Mobile phone beautification theme multi-platform adaptive optimization method and system

Abstract

The invention discloses a mobile phone beautification theme multi-platform adaptive optimization method and system, and the method comprises the steps: extracting the multi-dimensional attribute parameters of different terminal theme elements through a cross-end element adaptation decision model, determining the data loading priority based on a theme loading performance optimization algorithm, generating an adaptive dynamic effect scheme through an interface dynamic effect adaptation prediction model, and carrying out the optimization of the adaptive dynamic effect scheme. Visual parameters are adjusted through a theme visual effect calibration engine, code logic, resource paths and an interaction mechanism are optimized in combination with multi-platform compatibility requirements, and finally a theme installation package adaptive to multiple terminals is generated through integration. Wherein the steps of interface dynamic effect adaptation, visual effect calibration and compatibility adjustment all comprise multi-link refinement processing, the system achieves consistent presentation and efficient operation of themes in different terminals through collaborative operation of all functional units, the problems of element layout disorder, loading lagging, visual deviation and the like in multi-platform adaptation are solved, and the adaptability of the system is improved. And the accuracy and compatibility of theme cross-end adaptation are improved.

Description

Technical Field

[0001] This invention relates to the field of mobile phone theme optimization technology, and in particular to a multi-platform adaptive optimization method and system for mobile phone theme beautification. Background Technology

[0002] With the diversification of mobile terminal devices and the widespread adoption of terminals with different operating systems, hardware configurations, and screen parameters, mobile phone themes, as a core element for enhancing user experience, are facing increasingly urgent demands for multi-platform adaptation. Users are placing higher demands on the visual consistency, loading smoothness, and interactive adaptability of themes, requiring seamless presentation across different terminals such as smartphones and tablets, while also considering the efficiency of terminal hardware resource utilization and network transmission stability. Cross-platform component adaptation, loading performance optimization, animation adaptation prediction, visual effect calibration, and multi-platform compatibility adjustment have become key technical directions in mobile phone theme development, necessitating the construction of a systematic adaptive optimization solution to address the complexity of theme adaptation in multi-terminal environments.

[0003] Existing technologies have significant shortcomings in the multi-platform adaptation of mobile phone themes: On the one hand, they lack a precise cross-platform adaptation decision-making mechanism and comprehensive optimization model. Cross-platform applications are achieved only through simple parameter adjustments or fixed adaptation rules, which cannot fully combine terminal hardware performance, operating system characteristics, and the theme's own attributes for dynamic adaptation. This leads to problems such as component layout disorder and visual effect deviation on different terminals. On the other hand, a collaborative optimization system for loading performance, animation execution, and visual calibration has not been formed. Loading priority sorting does not fully consider the dynamic changes in network transmission rate and storage resource capacity. Animation adaptation does not accurately match the terminal display frame rate and processor computing efficiency. Visual calibration lacks a standardized deviation adjustment mechanism, resulting in high theme loading latency, stuttering animation operation, and poor visual consistency on different terminals, making it difficult to meet users' high-quality requirements for the theme usage experience. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a multi-platform adaptive optimization method and system for mobile phone theme customization.

[0005] The technical solution adopted in this invention is a multi-platform adaptive optimization method for mobile phone beautification themes, characterized by the following steps: S1, extracting theme element attribute parameters for different terminal operating systems through a cross-terminal element adaptation decision model, the parameters including element size specifications, layout hierarchy, interaction response threshold, and visual presentation characteristics; S2, prioritizing the extracted theme element data based on a theme loading performance optimization algorithm, and determining the data transmission order by combining terminal hardware computing power, network transmission rate, and storage resource capacity; S3, analyzing terminal display frame rate, processor computing efficiency, and memory usage using an interface animation adaptation prediction model to generate animation execution schemes adapted to different terminals; S4, comparing the actual presentation effect of theme elements on the target terminal with preset standards through a theme visual effect calibration engine, and adjusting color gamut parameters, clarity parameters, and contrast parameters; S5, adapting and adjusting the code execution logic, resource call path, and interaction response mechanism of theme elements according to the compatibility requirements of multi-platform adaptive optimization; S6, integrating the optimization results to generate a beautification theme installation package adapted to multiple terminal platforms, ensuring consistent presentation and operation of the theme on different terminals.

[0006] Furthermore, the expression for the cross-terminal component adaptation decision model is: ,in, To adapt decision coefficients across different platforms, For the size parameter weights, Let i be the size specification parameters of the i-th component. For layout parameter weights, Let i be the layout hierarchy parameter of the i-th element. For interaction parameter weights, Let be the interaction response threshold parameter for the i-th element. For terminal operating system compatibility coefficient, To adapt the threshold for hardware operations, To accommodate the correction factor, n represents the total number of theme elements.

[0007] Furthermore, the expression for the topic loading performance optimization algorithm is: ,in, To optimize loading performance index, For the network transmission rate parameter of the j-th type of data, This refers to the terminal hardware's processing capability parameter for the j-th type of data. The loading priority weight for the j-th type of data is... For the resource consumption parameter of the j-th type of data, This is the loading rate adjustment coefficient. Let j be the storage occupancy parameter for the j-th type of data. is the storage usage threshold, and m is the number of subject data categories.

[0008] Furthermore, the expression for the interface animation adaptation prediction model is: ,in, To adapt the motion effects to the predicted values, Display frame rate parameters on the terminal. These are parameters related to processor computational efficiency. This is a parameter related to memory usage. Let be the complexity parameter for the k-th animation. Let be the response delay parameter for the k-th motion effect. For motion effect adaptation correction coefficients, is the baseline parameter for motion smoothness, and p is the total number of motion effects included in the theme.

[0009] Furthermore, the calibration model expression of the theme visual effects calibration engine is: ,in, To calibrate the deviation value for visual effects, The actual color gamut parameters of the theme. To preset the standard parameters for color gamut, The actual sharpness parameter of the subject. To preset the standard resolution parameters, For the actual contrast parameters of the subject, To preset the standard contrast parameters, For color calibration coefficients, For sharpness calibration factor, This is the contrast calibration factor.

[0010] Furthermore, the comprehensive optimization model expression for the multi-platform adaptive optimization of the mobile phone beautification theme is as follows: ,in, To comprehensively optimize the index, To adapt decision coefficients across different platforms, To optimize loading performance index, To adapt the motion effects to the predicted values, To calibrate the deviation value for visual effects, To optimize the compensation coefficient, Let be the compatibility parameter for the q-th adapted scenario. Parameters are adapted for the resource call path in the q-th scenario. To adapt parameters for the interaction response mechanism of the q-th scenario, To adaptively optimize the number of scenes.

[0011] Further, S3 includes the following sub-steps: S31, collecting the display hardware parameters of the target terminal, including screen refresh rate range, pixel density level, and display color depth data, and establishing a terminal display capability database; S32, classifying the interface animations included in the theme according to motion trajectory complexity, color change frequency, and number of elements, and extracting the calibration execution parameters of each type of animation; S33, matching the animation calibration execution parameters with the data in the terminal display capability database, and initially screening animation schemes that meet the basic operating conditions of the terminal; S34, based on the interface animation adaptation prediction model, analyzing the frame rate stability, resource consumption, and interaction synchronization of the initially screened animation schemes, and determining the final animation execution scheme.

