Window management method, window management model training method and electronic equipment
By detecting window changes in the cloud desktop system, obtaining work environment and user habit information, and automatically adjusting windows using the window management model, it solves the cumbersome operation problems caused by window changes and improves the user experience.
Patent Information
- Application Number
- CN202311142706.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-05
- Publication Date
- 2025-03-07
AI Technical Summary
In the cloud desktop system, window changes lead to cumbersome operations by users, reducing the user experience.
By detecting the window change scene, obtaining the current working environment context and user usage habit information, input it into the training window management model, obtaining window parameters, and automatically adjusting the window in the desktop based on this parameter.
No need for users to manually adjust windows, and window adjustments in window changes are automatically completed, improving user experience.
Smart Images
Figure CN119576449A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of cloud desktop technology, and in particular to a window management method, a training method for a window management model, and an electronic device. Background Art
[0002] In cloud desktop systems, task management and window operations between multiple screens are relatively complex. Users need to manually adjust windows to adapt to different display devices, which makes operations cumbersome and reduces user experience. Summary of the invention
[0003] The embodiments of the present disclosure provide a window management method, a training method for a window management model, and an electronic device, which can solve the problem that window changes cause cumbersome user operations. The technical solution is as follows:
[0004] According to a first aspect of an embodiment of the present disclosure, there is provided a window management method, including:
[0005] When it is detected that the current window is changing, the current working environment context and the user's usage habits information are obtained;
[0006] Inputting the current working environment context and the user's usage habit information into the window management model to obtain window parameters;
[0007] According to the window parameters, the window in the current desktop is adjusted.
[0008] In one embodiment, when it is detected that the current window change scene is in progress, obtaining the current working environment context and the user's usage habit information includes:
[0009] When receiving the extended desktop command, the current working environment context and user usage habit information are obtained.
[0010] or,
[0011] When a new window is added or deleted, the current working environment context and user usage habit information are obtained.
[0012] In one embodiment, the current working environment context includes: operation information of the input device and / or hardware information of the display device within a preset time period before the current moment; the operation information of the input device includes: at least one of mouse click information, mouse scroll information and keyboard input information; the hardware information of the display device includes the resolution of the display device and / or the size information of the display device.
[0013] In one embodiment, the user's usage habit information includes at least one of the user's habit information on using applications, the user's habit information on accessing files and folders, the user's habit information on adjusting system settings, and the user's behavioral habit information on network connections.
[0014] In one embodiment, the window parameters include window position adjustment information and / or window size adjustment information.
[0015] In one embodiment, the method further comprises:
[0016] Obtain historical work environment context and historical user usage habits information;
[0017] The historical working environment context and the historical user usage habit information are input into the initial window management model for training until the training is completed to obtain the window management model.
[0018] In one embodiment, obtaining the historical work environment context and the historical user usage habit information includes:
[0019] During the operation of the desktop, the work environment context is obtained in real time as the historical work environment context; the user's usage habit information is obtained in real time as the historical user's usage habit information.
[0020] According to a second aspect of an embodiment of the present disclosure, a method for training a window management model is provided, comprising:
[0021] Obtain historical work environment context and historical user usage habits information;
[0022] The historical work environment context and the historical user usage habit information are input into the initial window management model for training until the training is completed to obtain a window management model. The window management model is used to obtain window parameters based on the current work environment context and the user's usage habit information. The window parameters are used to adjust the windows in the current desktop when in a window change scenario.
[0023] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising a processor and a memory, wherein the memory stores at least one computer instruction, and the instruction is loaded and executed by the processor to implement the window management method described in any one of the first aspects above.
[0024] According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising a processor and a memory, wherein the memory stores at least one computer instruction, and the instruction is loaded and executed by the processor to implement the steps performed in the training method of the window management model described in any one of the second aspects above.
[0025] According to a fifth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, in which at least one computer instruction is stored. The instruction is loaded and executed by a processor to implement the steps performed in the window management method described in the first aspect and any embodiment of the first aspect.
[0026] According to the sixth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, in which at least one computer instruction is stored, and the instruction is loaded and executed by a processor to implement the steps performed in the training method of the window management model described in any embodiment of the first aspect and the second aspect.
