A menu generation method and system based on liquid crystal screen

Abstract

The present invention relates to the field of liquid crystal display technology, and in particular to a menu generation method and system based on a liquid crystal screen. The method comprises the following steps: obtaining device hardware parameters and system resource data; performing device capability evaluation based on the device hardware parameters and system resource data to generate device performance index data; performing multi-dimensional device characteristic analysis based on the device performance index data to obtain device characteristic vector data; performing device type matching based on a preset device mode library and device characteristic vector data to obtain device prototype mode data; performing adaptive menu complexity division based on the device prototype mode data to generate menu complexity level data; performing responsive layout calculation based on the screen size and resolution information in the device hardware parameters to generate adaptive layout template data. The present invention can automatically adjust the complexity and layout of the menu according to the hardware parameters and system resources of the device to ensure the best performance on various devices.

Description

Technical Field

[0001] The present invention relates to the technical field of liquid crystal display, and in particular to a menu generation method and system based on a liquid crystal screen. Background Art

[0002] Liquid Crystal Display (LCD) is a common flat panel display device that uses the physical properties of liquid crystal to display images. Its basic structure includes: two layers of polarizers, two layers of glass substrates, a liquid crystal layer sandwiched between the glass substrates, and a backlight source (usually LED); the working principle is that the liquid crystal molecules are arranged in a spiral when there is no electric field, and when an electric field is applied, the liquid crystal molecules will rearrange, and this arrangement change controls the passage and blocking of light, thereby forming an image. After years of development, LCD screen technology has become one of the most commonly used display technologies; it is widely used in daily life and work, providing people with a clear and energy-saving visual experience.

[0003] Traditional LCD-based menu generation methods often have the following problems: In portable devices, such as smart watches or portable medical devices, the menu system needs to minimize power consumption while ensuring user experience, which requires finding a balance between complexity and energy consumption. The same menu system may need to run on screens of different sizes, such as mobile phones, tablets, and car displays. It is a challenge to ensure good layout and usability at various resolutions and aspect ratios. In embedded systems, such as simple home appliances or industrial control panels, processor performance and memory are often limited; this makes complex menu animations or high-resolution graphics difficult to implement, which may result in slow interface response or reduced display quality. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a menu generation method and system based on a liquid crystal screen to solve at least one of the above technical problems.

[0005] To achieve the above object, a menu generation method based on a liquid crystal screen includes the following steps:

[0006] Step S1: Obtain device hardware parameters and system resource data; perform device capability evaluation based on the device hardware parameters and system resource data, thereby generating device performance index data; perform multi-dimensional device characteristic analysis based on the device performance index data, thereby obtaining device characteristic vector data;

[0007] Step S2: performing device type matching according to a preset device mode library and device characteristic vector data, thereby obtaining device prototype mode data; performing adaptive menu complexity division according to the device prototype mode data, thereby generating menu complexity level data;

[0008] Step S3: performing responsive layout calculation according to the screen size and resolution information in the device hardware parameters, thereby generating adaptive layout template data; optimizing the menu element layout of the adaptive layout template data, thereby obtaining multi-size screen adaptation solution data;

[0009] Step S4: obtaining the current real-time power consumption data of the device; formulating a power management strategy according to the real-time power consumption data, and generating dynamic power consumption control parameter data; selecting a menu rendering mode based on the dynamic power consumption control parameter data, and obtaining energy-saving rendering solution data;

[0010] Step S5: performing menu element generation processing according to the menu complexity level data, the multi-size screen adaptation solution data, and the energy-saving rendering solution data to generate initial menu structure data; performing resource occupancy evaluation on the initial menu structure data to obtain resource occupancy index data; performing menu optimization processing based on the resource occupancy index data, and performing rendering effect simulation and user experience evaluation to obtain user experience score data;

[0011] Step S6: Determine whether the preset threshold is met based on the user experience score data. If not, return to step S5 for iterative optimization. If the preset threshold is met, generate final menu rendering instruction data to complete the adaptive menu generation based on the LCD screen.

[0012] The present invention can accurately evaluate the performance indicators of the device by comprehensively considering the device hardware parameters and system resource data, and provide a scientific basis for subsequent menu design; multi-dimensional device characteristic analysis ensures a comprehensive understanding of the device performance, so that the generated menu can maximize the potential of the device. By matching the device characteristic vector data with the preset device mode library, the menu prototype that best suits the user's device can be quickly determined; the adaptive menu complexity division ensures that the menu design not only meets the functional requirements, but also does not exceed the device processing capacity, providing a balanced user experience. The adaptive layout template data generated according to the device screen size and resolution information ensures that the menu can maintain a good display effect on screens of different sizes, and the menu element layout optimization further improves the aesthetics and ease of use of the interface. By real-time monitoring of power consumption data and formulating power management strategies, the energy consumption of the device can be optimized and the battery life can be extended; the selection of energy-saving rendering schemes further reduces the power consumption of menu operations, which is suitable for mobile devices that are sensitive to power. Taking into account the menu complexity, screen adaptation and energy-saving requirements, the generated initial menu structure data can meet a variety of usage scenarios, resource occupancy evaluation and optimization processing ensure the smoothness of menu operations, and user experience evaluation helps improve user satisfaction. The quality of menu design is judged by user experience scoring, and iterative optimization is performed based on the scoring results. This method ensures that the final menu design can meet the actual needs and expectations of users. The generated final menu rendering instruction data provides the device with an efficient, easy-to-use and energy-saving menu system. Overall, this adaptive menu generation method achieves full utilization of device performance and meticulous care for user experience through an intelligent design process; it not only improves the adaptability of the menu and user satisfaction, but also takes into account the energy consumption of the device. It is an efficient, environmentally friendly and user-friendly design method.

[0013] The present invention also provides a menu generation system based on a liquid crystal screen, which is used to execute the above-mentioned menu generation method based on a liquid crystal screen. The menu generation system based on a liquid crystal screen comprises:

[0014] The device performance analysis module is used to obtain device hardware parameters and system resource data; perform device capability evaluation based on the device hardware parameters and system resource data to generate device performance index data; perform multi-dimensional device characteristic analysis based on the device performance index data to obtain device characteristic vector data;

[0015] The menu complexity adaptation module is used to match the device type according to the preset device mode library and the device characteristic vector data, so as to obtain the device prototype mode data; and to perform adaptive menu complexity division according to the device prototype mode data, so as to generate menu complexity level data;

[0016] The multi-size screen layout module is used to perform responsive layout calculations based on the screen size and resolution information in the device hardware parameters, thereby generating adaptive layout template data; optimizing the menu element layout of the adaptive layout template data, thereby obtaining multi-size screen adaptation solution data;

[0017] Dynamic power consumption optimization module, used to obtain the current real-time power consumption data of the device; formulate power management strategy based on the real-time power consumption data, generate dynamic power consumption control parameter data; select menu rendering mode based on the dynamic power consumption control parameter data, and obtain energy-saving rendering solution data;

[0018] The menu generation and evaluation module is used to generate menu elements according to the menu complexity level data, the multi-size screen adaptation solution data and the energy-saving rendering solution data, and generate initial menu structure data; perform resource occupancy evaluation on the initial menu structure data to obtain resource occupancy index data; perform menu optimization based on the resource occupancy index data, and perform rendering effect simulation and user experience evaluation to obtain user experience score data;

[0019] The iterative optimization decision module is used to determine whether the preset threshold is met based on the user experience score data. If not, it returns to step S5 for iterative optimization; if the preset threshold is met, it generates the final menu rendering instruction data to complete the adaptive menu generation based on the LCD screen.

[0020] The present invention provides a comprehensive evaluation of device performance by collecting and analyzing device hardware parameters and system resource data. The generated device performance index data and device characteristic vector data provide a scientific basis for subsequent menu design and adaptation, ensuring the pertinence and effectiveness of the design. Based on the device characteristic vector data and the preset device mode library, the module can match the device type and determine the complexity level of the menu; this helps to create menus suitable for different device performance and characteristics, thereby providing a more personalized and optimized user experience. The device screen size and resolution data are used to generate an adaptive layout template and optimize the layout of menu elements; this ensures that the menu can maintain a good display effect and user interaction experience on screens of different sizes. By real-time monitoring of the power consumption data of the device and formulating corresponding power management strategies, the module helps to reduce the energy consumption of the device and extend the battery life; the formulation of energy-saving rendering schemes further optimizes the display performance of the menu, achieving energy consumption reduction while ensuring user experience. Combined with menu complexity, screen adaptation and power consumption optimization data, the module is responsible for generating the initial menu structure, and evaluating and optimizing its resource occupancy; by simulating and evaluating rendering effects and user experience, the module ensures the practicality and efficiency of menu design. The decision on whether to iterate and optimize the menu design is based on the user experience score data; if the score does not meet the standard, it is returned for optimization; if the threshold is met, the final menu rendering instructions are generated. This decision-making process ensures that the final product can meet the actual needs and expectations of users. Overall, the effect of this process is to provide a systematic and intelligent approach to design and optimize adaptive menus. It not only ensures the performance and user experience of the menu on different devices and usage scenarios, but also optimizes energy efficiency through real-time monitoring and dynamic adjustment. Through this iterative and decision-making mechanism, the design can be continuously improved until the best effect is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0022] Figure 1 The figure is a schematic diagram of the steps of the menu generation method based on the liquid crystal screen of the present invention;

