Information display method, cloud terminal, cloud virtual machine, and storage medium

Abstract

This application provides an information display method, a cloud terminal, a cloud virtual machine, and a storage medium. The method is applied to a cloud terminal. When a zoom interaction event is detected, the initial position of the event is first determined, and micro-motion information of the interaction process is obtained based on this position. Then, the cloud terminal determines the zoom intention and predicts the zoom level based on the micro-motion information, and then determines the predicted coordinate information of the region of interest corresponding to the event after zooming, based on the intention and zoom level. Subsequently, the cloud terminal obtains first rendering feedback information from the cloud virtual machine based on the coordinate information and the zoom level, and displays information based on the feedback information. When the event ends, the cloud terminal can collect the target position information of the event, and then obtain second rendering feedback information from the virtual machine based on the position information, updating the displayed information based on the second rendering feedback information. By predicting the user's zoom operation in advance and performing pre-rendering, screen latency can be effectively reduced and bandwidth resources saved.

Description

Technical Field

[0001] The embodiments of this application relate to, but are not limited to, the field of information processing technology, and particularly to information display methods, cloud terminals, cloud virtual machines, and storage media. Background Technology

[0002] Current cloud desktop technologies primarily rely on desktop virtualization protocols for image compression and transmission. They enhance user experience through techniques such as differentiated frame buffering and Region of Interest (ROI) encoding. Both of these technologies require detecting key indicators like real-time network status, screen stuttering, and image clarity before dynamically adjusting parameters based on pre-defined thresholds. However, both technologies currently employ a passive, reactive parameter adjustment logic, lacking the ability to dynamically perceive user actions and failing to perform real-time prediction and pre-rendering of the user's actual area of ​​interest. This ultimately leads to issues like screen lag during zooming and wasted bandwidth resources. Summary of the Invention

[0003] This application provides an information display method, a cloud terminal, a cloud virtual machine, and a storage medium, which can effectively reduce screen latency and save bandwidth resources.

[0004] This application provides an information display method applied to a cloud terminal. The method includes: upon detecting a user zoom interaction event, determining the initial position information of the user zoom interaction event, and obtaining micro-motion information of the user zoom interaction event using the initial position information as the starting position; obtaining a motion micro-variability rate based on the micro-variability rate, determining the zoom intention of the user zoom interaction event based on the motion micro-variability rate, predicting the predicted zoom level corresponding to the user zoom interaction event based on the micro-motion information, and determining the predicted coordinate information of the region of interest corresponding to the user zoom interaction event after scaling based on the zoom intention and the predicted zoom level; wherein, the motion micro-variability rate is used to reflect the change amplitude and direction of the distance between two fingers per unit time; obtaining first rendering feedback information from a cloud virtual machine based on the predicted coordinate information and the predicted zoom level, and performing preliminary information display based on the first rendering feedback information; and at the end of the user zoom interaction event, obtaining the target position information of the user zoom interaction event, obtaining second rendering feedback information from the cloud virtual machine based on the target position information, and updating the displayed information based on the second rendering feedback information.

[0005] On the other hand, this application provides an information display method applied to a cloud virtual machine. The method includes: receiving predicted coordinate information and predicted scaling level sent by a cloud terminal, wherein the predicted coordinate information is the scaled coordinate information of the region of interest corresponding to a user scaling interaction event determined by the cloud terminal according to the scaling intention and the predicted scaling level; the predicted scaling level is the scaling level corresponding to the user scaling interaction event predicted by the cloud terminal according to micro-motion information; the scaling intention is determined by the cloud terminal according to the motion micro-variability rate; the motion micro-variability rate is obtained according to the micro-motion information; the micro-motion information is obtained by the cloud terminal using the initial position information of the user scaling interaction event as the starting position when the user scaling interaction event is detected; the motion micro-variability rate is used to reflect the change amplitude and direction of the distance between two fingers per unit time; sending first rendering feedback information to the cloud terminal according to the predicted coordinate information and the predicted scaling level, the first rendering feedback information being used by the cloud terminal for preliminary information display; receiving the target scaling level corresponding to the user scaling interaction event sent by the cloud terminal at the end of the user scaling interaction event; and sending second rendering feedback information to the cloud terminal according to the target scaling level, the second rendering feedback information being used by the cloud terminal for updating the displayed information.

[0006] On the other hand, this application provides an information display method applied to a cloud desktop. The method includes: in response to detecting a two-finger touch event on a touchscreen, displaying a first region and a second region on the touchscreen; wherein the first region has a first resolution, the second region has a second resolution, the first resolution is higher than the second resolution, the first region is a closed region determined by two touch points of the two-finger touch event, and the second region is the outer region of the first region.

[0007] On the other hand, this application provides an information display method applied to a cloud desktop. The method includes: displaying a cursor on a display screen; detecting a mouse wheel scrolling event; and displaying a first region and a second region on the display screen in response to a first scrolling scale of the mouse wheel scrolling event. The first region has a first resolution, the second region has a second resolution, the first resolution is higher than the second resolution, the first region is a closed region determined based on the position of the cursor, and the second region is the outer region of the first region.

[0008] On the other hand, embodiments of this application provide a cloud terminal, including: at least one processor; at least one memory for storing at least one program; and when at least one of the programs is executed by at least one of the processors, it implements the information display method as described above.

[0009] On the other hand, embodiments of this application provide a cloud virtual machine, including: at least one processor; at least one memory for storing at least one program; and when at least one of the programs is executed by at least one of the processors, implementing the information display method as described above.

[0010] On the other hand, embodiments of this application provide a cloud desktop, including: at least one processor; at least one memory for storing at least one program; and when at least one of the programs is executed by at least one of the processors, implementing the information display method as described above.

[0011] On the other hand, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the information display method as described above.

[0012] On the other hand, embodiments of this application provide a computer program product, including a computer program or computer instructions, wherein the computer program or computer instructions are stored in a computer-readable storage medium, and a processor of a cloud terminal, a cloud virtual machine, or a cloud desktop reads the computer program or computer instructions from the computer-readable storage medium. The processor executes the computer program or computer instructions, causing the cloud terminal to execute the aforementioned information display method, or causing the cloud virtual machine to execute the aforementioned information display method, or causing the cloud desktop to execute the aforementioned information display method.

[0013] This application provides an information display method, a cloud terminal, a cloud virtual machine, a cloud desktop, a computer-readable storage medium, and a computer program product. The information display method is applied to a cloud terminal. When the cloud terminal detects a user zoom interaction event, it first determines the initial position information of the user zoom interaction event and uses this initial position information as the starting position to acquire micro-motion information during the zoom interaction process. Next, the cloud terminal determines the zoom intention of the user zoom interaction event based on the micro-motion information, and simultaneously predicts the predicted zoom level corresponding to the user zoom interaction event based on the micro-motion information. Then, based on the zoom intention and the predicted zoom level, it determines the predicted coordinate information of the region of interest corresponding to the user zoom interaction event after scaling. Subsequently, the cloud terminal obtains first rendering feedback information from the cloud virtual machine based on the predicted coordinate information and the predicted zoom level, and performs preliminary information display based on the first rendering feedback information. When the user zoom interaction event ends, the cloud terminal can collect the target position information of the user zoom interaction event, then obtain second rendering feedback information from the cloud virtual machine based on the target position information, and finally update the displayed information based on the second rendering feedback information. This application embodiment can effectively reduce screen latency during zooming by sensing user operations in advance and pre-rendering the screen; by determining the user's area of ​​interest in advance, it provides a basis for subsequent allocation of rendering and bandwidth resources, thereby effectively improving bandwidth utilization efficiency; by adjusting the current display information based on the pre-rendering and using the final actual parameters (i.e., the second rendering feedback information), it can not only ensure the accuracy of the final display, but also make the overall zooming interaction smoother than the user experience of the local device. Attached Figure Description

[0014] Figure 1 This is a flowchart of an information display method provided in one embodiment of this application; Figure 2 This is provided in one embodiment of the present application. Figure 1 The detailed flowchart of step S120; Figure 3 This is provided in one embodiment of the present application. Figure 1 The detailed flowchart of step S130; Figure 4 This is a flowchart illustrating the specific process of obtaining display adjustment instructions according to an embodiment of this application; Figure 5 This is provided in one embodiment of the present application. Figure 1 The detailed flowchart of step S150; Figure 6 This is a flowchart of an information display method provided in another embodiment of this application; Figure 7 This is a flowchart of an information display method provided in another embodiment of this application; Figure 8 This is a flowchart of an information display method provided in another embodiment of this application; Figure 9 This is a schematic diagram of an information display process provided in one embodiment of this application; Figure 10 This is a schematic diagram of the information display process provided in another embodiment of this application; Figure 11 This is a flowchart of an information display method provided in another embodiment of this application; Figure 12 This is a schematic diagram of the information display process provided in another embodiment of this application; Figure 13 This is a schematic diagram of the information display process provided in another embodiment of this application; Figure 14 This is a system architecture diagram of a cloud desktop scaling advance awareness system provided in a specific embodiment of this application; Figure 15 This is a timing diagram of a cloud desktop scaling advance perception system provided in a specific embodiment of this application; Figure 16 This is another timing diagram of a cloud desktop scaling advance perception system provided in a specific embodiment of this application; Figure 17 This is a schematic diagram of a cloud terminal structure provided in one embodiment of this application; Figure 18 This is a schematic diagram of a cloud virtual machine structure provided in one embodiment of this application; Figure 19 This is a schematic diagram of a cloud desktop structure provided in one embodiment of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0016] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0017] In this application, the terms "furthermore," "in this embodiment," or "optionally" are used as examples, illustrations, or descriptions and should not be construed as being more preferred or advantageous than other embodiments or designs. The use of terms such as "furthermore," "in this embodiment," or "optionally" is intended to present the relevant concepts in a specific manner.

