Brightness adjusting method and system for intelligent blackboard

Abstract

The invention discloses a brightness adjusting method and system for an intelligent blackboard, and relates to the technical field of intelligent touch control, and the method comprises the steps: detecting the contact between a touch pen and a blackboard screen according to the contact sensing data outputted by a capacitive sensor; integrating an ambient light sensor and a screen built-in brightness sensor, acquiring brightness sensing data, combining with contact sensing data, jointly inputting the brightness sensing data into a brightness stimulation evaluation network for eye discomfort evaluation, and outputting a brightness stimulation evaluation index; and inputting the brightness stimulation evaluation index into a brightness parameter adjustment network, generating a brightness adjustment parameter, transmitting the brightness adjustment parameter to an upper computer, and controlling the brightness of the blackboard screen. The technical problem that in the prior art, due to the influence of environment illumination and close-range use of a user, the brightness of the blackboard easily causes eye discomfort of the user is solved, and the technical effects that intelligent adjustment of the brightness of the screen is conducted through brightness stimulation evaluation, and the use comfort of the user is improved are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent touch technology, and in particular to a brightness adjustment method and system for a smart blackboard. Background Art

[0002] Smart blackboard is an innovative educational device that combines traditional blackboard and modern display technology. It integrates multiple functions such as touch screen, electronic whiteboard, projection display, etc., and is widely used in education, training, conference and other scenarios. Its core advantages are strong interactivity, intuitive information presentation, and real-time recording and sharing. However, due to the high brightness characteristics of its display screen and its application in various environments such as classrooms and conference rooms, the ambient lighting conditions are changeable, and users may feel eye discomfort due to excessive screen brightness during writing. Therefore, it is necessary to reasonably adjust the brightness of the smart blackboard to ensure the user's visual comfort. Summary of the invention

[0003] The present application provides a brightness adjustment method and system for a smart blackboard, which is used to solve the technical problem in the prior art that the brightness of the blackboard can easily cause eye discomfort to the user due to the influence of ambient lighting and close use by the user.

[0004] In a first aspect of the present application, a method for adjusting the brightness of a smart blackboard is provided, the method comprising: setting a stylus detection module, the stylus detection module comprising a capacitive sensor, the capacitive sensor being arranged in the stylus; detecting contact between the stylus and the blackboard screen according to contact sensing data output by the capacitive sensor, wherein the contact sensing data comprises contact pressure and contact duration; integrating an ambient light sensor and a built-in brightness sensor of the screen, obtaining a screen brightness detection module, and obtaining brightness sensing data according to the screen brightness detection module; inputting the contact sensing data and the brightness sensing data into a brightness stimulation evaluation network for eye discomfort evaluation, and outputting a brightness stimulation evaluation index; inputting the brightness stimulation evaluation index into a brightness parameter adjustment network, the brightness parameter adjustment network being connected to the brightness stimulation evaluation network, and generating brightness adjustment parameters according to preset brightness indicators; transmitting the brightness adjustment parameters to a host computer to control the brightness of the blackboard screen.

[0005] In a second aspect of the present application, a brightness adjustment system for a smart blackboard is provided, the system comprising: a stylus detection module setting module, the stylus detection module setting module is used to set the stylus detection module, the stylus detection module comprises a capacitive sensor, the capacitive sensor is arranged in the stylus; a contact sensing data acquisition module, the contact sensing data acquisition module is used to detect the contact between the stylus and the blackboard screen according to the contact sensing data output by the capacitive sensor, wherein the contact sensing data comprises contact pressure and contact duration; a brightness sensing data acquisition module, the brightness sensing data acquisition module is used to integrate the ambient light sensor and the screen built-in brightness sensor, and obtain the screen brightness detection data. A module, which obtains brightness sensor data according to the screen brightness detection module; a brightness stimulation evaluation module, which is used to input the contact sensing data and the brightness sensing data into a brightness stimulation evaluation network to evaluate eye discomfort and output a brightness stimulation evaluation index; a brightness adjustment parameter generation module, which is used to input the brightness stimulation evaluation index into a brightness parameter adjustment network, which is connected to the brightness stimulation evaluation network, and generates brightness adjustment parameters according to preset brightness indicators; a screen brightness control module, which is used to transmit the brightness adjustment parameters to a host computer to control the brightness of the blackboard screen.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application provides a brightness adjustment method and system for a smart blackboard, which relate to the field of intelligent touch technology. The method and system obtain contact sensing data between a stylus pen and a blackboard screen through a capacitive sensor, integrate an ambient light sensor and a built-in brightness sensor of the screen, obtain brightness sensing data, input the data into a brightness stimulation evaluation network for eye discomfort evaluation, obtain brightness stimulation evaluation indicators, input the indicators into a brightness parameter adjustment network, generate brightness adjustment parameters and transmit them to a host computer to control the brightness of the blackboard screen. The method solves the technical problem in the prior art that the brightness of the blackboard easily causes eye discomfort to the user due to the influence of ambient light and close use by the user, and realizes the technical effect of intelligently adjusting the screen brightness through brightness stimulation evaluation to improve the user's comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1A schematic diagram of a brightness adjustment method for a smart blackboard provided in an embodiment of the present application; Figure 2 A schematic diagram of a process for generating brightness adjustment parameters according to a preset brightness index in a brightness adjustment method for a smart blackboard provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a brightness adjustment system for a smart blackboard provided in an embodiment of the present application.

