Screen display adjusting method and system, terminal and storage medium

Abstract

The invention relates to a screen display adjusting method and system, a terminal and a storage medium, and the method comprises the steps: obtaining a task interface of a user computer, analyzing the task interface of the user computer, and obtaining a behavior mode of a user; inputting the behavior mode of the user into a pre-trained display model for comparative analysis to obtain a screen adjustment strategy; acquiring environment data of the environment where the user is located, and performing evaluation and judgment in combination with the screen adjustment strategy and the environment data of the environment where the user is located to obtain screen adjustment data of the computer of the user; and correspondingly adjusting the computer of the user according to the screen adjustment data of the computer of the user. The method has the effect of improving the visual experience of the user.

Description

Technical Field

[0001] The present application relates to the technical field of the Internet, and in particular to a screen display adjustment method, system, terminal and storage medium. Background Art

[0002] At present, with the development of science and technology, laptops have become an indispensable tool in daily work and study. At present, most laptops provide the function of manually adjusting screen brightness and color temperature. In different environments, the light intensity will affect the screen display effect. Usually, users need to adjust it by themselves to reduce the impact of the screen display effect on the user's visual comfort and work efficiency. However, this adjustment method requires users to operate frequently and is not convenient enough.

[0003] The above-mentioned prior art solutions have the following defects: the existing laptop screen display has low accuracy when adjusting and cannot effectively protect the user's eyes, so there is room for improvement. Summary of the invention

[0004] In order to enhance the user's visual experience, the present application provides a screen display adjustment method, system, terminal and storage medium.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A screen display adjustment method, the screen display adjustment method comprising: Acquire a task interface of a user's computer, and analyze the task interface of the user's computer to obtain a behavior pattern of the user; Inputting the user's behavior pattern into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy; Acquire environmental data of the environment where the user is located, and evaluate and judge the screen adjustment strategy and the environmental data of the environment where the user is located to obtain screen adjustment data of the user's computer; According to the screen adjustment data of the user's computer, the user's computer is adjusted accordingly.

[0006] By adopting the above technical solution, by acquiring and analyzing the task interface of the user's computer, the user's behavior pattern can be accurately understood to provide a basis for subsequent screen adjustment; by inputting the user's behavior pattern into a pre-trained display model for comparison and analysis, a personalized screen adjustment strategy can be generated to meet the user's specific needs; by acquiring the environmental data of the user's environment and combining it with the screen adjustment strategy for evaluation and judgment, accurate screen adjustment data can be generated, so that the user's computer can be adjusted according to this data to enhance the user's visual experience and usage comfort.

[0007] In a preferred example, the present application can be further configured as follows: the task interface of the user's computer is obtained, and the task interface of the user's computer is analyzed to obtain the user's behavior pattern, including: Acquire image data of the current task interface of the user's computer through image recognition technology; Performing clustering processing on the image data using a k-means algorithm to obtain feature values ​​of the image data, and analyzing the feature values ​​to obtain text content in the user computer task interface; The user's input data, operation records and text content are analyzed to obtain the user's behavior pattern.

[0008] By adopting the above technical solution, the image data of the current task interface of the user's computer is obtained through image recognition technology, so that the specific situation of the current task can be accurately obtained; the image data is clustered using the k-means algorithm to extract the characteristic values ​​of the image data, and further analyzed to obtain the text content in the user's computer task interface; combining the user's input data, operation records and text content for analysis can more comprehensively understand the user's behavior patterns, providing an accurate basis for generating personalized screen adjustment strategies.

[0009] In a preferred example, the present application may be further configured as follows: before inputting the user's behavior pattern into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy, the screen display adjustment method further includes: Acquire training data, the training data including the user's usage habits and preference settings in different environments and different task types, and preprocess and annotate the training data to obtain a training set; The training set is used to perform forward propagation and back propagation training on the long short-term memory network display model, and the long short-term memory network display model trained by the genetic algorithm is used to perform parameter optimization to obtain the pre-trained display model.