[0023] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0024] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 The present invention provides a menu generation method based on a liquid crystal screen, the method comprising the following steps:

[0029] Step S1: Obtain device hardware parameters and system resource data; perform device capability evaluation based on the device hardware parameters and system resource data, thereby generating device performance index data; perform multi-dimensional device characteristic analysis based on the device performance index data, thereby obtaining device characteristic vector data;

[0030] Step S2: performing device type matching according to a preset device mode library and device characteristic vector data, thereby obtaining device prototype mode data; performing adaptive menu complexity division according to the device prototype mode data, thereby generating menu complexity level data;

[0031] Step S3: performing responsive layout calculation according to the screen size and resolution information in the device hardware parameters, thereby generating adaptive layout template data; optimizing the menu element layout of the adaptive layout template data, thereby obtaining multi-size screen adaptation solution data;

[0032] Step S4: obtaining the current real-time power consumption data of the device; formulating a power management strategy according to the real-time power consumption data, and generating dynamic power consumption control parameter data; selecting a menu rendering mode based on the dynamic power consumption control parameter data, and obtaining energy-saving rendering solution data;

[0033] Step S5: performing menu element generation processing according to the menu complexity level data, the multi-size screen adaptation solution data, and the energy-saving rendering solution data to generate initial menu structure data; performing resource occupancy evaluation on the initial menu structure data to obtain resource occupancy index data; performing menu optimization processing based on the resource occupancy index data, and performing rendering effect simulation and user experience evaluation to obtain user experience score data;

[0034] Step S6: Determine whether the preset threshold is met based on the user experience score data. If not, return to step S5 for iterative optimization. If the preset threshold is met, generate final menu rendering instruction data to complete the adaptive menu generation based on the LCD screen.

[0035] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a menu generation method based on a liquid crystal screen of the present invention. In this example, the menu generation method based on a liquid crystal screen includes the following steps:

[0036] Step S1: Obtain device hardware parameters and system resource data; perform device capability evaluation based on the device hardware parameters and system resource data, thereby generating device performance index data; perform multi-dimensional device characteristic analysis based on the device performance index data, thereby obtaining device characteristic vector data;

[0037] The embodiment of the present invention obtains hardware parameters: processor information (processor model, number of cores, main frequency and other parameters), memory information, storage information, display information and battery information; obtains system resource data: CPU usage, collects current CPU usage, including peak and average values; memory usage: obtains current memory usage, including free memory and used memory; storage usage: obtains current storage space usage and I / O read and write rates. Analyze the computing power of the processor, including single-core and multi-core performance, and multi-tasking capabilities; evaluate the bandwidth and access delay of the memory, and determine the stability in a multi-tasking environment; analyze the read and write performance and I / O operation response time of the storage device; evaluate the battery life under the current power level and health status; analyze the maximum data transmission capacity and stability under the current network conditions. Based on various hardware and system resource parameters, the overall performance score of the computing device is comprehensively calculated; and data sets of various indicators are output, including processor performance, memory performance, storage performance, display performance, battery performance and network performance. Based on the equipment performance index data, principal component analysis (PCA) or cluster analysis is performed to extract the main performance characteristics of the equipment; the extracted main characteristics are expressed in vector form to generate equipment characteristic vector data as input for subsequent steps.

[0038] Step S2: performing device type matching according to a preset device mode library and device characteristic vector data, thereby obtaining device prototype mode data; performing adaptive menu complexity division according to the device prototype mode data, thereby generating menu complexity level data;

[0039] The embodiment of the present invention establishes a pattern library containing different device categories (such as smart phones, tablets, smart watches, etc.), each type corresponds to different usage scenarios and hardware configurations; the device feature vector is matched with the preset pattern in the pattern library, the most similar device type is identified, and the device prototype pattern data is generated. Based on the device type and its hardware capabilities, the complexity carrying capacity of the device in terms of UI presentation is evaluated; according to the data of the device prototype pattern, menus of different complexity levels (such as simple, medium, and complex) are defined, and menu complexity level data is generated.

[0040] Step S3: performing responsive layout calculation according to the screen size and resolution information in the device hardware parameters, thereby generating adaptive layout template data; optimizing the menu element layout of the adaptive layout template data, thereby obtaining multi-size screen adaptation solution data;

[0041] The embodiment of the present invention uses the screen information in the device hardware parameters to calculate a suitable responsive layout template; generates adapted layout template data according to different screen sizes to ensure good display effects on different screens. According to the layout template, set the arrangement, size, spacing and other parameters of the menu elements; optimize the layout of the menu elements for different screen sizes and resolutions, and generate multi-size screen adaptation solution data.

[0042] Step S4: obtaining the current real-time power consumption data of the device; formulating a power management strategy according to the real-time power consumption data, and generating dynamic power consumption control parameter data; selecting a menu rendering mode based on the dynamic power consumption control parameter data, and obtaining energy-saving rendering solution data;

[0043] The embodiment of the present invention collects the power consumption data of the device in real time, including the current battery consumption rate and the power consumption of each component; analyzes the real-time power consumption data, evaluates the current energy consumption status, and formulates corresponding power management strategies in combination with the remaining battery power and health status; generates dynamic power consumption control parameter data to optimize power usage and extend the battery life of the device. Based on the power consumption control parameters, select the appropriate menu rendering mode (such as high-performance mode, power saving mode); generate energy-saving rendering solution data to reduce the power consumption of graphics rendering when the battery is low.

[0044] Step S5: performing menu element generation processing according to the menu complexity level data, the multi-size screen adaptation solution data, and the energy-saving rendering solution data to generate initial menu structure data; performing resource occupancy evaluation on the initial menu structure data to obtain resource occupancy index data; performing menu optimization processing based on the resource occupancy index data, and performing rendering effect simulation and user experience evaluation to obtain user experience score data;

[0045] The embodiment of the present invention generates initial menu structure data based on menu complexity level data and screen adaptation solution data to ensure that the menu matches the device capabilities. The initial menu structure data is evaluated for resource occupancy, including the occupancy of CPU, memory, storage, etc.; resource usage indicators are output as a basis for menu optimization. Based on the resource occupancy indicators, menu elements are optimized, such as reducing unnecessary animation effects or compressing graphic resources; the menu's response speed, fluency and user experience are evaluated by simulating rendering effects; user experience evaluation is performed, and user experience score data is generated.

[0046] Step S6: Determine whether the preset threshold is met based on the user experience score data. If not, return to step S5 for iterative optimization. If the preset threshold is met, generate final menu rendering instruction data to complete the adaptive menu generation based on the LCD screen.

[0047] The embodiment of the present invention determines whether the preset threshold standard is met based on the user experience score data. If the score does not meet the standard, it returns to step S5 and performs menu optimization again. Once the user experience score meets the preset threshold, the final menu rendering instruction data is generated to complete the adaptive menu generation based on the LCD screen; the rendering instruction data is sent to the device to ensure that the menu can be displayed correctly and respond to user operations.

[0048] The present invention can accurately evaluate the performance indicators of the device by comprehensively considering the device hardware parameters and system resource data, and provide a scientific basis for subsequent menu design; multi-dimensional device characteristic analysis ensures a comprehensive understanding of the device performance, so that the generated menu can maximize the potential of the device. By matching the device characteristic vector data with the preset device mode library, the menu prototype that best suits the user's device can be quickly determined; the adaptive menu complexity division ensures that the menu design not only meets the functional requirements, but also does not exceed the device processing capacity, providing a balanced user experience. The adaptive layout template data generated according to the device screen size and resolution information ensures that the menu can maintain a good display effect on screens of different sizes, and the menu element layout optimization further improves the aesthetics and ease of use of the interface. By real-time monitoring of power consumption data and formulating power management strategies, the energy consumption of the device can be optimized and the battery life can be extended; the selection of energy-saving rendering schemes further reduces the power consumption of menu operations, which is suitable for mobile devices that are sensitive to power. Taking into account the menu complexity, screen adaptation and energy-saving requirements, the generated initial menu structure data can meet a variety of usage scenarios, resource occupancy evaluation and optimization processing ensure the smoothness of menu operations, and user experience evaluation helps improve user satisfaction. The quality of menu design is judged by user experience scoring, and iterative optimization is performed based on the scoring results. This method ensures that the final menu design can meet the actual needs and expectations of users. The generated final menu rendering instruction data provides the device with an efficient, easy-to-use and energy-saving menu system. Overall, this adaptive menu generation method achieves full utilization of device performance and meticulous care for user experience through an intelligent design process; it not only improves the adaptability of the menu and user satisfaction, but also takes into account the energy consumption of the device. It is an efficient, environmentally friendly and user-friendly design method.

