Display method and device based on capacitive touch screen and electronic equipment

Abstract

The invention discloses a display method and device based on a capacitive touch screen and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring user touch data and screen content; performing feature extraction on the screen content to obtain screen content features; determining a current working mode according to the user touch data and the screen content characteristics; matching a first display parameter corresponding to the current working mode in a preset display parameter database; predicting a future working mode according to the user touch data and the current working mode; matching a second display parameter corresponding to the future working mode in a preset display parameter database; and smoothly adjusting the first display parameter to a second display parameter through a gradual change mode, and determining to display second display content corresponding to the future working mode through the second display parameter. By implementing the technical scheme provided by the invention, automatic adjustment of the display parameters is realized by judging the current working mode and predicting the future working mode, and the experience feeling of a user is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a display method, device and electronic equipment based on a capacitive touch screen. Background Art

[0002] With the rapid development of information technology and the widespread popularity of smart devices, capacitive touch screens have become an indispensable component of modern electronic devices. Capacitive touch screens use human body current for position sensing and support multi-touch technology, greatly improving the interaction efficiency and experience between users and devices. In addition to their applications in smartphones and tablets, capacitive touch screens are also increasingly being used in a variety of fields, including automotive navigation, industrial control, and medical equipment.

[0003] At present, the display methods in the related art usually use a set of preset display parameters in a fixed manner. When the user needs to adjust the display parameters, the adjustment needs to be made manually, which reduces the user experience.

[0004] Therefore, there is an urgent need for a display method, device and electronic equipment based on a capacitive touch screen. Summary of the invention

[0005] The present application provides a display method, device and electronic device based on a capacitive touch screen, which realizes automatic adjustment of display parameters by judging the current working mode and predicting the future working mode, thereby improving the user experience.

[0006] In a first aspect of the present application, a display method based on a capacitive touch screen is provided, the method comprising: obtaining user touch data and screen content; performing feature extraction on the screen content to obtain screen content features, the screen content features including text density features and image color features; determining a current working mode according to the user touch data and the screen content features; matching a first display parameter corresponding to the current working mode in a preset display parameter database; displaying a first display content corresponding to the current working mode with the first display parameter; predicting a future working mode according to the user touch data and the current working mode; matching a second display parameter corresponding to the future working mode in the preset display parameter database; smoothly adjusting the first display parameter to the second display parameter in a gradual manner, and displaying a second display content corresponding to the future working mode with the second display parameter.

[0007] By adopting the above technical solution, by obtaining user touch data and screen content, and extracting features of the screen content, text density features and image color features reflecting the screen content features are obtained, and then the current working mode is determined in combination with the user touch data. This working mode judgment method based on screen content features and user interaction behavior can accurately perceive the user's current usage needs and interaction intentions, dynamically match the display parameters that best fit the current usage scenario, and present the interface content in real time with the optimal first display parameters, bringing a comfortable, smooth and personalized human-computer interaction experience. At the same time, the method can also intelligently predict the future working mode that the user may switch to based on the user touch data and the current working mode, match the display parameters that best fit the future working mode in advance, and smoothly transition the first display parameters and the second display parameters in a gradual manner, creating a coherent and natural display effect conversion, and reducing the eye discomfort caused by visual mutations. This display optimization solution greatly improves the visual effect and interactive experience of the capacitive touch screen at the physical level, enhances the user's immersion and sense of operation in different application scenarios, and thus improves the user's experience.

[0008] Optionally, the smoothly adjusting the first display parameter to the second display parameter in a gradual manner specifically includes: determining a preset gradual transition time for transitioning from the first display parameter to the second display parameter; calculating a parameter difference between the first display parameter and the second display parameter; according to the preset gradual transition time, evenly dividing the parameter difference into a plurality of parameter adjustment steps; within the preset gradual transition time, gradually adjusting the first display parameter to the second display parameter according to the plurality of parameter adjustment steps.

[0009] By adopting the above technical solution, when performing a gradual adjustment of the display parameters, the preset gradual change duration is first determined, so that the transition time will not appear abrupt due to being too short, nor will it appear sluggish due to being too long. Then, according to the difference between the first display parameter and the second display parameter, the entire gradual change process is evenly divided into a plurality of tiny parameter adjustment steps, and the parameter adjustment step of each step is strictly controlled to ensure that the parameter changes are carried out smoothly within the range of human eye perception, and the entire transition process is natural, smooth, and not abrupt. Through fine-grained gradual control and precise time synchronization, the dynamic optimization process of the display parameters is silent. This method of display parameter gradual change maintains the immersion and continuity of human-computer interaction to the greatest extent.

[0010] Optionally, the feature extraction of the screen content to obtain screen content features specifically includes: segmenting the screen content to obtain multiple sub-screen contents; counting the number of characters in each of the sub-screen contents to generate the text density feature; counting the colors contained in each of the sub-screen contents and the corresponding ratios of each of the colors to generate the image color feature.