[0018] With enterprise digital transformation, mobile office has become the norm, and cloud desktops have rapidly gained popularity due to their advantages such as centralized data management and cross-device access. However, traditional cloud desktops rely on remote server rendering and network backhaul, facing significant latency in high-frequency interactive scenarios such as scaling, resulting in a user experience far inferior to local computers (PCs). Current cloud desktop technology mainly relies on desktop virtualization protocols for image compression and transmission, and improves user experience through technologies such as differentiated frame buffering and Region of Interest (ROI) encoding. Both of these technologies require detecting key indicators such as real-time network status, screen stuttering, and image clarity before dynamically adjusting parameters based on preset thresholds. However, both are reactive mechanisms and cannot predict user intent in advance. These shortcomings also make it difficult for current technology to solve the following problems: (1) Differential frame buffering will have a latency accumulation problem. This technology relies on differential coding and partial screen updates to achieve transmission. When the user zooms in and out of the screen, the overall resolution will suddenly change, the original buffer will become invalid, and the cloud can only retransmit the entire frame. (2) ROI encoding has the limitation of being static. This technology will preset a fixed area of ​​interest, such as the center of the screen or the shortcut icon area. It cannot adjust the priority area synchronously with the user's real-time operation. When the user zooms in and out of the screen, the content that actually needs to be viewed is often in the non-priority encoding area. (3) Passive response will cause a lot of bandwidth waste. The cloud will encode and transmit the full-screen content in a unified manner, while the area of ​​the screen that the user actually pays attention to usually only accounts for 15% to 30% of the overall screen. Invalid screen data will occupy the transmission bandwidth. (4) The entire mechanism lacks the ability to predict the user's operating intentions. The system does not integrate touch trajectory perception data and cannot predict the user's zoom direction and zoom level in advance. It can only make remedial processing after the screen freezes.

[0019] In summary, current cloud desktop technology adopts a passive response-based parameter adjustment logic, which lacks the ability to dynamically perceive user operations and fails to achieve real-time prediction and pre-rendering of the user's actual focus area, ultimately leading to problems such as screen lag during scaling operations and wasted bandwidth resources.

[0020] To effectively reduce screen latency and save bandwidth resources, this application provides an information display method, a cloud terminal, a cloud virtual machine, a cloud desktop, a computer-readable storage medium, and a computer program product. The information display method is applied to a cloud terminal. When the cloud terminal detects a user zoom interaction event, it first determines the initial position information of the user zoom interaction event and uses this initial position information as the starting position to acquire micro-motion information during the zoom interaction process. Next, the cloud terminal determines the zoom intention of the user zoom interaction event based on the micro-motion information, and simultaneously predicts the predicted zoom level corresponding to the user zoom interaction event based on the micro-motion information. Then, based on the zoom intention and the predicted zoom level, it determines the predicted coordinate information of the region of interest corresponding to the user zoom interaction event after scaling. Subsequently, the cloud terminal obtains first rendering feedback information from the cloud virtual machine based on the predicted coordinate information and the predicted zoom level, and performs preliminary information display based on the first rendering feedback information. When the user zoom interaction event ends, the cloud terminal can collect the target position information of the user zoom interaction event, then obtain second rendering feedback information from the cloud virtual machine based on the target position information, and finally update the displayed information based on the second rendering feedback information. This application embodiment can effectively reduce screen latency during zooming by sensing user operations in advance and pre-rendering the screen; by determining the user's area of ​​interest in advance, it provides a basis for subsequent allocation of rendering and bandwidth resources, thereby effectively improving bandwidth utilization efficiency; by adjusting the current display information based on the pre-rendering and using the final actual parameters (i.e., the second rendering feedback information), it can not only ensure the accuracy of the final display, but also make the overall zooming interaction smoother than the user experience of the local device.

[0021] Based on the above analysis, the embodiments of this application will be further described below with reference to the accompanying drawings.

[0022] Reference Figure 1 , Figure 1 This is a flowchart of an information display method provided in one embodiment of this application. The method can be applied to cloud terminals, and the specific process includes, but is not limited to, steps S110 to S140.

[0023] Step S110: When a user zoom interaction event is detected, determine the initial position information of the user zoom interaction event, and use the initial position information as the starting position to obtain the micro-motion information of the user zoom interaction event; Step S120: Determine the scaling intent of the user scaling interaction event based on the micro-motion information, predict the predicted scaling level corresponding to the user scaling interaction event based on the micro-motion information, and determine the predicted coordinate information of the scaled region of interest corresponding to the user scaling interaction event based on the scaling intent and the predicted scaling level. Step S130: Obtain first rendering feedback information from the cloud virtual machine based on the predicted coordinate information and the predicted scaling level, and perform preliminary information display based on the first rendering feedback information; Step S140: When the user zoom interaction event ends, obtain the target position information of the user zoom interaction event, obtain the second rendering feedback information from the cloud virtual machine based on the target position information, and update the display information based on the second rendering feedback information.

[0024] In this embodiment, the cloud terminal refers to a cloud PC client, which is a software program running on a local device used to connect to and access virtual computers deployed in the cloud. A cloud PC client can also be called a cloud desktop client or remote desktop client. A cloud desktop is a technology that allows users to access virtual desktops on remote servers via a network; it can also be called a virtual desktop or cloud PC.

[0025] In this embodiment, the cloud virtual machine refers to a cloud desktop server, which is the backend infrastructure that configures, hosts, and manages virtualized computer instances in the cloud. It delivers complete computer capabilities, including the operating system, applications, and data, to end users via the network, typically accessed through client applications. A cloud desktop server can also be called a virtual machine host / cloud desktop server / cloud computer host.

[0026] In this embodiment, the cloud terminal is equipped with a touch-screen tablet display. Users can perform interactive operations by touching the tablet display with two or more fingers, or control the display interface using a mouse, keyboard, etc. When the cloud terminal detects that the user has triggered a zoom interaction event, it can first identify the initial position information corresponding to the zoom interaction event, and then use the initial position as the reference starting coordinate to collect the micro-motion information during this zoom interaction process.

[0027] In this embodiment, micro-motion information refers to a quantitative description of the continuous minute movements of the touch points during the user's touch operation in a zoom interaction event. Specifically, it refers to the real-time minute displacement data of each touch point relative to the initial touch position collected by the cloud terminal and its directly associated motion parameters. In a two-finger zoom interaction scenario, micro-motion information may include raw sampled data such as the real-time coordinate offset of each of the two touch points, the real-time spacing change between the touch points, the instantaneous movement rate of a single touch point, and the real-time sampled value of the touch pressure. This data can reflect the subtle movement trajectory and force changes of the user's finger on the screen.

[0028] In this embodiment, when determining the user's zooming intention based on micro-motion information, the motion micro-variability rate can be obtained first from the micro-motion information, and then the user's zooming intention can be determined based on the motion micro-variability rate. The motion micro-variability rate reflects the amplitude and direction of the change in the distance between two fingers per unit time. Its sign indicates the zooming direction, and its amplitude and stability eliminate accidental touch interference, thereby accurately identifying the user's zooming intention.

[0029] In this embodiment, determining the scaling intent of a user's scaling interaction event based on the motion micro-variability rate includes one of the following: when the motion micro-variability rate is greater than a first micro-variability rate threshold, the scaling intent of the user's scaling interaction event is determined to be a zoom-in intent; when the motion micro-variability rate is less than a second micro-variability rate threshold, the scaling intent of the user's scaling interaction event is determined to be a zoom-out intent. Specifically, in determining the scaling intent, two micro-variability rate thresholds in positive and negative directions can be set as judgment boundaries. When the rate of change of the distance between the two fingers exceeds the positive threshold, it is determined to be a zoom-in intent; when it is lower than the negative threshold, it is determined to be a zoom-out intent, thereby distinguishing between a valid scaling operation and minor finger tremors or accidental touches. For example, the first micro-variability rate threshold can be set to +0.5% / ms, and the second micro-variability rate threshold can be set to -0.5% / ms. When the motion micro-variability rate > +0.5% / ms, it indicates that the scaling intent of the user's scaling interaction event is a zoom-in intent; when the motion micro-variability rate < -0.5% / ms, it indicates that the scaling intent of the user's scaling interaction event is a zoom-out intent.

[0030] In this embodiment, if the absolute value of the motion micro-rate is less than the third micro-rate threshold, the user zoom interaction event can be determined to be a false touch event. For example, the third micro-rate threshold can be set to 0.2% / ms. When the absolute value of the motion micro-rate is < 0.2% / ms, it indicates that the user zoom interaction event is a false touch event.

[0031] It should be noted that the threshold setting in this embodiment is only an illustrative example. In actual applications, the threshold value can be adaptively adjusted according to factors such as the screen size of the terminal device and the application scenario requirements. This embodiment does not impose any specific limitations on this.

[0032] like Figure 2 As shown in this embodiment, the specific process of predicting the predicted zoom level corresponding to the user zoom interaction event based on the micro-motion information in step S120 includes, but is not limited to, steps S210 to S230.

[0033] Step S210: Determine the current scaling speed and current scaling acceleration based on the micro-motion information; Step S220: Predict the remaining operation time of the user zoom interaction event based on the micro-motion information; Step S230: Based on the current zoom speed, current zoom acceleration, and remaining operation time, predict the zoom level corresponding to the user zoom interaction event.

