UI self-adaptive self-healing method and device based on environmental intelligence and medium

Abstract

The invention discloses a UI self-adaptive self-healing method and device based on environment intelligence and a medium, and the method comprises the steps: carrying out interface data preprocessing on device interface monitoring data to obtain device environment sensing data; wherein the equipment environment sensing data comprises equipment state sensing data, user behavior sensing data and environment information sensing data; performing interface state analysis on the equipment state sensing data to obtain an interface element layout state; performing behavior state structure analysis on the user behavior perception data to obtain a user behavior interaction state; performing real-time environment state analysis on the environment information sensing data to obtain an environment adjusting state; and on the basis of the interface element layout state, the user behavior interaction state and the environment adjustment state, determining the UI self-healing state through multi-element state cooperative compensation. By means of the method, the technical problem that the UI is prone to deterioration in dynamic self-adaptive adjustment according to the use preference of the user is solved.

Description

Technical Field

[0001] This application relates to the technical field of computer human-computer interaction, and in particular to a UI adaptive self-healing method, device and medium based on ambient intelligence. Background Art

[0002] With the popularization of intelligent devices and the increasing complexity of application scenarios, users have put forward higher requirements for the usability, adaptability and stability of interfaces. Traditional UI designs usually rely on fixed layouts and interaction patterns, and it is difficult to cope with complex usage environments and changes in user needs. When users use the interface on different devices, in different environments or in different usage scenarios, they may encounter problems such as layout disorder, unsmooth interaction or poor visual effects.

[0003] The traditional UI architecture design lacks the ability to perceive user behavior and environmental changes, and cannot actively discover and repair problems in the interface. The interface UI adjustment architectures in the prior art usually perform conventional adjustments such as interface brightness based on the size of the user device and simple environmental changes, and cannot achieve dynamic adaptive adjustment of the interface IU according to the usage preferences of the device user. For the existing technical solutions that use adaptive learning methods to adjust the interface UI, problems such as UI degradation are likely to occur. Summary of the Invention

[0004] The embodiments of this application provide a UI adaptive self-healing method, device and medium based on ambient intelligence, which solves the technical problem that the interface UI is prone to degradation in dynamic adaptive adjustment according to user usage preferences.

[0005] In a first aspect, the embodiments of this application provide a UI adaptive self-healing method based on ambient intelligence, which is characterized in that the method includes: obtaining device interface monitoring data, and performing interface data preprocessing on the device interface monitoring data to obtain device environment perception data; wherein, the device environment perception data includes: device status perception data, user behavior perception data, and environmental information perception data; performing interface status analysis on the device status perception data to obtain the layout status of interface elements; performing behavior status structure analysis on the user behavior perception data to obtain the user behavior interaction status; performing real-time environmental status analysis on the environmental information perception data to obtain the environmental adjustment status; based on the layout status of interface elements, the user behavior interaction status and the environmental adjustment status, determining the self-healing status of the interface UI through multi-state collaborative compensation.

[0006] In an implementation manner of the present application, the device interface monitoring data further includes: screen interface monitoring data, user behavior monitoring data, and external environment monitoring data; performing interface data preprocessing on the screen interface monitoring data to obtain device environment perception data, specifically including: performing visual topology anomaly threshold analysis on the device interface monitoring data to obtain device status perception data; performing operation trajectory anomaly determination on the user behavior monitoring data to obtain user behavior perception data; performing environmental anomaly credibility assessment on the external environment monitoring data to obtain environmental information perception data.

[0007] In an implementation manner of the present application, performing interface state analysis on the device status perception data to obtain the interface element layout state, specifically including: performing interface element overlap threshold analysis on the device status perception data to determine the interface obstacle avoidance elements; based on the interface obstacle avoidance elements, obtaining the interface element migration path through Fermat spiral path analysis; performing interface table content matching state analysis on the device status perception data to determine the interface data density balancing strategy; according to the interface obstacle avoidance elements and the interface data density balancing strategy, obtaining the interface element layout state through performance load distribution.

[0008] In an implementation manner of the present application, performing behavior state structure analysis on the user behavior perception data to obtain the user behavior interaction state, specifically including: performing touch trajectory tracking on the behavior perception data to obtain the user's high-frequency mis-touch area; based on the user's high-frequency mis-touch area, determining the acceleration curve of the user's sliding operation and performing Fourier transform on the acceleration curve to obtain the user's operation intention; where the user's operation intention includes: fast page turning, precise scrolling; performing attention focus prediction on the user's operation intention to obtain the user behavior interaction state; where the attention focus prediction includes: eye movement tracking analysis, interface element weight assignment.

