A layout and switching method of a tablet computer multi-task window

Abstract

This invention provides a method for layout and switching of multi-tasking windows on a tablet computer. This method applies a priority scoring formula combined with multi-dimensional user behavior and device status data to achieve precise window layout adaptation. Compared to a fixed layout, it improves the completeness of application content display and operational efficiency. An adaptive window size and position calculation model avoids window overlap and screen overflow issues, while ensuring high-priority applications receive better display resources, reducing the accidental touch rate during multi-tasking operations. An intelligent switching trigger threshold algorithm, combining gestures, relevance, and user intent, reduces manual switching operations and improves switching response speed. A dynamic adjustment formula for transition animation parameters optimizes the visual experience based on device performance and application type, reducing resource consumption in low-power scenarios and improving battery life. A user habit learning mechanism enables personalized adaptation, resulting in a better user experience.

Description

Technical Field

[0001] This invention relates to the field of human-computer interaction technology, and in particular to a method for layout and switching of multi-tasking windows on a tablet computer. Background Technology

[0002] Current tablet multitasking window layouts are mostly fixed (such as 1:1 split-screen, top-bottom split-screen, and fixed-ratio three-screen), failing to consider differences in application types, user habits, and scenario requirements. This results in poor window size adaptation to application content (e.g., document editing windows are too small, video windows have an unreasonable proportion). Window switching relies on manually clicking taskbar icons or dragging, which is cumbersome and lacks intelligent triggering based on application relevance and user intent, leading to low multitasking efficiency. Window layouts do not consider device resource usage, easily causing lag and reduced battery life when multiple high-power applications are displayed in full screen simultaneously, and resource allocation lacks a dynamic optimization mechanism. Current technologies only support manual dragging for window size adjustment, failing to establish a dynamic adjustment logic of "user habit learning + content adaptation," resulting in insufficient personalized experience, and the adjusted window position is prone to overlapping or exceeding the screen range. Switching transition animation parameters are fixed and not dynamically adjusted according to application type (e.g., office software / video software) and device performance, leading to an imbalance between switching smoothness and visual experience (e.g., slow animation on high-performance devices, stuttering animation on low-performance devices). Summary of the Invention

[0003] The purpose of this invention is to provide a method for layout and switching of multitasking windows on a tablet computer, so as to solve at least one of the problems in the background art.

[0004] This invention provides a method for layout and switching of multitasking windows on a tablet computer, the method comprising:

[0005] S1: Collect multi-dimensional data on currently active applications on the tablet and calculate the priority of each application using the application priority scoring formula;

[0006] S2: Based on the application priority and screen resolution, determine the size and position of each window through an adaptive calculation model;

[0007] S3: Collect user operation data and application correlation, determine whether to trigger a switch through a switch trigger threshold algorithm, and dynamically adjust transition animation parameters based on device resource utilization and application type;

[0008] S4: Record user behavior data and optimize weight coefficients and switching thresholds through iterative algorithms to achieve adaptive user habits.

[0009] Furthermore, the application priority scoring formula is as follows:

[0010] ;

[0011] in, The value is a normalized value for the frequency of use, ranging from 0 to 1, and is obtained by normalizing the usage data of the past 30 days. To apply relevance, The application type weight ranges from 0.5 to 0.8. To determine the relevance, a value between 0 and 1 is used. This represents resource utilization, with a value between 0 and 1. - For dynamic weighting coefficients, =0.35, =0.25, =0.20, =0.15, =0.05, which can be iteratively optimized using user behavior data. The application is given a priority score, ranging from 0 to 1. The higher the score, the higher the priority of window layout and resource allocation.

[0012] Furthermore, the formula for the window size is:

[0013] ;

[0014] ;

[0015] in, For the first The width of each application window, No. Priority rating for each application. This is the sum of the priorities of all currently active applications, used to allocate screen space proportionally. The total width of the tablet screen. The number of currently active applications. Horizontal spacing of windows This sets a minimum window width threshold to prevent the window from becoming too narrow to operate. The maximum width threshold for the window. No. The height of each application window The total height of the tablet screen. Vertical spacing of windows Set a minimum window height threshold to accommodate one-handed operation. This is the maximum height threshold for the window.

[0016] Furthermore, the formula for the switching trigger threshold algorithm is as follows:

[0017] ;

[0018] The method for dynamically adjusting transition animation parameters based on both device resource utilization and application type is as follows:

[0019] ;

[0020] ;

[0021] in, The duration of the transition animation. =0.4, =0.3, =0.2, =0.1, which is the trigger weight coefficient. =500 pixels =100 pixels, which is the gesture operation threshold. This refers to the pressing pressure.