[0012] Further, step S4 includes the following sub-steps: S41, acquiring the actual display image data of the theme element on the target terminal through the terminal screen acquisition module, and extracting the color distribution data, sharpness feature value, and contrast quantization value from the image; S42, calling the preset standard parameter library of the theme visual effect calibration engine, and extracting the color gamut standard, sharpness standard, and contrast standard corresponding to the current theme; S43, calculating the difference between the actually extracted visual parameters and the preset standard parameters to determine the deviation amplitude and direction of each visual indicator; S44, adjusting the color adjustment parameters, sharpness enhancement parameters, and contrast correction parameters of the theme element according to the deviation calculation results to complete the visual effect calibration.

[0013] Further, S5 includes the following sub-steps: S51, analyzing the application runtime environment of different terminal operating systems, extracting system permission configurations, code execution rules, and resource call specification compatibility constraints; S52, adjusting the syntax adaptation of the code execution logic of the theme components based on the constraints to ensure that the code can be compiled and run normally in the target system; S53, optimizing the call path of theme resources, adjusting the resource index address and reading order according to the terminal storage structure and file access mechanism; S54, adapting the theme's touch response sensitivity, gesture recognition logic, and feedback mechanism parameters to the interactive hardware characteristics of different terminals.

[0014] A multi-platform adaptive optimization system for mobile phone themes, applied to a multi-platform adaptive optimization method for mobile phone themes, includes: a cross-platform component attribute parameter intelligent extraction unit, used to collect the theme component size, layout, interaction, and visual-related parameters of different terminal operating systems through a cross-platform component adaptation decision model, and establish a data connection with a multi-platform adaptation parameter library; a theme loading priority dynamic sorting unit, which receives the data output by the cross-platform component attribute parameter intelligent extraction unit, determines the loading order based on the theme loading performance optimization algorithm combined with terminal hardware and network parameters, and sends a sorting instruction to a resource transmission scheduling unit; and an interface animation adaptation scheme generation unit, which collects terminal display and hardware calculation parameters. The theme execution unit generates an adaptation scheme through an interface animation adaptation prediction model and establishes data interaction with the theme execution unit. The theme visual effect precision calibration unit receives the actual presentation data of the theme on the terminal, compares it with the preset standard through the theme visual effect calibration engine, adjusts the parameters, and outputs calibration instructions to the theme rendering unit. The multi-platform compatibility dynamic adjustment unit integrates the optimization data output by different units, adapts and adjusts the theme code logic, resource paths, and interaction mechanisms, and establishes communication with the theme packaging unit. The beautification theme integration and packaging output unit receives the optimization results of the multi-platform compatibility dynamic adjustment unit, integrates and encapsulates them according to the installation package format requirements of different terminals, and generates a theme installation file that can be directly deployed.

[0015] Beneficial Effects: This invention proposes a multi-platform adaptive optimization method and system for mobile phone theme customization. By accurately extracting the size, layout, interaction, and visual parameters of theme components on different terminals, and dynamically deciding on adaptation schemes based on terminal hardware and system characteristics, it replaces the traditional fixed-rule adaptation mode, solving the problems of cross-platform component layout disorder and visual effect deviation. By dynamically sorting loading priorities and accurately matching them with terminal hardware computing power, network transmission rate, and storage resources, it optimizes resource call paths and data transmission order. At the same time, relying on the motion effect adaptation prediction model to analyze terminal frame rate, computing efficiency, and memory usage, it generates adaptation schemes, solving the problems of high loading latency and motion effect stuttering. Through the visual effect calibration engine, it compares the actual presentation with the preset standard, adjusts color, clarity, and contrast parameters, and coordinates with the compatibility adjustments of code logic and interaction mechanisms to achieve consistency in the visual and interactive aspects of themes across multiple terminals. The various units of the system form a closed-loop optimization through data interaction and command transmission, ensuring that the theme runs efficiently on different terminals, and comprehensively improving the accuracy, smoothness, and compatibility of multi-platform adaptation. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method steps of the present invention; Figure 2 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, a multi-platform adaptive optimization method for mobile phone theme customization includes the following steps: S1. Extract the theme element attribute parameters of different terminal operating systems through the cross-terminal element adaptation decision model. The parameters include element size specifications, layout hierarchy, interaction response threshold and visual presentation characteristics. Specifically, step S1 extracts the attribute parameters of the theme components through the cross-platform component adaptation decision model. In practice, the model's multi-terminal system identification module is first activated to adapt to mainstream operating systems such as Android, iOS, and HarmonyOS. For each terminal system, all components included in the theme are traversed, with the total number of components controlled between 100 and 300. The size and specifications of each component are collected one by one, including length, width, and thickness. The length parameter range is set to 10 to 1500 pixels, the width parameter range to 5 to 1200 pixels, and the thickness parameter range to 1 to 50 pixels. Simultaneously, the component layout hierarchy parameters are extracted, dividing the layout hierarchy into 1 to 10 levels, with level 1 being the lowest level and level 10 being the lowest. At the top level, the hierarchical position of each element in the layout structure is clearly defined; interactive response threshold parameters are collected, including touch response trigger pressure threshold of 0.01 to 0.1 Newtons, click response latency threshold of 1 to 50 milliseconds, and swipe response sensitivity threshold of 1 to 20 pixels / millisecond; visual presentation feature parameters are extracted, including color values, transparency, and texture density. Color values ​​use RGB three-color channels, with each channel value ranging from 0 to 255; transparency parameters range from 0.1 to 1.0; and texture density parameters range from 10 to 500 pixels / square inch. All extracted parameters are stored according to element number and system type, forming a structured dataset of theme element attribute parameters, providing basic data support for subsequent adaptation and optimization.

[0019] S2, based on the topic loading performance optimization algorithm, sort the loading priority of the extracted topic element data, and determine the data transmission order by combining the terminal hardware computing power, network transmission rate and storage resource capacity; Specifically, step S2 executes loading priority sorting based on the topic loading performance optimization algorithm. During the implementation process, the terminal hardware detection module is first called to collect terminal hardware computing power parameters, including CPU clock speed of 1.0 to 3.5 GHz, GPU computing speed of 100 to 2000 GFLOPS, and memory capacity of 2 to 16 GB; at the same time, network transmission rate parameters are obtained, distinguished by network type, cellular network transmission rate of 1 to 100 Mbps, and Wi-Fi network transmission rate of 10 to 1000 Mbps; storage resource capacity parameters are collected, including remaining internal storage space of 10 to 512 GB and remaining RAM space of 0.5 to 8 GB. The hardware and network parameters are input into the algorithm, combined with the theme element data extracted in step S1. Data is categorized into four main types: image data, audio data, text data, and motion effect data. Each type is further subdivided into 5 to 10 subcategories. A loading weight value is calculated for each data type, ranging from 0.1 to 1.0. Specifically, image data weight values ​​are set to 0.7 to 1.0, audio data weight values ​​to 0.5 to 0.8, text data weight values ​​to 0.1 to 0.3, and motion effect data weight values ​​to 0.6 to 0.9. Based on the weight values ​​and the matching degree between the terminal hardware and network parameters, the data transmission order is determined. Data with a weight value higher than 0.8 and supported by efficient terminal hardware processing is transmitted first, followed by data with a weight value between 0.5 and 0.8, and finally data with a weight value lower than 0.5. A transmission buffer threshold is also set; when the network transmission rate is below 1 Mbps, non-core data transmission is paused to ensure the stability and efficiency of core data loading.