[0010] By adopting the above technical solutions, we can obtain the user's usage habits and preference settings in different environments and task types to form a rich training set, ensuring the diversity and comprehensiveness of the model training data; based on the long short-term memory network, the display model is constructed, and the forward propagation and back propagation training can improve the model's understanding and prediction of user behavior patterns. Using genetic algorithms for parameter optimization can further improve the accuracy and stability of the display model, thereby generating a more accurate screen adjustment strategy.

[0011] In a preferred example, the present application can be further configured as follows: the user's behavior pattern is input into a pre-trained display model for comparison and analysis, and the screen adjustment strategy is obtained, including: Preprocessing the user's behavior pattern to obtain preprocessed data, and extracting features from the preprocessed data to obtain feature data of the user's behavior pattern; The characteristic data is compared and analyzed with the data in the pre-trained display model to obtain a comparison result, and the screen adjustment strategy is generated according to the comparison result.

[0012] By adopting the above technical solution, through preprocessing and feature extraction of user behavior patterns, key feature data can be extracted to ensure the data consistency and accuracy of the input display model; by comparing and analyzing the feature data with the data in the pre-trained display model, a screen adjustment strategy that matches the user's current behavior pattern can be generated, thereby improving the adaptability of the screen display and user experience.

[0013] In a preferred example, the present application can be further configured as follows: the environmental data of the environment in which the user is located is obtained, and the screen adjustment data of the user's computer is obtained by comparing and judging the screen adjustment strategy and the environmental data of the environment in which the user is located, and the screen adjustment data of the user's computer is obtained, including: Using sensors to obtain light intensity, temperature, and noise level in the environment where the user is located, the light intensity, temperature, and noise level are all environmental data of the environment where the user is located; The user's visual comfort is evaluated by combining the environmental data and the screen adjustment strategy, and screen adjustment data of the user's computer is generated according to the user's visual comfort.

[0014] By adopting the above technical solution, by using sensors to obtain the light intensity, temperature and noise level in the user's environment, the user's current environmental conditions can be fully understood; combining environmental data and screen adjustment strategies, the user's visual comfort can be accurately evaluated, thereby generating screen adjustment data that adapts to the current environment, thereby improving the user's visual comfort and usage experience.

[0015] In a preferred example, the present application can be further configured as follows: the screen display adjustment method further includes: monitoring the user's eye movements and facial expressions in real time; Adjust the screen display parameters of the user's computer according to the user's eye movements and facial expressions.

[0016] By adopting the above technical solution, through real-time monitoring of the user's eye movements and facial expressions, the user's visual state and emotional changes can be dynamically understood; by adjusting the screen display parameters according to the user's eye movements and facial expressions, the screen display effect can be optimized in time, the user's visual experience can be enhanced, and at the same time the user's visual health can be protected, avoiding visual fatigue caused by long-term computer use.

[0017] The second object of the invention is achieved by the following technical solutions: A screen display adjustment system, the screen display adjustment system comprising: A behavior pattern acquisition module is used to acquire a task interface of a user's computer and analyze the task interface of the user's computer to obtain the behavior pattern of the user; A model analysis module, used to input the user's behavior pattern into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy; An adjustment parameter obtaining module is used to obtain the environmental data of the environment where the user is located, and to evaluate and judge the screen adjustment strategy and the environmental data of the environment where the user is located, so as to obtain the screen adjustment data of the user's computer; The adjustment module is used to make corresponding adjustments to the user's computer according to the screen adjustment data of the user's computer.