[0009] Description of the reference numerals: stylus detection module setting module 11 , contact sensing data acquisition module 12 , brightness sensing data acquisition module 13 , brightness stimulation evaluation module 14 , brightness adjustment parameter generation module 15 , screen brightness control module 16 . DETAILED DESCRIPTION

[0010] The present application provides a brightness adjustment method and system for a smart blackboard, which is used to solve the technical problem in the prior art that the brightness of the blackboard can easily cause eye discomfort to the user due to the influence of ambient lighting and close use by the user.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Embodiment 1

[0013] like Figure 1 As shown, the present application provides a brightness adjustment method for a smart blackboard, the method comprising: P10: Setting a stylus detection module, wherein the stylus detection module includes a capacitive sensor, and the capacitive sensor is set in the stylus.

[0014] Specifically, in order to achieve brightness adjustment of the smart blackboard, it is necessary to set up a stylus detection module first. The stylus detection module is one of the key components of the entire system, which is used to detect the user's actions when writing on the blackboard screen. The module includes a capacitive sensor, which is installed in the stylus. It is a device that can detect touch and pressure, and senses the contact between the stylus and the screen by measuring the change in capacitance value. The capacitive sensor is set in the stylus and can capture the contact data between the stylus and the blackboard screen in real time, such as contact pressure, contact duration, etc. These data can reflect the strength and frequency of the user's writing, and are an important basis for subsequent brightness adjustment.

[0015] To ensure the accuracy and sensitivity of the system, the stylus detection module requires a high-precision capacitive sensor. The high-precision sensor can accurately detect tiny changes in capacitance, thereby accurately capturing the user's writing movements. In addition, the design of the stylus also needs to be ergonomic, making the user feel comfortable during use and not affecting normal writing.

[0016] P20: Detecting contact between the stylus and the blackboard screen according to contact sensing data output by the capacitive sensor, wherein the contact sensing data includes contact pressure and contact duration.

[0017] It should be understood that the contact between the stylus and the blackboard screen is detected based on the contact sensing data output by the capacitive sensor. Among them, the contact sensing data mainly includes two key parameters: contact pressure and contact duration. Contact pressure refers to the force of the stylus pressing on the blackboard screen. This data can reflect the strength of the user when writing. Contact duration refers to the length of time the stylus stays on the blackboard screen. This data can show the frequency and speed of the user's writing.

[0018] When the user writes on the blackboard screen with a stylus, the capacitive sensor senses the contact of the stylus by measuring the change in capacitance value, capturing tiny capacitance changes, and thus providing accurate contact data. For example, when the user writes on the screen with greater pressure, the capacitive sensor detects a larger capacitance change and records the corresponding contact pressure. When the user stays in a certain position for a long time, the contact duration data will also increase accordingly. The contact sensing data is an important input parameter of the smart blackboard brightness adjustment system. Through the contact sensing data output by the capacitive sensor, the system can effectively detect the contact between the stylus and the blackboard screen, providing the necessary technical support for subsequent brightness adjustment. For example, when the contact pressure is large and the contact duration is long, it usually means that the user is concentrating on writing activities. At this time, the brightness of the screen may need to be adjusted accordingly to avoid excessive brightness from irritating the user's eyes.

[0019] P30: Integrate an ambient light sensor and a screen built-in brightness sensor, obtain a screen brightness detection module, and obtain brightness sensing data based on the screen brightness detection module.

