Blackboard interaction management method and system for smart teaching demonstration
By segmenting the blackboard into regions and using intelligent feature matching, the problems of low interactivity and cumbersome operation in existing blackboard interaction technologies have been solved, achieving efficient and intelligent teaching demonstrations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SUZHOU VOCATIONAL INSTITUTE OF INDUSTRIAL TECHNOLOGY
- Filing Date
- 2024-04-12
- Publication Date
- 2026-07-31
AI Technical Summary
Existing blackboard interactive technology suffers from low interactivity, poor display effects, and cumbersome demonstration and interactive operations.
By segmenting the blackboard into regions, a template space equipped with contact sensors is established. After the activation condition set is triggered, it enters a shared state. Combined with the image acquisition device to match the user account, intelligent auxiliary features are generated. Based on the contact sensor to capture the contact position, a preset trajectory is generated. Feature matching and trajectory reconstruction are performed, and finally the trajectory is shared to the synchronization device.
It improves interactivity and display effects, and enables convenient demonstration and interactive operations.
Smart Images
Figure CN118334918B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of teaching facilities technology, and in particular to a blackboard interactive management method and system for smart teaching demonstrations. Background Technology
[0002] As an important development direction for teaching demonstrations, intelligent blackboards provide a more intuitive and multi-dimensional demonstration path for classroom teaching. By introducing multimedia technology, teachers can display teaching content in various forms such as text, pictures, and videos on the blackboard, improving the demonstration effect. However, existing blackboard interactive technologies are limited by physical space, have low integration levels, and suffer from low interactivity, poor display effects, and cumbersome interactive operation. Summary of the Invention
[0003] The purpose of this application is to provide a blackboard interactive management method and system for smart teaching demonstrations, in order to solve the technical problems of low interactivity, poor display effect, and cumbersome demonstration and interactive operation in the existing technology.
[0004] In view of the above technical issues, this application provides a blackboard interactive management method and system for smart teaching demonstrations.
[0005] In a first aspect, this application provides a blackboard interaction management method for smart teaching demonstrations, wherein the method includes:
[0006] The blackboard is divided into regions to create a template space, wherein a contact sensor is configured in the template space;
[0007] Establish an activation condition set for the template space. When the activation condition set is triggered, the template space is activated into a shared state.
[0008] The system performs user image acquisition using an image acquisition device, matches the user account with the image acquisition results, and generates intelligent auxiliary features for the template.
[0009] When a preset intensity feature signal is received in the shared space, a preset trajectory is generated based on the contact position captured by the contact sensor.
[0010] When the template space receives an auxiliary signal during the preset trajectory generation process, it performs intelligent auxiliary feature matching based on the preset trajectory.
[0011] Based on the feature matching results, point selection and reconstruction are performed to generate the calibration trajectory;
[0012] When generating an arbitrary trajectory in the template space, the trajectory within the template space is shared to the synchronization device based on the shared state.
[0013] Secondly, this application also provides a blackboard interactive management system for smart teaching demonstrations, wherein the system includes:
[0014] A space segmentation module is used to segment the blackboard into regions and establish a template space, wherein the template space is equipped with a contact sensor;
[0015] The activation setting module is used to establish an activation condition set for the template space. When the activation condition set is triggered, the template space is activated into a shared state.
[0016] An auxiliary feature construction module is used to perform user image acquisition through an image acquisition device, match the user account based on the image acquisition results, and generate intelligent auxiliary features established by the template.
[0017] A trajectory generation module is used to generate a preset trajectory based on the capture contact position of the contact sensor when the template space receives a preset intensity feature signal in the shared state.
[0018] The feature matching module is used to perform intelligent auxiliary feature matching based on the preset trajectory when the template space receives an auxiliary signal during the preset trajectory generation process.
[0019] A trajectory reconstruction module is used to perform point reconstruction based on feature matching results to generate a calibration trajectory.
[0020] A synchronization sharing module is used to share the trajectory in the template space to the synchronization device based on the sharing status when an arbitrary trajectory is generated in the template space.
[0021] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0022] By segmenting the blackboard into regions, a template space is established, equipped with a contact sensor. An activation condition set is established for the template space; when the activation condition set is triggered, the template space is activated into a shared state. User images are captured via an image acquisition device, and the image capture results are matched with the user's account to generate intelligent auxiliary features for template creation. When the template space in the shared state receives a preset intensity feature signal, a preset trajectory is generated based on the contact position captured by the contact sensor. When the template space receives an auxiliary signal during the preset trajectory generation process, feature matching of the intelligent auxiliary features is performed according to the preset trajectory. Based on the feature matching results, point reconstruction is performed to generate a calibration trajectory. When any trajectory is generated in the template space, the trajectory within the template space is shared to a synchronization device based on the shared state. This achieves the technical effect of improved interactivity, better display effect, and convenient interactive operation.