[0009] In an implementation manner of the present application, the environmental information perception data further includes: environmental light intensity perception data, environmental network strength data; performing real-time environmental state analysis on the environmental light intensity perception data to obtain the environmental adjustment state, specifically including: performing light intensity dynamics on the environmental information perception data to construct a dynamic light field perception matrix; based on the dynamic light field perception matrix, determining the interface visual enhancement ratio through color temperature dynamic analysis; performing network delay two-dimensional decision analysis on the environmental network strength data to obtain network environment stress adaptation parameters; according to the interface visual enhancement ratio and the network environment stress adaptation parameters, obtaining the environmental adjustment state.

[0010] In an implementation manner of the present application, network delay two-dimensional decision analysis is performed on environmental network strength data to obtain network environment stress adaptation parameters, which specifically includes: performing network RRT threshold analysis on the environmental network strength data to determine interface interaction simplification parameters; wherein, the interface interaction simplification parameters include: animation rendering state, icon replacement parameters; in the case where the environmental state of the environmental network strength data is a 5G network slice, performing dynamic QoS level allocation on preset interface resources to determine application channel adaptation parameters; wherein, the dynamic QoS level allocation includes: high-frequency band channel allocation, low-frequency band channel allocation; based on the interface interaction simplification parameters and the application channel adaptation parameters, obtaining network environment stress adaptation parameters.

[0011] In an implementation manner of the present application, based on the interface element layout state, user behavior interaction state, and environmental adjustment state, through multi-state collaborative compensation, the interface UI self-healing state is determined, which specifically includes: performing adjustment amount analysis on the interface element layout state to determine the interface layout self-healing amount; obtaining the maximum allowable oscillation amplitude, and based on the interface layout self-healing amount and the maximum allowable oscillation amplitude, through stage damping control, determining the initial interface self-healing perturbation; performing Lyapunov exponent analysis on the initial interface self-healing perturbation to determine the interface UI self-healing state.

[0012] In an implementation manner of the present application, after determining the interface UI self-healing state through multi-state collaborative compensation based on the interface element layout state, user behavior interaction state, and environmental adjustment state, the method further includes: performing periodic cognitive calibration on the interface UI self-healing state to obtain the interface UI self-healing update state; based on the interface UI self-healing update state, through device energy consumption balance analysis, determining the device energy consumption adaptive configuration; synchronizing the device energy consumption adaptive configuration to the preset user personalized configuration in the cloud to obtain the cross-device synchronization state.

[0013] Second aspect, an embodiment of the present application further provides a UI self-adaptive and self-healing device based on ambient intelligence, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: obtain device interface monitoring data, and perform interface data preprocessing on the device interface monitoring data to obtain device environment perception data; wherein, the device environment perception data includes: device status perception data, user behavior perception data, and environmental information perception data; perform interface status analysis on the device status perception data to obtain the layout status of interface elements; perform behavior status structure analysis on the user behavior perception data to obtain the user behavior interaction status; perform real-time environment status analysis on the environmental information perception data to obtain the environmental adjustment status; based on the layout status of interface elements, the user behavior interaction status, and the environmental adjustment status, determine the self-healing status of the interface UI through multi-state collaborative compensation.

[0014] Third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for UI self-adaptive and self-healing based on ambient intelligence, storing computer-executable instructions, characterized in that the computer-executable instructions are set to: obtain device interface monitoring data, and perform interface data preprocessing on the device interface monitoring data to obtain device environment perception data; wherein, the device environment perception data includes: device status perception data, user behavior perception data, and environmental information perception data; perform interface status analysis on the device status perception data to obtain the layout status of interface elements; perform behavior status structure analysis on the user behavior perception data to obtain the user behavior interaction status; perform real-time environment status analysis on the environmental information perception data to obtain the environmental adjustment status; based on the layout status of interface elements, the user behavior interaction status, and the environmental adjustment status, determine the self-healing status of the interface UI through multi-state collaborative compensation.