[0020] Optionally, an ambient light sensor and a built-in screen brightness sensor are integrated to form a screen brightness detection module, and brightness sensing data is obtained through the module. Among them, the ambient light sensor is a sensor that can detect the intensity of ambient light. It is usually installed on the edge or top of the smart blackboard, and can sense the light changes in the classroom or conference room in real time. Its main function is to measure the brightness level of the external environment and provide a reference value. For example, when the ambient light is strong, the brightness of the screen may need to be appropriately increased to ensure visibility, while when the ambient light is weak, the screen brightness needs to be reduced to avoid over-stimulating the user's eyes. In addition, the built-in screen brightness sensor is a sensor installed inside the blackboard screen to detect the brightness of the screen itself. The sensor can accurately measure the brightness output of the screen under different usage states, and can provide real-time feedback on the brightness of the screen to ensure that the screen can provide appropriate brightness under different ambient light conditions.

[0021] The screen brightness detection module can comprehensively obtain data on both ambient light and screen brightness, provide comprehensive brightness information, and obtain brightness sensing data, which refers to the light intensity information obtained from the screen brightness detection module, including ambient brightness and screen brightness. These data can reflect the current lighting conditions and the brightness status of the screen. For example, when the ambient light sensor detects that the light in the classroom is dim, the system will obtain lower ambient brightness data, and combine it with the screen brightness data of the built-in brightness sensor of the screen to decide whether the screen brightness needs to be adjusted to adapt to the current ambient light conditions.

[0022] P40: Input the contact sensing data and the brightness sensing data into a brightness stimulation evaluation network to perform eye discomfort evaluation, and output a brightness stimulation evaluation index.

[0023] Furthermore, the brightness stimulus evaluation network is trained. The embodiment of the present application further includes step P40a. Step P40a further includes: P41a: Acquire brightness sensing samples, wherein the brightness sensing samples include sample data under different screen brightnesses and ambient light intensities; P42a: Acquire contact sensing samples, wherein the contact sensing samples include sample data under different contact pressures and contact durations; P43a: Perform time synchronization processing based on the brightness sensing samples and the contact sensing samples as a training data set; P44a: Record the user's eye discomfort feedback samples under the training data set, and use the eye discomfort feedback samples as a test data set; P45a: Train the initialized fully connected network based on the training data set and the test data set to obtain a brightness stimulus evaluation network.

[0024] It should be understood that the contact sensing data and the brightness sensing data are input into the brightness stimulation evaluation network to evaluate the user's eye discomfort and output a brightness stimulation evaluation index. The brightness stimulation evaluation network is a trained neural network model that can calculate a brightness stimulation evaluation index based on the user's writing behavior and ambient light conditions to guide subsequent screen brightness adjustment.

[0025] The specific training process of the brightness stimulus evaluation network may be, first, collecting sample data under different screen brightness and ambient light intensity conditions as brightness sensing samples, the brightness sensing samples include a variety of brightness combinations to ensure coverage of different usage scenarios. At the same time, collecting sample data under different contact pressures and contact duration conditions as contact sensing samples, the contact sensing samples record the user's behavior data under different writing intensities and frequencies. Furthermore, the brightness sensing samples and contact sensing samples are time-series synchronized to ensure the consistency of the sample data in the time dimension, and the brightness data, ambient light data, contact pressure data and contact duration data at the same time point are matched to form a complete training data set.

[0026] Furthermore, in order to verify the performance of the network and adjust its parameters to achieve the best effect, the user's eye discomfort feedback samples corresponding to the training data set are collected and used as a test data set to evaluate the effect of the training model. Furthermore, the initialized fully connected network is trained according to the training data set and the test data set. The fully connected network is a basic neural network structure. By learning a large amount of sample data, it can establish a complex mapping relationship between input and output, and by adjusting the network weights and biases, the model can accurately predict the user's eye discomfort level and obtain a brightness stimulation evaluation network.

[0027] Finally, by inputting the contact sensing data and brightness sensing data into the brightness stimulus evaluation network, the user's eye discomfort level can be accurately evaluated, and the screen brightness can be adjusted according to the evaluation results to improve the comfort and user experience of the smart blackboard.

[0028] Furthermore, the embodiment of the present application also includes step P40a further including: P46a: Obtain user distance samples according to the distance measurement module, the distance measurement module includes a distance measurement sensor, the distance measurement sensor is arranged in the stylus, and the distance measurement sensor is used to measure the distance between the blackboard screen and the user; P47a: According to the influence relationship between the user distance samples and the user's eye discomfort, the brightness stimulus evaluation network is incrementally learned to obtain an optimized brightness stimulus evaluation network.