[0018] By adopting the above technical solution, by acquiring and analyzing the task interface of the user's computer, the user's behavior pattern can be accurately understood to provide a basis for subsequent screen adjustment; by inputting the user's behavior pattern into a pre-trained display model for comparison and analysis, a personalized screen adjustment strategy can be generated to meet the user's specific needs; by acquiring the environmental data of the user's environment and combining it with the screen adjustment strategy for evaluation and judgment, accurate screen adjustment data can be generated, so that the user's computer can be adjusted according to this data to enhance the user's visual experience and usage comfort.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned screen display adjustment method when executing the computer program.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned screen display adjustment method are implemented.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. By acquiring and analyzing the task interface of the user's computer, the user's behavior pattern can be accurately understood, providing a basis for subsequent screen adjustment; the user's behavior pattern is input into the pre-trained display model for comparison and analysis, and a personalized screen adjustment strategy can be generated to meet the user's specific needs; 2. By obtaining environmental data of the user's environment and combining it with screen adjustment strategies for evaluation and judgment, accurate screen adjustment data can be generated, so that the user's computer can be adjusted according to this data to improve the user's visual experience and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a screen display adjustment method in an embodiment of the present application; Figure 2 is a flowchart for implementing step S10 in a screen display adjustment method in one embodiment of the present application; Figure 3 This is a flowchart for implementing step S20 in a screen display adjustment method in an embodiment of the present application; Figure 4 is a flowchart for implementing step S20 in a screen display adjustment method in an embodiment of the present application; Figure 5 is a flowchart for implementing step S30 in a screen display adjustment method in an embodiment of the present application; Figure 6 This is a flowchart of a method for adjusting screen display in an embodiment of the present application; Figure 7 This is a principle block diagram of a screen display adjustment system in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a screen display adjustment method, which specifically includes the following steps: S10: Acquire a task interface of the user's computer, and analyze the task interface of the user's computer to obtain the user's behavior pattern.

[0025] Specifically, the system first captures the computer interface that the user is currently using. This may include active windows, open applications, documents being edited, etc. By analyzing the captured task interface, this may involve identifying the applications that the user uses most frequently, the frequency and pattern of the user's interaction with the screen, and the time the user spends on different tasks, such as whether the user is doing text editing, image processing, or other types of work, to identify the user's behavior pattern.

[0026] S20: Inputting the user's behavior pattern into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy.

[0027] Specifically, the user's behavior pattern data is input into a pre-trained model, which may be used to predict or recommend display settings, such as brightness, contrast, color saturation, etc., to adapt to the user's behavior. The user behavior is analyzed through the display model, and a most suitable screen display strategy is provided based on the behavior pattern.

[0028] S30: Acquire environmental data of the user's environment, and make an evaluation and judgment based on the screen adjustment strategy and the environmental data of the user's environment to obtain screen adjustment data of the user's computer.

[0029] Specifically, the system collects data about the user's current environment, such as light intensity, color temperature, noise level, etc. These data can be obtained through various sensors, and then combines these data with the screen adjustment strategy for a comprehensive evaluation to determine the best screen display settings to ensure that the screen settings are in line with both user preferences and current environmental conditions. After evaluation and judgment, the system generates a set of screen adjustment parameters that the system believes are most suitable for the current user and environmental conditions.

[0030] S40: According to the screen adjustment data of the user's computer, the user's computer is adjusted accordingly.

[0031] Specifically, based on the comprehensive screen adjustment data, the system automatically adjusts the relevant settings of the computer screen, such as brightness, contrast, etc., to ensure that users can get the best visual effects and usage experience in different environments and different tasks.

[0032] By adopting the above technical solution, by acquiring and analyzing the task interface of the user's computer, the user's behavior pattern can be accurately understood to provide a basis for subsequent screen adjustment; by inputting the user's behavior pattern into a pre-trained display model for comparison and analysis, a personalized screen adjustment strategy can be generated to meet the user's specific needs; by acquiring the environmental data of the user's environment and combining it with the screen adjustment strategy for evaluation and judgment, accurate screen adjustment data can be generated, so that the user's computer can be adjusted according to this data to enhance the user's visual experience and usage comfort.