[0020] S3 uses an interface animation adaptation prediction model to analyze the terminal display frame rate, processor computing efficiency and memory usage, and generates animation execution schemes adapted to different terminals. Specifically, step S3 utilizes the interface animation adaptation prediction model to generate the animation scheme. During implementation, the display frame rate parameters of the target terminal are first collected using a terminal performance testing tool at a frequency of 10 times / second, and the average value is taken as the final display frame rate, ranging from 30 to 120 frames / second. Processor computation efficiency parameters are also collected, determined by calculating the number of instructions completed by the processor per unit time, ranging from 100 to 5000 MIPS. Memory usage parameters are also collected, including the memory required for theme operation (100 to 500 MB) and the remaining terminal memory (0.5 to 8 GB), calculating the memory usage rate, ranging from 5% to 80%. These parameters are input into the model, along with the animation data included in the theme. The total number of animations is controlled between 10 and 50. For each animation, core parameters such as motion trajectory complexity, color change frequency, and number of elements are extracted. Motion trajectory complexity is divided into levels 1 to 5, with level 1 being the simplest and level 5 the most complex. The color change frequency ranges from 1 to 30 times / second, and the number of elements ranges from 1 to 50. The model generates an adaptation scheme by analyzing the matching relationship between display frame rate and animation complexity, the balance between processor computing efficiency and animation computing requirements, and the coordination relationship between memory usage and animation resource consumption. For terminals with a display frame rate of 60 frames per second or higher, a processor efficiency of 2000 MIPS or higher, and a memory usage rate of less than 30%, animations with a complexity of 3 to 5 levels are adapted, with a color change frequency of up to 30 times per second and a maximum of 50 elements. For terminals with a display frame rate of 30 to 60 frames per second, a processor efficiency of 1000 to 2000 MIPS, and a memory usage rate of 30% to 60%, animations with a complexity of 2 to 3 levels are adapted, with a color change frequency of 10 to 20 times per second and a number of elements of 10 to 30. For terminals with a display frame rate of less than 30 frames per second, a processor efficiency of less than 1000 MIPS, and a memory usage rate of more than 60%, animations with a complexity of 1 to 2 levels are adapted, with a color change frequency of 1 to 10 times per second and a number of elements of 1 to 10, ensuring that the animations can run smoothly on different terminals.

[0021] S4 uses the theme visual effect calibration engine to compare the actual presentation effect of theme elements on the target terminal with the preset standard, and adjusts the color gamut parameters, sharpness parameters and contrast parameters accordingly. Specifically, step S4 adjusts visual parameters through the theme visual effect calibration engine. During implementation, the terminal screen capture module is first activated, using a combination of screenshots and real-time image capture to obtain the actual image of the theme elements on the target terminal. The capture resolution is set to the terminal's native resolution, ranging from 720×1280 to 3840×2160 pixels. The number of captures is 5 to 10, and the image with the highest clarity is used as the analysis sample. Color gamut parameters are extracted from the sample image, using the CIE1931 color space standard to extract the color gamut coverage of the three primary colors (red, green, and blue), ranging from 50% to 100%. Clarity parameters are extracted, determined by calculating the image edge sharpness value, ranging from 0.1 to 1.0. Contrast parameters are extracted, calculating the brightness ratio between the brightest and darkest areas of the image, ranging from 10:1 to 200:1. The calibration engine's preset standard parameter library is invoked, and preset color gamut standards are determined according to the theme design specifications. The coverage of the red, green, and blue primary color gamuts is no less than 90%. The preset sharpness standard parameter value is no less than 0.8, and the preset contrast standard parameter range is 50:1 to 150:1. The actual extracted parameters are compared with the preset standards one by one, and the deviation value is calculated. When the color gamut coverage deviation exceeds 10%, the color channel parameters are adjusted, with each channel adjusted by 1 to 20 numerical units. When the sharpness deviation exceeds 0.1, the image sharpening parameters are adjusted, with an adjustment range of 0.05 to 0.2. When the contrast deviation exceeds 30:1, the brightness adjustment parameters are adjusted, with the brightness of the brightest area adjusted by 5% to 20%, and the brightness of the darkest area adjusted by -20% to -5%. Through multiple rounds of adjustments, all visual parameters are made in accordance with the preset standards to ensure the consistency and standardization of the theme's visual effect.

[0022] S5 adapts and adjusts the code execution logic, resource call path, and interactive response mechanism of theme components to meet the compatibility requirements of multi-platform adaptive optimization. Specifically, step S5 performs adaptation adjustments based on the compatibility requirements of multi-platform adaptive optimization. During implementation, mainstream terminal operating systems are first categorized and analyzed, including Android versions 4.0 to 14.0, iOS versions 10 to 17.0, and HarmonyOS versions 2.0 to 4.0. For each system version, the application's runtime environment is analyzed, and system permission configuration parameters are extracted, including file access permissions, hardware access permissions, and background running permissions. The conditions for enabling different permissions and their calling specifications are clarified. Code execution rules are extracted, including programming language syntax requirements, code compilation standards, and runtime memory management mechanisms. Resource calling specifications are extracted, including resource file format requirements, storage path specifications, and calling interface standards. Based on these constraints, the code execution logic of the theme components is adjusted. A mixed Java and Kotlin programming syntax is used for Android, Swift syntax for iOS, and ArkTS syntax for HarmonyOS. Function calling methods, variable definition rules, and exception handling mechanisms in the code are adjusted to ensure that the code can be compiled and run normally on the target system. The resource access path has been optimized. Resource files are categorized and stored in designated system directories based on the terminal's storage structure. Resource index addresses have been adjusted to absolute paths, and the reading order has been optimized to sort by usage frequency, prioritizing frequently used resources. Touch response sensitivity parameters have been adjusted to suit the interactive hardware characteristics of different terminals. For touchscreen terminals, the trigger threshold has been adjusted to 0.01 to 0.1 Newtons. For foldable screen terminals, interactive area recognition parameters in the folded state have been added. For tablet terminals, gesture recognition logic has been adjusted to support accurate recognition of single-finger, two-finger, and multi-finger gestures. Feedback mechanism parameters have also been adjusted, with haptic feedback intensity set to levels 1 to 5 and auditory feedback frequency set to 100 to 1000 Hz to ensure accurate and adaptable interactive responses.

[0023] S6 integrates and optimizes the results to generate a beautification theme installation package adapted to multiple terminal platforms, ensuring consistent presentation and operation of the theme across different terminals.