[0027] The window management method provided by the embodiment of the present disclosure obtains the current working environment context and the user's usage habit information when detecting that the current window change scene is in progress, obtains the corresponding window parameters through the trained window management model, and automatically adjusts the windows in the desktop according to the window parameters. The user does not need to manually adjust the window, and the window adjustment in the window change scene can be completed automatically, thereby improving the user experience.
[0028] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0030] Figure 1 A flowchart of a window management method provided by an embodiment of the present disclosure;
[0031] Figure 2 A flowchart of a training method for a window management model provided in an embodiment of the present disclosure;
[0032] Figure 3 A flowchart of another window management method provided by an embodiment of the present disclosure;
[0033] Figure 4 A schematic diagram of the principles of a window management system provided in this embodiment. DETAILED DESCRIPTION
[0034] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.
[0035] With the development and maturity of cloud computing, cloud desktops are being used in more and more scenarios. Cloud desktops are becoming more and more popular in universities, medical care, government and other industries. Traditional IDV dual desktop switching technology provides the switching function of independent display devices, and the task management and window operation between multiple screens are relatively complicated. Users need to manually adjust the window to adapt to different display devices, which makes the operation cumbersome and reduces the user experience.
[0036] A window management method provided by an embodiment of the present disclosure obtains the current working environment context and the user's usage habit information when detecting that the current window change scene is in progress, obtains the corresponding window parameters through a trained window management model, and automatically adjusts the windows in the desktop according to the window parameters. The user does not need to manually adjust the window, and the window adjustment in the window change scene can be completed automatically, thereby improving the user experience.
[0037] The technical solution provided by the present disclosure is described in detail below with reference to specific embodiments.
[0038] See also Figure 1 , Figure 1 The present invention provides a flowchart of a window management method, which can be applied to a cloud desktop system. The window management method provided by the present embodiment is executed by a computer, a terminal, and other devices. The method provided by the present embodiment can include the following steps.
[0039] Step 101: When it is detected that the current window is changing, the current working environment context and the user's usage habit information are obtained.
[0040] The window change scenario refers to a scenario in which a window on the current desktop changes relative to a previous moment.
[0041] Optionally, the window change scenario may be to confirm that the current window change scenario is in place when receiving an extended desktop instruction; or to confirm that the current window change scenario is in place when adding or deleting a window, that is, when the number of windows changes.
[0042] The work environment context refers to information such as user operations or settings during the current desktop operation.
[0043] Optionally, the working environment context includes: operation information of the input device and / or hardware information of the display device within a preset time period before the current moment.
[0044] The operation information of the input device includes at least one of mouse click information, mouse scroll information and keyboard input information.
[0045] The hardware information of the display device includes the resolution of the display device and / or the size information of the display device.
[0046] The user's usage habit information is generated from the historical usage data of the user logged in on the current desktop, and specifically records the operation habits of the user logged in on the current desktop on windows and the like.
[0047] Optionally, the user's usage habit information includes at least one of the user's habit information on using applications, the user's habit information on accessing files and folders, the user's habit information on adjusting system settings, and the user's behavioral habit information on network connections.
[0048] Step 102: Input the current working environment context and the user's usage habit information into the window management model to obtain window parameters.
[0049] The window management model is trained based on the work environment context and user usage habit information. The window management model learns the window adjustment strategy through training, and can output window parameters based on the work environment context and user usage habit information. The windows in the current desktop can be adjusted based on the window parameters, which is equivalent to providing a strategy for the window layout in the current desktop.
[0050] Step 103: Adjust the window in the current desktop according to the window parameters.
[0051] The window parameters refer to the display parameters of the window, that is, the parameters related to the state of the window displayed on the desktop.
[0052] Optionally, the window parameters may include but are not limited to window position adjustment information and / or window size adjustment information.
[0053] The window management method provided by the embodiment of the present disclosure obtains the current working environment context and the user's usage habit information when detecting that the current window change scene is in progress, obtains the corresponding window parameters through the trained window management model, and automatically adjusts the windows in the desktop according to the window parameters. The user does not need to manually adjust the window, and the window adjustment in the window change scene can be completed automatically, thereby improving the user experience.
[0054] In some embodiments, step 101 may include step 1011 or step 1012 as follows.
[0055] Step 1011: When an extended desktop instruction is received, the current working environment context and user usage habit information are obtained.