[0033] In one embodiment, if Figure 2 As shown, in step S10, the task interface of the user's computer is obtained, and the task interface of the user's computer is analyzed to obtain the user's behavior pattern, which specifically includes: S11: Acquire image data of the current task interface of the user's computer through image recognition technology.

[0034] Specifically, image recognition technology, such as computer vision algorithms, is used to capture images of the current task interface of the user's computer, which may include documents, web pages, application interfaces, etc. that the user is viewing, in order to obtain accurate image data of the current task interface of the user's computer.

[0035] S12: Perform clustering processing on the image data using a k-means algorithm to obtain eigenvalues ​​of the image data, and analyze the eigenvalues ​​to obtain text content in the user computer task interface.

[0036] Specifically, the captured image data is input into the k-means algorithm, which is an unsupervised learning algorithm for grouping data points into k clusters so that the sum of the distances between each data point and the center of its cluster is minimized. The k-means algorithm extracts the feature values ​​of the image by clustering the image data; these feature values ​​include attributes such as color, texture, and shape, which are key information used to distinguish different images in image recognition. Using the extracted feature values, the system further analyzes the image data to identify and extract the text content therein, such as optical character recognition technology, by identifying the area containing text in the image, dividing the text area into separate lines or paragraphs, extracting features from each character, such as shape, edge, texture, etc., matching the extracted features with the trained classifier, identifying each character, and outputting the identified text in an editable and searchable format to convert the text in the image into editable and searchable text data.

[0037] S13: Analyze the user's input data, operation records, and text content to obtain the user's behavior pattern.

[0038] Specifically, the text content in the image is combined with the user's input and operation records, and the system uses data analysis technology to identify the user's behavior patterns. For example, if the user often edits documents at a certain time, or spends more time on specific types of tasks, these patterns can reveal the user's preferences and habits, and ultimately the user's behavior pattern is obtained through analysis.

[0039] In one embodiment, if Figure 3 As shown, before step S20, that is, before inputting the user's behavior pattern into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy, a screen display adjustment method further includes: S201: Acquire training data, where the training data includes the user's usage habits and preference settings in different environments and different task types, and preprocess and annotate the training data to obtain a training set.

[0040] Specifically, the system first collects users' usage habits and preference settings in different environments and different task types; this includes screen brightness, contrast, font size, layout preferences, etc. The collected data is preprocessed, including cleaning to remove invalid or erroneous data, normalization and feature engineering. At the same time, the data needs to be labeled, that is, given the correct labels to facilitate model training. These preprocessed data are summarized to form a training set.

[0041] S202: Perform forward propagation and back propagation training on the long short-term memory network display model using the training set, and optimize the parameters of the long short-term memory network display model after genetic algorithm training to obtain a pre-trained display model.

[0042] Specifically, a model is constructed using a long short-term memory network, which is a special type of recurrent neural network that can learn long-term dependencies and is suitable for processing sequence data. The model is trained using a training set. In the forward propagation stage, the input data is propagated forward in the network to calculate the output. In the back propagation stage, the loss is calculated based on the output and the actual value, and then the loss is propagated backward through the network to update the network weights. After training, a genetic algorithm is used to further optimize the model parameters. The solution is optimized through selection, crossover and mutation in an iterative process. After training and parameter optimization, a display model with good performance is obtained. This model can predict and adjust according to the user's usage habits and preference settings, that is, a pre-trained display model.

[0043] In one embodiment, if Figure 4 As shown, in step S20, the user's behavior pattern is input into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy, which specifically includes: S21: preprocessing the user's behavior pattern to obtain preprocessed data, and extracting features from the preprocessed data to obtain feature data of the user's behavior pattern.