[0049] Preferably, step S1 comprises the following steps:

[0050] Step S11: Obtain device hardware parameters and system resource data, wherein the device hardware parameters include CPU model and core number, GPU model and core number, main frequency data, display screen size, resolution, refresh rate data and battery capacity data, wherein the system resource data includes current CPU usage data, current memory occupancy data, current battery power data and current storage space usage data;

[0051] Step S12: Evaluate device capabilities based on device hardware parameters and system resource data to generate device performance indicator data, where the device capability evaluation includes processing capability evaluation, graphics rendering capability evaluation, multi-tasking capability evaluation, data reading and writing speed evaluation, display quality evaluation, and battery life evaluation;

[0052] Step S13: performing device characteristic analysis on the device performance indicator data based on CPU architecture characteristics, GPU rendering characteristics, memory management characteristics, storage access characteristics, power management characteristics, and display characteristics, thereby obtaining device characteristic analysis data;

[0053] Step S14: construct a multidimensional characteristic vector according to the device characteristic analysis data, and normalize the multidimensional characteristic vector to generate device characteristic vector data.

[0054] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0055] Step S11: Obtain device hardware parameters and system resource data, wherein the device hardware parameters include CPU model and core number, GPU model and core number, main frequency data, display screen size, resolution, refresh rate data and battery capacity data, wherein the system resource data includes current CPU usage data, current memory occupancy data, current battery power data and current storage space usage data;

[0056] The embodiment of the present invention uses system APIs (such as sysctl, / proc / cpuinfo, etc.) to obtain the processor model, the number of physical cores, and the number of logical cores; obtains the GPU model and the number of computing cores through drivers or system APIs (such as lspci, dxdiag, etc.); obtains the operating main frequency of the CPU and GPU, including the base frequency and dynamic frequency (such as the maximum turbo frequency); obtains the size, resolution (width × height, in pixels) and refresh rate (Hz) of the display screen; obtains the design capacity and current maximum capacity (mAh) of the battery through system APIs (such as upower, acpi). Real-time acquisition of current CPU usage data, including total usage and usage by core; obtains the current memory usage of the system, including used memory, available memory, and swap usage; obtains the current percentage of remaining battery power, as well as the charge and discharge status of the battery; obtains the total capacity, used space, and available space of the storage device, as well as the current I / O operation rate (such as read and write speed).

[0057] Step S12: Evaluate device capabilities based on device hardware parameters and system resource data to generate device performance indicator data, where the device capability evaluation includes processing capability evaluation, graphics rendering capability evaluation, multi-tasking capability evaluation, data reading and writing speed evaluation, display quality evaluation, and battery life evaluation;

[0058] The embodiment of the present invention evaluates the single-core and multi-core processing capabilities of the device by analyzing the CPU main frequency, core number and current usage rate, which can be measured using benchmark test data or actual computing tasks (such as floating-point operations, integer operations); analyzes the thread management capability of the CPU, and evaluates the multi-task processing capability of the device by simulating the concurrent execution of multi-threaded tasks. According to the model, core number and main frequency of the GPU, combined with the capacity and bandwidth of the video memory, the graphics rendering capability of the device is evaluated, which can be quantified using benchmark test data (such as 3DMark, GFXBench); by simulating real-time 3D rendering scenes, measuring the frame rate (FPS) and rendering delay, the performance of the device in complex graphics scenes is evaluated. By simulating a multi-task environment, the response time and task switching overhead of the CPU and memory when processing multiple tasks are analyzed; the allocation efficiency, memory bandwidth and access delay of the memory are evaluated, and the system stability under high memory occupancy is observed through stress testing. The performance of the memory is evaluated by measuring the read and write speed of the storage device (such as using the dd command or I / O benchmark tool); under multi-task read and write conditions, the concurrent read and write capability of the storage device and the response speed of I / O operations are evaluated. By adjusting the resolution and refresh rate, the smoothness of graphics rendering and image quality are measured to evaluate display performance; the color performance of the display is evaluated by combining the color depth and brightness parameters of the display. By simulating different usage scenarios (such as high-performance mode and energy-saving mode), the battery consumption rate is measured to estimate the battery life in various usage scenarios; combined with the power management strategy of the device, the power consumption management capability of the device in different usage scenarios is evaluated. The above evaluation results are summarized to calculate the comprehensive performance indicators of the device, including processor performance, graphics performance, memory management performance, storage performance, display performance and battery life; the data of various performance indicators are output to form a complete data set, which provides a basis for subsequent analysis.

[0059] Step S13: performing device characteristic analysis on the device performance indicator data based on CPU architecture characteristics, GPU rendering characteristics, memory management characteristics, storage access characteristics, power management characteristics, and display characteristics, thereby obtaining device characteristic analysis data;

[0060] The embodiment of the present invention analyzes the instruction set support (such as x86, ARM, etc.) and architectural features (such as pipeline depth, cache level) of the CPU to evaluate its processing capabilities for different task types; combines the main frequency and power consumption data to calculate the energy efficiency ratio of the CPU to determine its performance in high-performance and low-power consumption scenarios. Analyze the GPU's support for major graphics APIs (such as OpenGL, Vulkan, DirectX, etc.) to evaluate its compatibility and optimization effects; evaluate the GPU's shader performance, especially its performance in processing complex light and shadow effects and physical calculations. Analyze the memory hierarchy (such as L1 / L2 / L3 cache, main memory) to evaluate its performance in different access modes; combine the bandwidth and latency data of the memory controller to evaluate the overall efficiency of the memory system; analyze the interface type (such as SATA, NVMe) and protocol support of the storage device to evaluate its data transmission performance. Evaluate the performance consistency of the storage device under continuous read and write and random read and write conditions to determine its performance under different workloads. Analyze the DVFS mechanism of the device to evaluate its power management efficiency under different load conditions; combine the device's operating system and firmware to evaluate its power consumption and performance balance under different power modes. By analyzing the impact of resolution and refresh rate on device performance, the optimal settings for display performance are evaluated; screen types (such as LCD, OLED) and their characteristics in terms of color performance, contrast, and response time are analyzed. The results of various feature analyses are summarized to form a device feature analysis data set, which serves as the basis for constructing a multi-dimensional feature vector.

[0061] Step S14: construct a multidimensional characteristic vector according to the device characteristic analysis data, and normalize the multidimensional characteristic vector to generate device characteristic vector data.

[0062] The embodiment of the present invention selects the main features representing the performance of the device (such as CPU performance, GPU performance, memory bandwidth, etc.) from the device characteristic analysis data to construct a multi-dimensional characteristic vector; combines the various characteristics according to a unified scale (such as performance score, power consumption score, etc.) to form a complete characteristic vector. Select a suitable normalization method (such as Min-Max normalization, Z-score standardization) so that the characteristic vectors of different dimensions have the same magnitude to facilitate subsequent analysis; normalize the various components of the characteristic vector to ensure that the various dimensions of the characteristic vector are comparable on the same scale; generate normalized device characteristic vector data as input for subsequent device type matching and menu complexity division.

[0063] The present invention can fully understand the current hardware configuration and operating status of the device by collecting detailed device hardware parameters and system resource data; this provides the necessary raw data for subsequent performance evaluation and feature analysis, ensuring the accuracy and reliability of the evaluation results. Performing multi-faceted capability evaluation on the device, including processing capability, graphics rendering capability, etc., can reveal the performance level of the device in various aspects; this step helps to identify the advantages and limitations of the device, provides a basis for the complexity and function of menu design, and ensures that the menu design can both show the performance of the device and not exceed the processing capability. In-depth analysis of the device's CPU architecture, GPU rendering capability and other characteristics can provide a more detailed understanding of the performance of the device in actual applications. This analysis helps to optimize the menu design so that it can adapt to the specific characteristics of different devices, such as memory management and storage access speed, thereby improving the overall performance and user experience. Constructing a multidimensional feature vector and performing normalization processing can convert performance indicators of different dimensions into comparable formats; this step helps to standardize the performance evaluation of different devices, so that the device feature vector data can be used to accurately match the preset device mode library, providing a scientific and quantitative basis for generating adaptive menus. In general, the effect of these steps is to provide a systematic method to evaluate and understand the comprehensive performance and characteristics of the device. Through this method, accurate device performance indicators and characteristic vector data can be generated, providing a solid foundation for creating adaptive menus that adapt to different device performance and user needs; this method not only improves the adaptability and personalization level of menu design, but also helps to improve user satisfaction and device usage efficiency.

[0064] Preferably, step S12 comprises the following steps:

[0065] Step S121: performing a processing capability evaluation based on the CPU architecture type, cache size, and instruction set support according to the CPU model and core number, core number, and main frequency data in the device hardware parameters, and calculating the CPU performance score, thereby obtaining CPU processing capability index data;

[0066] The embodiment of the present invention determines the architecture type (such as ARM, x86, x64, etc.) and instruction set support of the CPU. Different architectures have different efficiency and energy efficiency performance when processing tasks; weights are assigned to different CPU architectures, and the more advanced the architecture performance, the higher the weight. Obtain the size of the L1, L2, and L3 caches and their impact on the processing speed. Large caches usually mean faster data access speeds; based on the cache size and level, calculate its contribution to the overall CPU performance and include it in the final CPU processing power index. Include the main frequency (GHz) and the number of cores in the calculation, and calculate the performance scores of single-core and multi-core through benchmarks or known performance formulas; consider the weight of multi-core performance according to the application scenario. For example, for multi-tasking, the weight of multi-core performance will be higher; combine the above evaluation results to generate a comprehensive CPU performance score as the CPU processing power index data.