[0024] Specifically, step S6 integrates and optimizes the results to generate the installation package. During implementation, an optimization result integration database is first established, storing all optimization data generated in steps S1 to S5 categorized by data type. This includes component attribute parameter optimization data, loading priority sorting data, animation adaptation scheme data, visual parameter calibration data, and compatibility adjustment data. Each category is further subdivided according to terminal system type and hardware configuration level. The installation package generation tool is then launched, importing the integrated optimization data and packaging it according to the installation package format requirements of different terminal platforms. For Android systems, an APK format installation package is generated, with a file size controlled between 5 and 50 MB, supporting architectures such as ARM and x86. For iOS systems, an IPA format installation package is generated, with a file size controlled between 5 and 40 MB, adapting to devices such as iPhone and iPad. For HarmonyOS systems, an APP format installation package is generated, with a file size controlled between 5 and 45 MB, supporting all HarmonyOS terminal devices. An adaptation detection module is embedded during the packaging process. This module includes 100 to 200 adaptation detection points, covering multiple dimensions such as component display, loading speed, animation operation, visual effects, and interactive response. Each detection point has a set pass / fail threshold: the component display detection threshold is a parameter matching degree of no less than 95%; the loading speed detection threshold is a startup time of no more than 3 seconds; the animation operation detection threshold is a frame rate fluctuation of no more than 5 frames / second; the visual effect detection threshold is that the parameters meet preset standards; and the interactive response detection threshold is a response latency of no more than 50 milliseconds. The generated installation package undergoes full-dimensional testing. Installation packages that pass the tests are marked as deployable versions, while those that fail are returned to the corresponding steps for re-optimization. Finally, a beautified theme installation package adapted to different terminal platforms is output, achieving consistent presentation and efficient operation of the theme across multiple terminals.

[0025] Preferably, the expression for the cross-terminal component adaptation decision model is: ,in, To adapt decision coefficients across different platforms, For the size parameter weights, Let i be the size specification parameters of the i-th component. For layout parameter weights, Let i be the layout hierarchy parameter of the i-th element. For interaction parameter weights, Let be the interaction response threshold parameter for the i-th element. For terminal operating system compatibility coefficient, To adapt the threshold for hardware operations, To accommodate the correction factor, n represents the total number of theme elements.

[0026] Specifically, in the implementation of the cross-platform component adaptation decision model, three types of core parameter weights are first set: size parameter weights range from 0.3 to 0.5, layout parameter weights range from 0.2 to 0.4, and interaction parameter weights range from 0.2 to 0.4. The sum of the three weights is fixed at 1.0. Specific values ​​are dynamically allocated according to the theme type and terminal characteristics. For example, for icon-type themes, the size parameter weight is set to 0.5, and the layout and interaction parameter weights are each set to 0.25; for desktop wallpaper-type themes, the layout parameter weight is set to 0.4, and the size and interaction parameter weights are set to 0.3 and 0.3 respectively. For each component, size specifications are quantified and collected according to a length of 10 to 1500 pixels, a width of 5 to 1200 pixels, and a thickness of 1 to 50 pixels. Layout hierarchy parameters are assigned values ​​according to a hierarchy of 1 to 10 levels. Interaction response threshold parameters are recorded according to touch pressure of 0.01 to 0.1 Newtons, click latency of 1 to 50 milliseconds, and swipe sensitivity of 1 to 20 pixels / millisecond. Simultaneously, the terminal operating system compatibility coefficient is collected, ranging from 0.6 to 1.0, with mainstream new versions set to 1.0 and older versions set to 0.6 to 0.8. The hardware computing adaptation threshold is set from 0.5 to 1.0 according to the terminal CPU frequency of 1.0 to 3.5 GHz. The adaptation correction coefficient is calibrated and set from 0.05 to 0.2 based on historical adaptation data. By integrating the above parameters through model calculation, a cross-platform adaptation decision coefficient in the range of 0.1 to 1.0 is obtained. A coefficient higher than 0.8 is considered excellent adaptation, 0.6 to 0.8 is considered good adaptation, and a coefficient lower than 0.6 requires readjustment of component parameters to ensure accurate adaptation of the structure and interaction of the theme component in different terminal systems.

[0027] Preferably, the expression for the topic loading performance optimization algorithm is: ,in, To optimize loading performance index, For the network transmission rate parameter of the j-th type of data, This refers to the terminal hardware's processing capability parameter for the j-th type of data. The loading priority weight for the j-th type of data is... For the resource consumption parameter of the j-th type of data, This is the loading rate adjustment coefficient. Let j be the storage occupancy parameter for the j-th type of data. is the storage usage threshold, and m is the number of subject data categories.

[0028] Specifically, when implementing the topic loading performance optimization algorithm, topic resources are divided into four major categories—images, audio, text, and animations—and 5 to 10 subcategories based on data type. A loading priority weight is assigned to each data category, ranging from 0.1 to 1.0. Core image data has a weight of 0.9 to 1.0, secondary text data has a weight of 0.1 to 0.3, and the remaining data is weighted according to importance, with a weight of 0.5 to 0.8. Network transmission rate parameters for each data category are collected: 1 to 100 Mbps for cellular networks and 10 to 1000 Mbps for Wi-Fi networks. Terminal hardware processing power parameters are quantized to 0.3 to 1.0 for CPU frequencies of 1.0 to 3.5 GHz and GPU processing speeds of 100 to 2000 GFLOPS. Resource usage parameters are quantized to 0.1 to 0.9 for file sizes of 10KB to 50MB. Storage usage parameters are calculated as 5% to 80% of the remaining terminal storage (10 to 512 GB), with a storage usage threshold set at 30%. The loading rate adjustment coefficient is set from 0.5 to 2.0 based on the terminal type, with 2.0 for high-performance terminals and 0.5 for entry-level terminals. An algorithm integrates these parameters to calculate a loading performance optimization index, ranging from 0.1 to 1.0. An index above 0.8 employs a high-speed loading strategy, between 0.5 and 0.8 a balanced loading strategy, and below 0.5 a power-saving loading strategy. Simultaneously, the loading rhythm is dynamically adjusted based on a comparison of storage occupancy and a threshold. When storage occupancy exceeds the threshold, the loading priority of low-weight data is reduced, ensuring a high degree of matching between the topic loading process and terminal hardware, network status, and storage resources, thereby improving loading efficiency and stability.

[0029] Preferably, the expression for the interface animation adaptation prediction model is: ,in, To adapt the motion effects to the predicted values, Display frame rate parameters on the terminal. These are parameters related to processor computational efficiency. This is a parameter related to memory usage. Let be the complexity parameter for the k-th animation. Let be the response delay parameter for the k-th motion effect. For motion effect adaptation correction coefficients, is the baseline parameter for motion smoothness, and p is the total number of motion effects included in the theme.

[0030] Specifically, when implementing the interface animation adaptation prediction model, the terminal display frame rate parameter is collected and recorded based on actual detection values ​​of 30 to 120 frames per second. High-performance terminals typically achieve 60 to 120 frames per second, while older terminals achieve 30 to 60 frames per second. The processor computing efficiency parameter is quantified based on calculation results of 100 to 5000 MIPS, with flagship terminals reaching 3000 to 5000 MIPS and entry-level terminals achieving 100 to 1000 MIPS. The memory usage parameter is calculated as 5% to 80% based on the 100 to 500 MB of memory required for the theme to run and the remaining 0.5 to 8 GB of memory on the terminal. At the same time, the motion trajectory complexity of each animation is divided into 1 to 5 levels, with level 1 corresponding to a single linear motion and level 5 corresponding to a multi-curve composite motion. The color change frequency is recorded as 1 to 30 times per second, with simple animations at 1 to 10 times per second and complex animations at 20 to 30 times per second. The number of elements is counted as 1 to 50. The motion effect adaptation correction coefficient is set between 0.8 and 1.2, and dynamically adjusted according to the type of motion effect. The smoothness benchmark parameter is set to 0.7 according to industry standards. The motion effect adaptation prediction value in the range of 0.1 to 1.0 is obtained through model calculation. When the prediction value is higher than 0.8, it adapts to high-complexity motion effects; when it is between 0.5 and 0.8, it adapts to medium-complexity motion effects; and when it is lower than 0.5, it adapts to low-complexity motion effects. At the same time, combined with the analysis results of frame rate stability, resource consumption, and interaction synchronization, the complexity level, color change frequency, and number of elements of the motion effect are adjusted to ensure that the motion effect runs smoothly and does not exceed the performance capacity of the terminal.