[0044] Specifically, user behavior pattern data may include various information about the user's interaction with the device, such as clicks, scrolling, input habits, etc. These raw data are converted into a format suitable for further analysis, including data cleaning, removing noise and outliers, adjusting the data scale to eliminate the impact of different dimensions, and data conversion, such as discretizing time series data. Based on the preprocessed data, feature extraction is performed to identify key information. These features include the time pattern of user activities, interaction frequency, preference settings, etc. After preprocessing and feature extraction, the data obtained is called feature data, which captures the key aspects of user behavior patterns.

[0045] S22: Compare and analyze the feature data with the data in the pre-trained display model to obtain a comparison result, and generate a screen adjustment strategy based on the comparison result.

[0046] Specifically, the extracted feature data is compared with the data in a pre-trained display model. This model has learned the association between different user behavior patterns and screen adjustment settings. The purpose of the comparison analysis is to find out the similarities or differences between the user's current behavior pattern and the existing data in the model. The comparison results may include the degree of match between the user behavior pattern and the model prediction, recommended screen settings, etc. Based on the comparison results, the system generates a corresponding screen adjustment strategy, which includes adjusting the screen brightness, contrast, font size, layout, etc. to adapt to the user's behavior pattern and improve the user experience.

[0047] In one embodiment, if Figure 5 As shown, in step S30, the environmental data of the user's environment is obtained, and the screen adjustment strategy and the environmental data of the user's environment are combined for evaluation and judgment to obtain the screen adjustment data of the user's computer, which specifically includes: S31: Using sensors to obtain light intensity, temperature, and noise level in the environment where the user is located, where the light intensity, temperature, and noise level are all environmental data of the environment where the user is located.

[0048] Specifically, because measuring the brightness of light in the environment may affect the visibility of the screen and the user's visual comfort, the ambient temperature may affect the user's comfort and the performance of the screen display technology. Although noise directly affects hearing, a high-noise environment may distract the user and indirectly affect visual concentration. Therefore, different types of sensors are used to collect the light intensity, temperature and noise level of the user's environment.

[0049] S32: Evaluate the user's visual comfort by combining the environmental data and the screen adjustment strategy, and generate screen adjustment data for the user's computer according to the user's visual comfort.

[0050] Specifically, the collected environmental data is combined with the user's behavior patterns and screen adjustment strategies. For example, if the ambient light intensity is high, the system may recommend increasing the screen brightness to improve visibility. Using environmental data and screen adjustment strategies, the system evaluates the user's visual comfort under current conditions, such as the contrast between screen brightness and ambient light, the coordination of color temperature and ambient light, the matching of screen font size with user vision, the use of eye tracking technology to analyze the user's gaze point and line of sight movement, and asking the user whether they feel eye fatigue, headache or other discomfort after using the screen. Based on the evaluation results, the system generates specific screen adjustment data, including adjusting screen brightness, contrast, color temperature, font size and other display settings to optimize the user's visual experience.

[0051] In one embodiment, if Figure 6 As shown, the screen display adjustment method also includes: S51: Real-time monitoring of user's eye movements and facial expressions.

[0052] Specifically, eye tracking technology is used to monitor the user's eye movements, which can reflect the user's attention focus, reading habits and visual comfort. At the same time, facial expression recognition technology is used to analyze the user's emotional state, such as whether he or she feels tired, confused or satisfied.

[0053] S52: Adjust the screen display parameters of the user's computer according to the user's eye movements and facial expressions to enhance the user's visual experience and health protection.

[0054] Specifically, the system analyzes eye movement data, such as gaze point, eye saccade patterns, and pupil size, to understand how users interact with screen content. Based on this information, the system can adjust the screen display parameters. For example, if the user's pupils dilate while reading, it may mean that the environment is dark. The system can automatically increase the screen brightness and adjust the contrast of the screen content based on the user's gaze point and eye saccades to reduce visual fatigue. If the user's eye movements show that they need to frequently adjust their line of sight to track the text, the system can increase the font size or adjust the font style to improve readability. The screen display parameters of the user's computer are adjusted based on the user's eye movements and facial expressions.