[0015] An embodiment of the present application provides a UI self-adaptive and self-healing method, device and medium based on ambient intelligence. Through multi-state collaborative compensation of the interface elements of the device interface, the environment where the interface is located, and user behavior analysis, it solves the technical problem that the interface UI is prone to deterioration in dynamic adaptive adjustment according to user usage preferences, realizes the dynamic adaptive adjustment of the device interface elements, improves the adaptability of the device interface UI self-healing to user habits, optimizes the comprehensive status of the interface UI self-healing, and reduces the possibility of UI deterioration in the interface UI self-healing due to multi-analysis. Description of the Drawings

[0016] The accompanying drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0017] Figure 1 It is a flowchart of a UI self - adapting and self - healing method based on ambient intelligence provided by an embodiment of the present application;

[0018] Figure 2 It is a schematic internal structure diagram of a UI self - adapting and self - healing device based on ambient intelligence provided by an embodiment of the present application. Detailed implementation manners

[0019] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0020] The embodiments of the present application provide a UI self - adapting and self - healing method, device and medium based on ambient intelligence. Through the multi - state collaborative compensation of the interface elements of the device interface, the environment where the interface is located, and user behavior analysis, it solves the technical problem that the interface UI is prone to deterioration in the dynamic adaptive adjustment according to user usage preferences, realizes the dynamic adaptive adjustment of the device interface elements, improves the adaptability of the device interface UI self - healing to user habits, optimizes the comprehensive state of the interface UI self - healing, and reduces the possibility of UI deterioration of the interface UI self - healing caused by multi - analysis.

[0021] The technical solutions proposed in the embodiments of the present application will be described in detail below with reference to the drawings.

[0022] Figure 1 It is a flowchart of a UI self - adapting and self - healing method based on ambient intelligence provided by an embodiment of the present application. As Figure 1 shown, a UI self - adapting and self - healing method based on ambient intelligence provided by an embodiment of the present application specifically includes the following steps:

[0023] Step 101: Obtain device interface monitoring data, and perform interface data pre - processing on the device interface monitoring data to obtain device environment perception data.

[0024] Exemplarily, after obtaining the device interface monitoring data, it is necessary to perform interface data pre - processing on the device interface monitoring data to screen out the useless data under special angles of the device interface, special environments, and abnormal user operations, thereby improving the data purity of the device environment perception data.

[0025] Among them, device environment perception data includes: device status perception data, user behavior perception data, and environmental information perception data.

[0026] Furthermore, the device interface monitoring data also includes: screen interface monitoring data, user behavior monitoring data, and external environment monitoring data.

[0027] Specifically, the screen interface monitoring data is preprocessed to obtain device environment perception data, including: performing visual topology anomaly threshold analysis on the device interface monitoring data to obtain device state perception data; performing operation trajectory anomaly judgment on the user behavior monitoring data to obtain user behavior perception data; and performing environmental anomaly credibility assessment on the external environment monitoring data to obtain environmental information perception data.

[0028] In one embodiment, the visual topology anomaly threshold analysis needs to construct a Z-order hierarchical tree of interface elements to detect the unconventional occlusion of device interface elements. The initial design draft of the device interface is compared with the SSIM structure similarity of the real-time rendering. When the difference is greater than 15%, an abnormal alarm is triggered, and data with a difference exceeding the abnormal threshold is eliminated to obtain device status perception data.

[0029] The judgment of abnormal operation trajectory requires monitoring the user's operation trajectory on the device interface (touch interactive interface such as the screen), analyzing the sliding speed of the user's operation trajectory, and determining whether the operation trajectory is a false touch through the abnormal operation trajectory score. When the abnormal score is greater than the predetermined threshold, the sliding trajectory is judged to be a false touch.

[0030] Environmental anomaly credibility assessment can perform a cross-validation matrix analysis on the device's sensors. First, the device's ambient light sensor, camera, and distance sensor are determined.

[0031] It should be noted that the type and distribution of sensors are related to the model of the device itself, and the composition of the cross-validation matrix is also related to the type and number of sensors and is not fixed. The same is true for the abnormal logic setting of sensors, which can meet the device and usage requirements.

[0032] After constructing a cross-validation matrix that includes sensor type, normal range of physical quantity and abnormal logic, a credibility analysis is performed on the sudden change in screen reflectivity of the device interface based on the cross-validation matrix. When the screen reflectivity change rate is greater than the preset threshold, it is determined to be abnormal reflective interference, and the abnormal data is eliminated, ultimately obtaining environmental information perception data without abnormal data.