[0023] The above description is merely an overview of the technical solution of this application. In order to more clearly explain the technical means of this application, and to enable its implementation in accordance with the contents of the specification, and to make the above and other objectives, features and advantages of this application more apparent and understandable, specific embodiments of this application are described below. Attached Figure Description
[0024] The embodiments of the present invention and the following brief description are illustrated in conjunction with the figures, which are described below:
[0025] Figure 1 This is a flowchart illustrating the blackboard interaction management method used in this application for smart teaching demonstrations;
[0026] Figure 2 This is a flowchart illustrating the setting and management of auxiliary signals in the blackboard interaction management method for smart teaching demonstrations in this application;
[0027] Figure 3 This is a schematic diagram of the blackboard interactive management system used for smart teaching demonstrations in this application.
[0028] Explanation of reference numerals in the attached diagram: Spatial segmentation module 11, Activation setting module 12, Auxiliary feature construction module 13, Trajectory generation module 14, Feature matching module 15, Trajectory reconstruction module 16, Synchronization sharing module 17. Detailed Implementation
[0029] This application provides a blackboard interaction management method and system for smart teaching demonstrations, which solves the technical problems of low interactivity, poor display effect, and cumbersome demonstration and interaction operations faced by existing technologies.
[0030] The overall approach adopted in this technical embodiment to solve the above problems is as follows:
[0031] The blackboard is segmented to create a template space, which includes a contact sensor. An activation condition set is established for the template space; when the activation condition set is triggered, the template space is activated into a shared state. User images are captured via an image acquisition device, and the user account is matched with the image capture results to generate intelligent auxiliary features for template creation. When the template space in shared state receives a preset intensity feature signal, a preset trajectory is generated based on the contact position captured by the contact sensor. When the template space receives an auxiliary signal during the preset trajectory generation process, feature matching of the intelligent auxiliary features is performed according to the preset trajectory. Point selection and reconstruction are performed based on the feature matching results to generate a calibration trajectory. When any trajectory is generated in the template space, the trajectory within the template space is shared to a synchronization device based on the shared state. This achieves the technical effects of improved interactivity, better display effects, and convenient interactive operation.
[0032] To better understand the above technical solutions, the following detailed description will be provided in conjunction with the accompanying drawings and specific embodiments. It should be noted that the described embodiments are only a part of the embodiments of this application, not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. Furthermore, it should be noted that, for ease of description, only the parts related to this invention are shown in the accompanying drawings, not all of them.
[0033] Example 1
[0034] like Figure 1 As shown, this application provides a blackboard interaction management method for smart teaching demonstrations, the method comprising:
[0035] S100: Divide the blackboard into regions to establish a template space, wherein the template space is equipped with a contact sensor;
[0036] Optionally, the blackboard can be divided into a template space and a non-template space, with contact sensors deployed in the template space. The template space refers to an area on the target blackboard with a specific location and range. Contact within this area with the target blackboard can be identified and captured. Contact sensors are used to identify contact between the interactive object and the template space of the blackboard. Specifically, interactive objects include limbs, dedicated contact pens or components, and other available objects. Furthermore, various types of contact sensors can be used, such as pressure sensors, capacitive sensors, electromagnetic sensors, infrared sensors, and ultrasonic sensors. These sensor types can be used individually or in combination to provide more information and finer touch detection. The type and combination of contact sensors configured depend on factors such as the application scenario, cost, and required accuracy.
[0037] Optionally, the boundary between the template space and the non-template space is marked with a boundary line to facilitate the user's distinction between the template space and the non-template space. In addition, the surfaces of the template space and the non-template space are flush, and their appearance, feel, coefficient of friction and other characteristics are the same or similar, so that the template space has the same functionality as the non-template space while having the contact capture function, and can be used as a non-template space.
[0038] S200: Establish an activation condition set for the template space. When the activation condition set is triggered, the template space is activated into a shared state.