[0034] In this embodiment, the micro-motion information includes the real-time coordinate offset of the two touch points, the real-time distance between the touch points, the timestamps at each sampling moment, and the touch pressure value. When predicting the predicted zoom level corresponding to the user zoom interaction event, the current zoom speed and current zoom acceleration can be calculated first based on the continuous multi-frame spacing data and corresponding timestamps in the micro-motion information. The current zoom speed can be divided into three levels: fast, medium, and slow. Then, based on the current zoom speed, acceleration, and historical operation habit data, the remaining operation time of the user zoom interaction event is predicted. Finally, based on the current zoom speed, current zoom acceleration, and remaining operation time, the predicted zoom level Z_pred corresponding to the user zoom interaction event is predicted. For example, when the velocity is stable (acceleration ≈ 0), linear extrapolation is used, and the predicted scaling level is Z_pred = Z_0 + v × t_remaining, where Z_0 is the scaling level at the current moment, v is the current scaling velocity, and t_remaining is the predicted remaining operation time. When the velocity increases (acceleration > 0), accelerated extrapolation is used, and the predicted scaling level is Z_pred = Z_0 + v × t + 0.5 × a × t², where a is the current scaling acceleration and t is the remaining operation time. When the velocity decreases (acceleration < 0), decelerated extrapolation is used, and the predicted scaling level is Z_pred = Z_0 + v × t + 0.5 × a × t². The calculation process continues until the velocity is zero, at which point the extrapolation stops.

[0035] In this embodiment, when determining the scaled predicted coordinates of the Region of Interest (ROI) corresponding to a user's zoom interaction event based on the zoom intention and predicted zoom level, the radius of the ROI is first adjusted according to the zoom intention and predicted zoom level to obtain the target area radius. Then, the scaled predicted coordinates of the ROI are determined based on the target area radius. In other words, the size of the ROI can be dynamically adjusted following the user's zoom operation, specifically implemented in two steps: first, the radius of the current ROI is adjusted according to the user's zoom intention and predicted zoom level. If the predicted zoom operation is a zoom-in operation and the higher the zoom level, the narrower the user's focus area is, indicating a desire to see more detailed content; therefore, the target area radius will decrease accordingly. Conversely, if the predicted zoom operation is a zoom-out operation and the lower the zoom level, the wider the user's focus area is, indicating a desire to see more global content; therefore, the target area radius will increase accordingly. After obtaining the target area radius, the scaled rectangular predicted coordinates of the ROI can be calculated using the ROI center point coordinates, including the top-left corner (x1, y1) and the bottom-right corner (x2, y2). During this calculation process, the center point of the ROI is always confined to the effective area of ​​the screen to ensure that the final generated ROI rectangle does not exceed the screen boundary.

[0036] In this embodiment, when the predicted scaling level is obtained, the prediction confidence level corresponding to that level is also obtained. The prediction confidence level indicates the reliability of the predicted scaling level and reflects the probability of matching the scaling result calculated based on the current micro-motion information with the user's actual operational intent. The prediction confidence level typically ranges from 0 to 1; a higher value indicates higher reliability of the prediction result.

[0037] like Figure 3 As shown, the specific process of obtaining the first rendering feedback information from the cloud virtual machine based on the predicted coordinate information and the predicted scaling level in step S130 includes, but is not limited to, steps S310 and S320.

[0038] Step S310: Send the prediction coordinates, prediction scaling level, and prediction confidence to the cloud virtual machine; Step S320: Receive the first rendering feedback information sent by the cloud virtual machine based on the predicted coordinate information, the predicted scaling level, and the predicted confidence.

[0039] In this embodiment, the first rendering feedback information refers to the image data returned by the cloud virtual machine after pre-rendering based on the scaling operation parameters predicted by the cloud terminal. The cloud terminal can first send the predicted ROI coordinate information, predicted scaling level, and predicted confidence level to the cloud virtual machine. After receiving the data, if the cloud virtual machine determines that the confidence level meets the pre-rendering conditions, it will prioritize the first round of rendering for the predicted area of ​​interest and scaling level, generate the corresponding image frames, and send them back to the cloud terminal. In this way, when the user completes the actual scaling operation, the cloud terminal has already obtained the rendered image in advance and can display it directly, reducing the user's waiting delay.

[0040] In this embodiment, when the prediction confidence is greater than the first confidence threshold, the first rendering feedback information is a full pre-rendering instruction. When the prediction confidence is less than or equal to the first confidence threshold and greater than or equal to the second confidence threshold, the first rendering feedback information is a hierarchical pre-rendering instruction. When the prediction confidence is less than the second confidence threshold, the first rendering feedback information is the scaled image data of the region of interest. For example, the first confidence threshold can be set to 0.9, and the second confidence threshold can be set to 0.7. When the prediction confidence is >0.9, it is considered high confidence, indicating that the user's scaling intention is clear and the historical operation matching degree is high. The cloud virtual machine can directly perform full pre-rendering, generate the rendering result of the complete screen and return it. When the prediction confidence is ∈ [0.7, 0.9], it is considered medium confidence, indicating that the scaling trend is clear but the final scaling level may be slightly adjusted. The cloud virtual machine adopts a hierarchical pre-rendering strategy, prioritizing the rendering of the core area of ​​interest and then gradually completing the surrounding areas. When the prediction confidence is ∈ [0.5, 0.7), it is considered low confidence, indicating that the user's intention is not yet certain. The cloud virtual machine only pre-requests rendering resources and returns the image data of the scaled area of ​​interest for temporary display, without performing full-screen rendering. When the prediction confidence is <0.5, it is considered extremely low confidence, indicating that the touch signal is chaotic and cannot be reliably predicted. The cloud virtual machine does not perform predictive rendering and responds to the rendering request in the traditional way after the user's operation is completed.

[0041] In this embodiment, when the first rendering feedback information is a full pre-rendering instruction, the execution process of preliminary information display based on the first rendering feedback information in step S130 is as follows: Obtain the pre-requested image processor (GPU) resources according to the full pre-rendering instruction, and then perform preliminary full-rendering display on the scaled region of interest based on the GPU resources. For example, when a user browses a drawing on a cloud desktop and performs a two-finger zoom operation, if the prediction confidence reaches 0.95 and the prediction zoom level is 150% of the original image, after receiving the full pre-rendering instruction, the cloud virtual machine can allocate corresponding computing power from the pre-requested GPU resource pool and perform full-resolution rendering of the entire drawing at a 150% zoom ratio. After rendering, the image frames are temporarily stored in the cache. When the user releases their finger and the zoom operation actually occurs, the cloud virtual machine does not need to re-render and directly pushes the cached high-definition image to the cloud terminal for display, so the user hardly perceives any delay.

[0042] In this embodiment, when the first rendering feedback information is a hierarchical pre-rendering instruction, the execution process of preliminary information display based on the first rendering feedback information in step S130 is as follows: Preliminary hierarchical rendering display is performed on the scaled region of interest (ROI) and its corresponding peripheral region according to the hierarchical pre-rendering instruction. For example, when a user browses a drawing on a cloud desktop and performs a two-finger zoom operation, if the prediction confidence is 0.82 and the prediction zoom level is 200%, the cloud virtual machine can perform rendering in two levels after receiving the hierarchical pre-rendering instruction. The first level targets the predicted core ROI region, calling GPU resources to render a high-definition image at a 200% scale to ensure that the central area focused on by the user's gaze is clearly displayed first. The second level quickly renders a blurry preview of the peripheral transition area of ​​the ROI at a lower resolution. After the user's zoom operation is finally confirmed and the zoom level is completely locked, the high-definition details of the peripheral area are then filled in. This ensures the immediate clarity of the core area while reserving adjustment space, avoiding the waste of computing power caused by prediction deviations.

[0043] In this embodiment, when the first rendering feedback information is image data of the scaled region of interest, the execution process of preliminary information display based on the first rendering feedback information in step S130 is as follows: preliminary rendering display based on the image data. For example, when a user browses a drawing on a cloud desktop and performs a two-finger zoom operation, if the prediction confidence is 0.6, which is in the low confidence range, the cloud virtual machine does not perform full-screen pre-rendering, but only processes the local area corresponding to the predicted ROI according to the prediction scaling level and returns the image data. After receiving the data, the cloud terminal can paste this scaled local image onto the corresponding position on the screen for preliminary display, allowing the user to see the magnified effect of the central area in advance, while the rest of the screen temporarily maintains its original scaling state. After the user releases their finger and the zoom operation is finally confirmed, the cloud virtual machine is requested to display the complete full-screen rendering result to replace the preliminary displayed local image.

[0044] In this embodiment, during the continuous process of the user's zoom interaction event, the cloud terminal can acquire the continuous process information of the user's zoom interaction event, and then obtain display adjustment instructions from the cloud virtual machine based on the continuous process information, and then adjust the current display information according to the display adjustment instructions. Specifically, during the period before the zoom operation ends, the cloud terminal can continuously collect the user's real-time operation data, compare the actual operation with the previous prediction results, and if a deviation is found, it can be synchronized to the cloud virtual machine. The cloud virtual machine can generate corresponding display adjustment instructions based on the magnitude of the deviation and send them back. The cloud terminal adjusts the current screen in real time according to the instructions, without having to wait for the entire zoom to be completed before making corrections, thereby avoiding sudden screen jumps.

[0045] In this embodiment, as Figure 4 As shown, the specific process of obtaining display adjustment instructions from the cloud virtual machine based on continuous process information includes, but is not limited to, steps S410 to S430.

[0046] Step S410: Based on the continuous process information, predict the scaling adjustment level corresponding to the predicted scaling level and the coordinate adjustment information corresponding to the predicted coordinate information; Step S420: Send scaling adjustment level and coordinate adjustment information to the cloud virtual machine; Step S430: Receive display adjustment instructions sent by the cloud virtual machine based on scaling adjustment level and coordinate adjustment information.

[0047] In this embodiment, the display adjustment command refers to the screen data returned by the cloud virtual machine after correcting and updating the initial pre-rendered result based on the dynamic adjustment parameters sent by the cloud terminal during the continuous user scaling operation. When the cloud terminal obtains the display adjustment command from the cloud virtual machine, it can first correct the previously predicted scaling level and ROI coordinates based on the continuously collected micro-motion process information, obtaining the scaling adjustment level difference corresponding to the original predicted scaling level, and the coordinate offset adjustment information corresponding to the original predicted coordinate information; then it sends these two adjustment parameters to the cloud virtual machine; subsequently, it receives the display adjustment command returned by the cloud virtual machine after correcting the original pre-rendered result based on the scaling adjustment level and coordinate adjustment information. In this way, as the user operation continues, the rendering result can gradually match the user's actual operation trajectory, which reduces the waiting time for final rendering and avoids the waste of computing power caused by the initial prediction deviation.