[0055] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0056] In one embodiment, a screen display adjustment system is provided, and the screen display adjustment system corresponds one-to-one to a screen display adjustment method in the above embodiment. Figure 7 As shown, the screen display adjustment system includes a behavior pattern acquisition module, a model analysis module, an adjustment parameter acquisition module and an adjustment module. The detailed description of each functional module is as follows: The behavior pattern acquisition module is used to acquire the task interface of the user's computer and analyze the task interface of the user's computer to obtain the user's behavior pattern; The model analysis module is used to input the user's behavior pattern into the pre-trained display model for comparison and analysis to obtain the screen adjustment strategy; The adjustment parameter acquisition module is used to acquire the environmental data of the user's environment, and to evaluate and judge the screen adjustment strategy and the environmental data of the user's environment to obtain the screen adjustment data of the user's computer; The adjustment module is used to adjust the user's computer accordingly according to the screen adjustment data of the user's computer.

[0057] Optionally, the screen display adjustment system further includes: Monitoring module, used to monitor the user's eye movements and facial expressions in real time; The display adjustment module is used to adjust the screen display parameters of the user's computer according to the user's eye movements and facial expressions to enhance the user's visual experience and health protection.

[0058] The training set acquisition module is used to acquire training data, which includes the user's usage habits and preference settings in different environments and different task types, and preprocess and annotate the training data to obtain a training set; The model training module is used to use the training set to perform forward propagation and back propagation training on the long short-term memory network display model, and to optimize the parameters of the long short-term memory network display model trained by the genetic algorithm to obtain a pre-trained display model.

[0059] Optionally, the behavior pattern acquisition module includes: The image recognition submodule is used to obtain the image data of the current task interface of the user's computer through image recognition technology; The cluster analysis submodule is used to perform clustering processing on the image data using the k-means algorithm to obtain the eigenvalues ​​of the image data, and to analyze the eigenvalues ​​to obtain the text content in the user computer task interface; The data analysis submodule is used to analyze the user's input data, operation records and text content to obtain the user's behavior pattern.

[0060] Optionally, the model analysis module includes: The data preprocessing submodule is used to preprocess the user's behavior pattern to obtain preprocessed data, and to extract features from the preprocessed data to obtain feature data of the user's behavior pattern; The comparison and analysis submodule is used to compare and analyze the feature data with the data in the pre-trained display model to obtain the comparison results, and generate the screen adjustment strategy based on the comparison results.

[0061] Optionally, the module for obtaining adjustment parameters includes: The sensor detection submodule is used to obtain the light intensity, temperature and noise level of the user's environment using sensors. The light intensity, temperature and noise level are all environmental data of the user's environment. The evaluation submodule is used to combine environmental data and screen adjustment strategy to evaluate the user's visual comfort and generate screen adjustment data for the user's computer based on the user's visual comfort.

[0062] For the specific definition of a screen display adjustment system, please refer to the definition of a screen display adjustment method above, which will not be repeated here. Each module in the above-mentioned screen display adjustment system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0063] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a screen display adjustment method is implemented.

[0064] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtaining a task interface of a user's computer, and analyzing the task interface of the user's computer to obtain the user's behavior pattern; Input the user's behavior pattern into the pre-trained display model for comparison and analysis to obtain the screen adjustment strategy; Acquire the environmental data of the user's environment, and make an evaluation and judgment based on the screen adjustment strategy and the environmental data of the user's environment to obtain the screen adjustment data of the user's computer; According to the screen adjustment data of the user's computer, the user's computer is adjusted accordingly.

[0065] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtaining a task interface of a user's computer, and analyzing the task interface of the user's computer to obtain the user's behavior pattern; Input the user's behavior pattern into the pre-trained display model for comparison and analysis to obtain the screen adjustment strategy; Acquire the environmental data of the user's environment, and make an evaluation and judgment based on the screen adjustment strategy and the environmental data of the user's environment to obtain the screen adjustment data of the user's computer; According to the screen adjustment data of the user's computer, the user's computer is adjusted accordingly.