[0067] Step S122: Based on the device hardware parameters and the display screen size, resolution, refresh rate data in the system resource data, as well as the GPU model and core number, a graphics rendering capability evaluation based on the memory bandwidth and the supported graphics API is performed to obtain GPU rendering capability index data;

[0068] The embodiment of the present invention analyzes the video memory bandwidth of the GPU. The higher the bandwidth, the stronger the graphics data processing capability. The GPU's support for mainstream graphics APIs (such as DirectX, OpenGL, Vulkan, etc.) is determined. The support of different APIs will affect graphics rendering performance and compatibility. High resolution and high refresh rate will increase the rendering burden of the GPU, so higher performance is required to maintain smooth graphics output. The expected performance of the GPU at a specific resolution and refresh rate is calculated to generate a corresponding rendering capability score. Comprehensive factors such as memory bandwidth, graphics API support, display resolution and refresh rate are considered to calculate the GPU's comprehensive rendering capability score and generate GPU rendering capability index data.

[0069] Step S123: performing a multi-task processing capability evaluation based on memory speed, number of channels, and available memory size according to the current CPU usage data and the current memory occupancy data, thereby obtaining multi-task processing capability index data;

[0070] The embodiment of the present invention acquires and analyzes the current CPU usage and memory occupancy in real time to evaluate the multi-tasking capability of the system; analyzes the memory speed (MHz) and the number of channels (such as single channel and dual channel) to evaluate the impact of data transmission efficiency on multi-tasking. By simulating the concurrent execution of multiple tasks, the response time and stability of the system under high load are evaluated; and the multi-tasking capability index data is generated by combining the CPU usage, memory speed and number of channels.

[0071] Step S124: Based on the current memory occupancy rate data and the current storage space usage data, data read and write speed evaluation is performed based on the storage medium type, interface speed, and remaining available space, thereby obtaining read and write capacity index data;

[0072] The embodiments of the present invention identify the type of storage media used by the device (such as SSD, HDD, NVMe). Different media have different read and write speeds and response times. The speed of the storage interface (such as SATA, PCIe) is analyzed. Different interface types affect the data transmission speed. The current storage space utilization rate is evaluated. The less remaining space, the lower the read and write speed may be. The actual read and write speed of the storage device is measured through benchmark tests (such as Sequential Read / Write and Random Read / Write tests). The storage medium type, interface speed and current usage are combined to generate a comprehensive read and write capability score for the storage device as read and write capability indicator data.

[0073] Step S125: performing display quality evaluation based on pixel density, color range, and touch response speed according to display screen size, resolution, and refresh rate data in the device hardware parameters, thereby obtaining display quality index data;

[0074] The embodiment of the present invention calculates the pixel density (PPI) according to the size and resolution of the display screen. A higher PPI generally provides a clearer display effect; the color range of the display screen (such as sRGB coverage) is analyzed. The wider the color range, the richer the color expression. By testing the touch response time of the screen, the user's fluency and feedback speed during use are evaluated. Combining pixel density, color range and touch response speed, a display quality score is generated as display quality indicator data.

[0075] Step S126: Based on the battery capacity data and the current battery power data, a battery life evaluation is performed based on power consumption efficiency, charging speed, and battery health status, thereby obtaining battery life indicator data;

[0076] The embodiment of the present invention evaluates the actual capacity and health of the battery. The better the health, the longer the battery life. By analyzing the power consumption management strategy and actual power consumption of the device, the battery life performance in different usage scenarios is evaluated. The charging time from 0% to 100% power is tested to evaluate the compensation effect of charging speed on battery life. The battery capacity, power consumption efficiency and charging speed are combined to generate battery life index data.

[0077] Step S127: The CPU processing capability index data, GPU rendering capability index data, multi-tasking capability index data, reading and writing capability index data, display quality index data and battery life index data are weighted and integrated based on the device type and expected usage scenario to obtain device performance index data.

[0078] The embodiment of the present invention determines the type of device (such as a smart phone, tablet computer, laptop computer, etc.) based on the device hardware parameters; identifies the main usage scenarios of the device (such as games, office, multimedia, etc.), and different scenarios have different requirements for various performances. According to the device type and usage scenario, different weights are assigned to indicators such as CPU processing power, GPU rendering power, multi-tasking processing power, reading and writing capabilities, display quality, and battery life; the performance indicator data are weighted and summed according to the weights to obtain a comprehensive device performance score; and the comprehensive performance indicator data of the device is generated for device characteristic analysis and adaptive menu generation in subsequent steps.

[0079] The present invention can accurately quantify the processing power of the CPU by evaluating the CPU architecture, cache size and instruction set, and calculating the CPU performance score; this helps determine the device's ability to perform complex calculations and multi-threaded tasks, and provides performance basis for applications that require a lot of calculations. Evaluating the rendering capabilities of the GPU, including memory bandwidth and graphics API support, can determine the device's ability to process high-quality graphics and animations; this is particularly important for graphics-intensive applications such as games and professional graphic design software. Evaluating multi-tasking capabilities based on CPU and memory usage data helps understand the performance of the device when multiple applications are running simultaneously, which is crucial to ensuring a smooth experience for users when using multiple applications. Evaluating the read and write speed of the storage system, including the storage medium type and interface speed, can ensure the efficiency of data access, which is of great significance for applications that require fast reading and writing of large amounts of data, such as database management and large file transfer. By evaluating the pixel density, color range and touch response speed of the display screen, the quality of the visual experience provided by the device can be determined, which is crucial to improving the aesthetics and ease of use of the user interface and meeting the needs of professional visual work. Evaluating battery life, including power efficiency and health, ensures how long the device can last without being plugged in, which is especially important for mobile devices and scenarios where the device needs to be used for a long time and cannot be charged immediately. Weighting all performance indicator data and taking into account device type and expected usage scenarios can generate a comprehensive device performance indicator, which helps to balance different performance aspects when designing applications and menus to achieve the best user experience. Overall, the effect of these steps is to provide a comprehensive and detailed approach to evaluating various performance indicators of the device; this approach not only helps to understand how the device performs under different workloads, but also provides a basis for the personalized design of applications and menus, ensuring an efficient, smooth and user-friendly experience on different devices.

[0080] Preferably, step S2 comprises the following steps:

[0081] Step S21: standardizing the device characteristic vector data and performing format unification based on a preset device mode library, thereby generating device characteristic standard data;

[0082] Step S22: Calculate the similarity between the device characteristic standard data and the templates in the preset device pattern library by using the K nearest neighbor algorithm, so as to obtain device type matching result data;

[0083] Step S23: determining the most matching device prototype pattern according to the device type matching result data, thereby generating device prototype pattern data, wherein if there are multiple similar matching results when matching the device prototype patterns, the one with the highest similarity is selected as the primary pattern, and the others are selected as secondary patterns;

[0084] Step S24: determining the performance level of the device according to the device prototype mode data and the device performance indicator data, thereby generating device performance level data, wherein the performance level of the device includes high performance, medium performance and low performance;

[0085] Step S25: formulating a preliminary menu complexity strategy based on the device performance level data and the device prototype mode data, and adjusting the preliminary menu complexity strategy based on the real-time status of the device, thereby obtaining menu complexity strategy data;

[0086] Step S26: Map the menu complexity policy data to the complexity level, thereby generating menu complexity level data.

[0087] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0088] Step S21: standardizing the device characteristic vector data and performing format unification based on a preset device mode library, thereby generating device characteristic standard data;

[0089] The embodiment of the present invention standardizes the characteristic values ​​in the device characteristic vector so that the data range is between 0 and 1; this step ensures the comparability between different characteristics, for example, various indicators such as processing power, graphics rendering power, and battery life are standardized to the same scale; outliers or missing values ​​are identified and processed, and appropriate statistical methods (such as mean filling or median filling) are used for processing to ensure the integrity and accuracy of the data. According to the format requirements of the preset device mode library, the device characteristic vector data is subjected to necessary format conversion (such as data type, field order, etc.) to ensure consistency with the template data format in the device mode library; if there are structural differences between the device characteristic vector data and the data in the device mode library, structural adjustments are made, such as adding or deleting certain fields, so that the two have the same dimensions and structure. The standardized and format-consistent device characteristic vector data is saved as device characteristic standard data, which serves as the basic data for device type matching in subsequent steps.

[0090] Step S22: Calculate the similarity between the device characteristic standard data and the templates in the preset device pattern library by using the K nearest neighbor algorithm, so as to obtain device type matching result data;

[0091] The embodiment of the present invention selects an appropriate K value according to the complexity of the device characteristic standard data and the size of the device pattern library. Usually, a smaller K value (such as 3 to 5) is selected to ensure high-precision matching; the Euclidean distance metric in the K nearest neighbor algorithm is used to calculate the distance between the device characteristic standard data and each template data in the device pattern library. The smaller the distance, the more similar the two are; based on the calculated distance, the templates in the device pattern library are sorted in order from small to large distance to obtain device type matching result data. A similarity score is assigned to each matching result, and a higher score indicates a higher matching degree; the top K matching results with the highest similarity are retained and stored as device type matching result data.