[0048] In this embodiment, when the cloud terminal adjusts the current display information according to the display adjustment command, it can progressively adjust the display information of the region of interest (ROI) and its corresponding peripheral regions. Specifically, the display adjustment command can carry information such as the corrected ROI position, scaling level, and region update priority. After receiving the display adjustment command, the cloud terminal can first update the high-definition image of the core ROI region according to the command, and then gradually refresh the peripheral transition areas sequentially. This layered and progressive approach ensures a smooth transition during the scaling process.

[0049] In this embodiment, when there is a deviation between the scaling adjustment level and coordinate adjustment information and the actual scaling level and coordinate information of the user's actual operation, the cloud terminal can send the deviation to the cloud virtual machine. After receiving the correction instruction sent by the cloud virtual machine based on the deviation, the cloud terminal can further obtain correction image data from the cloud virtual machine to correct the deviation according to the correction instruction, and correct the current display information according to the correction image data. This process is the deviation correction stage. It should be understood that since the previous scaling adjustment level and coordinate adjustment are based on the prediction of the previous operation trend, the user may adjust the gesture speed, direction, or final landing point in the actual operation, resulting in a deviation between the predicted value and the actual scaling level, coordinate information, etc. After the cloud terminal detects the deviation in real time, it can send the deviation data to the cloud virtual machine. The cloud virtual machine generates a correction instruction based on the deviation and returns the correction instruction to the cloud terminal. After receiving the correction instruction, the cloud terminal can obtain the corresponding correction image data from the cloud virtual machine according to the correction instruction, and replace the screen of the deviation area in the current display with the correction image data to complete the display correction. This ensures that the final screen completely matches the user's actual operation, without having to re-render the entire screen, thus ensuring both display accuracy and rendering efficiency.

[0050] In this embodiment, the specific process of obtaining the second rendering feedback information from the cloud virtual machine based on the target location information in step S150 is as follows: Figure 5 As shown, steps S510 to S530 are included, but are not limited to.

[0051] Step S510: Determine the target zoom level corresponding to the user zoom interaction event based on the target location information; Step S520: Send the target scaling level to the cloud virtual machine; Step S530: Receive the second rendering feedback information sent by the cloud virtual machine according to the target scaling level.

[0052] In this embodiment, when the cloud terminal detects the end of the user's zoom interaction event, it can collect the final target position information of this zoom operation, calculate the user's actual target zoom level based on the target position information, and send the final target zoom level to the cloud virtual machine. Upon receiving this actual target zoom level, the cloud virtual machine can perform a full-resolution final rendering of the complete image according to the user's final zoom parameters, generating second rendering feedback information and returning it to the cloud terminal. After receiving the second rendering feedback information from the cloud virtual machine based on the target zoom level, the cloud terminal can replace the previous pre-rendered content with the final full-rendered image, completing the final update of the display information and ensuring that the user sees a full HD clear image that perfectly matches the actual operation.

[0053] In this embodiment, when the second rendering feedback information is a rendering confirmation command, the cloud terminal, while updating the display information according to the second rendering feedback information, can first obtain the frame information of the currently displayed screen according to the rendering confirmation command, and then update the current display information according to the frame information. The rendering confirmation command is a confirmation signal sent to the terminal by the cloud virtual machine after completing the final full-resolution rendering. It contains the frame information of the final rendered frame, such as frame number, resolution, and timestamp. After receiving the command, the cloud terminal can first check whether the frame corresponds to the final rendered frame of the current scaling operation through the frame information. After confirming the match, it replaces the final high-definition rendered frame into the display buffer to complete the screen update.

[0054] Reference Figure 6 , Figure 6 This is a flowchart of an information display method provided in another embodiment of this application. The method can be applied to cloud virtual machines, and the specific process includes, but is not limited to, steps S610 to S640.

[0055] Step S610: Receive the predicted coordinate information and predicted zoom level sent by the cloud terminal. The predicted coordinate information is the zoomed coordinate information of the region of interest corresponding to the user zoom interaction event determined by the cloud terminal according to the zoom intention and the predicted zoom level. The predicted zoom level is the zoom level corresponding to the user zoom interaction event predicted by the cloud terminal according to the micro-motion information. The zoom intention is determined by the cloud terminal according to the micro-motion information. The micro-motion information is obtained by the cloud terminal using the initial position information of the user zoom interaction event as the starting position when the user zoom interaction event is detected. Step S620: Send the first rendering feedback information to the cloud terminal according to the predicted coordinate information and the predicted scaling level. The first rendering feedback information is used by the cloud terminal to display preliminary information. Step S630: Receive the target zoom level corresponding to the user zoom interaction event sent by the cloud terminal at the end of the user zoom interaction event; Step S640: Send second rendering feedback information to the cloud terminal according to the target scaling level. The second rendering feedback information is used by the cloud terminal to update the display information.

[0056] In this embodiment, when the cloud terminal detects a user-triggered zoom interaction event, it first determines the initial position information corresponding to the event and collects micro-motion information of the zoom interaction event starting from the initial position. Based on the micro-motion information, the cloud terminal determines the zoom intention of the zoom interaction event and predicts the corresponding zoom level. Then, based on the zoom intention and the predicted zoom level, it calculates the predicted coordinates of the region of interest corresponding to the zoom interaction event after scaling, and sends the predicted coordinates and the predicted zoom level to the cloud virtual machine. Upon receiving the predicted coordinates and the predicted zoom level, the cloud virtual machine returns first rendering feedback information to the cloud terminal based on the information and level. After receiving this information, the cloud terminal completes the initial display based on the first rendering feedback information. After the zoom interaction event ends, the cloud terminal can collect the target zoom level corresponding to the user's zoom interaction event at the end of the event and send this target zoom level to the cloud virtual machine. The cloud virtual machine returns second rendering feedback information to the cloud terminal based on the received target zoom level. After receiving this information, the cloud terminal updates the current content based on the second rendering feedback information.

[0057] In this embodiment, when the cloud virtual machine receives the predicted coordinate information and predicted scaling level sent by the cloud terminal, it can also receive the predicted confidence level corresponding to the predicted scaling level sent by the cloud terminal. When the cloud virtual machine sends the first rendering feedback information to the cloud terminal based on the predicted coordinate information and predicted scaling level, it can first determine the corresponding rendering strategy based on the predicted confidence level, predicted coordinate information, and predicted scaling level, and then send the first rendering feedback information to the cloud terminal based on the rendering strategy.

[0058] In this embodiment, the corresponding rendering strategy is determined based on the prediction confidence level, prediction coordinate information, and prediction scaling level, including one of the following: when the prediction confidence level is greater than a first confidence threshold, the corresponding rendering strategy is full pre-rendering based on the prediction coordinate information and prediction scaling level; when the prediction confidence level is less than or equal to the first confidence threshold but greater than or equal to a second confidence threshold, the corresponding rendering strategy is hierarchical pre-rendering based on the prediction coordinate information and prediction scaling level; when the prediction confidence level is less than the second confidence threshold, the corresponding rendering strategy is sending the image data to be rendered from the cloud virtual machine to the cloud terminal based on the prediction coordinate information and prediction scaling level. For example, when a user views a drawing on the cloud desktop and performs a two-finger zoom operation, after receiving the prediction coordinates, prediction scaling level, and prediction confidence level from the cloud terminal, the cloud virtual machine can first match the rendering strategy based on the confidence level. If the prediction confidence is 0.93, which is higher than the first confidence threshold of 0.9, it indicates that the scaling intention is clear and the prediction reliability is high. In this case, a full pre-rendering strategy is adopted, sending a full pre-rendering command to the cloud terminal and simultaneously returning the complete full-resolution pre-rendered image. If the prediction confidence is 0.81, which is in the middle confidence range between the second confidence threshold of 0.7 and the first confidence threshold of 0.9, it indicates that the trend is clear but the endpoint may be slightly adjusted. In this case, a hierarchical pre-rendering strategy is adopted, sending a hierarchical pre-rendering command to the cloud terminal, prioritizing the return of high-definition images of the core region of interest, and gradually completing the peripheral areas later. If the prediction confidence is 0.62, which is lower than the second confidence threshold of 0.7, it indicates that the intention is not yet certain. In this case, a partial pre-rendering strategy can be adopted, returning only the scaled region of interest image data to the cloud terminal for temporary display, without performing full-screen rendering.

[0059] It should be noted that the values ​​of parameters such as the micro-variance rate threshold and confidence threshold in the embodiments of this application are only illustrative examples. In specific application scenarios, they can be flexibly adapted and adjusted according to factors such as the touch sampling rate of the terminal device, screen size, or the operating habits of the target user. The embodiments of this application do not limit the specific values ​​of the above parameters.

[0060] In this embodiment, when the rendering strategy is full pre-rendering, the first rendering feedback information is the full pre-rendering instruction. When the rendering strategy is hierarchical pre-rendering, the first rendering feedback information is the hierarchical pre-rendering instruction. When the rendering strategy involves the cloud virtual machine sending the image data to be rendered to the cloud terminal, the first rendering feedback information is the image data. That is to say, in the full pre-rendering scenario with the highest confidence, the cloud directly completes the rendering of the entire image and returns the full pre-rendering instruction and the finished image; in the hierarchical pre-rendering scenario with medium confidence, the cloud advances the rendering in stages and returns the hierarchical pre-rendering instruction and the layered image; when the confidence is low, the cloud does not perform full-screen rendering, but only returns the image data of the region of interest to be displayed for temporary display, thereby simultaneously taking into account response speed and computing efficiency.