[0066] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0067] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0068] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A screen display adjustment method, characterized in that: The screen display adjustment method comprises: Acquire a task interface of a user's computer, and analyze the task interface of the user's computer to obtain a behavior pattern of the user; Inputting the user's behavior pattern into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy; Acquire environmental data of the environment where the user is located, and evaluate and judge the screen adjustment strategy and the environmental data of the environment where the user is located to obtain screen adjustment data of the user's computer; According to the screen adjustment data of the user's computer, the user's computer is adjusted accordingly.

2. A screen display adjustment method according to claim 1, characterized in that: The step of obtaining a task interface of a user's computer and analyzing the task interface of the user's computer to obtain the user's behavior pattern includes: Acquire image data of the current task interface of the user's computer through image recognition technology; Performing clustering processing on the image data using a k-means algorithm to obtain feature values ​​of the image data, and analyzing the feature values ​​to obtain text content in the user computer task interface; The user's input data, operation records and text content are analyzed to obtain the user's behavior pattern.

3. A screen display adjustment method according to claim 1, characterized in that: Before inputting the user's behavior pattern into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy, the screen display adjustment method further includes: Acquire training data, the training data including the user's usage habits and preference settings in different environments and different task types, and preprocess and annotate the training data to obtain a training set; The training set is used to perform forward propagation and back propagation training on the long short-term memory network display model, and the long short-term memory network display model trained by the genetic algorithm is used to perform parameter optimization to obtain the pre-trained display model.

4. A screen display adjustment method according to claim 1, characterized in that: The inputting the user's behavior pattern into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy includes: Preprocessing the user's behavior pattern to obtain preprocessed data, and extracting features from the preprocessed data to obtain feature data of the user's behavior pattern; The characteristic data is compared and analyzed with the data in the pre-trained display model to obtain a comparison result, and the screen adjustment strategy is generated according to the comparison result.

5. A screen display adjustment method according to claim 1, characterized in that: The step of obtaining the environmental data of the environment in which the user is located, and performing evaluation and judgment in combination with the screen adjustment strategy and the environmental data of the environment in which the user is located to obtain the screen adjustment data of the user's computer includes: Using sensors to obtain light intensity, temperature, and noise level in the environment where the user is located, the light intensity, temperature, and noise level are all environmental data of the environment where the user is located; The user's visual comfort is evaluated by combining the environmental data and the screen adjustment strategy, and screen adjustment data of the user's computer is generated according to the user's visual comfort.

6. A screen display adjustment method according to claim 1, characterized in that: The screen display adjustment method further includes: monitoring the user's eye movements and facial expressions in real time; Adjust the screen display parameters of the user's computer according to the user's eye movements and facial expressions.

7. A screen display adjustment system, characterized in that: The screen display adjustment system comprises: A behavior pattern acquisition module is used to acquire a task interface of a user's computer and analyze the task interface of the user's computer to obtain the behavior pattern of the user; A model analysis module, used to input the user's behavior pattern into a pre-trained display model for comparison and analysis to obtain a screen adjustment strategy; An adjustment parameter obtaining module is used to obtain the environmental data of the environment where the user is located, and to evaluate and judge the screen adjustment strategy and the environmental data of the environment where the user is located, so as to obtain the screen adjustment data of the user's computer; The adjustment module is used to make corresponding adjustments to the user's computer according to the screen adjustment data of the user's computer.

8. A screen display adjustment system according to claim 7, characterized in that: The screen display adjustment system further comprises: A monitoring module, used for monitoring the eye movements and facial expressions of the user in real time; The display adjustment module is used to adjust the screen display parameters of the user's computer according to the user's eye movements and facial expressions to enhance the user's visual experience and health protection.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the screen display adjustment method as claimed in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a screen display adjustment method as claimed in any one of claims 1 to 6 are implemented.

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