[0092] Step S23: determining the most matching device prototype pattern according to the device type matching result data, thereby generating device prototype pattern data, wherein if there are multiple similar matching results when matching the device prototype patterns, the one with the highest similarity is selected as the primary pattern, and the others are selected as secondary patterns;

[0093] In the embodiment of the present invention, in the device type matching results, the device mode with the highest similarity is selected as the primary device prototype mode. The primary mode is usually the mode that is closest to the device characteristics; the characteristic data of the primary mode is saved to generate preliminary device prototype mode data. If there are multiple matching results that are relatively close to the device characteristics (but the similarity is slightly lower than the primary mode), these modes are retained as secondary modes; as needed, the data of the secondary mode is integrated into the device prototype mode data as supplementary or backup information. The matching data of the primary mode and the secondary mode are integrated into the final device prototype mode data for subsequent performance level evaluation and menu complexity strategy formulation.

[0094] Step S24: determining the performance level of the device according to the device prototype mode data and the device performance indicator data, thereby generating device performance level data, wherein the performance level of the device includes high performance, medium performance and low performance;

[0095] The embodiment of the present invention defines the classification rules of the device performance level based on the device prototype mode data and the device performance indicator data; for example, the device performance level can be divided into three levels: high performance, medium performance and low performance; considering multiple performance indicators such as CPU processing power, GPU rendering power, multi-tasking processing power, etc., a comprehensive evaluation is performed through a preset threshold or weighted average method. The comprehensive performance score of the device is compared with the preset performance level standard to determine which performance level the device belongs to; for devices at the critical point, a detailed analysis is performed to ensure the accuracy of the performance level division. The performance level data of the device is generated based on the evaluation results as an important reference for the formulation of menu complexity strategies.

[0096] Step S25: formulating a preliminary menu complexity strategy based on the device performance level data and the device prototype mode data, and adjusting the preliminary menu complexity strategy based on the real-time status of the device, thereby obtaining menu complexity strategy data;

[0097] The embodiment of the present invention formulates a corresponding preliminary menu complexity strategy based on the device performance level data. For example, high-performance devices can support more complex menu animations and graphic elements, while low-performance devices should simplify the menu design; menu strategies of different complexity levels are designed in consideration of user experience, menu operation smoothness, and rational use of device performance. The real-time status data of the device (such as current CPU load, memory usage, power consumption, etc.) is obtained, and the impact of these data on menu rendering and operation is analyzed; according to the real-time status data, the preliminary formulated menu complexity strategy is fine-tuned, such as reducing the complexity of graphic special effects or simplifying animation effects, to ensure the smoothness of menu operation. The adjusted menu complexity strategy is saved as menu complexity strategy data to ensure that the device can provide the best user experience in different states.

[0098] Step S26: Map the menu complexity policy data to the complexity level, thereby generating menu complexity level data.

[0099] The embodiment of the present invention defines different complexity level mapping standards according to the menu complexity policy data. For example, the complexity can be divided into three levels: simple, medium, and complex. Each level corresponds to a different number of menu elements, animation effects, and graphics rendering requirements. Through the mapping algorithm, the various parameters in the menu complexity policy data are converted into specific complexity levels. The policy data is input into the mapping algorithm to obtain the corresponding complexity level, and further refined and adjusted according to the current status and performance level of the device. The final menu complexity level data is output as the basis for menu generation and rendering. The generated menu complexity level data is verified to ensure its rationality and applicability. According to the verification results, necessary adjustments are made to ensure that the device can flexibly adjust the menu complexity level according to actual conditions during operation.

[0100] The present invention performs standardized processing on device characteristic vector data to ensure that the data format is consistent with the preset device mode library, which helps to improve the comparability of data; this format consistency is the basis for achieving accurate device matching and ensures the fairness and accuracy of the evaluation process. The similarity between the device characteristic standard data and the device mode library template is calculated using the K nearest neighbor algorithm, and the device prototype closest to the current device characteristics can be quickly found. This method helps to achieve automatic matching of device types and provides a basis for subsequent menu design. The most matching device prototype mode is determined according to the matching results, and the one with the highest similarity is selected as the main mode among multiple similar matching results, which ensures that the menu design can be optimized for the prototype that best meets the current device characteristics, while retaining other modes as alternatives. The performance level of the device is determined according to the device prototype mode data and the device performance index data, and the device is divided into high performance, medium performance and low performance levels; this classification helps to customize different menu complexity strategies for devices of different performance levels, ensuring that the menu design not only meets the functional requirements but also adapts to the device capabilities. Based on the device performance level and prototype mode data, a menu complexity strategy is initially formulated and adjusted according to the real-time status of the device; this dynamic adjustment mechanism ensures that the menu design can adapt to the current operating environment and resource status of the device and improve the user experience. Mapping menu complexity strategy data to specific complexity levels to generate menu complexity level data provides clear guidance for menu design, allowing designers to adjust the number, hierarchy, and interaction methods of menu elements according to different complexity levels. Overall, the effect of these steps is to provide a systematic approach to evaluate device characteristics and performance, and formulate appropriate menu complexity strategies based on the evaluation results. This approach not only improves the adaptability and personalization of menu design, but also helps to provide an efficient, smooth, and user-friendly experience on different devices. Through dynamic adjustment and real-time optimization, it ensures that the user interface can maintain optimal performance even when device performance or status changes.

[0101] Preferably, step S3 comprises the following steps:

[0102] Step S31: extracting key display parameters of screen size, resolution and pixel density from device hardware parameters to generate screen characteristic data;

[0103] The embodiment of the present invention extracts the physical size of the screen (usually in inches) from the device hardware parameters for subsequent layout calculations; extracts the screen resolution data (such as 1920x1080 pixels), which is the basic data for layout calculations; and calculates or directly extracts the pixel density (PPI, Pixels Per Inch), which determines the clarity and delicacy of UI elements. Parameters such as screen size, resolution, and pixel density are integrated into a set of screen characteristic data and saved as screen characteristic data, which will be used to determine the layout grid and formulate subsequent responsive layout strategies.

[0104] Step S32: Divide the screen into a number of columns and rows according to the screen characteristic data, determine a basic grid system, and thereby generate basic layout grid data;

[0105] The embodiment of the present invention divides the screen into several rows and columns according to the screen characteristic data to form a basic grid system; for example, the number of rows and columns divided is determined according to the resolution and pixel density of the screen to ensure that the layout elements can have a good display effect at different resolutions; the proportion of each grid unit is determined according to the aspect ratio of the screen and common design standards. The golden ratio or other suitable proportions are usually used to ensure visual beauty. The data of the basic grid system is output to divide the screen into several grid units, which will serve as the basis for the arrangement of UI components in the layout.

[0106] Step S33: selecting a responsive layout strategy based on flow layout, elastic layout or grid layout according to the basic layout grid data and the device prototype mode data, thereby obtaining layout strategy data;

[0107] The embodiments of the present invention are applicable to scenarios with a large amount of content and the need for vertical scrolling. The grid units can be adaptively adjusted as the screen width changes. They are applicable to scenarios with a small amount of content but the need for dynamic adjustment according to the screen width. The layout elements can expand or shrink with the screen width. They are applicable to scenarios with a large amount of content and the need for neat arrangement. All the content will be arranged according to the predefined grid system. According to the device prototype mode data, the most suitable layout strategy is selected. For example, high-performance devices may be suitable for elastic layout, while low-performance devices may be suitable for streaming layout. According to the selected layout strategy, the corresponding layout strategy data is generated and saved for subsequent layout design.

[0108] Step S34: identifying key layout breakpoints based on layout change points under different screen widths according to the layout strategy data, thereby obtaining layout breakpoint data;

[0109] The embodiment of the present invention identifies key layout breakpoints based on the layout change characteristics under different screen widths. These breakpoints are usually points where the layout structure needs to be adjusted when the screen width changes significantly; these layout breakpoints are determined using preset breakpoint rules, such as common device sizes (mobile phones, tablets, desktops, etc.), and the identified key layout breakpoint data is saved as important reference data for subsequent layout design.

[0110] Step S35: designing a corresponding layout structure for each breakpoint interval based on the layout breakpoint data, and generating multi-breakpoint layout structure data, wherein the layout structure includes the arrangement, spacing, and size ratio of the menu items;

[0111] According to different layout breakpoints, the embodiments of the present invention design corresponding layout structures for each breakpoint interval, including the arrangement, spacing, size ratio, etc. of menu items; for example, on a narrow screen, menu items may need to be displayed in a stacked manner, while on a wide screen, they can be arranged side by side; ensuring that the layout of each breakpoint interval can adapt to different screen widths, maintaining visual consistency and functional usability. The layout structure data designed for each breakpoint is saved as multi-breakpoint layout structure data. This data will be used for subsequent layout optimization and adaptive UI component design.

[0112] Step S36: adjusting and optimizing the multi-breakpoint layout structure data based on element density and complexity according to the menu complexity level data, thereby obtaining layout structure optimization data;

[0113] The embodiment of the present invention optimizes the multi-breakpoint layout structure, taking into account the complexity level of the menu. A complex menu may need to be simplified on a small screen, while more details can be displayed on a large screen. The layout structure is optimized by adjusting the density of elements. For example, appropriate spacing is added to a dense menu layout, or more elements are filled in a sparse layout to improve the user experience; the optimized layout structure data is saved as layout structure optimization data for the design and layout combination of adaptive UI components.