[0061] In this embodiment, when the second rendering feedback information is a rendering confirmation instruction, the rendering confirmation instruction is used by the cloud terminal to obtain the frame information of the currently displayed screen and update the current display information according to the frame information.

[0062] In this embodiment, the cloud virtual machine can also receive zoom adjustment level and coordinate adjustment information corresponding to the predicted zoom level from the cloud terminal during the continuous process of the user zoom interaction event. Based on the zoom adjustment level and coordinate adjustment information, the cloud terminal sends a display adjustment command to the cloud terminal. This display adjustment command is used by the cloud terminal to progressively adjust the display information of the region of interest and its corresponding peripheral region. In other words, during the continuous process of the zoom interaction event, the cloud terminal continuously collects information about the event's progress and calculates the zoom adjustment level relative to the original predicted zoom level and the coordinate adjustment information relative to the original predicted coordinate information, and sends this information to the cloud virtual machine. The cloud virtual machine returns the corresponding display adjustment command to the cloud terminal based on the received adjustment information; upon receiving the command, the cloud terminal progressively adjusts the currently displayed content according to the display adjustment command.

[0063] In this embodiment, the cloud virtual machine can also execute, for example... Figure 7 The information display method shown includes, but is not limited to, steps S710 to S740.

[0064] Step S710: Receive the deviation sent by the cloud terminal. The deviation is the difference between the scaling adjustment level and coordinate adjustment information and the actual scaling level and actual coordinate information of the user's actual operation. Step S720: Send the corresponding correction command to the cloud terminal according to the deviation; Step S730: Receive image data request information sent by the cloud terminal according to the correction instruction; Step S740: Send the corresponding corrected image data to the cloud terminal according to the image data request information. The corrected image data is used by the cloud terminal to correct the currently displayed information.

[0065] In this embodiment, the cloud terminal can monitor the actual operation data during the user's zoom interaction in real time. When a deviation is detected between the zoom adjustment level and coordinate adjustment information and the actual zoom level and coordinate information corresponding to the user's actual operation, the deviation data can be sent to the cloud virtual machine. After receiving the deviation data sent by the cloud terminal, the cloud virtual machine generates a corresponding correction instruction based on the deviation and returns it to the cloud terminal. After receiving the correction instruction, the cloud terminal can send an image data request to the cloud virtual machine to request corrected image data for correcting the deviation. After receiving the image data request sent by the cloud terminal based on the correction instruction, the cloud virtual machine returns the corresponding corrected image data to the cloud terminal according to the request content, so that the cloud terminal can correct the deviation of the currently displayed content.

[0066] It should be noted that the information display method embodiment applied to the cloud virtual machine corresponds to the information display method embodiment applied to the cloud terminal. That is to say, the two are mutually compatible in terms of process logic, interaction rules, and function implementation. The method embodiment on the cloud virtual machine side can be understood by referring to the relevant embodiment on the cloud terminal side, and will not be repeated here.

[0067] Reference Figure 8 , Figure 8 This is a flowchart of an information display method provided in another embodiment of this application. The method can be applied to cloud desktops, and the specific process includes, but is not limited to, step S810.

[0068] Step S810: In response to detecting a two-finger touch event on the touchscreen, a first region and a second region are displayed on the touchscreen; wherein the first region has a first resolution, the second region has a second resolution, the first resolution is higher than the second resolution, the first region is a closed region defined by the two touch points of the two-finger touch event, and the second region is the outer region of the first region.

[0069] In this embodiment, cloud desktop is a technology that allows users to access virtual desktops on remote servers via the network. It consists of two parts: a cloud terminal and a cloud virtual machine. The various functions of cloud desktop can be realized through information interaction between the two.

[0070] In this embodiment, when the cloud desktop detects a two-finger touch event on the touchscreen, it can divide the display screen into a first region and a second region. The first region is a closed core area defined by the two touch points of the two-finger touch event, corresponding to the user's main focus area during zooming operations; the second region is the peripheral area outside the first region. Since the positions of the two regions correspond to the user's core focus area and non-focused peripheral area, the user's gaze is mainly focused on the first region surrounded by the two fingers when zooming, and the perception of the clarity of the peripheral area is weaker. At the same time, in order to prioritize the rendering speed of the core area and save bandwidth resources, the cloud desktop will allocate more rendering computing power and bandwidth to the first region. Therefore, the clarity of the first region is higher than that of the second region.

[0071] In this embodiment, as the distance between the two touch points changes, the area of ​​the first region is updated in real time; or, as the distance between the two touch points changes, the area of ​​the first region remains unchanged, and the displayed content within the first region is updated in real time. For example... Figure 9 As shown, the left side represents the initial state of pinch-to-zoom, and the right side represents the state after pinching two fingers outwards to enlarge. When a user browses high-definition photos on a touchscreen, pressing and holding the photo with two fingers to enlarge it outwards, as the distance between the two touch points increases, the first area (the area of ​​the image the user is primarily focused on) expands outwards synchronously with the touch points, maintaining high-definition display in the core area; the outer second area retains a lower resolution. This ensures that the core area the user is focused on is clearly visible without transmitting high-definition data across the entire screen, effectively saving bandwidth resources. Figure 10 As shown, the left side represents the initial zoom state, and the right side represents the zoom state after two fingers are spread outwards. When a user browses a high-resolution photo and zooms in to check only a specific detail, the first area (the dotted box in the image) remains fixed within the area of ​​focus during the zooming process. Only the photo content within the first area adjusts its resolution in real-time with the zoom operation, updating to the corresponding clarity when zoomed in to the appropriate scale. The range and clarity of the outer second area remain unchanged. This method upgrades the clarity of only the fixed area of ​​focus without adjusting the rendering parameters of the entire screen, thus saving bandwidth while still ensuring the need for detailed viewing.

[0072] Reference Figure 11 , Figure 11 This is a flowchart of an information display method provided in another embodiment of this application. The method can be applied to cloud desktops, and the specific process includes, but is not limited to, steps S1110 to S1130.

[0073] Step S1110: Display the cursor on the screen; Step S1120: Mouse wheel scrolling event detected; Step S1130: In response to the first scroll mark of the mouse wheel scrolling event, display a first region and a second region on the display screen; wherein the first region has a first resolution, the second region has a second resolution, the first resolution is higher than the second resolution, the first region is a closed region determined based on the cursor position, and the second region is the outer region of the first region.

[0074] In this embodiment, the first region can be a circular region centered on the cursor position, or a rectangular region centered on the cursor position.

[0075] In this embodiment, the area of ​​the first region is updated in real time as the mouse wheel is continuously scrolled; or, the area of ​​the first region remains unchanged while the content displayed within the first region is updated in real time as the mouse wheel is continuously scrolled.

[0076] In this embodiment, the difference between the first and second levels of resolution persists from the start to the stop of mouse wheel scrolling. Since the mouse cursor is located at the center of the first area, the core area around the cursor maintains higher resolution throughout the scrolling and zooming process, while the peripheral, uninterested areas maintain lower resolution. This matches the user's habit of following the cursor with their eye when operating the mouse, ensuring sufficient clarity for the area the user is actually focused on without transmitting high-definition content across the entire screen, thus saving bandwidth and accelerating zoom response.

[0077] like Figure 12 As shown, the left side represents the initial zoom state, and the right side represents the zoomed-in state. The mouse cursor is located at the center of the first area. Therefore, throughout the zoom process, the core area around the cursor maintains higher resolution, while the peripheral, less-discussed areas maintain lower resolution. When the scroll wheel zooms forward, the content in the first area centered on the cursor simultaneously increases in resolution and magnifies details; when the scroll wheel zooms backward, the resolution adjusts accordingly. Throughout the process, the peripheral second area maintains a lower resolution, avoiding the transmission of high-definition data across the entire screen. This ensures sufficient clarity for the area the user is actually interested in, while also saving bandwidth and accelerating zoom response. Figure 13 As shown, the left side represents the initial zoom state, and the right side represents the zoomed-in state. During continuous mouse wheel scrolling, the area of ​​the first region remains constant, while the content displayed within that region updates in real time. As the wheel scrolls forward to zoom in, the details within the first region gradually become clearer, and the resolution increases accordingly; as the wheel scrolls backward to zoom out, the resolution within that region decreases accordingly. This mode is suitable for scenarios where users need to focus on a specific detail, such as checking small print in a document or zooming in on a part of an image during editing. Only the rendering parameters need to be adjusted for the fixed first region, without needing to adjust the entire screen simultaneously. This satisfies the need for detailed viewing while saving bandwidth and speeding up zoom response.

[0078] The embodiments of this application can be applied to client software, thin client firmware, and cloud virtual machine server programs in scenarios such as cloud desktop, remote desktop, and virtual office, and are applicable to all remote desktop protocol environments.

[0079] The core of this application's embodiments lies in constructing a pre-perception and pre-rendering mechanism for end-to-end cloud collaboration, breaking through the technical bottleneck of traditional cloud desktops' passive response. The specific execution logic is as follows: the moment a user triggers two-finger touch zoom, multi-finger touch zoom, or keyboard and mouse zoom, the cloud terminal immediately collects the initial zoom state and synchronously initiates the prediction process. The prediction stage includes: based on velocity and acceleration dynamics models, correcting and calculating the predicted zoom level at the zoom endpoint; and fusing the geometric center of the zoom operation to dynamically calculate the true ROI center. After obtaining the prediction result, the cloud terminal first executes a hierarchical pre-rendering strategy locally, prioritizing the rendering of high-definition content in the ROI's focus area, while temporarily using low-resolution images to occupy unfocused peripheral areas, ensuring that the area the user's gaze is focused on is clearly displayed first. After the user's zoom operation ends, the cloud terminal compares the actual zoom result with the predicted target; if the deviation exceeds a preset threshold, it triggers cloud-based supplementary rendering to correct the image. This application's embodiments, by combining predictive pre-rendering and deviation correction, can effectively reduce the response latency of zoom operations, making the cloud desktop's zoom interaction experience approach the smoothness of a local PC.