[0031] Preferably, the calibration model expression of the theme visual effects calibration engine is: ,in, To calibrate the deviation value for visual effects, The actual color gamut parameters of the theme. To preset the standard parameters for color gamut, The actual sharpness parameter of the subject. To preset the standard resolution parameters, For the actual contrast parameters of the subject, To preset the standard contrast parameters, For color calibration coefficients, For sharpness calibration factor, This is the contrast calibration factor.

[0032] Specifically, during the implementation of the calibration model of the theme visual effect calibration engine, the actual color gamut parameters of the theme are obtained through the terminal screen acquisition module. The color gamut coverage of the three primary colors (red, green, and blue) is extracted according to the CIE1931 color space standard, with actual values ​​typically ranging from 50% to 100%. The actual sharpness parameter is calculated as 0.1 to 1.0 based on image edge sharpness; the actual contrast ratio is calculated as 10:1 to 200:1 based on the ratio of the brightest to the darkest area. A preset standard parameter library is called, and according to the theme design specifications, the color gamut coverage of the three primary colors (red, green, and blue) is set to be no less than 90%, the sharpness standard parameter value to be no less than 0.8, and the contrast standard parameter range to be 50:1 to 150:1. The color calibration coefficient is set to 0.8 to 1.2, the sharpness calibration coefficient to 0.9 to 1.1, and the contrast calibration coefficient to 0.7 to 1.3, adjusted according to the terminal screen type: OLED screens have a calibration coefficient set to 1.2 to 1.3, and LCD screens to 0.8 to 0.9. The model calculates the visual effect calibration deviation value, which ranges from 0 to 0.5. When the deviation value is below 0.1, the visual effect is considered acceptable and no adjustment is needed. When the deviation value is between 0.1 and 0.3, slight adjustments are made, with color channel parameters adjusted by 1 to 10 numerical units, sharpness parameters adjusted by 0.05 to 0.1, and contrast parameters adjusted by 5:1 to 15:1. When the deviation value is above 0.3, deep adjustments are made, with color channel parameters adjusted by 10 to 20 numerical units, sharpness parameters adjusted by 0.1 to 0.2, and contrast parameters adjusted by 15:1 to 30:1. Multiple rounds of calibration calculations are performed until the deviation value is below 0.1, ensuring that the visual presentation of the theme on different terminals is highly consistent with the preset standard.

[0033] Preferably, the comprehensive optimization model expression for the multi-platform adaptive optimization of the mobile phone beautification theme is: ,in, To comprehensively optimize the index, To adapt decision coefficients across different platforms, To optimize loading performance index, To adapt the motion effects to the predicted values, To calibrate the deviation value for visual effects, To optimize the compensation coefficient, Let be the compatibility parameter for the q-th adapted scenario. Parameters are adapted for the resource call path in the q-th scenario. To adapt parameters for the interaction response mechanism of the q-th scenario, To adaptively optimize the number of scenes.

[0034] Specifically, when implementing the comprehensive optimization model for multi-platform adaptive optimization of mobile phone beautification themes, the cross-platform adaptation decision coefficient (0.1 to 1.0), loading performance optimization index (0.1 to 1.0), motion effect adaptation prediction value (0.1 to 1.0), and visual effect calibration deviation value (0 to 0.5) generated by the preceding model are integrated, and the optimization compensation coefficient is set to 0.05 to 0.2 to offset the impact of parameter measurement errors. For adaptive optimization scenarios, 10 to 20 scenarios are categorized according to terminal system type, hardware configuration level, and usage scenario. Compatibility parameters are collected for each scenario. The system version adaptation level is set to 0.6 to 1.0, with newer versions set to 1.0 and older versions set to 0.6 to 0.8. Resource call path adaptation parameters are set to 0.7 to 1.0, with direct call paths set to 1.0 and indirect call paths set to 0.7 to 0.9. Interaction response mechanism adaptation parameters are set to 0.6 to 1.0, with complete matching set to 1.0 and partial matching set to 0.6 to 0.9. The model calculates a comprehensive optimization index ranging from 0.1 to 1.0. An index above 0.8 is considered to have met the optimization criteria, and the optimization result is directly output. For indices between 0.6 and 0.8, secondary optimization is performed on individual parameters below 0.8, focusing on adjusting compatibility parameters or resource call path parameters. For indices below 0.6, the entire optimization process is re-executed, correcting the weak parameters in cross-platform adaptation, loading performance, animation adaptation, and visual calibration one by one, ensuring the comprehensiveness and effectiveness of the theme's multi-platform adaptive optimization, and achieving the optimal improvement in overall performance.

[0035] Preferably, step S3 includes the following sub-steps: S31, collecting display hardware parameters of the target terminal, including screen refresh rate range, pixel density level, and display color depth data, and establishing a terminal display capability database; S32, classifying the interface animations included in the theme according to motion trajectory complexity, color change frequency, and number of elements, and extracting the calibration execution parameters of each type of animation; S33, matching the animation calibration execution parameters with the data in the terminal display capability database, and initially screening animation schemes that meet the basic operating conditions of the terminal; S34, analyzing the frame rate stability, resource consumption, and interaction synchronization of the initially screened animation schemes based on the interface animation adaptation prediction model, and determining the final animation execution scheme.

[0036] Specifically, in the implementation of step S3, S31 first starts the terminal hardware detection tool to collect parameters of the target terminal's display hardware. The screen refresh rate is recorded in the range of 30 to 120 frames per second, with a sample size of no less than 30 sets, and the average value is taken as the final data. The pixel density level is divided according to the standard of 120 to 640 ppi, accurate to every 10 ppi level. The display color depth is classified and identified according to 8-bit, 16-bit, 24-bit, and 32-bit. At the same time, the screen resolution parameters are recorded, ranging from 720×1280 to 3840×2160 pixels. All collected data is stored according to the terminal model, establishing a display capability database including 100 to 500 terminal models. S32 classifies the 10 to 50 interface animations included in the theme, and the motion trajectory complexity is divided into 1 to 5 levels, with level 1 corresponding to linear motion in a single direction and level 5 corresponding to multiple... The motion is a composite motion with directional curves. The color change frequency is counted at 1 to 30 times / second, and the number of elements is counted at 1 to 50. The core execution parameters of each motion effect, such as motion trajectory parameters, color change cycle, and element rendering level, are extracted. In S33, the core execution parameters of the motion effect are matched with the terminal display capability data in the database. The matching threshold is set to 0.7, and the schemes that meet the basic requirements for the operation of the motion effect are selected. The motion effect combinations with a matching degree of less than 0.7 are eliminated. In S34, the schemes after preliminary screening are input into the interface motion effect adaptation prediction model to analyze the frame rate stability. It is required that the frame rate fluctuation does not exceed 5 frames / second within 5 minutes of continuous operation, the resource consumption is controlled within 30% of the terminal's remaining memory, and the interaction synchronization requires that the delay between the motion effect response and the user operation does not exceed 50 milliseconds. The final motion effect execution scheme is determined by combining the three indicators to ensure that the motion effect and the terminal display capability are accurately matched.