[0114] Step S37: designing an adaptively scalable UI component according to the layout structure optimization data, and combining the layout structure with the UI component to obtain adaptive layout template data;

[0115] When designing UI components in the embodiments of the present invention, it is ensured that they can be adaptively scaled under different screen sizes and resolutions. For example, buttons, icons and texts are displayed in reduced size on a small screen and enlarged on a large screen, while maintaining good readability and operability; the size, position and spacing of the components are dynamically adjusted according to the screen width and layout strategy. The designed adaptive UI components are combined with the optimized layout structure so that the entire interface can be adaptively adjusted according to the changes in screen size; tests are performed under different screen sizes and resolutions to ensure that the combination of UI components and layout is good. If problems are found, adjustments and optimizations are made in a timely manner. The data after the final adaptive layout and UI component combination is saved as adaptive layout template data, which will be used for the final multi-size screen adaptation.

[0116] Step S38: Optimizing the menu element layout of the adaptive layout template data, thereby obtaining multi-size screen adaptation solution data.

[0117] The embodiment of the present invention optimizes and adjusts the layout of menu elements for screens of different sizes to ensure the best display effect and user operation experience; for example, more menu items can be displayed on a large screen, while the layout needs to be simplified on a small screen; the position and order of menu elements are adjusted according to the screen size, so that users can easily access the main functions on any screen size. The optimized layout of menu elements is saved as multi-size screen adaptation solution data to ensure that the menu can be perfectly adapted on different screen sizes.

[0118] The present invention extracts key display parameters, such as screen size, resolution and pixel density, and can accurately grasp the display capability of the screen. The generated screen characteristic data provides basic information for subsequent layout design, ensuring that the layout design can adapt to the specific display environment. By determining the basic grid system through screen characteristic data, a flexible and consistent layout framework can be created; the basic layout grid data enables the design elements to be arranged according to predefined paths and areas, improving the standardization and consistency of the layout. Selecting a streaming, elastic or grid layout strategy according to the basic grid and device prototype mode can ensure that the layout can adapt to different screen sizes and directions, and the layout strategy data guides how to dynamically adjust the size and position of elements as the screen size changes. Identifying key layout breakpoints allows designers to define different layout rules for different screen widths; the layout breakpoint data enables the interface to be significantly adjusted at a specific width to optimize the user experience. Designing a specific layout structure for each breakpoint interval can ensure the best display effect at different screen sizes; the multi-breakpoint layout structure data provides a set of flexible rules to adapt to various devices from small mobile phones to large desktop displays. Adjusting and optimizing the layout structure according to the menu complexity level can balance the density and complexity of elements on the interface; the layout structure optimization data ensures that the user interface remains clear and easy to navigate even on devices with limited display space. Designing adaptively scalable UI components and combining them with the layout structure can create an interface that can automatically adjust to different screen sizes; the adaptive layout template data improves the flexibility and reusability of interface elements. Further optimization of the adaptive layout template ensures that the menu elements can achieve the best layout effect on screens of different sizes; the multi-size screen adaptation solution data enables the menu design to seamlessly adapt to various devices and provide a consistent user experience. Overall, the effect of these steps is to provide a systematic approach to creating responsive and adaptive user interfaces. This approach not only ensures that the menu provides a good user experience on different devices and screen sizes, but also improves the flexibility and maintainability of the design. By finely controlling the layout breakpoints and optimizing the UI components, it can ensure that the user interface remains functional and beautiful even under extreme conditions.

[0119] Preferably, step S38 includes the following steps:

[0120] Step S381: Evaluate the display effect and interactive convenience of the adaptive layout template data under different screen sizes and directions to generate layout usability evaluation data;

[0121] The embodiment of the present invention displays an adaptive layout template on various screen sizes to simulate actual usage; ensure that the layout can maintain good visual effects on both small screens (such as smart phones) and large screens (such as tablets or desktop displays); test the layout in horizontal and vertical modes respectively, observe the adaptability and display effect of element arrangement, and ensure that the layout can maintain visual consistency in different directions; check whether the size, spacing, color and font of the elements are consistent under different screen sizes and directions to avoid visual imbalance or operational difficulties caused by screen differences. Simulate user interaction behaviors, such as clicks, slides and drag operations, evaluate the interactive convenience under different screen sizes and directions, ensure that the operation area is large enough and easy to access, especially on small-screen devices; test the response speed of interactive operations to ensure a smooth user experience on all devices, especially on low-performance devices, and ensure that the layout response and animation effects do not affect the operational experience. Record the evaluation results of the display effect and interactive convenience to generate layout usability evaluation data. Include the advantages and disadvantages of each screen size and direction for subsequent optimization and adjustment.

[0122] Step S382: fine-tuning and optimizing the adaptive layout template data based on element size, position and spacing according to the layout usability evaluation data, thereby obtaining layout template optimization data;

[0123] The embodiment of the present invention adjusts the size of elements according to the evaluation data to ensure that the elements are large enough to click on the small screen and do not appear too sparse on the large screen; in particular, for the main operation area, it is ensured that the user can conveniently perform touch operations. According to the evaluation results of the interactive convenience, the position of the elements is fine-tuned. For example, common functions are placed in more accessible locations to reduce the gesture span of user operations; ensure that the position of elements is reasonably adjusted in horizontal and vertical modes. For example, elements may need to be stacked in vertical mode, while they can be displayed side by side in horizontal mode. According to the screen size, the spacing between elements is adjusted to improve the clarity and aesthetics of the interface. The spacing may need to be reduced on a small screen to save space, while the spacing can be appropriately increased on a large screen to avoid an overcrowded interface. All adjustment and optimization results are saved as layout template optimization data to ensure that these data can be used in subsequent multi-size screen adaptation solutions to further improve the usability and aesthetics of the layout.

[0124] Step S383: Generate specific layout solutions corresponding to different screen sizes and directions based on the layout template optimization data, thereby obtaining multi-size screen adaptation solution data, where the specific layout solution includes menu element arrangement and size specifications in horizontal and vertical modes.

[0125] The embodiment of the present invention optimizes the data based on the layout template and designs a specific layout scheme in the horizontal screen mode. Considering the increase in screen width, it may be necessary to increase the number of columns or adjust the arrangement order of elements to ensure the convenience of operation in the horizontal screen mode; ensure that in the horizontal screen mode, the arrangement of menu elements is reasonable, the size of the elements matches the screen ratio, and provide the best user experience. For the vertical screen mode, optimize the vertical arrangement of elements to ensure that the menu items do not appear too crowded or scattered in the vertical screen, and adjust the height and width ratio of the elements to adapt to the visual and operating habits in the vertical screen. In the vertical screen mode, it may be necessary to stack the elements vertically to reduce the width occupation and ensure that the main functions are in the middle and upper part of the screen for easy touch. According to the screen size and orientation of the device, integrate the horizontal and vertical screen layout schemes to ensure that the best display effect and operating experience can be obtained on all devices; test on devices of different sizes and orientations to ensure the practicality and aesthetics of the layout scheme. Once a problem is found, fine-tune it in time to ensure the integrity and adaptability of the scheme. The final integrated and optimized layout scheme data is saved as multi-size screen adaptation scheme data; this data will be used for layout rendering in actual applications to ensure that the adaptive layout can be displayed and operated normally on all devices.

[0126] The present invention can identify possible problems and deficiencies in the layout by evaluating the display effect and interactive convenience of the adaptive layout template under different screen sizes and directions; the layout usability evaluation data provides key feedback information, which helps to understand the actual experience of users on different devices and provides a basis for subsequent optimization. Fine-tuning and optimizing the layout template according to the evaluation data, including adjusting the size, position and spacing of elements, can solve problems that may affect the user experience under a specific screen size or direction; such fine-tuning helps to improve the intuitiveness, usability and accessibility of the interface, ensuring that all users can easily interact with the interface elements. Based on the optimized layout template data, a specific layout scheme corresponding to different screen sizes and directions is generated, which can ensure that the display effect and interactive experience of the menu on various devices are optimal; the specific layout scheme includes the arrangement and size specifications of menu elements in horizontal and vertical modes, which provides a set of detailed guidelines for designers to ensure that a consistent user experience can be achieved on different devices. In general, the effect of these steps is to provide a detailed and systematic method to evaluate and optimize the adaptive layout template. In this way, it can be ensured that the menu design can provide a high-quality user experience under different devices and usage scenarios. This ongoing evaluation and optimization process helps improve user satisfaction, strengthen brand loyalty, and ensure that designs can adapt to changing technology and market needs.

[0127] Preferably, step S4 comprises the following steps:

[0128] Step S41: obtaining the current real-time power consumption data of the device, including the overall power consumption and the sub-item power consumption of each major hardware component;

[0129] The embodiment of the present invention monitors the battery power consumption of the device in real time and records the current overall power consumption data; this includes the total power consumed by the device during operation, which is used to evaluate the overall battery consumption. Obtain and record the current CPU power consumption, taking into account the usage of different cores and the current workload; obtain and record the GPU power consumption data, especially the power consumption under the graphics processing task load; obtain the screen power consumption data, combined with the current brightness, refresh rate and other parameters, to evaluate the impact of the screen display on power consumption; obtain the power consumption data of the storage device (such as SSD or HDD), especially the power consumption changes during high-load read and write operations; such as the real-time power consumption of key components such as wireless communication modules and cooling fans, and record their contribution to the overall power consumption.