[0056] In this embodiment, when it is necessary to extend the desktop through a connected display device, it is determined that the current window change scene is in progress, and the window display needs to be displayed in the original display device and the extended display device. Therefore, the current working environment context and the user's usage habits are obtained, and the window parameters are obtained through the window management model. The window in the current desktop is adjusted using the window parameters. The user does not need to manually adjust the window, and the window adjustment in the window change scene can be completed automatically, thereby improving the user experience.
[0057] Step 1012: When a new window is added or deleted, the current working environment context and the user's usage habit information are obtained.
[0058] In this embodiment, when adding or deleting a window, it is determined that the current window is in a window change scenario. For example, if a user opens an application, the window of the application will be added to the current desktop. The current working environment context and the user's usage habits are obtained, and the window parameters are obtained through the window management model. The window parameters are used to adjust the window in the current desktop. The user does not need to manually adjust the window, and the window adjustment in the window change scenario can be automatically completed, thereby improving the user experience.
[0059] In some scenarios, the window management model may be trained, which is described in detail below with a specific embodiment. This embodiment may be implemented alone or in combination with any of the above embodiments, and the present disclosure does not limit this.
[0060] See also Figure 2 , Figure 2 This is a flow chart of a training method for a window management model provided by an embodiment of the present disclosure. The method of this embodiment can be executed by a computer or a server, etc. The method of this embodiment can include the following steps.
[0061] Step 201: Obtain historical work environment context and historical user usage habit information.
[0062] Optionally, step 201 may be implemented through the following step 2011.
[0063] Step 2011: During the operation of the desktop, the work environment context is obtained in real time as the historical work environment context; and the user's usage habit information is obtained in real time as the historical user's usage habit information.
[0064] Step 202: Input the historical work environment context and historical user usage habit information into the initial window management model for training until the training is completed to obtain the window management model.
[0065] The window management model is used to obtain window parameters according to the current working environment context and the user's usage habit information, and the window parameters are used to adjust the windows in the current desktop when the window changes.
[0066] In this embodiment, the initial window management model is trained through the historical work environment context and the historical user usage habit information until the training is completed to obtain the window management model, thereby completing the training. When it is detected that the current window change scene is in progress, the current work environment context and the user's usage habit information are obtained, and the corresponding window parameters are obtained through the trained window management model, thereby automatically adjusting the windows in the desktop according to the window parameters. The user does not need to manually adjust the window, and the window adjustment in the window change scene can be completed automatically, thereby improving the user experience.
[0067] An example is shown below to illustrate a system for executing the window management method provided by an embodiment of the present disclosure, thereby also illustrating the window management method provided by the present disclosure.
[0068] The following introduces a window management system.
[0069] The window management system includes: a log collection system, a context perception module, a data processing module, a decision module and a window management control module.
[0070] The log collection system is used to collect application usage habits on the desktop, display device connection status, file and folder access habits, system settings adjustment habits, network connection behavior habits, etc., and collect and store them in a historical database.
[0071] The context-aware module is used to build the user's work environment context by monitoring the user's operating behavior (such as mouse clicks, scrolling, input, etc.), the opening and closing of applications, and the status of currently connected external display devices.
[0072] The data processing module is used to collect, organize and analyze contextual data, and use machine learning algorithms to train and generate intelligent window management models, including window position adjustment rules, window size adaptation rules, etc.
[0073] The decision module is used to predict the user's window management intention based on the intelligent window management model and the user's current working environment context, and generate corresponding window adjustment strategies.
[0074] The window management control module is used to automatically adjust the position and size of the application window between multiple screens according to the strategy generated by the decision module, so as to realize intelligent window management.
[0075] See also Figure 3 , Figure 3 This is a flow chart of another window management method provided by an embodiment of the present disclosure. The window management method of this embodiment is introduced below in combination with the above-mentioned window management system.
[0076] Step 301: Obtain user behavior logs through a log collection system.
[0077] Step 302: Acquire the work environment context through the context awareness module.
[0078] Step 303: Obtain the window management model through the data processing module.
[0079] Step 304: Generate a window adjustment strategy through a decision module.
[0080] The window adjustment strategy includes window parameters.
[0081] Step 305: Window display.
[0082] In this embodiment, by analyzing the user's work environment context and usage habits, the system can intelligently adjust and manage the position and size of application windows across multiple screens. It can prioritize and reasonably allocate windows based on factors such as importance, relevance, and usage frequency, providing a more efficient window layout that meets user expectations.