[0037] Preferably, step S4 includes the following sub-steps: S41, acquiring the actual display image data of the theme element on the target terminal through the terminal screen acquisition module, and extracting the color distribution data, sharpness feature value, and contrast quantization value from the image; S42, calling the preset standard parameter library of the theme visual effect calibration engine, and extracting the color gamut standard, sharpness standard, and contrast standard corresponding to the current theme; S43, calculating the difference between the actually extracted visual parameters and the preset standard parameters to determine the deviation amplitude and direction of each visual indicator; S44, adjusting the color adjustment parameters, sharpness enhancement parameters, and contrast correction parameters of the theme element according to the deviation calculation results to complete the visual effect calibration.

[0038] Specifically, in the implementation of step S4, S31 activates the terminal screen acquisition module, employs real-time image capture technology, and acquires the actual displayed image of the theme element at the terminal's native resolution. The acquisition frequency is 10 times / second, and the acquisition continues for 10 seconds. From 100 frames, the 5 frames with the highest clarity and no afterimages are selected as analysis samples. Color distribution data is extracted using an image pixel analysis tool, and the pixel value distribution range of each channel is statistically analyzed according to the RGB three color channels. The clarity feature value is calculated using an edge detection algorithm, ranging from 0.1 to 1.0. The contrast quantization value is determined by calculating the ratio of the maximum brightness to the minimum brightness of the image, ranging from 10:1 to 200:1. S42 calls the preset standard parameter library of the theme visual effect calibration engine, extracts the corresponding color gamut standard according to the theme design file, and sets the coverage range of the red, green, and blue primary color gamuts to 90% to 100%. The clarity standard parameter value is set to 0.8 to 1.0, and the contrast standard parameter value is set to 0.8 to 1.0. The aspect ratio is set to 50:1 to 150:1 to ensure that the standard parameters are consistent with the original design intent. S43 calculates the difference between the actual extracted visual parameters and the preset standard parameters item by item. The color gamut deviation is calculated according to the difference in coverage of each channel. The sharpness deviation is calculated according to the absolute difference between the actual feature value and the standard value. The contrast deviation is calculated according to the ratio of the difference between the actual ratio and the standard ratio. The deviation magnitude and direction of each indicator are clearly defined, and the deviation magnitude is accurate to 1%. S44 performs parameter adjustment according to the deviation calculation results. The color adjustment parameters are adjusted separately for each RGB channel, with an adjustment magnitude of 1 to 20 numerical units for each channel. The sharpness enhancement parameter is achieved by adjusting the intensity of the image sharpening filter, with an adjustment range of 0.05 to 0.2. The contrast correction parameter is achieved by adjusting the slope of the brightness curve, with an adjustment magnitude of 5:1 to 30:1. After each round of adjustment, the image is re-acquired for verification. The adjustment is repeated until the deviation magnitude of each visual parameter is less than 10%, and the visual effect calibration is completed.

[0039] Preferably, step S5 includes the following sub-steps: S51, analyzing the application runtime environment of different terminal operating systems, and extracting system permission configurations, code execution rules, and resource call specification compatibility constraints; S52, adjusting the syntax adaptation of the code execution logic of the theme components based on the constraints to ensure that the code can be compiled and run normally in the target system; S53, optimizing the call path of theme resources, and adjusting the resource index address and reading order according to the terminal storage structure and file access mechanism; S54, adjusting the touch response sensitivity, gesture recognition logic, and feedback mechanism parameters of the theme according to the interactive hardware characteristics of different terminals to adapt the interactive response mechanism.

[0040] Specifically, in the implementation of step S5, S51 analyzes the application runtime environment of mainstream operating systems such as Android 4.0 to 14.0, iOS 10 to 17.0, and HarmonyOS 2.0 to 4.0. System permission configuration focuses on clarifying the conditions for enabling file read / write permissions, hardware access permissions, and background running permissions. Code execution rules clarify the programming language syntax, function call specifications, and memory management mechanisms supported by different systems. Resource call specifications determine resource file format requirements, including PNG and JPG images, MP3 and WAV audio formats, storage path specifications, and API versions, compiling a list of multi-platform compatibility constraints. S52 adjusts the code execution logic of the theme components based on these constraints. Android system code is adapted using Java and Kotlin syntax, iOS system code uses Swift syntax, and HarmonyOS system code uses ArkTS syntax. Function parameter definitions are also adjusted. The code branch structure and exception handling process are optimized to ensure that the code can be compiled and run without runtime errors on the target system. S53 optimizes resource call paths, classifying and storing resource files in designated system directories according to the terminal's storage structure. The application directory stores core code files, while the resource directory stores images, audio, and other files. Resource index addresses are adjusted to absolute paths, and the reading order is sorted according to resource usage frequency, with frequently used resources having a priority higher than 0.8 and infrequently used resources having a priority lower than 0.5. S54 adjusts touch response sensitivity for different terminals such as touchscreens, foldable screens, and tablets, setting the trigger pressure threshold to 0.01 to 0.1 Newtons. The gesture recognition logic adds algorithms to distinguish between single-finger, two-finger, and multi-finger operations. The tactile feedback intensity in the interaction feedback mechanism parameters is adjusted from 1 to 5 levels, and the auditory feedback frequency is set from 100 to 1000Hz, ensuring accurate adaptation of the interaction response to the terminal hardware characteristics.

[0041] The cross-platform component adaptation decision model is the core adaptation decision tool designed for the differences in multi-terminal systems in this invention. It achieves accurate matching between theme components and different terminals through multi-dimensional parameter fusion calculation. The implementation process of this model requires first extracting core attribute parameters of theme components, such as size specifications, layout level, and interaction response threshold. Size parameters include quantitative data such as length of 10 to 1500 pixels and width of 5 to 1200 pixels. The layout level is divided into 1 to 10 levels. The interaction response threshold includes parameters such as touch pressure of 0.01 to 0.1 Newtons. At the same time, terminal characteristic parameters such as terminal operating system compatibility coefficient and hardware computing adaptation threshold are collected. By setting the weights of the three types of parameters, size, layout, and interaction (with values ​​ranging from 0.2 to 0.5), and combining them with the adaptation correction coefficient, a comprehensive calculation is performed to obtain an adaptation decision coefficient in the range of 0.1 to 1.0. This replaces the traditional fixed rule adaptation mode, dynamically judging the adaptation feasibility of theme components on different terminals. A coefficient higher than 0.8 is considered excellent adaptation, and a coefficient lower than 0.6 triggers parameter adjustment. This model addresses industry pain points such as misaligned component layouts and inconsistent interactive responses in multi-terminal systems. Through quantitative parameters and dynamic decision-making mechanisms, it significantly improves the accuracy of cross-platform theme adaptation, laying the foundation for consistent presentation across multiple platforms.