[0011] By adopting the above technical solution, when extracting screen content features, the screen content is first segmented into fine-grained segments to extract sub-screen content, fully considering the spatial differences in content layout; then, the display characteristics of each sub-screen content are quantitatively characterized from the two dimensions of text density and color composition, taking into account both the semantic features of the content and the visual features of the content. By comprehensively and meticulously analyzing the structure and distribution of screen content, a feature representation mechanism that emphasizes both semantics and vision is formed, laying a data foundation for the subsequent determination of the working model.

[0012] Optionally, determining the current working mode based on the user touch data and the screen content features specifically includes: inputting the user touch data and the screen content features into a preset working mode classification model, the preset working mode classification model is based on a machine learning algorithm, and is trained with historical user touch data and historical screen content as training data, the preset working mode classification model including user touch data, screen content features and a correspondence between working modes; obtaining the confidence corresponding to each preset working mode output by the preset working mode classification model; sorting each of the confidences from large to small to obtain the maximum confidence, and determining the preset working mode corresponding to the maximum confidence as the current working mode.

[0013] By adopting the above technical solution, when judging the current working mode, an intelligent classification method based on machine learning is adopted. By analyzing the user's historical touch behavior data and historical screen content, a correlation model between user touch data, screen content features and working modes is constructed. When new user touch data and screen content features arrive, the trained preset working mode classification model can quickly and accurately infer the working mode of the user, and give the matching confidence of various possible working modes. By sorting the confidence, the working mode with the highest confidence is selected as the current working mode. This working mode recognition method based on machine learning makes full use of the user's usage habits and screen content characteristics contained in the historical user touch data and historical screen content. The preset working mode classification model obtained through training can automatically learn and summarize the correlation between user behavior and working mode, thereby realizing intelligent recognition and dynamic prediction of working mode.

[0014] Optionally, the first display parameter includes a first brightness, and after matching the first display parameter corresponding to the current operating mode in the preset display parameter database, the method further includes: obtaining the ambient light intensity; if it is determined that the ambient light intensity is higher than a preset high light intensity threshold, increasing the preset brightness value on the basis of the first brightness to obtain a second brightness; if it is determined that the ambient light intensity is lower than a preset low light intensity threshold, reducing the preset brightness value on the basis of the first brightness to obtain the second brightness; and using the second brightness as the brightness parameter of the first display parameter.

[0015] By adopting the above technical solution, when determining the brightness parameters in the display parameters, not only the current working mode requirements are taken into account, but also the influence of ambient light conditions is fully considered. By acquiring the data of ambient light intensity in real time, it is determined whether the current ambient light intensity is higher than the preset high light intensity threshold. If the ambient light intensity is higher than the preset high light intensity threshold, the screen brightness is increased accordingly to avoid the screen content appearing dim and distorted under strong light; if the ambient light intensity is lower than the preset low light intensity threshold, the screen brightness is reduced accordingly to avoid the screen content appearing dazzling in the dark. By combining environmental perception with brightness compensation, the display brightness is dynamically adjusted to match the light conditions, and the screen brightness is always controlled within the most suitable range for human eyes to watch, so that users can get a comfortable and natural visual experience in any lighting environment, reduce visual fatigue, and provide a comfortable viewing experience.

[0016] Optionally, predicting the future working mode based on the user touch data and the current working mode specifically includes: acquiring a historical touch data sequence and a historical working mode; performing feature extraction on the historical touch data sequence to obtain a historical touch feature vector sequence; inputting the historical touch feature vector sequence and the historical working mode into a neural network model for training to obtain a preset working mode transfer model; and predicting the future working mode corresponding to the user touch data and the current working mode through the preset working mode transfer model.

[0017] By adopting the above technical solution, the historical touch data sequence and historical working mode are obtained, and the feature extraction of the historical touch data sequence is performed to obtain the historical touch feature vector sequence; then, according to the historical touch vector sequence and the historical working mode, the neural network model is input for training to obtain the preset working mode transfer model; finally, the future working mode corresponding to the user touch data and the current working mode is predicted through the working mode transfer model. This touch sequence learning and working mode transfer prediction method based on neural network can deeply mine the time series correlation characteristics of user behavior data, fully learn the user's operation behavior sequence and mode switching rules in different working scenarios, and by predicting the future working mode in advance, it can provide leading information for subsequent display content and parameter optimization, realize advance preparation and smooth transition, thereby improving the user's interactive experience and fluency of use.

[0018] Optionally, after smoothly adjusting the first display parameter to the second display parameter in a gradual manner and determining that the second display content corresponding to the future working mode is displayed with the second display parameter, the method also includes: determining whether a touch operation is detected within a preset time period; if it is determined that the touch operation is not detected within the preset time period, turning off the power supply of the capacitive touch screen and adjusting the capacitive touch screen to a low-power standby mode.