[0039] Optionally, the template space has three states: shared, dormant, and off. The shared state refers to the state where the contact sensor is in contact monitoring mode. In this state, the contact sensor collects contact events occurring on the template space and records contact characteristics, including user behaviors such as touching, drawing, and writing. Specifically, the contact sensor in the shared state has a higher sampling rate, enabling it to detect contact events on the template space in real time, and the sampling results are highly accurate. In the dormant state, the contact sensor has a lower sampling rate, maintaining low power consumption to save energy. The dormant state can be automatically switched by the system based on certain triggering conditions. For example, if no user activity is detected for a period of time, and when an activation condition set is triggered, the template space can quickly switch from dormant to shared state. Furthermore, the off state means that the contact sensor completely stops working and does not perform any detection. In the off state, the contact sensor does not collect any contact events to minimize power consumption. The off state is triggered manually by the system or the user.
[0040] Specifically, the design of these multiple operating states allows the system to switch between different operating modes to balance the need for real-time monitoring with energy consumption considerations. For example, when a user leaves the template space for a period of time, the sensors are automatically switched to a sleep or off state to save energy. When the user returns to the template space, the sensors are switched to a shared state based on an activation condition set to monitor the user's interaction in real time.
[0041] Optionally, the shared state can be activated through a set of activation conditions. These conditions define the conditions under which the template space should be activated into a shared state. These conditions can be determined based on factors such as time, user behavior, and system state, and can include various methods such as sound recognition, interactive action detection, and proximity detection. Examples include recognizing specific keywords, detecting specific interactive signals, detecting the approach of a specific object, recording contact time or pressure on the template space reaching a preset constraint value, detecting contact at a specific location in the template space, and detecting a specific trajectory in the template space.
[0042] Optionally, the identified activation conditions can be combined into an activation condition set. This can be a logical set comprising combinations of multiple conditions. The contact sensor is then configured to monitor the conditions defined in the activation condition set. This may involve setting parameters such as sensor sensitivity and detection frequency.
[0043] S300: Performs user image acquisition through an image acquisition device, matches the user account with the image acquisition results, and generates intelligent auxiliary features for template establishment;
[0044] The image acquisition device is an auxiliary acquisition equipment, including cameras, structured light cameras, etc. It is used to acquire relevant images of the target user, thereby completing identity authentication and account matching.
[0045] Optionally, the acquired user images are processed, including image preprocessing, face detection, and key point extraction. Useful information, particularly information related to the user's facial features, is extracted from the images to complete subsequent steps such as user account matching. Furthermore, the acquired user images are matched with registered user accounts. Technologies such as face recognition, feature matching, and biometric identification are used to ensure matching accuracy.
[0046] Intelligent auxiliary features are generated based on the user's historical data, preferences, or other relevant information to assist in creating presentation templates. These features include the user's areas of interest, usage habits, and personalized settings, providing a more personalized and intelligent service. For example, intelligent auxiliary features might manifest as the user's frequently used fields (subjects, industries, etc.) and presentation style (language, font characteristics, commonly used symbols and graphics, etc.).
[0047] Furthermore, such as Figure 2 As shown, step S300 further includes:
[0048] The trigger frequency of the intelligent auxiliary features is statistically analyzed to establish a frequency statistics set;
[0049] The classification coefficients of the intelligent auxiliary features are initialized based on the frequency statistics set, and the feature level segmentation of the intelligent auxiliary features is performed using the initialized classification coefficients.
[0050] Auxiliary signal settings and management are based on feature-level segmentation results.
[0051] Optionally, the trigger logs of intelligent assistance features are traversed to count the trigger frequencies of multiple features within the intelligent assistance features, and a frequency statistics set is established based on this. The intelligent assistance feature trigger logs record the triggering of intelligent assistance features over a period of time, and the frequency statistics set includes various features and their corresponding trigger counts. Then, classification coefficients are initialized for the intelligent assistance features based on the frequency statistics set. For example, intelligent assistance features with higher trigger frequencies have larger classification coefficient values, indicating higher importance, probability of occurrence, and priority for these features. Subsequently, based on the initialized classification coefficients, the intelligent assistance features are segmented into feature levels. The priority and influence of features are determined through the classification coefficients, and corresponding feature levels are assigned to the intelligent assistance features to better understand and manage user behavior patterns.
[0052] Furthermore, the steps for setting and managing auxiliary signals based on feature-level segmentation results also include:
[0053] Determine whether a first feature level exists in the feature level segmentation result;
[0054] If a first feature level exists, the auxiliary signal of the feature corresponding to the first feature level is set as the automatic detection signal, and the detection response sensitivity is configured according to the frequency feature of the first feature level.