[0033] Step 102: Perform interface status analysis on the device status perception data to obtain the layout status of the interface elements.

[0034] Exemplarily, performing interface state analysis on device state perception data is a topological optimization of the element relationships in the device interface, achieving dynamic resolution of visual conflicts among UI elements in the device interface and optimizing the allocation of device performance load.

[0035] Specifically, performing interface state analysis on device state perception data to obtain the layout state of interface elements includes: performing interface element overlap threshold analysis on device state perception data to determine interface obstacle avoidance elements; based on the interface obstacle avoidance elements, obtaining the interface element migration path through Fermat spiral path analysis; performing interface table content matching state analysis on device state perception data to determine the interface data density balancing strategy; and obtaining the layout state of interface elements through performance load allocation according to the interface obstacle avoidance elements and the interface data density balancing strategy.

[0036] In one embodiment, when the pop-up window overlaps with the core operation button by more than 20% in area, through Fermat spiral path analysis, the pop-up window is migrated to the nearest blank area along the interface element migration path.

[0037] When it is detected that the column width of the table does not match the content length, for numeric fields, right alignment and equal-width fonts are adopted, and for text fields, automatic line wrapping and floating summary are enabled for changes.

[0038] For the device in the low power mode, the GPU rendering frequency of the device can be reduced. Similarly, when the device temperature is too high, the image decoding task can be transferred to the coprocessor, and the main thread focuses on ensuring interactive response.

[0039] Step 103: Perform behavioral state structure analysis on user behavior perception data to obtain the user behavior interaction state.

[0040] Specifically, performing behavioral state structure analysis on user behavior perception data to obtain the user behavior interaction state includes: tracking the touch trajectory of the behavioral perception data to obtain the high-frequency accidental touch area of the user; based on the high-frequency accidental touch area of the user, determining the acceleration curve of the user's sliding operation and performing Fourier transform on the acceleration curve to obtain the user's operation intention; where the user's operation intention includes: quick page turning, precise scrolling; predicting the attention focus of the user's operation intention to obtain the user behavior interaction state; where the attention focus prediction includes: eye movement tracking analysis, interface element weight allocation.

[0041] In one embodiment, a user behavior entropy value model can be established through touch trajectory capture, and the determination threshold of the high-frequency accidental touch area (such as the screen edge) is expanded to reduce the impact of the sliding part of the accidental touch area on the normal user behavior.

[0042] Then, perform a Fourier transform on the acceleration curve of the user's sliding operation to identify the operation intention. A rapid short slide represents triggering fast page turning, and a slow long slide represents precise scrolling.

[0043] When the user's line of sight stays in the search box for more than 2 seconds, the historical record panel is automatically expanded and the input method prediction accuracy is improved. When it is detected that the user's eyes frequently make regressive saccades in a reading app, the paragraph spacing is dynamically adjusted to 1.5 times the line height and auxiliary positioning is performed.

[0044] Step 104: Perform real-time environmental state analysis on the environmental information perception data to obtain the environmental adjustment state.

[0045] Furthermore, the environmental information perception data also includes: environmental light intensity perception data, environmental network strength data.

[0046] Exemplarily, by performing real-time environmental state analysis on the environmental information perception data, dynamic analysis of the environmental adjustment state is achieved, and the adjustment sensitivity of the environmental light intensity and network environment is optimized.

[0047] Specifically, performing real-time environmental state analysis on the environmental light intensity perception data to obtain the environmental adjustment state specifically includes: performing light intensity dynamics on the environmental information perception data to construct a dynamic light field perception matrix; based on the dynamic light field perception matrix, determining the interface visual enhancement ratio through color temperature dynamic analysis; performing network delay two-dimensional decision analysis on the environmental network strength data to obtain network environment stress adaptation parameters; and obtaining the environmental adjustment state according to the interface visual enhancement ratio and the network environment stress adaptation parameters.

[0048] Furthermore, performing network delay two-dimensional decision analysis on the environmental network strength data to obtain network environment stress adaptation parameters specifically includes: performing network RRT threshold analysis on the network environment strength data to determine the interface interaction simplification parameters; where the interface interaction simplification parameters include: animation rendering state, icon replacement parameters; in the case where the environmental state of the network environment strength data is a 5G network slice, performing dynamic QoS level allocation on the preset interface resources to determine the application channel adaptation parameters; where the dynamic QoS level allocation includes: high-frequency band channel allocation, low-frequency band channel allocation; and obtaining the network environment stress adaptation parameters based on the interface interaction simplification parameters and the application channel adaptation parameters.