[0029] In a possible embodiment of the present application, in order to further improve the accuracy and practicality of the brightness stimulus evaluation network, the embodiment of the present application introduces a distance measurement module as an additional data input source. The core of the distance measurement module is a distance measurement sensor arranged in the stylus, which is like a miniature radar, and can measure and record the distance between the user and the blackboard screen when the stylus is used in real time. Specifically, the distance measurement sensor can accurately measure the distance between the blackboard screen and the user's eyes by transmitting and receiving signals (such as laser or ultrasonic waves). When the user writes on the blackboard with a stylus, the distance measurement sensor measures and records the distance between the user and the blackboard in real time as a user distance sample, which can provide additional dimensional information for evaluating the user's visual comfort. The closer the distance, the greater the impact of the screen brightness on the user's eyes, so accurate distance measurement is particularly important for brightness adjustment.

[0030] Furthermore, the relationship between the user distance sample and the user's eye discomfort is used as a new input feature for incremental learning to further train the brightness stimulation evaluation network. Incremental learning is a method that continuously updates and optimizes a model through new data based on an existing model. Through data statistics, the relationship between the user distance sample and the user's eye discomfort feedback can be analyzed to find out the influence of the distance between the user and the blackboard screen on eye discomfort. Generally, the closer the distance, the greater the stimulation of the screen brightness to the eyes. Then, using the user distance sample as a newly added input feature, combined with the original contact sensor data and brightness sensor data, the brightness stimulation evaluation network is incrementally learned, and the network model is fine-tuned, for example, by adjusting the network parameters and weights to optimize its prediction ability, so that it can more accurately reflect the actual usage of the user and output a more accurate brightness stimulation evaluation index.

[0031] P50: Inputting the brightness stimulus evaluation index into a brightness parameter adjustment network, the brightness parameter adjustment network is connected to the brightness stimulus evaluation network, and a brightness adjustment parameter is generated according to a preset brightness index.

[0032] Further, such as Figure 2 As shown, step P50 of the embodiment of the present application also includes: P51: Determine a preset brightness index, wherein the preset brightness index is within the brightness tolerance range of the blackboard screen; P52: Obtain the real-time brightness parameter of the blackboard screen; P53: The brightness parameter adjustment network adjusts the real-time brightness parameter according to the preset brightness index to generate a brightness adjustment parameter, wherein the brightness adjustment parameter includes an adjustment step, an adjustment frequency, and an adjustment error compensation coefficient.

[0033] It should be understood that the brightness stimulus evaluation index is input into the brightness parameter adjustment network, and the brightness adjustment parameter is generated according to the preset brightness index. The brightness parameter adjustment network is a neural network-based adjustment system, which is connected to the brightness stimulus evaluation network, can receive the brightness stimulus evaluation index output by the brightness stimulus evaluation network, and generate specific brightness adjustment parameters according to these evaluation indicators and the preset brightness index.

[0034] Specifically, firstly, an ideal brightness value range is set based on the physical characteristics of the blackboard screen (such as the brightness tolerance range) and user expectations (such as optimal viewing comfort) as the preset brightness index. The brightness tolerance range refers to the brightness range in which the screen can work stably under the premise of ensuring picture clarity and color accuracy. The preset brightness index is within this range, ensuring the feasibility and effectiveness of the adjustment.

[0035] Furthermore, the real-time brightness parameters of the blackboard screen are obtained by the ambient light sensor and the built-in brightness sensor of the screen, and the real-time brightness parameters include the current ambient light intensity and the actual brightness value of the screen. Furthermore, the brightness parameter adjustment network adjusts the real-time brightness parameters according to the preset brightness index to generate specific brightness adjustment parameters. Among them, the brightness adjustment parameters include the adjustment step size, the adjustment frequency and the adjustment error compensation coefficient. The adjustment step size determines the magnitude of each brightness adjustment, which not only avoids the user discomfort caused by too fast brightness changes, but also ensures the adjustment efficiency. The adjustment frequency is adjusted according to the speed of ambient light changes and the user activity mode to maintain the best viewing state. The adjustment error compensation coefficient is used to correct the brightness deviation that may be caused by system errors or insufficient sensor accuracy to ensure the accuracy of the adjustment result. Through the brightness parameter adjustment network, the system can dynamically adjust the screen brightness so that it is always within the user's visual comfort range.

[0036] Furthermore, the embodiment of the present application also includes step P50a: transmitting to a host computer according to the brightness adjustment parameter, the host computer smoothing the adjustment step based on a PID control algorithm, and adjusting the screen brightness according to the smoothed adjustment step.