[0019] By adopting the above technical solution, it is determined whether a touch operation is detected within a preset time period. If no touch operation is detected, the power supply of the capacitive touch screen is turned off and the capacitive touch screen is adjusted to a low-power standby mode. This adaptive touch detection and power consumption control strategy can dynamically adjust the working state of the capacitive touch screen according to the user's real-time operation behavior, and automatically cut off the power supply of the capacitive screen when the user does not operate for a long time, thereby reducing power consumption and extending battery life.

[0020] In a second aspect of the present application, a display device based on a capacitive touch screen is provided, the device comprising: an acquisition module and a processing module; the acquisition module is used to acquire user touch data and screen content; the processing module is used to extract features of the screen content to obtain screen content features, the screen content features including text density features and image color features; the processing module is also used to determine a current working mode according to the user touch data and the screen content features; the processing module is also used to match a first display parameter corresponding to the current working mode in a preset display parameter database; the processing module is also used to display a first display content corresponding to the current working mode with the first display parameter; the processing module is also used to predict a future working mode according to the user touch data and the current working mode; the processing module is also used to match a second display parameter corresponding to the future working mode in the preset display parameter database; the processing module is also used to smoothly adjust the first display parameter to the second display parameter in a gradual manner, and display the second display content corresponding to the future working mode with the second display parameter.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By acquiring user touch data and screen content, and extracting features of the screen content, text density features and image color features reflecting the screen content features are obtained, and then the current working mode is determined in combination with the user touch data. This working mode judgment method based on screen content features and user interaction behavior can accurately perceive the user's current usage needs and interaction intentions, dynamically match the display parameters that best fit the current usage scenario, and present the interface content in real time with the optimal first display parameters, bringing a comfortable, smooth, and personalized human-computer interaction experience. At the same time, the method can also intelligently predict the future working mode that the user may switch to based on the user touch data and the current working mode, match the display parameters that best fit the future working mode in advance, and smoothly transition the first display parameters and the second display parameters in a gradual manner, creating a coherent and natural display effect conversion, and reducing the eye discomfort caused by visual mutations. This intimate display optimization solution greatly improves the visual effects and interactive experience of capacitive touch screens at the physical level, enhances the user's immersion and sense of operation in different application scenarios, and makes human-computer interaction more humane and intelligent.

[0024] 2. When making gradual adjustments to display parameters, first determine the preset gradual duration, which will neither appear abrupt due to a short transition time nor appear sluggish due to a long transition time. Then, according to the difference between the first display parameter and the second display parameter, evenly divide the entire gradual process into multiple tiny parameter adjustment steps, and strictly control the parameter adjustment step of each step to ensure that the parameter changes are smooth within the range of human eye perception, and the entire transition process is natural, smooth, and not abrupt. Through fine-grained gradual control and precise time synchronization, the dynamic optimization process of display parameters is silent. This method of display parameter gradual change maintains the immersion and coherence of human-computer interaction to the greatest extent.

[0025] 3. When judging the current working mode, an intelligent classification method based on machine learning is adopted. By analyzing the user's historical touch behavior data and historical screen content, a correlation model between user touch data, screen content features and working modes is constructed. When new user touch data and screen content features arrive, the trained preset working mode classification model can quickly and accurately infer the user's working mode and give the matching confidence of various possible working modes. By sorting the confidences, the working mode with the highest confidence is selected as the current working mode. This working mode recognition method based on machine learning makes full use of the user's usage habits and screen content characteristics contained in the historical user touch data and historical screen content. The preset working mode classification model obtained through training can automatically learn and summarize the correlation between user behavior and working mode, thereby realizing intelligent recognition and dynamic prediction of working modes. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a display method based on a capacitive touch screen disclosed in an embodiment of the present application; Figure 2 It is a module schematic diagram of a display device based on a capacitive touch screen disclosed in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0027] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0028] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0029] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0030] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0031] The present application provides a display method based on a capacitive touch screen, referring to Figure 1 , Figure 11 is a flow chart of a display method based on a capacitive touch screen provided in an embodiment of the present application. The method is applied to a user device, which refers to any electronic device equipped with a capacitive touch screen, and the device can execute the display method based on a capacitive touch screen. These devices may include smart phones, tablet computers, laptop computers, and desktop displays. The method includes steps S101 to S108, and the above steps are as follows: Step S101: Acquire user touch data and screen content.

[0032] In step S101, the user device acquires two types of data from the capacitive touch screen in real time: user touch data and current screen content. The user touch data is collected by the sensor array of the capacitive touch screen. When the user's finger clicks, slides, and zooms on the screen, the sensor can record the coordinate position, sliding trajectory, and gesture type information of the touch point, and transmit it to the user device in a certain data format. At the same time, the user device can also obtain the screen content currently displayed on the touch screen through a screenshot as the object of subsequent content analysis and feature extraction.

[0033] Step S102: extracting features of the screen content to obtain screen content features, which include text density features and image color features.

[0034] In step S102, the screen content is segmented to obtain a plurality of sub-screen contents; the number of characters in each sub-screen content is counted to generate a text density feature; the colors contained in each sub-screen content and the corresponding ratios of each color are counted to generate an image color feature.