[0055] The auxiliary signal settings for the first feature level are managed based on the configured detection response sensitivity.
[0056] Optionally, the intelligent auxiliary feature with the largest classification coefficient value is selected as the normal trigger feature, an automatic detection signal is set, and real-time feature detection and recognition are performed in the target scene based on this feature and the automatic detection signal. A deeper understanding of user behavior is achieved through trigger frequency statistics and feature level segmentation, and this information is applied in the management of auxiliary signal settings. This ultimately achieves the technical effect of providing personalized services and improving user experience.
[0057] Optionally, the detection response sensitivity can be configured based on the frequency characteristics of the first feature level. The configuration methods for the detection response sensitivity include setting parameters such as thresholds, time windows, and trigger conditions to adapt to changes in different frequency characteristics.
[0058] Based on the configured detection response sensitivity, the auxiliary signal settings management of the first characteristic level is carried out, including the management settings (detection response sensitivity) for starting, stopping, updating, or auxiliary signals.
[0059] S400: When the template space in the shared state receives a preset intensity feature signal, a preset trajectory is generated based on the contact position captured by the contact sensor.
[0060] When a preset intensity characteristic signal is received in the shared space, a preset trajectory is generated based on the contact position captured by the contact sensor. For example, firstly, the shared space receives an intensity characteristic signal. This signal, from the aforementioned contact sensor, indicates a specific force or intensity. When the received intensity characteristic signal satisfies the preset intensity characteristic signal, the contact sensor continuously captures the current contact position, and a preset trajectory is generated based on the captured contact position. The preset trajectory is the motion trajectory of the user's contact surface, reflecting the force and direction of movement applied by the user in the shared state.
[0061] Optionally, generating a preset trajectory also includes connecting the acquired set of original contact positions in chronological order to form a continuous trajectory. Then, the connected trajectory is smoothed to reduce jitter or noise during motion. The smoothed trajectory can employ techniques such as mathematical interpolation and filtering to ensure the generated preset trajectory is more continuous and stable.
[0062] S500: When the template space receives an auxiliary signal during the preset trajectory generation process, it performs intelligent auxiliary feature matching based on the preset trajectory.
[0063] Optionally, when the template space receives an auxiliary signal during the generation of the preset trajectory, feature matching of intelligent auxiliary features is performed based on the preset trajectory. Optionally, the auxiliary signal includes the target user's image signal, audio signal, digital signature, digital tag, entity tag, etc. The auxiliary signal is used to determine the target user's account, and then retrieve the intelligent auxiliary features corresponding to the target user's account. Further, based on the intelligent auxiliary features, feature matching of the preset trajectory is performed, including setting feature matching range constraints based on the intelligent auxiliary features and comparing them with the preset trajectory to determine whether the preset trajectory matches the expected features.
[0064] S600: Perform point selection and reconstruction based on feature matching results to generate calibration trajectory;
[0065] Furthermore, based on the feature matching results, point selection and reconstruction are performed to generate a calibration trajectory. Step S600 includes:
[0066] Set continuous trigger constraints;
[0067] When any preset trajectory satisfies the continuous triggering constraint, trajectory matching is performed on the continuous preset trajectory according to the feature corresponding to the first feature level;
[0068] If the matching result meets the detection response sensitivity, then the auxiliary signal triggering is completed, and the selection and reconstruction of the continuous preset trajectory is performed.
[0069] Optionally, a continuous trigger constraint can be defined. The continuous trigger constraint specifies the continuous triggering requirements of the preset trajectory in time and is used to determine whether the preset trajectory is a continuous preset trajectory. Specifically, the continuous trigger constraint involves the time interval between consecutive points on the preset trajectory, the smoothness of the time interval between consecutive points on the preset trajectory, the average interval between consecutive points on the preset trajectory, or other relevant conditions.
[0070] Optionally, when any preset trajectory satisfies the continuous triggering constraint, trajectory matching is performed on the continuous preset trajectories based on the features corresponding to the first feature level. Examples include feature similarity comparison and pattern matching. If the trajectory matching result satisfies the detection response sensitivity condition, it indicates that the trigger signal has reached sufficient sensitivity, and auxiliary signal triggering can be executed. A point selection and reconstruction operation for the continuous preset trajectories is then performed. For example, key points are reselected based on the matched features to generate a calibration trajectory.