[0080] In this embodiment, the pre-sensing and pre-rendering mechanism of edge-cloud collaboration includes a micro-variability calculation model, a two-finger movement vector angle calculation model, a two-finger speed comparison model, a pressure change trend analysis model, a dynamic prediction model, an ROI center calculation model, a confidence assessment model, and a deviation monitoring model. The functions of each model are as follows: The micro-variability calculation model is responsible for calculating the rate of change of the distance between two fingers per unit time, used to initially determine the zoom direction and magnitude; the two-finger movement vector angle calculation model is used to calculate the angle between the movement directions of the two touch points, used to help verify whether the current operation is zoom, excluding other gestures such as translation and rotation; the two-finger speed comparison model is used to compare the ratio of the movement speeds of the two touch points, verifying whether the two fingers move synchronously, and further confirming the validity of the zoom operation; the pressure change trend analysis model is used to analyze the change trend of the two-finger touch pressure, to help corroborate the zoom intention and improve the accuracy of intention recognition; the dynamics prediction model has three modes: uniform speed, acceleration, and deceleration. Based on the current zoom speed and acceleration, it extrapolates using the corresponding kinematic formulas to predict the target level of the zoom endpoint; the ROI center calculation model is responsible for fusing and calculating the center coordinates of the user's attention area, and can also limit the center to the effective range of the screen, providing a regional basis for subsequent graded rendering; the confidence assessment model calculates the credibility of the prediction results by integrating multi-dimensional features, providing a graded basis for subsequent selection of which rendering strategy to use; the deviation monitoring model compares the deviation between the predicted value and the user's actual operation value in real time, triggering the subsequent dynamic correction process. It should be noted that each model has a mature corresponding implementation in current touch gesture recognition and motion prediction technologies, and the algorithm design of the model itself is not the focus of this application.

[0081] The information display method of this application will be described in detail below with reference to a specific embodiment.

[0082] This embodiment illustrates the modular interaction process between a cloud terminal and a cloud-based virtual machine in a scaling interaction scenario, with both ends establishing bidirectional communication via Socket. For example... Figure 14As shown, in a cloud desktop scaling advance perception system composed of cloud terminals and cloud virtual machines, the cloud terminal side includes a perception module, a prediction module, and a pre-rendering module. The perception module corresponds to the actual touch input hardware, such as the physical touchscreen, touchpad, and keyboard / mouse peripherals used by the user. It is responsible for collecting all user interaction input data, such as the initial touch point position of scaling interactions, real-time micro-motion information, operation duration data, and keyboard / mouse sliding distance and click commands. All collected perception data can be synchronized to the prediction module in real time as a basis for prediction. The prediction module is mainly responsible for predicting the user's operational intent based on the perception data collected by the perception module. Specifically, it can determine the user's scaling intent based on micro-motion information, predict the scaling level and corresponding prediction confidence, and calculate the predicted coordinates of the region of interest after scaling. During the continuous scaling operation, it can also calculate the scaling adjustment level and coordinate adjustment information in real time, and continuously compare the deviation between the predicted value and the user's actual operation value. All prediction results can be synchronously sent to the cloud computer server. The pre-rendering module is responsible for the screen processing and display on the cloud terminal side, including protocol decoding, layered screen rendering, and display updates. Upon receiving the first rendering feedback information from the cloud virtual machine, the cloud terminal can complete the initial screen display. Upon receiving a display adjustment instruction, it can perform a gradual display update. Upon receiving corrected image data, it can refresh the screen after deviation correction and present the processed image to the user. Upon receiving the second rendering feedback information, the cloud terminal can update the current display information and present the final processed image to the user. The cloud virtual machine side includes a decision layer, a cloud-based screen capture encoding module, and a feedback optimization module. The decision layer is primarily responsible for receiving various prediction results from the cloud terminal (such as prediction coordinate information, prediction scaling level, prediction confidence, etc.), matching the corresponding rendering strategy based on the prediction results, identifying the user's areas of interest in advance, clarifying the priority and parameter requirements for hierarchical rendering, and then issuing rendering scheduling instructions to the cloud-based screen capture encoding module. The cloud-based screen capture and encoding module is responsible for performing the actual desktop screen capture and layered encoding. According to the ROI range, resolution level, and encoding priority specified by the decision-making layer, it performs high-frame-rate, high-definition encoding on the region of interest (ROI) and low-resolution, fast encoding on the peripheral, non-interested regions. After encoding, it returns the corresponding first rendering feedback information, second rendering feedback information, or corrected image data as a rendering data stream to the cloud terminal, coordinating with the terminal side to execute the predictive rendering process. Furthermore, the system forms a complete model optimization closed loop through feedback optimization mechanisms on both sides. After each scaling operation, the cloud terminal's prediction module can send the deviation data between the actual operation result and the predicted target back to the cloud virtual machine. The cloud virtual machine's feedback optimization module is responsible for collecting all deviation samples and periodically iterating and updating each model to continuously improve prediction accuracy, making subsequent predictive rendering more aligned with the user's actual operating habits.

[0083] like Figure 15 and Figure 16 As shown, the perception prediction rendering (i.e., information display) process of this system is as follows: Step 1: Touch Data Acquisition. When a user triggers zoom interaction via two-finger / multi-finger touchscreen or keyboard / mouse operation, the sensing module switches to high-frequency sampling mode to record data such as initial coordinate position / spacing / pressure at the start of the operation. For example, in the case of two-finger operation, the initial coordinate position is the initial coordinates of the two touch points (Finger1_x, Finger1_y) and (Finger2_x, Finger2_y). These initial coordinates serve as the reference zero point for the entire zoom operation. The initial data is packaged into a zoom reference state data package containing coordinates, timestamps, and operation type, which serves as the input basis for all subsequent prediction calculations.

[0084] Step 2: Sensing Data Preprocessing. Based on the scaled baseline state data packet output in Step 1, the sensing module performs convergence and fusion processing on the multi-source collected data. First, the data from different sources are time-aligned and unified to the baseline at the T0 starting time. If there are insufficient historical data samples, the historical offset correction function is temporarily disabled. Finally, a standardized sensing feature vector is generated and input into the prediction module.

[0085] Step 3: Micro-motion Intent Recognition. The prediction module recognizes micro-motion intent (i.e., zooming intent) based on standardized perceptual feature vectors. It calculates the zooming direction (zoom in / zoom out), initial speed level (fast / medium / slow), and intent confidence score through several dimensions: micro-variability rate, angle between two-finger movement vectors, comparison of two-finger speeds, and pressure change trend. The module outputs an operation intent recognition report. Specific judgment rules are as follows: a micro-variability rate greater than +0.5% / ms indicates a zooming intent, with a corresponding increase in confidence score; a micro-variability rate less than -0.5% / ms indicates a zooming intent, with a corresponding increase in confidence score; an absolute value of micro-variability rate less than 0.2% / ms indicates a stationary or accidental touch, which is directly filtered out. Auxiliary verification rules include: a two-finger movement vector angle greater than 150° confirms a zooming operation, excluding translation and rotation interference; a two-finger speed ratio between 0.8 and 1.2 confirms synchronous zooming; synchronous two-finger pressure increases correspond to pinching to zoom out, and synchronous pressure decreases correspond to opening to zoom out, further verifying the accuracy of the intent.

[0086] Step 4: Target Scaling Level Prediction. The prediction module performs dynamic trend extrapolation based on the operation intent recognition report. According to the movement speed and acceleration of the gesture or mouse, it infers the target scaling level Z_pred, prediction confidence, and estimated completion time according to corresponding rules. Linear extrapolation is used when the speed is stable and acceleration ≈ 0, with the formula Z_pred = Z_0 + v × t_remaining; accelerated extrapolation is used when the speed increases and acceleration > 0, with the formula Z_pred = Z_0 + v × t + 0.5 × a × t²; decelerated extrapolation is used when the speed decreases and acceleration < 0, with the formula Z_pred = Z_0 + v × t - 0.5 × a × t². The calculation stops when the speed drops to zero.

[0087] Step 5: ROI Center Fusion Calculation. The prediction module performs ROI center fusion calculation based on the target scaling level Z_pred. First, it restricts the ROI center to the effective area of ​​the image (0 < ROI coordinates < image width / height), and then dynamically adjusts the radius of the area of ​​interest according to the target scaling level. The higher the scaling level and the greater the magnification, the smaller the radius, ultimately generating the rectangular coordinates of the ROI, namely the upper left corner (x1, y1) and the lower right corner (x2, y2).

[0088] Step 6: Prediction Result Output and Tiered Decision-Making. The prediction module outputs a comprehensive prediction result, including the target scaling level, ROI coordinates, and prediction confidence, and sends it to the server's decision-making layer. The decision-making layer selects a tiered strategy based on the confidence threshold: high confidence (>0.9) corresponds to a full pre-rendering strategy, medium confidence (0.7 to 0.9) corresponds to a tiered pre-rendering strategy, low confidence (0.5 to 0.7) corresponds to a resource pre-allocation strategy, and if the confidence is <0.5, the prediction is rejected, and the system waits for the operation to complete before responding.

[0089] Step 7: Cloud Rendering Preparation. The cloud-based screen capture encoding module of the cloud virtual machine prepares for rendering according to the hierarchical strategy of the decision layer: in high-confidence scenarios, it sends a full rendering command to the pre-rendering module; in medium-confidence scenarios, it sends hierarchical pre-rendering commands; in low-confidence scenarios, it does not perform full rendering for the time being, and waits for subsequent image data requests from the terminal.