[0035] Specifically, the user device segments the acquired screen content data according to certain semantic and layout rules to divide it into multiple independent sub-regions. This process can be aided by image segmentation algorithms, such as region growing algorithms based on color, texture and edge features, and segmentation methods based on graph theory. The goal of segmentation is to maintain the content integrity and semantic independence of each sub-region as much as possible, providing a good foundation for subsequent feature analysis. After the segmentation is completed, the user device extracts two types of core features from each sub-region: text density features and image color features. For text density features, the user device uses OCR (optical character recognition) technology to identify text information in each sub-region and count the number, density, and proportion of text. These indicators can reflect the amount and distribution of text information in the screen content. For example, when the text density of a sub-region is very high, it can be inferred that the sub-region is an article or an instruction document; and when the number of texts on the screen as a whole is small, it can be inferred that the current content is mainly pictures and videos. When extracting image color features, the user device performs quantitative analysis on the pixel data of each sub-region. On the one hand, it counts the colors that appear in each sub-region and the corresponding proportions of each color to generate a color histogram. By analyzing the distribution of the color histogram, you can find the color richness and main color tone of the screen content.

[0036] Step S103: Determine the current working mode according to the user touch data and the screen content characteristics.

[0037] In step S103, the user touch data and screen content features are input into a preset working mode classification model. The preset working mode classification model is based on a machine learning algorithm and is trained with historical user touch data and historical screen content as training data. The preset working mode classification model includes user touch data, screen content features, and the correspondence between working modes. The confidence corresponding to each preset working mode output by the preset working mode classification model is obtained. The confidences are sorted from large to small to obtain the maximum confidence, and the preset working mode corresponding to the maximum confidence is determined as the current working mode.

[0038] Specifically, the user device comprehensively determines the user's current working mode based on the user touch data and screen content features obtained in the previous steps. The working mode refers to a typical interactive behavior mode of the user in a specific task scenario, such as browsing mode, editing mode, search mode, and video mode. In order to realize automatic recognition of the working mode, the user device pre-builds a preset working mode classification model. The preset working mode classification model uses a machine learning algorithm and is trained through a large amount of historical user data. These historical data include two categories: one is the historical touch interaction data of the user in various operation scenarios, such as the time, position, and force information of click, slide, and zoom operations; the other is the corresponding historical screen content, such as statistical features of different types of content such as text, images, and videos. Through learning from massive historical user data, the preset working mode classification model can summarize the association rules between user touch data and screen content features in different working modes. For example, in browsing mode, the user's sliding operation is usually more frequent, and most of them are scrolling up and down, and the content is mainly long text and large pictures; while in editing mode, the user's click operation is more intensive, and is mostly concentrated in the input area at the bottom of the screen, and the content is mainly text, accompanied by frequent additions, deletions, and modifications.

[0039] When the user device receives a new set of user touch data and screen content features, it will input them into the preset working mode classification model. The preset working mode classification model will calculate the matching degree between the input data and the typical features of various working modes that have been learned, and obtain a set of confidences. The confidence reflects the similarity between the current input data and various working modes. The higher the confidence, the more the input data conforms to the interactive behavior and content characteristics of the working mode. The user device will sort these confidences and select the working mode corresponding to the confidence with the highest score as the best explanation for the current user behavior, that is, the current working mode.

[0040] It should be noted that the training and updating of the preset work mode classification model is a continuous iterative process. On the one hand, as user interaction data continues to accumulate, the preset work mode classification model needs to be retrained regularly to learn new behavior patterns and content features. On the other hand, the classification effect of the preset work mode classification model also needs to be evaluated and optimized through online user feedback.

[0041] Step S104: matching the first display parameter corresponding to the current working mode in the preset display parameter database.

[0042] In step S104, the user device matches the corresponding display parameters from the preset display parameter database according to the current working mode of the identified user. The preset display parameter database stores the optimal display parameter configurations under different working modes, which are set by field experts based on experiments. Display parameters include key indicators that affect the display effect, such as screen brightness, contrast, and refresh rate. For example, in browsing mode, since users mainly perform fast information scanning and reading, the screen brightness and contrast can be appropriately lowered to reduce visual fatigue; and the refresh rate can be appropriately increased to improve the smoothness of page scrolling and switching. For another example, in editing mode, since users need to stare at the screen for a long time to input text, the screen brightness can be further reduced, and the contrast can be appropriately increased to highlight the clarity of the text; at the same time, in order to reduce the discomfort caused by screen flickering, the refresh rate can be maintained within a more comfortable range. The setting of display parameters comprehensively considers many factors. On the one hand, it is necessary to refer to the research results of ergonomics and visual psychology to understand the influence of different display parameters on human visual perception. On the other hand, a large amount of user subjective perception data is also needed as supplement and verification. Users can be invited to perform tasks in specific working modes under different display parameter combinations, and subjective evaluations and feedback from users can be collected through questionnaires, interviews, and physiological indicators to find the most popular parameter combinations. After establishing a complete preset display parameter database, the user device can quickly match the corresponding optimal display parameters according to the user's working mode. The matching process uses a table lookup operation with low time complexity. Through the matching process, the user device matches the first display parameter corresponding to the current working mode.