[0071] Furthermore, based on the feature matching results, point selection and reconstruction are performed to generate the calibration trajectory. Step S600 also includes:
[0072] Set timing constraints;
[0073] If a new continuous trajectory is generated within the preset time period during which the continuous triggering constraint is triggered, then the time-series confinement constraint is triggered.
[0074] The point selection and reconstruction results are retained by the temporal delineation constraints, and the process waits for the newly generated continuous trajectory to be drawn.
[0075] Once the newly generated trajectory is drawn, a continuous trigger constraint determination is performed on the newly generated trajectory;
[0076] Based on the judgment and retention results, the selected points under the time sequence are reconstructed.
[0077] Optionally, the temporal confinement constraint specifies the conditions for generating a new continuous trajectory within a preset time period after the continuous trigger constraint is triggered, including time limits for trajectory generation. When the contact sensor detects a newly generated continuous trajectory within the preset time period after the continuous trigger constraint is triggered, the temporal confinement constraint is triggered. For example, if a new trajectory is detected after the continuous temporal confinement constraint length is reached within the preset time period after the continuous trigger constraint is triggered, the temporal confinement constraint is activated, and the trajectory acquired after the continuous temporal confinement constraint length is continuously acquired, while retaining the existing point selection reconstruction results.
[0078] Optionally, based on the judgment results of continuous trigger constraints and the retained point reconstruction results, the point reconstruction under the temporal delineation is completed. This includes merging the newly generated trajectory with the previously retained trajectory or other operations. Through reasonable temporal delineation constraints, it is ensured that even if the contact of a trajectory belonging to the same continuous trajectory is interrupted for various reasons, it can still be regarded as a complete whole for point reconstruction.
[0079] S700: When generating an arbitrary trajectory in the template space, the trajectory in the template space is shared to the synchronization device based on the sharing state.
[0080] Optionally, a synchronization device refers to a device that waits to acquire the trajectory within the template space and then displays or demonstrates it. Upon receiving the trajectory information, the synchronization device parses and displays or demonstrates the same trajectory. This ensures that the display on the synchronization device is consistent with the trajectory within the template space. Exemplary synchronization devices include: other computer devices: desktop computers, laptops, etc.; mobile devices: smartphones, tablets, etc.; large-screen display devices: televisions or projectors, etc.; virtual reality (VR) devices or augmented reality (AR) devices. This synchronization mechanism can provide a more collaborative and interactive experience, removing the constraints of spatial teaching demonstrations and ensuring trajectory consistency across multiple environments.
[0081] Furthermore, after the system detects that a trajectory has been marked as shared, it synchronizes this trajectory information to the synchronization device via a communication protocol. Synchronization is achieved through network communication or other appropriate communication methods.
[0082] Furthermore, the method also includes:
[0083] Record the generated sample drafts and extract their features;
[0084] Obtain the user's pre-stored identifier, and establish an index for the template records based on the pre-stored identifier and the template features;
[0085] The non-model space image of the blackboard is acquired through an auxiliary device, and image recognition features are generated.
[0086] The index is matched and evaluated based on the image recognition features;
[0087] Based on the matching evaluation results, the corresponding sample manuscript record is retrieved from the index, and the retrieved result is adjusted to a floating state and displayed in the pre-selected area of the sample manuscript space.
[0088] Optionally, the generated templates are recorded and stored. Then, feature extraction is performed on the templates to extract key features, including text content, trajectory color, graphic shape, and size. Next, user identity information or other pre-stored identifiers are obtained to generate a pre-stored user identifier. This pre-stored identifier is used to associate the generated template with a specific user. Furthermore, based on the association between the pre-stored identifier and the extracted template features, an index of template records is established. This index is a database index that reflects the correspondence between users and template records, facilitating subsequent retrieval and access to these records.
[0089] Optionally, an auxiliary device can be used to acquire images of the non-patterned space of the blackboard, obtaining image information of the entire blackboard. For example, the auxiliary device includes image acquisition devices such as cameras or video cameras. The acquired images are processed to extract image recognition features, including recognizing the trajectory, shape, and color of the non-patterned space.
[0090] Optionally, the extracted image recognition features are matched and evaluated against the previously established index to determine the content in the template record that matches the current image in the non-template space. Based on the matching evaluation results, the template record that best matches the current image is retrieved. The matched template record is then displayed in a pre-selected area of the template space, and its display status is adjusted to floating, allowing users to view or select it in the non-template space.
[0091] Through the above steps, trajectory prediction based on the target user's sample records is achieved, providing the target user with a convenient means of demonstration and interaction, improving the efficiency of demonstration and interaction and the user experience.