[0022] Furthermore, in step S1, the number of active applications is less than or equal to 5, based on the upper limit of tablet computer performance, and the application correlation is obtained through training using application interaction logs.

[0023] Furthermore, in step S2, the window position calculation follows the rule that the application with the highest priority is placed in the core area of ​​the screen, and the rest are placed in descending order of priority to avoid overlap.

[0024] Furthermore, in step S3, the condition for triggering the switch is that the calculated result of the operation parameter is greater than or equal to a preset threshold, and the switch target is the application with the highest relevance and the second highest priority.

[0025] Furthermore, in step S4, the learning rate η of the iterative algorithm is 0.05, and the weight and threshold optimization is completed once every 100 user behavior data are accumulated.

[0026] Furthermore, the application types include office applications, video applications, social applications, and utility applications, with corresponding weights of 0.8, 0.7, 0.6, and 0.5, respectively.

[0027] Furthermore, the maximum window size threshold = -2 Maximum window height threshold = -2 Ensure that the window does not extend beyond the screen area.

[0028] The aforementioned method for layout and switching multitasking windows on a tablet computer utilizes a priority scoring formula combined with multi-dimensional user behavior and device status data to achieve precise window layout adaptation. Compared to a fixed layout, this improves the completeness of application content display and operational efficiency. An adaptive window size and position calculation model avoids window overlap and screen overflow issues, while ensuring high-priority applications receive better display resources, reducing the accidental touch rate during multitasking. An intelligent switching trigger threshold algorithm, combining gestures, relevance, and user intent, reduces manual switching operations and improves switching response speed. A dynamic adjustment formula for transition animation parameters optimizes the visual experience based on device performance and application type, reducing resource consumption and improving battery life in low-power scenarios. A user habit learning mechanism enables personalized adaptation, resulting in a superior user experience. Attached Figure Description

[0029] Figure 1 This is a flowchart of a method for layout and switching of a multi-tasking window on a tablet computer according to the first embodiment of the present invention.

[0030] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0031] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0032] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0034] Please see Figure 1 The first embodiment of the present invention provides a method for layout and switching of multitasking windows on a tablet computer, the method comprising:

[0035] S1: Collect multi-dimensional data on currently active applications on the tablet and calculate the priority of each application using the application priority scoring formula;

[0036] S2: Based on the application priority and screen resolution, determine the size and position of each window through an adaptive calculation model;

[0037] S3: Collect user operation data and application correlation, determine whether to trigger a switch through a switch trigger threshold algorithm, and dynamically adjust transition animation parameters based on device resource utilization and application type;

[0038] S4: Record user behavior data and optimize weight coefficients and switching thresholds through iterative algorithms to achieve adaptive user habits.

[0039] In one embodiment of the present invention, the application priority scoring formula is:

[0040] ;

[0041] in, The value is a normalized value for the frequency of use, ranging from 0 to 1, and is obtained by normalizing the usage data of the past 30 days. To apply relevance, The application type weight ranges from 0.5 to 0.8. To determine the relevance, a value between 0 and 1 is used. This represents resource utilization, with a value between 0 and 1. - For dynamic weighting coefficients, =0.35, =0.25, =0.20, =0.15, =0.05, which can be iteratively optimized using user behavior data. The application is given a priority score, ranging from 0 to 1. The higher the score, the higher the priority of window layout and resource allocation.

[0042] In one embodiment of the present invention, the window size formula is:

[0043] ;

[0044] ;

[0045] in, For the first The width of each application window, No. Priority rating for each application. This is the sum of the priorities of all currently active applications, used to allocate screen space proportionally. The total width of the tablet screen. The number of currently active applications. Horizontal spacing of windows This sets a minimum window width threshold to prevent the window from becoming too narrow to operate. The maximum width threshold for the window. No. The height of each application window The total height of the tablet screen. Vertical spacing of windows Set a minimum window height threshold to accommodate one-handed operation. This is the maximum height threshold for the window.

[0046] In one embodiment of the present invention, the formula for the switching trigger threshold algorithm is:

[0047] ;

[0048] The method for dynamically adjusting transition animation parameters based on both device resource utilization and application type is as follows:

[0049] ;

[0050] ;

[0051] in, The duration of the transition animation. =0.4, =0.3, =0.2, =0.1, which is the trigger weight coefficient. =500 pixels =100 pixels, which is the gesture operation threshold. This refers to the pressing pressure.

[0052] In one embodiment of the present invention, the number of active applications in step S1 is less than or equal to 5, based on the upper limit of tablet computer performance, and the application correlation is obtained by training through application interaction logs.