[0037] Optionally, in order to achieve more accurate and smooth brightness adjustment, the host computer is transmitted according to the brightness adjustment parameters, and the host computer smoothes the adjustment step based on the PID control algorithm, and adjusts the screen brightness according to the smoothed adjustment step. The host computer refers to a computing device used to control and manage the brightness adjustment of the blackboard screen, which is responsible for integrating data and executing advanced control strategies.

[0038] After receiving the brightness adjustment parameters, the host computer will pay special attention to the key parameter of the adjustment step length, because the adjustment step length is directly related to the amplitude and speed of the screen brightness change. In order to avoid abruptness or oscillation during the brightness adjustment process, the host computer uses the PID control algorithm to smooth the adjustment step length. The PID control algorithm is a classic and widely used closed-loop feedback control strategy. It can dynamically adjust the control amount (here is the adjustment step length) by weighted calculation of the proportion, integration and differentiation of the error (that is, the difference between the preset brightness index and the current screen brightness) to achieve fast, accurate and smooth control effects.

[0039] During the smoothing process, the PID control algorithm will intelligently adjust the adjustment step size according to the size and change trend of the error. When the error is large, the algorithm will increase the effect of the proportional term so that the adjustment step size quickly approaches the target value. At the same time, the integral term will accumulate past errors to ensure that the system can eventually eliminate steady-state errors, and the differential term will predict the change trend of the error and make adjustments in advance to reduce overshoot and oscillation. The adjustment step size after smoothing by the PID control algorithm will be more in line with the user's visual habits and the stable operation requirements of the system.

[0040] Finally, the host computer will send brightness adjustment instructions to the blackboard screen through the corresponding hardware interface according to the optimized adjustment step, so as to achieve accurate and smooth adjustment of the screen brightness, which not only improves the user's visual experience, but also enhances the reliability and stability of the system.

[0041] P60: Transmit the brightness adjustment parameters to the host computer to control the brightness of the blackboard screen.

[0042] Furthermore, step P60 of the embodiment of the present application also includes: P61: Divide the blackboard screen into areas to obtain multiple sub-area screens; P62: Position the contact position of the contact sensing data among the multiple sub-area screens and output the first sub-area screen; P63: Transmit the brightness adjustment parameters to the host computer to control the brightness of the first sub-area screen.

[0043] In a possible embodiment of the present application, the brightness adjustment parameters are transmitted from the brightness parameter adjustment network to the host computer, and these parameters are used to control the brightness of the blackboard screen. First, the blackboard screen is divided into regions, and based on the physical size of the screen and usage requirements, the large screen is divided into a plurality of independently controllable small regions, i.e., sub-region screens, so as to achieve more refined brightness adjustment. Through regional division, the brightness of a specific area can be adjusted according to the specific writing position and requirements, so as to reduce unnecessary brightness changes and improve the accuracy of adjustment and user experience.

[0044] Furthermore, by using the contact position in the contact sensing data to locate in multiple sub-area screens, the position information can be compared with the pre-set sub-area screen coordinates to quickly and accurately output the screen coordinates of the area currently touched by the user, thereby determining the area where the user actually writes, that is, the first sub-area screen. Furthermore, after the brightness adjustment parameters are transmitted to the host computer, the host computer uses the PID control algorithm based on the received brightness adjustment parameters to adjust the brightness of the first sub-area screen. By smoothing the adjustment step length, the continuity and stability of the brightness change are ensured.

[0045] Furthermore, step P63 of the embodiment of the present application also includes: P63-1: Predict the user's contact trajectory based on the contact sensing data and obtain the predicted contact trajectory; P63-2: Position the user in the multiple sub-area screens based on the predicted contact trajectory and output the predicted sub-area screen; P63-3: Transmit the brightness adjustment parameters to the host computer to control the brightness of the predicted sub-area screen.

[0046] Specifically, based on the above steps, the embodiment of the present application further introduces a predictive control strategy to adjust the screen brightness in advance by predicting the user's contact trajectory, thereby providing a smoother and more forward-looking user experience. First: predict the user's contact trajectory based on the contact sensing data. Exemplarily, obtain the user's current contact position and historical contact path data from the stylus detection module. Use a machine learning algorithm, such as an LSTM network, to predict the contact trajectory. LSTM (Long Short-Term Memory Network) is a neural network suitable for time series data prediction, which can predict future writing paths based on historical trajectory data. Through the trajectory prediction algorithm, a predicted contact trajectory that the user is about to write is generated.