[0092] Furthermore, the method also includes:
[0093] The calibration trajectory is canceled from recording, and abnormal matching features are identified in reverse based on the cancellation results;
[0094] The abnormal matching features are added to the abnormal recognition network of the intelligent auxiliary features to complete the matching optimization of the intelligent auxiliary features.
[0095] Optionally, based on the user's selection or cancellation operation of the calibration trajectory, abnormal matching features are extracted according to the cancellation records, and intelligent auxiliary feature matching optimization based on the abnormal matching features is performed. Here, cancellation records refer to deleting or marking unnecessary calibration trajectories.
[0096] Optionally, in the unrecorded calibration trajectory, the system identifies anomalous matching features through reverse identification analysis. This may involve analyzing key points, shapes, colors, etc., of the calibration trajectory to identify features inconsistent with normal trajectories. Then, the anomalous matching features identified from the unrecorded calibration trajectory are added to the anomaly detection network of the intelligent auxiliary features. For example, this anomaly detection network is a neural network, machine learning model, or other algorithm used to identify anomalous features. After the anomalous matching features are added to the anomaly detection network, the model parameters are retrained or updated based on backpropagation and gradient descent methods to optimize the matching performance of the intelligent auxiliary features. This helps improve the system's accurate identification of anomalous trajectories and enhances the user experience.
[0097] In summary, the blackboard interactive management method for smart teaching demonstrations provided by this invention has the following technical effects:
[0098] By segmenting the blackboard into regions, a template space is established, equipped with a contact sensor. An activation condition set is established for the template space; when the activation condition set is triggered, the template space is activated into a shared state. User images are captured via an image acquisition device, and the image capture results are matched with the user's account to generate intelligent auxiliary features for template creation. When the template space in the shared state receives a preset intensity feature signal, a preset trajectory is generated based on the contact position captured by the contact sensor. When the template space receives an auxiliary signal during the preset trajectory generation process, feature matching of the intelligent auxiliary features is performed according to the preset trajectory. Based on the feature matching results, point reconstruction is performed to generate a calibration trajectory. When any trajectory is generated in the template space, the trajectory within the template space is shared to a synchronization device based on the shared state. This achieves the technical effect of improved interactivity, better display effect, and convenient interactive operation.
[0099] Example 2
[0100] Based on the same concept as the blackboard interactive management method for smart teaching demonstrations in the embodiments described above, such as Figure 3 As shown, this application also provides a blackboard interactive management system for smart teaching demonstrations, the system comprising:
[0101] The space segmentation module 11 is used to segment the blackboard into regions and establish a template space, wherein the template space is equipped with a contact sensor;
[0102] The activation setting module 12 is used to establish an activation condition set for the template space. When the activation condition set is triggered, the template space is activated into a shared state.
[0103] The auxiliary feature construction module 13 is used to perform user image acquisition through the image acquisition device, match the user account according to the image acquisition results, and generate intelligent auxiliary features established by the template.
[0104] The trajectory generation module 14 is used to generate a preset trajectory based on the capture contact position of the contact sensor when the template space receives a preset intensity feature signal in the shared state.
[0105] The feature matching module 15 is used to perform intelligent auxiliary feature matching based on the preset trajectory when the template space receives an auxiliary signal during the preset trajectory generation process.
[0106] Trajectory reconstruction module 16 is used to perform point reconstruction based on feature matching results and generate calibration trajectory;
[0107] The synchronization sharing module 17 is used to share the trajectory in the template space to the synchronization device based on the sharing status when an arbitrary trajectory is generated in the template space.
[0108] Furthermore, the auxiliary feature construction module 13 also includes:
[0109] The frequency statistics unit is used to perform trigger frequency statistics on the intelligent auxiliary features and establish a frequency statistics set.
[0110] The feature level segmentation unit is used to initialize the classification coefficients of the intelligent auxiliary features based on the frequency statistics set, and to perform feature level segmentation of the intelligent auxiliary features using the initialized classification coefficients.
[0111] The configuration management unit is used to configure and manage auxiliary signals based on the feature level segmentation results.
[0112] Furthermore, the configuration management unit also includes:
[0113] The discrimination unit is used to determine whether a first feature level exists in the feature level segmentation result;
[0114] The detection response configuration unit is used to set the auxiliary signal of the feature corresponding to the first feature level as an automatic detection signal if a first feature level exists, and to configure the detection response sensitivity according to the frequency characteristics of the first feature level.