[0053] In one embodiment of the present invention, the window position calculation in step S2 follows the rule that the application with the highest priority is placed in the core area of ​​the screen, and the rest are placed in descending order of priority to avoid overlap.

[0054] In one embodiment of the present invention, the switching trigger condition in step S3 is that the calculation result of the operation parameter is greater than or equal to a preset threshold, and the switching target is the application with the highest relevance and the second highest priority.

[0055] In one embodiment of the present invention, the learning rate η of the iterative algorithm in step S4 is 0.05, and the weight and threshold optimization is completed once every 100 user behavior data are accumulated.

[0056] In one embodiment of the present invention, the application types include office applications, video applications, social applications, and tool applications, with corresponding weights of 0.8, 0.7, 0.6, and 0.5, respectively.

[0057] In one embodiment of the present invention, the maximum window size threshold = -2 Maximum window height threshold = -2 Ensure that the window does not extend beyond the screen area.

[0058] The aforementioned method for layout and switching multitasking windows on a tablet computer utilizes a priority scoring formula combined with multi-dimensional user behavior and device status data to achieve precise window layout adaptation. Compared to a fixed layout, this improves the completeness of application content display and operational efficiency. An adaptive window size and position calculation model avoids window overlap and screen overflow issues, while ensuring high-priority applications receive better display resources, reducing the accidental touch rate during multitasking. An intelligent switching trigger threshold algorithm, combining gestures, relevance, and user intent, reduces manual switching operations and improves switching response speed. A dynamic adjustment formula for transition animation parameters optimizes the visual experience based on device performance and application type, reducing resource consumption and improving battery life in low-power scenarios. A user habit learning mechanism enables personalized adaptation, resulting in a superior user experience.

[0059] Specifically, in one embodiment of the present invention, in step S1, the tablet computer screen resolution is 2560*1600 pixels, and the currently active applications are: App1 (document editing, office application), App2 (email, office application), and App3 (browser, office application); user history data: =25 times / day =18 times / day =12 times / day, after normalization =1.0, =0.72, =0.48; Current state: =1 (front desk), =0.6 (Active in the background) =0.6 (Active in the background); Application type: = = =0.8; Relevance: =1.0 (Main Application) =0.9 (email and document association), =0.7 (browser and document association); resource usage: =0.3, =0.2, =0.25. Calculation priority:

[0060] =(0.35*1.0+0.25*1+0.2*0.8+0.15*1.0-0.05+0.3) / (0.35+0.25+0.2+0.15+0.05)=(0.35+0.25+0.16+0.15-0.015) / 1=0.895;

[0061] =(0.35*0.72+0.25*0.6+0.2*0.8+0.15*0.9-0.05*0.2) / 1=(0.252+0.15+0.16+0.135-0.01) / 1=0.687;

[0062] =(0.35*0.48+0.25*0.6+0.2*0.8+0.15*0.7-0.05*0.25) / 1=(0.168+0.15+0.16+0.105-0.0125) / 1=0.5705;

[0063] Priority ranking: App1 > App2 > App3.

[0064] In step S2: Window size calculation: =0.895+0.687+0.5705≈2.1525; =(0.895 / 2.1525)×(2560-3×20)+300≈0.416×2500+300≈1340 pixels; =(0.895 / 2.1525)×(1600-3×20)+400≈0.416×1540+400≈1041 pixels; Similarly, ≈(0.687 / 2.1525)×2500+300≈998 pixels ≈(0.687 / 2.1525)×1540+400≈832 pixels; ≈(0.5705 / 2.1525)×2500+300≈862 pixels ≈(0.5705 / 2.1525)×1540+400≈727 pixels; Window position calculation: App1: X1=20, Y1=20 (core area); App2: 20+1340+20=1380≤2560, therefore X2=1380, Y2=20 (right side of the same column); App3: 1380+998+20=2398≤2560, therefore X3 =2398>2560-862-20=1678, line break layout: X3=20, Y3=20+1041+20=1081; final layout: App1 (1340×1041, (20, 20)), App2 (998×832, (1380, 20)), App,3 (862×727, (20, 1081)), no overlap and complete display.

[0065] In step S3: The user gestures by swiping right within the App1 window, with a trajectory length L = 400 pixels, a speed V = 80 pixels / ms, and a pressing pressure... =0.6; Application Relevance =0.9, calculate the trigger threshold:

[0066] =0.4×(400 / 500)+0.3×(80 / 100)+0.2×0.9+0.1×0.6=0.4×0.8+0.3×0.8+0.18+0.06=0.32+0.24+0.18+0.06=0.8≥0.6, triggering a switch;

[0067] Transition animation parameters: Current resource utilization R=0.4, application type is office;

[0068] =60-40×0.4=44fps;

[0069] =0.2 + 0.1 × 0.4 = 0.24 s;

[0070] Switching result: The app switches from the foreground of App1 to the foreground of App2. App1 becomes active in the background, and the layout position remains unchanged.