[0049] In one embodiment, the interface color contrast is automatically adjusted through color temperature recognition (such as warm light / cold light). In a strong backlight scenario, the text stroke is strengthened to 3px and the saturation is increased by 15%. In the case of on-demand playback and handheld jitter, the click determination delay of the device interface is increased from 50ms to 200ms, and at the same time, the touch hot zone range is shrunk.

[0050] When the network RTT (Round-Trip Time) is detected to be > 300 ms, disable the device's animation rendering and replace the icon with a geometric placeholder. In a 5G network slice environment, dynamically allocate interface resources according to the QoS level. Video applications preferentially occupy high-frequency channels, and text services are bound to low-power narrowband channels.

[0051] Step 105: Based on the interface element layout state, user behavior interaction state, and environmental adjustment state, determine the self-healing state of the interface UI through multi-state collaborative compensation.

[0052] Exemplarily, through multi-state collaborative compensation, the adjustments of the interface element layout state, user behavior interaction state, and environmental adjustment state tend to be smoothed in the self-healing of the same device interface, avoiding severe conflicts in the adjustment of UI elements during the self-healing of multi-states, and improving the user experience and the robustness of the self-healing of the interface UI.

[0053] Specifically, based on the interface element layout state, user behavior interaction state, and environmental adjustment state, determine the self-healing state of the interface UI through multi-state collaborative compensation, which specifically includes: analyzing the adjustment amount of the interface element layout state to determine the self-healing amount of the interface layout; obtaining the maximum allowable oscillation amplitude, and based on the self-healing amount of the interface layout and the maximum allowable oscillation amplitude, determine the initial disturbance of the interface self-healing through stage damping control; performing Lyapunov exponent analysis on the initial disturbance of the interface self-healing to determine the self-healing state of the interface UI.

[0054] In one embodiment, the adjustment amount analysis first performs stage decomposition on the total number of adjustment stages and the current adjustment requirements. To ensure the smooth adjustment of UI self-healing, a rapid response is made in the initial adjustment stage, and smooth convergence is achieved in the later adjustment stage to suppress overshoot in the final stage.

[0055] At this time, under the control of its oscillation term in the stage damping control process, Δactual representing the initial disturbance can be obtained, and then the initial disturbance parameter of the Lyapunov exponent is determined.

[0056] Through Lyapunov exponent analysis, determine the stability criterion for UI self-healing, and perform control response on the self-healing process of the same state through threshold determination to determine the self-healing state of the interface UI.

[0057] Further, after determining the self-healing state of the interface UI through multi-state collaborative compensation based on the layout state of interface elements, the user behavior interaction state, and the environmental adjustment state, the method further includes: performing periodic cognitive calibration on the self-healing state of the interface UI to obtain the self-healing update state of the interface UI; based on the self-healing update state of the interface UI, determining the adaptive configuration of device energy consumption through device energy consumption balance analysis; and synchronizing the adaptive configuration of device energy consumption to the preset user personalized configuration in the cloud to obtain the cross-device synchronization state.

[0058] In one embodiment, by inserting micro-interaction guidance and matching the indicator UI elements of the micro-interaction guidance with the interface elements after the self-healing of the interface UI, it helps users quickly adapt to the new interface layout. After the user uses this interface UI layout, if it is found that the actions of the user's usage habits change less than the mutation threshold within a certain period of time, it can be determined that the user is temporarily accustomed to the current UI layout, and this UI layout is uploaded to the user's classic operation layout library.

[0059] When the user makes an operation change, it can be restored to the most accustomed UI layout according to the data in the user's classic operation layout library. Monitor the memory / GPU occupancy rate after adjustment, perform lightweight rendering on high-frequency adjustment components, and dynamically switch and adjust the rendering precision according to the remaining battery power of the device.

[0060] The above is the method embodiment proposed by this application. Based on the same inventive concept, the embodiment of this application also provides a UI adaptive self-healing device based on ambient intelligence, and its structure is as Figure 2 shown.