[0130] Step S42: predicting the remaining usage time according to the battery capacity data and the current battery power data, thereby generating power prediction data;

[0131] The embodiment of the present invention calculates the total amount of currently available electrical energy based on the nominal battery capacity (such as mAh) of the device and the current battery power percentage. Combined with the real-time power consumption data and the current battery power data, the remaining use time of the device at the current power consumption level is predicted, and the battery life under the current use mode is estimated through a linear or nonlinear model; the predicted remaining use time is recorded as power prediction data for subsequent power management decisions.

[0132] Step S43: determining the current power state level of the device according to the power prediction data and the real-time power consumption data, thereby generating power state level data, wherein the power state levels include sufficient power, normal power, low power and extremely low power;

[0133] The embodiment of the present invention divides the power status of the device into four levels: sufficient, normal, low power and very low power according to the power prediction data; the division of each level can be based on the remaining usage time (such as sufficient: >4 hours, normal: 2-4 hours, low power: 1-2 hours, very low power: <1 hour). According to the remaining usage time, the power status level of the current device is determined, and the corresponding power status level data is generated for use by subsequent power management strategies.

[0134] Step S44: formulating a preliminary power management strategy including CPU frequency adjustment, GPU usage restriction and screen brightness adjustment based on the power state level data and the device performance level data, thereby generating preliminary power management strategy data;

[0135] In the low power or extremely low power state, the embodiment of the present invention considers reducing the maximum frequency of the CPU to reduce power consumption. For sufficient or normal power state, the frequency can be maintained or increased to maintain performance. In the low power state, the GPU performance mode is limited (such as switching to energy-saving mode) or the resolution rendering is reduced to reduce the battery consumption of graphics processing tasks. The screen brightness is automatically adjusted according to the power state level. For example, the brightness is reduced when the power is low to reduce display power consumption. The above adjustment strategy is recorded as preliminary power management strategy data to provide a basis for further optimization and execution.

[0136] Step S45: obtaining the user's current usage scenario data; and optimizing the preliminary power management strategy data to balance the energy saving requirements and the user experience according to the usage scenario data, thereby obtaining the power management strategy data;

[0137] The embodiment of the present invention determines the current usage scenario, such as entertainment, office, standby, etc., through sensor data (such as location, application usage, etc.) or user behavior patterns. The preliminary power management strategy is adjusted according to the user usage scenario. For example, in entertainment mode, even in a low-power state, it may be necessary to maintain a high screen brightness and GPU performance to ensure user experience; the optimized power management strategy is saved as the final power management strategy data to ensure that in different usage scenarios, both power can be saved and a good user experience can be provided.

[0138] Step S46: generating dynamic power consumption control parameter data including CPU maximum frequency limit, GPU performance mode selection and display refresh rate adjustment according to the power management policy data;

[0139] The embodiment of the present invention generates CPU maximum frequency limit parameters suitable for the current power state and usage scenario according to the power management strategy; selects a suitable GPU performance mode (such as energy-saving mode or high-performance mode), and generates corresponding dynamic power consumption control parameters; adjusts the screen refresh rate to save power consumption according to the usage scenario and power state, and generates corresponding control parameters; and records the above-generated control parameters as dynamic power consumption control parameter data for use when the system executes the power management strategy.

[0140] Step S47: Selecting a menu rendering mode based on the dynamic power consumption control parameter data to obtain energy-saving rendering solution data.

[0141] An embodiment of the present invention selects an appropriate menu rendering mode according to dynamic power consumption control parameters. For example, in energy-saving mode, a low-power rendering mode may be selected to reduce the use of animation effects and special effects. At the same time, it ensures that the selection of the rendering mode will not significantly affect the user experience, such as maintaining a smooth operating interface in scenarios requiring a high response speed. The selected rendering mode is recorded as energy-saving rendering scheme data for menu rendering during actual operation of the device.

[0142] The present invention can understand the energy consumption of the device in detail by obtaining the overall power consumption of the device and the sub-item power consumption of each major hardware component, which helps to identify components or processes with excessive energy consumption and provide data support for formulating effective power management strategies. The remaining usage time is predicted according to the battery capacity and the current power, so that both the user and the system can have a clear expectation of the battery life, which helps the user to plan the usage time and provides a basis for automatically adjusting the power management strategy. By determining the power state level, the battery status of the device can be classified, and different power management measures can be taken according to different levels, which helps to optimize the performance when the power is sufficient and extend the usage time when the power is low. According to the power state and the performance level of the device, a preliminary power management strategy is formulated, such as CPU frequency adjustment and GPU usage restriction, which helps to reasonably control energy consumption while ensuring the performance of the device. Obtaining the user's current usage scenario data and optimizing the power management strategy accordingly can balance energy saving requirements and user experience; for example, in scenarios where high performance is required, the energy consumption limit can be appropriately relaxed. Dynamic power consumption control parameters are generated according to the power management strategy, such as CPU maximum frequency limit and display refresh rate adjustment, and these parameters can be dynamically adjusted according to actual conditions to achieve the best balance between energy consumption and performance. By selecting the most appropriate menu rendering mode based on dynamic power control parameters, an energy-saving rendering solution can be obtained, which helps to reduce unnecessary energy consumption and extend battery life while ensuring the smoothness of the user interface. Overall, the effect of these steps is to provide an intelligent power management method that can dynamically adjust power management strategies and power control parameters based on the device's real-time power consumption data, power forecasts, usage scenarios, and performance requirements. This method not only helps to extend the battery life of the device and reduce energy consumption, but also ensures that users are provided with satisfactory performance and experience in different usage scenarios. Through continuous optimization and adjustment, the power management strategy can adapt to changes in user behavior and device status to achieve more intelligent and personalized power management.

[0143] Preferably, step S47 includes the following steps:

[0144] Step S471: determining a menu rendering mode according to the dynamic power consumption control parameter data and the menu complexity level data, thereby generating menu rendering mode selection data, wherein the menu rendering mode includes a full-function mode, a standard mode and a power saving mode;

[0145] The embodiments of the present invention are suitable for enabling all rendering functions, such as high-resolution graphics, complex animation effects, rich colors and graphic details, when the power is sufficient and the device performance is high; retaining relatively balanced graphics and animation effects, providing moderate rendering quality, and reducing some resource consumption when the power is normal or slightly low; simplifying graphics rendering, turning off most animation effects, reducing the level of graphics details, and giving priority to saving power when the power is low or very low. Check the dynamic power consumption control parameter data, and determine the appropriate rendering mode in combination with the current device performance level and power status; decide whether to enable or disable certain complex graphics or special effects functions in the current rendering mode based on the menu complexity level data. Record the selected menu rendering mode (full-function mode, standard mode, or power saving mode) as menu rendering mode selection data for subsequent rendering strategy formulation.

[0146] Step S472: Formulate a specific rendering strategy according to the menu rendering mode selection data, including whether to enable animation effects, graphics detail level, and color depth selection, thereby generating rendering strategy data;

[0147] The embodiment of the present invention determines whether to enable animation effects based on menu rendering mode selection data. For example, in power saving mode, animation effects may be turned off or reduced. The graphics detail level is adjusted according to the selected rendering mode. For example, in full-function mode, high-resolution textures and complex light and shadow effects are enabled. In power saving mode, textures are simplified and graphics details are reduced. According to the power and performance status, an appropriate color depth is selected. For example, in power saving mode, the color depth may be reduced to reduce the rendering load. The above rendering strategies are recorded as rendering strategy data, including detailed parameter settings for each strategy, to ensure that graphics and animation effects are adjusted as needed during the rendering process.

[0148] Step S473: Based on the rendering strategy data and the multi-size screen adaptation solution data, the menu elements are optimized by simplifying complex graphics, adjusting animation frame rates, and optimizing text rendering, thereby obtaining menu element rendering data;

[0149] The embodiment of the present invention simplifies complex graphic elements according to the rendering strategy data, such as reducing the sampling points of the curve, reducing the number of polygons, or reducing the calculation of shadows and lighting; in power saving mode, the animation frame rate is reduced to reduce the calculation burden, while ensuring that the smoothness of the animation does not affect the user experience. The text rendering method is optimized, and a lower font rendering accuracy or a simplified font style is selected to reduce the load on the CPU and GPU; for elements that combine graphics and text, such as buttons or labels, comprehensive optimization is performed to ensure that the content can still be clearly presented in low power consumption mode; the above-mentioned optimized menu element rendering details are recorded as menu element rendering data to provide support for the final rendering instruction generation.

[0150] Step S474: Generate energy-saving rendering solution data including rendering instructions, resource loading strategies, and cache usage solutions according to the menu element rendering data.

[0151] The embodiment of the present invention generates specific rendering instructions based on the menu element rendering data, and controls the rendering method of each menu element in the actual display process, including the rendering order, animation playback, and graphics generation method. According to the current power status and rendering mode, decide whether to preload, delay loading, or dynamically load graphic resources to reduce instantaneous resource occupancy and power consumption; design a reasonable cache strategy, such as caching commonly used graphics and animation elements to reduce repeated calculations and resource occupancy. Integrate rendering instructions, resource loading strategies, and cache usage plans into energy-saving rendering plan data to ensure that the final rendering process is both energy-saving and efficient.