[0071] In step S4: During user habit learning, the user manually adjusts the App3 window to 1000×800 pixels. The system records this preference and iteratively optimizes the weight coefficients. The weight of application type has been adjusted from 0.2 to 0.22 (due to users increasing the browser window size, the weight of office applications has increased). The App3 window size will automatically adapt to 1000×800 pixels during the next layout.

[0072] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for layout and switching of multitasking windows on a tablet computer, characterized in that, The method includes: S1: Collect multi-dimensional data on currently active applications on the tablet and calculate the priority of each application using the application priority scoring formula; S2: Based on the application priority and screen resolution, determine the size and position of each window through an adaptive calculation model; S3: Collect user operation data and application correlation, determine whether to trigger a switch through a switch trigger threshold algorithm, and dynamically adjust transition animation parameters based on device resource utilization and application type; S4: Record user behavior data and optimize weight coefficients and switching thresholds through iterative algorithms to achieve adaptive user habits.

2. The method for layout and switching of multi-tasking windows on a tablet computer according to claim 1, characterized in that, The application priority scoring formula is as follows: ； in, The value is a normalized value for the frequency of application use, ranging from 0 to 1, and is obtained by normalizing the historical usage data of the past 30 days. To apply relevance, The application type weight ranges from 0.5 to 0.

8. To determine the relevance, a value between 0 and 1 is used. This represents resource utilization, with a value between 0 and 1. - For dynamic weighting coefficients, =0.35, =0.25, =0.20, =0.15, =0.05, which can be iteratively optimized using user behavior data. The application is given a priority score, ranging from 0 to 1. The higher the score, the higher the priority of window layout and resource allocation.

3. The method for layout and switching of multi-tasking windows on a tablet computer according to claim 1, characterized in that, The formula for the window size is: ； ； in, For the first The width of each application window, No. Priority rating for each application. This is the sum of the priorities of all currently active applications, used to allocate screen space proportionally. The total width of the tablet screen. The number of currently active applications. Horizontal spacing of windows This sets a minimum window width threshold to prevent the window from becoming too narrow to operate. The maximum width threshold for the window. No. The height of each application window The total height of the tablet screen. Vertical spacing of windows Set a minimum window height threshold to accommodate one-handed operation. This is the maximum height threshold for the window.

4. The method for layout and switching of multi-tasking windows on a tablet computer according to claim 1, characterized in that, The formula for the switching trigger threshold algorithm is: ； The method for dynamically adjusting transition animation parameters based on both device resource utilization and application type is as follows: ； ； in, The duration of the transition animation. =0.4, =0.3, =0.2, =0.1, which is the trigger weight coefficient. =500 pixels =100 pixels, which is the gesture operation threshold. This refers to the pressing pressure.

5. The method for layout and switching of multi-tasking windows on a tablet computer according to claim 1, characterized in that, In step S1, the number of active applications is less than or equal to 5, which is based on the performance limit of the tablet computer. The application correlation is obtained by training through application interaction logs.

6. The method for layout and switching of multi-tasking windows on a tablet computer according to claim 1, characterized in that, In step S2, the window position calculation follows the rule that the application with the highest priority is placed in the core area of ​​the screen, and the rest are placed in descending order of priority to avoid overlap.

7. The method for layout and switching of a multi-tasking window on a tablet computer according to claim 1, characterized in that, In step S3, the switching trigger condition is that the calculated result of the operation parameter is greater than or equal to the preset threshold, and the switching target is the application with the highest relevance and the second highest priority.

8. The method for layout and switching of multi-tasking windows on a tablet computer according to claim 1, characterized in that, In step S4, the learning rate η of the iterative algorithm is 0.05, and the weight and threshold optimization is completed once every 100 user behavior data are accumulated.

9. The method for layout and switching of multi-tasking windows on a tablet computer according to claim 1, characterized in that, The application types include office applications, video applications, social applications, and utility applications, with corresponding weights of 0.8, 0.7, 0.6, and 0.5, respectively.

10. The method for layout and switching of a multi-tasking window on a tablet computer according to claim 3, characterized in that, The maximum window size threshold = -2 Maximum window height threshold = -2 Ensure that the window does not extend beyond the screen area.

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