[0043] After step S104, the method further includes: obtaining the ambient light intensity; if it is determined that the ambient light intensity is higher than a preset high light intensity threshold, then increasing the preset brightness value on the basis of the first brightness to obtain a second brightness, and the first display parameter includes the first brightness; if it is determined that the ambient light intensity is lower than a preset low light intensity threshold, then reducing the preset brightness value on the basis of the first brightness to obtain a second brightness; and using the second brightness as the brightness parameter of the first display parameter.

[0044] Specifically, the user device obtains the light intensity information of the environment in real time through the light sensor. The light sensor can continuously detect the ambient light intensity and report the ambient light intensity to the user device at a certain frequency. The working principle of the light sensor is to convert the incident light into an electrical signal through a photosensitive diode, and then convert the analog signal into a digital quantity through an analog-to-digital conversion circuit, thereby obtaining a light intensity value in lux. After obtaining the ambient light intensity, the user device will compare it with the preset light intensity threshold, which includes a preset high light intensity threshold and a preset low light intensity threshold. Based on this, the user device can determine whether the current lighting condition belongs to a high-brightness environment or a low-brightness environment. The preset high light intensity threshold and the preset low light intensity threshold can be set according to the visual characteristics of the human eye and the user's usage habits. Generally, when the ambient light intensity exceeds 1000 lux, it can be considered a high-brightness environment, such as outdoor strong light; and when the light intensity is less than 100 lux, it can be regarded as a low-brightness environment, such as a dim indoor environment. The preset high light intensity threshold and the preset low light intensity threshold may also be dynamically adjusted according to actual application scenarios and user feedback, and this application does not limit this.

[0045] If it is determined that the ambient light intensity is higher than the preset high light intensity threshold, the user device determines that the current lighting condition is a high-brightness environment, and the user device will appropriately increase a preset brightness value on the basis of the original first brightness to obtain a second brightness. The size of the preset brightness value comprehensively considers multiple factors, such as the specific value of the ambient light illumination, the maximum brightness value of the screen, and the user's brightness preference. Generally speaking, the preset brightness value will gradually increase with the increase of light intensity, but it cannot exceed the maximum brightness limit of the screen hardware. At the same time, due to certain subjective differences in users' perception of brightness, the user device can also set different brightness increment strategies for different users. For example, for users who prefer bright screens, the preset brightness value can be appropriately increased; for users who prefer soft screens, the preset brightness value can be appropriately reduced. This application does not limit the specific value of the preset brightness value.

[0046] On the contrary, if it is determined that the ambient light intensity is lower than the preset low light intensity threshold, the user device determines that the current lighting condition is a low light environment, and the user device will reduce the preset brightness value in the above step based on the first brightness to obtain a second brightness. The user device will use the adjusted second brightness as a corrected brightness display parameter, and transmit it to the user device together with other display parameters to control the final display effect of the screen.

[0047] Step S105: displaying the first display content corresponding to the current working mode with the first display parameter.

[0048] In step S105, the user equipment generates first display content corresponding to the current working mode from three dimensions of brightness, contrast and refresh rate according to the first display parameter determined in step S104.

[0049] Step S106: predicting the future working mode according to the user touch data and the current working mode.

[0050] In step S106, historical touch data sequences and historical working modes are obtained; features are extracted from the historical touch data sequences to obtain historical touch feature vector sequences; a neural network model is trained based on the historical touch vector sequences and historical working modes to obtain a preset working mode transfer model; and the preset working mode transfer model is used to predict the future working mode corresponding to the user touch data and the current working mode.