[0115] The detection management unit is used to manage the setting of auxiliary signals for the first characteristic level based on the configured detection response sensitivity.
[0116] Furthermore, the trajectory reconstruction module 16 also includes:
[0117] The continuous trigger constraint setting unit is used to set continuous trigger constraints;
[0118] A continuous trigger constraint monitoring unit is used to perform trajectory matching on the continuous preset trajectory according to the feature corresponding to the first feature level when any preset trajectory satisfies the continuous trigger constraint.
[0119] The trigger reconstruction unit is used to complete the auxiliary signal triggering and perform point reconstruction of a continuous preset trajectory if the matching result meets the detection response sensitivity.
[0120] Furthermore, the trajectory reconstruction module 16 also includes:
[0121] The timing confinement constraint setting unit is used to set timing confinement constraints;
[0122] The timing-delineation constraint monitoring unit is used to trigger the timing-delineation constraint if a new continuous trajectory is generated within a preset time period when the continuous triggering constraint is triggered.
[0123] The filtering and retention unit is used to retain the point reconstruction results through the temporal delimitation constraints and wait for the newly generated continuous trajectory to be drawn.
[0124] The continuous trigger constraint determination unit is used to perform continuous trigger constraint determination on the newly generated trajectory after the newly generated trajectory is drawn.
[0125] The dual reconstruction unit is used to complete the reconstruction of selected points under the time sequence delineation based on the judgment result and the retention result.
[0126] Furthermore, the system also includes:
[0127] The template recording unit is used to record the generated templates and extract their features;
[0128] The associated index unit is used to obtain the user's pre-stored identifier and establish an index for the template records based on the pre-stored identifier and the template features;
[0129] The image acquisition unit is used to acquire non-patterned space images of the blackboard through an auxiliary device and generate image recognition features;
[0130] A matching evaluation unit is used to perform index matching evaluation based on the image recognition features;
[0131] The pre-selection display unit is used to call the corresponding sample manuscript record based on the matching evaluation results, and adjust the call results to a floating state to display them in the pre-selection area of the sample manuscript space.
[0132] Furthermore, it also includes:
[0133] An anomaly identification unit is used to cancel the recording of the calibration trajectory and to reverse identify anomaly matching features based on the cancellation result;
[0134] The matching optimization unit is used to add the abnormal matching features to the anomaly recognition network of the intelligent auxiliary features to complete the matching optimization of the intelligent auxiliary features.
[0135] It should be understood that the focus of the embodiments mentioned in this specification is their difference from other embodiments. The specific embodiments in the aforementioned Embodiment 1 are also applicable to the blackboard interactive management system for smart teaching demonstrations described in Embodiment 2. For the sake of brevity, they will not be elaborated further here.
[0136] It should be understood that the embodiments disclosed in this application and the above description can enable those skilled in the art to implement this application. At the same time, this application is not limited to the embodiments mentioned above; obvious modifications, combinations, and substitutions to the embodiments mentioned in this application also fall within the scope of protection of this application.
Claims
1. A blackboard interactive management method for smart teaching demonstrations, characterized in that, The method includes: The blackboard is divided into regions to create a template space, wherein a contact sensor is configured in the template space; Establish an activation condition set for the template space. When the activation condition set is triggered, the template space is activated into a shared state. The system performs user image acquisition using an image acquisition device, matches the user account with the image acquisition results, and generates intelligent auxiliary features for the template. When a preset intensity feature signal is received in the shared space, a preset trajectory is generated based on the contact position captured by the contact sensor. When the template space receives an auxiliary signal during the preset trajectory generation process, it performs intelligent auxiliary feature matching based on the preset trajectory. Based on the feature matching results, point selection and reconstruction are performed to generate the calibration trajectory; When generating an arbitrary trajectory in the template space, the trajectory within the template space is shared to the synchronization device based on the sharing state; The method further includes: The trigger frequency of the intelligent auxiliary features is statistically analyzed to establish a frequency statistics set; The classification coefficients of the intelligent auxiliary features are initialized based on the frequency statistics set. The intelligent auxiliary features are then segmented by feature level using the initialized classification coefficients. The intelligent auxiliary features with high trigger frequency have larger classification coefficient values. Auxiliary signal setting and management based on feature-level segmentation results; The method further includes: Determine whether a first feature level exists in the feature level segmentation result, where the feature corresponding to the first feature level is the intelligent auxiliary feature with the largest classification coefficient value; If a first feature level exists, the auxiliary signal of the feature corresponding to the first feature level is set as the automatic detection signal, and the detection response sensitivity is configured according to the frequency feature of the first feature level. The auxiliary signal settings for the first feature level are managed based on the configured detection response sensitivity. The method further includes: Set continuous trigger constraints; When any preset trajectory satisfies the continuous triggering constraint, trajectory matching is performed on the continuous preset trajectory according to the feature corresponding to the first feature level; If the matching result meets the detection response sensitivity, then the auxiliary signal triggering is completed, and the selection and reconstruction of the continuous preset trajectory is performed.