[0047] Furthermore, the predicted contact trajectory is matched with the pre-divided sub-area screen to determine the sub-area that the predicted trajectory passes through, and the positioning result is output as the predicted sub-area screen, that is, the area where the system predicts that the user is about to write. The brightness adjustment parameters are then transmitted to the host computer, and the host computer uses the PID control algorithm based on the received brightness adjustment parameters to perform forward-looking brightness adjustment on the predicted sub-area screen. This adjustment method can not only improve the user's visual comfort, but also reduce the abruptness caused by brightness changes, making the overall user experience smoother.

[0048] Furthermore, the embodiment of the present application further includes step P70, and step P70 further includes: P71: Obtain basic eye characteristics of the user, including at least eye power characteristics, eye astigmatism characteristics and glasses wearing characteristics; P72: Obtain brightness sensitivity coefficient based on the basic eye characteristics of the user; P73: Perform feedback optimization on the brightness stimulus evaluation network based on the brightness sensitivity coefficient.

[0049] Optionally, the brightness stimulus evaluation network can be personalized according to the basic eye characteristics of the user to achieve a more accurate and comfortable visual experience. Specifically, the basic eye characteristics of the user are obtained through user input, vision testing equipment, or combined with biometric technology (such as eye tracking). The basic eye characteristics of the user include eye degree characteristics, that is, the user's refractive degree information, such as myopia and hyperopia. This information can reflect the user's sensitivity to different brightness. Eye astigmatism characteristics, that is, the user's astigmatism degree and axis information. Astigmatism characteristics affect the user's perception of light, especially in high-contrast environments. Glasses wearing characteristics, that is, whether the user wears glasses, and the type of glasses (such as myopia glasses, hyperopia glasses, or blue light protection glasses). Wearing glasses will affect the refraction and focusing effect of light on the eyeball, thereby indirectly affecting the user's brightness perception.

[0050] Furthermore, the brightness sensitivity coefficient is calculated based on the basic eye characteristics of the user. The brightness sensitivity coefficient is a quantitative indicator used to describe the user's sensitivity to visual stimulation under different brightness conditions. The user's eye degree, astigmatism characteristics and glasses wearing conditions can be analyzed, and based on the analysis results and combined with standard visual science formulas, the user's brightness sensitivity coefficient can be calculated. Then, the brightness stimulus evaluation network is feedback optimized based on the brightness sensitivity coefficient, for example, the parameters of the evaluation network are adjusted so that the network can more accurately predict the user's eye discomfort. The optimized evaluation network can provide personalized brightness adjustment solutions based on the visual characteristics of different users to enhance the user experience.

[0051] In summary, the embodiments of the present application have at least the following technical effects: The present application obtains contact sensing data between the stylus and the blackboard screen through a capacitive sensor, integrates an ambient light sensor and a built-in brightness sensor of the screen, obtains brightness sensing data, inputs the data into a brightness stimulus evaluation network for eye discomfort evaluation, obtains brightness stimulus evaluation indicators, inputs the indicators into a brightness parameter adjustment network, generates brightness adjustment parameters and transmits them to a host computer to control the brightness of the blackboard screen.

[0052] The technical effect of intelligently adjusting the screen brightness through brightness stimulation evaluation and improving user comfort is achieved. Embodiment 2

[0053] Based on the same inventive concept as the brightness adjustment method of a smart blackboard in the aforementioned embodiment, Figure 3 As shown, the present application provides a brightness adjustment system for a smart blackboard, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: A stylus detection module setting module 11, wherein the stylus detection module setting module 11 is used to set a stylus detection module, wherein the stylus detection module includes a capacitive sensor, and the capacitive sensor is set in the stylus; A contact sensing data acquisition module 12, the contact sensing data acquisition module 12 is used to detect the contact between the stylus and the blackboard screen according to the contact sensing data output by the capacitive sensor, wherein the contact sensing data includes contact pressure and contact duration; A brightness sensing data acquisition module 13, wherein the brightness sensing data acquisition module 13 is used to integrate an ambient light sensor and a screen built-in brightness sensor, acquire a screen brightness detection module, and acquire brightness sensing data according to the screen brightness detection module; A brightness stimulation evaluation module 14, wherein the brightness stimulation evaluation module 14 is used to input the contact sensing data and the brightness sensing data into a brightness stimulation evaluation network to perform eye discomfort evaluation and output a brightness stimulation evaluation index; A brightness adjustment parameter generating module 15, wherein the brightness adjustment parameter generating module 15 is used to input the brightness stimulus evaluation index into a brightness parameter adjusting network, wherein the brightness parameter adjusting network is connected to the brightness stimulus evaluation network, and generates a brightness adjustment parameter according to a preset brightness index; The screen brightness control module 16 is used to transmit the brightness adjustment parameters to the host computer to control the brightness of the blackboard screen.