[0083] According to the resolution and size characteristics of different display devices, the system can automatically adjust the size of the application window to ensure the best display effect on each screen. This not only reduces the tedious operation of users manually adjusting the window size, but also improves the visibility and readability of the window content.
[0084] Through the context-aware module and machine learning algorithm, the system can predict the user's window management intention based on the user's current working environment context. For example, when the user connects a new display device or opens a specific type of application, the system can automatically adjust the window layout or restore the last layout setting, providing a more intelligent and personalized window management experience.
[0085] Use machine learning to learn user behavior and historical data, and automatically generate optimal window parameters based on the hardware environment to achieve the best experience.
[0086] See also Figure 4 , Figure 4This is a schematic diagram of the principle of a window management system provided by this embodiment. User behavior data is collected through a user behavior collection detector. Different users log in to the desktop, and terminal window display data parameters are generated according to the detection results. Terminal display is performed according to the terminal data parameters.
[0087] An embodiment of the present disclosure provides an electronic device, including a processor and a memory, wherein the memory stores at least one computer instruction, and the instruction is loaded and executed by the processor to implement the window management method of any of the above embodiments.
[0088] An embodiment of the present disclosure provides an electronic device, including a processor and a memory, wherein the memory stores at least one computer instruction, and the instruction is loaded and executed by the processor to implement the steps performed in the training method of the window management model of any of the above embodiments.
[0089] The implementation principle and beneficial effects of the device provided in the embodiment of the present disclosure are similar to those of the method embodiment provided above, and will not be repeated here.
[0090] Based on the window management method described in the above embodiments, the embodiment of the present disclosure further provides a computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc. The storage medium stores computer instructions for executing the window management method described in any of the above embodiments, which will not be described in detail here.
[0091] Based on the training method of the window management model described in the above embodiments, the embodiment of the present disclosure further provides a computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc. The storage medium stores computer instructions for executing the training method of the window management model described in any of the above embodiments, which will not be described in detail here.
[0092] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.
[0093] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.
Claims
1. A window management method, characterized in that: include: When it is detected that the current window is changing, the current working environment context and the user's usage habits information are obtained; Inputting the current working environment context and the user's usage habit information into the window management model to obtain window parameters; According to the window parameters, the window in the current desktop is adjusted.
2. The method according to claim 1, characterized in that When the window change scene is detected, the current working environment context and the user's usage habit information are obtained, including: When receiving the extended desktop command, the current working environment context and user usage habit information are obtained. or, When a new window is added or deleted, the current working environment context and user usage habit information are obtained.
3. The method according to claim 1, characterized in that The current working environment context includes: the operation information of the input device and / or the hardware information of the display device within a preset time period before the current moment; the operation information of the input device includes: at least one of mouse click information, mouse scroll information and keyboard input information; the hardware information of the display device includes the resolution of the display device and / or the size information of the display device.
4. The method according to claim 1, characterized in that The user's usage habit information includes at least one of the user's habit information on using applications, the user's habit information on accessing files and folders, the user's habit information on adjusting system settings, and the user's behavioral habit information on network connections.
5. The method according to claim 1, characterized in that The window parameters include window position adjustment information and / or window size adjustment information.
6. The method according to claim 1, characterized in that The method further comprises: Obtain historical work environment context and historical user usage habits information; The historical working environment context and the historical user usage habit information are input into the initial window management model for training until the training is completed to obtain the window management model.
7. The method according to claim 6, characterized in that The obtaining of historical work environment context and historical user usage habit information includes: During the operation of the desktop, the work environment context is obtained in real time as the historical work environment context; the user's usage habit information is obtained in real time as the historical user's usage habit information.
8. A training method for a window management model, characterized in that: include: Obtain historical work environment context and historical user usage habits information; The historical work environment context and the historical user usage habit information are input into the initial window management model for training until the training is completed to obtain a window management model. The window management model is used to obtain window parameters based on the current work environment context and the user's usage habit information. The window parameters are used to adjust the windows in the current desktop when in a window change scenario.
9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores at least one computer instruction, and the instruction is loaded and executed by the processor to implement the window management method described in any one of claims 1 to 7, or the steps performed in the training method of the window management model described in claim 8.
10. A computer-readable storage medium, characterized in that: The storage medium stores at least one computer instruction, which is loaded and executed by the processor to implement the window management method described in any one of claims 1 to 7, or the steps performed in the window management model training method described in claim 8.