[0051] Specifically, the user device obtains the user's historical touch data sequence and the corresponding historical working mode. The historical touch data sequence refers to the user's continuous touch events within a period of time, such as the position, time, and force of touching the screen; the historical working mode refers to the working state of the user during this period, such as reading mode, editing mode, and video mode. These data can be obtained from the log system or user behavior database of the user device, or collected in real time through the embedding point and user behavior tracking technology of the front-end device. The user device needs to perform feature extraction and representation learning on the historical touch data sequence to convert the original touch event into a series of touch feature vectors. The user device uses word embedding technology based on the field of natural language processing, regards each touch event as a "word", and maps high-dimensional and sparse touch events to low-dimensional and dense vector space through word embedding models such as Word2Vec. In this vector space, semantically similar touch events will have similar vector representations, such as click, slide, zoom and other different types of touch events will be clustered. Through this touch event embedding, the user device can obtain a historical touch feature vector sequence, each vector corresponds to a touch event, and the dimension of the vector is between tens and hundreds. With the historical touch feature vector sequence and the corresponding historical working mode, the user device can build a preset working mode transfer model. The user device adopts a sequence labeling model based on a neural network to predict the most likely working mode transfer path under a given touch sequence by learning the mapping relationship between the historical touch feature vector sequence and the corresponding historical working mode. Specifically, the user device uses a bidirectional recurrent neural network structure based on LSTM (Long Short-Term Memory Network) to input the historical touch feature vector sequence into the forward and reverse directions of LSTM to capture the forward and backward dependencies of touch events; in the output layer of LSTM, the CRF (Conditional Random Field) layer is used to sequence label the working mode of each time step, considering the transfer constraints between adjacent working modes. The entire training process is achieved by minimizing the cross entropy loss function between the predicted working mode sequence and the real working mode sequence, and the back propagation algorithm is used to update the parameters of the model to continuously improve the prediction accuracy of the model. When the model training is completed, the preset working mode transfer model is obtained. The preset working mode transfer model can predict which working mode the user is most likely to be in at different time steps based on the user's user touch data. In the actual prediction process, the user device will input the user's latest user touch data and current working mode into the preset working mode transfer model. The preset working mode transfer model will automatically extract the feature vector representation of the user touch data, and combine it with the current working mode to predict the working mode that is most likely to be switched to in the future, and obtain the future working mode.For example, if the user is currently in reading mode and the recent touch events include a large number of sliding and page turning operations, the preset working mode transition model may predict that the user will continue to remain in reading mode for some time in the future; but if the user's touch events suddenly include a large number of keyboard input and text selection operations, the preset working mode transition model may predict that the user is about to switch to editing mode.

[0052] Step S107: Match the second display parameter corresponding to the future working mode in the preset display parameter database.

[0053] In step S107, the user device matches the corresponding second display parameters in the preset display parameter database according to the future working mode predicted in the previous step, and the second display parameters also include screen brightness, contrast and refresh rate.

[0054] Step S108: smoothly adjusting the first display parameter to the second display parameter in a gradual manner, and displaying the second display content corresponding to the future working mode with the second display parameter.

[0055] In step S108, a preset gradient duration for transitioning from a first display parameter to a second display parameter is determined; a parameter difference between the first display parameter and the second display parameter is calculated; the parameter difference is evenly divided into a plurality of parameter adjustment steps according to the preset gradient duration; and within the preset gradient duration, the first display parameter is gradually adjusted to the second display parameter according to the parameter adjustment step.

[0056] Specifically, the user device determines the preset gradient duration for the smooth transition of the display parameters, that is, the time required for the entire process from adjusting the current display parameters (first display parameters) to stabilizing the target display parameters (second display parameters). The setting of the preset gradient duration needs to comprehensively consider factors such as the user's visual adaptation characteristics and the frequency of working mode switching. Generally speaking, the setting of the preset gradient duration should follow the principle of "short, flat and fast", that is, to complete the parameter adjustment in the shortest possible time, so that the user can enter the new display environment as soon as possible; but at the same time, it should not be too short, so as not to be too abrupt and cause visual discomfort. The general transition duration is about 1-2 seconds, which will neither obviously perceive the abruptness of the parameter change, nor appear to be too slow to affect the work rhythm. The preset gradient duration can also be dynamically adjusted according to the user's personal preferences and usage scenarios. This application does not limit the specific value of the preset gradient duration.

[0057] After determining the preset gradient duration, the user device calculates the difference between the first display parameter and the second display parameter, and this difference represents the total amount that each display parameter needs to be adjusted. For example, when the screen brightness is adjusted from 200 nits to 300 nits, the difference in the brightness parameter is 100 nits; when the screen refresh rate is adjusted from 60 Hz to 120 Hz, the difference in the refresh rate parameter is 60 Hz. The user device divides the total difference of each display parameter into several small adjustment steps according to the preset gradient duration. The calculation formula for the adjustment step is: step = difference / (transition duration / refresh interval), where the refresh interval refers to the time it takes for the display to refresh a frame, usually 16.7 ms (corresponding to a 60 Hz refresh rate). For example, if the screen brightness difference is 100 nits and the transition duration is 1 second, then when each frame is refreshed, the screen brightness needs to be adjusted by 100 / (1000 / 16.7)≈1.67 nits until the last frame is adjusted to the second display parameter. In this way, within the entire 1 second transition period, the screen brightness will increase evenly and smoothly by 100 nits, and the user will not feel a sudden change in brightness, but a natural and soft brightness gradient. The user device will adjust the step size and refresh interval according to the set parameters within the preset gradient period, and adjust the display parameters from the first display parameter to the second display parameter frame by frame.

[0058] After step S108, the method further includes: determining whether a touch operation is detected within a preset time period; if it is determined that no touch operation is detected within the preset time period, turning off the power supply of the capacitive touch screen and adjusting the capacitive touch screen to a low power standby mode.

