[0031] In order to make the objectives, technical solutions and advantages of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other arbitrarily if there is no conflict.
[0032] figure 1 Is a flowchart of a display method according to an embodiment of the present invention, such as figure 1 As shown, the method of this embodiment includes the following steps:
[0033] Step S11, extract file attributes of files browsed by the user;
[0034] Step S12: Set a corresponding screen pixel density according to the file attribute, and establish a model of the relationship between the file attribute and the screen pixel density;
[0035] Step S13: When displaying the file, the model is searched, and the screen pixel density corresponding to the file attribute of the file to be displayed is used for display.
[0036] The method of this embodiment lies in machine learning. The user model is established through machine learning, so as to realize the adjustment of screen pixel density. According to different screen pixel densities, the power consumption of the screen can be reduced to varying degrees. The endurance of the terminal itself will also be improved.
[0037] In an embodiment, step S12, after establishing a model of the relationship between file attributes and screen pixel density, may further include:
[0038] After timing or receiving an instruction, the files that have been browsed within a specified period are analyzed, and the screen pixel density of the corresponding file attributes in the model is adjusted according to the number of times the files have been browsed.
[0039] After the model is established, the model can be further optimized through machine learning. As the model is applied, the model will be improved accordingly. In the application of the model, we will continue to learn and continue to optimize the model.
[0040] The method provided in this embodiment is to dynamically adjust the pixel density of the screen based on machine learning to reduce the power consumption of the LCD, and at the same time, it can also improve the battery life of the mobile terminal.
[0041] After the user gets a mobile terminal (such as a mobile phone), the mobile phone system has already modeled machine learning based on big data processing, and automatically selects the appropriate screen pixel density based on the type of sample, and also the attributes of the phone file. For example, text documents can use a lower screen pixel density, and pictures, etc., use a slightly higher screen pixel density.
[0042] With the passage of time, the user's browsing habits are recorded. Different browsing habits can be classified by file type, image binary array type, and other file types by analogy. After machine learning acquires the user’s screen browsing habits, a machine learning model is established to display the file types most frequently browsed by the current user in high resolution, and the rest adopt normal resolution. During the process of model application, the model is continuously optimized. In order to achieve a more humanized display effect.
[0043] Combine below figure 2 Explain the automatic adjustment of screen pixel density based on machine learning:
[0044] Step S101: Obtain a sample file, such as a browsed object;
[0045] Step S102: Extract file attributes, mainly classify file types.
[0046] Step S103: Set different screen pixel densities according to the extracted file attributes. For example, if the file attribute is a text file, the screen pixel density can be smaller.
[0047] Step S104: When the file is displayed, the model is searched and displayed according to different screen pixel densities corresponding to the file attributes.
[0048] In this way, the display effect of the screen is different, and the power consumption of the screen is also different, and the endurance of the whole machine will also be affected.
[0049] Step S105: Collect and analyze user browsing habits data regularly;
[0050] On the basis of the original model, a new model is optimized for individual users. The premise of the optimized model is the collection and analysis of sample data information. On the basis of sample attributes, record the user's browsing habits, optimize models for different user browsing habits, samples with many browsing times can appropriately increase the screen pixel density, and samples that are hardly browsed can appropriately reduce the pixel density.
[0051] Step S106: Perform feature information extraction on files browsed by the user;
[0052] Feature information is mainly file attributes and user browsing times, and big data analysis and processing of users' browsing habits.
[0053] Step S107: Adjust the screen pixel density of the corresponding file attribute in the model according to the number of times the file is viewed;
[0054] For example, increase the screen pixel density corresponding to the file attribute of the file whose number of times is greater than the first specified value (for example, set to 10 times) to the first threshold (for example, set to 305), and the number of times to be viewed The screen pixel density corresponding to the file attribute of the file that is less than the second specified value (for example, set 20 times) is reduced to a second threshold (for example, the set is 350), and the first specified value is greater than the second specified Value, the first threshold is greater than the second threshold.
[0055] If the file attribute of the browsed file is new, the screen pixel density corresponding to the file attribute is set according to the number of times the file has been browsed, and the corresponding relationship between the file attribute and the screen pixel density is added to the model.
[0056] This embodiment collects and analyzes user browsing habit data on a regular basis. Of course, it can also start collecting and analyzing user browsing habit data upon receiving an instruction output by the user.
[0057] The method provided in this embodiment can dynamically adjust the pixel density of the screen through machine learning, thereby reducing the power consumption of the LCD, the power consumption is reduced, and the endurance of the whole machine is also improved.
[0058] image 3 Is a schematic diagram of a display device according to an embodiment of the present invention, such as image 3 As shown, the device of this embodiment includes:
[0059] The extraction module is used to extract the file attributes of the files browsed by the user;
[0060] The establishment module is used to set the corresponding screen pixel density according to the file attribute, and establish a model of the relationship between the file attribute and the screen pixel density;
[0061] The display module is used to search for the model when displaying the file, and display it using the screen pixel density corresponding to the file attribute of the file to be displayed.
[0062] In an embodiment, the device may further include:
[0063] The adjustment module is used to analyze the files that have been browsed within a specified period of time or after receiving instructions, and adjust the screen pixel density of the corresponding file attributes in the model according to the number of times the files have been browsed.
[0064] Wherein, the adjustment module adjusting the screen pixel density corresponding to the file attribute according to the number of times the file has been viewed may include: increasing the screen pixel density corresponding to the file attribute of the file whose number of times has been viewed greater than the first specified value to a first threshold , Reducing the screen pixel density corresponding to the file attribute of the file whose browsing times are less than the second specified value to a second threshold, the first specified value is greater than the second specified value, and the first threshold is greater than the first threshold Two thresholds.
[0065] Wherein, the adjustment module, if the file attribute of the browsed file is new, sets the screen pixel density corresponding to the file attribute according to the number of times the file has been browsed, and adds the file attribute and file attribute to the model. Correspondence of screen pixel density.
[0066] The device of this embodiment can dynamically adjust the pixel density of the screen, thereby reducing the power consumption of the LCD, the power consumption is reduced, and the endurance of the whole machine is also improved.
[0067] This embodiment also provides a mobile terminal including the above-mentioned display device.
[0068] This embodiment also provides a mobile terminal, including: a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor implements the following steps when the program is executed:
[0069] Extract file attributes of files browsed by users;
[0070] Set the corresponding screen pixel density according to the file attribute, and establish a model of the relationship between the file attribute and the screen pixel density;
[0071] When the file is displayed, the model is searched, and the screen pixel density corresponding to the file attribute of the file to be displayed is used for display.
[0072] The embodiment of the present invention also provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions implement the display method when executed.
[0073] Those of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk. Optionally, all or part of the steps of the foregoing embodiments may also be implemented by using one or more integrated circuits. Correspondingly, each module/unit in the above-mentioned embodiment can be implemented in the form of hardware or software functional module. The present invention is not limited to the combination of any specific form of hardware and software.
[0074] The above are only the preferred embodiments of the present invention. Of course, the present invention can also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various modifications according to the present invention. Corresponding changes and deformations, but these corresponding changes and deformations should fall within the protection scope of the appended claims of the present invention.