[0090] Step 8: Local Pre-rendering on the Terminal. The pre-rendering module performs local pre-rendering based on the prediction results and the rendering data returned from the cloud. First, a rendering context is established: In high-confidence scenarios, high-definition content in the ROI area is decoded first, the outer area is rendered, and after synthesizing a complete frame, it is pushed to the display buffer to directly display the pre-rendered image; In medium-confidence scenarios, only the low-definition preview image of the core ROI is rendered, and the outer area is temporarily used for placeholder content; In low-confidence scenarios, local pre-rendering is not performed for the time being, and the rendering data returned from the cloud is awaited.

[0091] Step 9: Real-time Deviation Monitoring and Dynamic Correction. During the continuous scaling operation, the perception module collects process data in real time, and the prediction module updates the trajectory and corrects the prediction results synchronously. If a deviation occurs between the predicted value and the actual operation, an adjustment request is immediately sent to the decision-making layer. The decision-making layer returns a dynamic adjustment command, and the cloud-based screen capture and encoding module gradually completes the rendering content of non-ROI areas, while the pre-rendering module simultaneously performs progressive image updates. The correction process employs a double-buffering mechanism to ensure image continuity; while the background performs correction calculations, the foreground continues to display the old frames, and seamless switching occurs after correction is complete, avoiding black screens and flickering.

[0092] Step 10: Scaling Completion Confirmation and Final Image Compositing. After the perception module detects a scaling completion event (finger leaves the screen, scroll wheel stops scrolling), it sends a scaling completion signal to the prediction module, confirming the final scaling level Z_final and terminating the prediction iteration. The decision layer triggers final rendering. The cloud-based screen capture and encoding module performs full-screen high-definition rendering and compositing according to the final scaling level. After color correction and edge smoothing, it sends the final image to the pre-rendering module and pushes it to the display front end to present a clear final image.

[0093] Furthermore, the system can form a complete model optimization closed loop through the feedback optimization module. Specifically, after each scaling operation, the system compares the deviation between the actual operation endpoint and the predicted target. If the deviation exceeds a threshold, it triggers supplementary rendering and sends the deviation samples to the feedback optimization module to periodically iterate and update the model, continuously optimizing the accuracy of subsequent predictions. It should be noted that the values ​​of parameters such as the micro-variance threshold and confidence threshold in this specific embodiment are only illustrative examples. In specific application scenarios, they can be flexibly adapted and adjusted according to factors such as the touch sampling rate of the terminal device, screen size, or the operating habits of the target user. This specific embodiment does not limit the specific values ​​of the above parameters.

[0094] Based on the above embodiments, this application embodiment achieves significant optimization of the cloud desktop scaling operation experience by constructing a pre-awareness and pre-rendering mechanism for end-to-cloud collaboration. The specific beneficial effects include the following aspects: 1. Regarding reducing interaction latency, traditional cloud desktops rely on remote server rendering and network backhaul, resulting in noticeably slow response times for users. This application achieves a smooth interactive experience close to that of a local PC through touch-sensitive interaction, micro-motion intent judgment, and trend-based endpoint prediction.

[0095] 2. Regarding improving image clarity response, traditional solutions initially present a blurry, low-resolution image during zooming, requiring users to wait for a high-resolution image to be transmitted from the cloud before achieving clear viewing, resulting in noticeable visual gaps. This application utilizes a ROI dynamic focus pre-rendering strategy to prioritize rendering high-resolution content in areas of actual user interest, eliminating the visual gaps inherent in traditional solutions.

[0096] 3. Regarding saving network bandwidth, traditional cloud desktops use full-screen unified encoding for transmission, but the area that users actually focus on often only occupies 15% to 30% of the screen, resulting in a large amount of invalid data transmission. This application uses a hierarchical pre-rendering strategy, employing high-resolution, low-compression encoding for the ROI center area and low-resolution, high-compression encoding or blurring for non-ROI areas, significantly reducing overall bandwidth consumption by 40% to 60%.

[0097] 4. Regarding enhanced adaptability in weak network environments, traditional cloud desktops experience a sharp decline in performance when network quality deteriorates, with zooming operations becoming noticeably laggy or even completely unusable. This application, due to the presence of local pre-rendering on the terminal, ensures basic smooth display of the ROI area even in weak or offline environments. The system dynamically adjusts the pre-rendering strategy based on real-time network conditions, prioritizing a high-definition experience when the network is good and ensuring basic usability when the network deteriorates, achieving adaptive degradation of the experience rather than a precipitous collapse.

[0098] Reference Figure 17 This application also discloses a cloud terminal 1700, which includes a memory 1710, a processor 1720, and a computer program stored on the memory 1710 and executable on the processor 1720. When the processor 1720 executes the computer program, it can implement the information display method as described in any of the previous embodiments applied to the cloud terminal.

[0099] Reference Figure 18 This application also discloses a cloud virtual machine 1800, which includes a memory 1810, a processor 1820, and a computer program stored on the memory 1810 and executable on the processor 1820. When the processor 1820 executes the computer program, it can implement the information display method as described in any of the previous embodiments applied to the cloud virtual machine.

[0100] Reference Figure 19 This application also discloses a cloud desktop 1900, which includes a memory 1910, a processor 1920, and a computer program stored on the memory 1910 and executable on the processor 1920. When the processor 1920 executes the computer program, it can implement the information display method as described in any of the previous embodiments applied to the cloud desktop.

[0101] One embodiment of this application also provides a computer-readable storage medium storing computer-executable instructions for performing the information display method provided in any embodiment of this application.

[0102] An embodiment of this application also provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a cloud terminal, a cloud virtual machine, or a cloud desktop reads the computer program or computer instructions from the computer-readable storage medium. The processor executes the computer program or computer instructions, causing the cloud terminal to perform the information display method in any of the embodiments previously applied to the cloud terminal, or causing the cloud virtual machine to perform the information display method in any of the embodiments previously applied to the cloud virtual machine, or causing the cloud desktop to perform the information display method in any of the embodiments previously applied to the cloud desktop.

[0103] The system architecture and application scenarios described in this application are for the purpose of more clearly illustrating the technical solutions of this application, and do not constitute a limitation on the technical solutions provided in this application. Those skilled in the art will know that as system architectures evolve and new application scenarios emerge, the technical solutions provided in this application are also applicable to similar technical problems.

[0104] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0105] In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0106] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process or execution thread, and components may be located on a single computer or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, or a network, such as the Internet interacting with other systems via signals).

Claims

1. An information display method applied to a cloud terminal, the method comprising: Upon detecting a user zoom interaction event, the initial position information of the user zoom interaction event is determined, and the micro-motion information of the user zoom interaction event is obtained using the initial position information as the starting position. Based on the micro-motion information, a motion micro-variability rate is obtained; based on the motion micro-variability rate, the scaling intention of the user scaling interaction event is determined; based on the micro-motion information, the predicted scaling level corresponding to the user scaling interaction event is predicted; and based on the scaling intention and the predicted scaling level, the predicted coordinate information of the region of interest corresponding to the user scaling interaction event after scaling is determined; wherein, the motion micro-variability rate is used to reflect the change amplitude and direction of the distance between two fingers per unit time. The first rendering feedback information is obtained from the cloud virtual machine based on the predicted coordinate information and the predicted scaling level, and preliminary information is displayed based on the first rendering feedback information. When the user zoom interaction event ends, the target position information of the user zoom interaction event is obtained, the second rendering feedback information is obtained from the cloud virtual machine based on the target position information, and the display information is updated based on the second rendering feedback information.

2. The information display method according to claim 1, characterized in that, Determining the zooming intent of the user zooming interaction event based on the motion micro-variability includes one of the following: If the rate of change of movement is greater than the first rate of change threshold, the zooming intention of the user zoom interaction event is determined to be a zoom-in intention. If the rate of change of movement is less than the second rate of change threshold, the zooming intention of the user zooming interaction event is determined to be a zooming intention.

3. The information display method according to claim 1, characterized in that, The method further includes: If the absolute value of the movement micro-rate is less than the third micro-rate threshold, the user zoom interaction event is determined to be a false touch event.

4. The information display method according to claim 1, characterized in that, The step of predicting the predicted zoom level corresponding to the user zoom interaction event based on the micro-motion information includes: The current scaling speed and current scaling acceleration are determined based on the micro-motion information; The remaining operation time of the user zoom interaction event is predicted based on the micro-motion information. Based on the current zoom speed, the current zoom acceleration, and the remaining operation time, the predicted zoom level corresponding to the user zoom interaction event is predicted.

5. The information display method according to claim 1, characterized in that, The step of determining the predicted coordinate information of the region of interest corresponding to the user zoom interaction event after scaling, based on the zoom intention and the predicted zoom level, includes: Based on the scaling intent and the predicted scaling level, the radius of the region of interest corresponding to the user scaling interaction event is scaled and adjusted to obtain the target region radius. The scaled predicted coordinates of the region of interest are determined based on the radius of the target region.

6. The information display method according to claim 1, characterized in that, When the prediction scaling level is obtained, the prediction confidence level corresponding to the prediction scaling level is also obtained. The step of obtaining the first rendering feedback information from the cloud virtual machine based on the predicted coordinate information and the predicted scaling level includes: Send the predicted coordinate information, the predicted scaling level, and the predicted confidence level to the cloud virtual machine; Receive first rendering feedback information sent by the cloud virtual machine based on the predicted coordinate information, the predicted scaling level, and the predicted confidence level.

7. The information display method according to claim 6, characterized in that: If the prediction confidence is greater than the first confidence threshold, the first rendering feedback information is a full pre-rendering instruction; Alternatively, if the prediction confidence is less than or equal to a first confidence threshold and greater than or equal to a second confidence threshold, the first rendering feedback information is a hierarchical pre-rendering instruction. Alternatively, if the prediction confidence is less than the second confidence threshold, the first rendering feedback information is the scaled image data of the region of interest.