[0054] Furthermore, the brightness stimulus evaluation module 14 is further configured to perform the following steps: Acquire brightness sensing samples, wherein the brightness sensing samples include sample data under different screen brightness and ambient light intensities; acquire contact sensing samples, wherein the contact sensing samples include sample data under different contact pressures and contact durations; perform time synchronization processing based on the brightness sensing samples and the contact sensing samples as a training data set; record the user's eye discomfort feedback samples under the training data set, and use the eye discomfort feedback samples as a test data set; train an initialized fully connected network based on the training data set and the test data set to obtain a brightness stimulus evaluation network.

[0055] Furthermore, the brightness stimulus evaluation module 14 is further configured to perform the following steps: A user distance sample is obtained according to a distance measurement module, wherein the distance measurement module includes a distance measurement sensor, which is arranged in a stylus and is used to measure the distance between the blackboard screen and the user; and according to the influence relationship between the user distance sample and the user's eye discomfort, the brightness stimulus evaluation network is incrementally learned to obtain an optimized brightness stimulus evaluation network.

[0056] Furthermore, the brightness adjustment parameter generating module 15 is further configured to perform the following steps: Determine a preset brightness index, wherein the preset brightness index is within the brightness tolerance range of the blackboard screen; obtain the real-time brightness parameter of the blackboard screen; the brightness parameter adjustment network adjusts the real-time brightness parameter according to the preset brightness index to generate a brightness adjustment parameter, wherein the brightness adjustment parameter includes an adjustment step, an adjustment frequency, and an adjustment error compensation coefficient.

[0057] Furthermore, the brightness adjustment parameter generating module 15 is further configured to perform the following steps: The brightness adjustment parameters are transmitted to a host computer, the host computer smoothes the adjustment step length based on a PID control algorithm, and adjusts the screen brightness according to the smoothed adjustment step length.

[0058] Furthermore, the screen brightness control module 16 is further configured to perform the following steps: The blackboard screen is divided into regions to obtain a plurality of sub-region screens; the contact position of the contact sensing data is positioned in the plurality of sub-region screens to output a first sub-region screen; the brightness adjustment parameter is transmitted to a host computer to control the brightness of the first sub-region screen.

[0059] Furthermore, the screen brightness control module 16 is further configured to perform the following steps: The user's contact trajectory is predicted according to the contact sensing data to obtain the predicted contact trajectory; positioning is performed in the multiple sub-area screens according to the predicted contact trajectory, and the predicted sub-area screens are output; the brightness adjustment parameters are transmitted to the host computer to control the brightness of the predicted sub-area screens.

[0060] Furthermore, the system also includes: A user eye basic feature acquisition module, the user eye basic feature acquisition module is used to acquire the user's eye basic features, including at least eye degree features, eye astigmatism features and glasses wearing features; a brightness sensitivity coefficient acquisition module, the brightness sensitivity coefficient acquisition module is used to acquire the brightness sensitivity coefficient according to the user's eye basic features; a feedback optimization module, the feedback optimization module is used to perform feedback optimization on the brightness stimulus evaluation network according to the brightness sensitivity coefficient.

[0061] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0063] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A brightness adjustment method for a smart blackboard, characterized in that: The method comprises: Setting a stylus detection module, wherein the stylus detection module includes a capacitive sensor, and the capacitive sensor is set in the stylus; Detecting contact between the stylus and the blackboard screen according to contact sensing data output by the capacitive sensor, wherein the contact sensing data includes contact pressure and contact duration; Integrate an ambient light sensor and a screen built-in brightness sensor to obtain a screen brightness detection module, and obtain brightness sensing data according to the screen brightness detection module; Inputting the contact sensing data and the brightness sensing data into a brightness stimulation evaluation network to perform eye discomfort evaluation, and outputting a brightness stimulation evaluation index; Inputting the brightness stimulus evaluation index into a brightness parameter adjustment network, the brightness parameter adjustment network is connected to the brightness stimulus evaluation network, and generating brightness adjustment parameters according to a preset brightness index; The brightness adjustment parameters are transmitted to a host computer to control the brightness of the blackboard screen.