[0059] Specifically, the user device first determines a time window (preset time period) for touch interaction detection, that is, how long does it take for no touch operation to be detected before it can be determined that the user is currently in a non-interactive state. The setting of the preset time period needs to balance two factors: first, it must be long enough to prevent the user from misjudging the non-interactive state due to a long thinking or reading time between two consecutive operations; second, it must switch to a low-power state as soon as possible after the user actually stops interacting to avoid unnecessary energy waste. According to user behavior research and device usage statistics, the preset time period is generally set at around 5-10 minutes, which will neither reduce the response sensitivity nor switch the touch screen state too frequently. Of course, the preset time period can also be personalized and dynamically adjusted based on user preferences, application types, usage scenarios and other factors, and this application does not limit this.

[0060] After determining the preset time period, the user device will start a timer each time the display parameter transition is completed, and begin to continuously record and compare the current time with the timestamp of the last touch operation. If no new touch operation events are detected during the entire preset time period, it means that the user has been in a non-interactive state during this period. At this time, the user device will send a control instruction to the client, instructing the client to cut off the power supply of the capacitive touch screen, and adjust the working clock, scanning frequency and other parameters of the touch screen chip to the lowest level, so that it enters a low-power standby mode. In this state, the capacitive touch screen is basically in a dormant state and will not respond to external touch operations, but it also consumes almost no energy.

[0061] Reference Figure 2 The present application also provides a display device based on a capacitive touch screen, which is a user device. The user device includes an acquisition module 201 and a processing module 202. The acquisition module 201 is used to acquire user touch data and screen content; the processing module 202 is used to extract features of the screen content to obtain screen content features, and the screen content features include text density features and image color features; the processing module 202 is also used to determine the current working mode according to the user touch data and the screen content features; the processing module 202 is also used to match the first display parameter corresponding to the current working mode in a preset display parameter database; the processing module 202 is also used to display the first display content corresponding to the current working mode with the first display parameter; the processing module 202 is also used to predict the future working mode according to the user touch data and the current working mode; the processing module 202 is also used to match the second display parameter corresponding to the future working mode in the preset display parameter database; the processing module 202 is also used to smoothly adjust the first display parameter to the second display parameter in a gradual manner, and display the second display content corresponding to the future working mode with the second display parameter.

[0062] In a possible implementation, the processing module 202 smoothly adjusts the first display parameter to the second display parameter in a gradual manner, specifically including: the processing module 202 determines a preset gradual transition time from the first display parameter to the second display parameter; the processing module 202 calculates the parameter difference between the first display parameter and the second display parameter; the processing module 202 evenly divides the parameter difference into a plurality of parameter adjustment steps according to the preset gradual transition time; the processing module 202 gradually adjusts the first display parameter to the second display parameter according to the plurality of parameter adjustment steps within the preset gradual transition time.

[0063] In a possible implementation, the processing module 202 performs feature extraction on the screen content to obtain screen content features, which specifically includes: the processing module 202 segments the screen content to obtain multiple sub-screen contents; the processing module 202 counts the number of characters in each sub-screen content to generate a text density feature; the processing module 202 counts the colors contained in each sub-screen content and the corresponding ratios of each color to generate an image color feature.

[0064] In a possible implementation, the processing module 202 determines the current working mode based on the user touch data and the screen content features, specifically including: the processing module 202 inputs the user touch data and the screen content features into a preset working mode classification model, the preset working mode classification model is based on a machine learning algorithm, and is trained with historical user touch data and historical screen content as training data, the preset working mode classification model includes the user touch data, the screen content features and the correspondence between the working modes; the acquisition module 201 acquires the confidence corresponding to each preset working mode output by the preset working mode classification model; the processing module 202 sorts the confidences from large to small to obtain the maximum confidence, and determines the preset working mode corresponding to the maximum confidence as the current working mode.

[0065] In a possible implementation, the first display parameter includes a first brightness. After the processing module 202 matches the first display parameter corresponding to the current working mode in the preset display parameter database, the method further includes: the acquisition module 201 acquires the ambient light intensity; if the processing module 202 determines that the ambient light intensity is higher than the preset high light intensity threshold, the processing module 202 increases the preset brightness value on the basis of the first brightness to obtain a second brightness; if the processing module 202 determines that the ambient light intensity is lower than the preset low light intensity threshold, the processing module 202 reduces the preset brightness value on the basis of the first brightness to obtain a second brightness; the processing module 202 uses the second brightness as the brightness parameter of the first display parameter.

[0066] In a possible implementation, the processing module 202 predicts the future working mode based on the user touch data and the current working mode, specifically including: the acquisition module 201 acquires the historical touch data sequence and the historical working mode; the processing module 202 performs feature extraction on the historical touch data sequence to obtain a historical touch feature vector sequence; the processing module 202 inputs the neural network model for training based on the historical touch vector sequence and the historical working mode to obtain a preset working mode transfer model; the processing module 202 predicts the future working mode corresponding to the user touch data and the current working mode through the preset working mode transfer model.