8. The information display method according to claim 7, characterized in that: The first rendering feedback information is a full pre-rendering instruction. The step of displaying preliminary information based on the first rendering feedback information includes: obtaining pre-requested image processor resources based on the full pre-rendering instruction, and performing preliminary full rendering display on the scaled region of interest based on the image processor resources. or, The first rendering feedback information is a hierarchical pre-rendering instruction. The step of displaying preliminary information based on the first rendering feedback information includes: performing preliminary hierarchical rendering display on the scaled region of interest and the corresponding peripheral region based on the hierarchical pre-rendering instruction. or, The first rendering feedback information is the image data of the region of interest after scaling. The step of performing preliminary information display based on the first rendering feedback information includes: performing preliminary rendering display based on the image data.

9. The information display method according to claim 1, characterized in that, The method further includes: During the continuous execution of the user zoom interaction event, information about the duration of the user zoom interaction event is obtained. Based on the continuous process information, display adjustment instructions are obtained from the cloud virtual machine; Adjust the current display information according to the display adjustment command.

10. The information display method according to claim 9, characterized in that, The step of obtaining display adjustment instructions from the cloud virtual machine based on the continuous process information includes: Based on the continuous process information, a scaling adjustment level corresponding to the predicted scaling level and coordinate adjustment information corresponding to the predicted coordinate information are predicted. Send the scaling adjustment level and the coordinate adjustment information to the cloud virtual machine; Receive display adjustment instructions sent by the cloud virtual machine based on the scaling adjustment level and the coordinate adjustment information.

11. The information display method according to claim 10, characterized in that, The step of adjusting the current display information according to the display adjustment instruction includes: The display information of the region of interest and its corresponding peripheral region is progressively adjusted according to the display adjustment command.

12. The information display method according to claim 10, characterized in that, The method further includes: When there is a deviation between the zoom adjustment level and the coordinate adjustment information and the actual zoom level and actual coordinate information of the user's actual operation, the deviation is sent to the cloud virtual machine; Receive correction instructions sent by the cloud virtual machine based on the deviation; According to the correction instruction, the corrected image data for correcting the deviation is obtained from the cloud virtual machine; The currently displayed information is corrected based on the corrected image data.

13. The information display method according to claim 1, characterized in that, The step of obtaining the second rendering feedback information from the cloud virtual machine based on the target location information includes: Determine the target zoom level corresponding to the user zoom interaction event based on the target location information; Send the target scaling level to the cloud virtual machine; Receive the second rendering feedback information sent by the cloud virtual machine according to the target scaling level.

14. The information display method according to claim 13, characterized in that, The second rendering feedback information is a rendering confirmation instruction; updating the display information based on the second rendering feedback information includes: The frame information of the currently displayed screen is obtained according to the rendering confirmation command; The current display information is updated based on the frame information.

15. An information display method applied to a cloud-based virtual machine, the method comprising: The system receives predicted coordinate information and predicted zoom level from a cloud terminal. The predicted coordinate information is the zoomed coordinate information of the region of interest corresponding to the user zoom interaction event, determined by the cloud terminal based on the zoom intention and the predicted zoom level. The predicted zoom level is the zoom level corresponding to the user zoom interaction event predicted by the cloud terminal based on micro-motion information. The zoom intention is determined by the cloud terminal based on the motion micro-variability rate, which is obtained based on the micro-motion information. The micro-motion information is obtained by the cloud terminal using the initial position information of the user zoom interaction event as the starting position when the user zoom interaction event is detected. The motion micro-variability rate reflects the change amplitude and direction of the distance between the two fingers per unit time. Based on the predicted coordinate information and the predicted scaling level, a first rendering feedback information is sent to the cloud terminal, and the first rendering feedback information is used by the cloud terminal to perform preliminary information display. Receive the target zoom level corresponding to the user zoom interaction event sent by the cloud terminal when the user zoom interaction event ends; The cloud terminal is sent a second rendering feedback message according to the target scaling level. The second rendering feedback message is used by the cloud terminal to update the display information.

16. The information display method according to claim 15, characterized in that, Upon receiving the predicted coordinate information and the predicted scaling level sent by the cloud terminal, the system also receives the predicted confidence level corresponding to the predicted scaling level sent by the cloud terminal. Sending first rendering feedback information to the cloud terminal based on the predicted coordinate information and the predicted scaling level includes: The corresponding rendering strategy is determined based on the prediction confidence, the prediction coordinate information, and the prediction scaling level. The first rendering feedback information is sent to the cloud terminal according to the rendering strategy.

17. The information display method according to claim 16, characterized in that, Determining the corresponding rendering strategy based on the prediction confidence level, the prediction coordinate information, and the prediction scaling level includes one of the following: If the prediction confidence is greater than the first confidence threshold, the corresponding rendering strategy is determined to be full pre-rendering based on the prediction coordinate information and the prediction scaling level. If the prediction confidence is less than or equal to the first confidence threshold and greater than or equal to the second confidence threshold, the corresponding rendering strategy is determined as hierarchical pre-rendering based on the prediction coordinate information and the prediction scaling level. If the prediction confidence is less than the second confidence threshold, the corresponding rendering strategy is determined based on the prediction coordinate information and the prediction scaling level, which is to send the image data to be rendered from the cloud virtual machine to the cloud terminal.

18. The information display method according to claim 17, characterized in that: The rendering strategy is full pre-rendering, and the first rendering feedback information is a full pre-rendering instruction; Alternatively, the rendering strategy is hierarchical pre-rendering, and the first rendering feedback information is a hierarchical pre-rendering instruction; Alternatively, the rendering strategy may involve the cloud virtual machine sending the image data to be rendered to the cloud terminal, with the first rendering feedback information being the image data.

19. The information display method according to claim 15, characterized in that, The second rendering feedback information is a rendering confirmation instruction, which is used by the cloud terminal to obtain the frame information of the currently displayed screen and update the current display information according to the frame information.

20. The information display method according to claim 15, characterized in that, The method further includes: The cloud terminal receives the scaling adjustment level corresponding to the predicted scaling level and the coordinate adjustment information corresponding to the predicted coordinate information, which are sent during the continuous process of the user scaling interaction event. Based on the scaling adjustment level and the coordinate adjustment information, a display adjustment command is sent to the cloud terminal. The display adjustment command is used by the cloud terminal to progressively adjust the display information of the region of interest and the corresponding peripheral region.

21. The information display method according to claim 15, characterized in that, The method further includes: The system receives the deviation sent by the cloud terminal, whereby the deviation is the discrepancy between the scaling adjustment level and the coordinate adjustment information and the actual scaling level and actual coordinate information of the user's actual operation. A corresponding correction instruction is sent to the cloud terminal based on the deviation; Receive image data request information sent by the cloud terminal according to the correction instruction; The cloud terminal sends corresponding corrected image data according to the image data request information. The corrected image data is used by the cloud terminal to correct the currently displayed information.

22. An information display method applied to a cloud desktop, the method comprising: In response to detecting a two-finger touch event on the touchscreen, a first area and a second area are displayed on the touchscreen. Wherein, the first region has a first level of clarity, the second region has a second level of clarity, the first level of clarity is higher than the second level of clarity, the first region is a closed region determined by the two touch points of the two-finger touch event, and the second region is the outer region of the first region.

23. The information display method in a cloud desktop according to claim 22, characterized in that: As the distance between the two touch points changes, the area of ​​the first region is updated in real time. or, As the distance between the two touch points changes, the area of ​​the first region remains unchanged, and the displayed content within the first region is updated in real time.

24. The information display method in a cloud desktop according to claim 22, characterized in that, During the period from the first frame where the two-finger touch event occurs to the last frame where the two-finger touch event ends, the difference between the first sharpness and the second sharpness persists.

25. An information display method applied to a cloud desktop, the method comprising: Display the cursor on the screen; Mouse wheel scrolling event detected; In response to the first scroll increment of the mouse wheel scrolling event, a first area and a second area are displayed on the screen; The first region has a first level of clarity, the second region has a second level of clarity, the first level of clarity is higher than the second level of clarity, the first region is a closed region determined based on the location of the cursor, and the second region is the outer region of the first region.

26. The information display method in a cloud desktop according to claim 25, characterized in that: The first region is a circular region centered at the location of the cursor; or, The first region is a rectangular area centered on the location of the cursor.

27. The information display method in a cloud desktop according to claim 25, characterized in that: As the mouse wheel scrolls continuously, the area of ​​the first region is updated in real time. or, As the mouse wheel scrolls continuously, the area of ​​the first region remains unchanged, and the content displayed within the first region is updated in real time.

28. The information display method in a cloud desktop according to claim 25, characterized in that, The difference between the first sharpness and the second sharpness persists from the time the mouse wheel starts scrolling until it stops scrolling.

29. A cloud terminal, characterized in that, include: At least one processor; At least one memory for storing at least one program; The information display method as described in any one of claims 1 to 14 is implemented when at least one of the programs is executed by at least one of the processors.

30. A cloud-based virtual machine, characterized in that, include: At least one processor; At least one memory for storing at least one program; The information display method as described in any one of claims 15 to 21 is implemented when at least one of the programs is executed by at least one of the processors.

31. A cloud desktop, characterized in that, include: At least one processor; At least one memory for storing at least one program; The information display method as described in any one of claims 22 to 28 is implemented when at least one of the programs is executed by at least one of the processors.

32. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the information display method as described in any one of claims 1 to 21, or to execute the information display method in a cloud desktop as described in any one of claims 22 to 28.

33. A computer program product, comprising a computer program or computer instructions, characterized in that, The computer program or the computer instructions are stored in a computer-readable storage medium. The processor of the cloud terminal, cloud virtual machine, or cloud desktop reads the computer program or the computer instructions from the computer-readable storage medium. The processor executes the computer program or the computer instructions, causing the cloud terminal to perform the information display method according to any one of claims 1 to 14, or causing the cloud virtual machine to perform the information display method according to any one of claims 15 to 21, or causing the cloud desktop to perform the information display method according to any one of claims 22 to 28.

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