2. The method according to claim 1, characterized in that Training a brightness stimulus evaluation network, including: Acquire brightness sensing samples, wherein the brightness sensing samples include sample data under different screen brightness and ambient light intensity; Acquiring contact sensing samples, wherein the contact sensing samples include sample data under different contact pressures and contact durations; Performing time-series synchronization processing according to the brightness sensing samples and the contact sensing samples as a training data set; Recording the eye discomfort feedback samples of the users in the training data set, and using the eye discomfort feedback samples as the test data set; The initialized fully connected network is trained according to the training data set and the test data set to obtain a brightness stimulus evaluation network.

3. The method according to claim 2, characterized in that Training a brightness stimulus evaluation network also includes: Acquire a user distance sample according to a distance measurement module, wherein the distance measurement module includes a distance measurement sensor, the distance measurement sensor is arranged in the stylus, and the distance measurement sensor is used to measure the distance between the blackboard screen and the user; According to the influence relationship between the user distance samples and the user's eye discomfort, incremental learning is performed on the brightness stimulus evaluation network to obtain an optimized brightness stimulus evaluation network.

4. The method according to claim 1, characterized in that The brightness adjustment parameter is generated according to the preset brightness index, and the method includes: Determining a preset brightness index, wherein the preset brightness index is within a brightness tolerance range of the blackboard screen; Obtaining real-time brightness parameters of the blackboard screen; The brightness parameter adjustment network adjusts the real-time brightness parameter according to the preset brightness index to generate a brightness adjustment parameter, wherein the brightness adjustment parameter includes an adjustment step, an adjustment frequency, and an adjustment error compensation coefficient.

5. The method according to claim 4, characterized in that The brightness adjustment parameters are transmitted to a host computer, the host computer smoothes the adjustment step length based on a PID control algorithm, and adjusts the screen brightness according to the smoothed adjustment step length.

6. The method according to claim 1, characterized in that The brightness adjustment parameter is transmitted to the host computer to control the brightness of the blackboard screen, and the method includes: Dividing the blackboard screen into regions to obtain multiple sub-region screens; Positioning the contact position of the contact sensing data in the plurality of sub-region screens and outputting a first sub-region screen; The brightness adjustment parameter is transmitted to a host computer to control the brightness of the screen of the first sub-area.

7. The method according to claim 6, characterized in that The brightness adjustment parameter is transmitted to a host computer to control the brightness of the first sub-area screen. The method further includes: Predicting a contact trajectory of the user according to the contact sensing data to obtain a predicted contact trajectory; Positioning the plurality of sub-region screens according to the predicted contact trajectory, and outputting a predicted sub-region screen; The brightness adjustment parameters are transmitted to a host computer to control the brightness of the predicted sub-area screen.

8. The method according to claim 3, characterized in that The method further comprises: Obtaining basic eye characteristics of the user, including at least eye power characteristics, eye astigmatism characteristics, and glasses wearing characteristics; Obtaining a brightness sensitivity coefficient according to the basic eye characteristics of the user; Feedback optimization is performed on the brightness stimulus evaluation network according to the brightness sensitivity coefficient.

9. A brightness adjustment system for a smart blackboard, characterized in that: The system comprises: A stylus detection module setting module, the stylus detection module setting module is used to set the stylus detection module, the stylus detection module includes a capacitive sensor, and the capacitive sensor is set in the stylus; a contact sensing data acquisition module, the contact sensing data acquisition module being used to detect the contact between the stylus and the blackboard screen according to the contact sensing data output by the capacitive sensor, wherein the contact sensing data includes contact pressure and contact duration; A brightness sensing data acquisition module, wherein the brightness sensing data acquisition module is used to integrate an ambient light sensor and a screen built-in brightness sensor, acquire a screen brightness detection module, and acquire brightness sensing data according to the screen brightness detection module; A brightness stimulation evaluation module, the brightness stimulation evaluation module is used to input the contact sensing data and the brightness sensing data into a brightness stimulation evaluation network to perform eye discomfort evaluation and output a brightness stimulation evaluation index; A brightness adjustment parameter generation module, the brightness adjustment parameter generation module is used to input the brightness stimulus evaluation index into a brightness parameter adjustment network, the brightness parameter adjustment network is connected to the brightness stimulus evaluation network, and generates a brightness adjustment parameter according to a preset brightness index; A screen brightness control module, wherein the screen brightness control module is used to transmit the brightness adjustment parameters to a host computer to control the brightness of the blackboard screen.

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