[0042] The theme loading performance optimization algorithm is a technology that ensures efficient theme loading. It dynamically adjusts resource loading strategies based on terminal hardware and network status. In implementation, theme resources are first divided into four main categories—images, audio, text, and animations—and multiple subcategories. Each category is assigned a loading priority weight of 0.1 to 1.0, with core image data weighted at 0.9 to 1.0 and secondary text data at 0.1 to 0.3. Simultaneously, hardware parameters such as terminal CPU frequency, GPU processing speed, and memory capacity are collected, along with transmission rates of 1 to 100 Mbps for cellular networks and 10 to 1000 Mbps for Wi-Fi networks. Combining this with a comparison of storage occupancy and thresholds, the algorithm calculates a loading performance optimization index. Based on the index (above 0.8 for high-speed loading, 0.5 to 0.8 for balanced loading, and below 0.5 for energy-saving loading), the data transmission order is dynamically adjusted, prioritizing the loading of high-weight core data. When the network speed drops below 1 Mbps, non-core data transmission is paused. This algorithm breaks through the limitations of traditional loading strategies that ignore terminal differences, and achieves dynamic matching of loading efficiency with terminal hardware and network status, effectively reducing loading latency, improving the stability of theme startup and operation, and enhancing the user experience.

[0043] The UI animation adaptation prediction model is a key tool for achieving smooth cross-platform animation operation. It generates adaptation solutions through matching analysis of terminal performance and animation characteristics. The implementation first collects performance parameters such as terminal display frame rate (30 to 120 frames / second), processor computing efficiency (100 to 5000 MIPS), and memory usage (5% to 80%). Then, it extracts core features such as the motion trajectory complexity (level 1 to 5), color change frequency (1 to 30 times / second), and number of elements (1 to 50) of the theme animation. The two types of parameters are input into the model to analyze three major indicators: frame rate stability, resource consumption, and interaction synchronization. Adaptation prediction values ​​of 0.1 to 1.0 are generated. The animation scheme is dynamically adjusted according to the prediction values. High-configuration terminals (prediction value higher than 0.8) are adapted to animations with complexity levels of 3 to 5, medium-configuration terminals (0.5 to 0.8) are adapted to levels 2 to 3, and low-configuration terminals (lower than 0.5) are adapted to levels 1 to 2, ensuring that the frame rate fluctuation of the animation does not exceed 5 frames / second and the resource consumption does not exceed 30% of the remaining memory. This model solves the stuttering and imbalance problems caused by traditional motion effect adaptation. By accurately matching terminal performance and motion effect complexity, it achieves a balance between smooth operation of motion effects on multiple terminals and consistency of visual experience, thereby improving the interactive quality of the theme.

[0044] The theme visual effect calibration engine is a technology that ensures visual consistency across multiple terminals. It optimizes visual parameters by comparing and adjusting the actual display with preset standards. The process begins by acquiring the actual displayed image of the theme at the terminal's native resolution, extracting the color gamut coverage (50% to 100%) of the red, green, and blue primary colors under the CIE1931 color space, the sharpness feature value (0.1 to 1.0), and the contrast ratio quantization value (10:1 to 200:1). Then, it calls upon the preset standard parameter library to define the standard requirements: color gamut coverage no less than 90%, sharpness no less than 0.8, and contrast ratio 50:1 to 150:1. By calculating the deviation between the actual parameters and the standards, the parameters are adjusted in increments of 1 to 20 units for color channels, 0.05 to 0.2 for sharpness, and 5:1 to 30:1 for contrast ratio. Multiple rounds of calibration are performed until the deviation is less than 10%, correcting visual deviations caused by different terminal screen characteristics and ensuring that parameters such as color, sharpness, and contrast meet the design standards. This engine breaks down the visual inconsistencies caused by differences in terminal screens. Through a standardized calibration mechanism, it ensures that the theme presents a unified visual effect on different types of screens such as OLED and LCD, as well as terminals with different resolutions. This guarantees the accurate realization of the theme design intention and enhances brand recognition and user visual experience.

[0045] like Figure 2As shown, a multi-platform adaptive optimization system for mobile phone themes is applied to a multi-platform adaptive optimization method for mobile phone themes. The system includes: a cross-platform component attribute parameter intelligent extraction unit, used to collect the theme component size, layout, interaction, and visual-related parameters of different terminal operating systems through a cross-platform component adaptation decision model, and establish a data connection with a multi-platform adaptation parameter library; a theme loading priority dynamic sorting unit, which receives data output from the cross-platform component attribute parameter intelligent extraction unit, determines the loading order based on a theme loading performance optimization algorithm combined with terminal hardware and network parameters, and sends a sorting instruction to a resource transmission scheduling unit; and an interface animation adaptation scheme generation unit, which collects terminal display and hardware calculation parameters. The system generates adaptation schemes through the interface animation adaptation prediction model and establishes data interaction with the theme execution unit; the theme visual effect precision calibration unit receives the actual presentation data of the theme on the terminal, compares it with the preset standard through the theme visual effect calibration engine and adjusts the parameters, and outputs calibration instructions to the theme rendering unit; the multi-platform compatibility dynamic adjustment unit integrates the optimization data output by different units, adapts and adjusts the theme code logic, resource paths and interaction mechanisms, and establishes communication with the theme packaging unit; the beautification theme integration and packaging output unit receives the optimization results of the multi-platform compatibility dynamic adjustment unit, integrates and encapsulates them according to the installation package format requirements of different terminals, and generates a theme installation file that can be directly deployed.

[0046] A multi-platform adaptive optimization method and system for mobile phone theme customization is proposed. Through a cross-platform component adaptation decision mechanism, it comprehensively extracts multi-dimensional attribute parameters of theme components and makes dynamic decisions based on terminal system and hardware characteristics, replacing the traditional single-parameter adaptation mode. This fundamentally solves the problems of component layout disorder and inconsistent interactive response between different terminals. Through theme loading performance optimization technology, it dynamically sorts loading priorities based on terminal hardware computing, network transmission and storage resource status, optimizes resource call paths, significantly improves theme loading efficiency, and effectively improves the drawback of excessive loading latency.

[0047] Meanwhile, the interface animation adaptation prediction technology analyzes the terminal display frame rate, computing efficiency, and memory usage to generate targeted animation execution plans, avoiding animation stuttering and adaptation imbalances. The theme visual effect calibration engine adjusts parameters through precise comparison between actual presentation and preset standards, ensuring consistent visual presentation across multiple terminals. Multi-platform compatibility adjustment technology provides comprehensive adaptation of code logic, resource paths, and interaction mechanisms, further enhancing the theme's cross-platform compatibility. Each unit of the system forms a closed-loop optimization through data interaction and command transmission. The collaborative efforts of each technical module comprehensively overcome the shortcomings of existing technologies, such as insufficient adaptation accuracy and the difficulty in balancing performance and visual effects, achieving efficient and stable operation and consistent presentation of the theme across different terminals.