[0061] Figure 2 It is a schematic internal structure diagram of a UI adaptive self-healing device based on ambient intelligence provided by the embodiment of this application. As Figure 2 shown, the device includes:

[0062] At least one processor 201;

[0063] And a memory 202 communicatively connected to at least one processor;

[0064] Wherein, the memory 202 stores instructions executable by at least one processor, and the instructions are executed by at least one processor 201 so that at least one processor 201 can:

[0065] Obtain device interface monitoring data, and perform interface data preprocessing on the device interface monitoring data to obtain device environment perception data; wherein, the device environment perception data includes: device status perception data, user behavior perception data, and environmental information perception data; perform interface status analysis on the device status perception data to obtain the interface element layout status; perform behavior status structure analysis on the user behavior perception data to obtain the user behavior interaction status; perform real-time environmental status analysis on the environmental information perception data to obtain the environmental adjustment status; based on the interface element layout status, user behavior interaction status, and environmental adjustment status, determine the interface UI self-healing status through multi-state collaborative compensation.

[0066] Some embodiments of the present application provide a Figure 1 non-volatile computer storage medium for UI self-adaptation and self-healing based on ambient intelligence, storing computer-executable instructions, and the computer-executable instructions are set as:

[0067] Obtain device interface monitoring data, and perform interface data preprocessing on the device interface monitoring data to obtain device environment perception data; wherein, the device environment perception data includes: device status perception data, user behavior perception data, and environmental information perception data; perform interface status analysis on the device status perception data to obtain the interface element layout status; perform behavior status structure analysis on the user behavior perception data to obtain the user behavior interaction status; perform real-time environmental status analysis on the environmental information perception data to obtain the environmental adjustment status; based on the interface element layout status, user behavior interaction status, and environmental adjustment status, determine the interface UI self-healing status through multi-state collaborative compensation.

[0068] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the embodiments of the Internet of Things devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0069] The systems and media provided by the embodiments of the present application correspond one-to-one with the methods. Therefore, the systems and media also have beneficial technical effects similar to those of the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be elaborated here.

[0070] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0071] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0072] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0074] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0075] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0076] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0077] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0078] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A UI self - adapting and self - healing method based on ambient intelligence, characterized in that, The method includes: Obtaining device interface monitoring data, and performing preprocessing on the device interface monitoring data to obtain device environment perception data; wherein, the device environment perception data includes: device status perception data, user behavior perception data, and environmental information perception data; Performing interface status analysis on the device status perception data to obtain the layout status of interface elements; Performing behavior status structure analysis on the user behavior perception data to obtain the user behavior interaction status; Performing real-time environment status analysis on the environmental information perception data to obtain the environmental adjustment status; Based on the layout status of interface elements, the user behavior interaction status, and the environmental adjustment status, determining the self-healing status of the interface UI through multi-state collaborative compensation.

2. The UI self - adapting and self - healing method based on ambient intelligence according to claim 1, characterized in that, The device interface monitoring data further includes: screen interface monitoring data, user behavior monitoring data, and external environment monitoring data; Performing preprocessing on the screen interface monitoring data to obtain device environment perception data, specifically including: Performing visual topology anomaly threshold analysis on the device interface monitoring data to obtain the device status perception data; Performing operation trajectory anomaly determination on the user behavior monitoring data to obtain the user behavior perception data; Performing environmental anomaly credibility assessment on the external environment monitoring data to obtain the environmental information perception data.

3. A UI self - adapting and self - healing method based on ambient intelligence according to claim 1, characterized in that Performing interface status analysis on the device status perception data to obtain the layout status of interface elements, specifically including: Performing interface element overlap threshold analysis on the device status perception data to determine interface obstacle avoidance elements; Based on the interface obstacle avoidance elements, obtaining the migration path of interface elements through Fermat spiral path analysis; Performing interface table content matching status analysis on the device status perception data to determine the interface data density balancing strategy; According to the interface obstacle avoidance elements and the interface data density balancing strategy, obtaining the layout status of interface elements through performance load distribution.

4. An UI self-adaptive and self-healing method based on ambient intelligence according to claim 1, characterized in that, Performing behavior status structure analysis on the user behavior perception data to obtain the user behavior interaction status, specifically including: Performing touch trajectory tracking on the behavior perception data to obtain the user's high-frequency mis-touch area; Based on the user's high-frequency mis-touch area, determining the acceleration curve of the user's sliding operation, and performing Fourier transform on the acceleration curve to obtain the user's operation intention; wherein, the user's operation intention includes: fast page turning, precise scrolling; Performing attention focus prediction on the user's operation intention to obtain the user behavior interaction status; wherein, the attention focus prediction includes: eye movement tracking analysis, interface element weight assignment.