2. The method as described in claim 1, characterized in that, The method further includes: Set timing constraints; If a new continuous trajectory is generated within the preset time period during which the continuous triggering constraint is triggered, then the time-series confinement constraint is triggered. The point selection and reconstruction results are retained by the temporal delineation constraints, and the process waits for the newly generated continuous trajectory to be drawn. Once the newly generated trajectory is drawn, a continuous trigger constraint determination is performed on the newly generated trajectory; Based on the judgment and retention results, the selected points under the time sequence are reconstructed.
3. The method as described in claim 1, characterized in that, The method further includes: Record the generated sample drafts and extract their features; Obtain the user's pre-stored identifier, and establish an index for the template records based on the pre-stored identifier and the template features; The non-model space image of the blackboard is acquired through an auxiliary device, and image recognition features are generated. The index is matched and evaluated based on the image recognition features; Based on the matching evaluation results, the corresponding sample manuscript record is retrieved from the index, and the retrieved result is adjusted to a floating state and displayed in the pre-selected area of the sample manuscript space.
4. The method as described in claim 1, characterized in that, The method further includes: The calibration trajectory is canceled from recording, and abnormal matching features are identified in reverse based on the cancellation results; The abnormal matching features are added to the abnormal recognition network of the intelligent auxiliary features to complete the matching optimization of the intelligent auxiliary features.
5. A blackboard interactive management system for intelligent teaching demonstrations, characterized in that, The system includes: A space segmentation module is used to segment the blackboard into regions and establish a template space, wherein the template space is equipped with a contact sensor; The activation setting module is used to establish an activation condition set for the template space. When the activation condition set is triggered, the template space is activated into a shared state. An auxiliary feature construction module is used to perform user image acquisition through an image acquisition device, match the user account based on the image acquisition results, and generate intelligent auxiliary features established by the template. A trajectory generation module is used to generate a preset trajectory based on the capture contact position of the contact sensor when the template space receives a preset intensity feature signal in the shared state. The feature matching module is used to perform intelligent auxiliary feature matching based on the preset trajectory when the template space receives an auxiliary signal during the preset trajectory generation process. A trajectory reconstruction module is used to perform point reconstruction based on feature matching results to generate a calibration trajectory. A synchronization sharing module is used to share the trajectory in the template space to the synchronization device based on the sharing status when an arbitrary trajectory is generated in the template space. The auxiliary feature construction module also includes: The frequency statistics unit is used to perform trigger frequency statistics on the intelligent auxiliary features and establish a frequency statistics set. The feature level segmentation unit is used to initialize the classification coefficients of the intelligent auxiliary features based on the frequency statistics set, and to perform feature level segmentation of the intelligent auxiliary features using the initialized classification coefficients. Among them, the intelligent auxiliary features with high trigger frequency have large classification coefficient values. A management unit is set up to manage the setting of auxiliary signals based on the feature level segmentation results; The configuration management unit also includes: The discrimination unit is used to determine whether there is a first feature level in the feature level segmentation result, wherein the feature corresponding to the first feature level is the intelligent auxiliary feature with the largest classification coefficient value; The detection response configuration unit is used to set the auxiliary signal of the feature corresponding to the first feature level as an automatic detection signal if a first feature level exists, and to configure the detection response sensitivity according to the frequency characteristics of the first feature level. The detection management unit is used to manage the setting of auxiliary signals for the first characteristic level based on the configured detection response sensitivity. The trajectory reconstruction module also includes: The continuous trigger constraint setting unit is used to set continuous trigger constraints; A continuous trigger constraint monitoring unit is used to perform trajectory matching on the continuous preset trajectory according to the feature corresponding to the first feature level when any preset trajectory satisfies the continuous trigger constraint. The trigger reconstruction unit is used to complete the auxiliary signal triggering and perform point reconstruction of a continuous preset trajectory if the matching result meets the detection response sensitivity.