[0048] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-platform adaptive optimization method for mobile phone theme customization, characterized in that, Includes the following steps: S1. Extract the theme element attribute parameters of different terminal operating systems through the cross-terminal element adaptation decision model. The parameters include element size specifications, layout hierarchy, interaction response threshold and visual presentation characteristics. S2, Based on the theme loading performance optimization algorithm, the extracted theme element data is sorted by loading priority, and the data transmission order is determined by combining the terminal hardware computing power, network transmission rate and storage resource capacity; S3, The interface animation adaptation prediction model is used to analyze the terminal display frame rate, processor computing efficiency and memory usage, and generate animation execution schemes adapted to different terminals. S4. By comparing the actual presentation effect of theme elements on the target terminal with the preset standard through the theme visual effect calibration engine, the color gamut parameters, sharpness parameters, and contrast parameters are adjusted; S5. According to the compatibility requirements of multi-platform adaptive optimization, the code execution logic, resource call path, and interactive response mechanism of theme elements are adapted and adjusted; S6. The optimization results are integrated to generate a beautification theme installation package adapted to multiple terminal platforms, so as to ensure the consistent presentation and operation of the theme on different terminals.

2. The mobile phone theme customization method with multi-platform adaptive optimization according to claim 1, characterized in that, The expression for the cross-terminal component adaptation decision model is: ,in, To adapt decision coefficients across different platforms, For the size parameter weights, Let i be the size specification parameters of the i-th component. For layout parameter weights, Let i be the layout hierarchy parameter of the i-th element. For interaction parameter weights, Let be the interaction response threshold parameter for the i-th element. For terminal operating system compatibility coefficient, To adapt the threshold for hardware operations, To accommodate the correction factor, n represents the total number of theme elements.

3. The mobile phone theme customization method with multi-platform adaptive optimization according to claim 1, characterized in that, The expression for the topic loading performance optimization algorithm is: ,in, To optimize loading performance index, For the network transmission rate parameter of the j-th type of data, This refers to the terminal hardware's processing capability parameter for the j-th type of data. The loading priority weight for the j-th type of data is... For the resource consumption parameter of the j-th type of data, This is the loading rate adjustment coefficient. Let j be the storage occupancy parameter for the j-th type of data. is the storage usage threshold, and m is the number of subject data categories.

4. The mobile phone theme customization method with multi-platform adaptive optimization according to claim 1, characterized in that, The expression for the interface animation adaptation prediction model is: ,in, To adapt the motion effects to the predicted values, Display frame rate parameters on the terminal. These are parameters related to processor computational efficiency. This is a parameter related to memory usage. Let be the complexity parameter for the k-th animation. Let be the response delay parameter for the k-th motion effect. For motion effect adaptation correction coefficients, is the baseline parameter for motion smoothness, and p is the total number of motion effects included in the theme.

5. The mobile phone theme customization method with multi-platform adaptive optimization according to claim 1, characterized in that, The calibration model expression for the theme visual effects calibration engine is: ,in, To calibrate the deviation value for visual effects, The actual color gamut parameters of the theme. To preset the standard parameters for color gamut, For the actual sharpness parameter of the subject, To preset the standard resolution parameters, For the actual contrast parameters of the subject, To preset the standard contrast parameters, For color calibration coefficients, For sharpness calibration factor, This is the contrast calibration factor.

6. The mobile phone theme customization method with multi-platform adaptive optimization according to claim 1, characterized in that, The comprehensive optimization model expression for the multi-platform adaptive optimization of mobile phone beautification themes is as follows: ,in, To comprehensively optimize the index, To adapt decision coefficients across different platforms, To optimize loading performance index, To adapt the motion effects to the predicted values, To calibrate the deviation value for visual effects, To optimize the compensation coefficient, Let be the compatibility parameter for the q-th adapted scenario. Parameters are adapted for the resource call path in the q-th scenario. To adapt parameters for the interaction response mechanism of the q-th scenario, To adaptively optimize the number of scenes.

7. The mobile phone theme customization method with multi-platform adaptive optimization according to claim 1, characterized in that, S3 includes the following steps: S31, collecting display hardware parameters of the target terminal, including screen refresh rate range, pixel density level and display color depth data, and establishing a terminal display capability database; S32, classifying the interface animations included in the theme according to motion trajectory complexity, color change frequency and number of elements, and extracting the calibration execution parameters of each type of animation; S33, matching the animation calibration execution parameters with the data in the terminal display capability database, and initially screening animation schemes that meet the basic operating conditions of the terminal; S34. Based on the interface animation adaptation prediction model, the frame rate stability, resource consumption and interaction synchronization of the initially screened animation schemes are analyzed to determine the final animation execution scheme.

8. The mobile phone theme customization method with multi-platform adaptive optimization according to claim 1, characterized in that, S4 includes the following sub-steps: S41, acquiring the actual display image data of the theme element on the target terminal through the terminal screen acquisition module, and extracting the color distribution data, sharpness feature value and contrast quantization value in the image; S42, calling the preset standard parameter library of the theme visual effect calibration engine, and extracting the color gamut standard, sharpness standard and contrast standard corresponding to the current theme; S43, calculating the difference between the actually extracted visual parameters and the preset standard parameters to determine the deviation magnitude and direction of each visual indicator; S44, based on the deviation calculation results, adjust the color adjustment parameters, sharpness enhancement parameters, and contrast correction parameters of the theme element to complete the visual effect calibration.

9. The mobile phone theme customization method with multi-platform adaptive optimization according to claim 1, characterized in that, S5 includes the following sub-steps: S51, analyze the application runtime environment of different terminal operating systems, and extract system permission configuration, code execution rules, and resource call specification compatibility constraints; S52, adjust the syntax adaptation of the code execution logic of the theme components based on the constraints to ensure that the code can be compiled and run normally in the target system; S53, optimize the call path of theme resources, and adjust the resource index address and reading order according to the terminal storage structure and file access mechanism; S54, adjust the touch response sensitivity, gesture recognition logic, and feedback mechanism parameters of the theme according to the interactive hardware characteristics of different terminals to adapt the interactive response mechanism.

10. A multi-platform adaptive optimization system for mobile phone theme customization, characterized in that, This system is applied to a multi-platform adaptive optimization method for mobile phone themes as described in claim 1, comprising: a cross-platform component attribute parameter intelligent extraction unit, used to collect the theme component size, layout, interaction, and visual-related parameters of different terminal operating systems through a cross-platform component adaptation decision model, and establish a data connection with a multi-platform adaptation parameter library; a theme loading priority dynamic sorting unit, which receives the data output by the cross-platform component attribute parameter intelligent extraction unit, determines the loading order based on the theme loading performance optimization algorithm combined with terminal hardware and network parameters, and sends a sorting instruction to a resource transmission scheduling unit; and an interface animation adaptation scheme generation unit, which collects terminal display and hardware calculation parameters, and generates the interface animation adaptation scheme. The performance adaptation prediction model generates an adaptation scheme and establishes data interaction with the theme execution unit; the theme visual effect precision calibration unit receives the actual presentation data of the theme on the terminal, compares it with the preset standard through the theme visual effect calibration engine and adjusts the parameters, and outputs calibration instructions to the theme rendering unit; the multi-platform compatibility dynamic adjustment unit integrates the optimization data output by different units, adapts and adjusts the theme code logic, resource paths and interaction mechanisms, and establishes communication with the theme packaging unit; the beautification theme integration and packaging output unit receives the optimization results of the multi-platform compatibility dynamic adjustment unit, integrates and encapsulates them according to the installation package format requirements of different terminals, and generates a theme installation file that can be directly deployed.

Images