5. The UI self - adapting and self - healing method based on ambient intelligence according to claim 1, wherein, The environmental information perception data further includes: environmental light intensity perception data, environmental network strength data; Performing real-time environment status analysis on the environmental light intensity perception data to obtain the environmental adjustment status, specifically including: Performing light intensity dynamics on the environmental information perception data to construct a dynamic light field perception matrix; Based on the dynamic light field perception matrix, determining the interface visual enhancement ratio through color temperature dynamic analysis; Perform network delay two-dimensional decision analysis on the environmental network strength data to obtain network environment stress adaptation parameters; Obtain the environmental adjustment state according to the interface visual enhancement ratio and the network environment stress adaptation parameters.

6. The UI self-adaptive and self-healing method based on ambient intelligence according to claim 5, wherein Performing network delay two-dimensional decision analysis on the environmental network strength data to obtain network environment stress adaptation parameters specifically includes: Perform network RRT threshold analysis on the network environment strength data to determine interface interaction simplification parameters; wherein, the interface interaction simplification parameters include: animation rendering state, icon replacement parameters; When the environmental state of the network environment strength data is a 5G network slice, perform dynamic QoS level allocation on preset interface resources to determine application channel adaptation parameters; wherein, the QoS level dynamic allocation includes: high-frequency band channel allocation, low-channel channel allocation; Obtain the network environment stress adaptation parameters based on the interface interaction simplification parameters and the application channel adaptation parameters.

7. A UI self - adapting and self - healing method based on ambient intelligence according to claim 1, characterized in that, Based on the interface element layout state, user behavior interaction state, and the environmental adjustment state, determine the interface UI self-healing state through multi-state collaborative compensation, specifically including: Perform adjustment amount analysis on the interface element layout state to determine the interface layout self-healing amount; Obtain the maximum allowable oscillation amplitude, and based on the interface layout self-healing amount and the maximum allowable oscillation amplitude, determine the initial perturbation of interface self-healing through stage damping control; Perform Lyapunov exponent analysis on the initial perturbation of interface self-healing to determine the interface UI self-healing state.

8. A UI self - adapting and self - healing method based on ambient intelligence according to claim 1, characterized in that, After determining the interface UI self-healing state through multi-state collaborative compensation based on the interface element layout state, user behavior interaction state, and the environmental adjustment state, the method further includes: Perform periodic cognitive calibration on the interface UI self-healing state to obtain the interface UI self-healing update state; Determine the device energy consumption adaptive configuration through device energy consumption balance analysis based on the interface UI self-healing update state; Synchronize the device energy consumption adaptive configuration to the preset user personalized configuration in the cloud to obtain the cross-device synchronization state.

9. A UI self-adaptive and self-healing device based on ambient intelligence, characterized in that, The device includes: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Obtain device interface monitoring data, and perform interface data preprocessing on the device interface monitoring data to obtain device environment perception data; wherein, the device environment perception data includes: device state perception data, user behavior perception data, environmental information perception data; Perform interface state analysis on the device state perception data to obtain the interface element layout state; Perform behavior state structure analysis on the user behavior perception data to obtain the user behavior interaction state; Perform real-time environmental state analysis on the environmental information perception data to obtain the environmental adjustment state; Based on the layout state of the interface elements, the user behavior interaction state, and the environment adjustment state, determine the self-healing state of the interface UI through multi-state collaborative compensation.

10. A non-volatile computer storage medium for UI self-adaptation and self-healing based on ambient intelligence, storing computer-executable instructions, characterized in that, The computer-executable instructions are set to: Obtain device interface monitoring data, and perform preprocessing on the device interface monitoring data to obtain device environment perception data; wherein, the device environment perception data includes: device state perception data, user behavior perception data, and environmental information perception data; Perform interface state analysis on the device state perception data to obtain the layout state of the interface elements; Perform behavior state structure analysis on the user behavior perception data to obtain the user behavior interaction state; Perform real-time environment state analysis on the environmental information perception data to obtain the environment adjustment state; Based on the layout state of the interface elements, the user behavior interaction state, and the environment adjustment state, determine the self-healing state of the interface UI through multi-state collaborative compensation.

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