[0067] In a possible implementation, after the processing module 202 smoothly adjusts the first display parameter to the second display parameter in a gradual manner and determines to display the second display content corresponding to the future working mode with the second display parameter, the method also includes: the processing module 202 determines whether a touch operation is detected within a preset time period; if the processing module 202 determines that no touch operation is detected within the preset time period, the processing module 202 turns off the power supply of the capacitive touch screen and adjusts the capacitive touch screen to a low-power standby mode.

[0068] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0069] The present application also provides an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0070] The communication bus 302 is used to realize the connection and communication between these components.

[0071] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0072] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0073] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0074] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a display method based on a capacitive touch screen.

[0075] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a display method based on a capacitive touch screen, and when executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0076] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments.

[0077] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0078] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0081] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0082] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0083] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A display method based on a capacitive touch screen, characterized in that: The method comprises: Get user touch data and screen content; Extracting features of the screen content to obtain screen content features, wherein the screen content features include text density features and image color features; Determine the current working mode according to the user touch data and the screen content characteristics; Matching the first display parameter corresponding to the current working mode in a preset display parameter database; Displaying a first display content corresponding to the current working mode using the first display parameter; Predicting a future working mode according to the user touch data and the current working mode; Matching the second display parameter corresponding to the future working mode in the preset display parameter database; The first display parameter is smoothly adjusted to the second display parameter in a gradual manner, and the second display content corresponding to the future working mode is displayed with the second display parameter.

2. The method according to claim 1, characterized in that The step of smoothly adjusting the first display parameter to the second display parameter in a gradual manner specifically includes: Determining a preset gradual transition duration from the first display parameter to the second display parameter; Calculating a parameter difference between the first display parameter and the second display parameter; According to the preset gradual change time, the parameter difference is evenly divided into a plurality of parameter adjustment steps; Within the preset gradual change time, the first display parameter is gradually adjusted to the second display parameter according to a plurality of the parameter adjustment steps.

3. The method according to claim 1, characterized in that The extracting features of the screen content to obtain screen content features specifically includes: Splitting the screen content to obtain multiple sub-screen contents; Counting the number of characters in the content of each sub-screen to generate the text density feature; The colors contained in each of the sub-screen contents and the corresponding ratios of each of the colors are counted to generate the image color feature.

4. The method according to claim 1, characterized in that: The determining the current working mode according to the user touch data and the screen content characteristics specifically includes: Inputting the user touch data and the screen content features into a preset working mode classification model, wherein the preset working mode classification model is based on a machine learning algorithm and is trained using historical user touch data and historical screen content as training data, and the preset working mode classification model includes a correspondence between the user touch data, the screen content features, and the working mode; Obtaining the confidence corresponding to each preset working mode output by the preset working mode classification model; The confidences are sorted from large to small to obtain a maximum confidence, and the preset working mode corresponding to the maximum confidence is determined as the current working mode.

5. The method according to claim 1, characterized in that The first display parameter includes a first brightness. After matching the first display parameter corresponding to the current working mode in the preset display parameter database, the method further includes: Get the ambient light intensity; If it is determined that the ambient light intensity is higher than the preset high light intensity threshold, then increasing the preset brightness value on the basis of the first brightness to obtain a second brightness; If it is determined that the ambient light intensity is lower than a preset low light intensity threshold, reducing the preset brightness value on the basis of the first brightness to obtain the second brightness; The second brightness is used as a brightness parameter of the first display parameter.

6. The method according to claim 1, characterized in that The predicting of the future working mode according to the user touch data and the current working mode specifically includes: Get historical touch data sequence and historical working mode; Performing feature extraction on the historical touch data sequence to obtain a historical touch feature vector sequence; Input a neural network model for training according to the historical touch feature vector sequence and the historical working mode to obtain a preset working mode transfer model; The preset working mode transfer model is used to predict the future working mode corresponding to the user touch data and the current working mode.

7. The method according to claim 1, characterized in that After smoothly adjusting the first display parameter to the second display parameter in a gradual manner and displaying the second display content corresponding to the future working mode with the second display parameter, the method further includes: Determine whether a touch operation is detected within a preset time period; If it is determined that the touch operation is not detected within the preset time period, the power supply of the capacitive touch screen is turned off, and the capacitive touch screen is adjusted to a low power standby mode.

8. A display device based on a capacitive touch screen, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire user touch data and screen content; The processing module (202) is used to extract features from the screen content to obtain screen content features, wherein the screen content features include text density features and image color features; The processing module (202) is further used to determine the current working mode according to the user touch data and the screen content characteristics; The processing module (202) is further used to match the first display parameter corresponding to the current working mode in a preset display parameter database; The processing module (202) is further configured to display a first display content corresponding to the current working mode using the first display parameter; The processing module (202) is further used to predict the future working mode according to the user touch data and the current working mode; The processing module (202) is further configured to match the second display parameter corresponding to the future working mode in the preset display parameter database; The processing module (202) is further configured to smoothly adjust the first display parameter to the second display parameter in a gradual manner, and to display the second display content corresponding to the future working mode using the second display parameter.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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