[0152] The present invention determines the rendering mode of the menu by combining dynamic power consumption control parameters and menu complexity level, and can intelligently switch between full-function mode, standard mode and power saving mode; this ensures that under different power and performance requirements, the device can render the menu in the most appropriate way, balancing performance and power consumption. According to the selected menu rendering mode, a specific rendering strategy is formulated, including the adjustment of animation effects, graphic details and color depth; the formulation of such a strategy helps to further optimize power usage, while providing customized visual effects according to user preferences and device status. Based on the rendering strategy and multi-size screen adaptation scheme, the menu elements are optimized, such as simplifying complex graphics, adjusting animation frame rate and optimizing text rendering; these optimizations can not only reduce resource consumption and improve rendering efficiency, but also ensure that a clear and easy-to-read menu interface can be provided on different devices and screen sizes. Energy-saving rendering scheme data including rendering instructions, resource loading strategies and cache usage schemes are generated, providing a detailed execution guide for actual menu rendering; this helps to improve the responsiveness and fluency of the menu, while reducing unnecessary power consumption by intelligently managing resources and caches. In general, the effect of these steps is to provide an intelligent menu rendering method that comprehensively considers power consumption control, menu complexity and user experience. This method optimizes resource usage and performance by dynamically adjusting rendering modes and strategies, effectively reducing energy consumption and extending device usage time while ensuring user experience. Through this intelligent adaptive adjustment, users can be guaranteed to have a satisfactory operating experience in various usage scenarios.

[0153] Preferably, step S5 comprises the following steps:

[0154] Step S51: determining the menu structure and element type according to the menu complexity level data to obtain preliminary menu structure solution data; and adjusting the layout of the preliminary menu structure solution data based on screen size adaptation optimization according to the multi-size screen adaptation solution data to obtain menu structure data;

[0155] The embodiment of the present invention determines the overall structure of the menu, including the hierarchical relationship between the main menu and the submenu, the number of menu items, and the type of each element (such as icons, texts, buttons, etc.), based on the menu complexity level data generated in step S26; and records the above-determined menu structure as preliminary menu structure solution data. According to the multi-size screen adaptation solution data generated in step S38, the preliminary menu structure is optimized for screen size adaptation, and the layout of menu elements on different screen sizes is adjusted, such as the arrangement, spacing, and size ratio of the elements; the position and arrangement of the menu elements are slightly adjusted according to the screen size, resolution, orientation (horizontal or vertical) and other characteristics of the specific device to ensure that the menu can be effectively displayed and operated under various screen conditions; the optimized menu structure is recorded as menu structure data for use in subsequent steps.

[0156] Step S52: optimizing the visual effects of menu elements in the menu structure data according to the energy-saving rendering solution data, thereby obtaining initial menu structure data;

[0157] The embodiment of the present invention optimizes the visual effects of menu elements according to the energy-saving rendering solution data generated in step S47, including simplifying graphics, reducing color depth, reducing the use of shadows and lighting effects, etc., to reduce resource consumption; under the premise of meeting power saving requirements, further simplifying menu animation effects, reducing the complexity of graphics rendering, and optimizing visual performance; recording the optimized menu visual effects as initial menu structure data to ensure that the menu can provide the best visual experience under different performance states.

[0158] Step S53: performing resource occupancy evaluation on the initial menu structure data based on memory occupancy, CPU usage rate and GPU load, thereby obtaining resource occupancy index data;

[0159] The embodiment of the present invention evaluates the occupancy of system memory by each element in the initial menu structure data, analyzes the memory resources required by the menu during loading and interaction, and performs actual memory usage measurement, including the occupancy of textures, fonts, and cached data of menu elements. Analyze the CPU usage during menu rendering, especially the demand for CPU by complex animations and dynamic content; generate CPU load occupancy data based on the CPU usage of the simulated running menu. Evaluate the GPU load of the initial menu structure in different rendering modes, especially the demand for GPU by rendering high-resolution graphics and special effects; generate GPU load occupancy data through specific measurement. Integrate the measurement results of memory occupancy, CPU usage, and GPU load into resource occupancy index data to reflect the overall demand of the menu for system resources.

[0160] Step S54: performing menu optimization processing on the initial menu structure according to the resource occupancy index data, and performing rendering effect simulation, thereby obtaining rendering effect simulation data;

[0161] According to the resource occupancy index data generated in step S53, the embodiment of the present invention optimizes the initial menu structure, reduces the use of high-load elements, or adjusts the menu layout to balance resource occupancy; such as reducing the complexity of menu animation, reducing the resolution of graphics, adjusting the layout to reduce memory and CPU / GPU occupancy, etc. Simulate the operation effect of the menu in different performance modes, evaluate the rendering quality, animation fluency and response speed; record key data in the rendering process, such as frame rate, response time and visual performance, and generate rendering effect simulation data.

[0162] Step S55: Perform a user experience evaluation based on visual appeal, operation fluency, and information readability based on the rendering effect simulation data, thereby obtaining user experience score data.

[0163] The embodiment of the present invention evaluates the visual appeal of the menu and determines its visual effects on different screens and rendering modes by analyzing the color, layout, and graphic details of the menu interface. Evaluate the smoothness of the menu during user operation, including click response, menu switching speed, animation playback smoothness, etc., to ensure that the menu can provide a smooth operating experience in different device states; evaluate the readability of text and the clarity of graphic information in the menu to ensure that information can be presented clearly and accurately in various layouts and sizes. Based on the above evaluation results, the visual appeal, operation smoothness and information readability are comprehensively considered to generate user experience score data to reflect the overall user experience of the menu in actual use.

[0164] By considering the menu complexity level and the multi-size screen adaptation scheme, the present invention can determine a preliminary menu structure scheme and optimize it to adapt to devices with different screen sizes; this ensures that the menu can provide a consistent user experience on various devices, while optimizing the layout to adapt to different display requirements. Optimizing the visual effects of menu elements according to the energy-saving rendering scheme can improve the aesthetics and attractiveness of the menu while maintaining energy efficiency, which helps to provide a richer and more attractive visual experience without sacrificing performance. Evaluating the resource usage of the menu structure on memory, CPU and GPU can identify possible performance bottlenecks and optimization opportunities; resource usage index data provides a quantitative basis for subsequent optimization, ensuring that the menu can run smoothly on various devices. Optimizing the menu according to the resource usage index and simulating the rendering effect can foresee the performance of the final user interface and make necessary adjustments, which helps to ensure that the performance and appearance of the menu meet the expected standards before actual deployment. User experience evaluation is performed based on the rendering effect simulation data, focusing on key factors such as visual appeal, operation fluency and information readability; user experience score data provides an evaluation of the comprehensive quality of menu design and guides further optimization to meet the actual needs and preferences of users. Overall, the effect of these steps is to provide a systematic approach to designing and optimizing menu structures, which takes into account multiple dimensions, including adaptability, visual effects, resource usage, and user experience. This comprehensive consideration ensures that the final menu design is both beautiful and practical, and can provide a high-quality user experience on a variety of devices and usage scenarios while maintaining energy efficiency and performance optimization.

[0165] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0166] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A menu generation method based on a liquid crystal screen, characterized in that: The following steps are involved: Step S1: Obtain device hardware parameters and system resource data; perform device capability evaluation based on the device hardware parameters and system resource data, thereby generating device performance indicator data; Perform multi-dimensional equipment characteristic analysis based on equipment performance index data to obtain equipment characteristic vector data; step S1 includes: Step S11: Obtain device hardware parameters and system resource data, wherein the device hardware parameters include CPU model and core number, GPU model and core number, main frequency data, display screen size, resolution, refresh rate data and battery capacity data, wherein the system resource data includes current CPU usage data, current memory occupancy data, current battery power data and current storage space usage data; Step S12: Evaluate device capabilities based on device hardware parameters and system resource data to generate device performance indicator data, where the device capability evaluation includes processing capability evaluation, graphics rendering capability evaluation, multi-tasking capability evaluation, data reading and writing speed evaluation, display quality evaluation, and battery life evaluation; Step S13: performing device characteristic analysis on the device performance indicator data based on CPU architecture characteristics, GPU rendering characteristics, memory management characteristics, storage access characteristics, power management characteristics, and display characteristics, thereby obtaining device characteristic analysis data; Step S14: constructing a multidimensional characteristic vector according to the device characteristic analysis data, and normalizing the multidimensional characteristic vector to generate device characteristic vector data; Step S2: performing device type matching according to a preset device mode library and device characteristic vector data, thereby obtaining device prototype mode data; performing adaptive menu complexity division according to the device prototype mode data, thereby generating menu complexity level data; Step S3: performing responsive layout calculation according to the screen size and resolution information in the device hardware parameters, thereby generating adaptive layout template data; optimizing the menu element layout of the adaptive layout template data, thereby obtaining multi-size screen adaptation solution data; Step S4: obtaining the current real-time power consumption data of the device; formulating a power management strategy according to the real-time power consumption data, and generating dynamic power consumption control parameter data; selecting a menu rendering mode based on the dynamic power consumption control parameter data, and obtaining energy-saving rendering solution data; Step S5: performing menu element generation processing according to the menu complexity level data, the multi-size screen adaptation solution data, and the energy-saving rendering solution data to generate initial menu structure data; performing resource occupancy evaluation on the initial menu structure data to obtain resource occupancy index data; performing menu optimization processing based on the resource occupancy index data, and performing rendering effect simulation and user experience evaluation to obtain user experience score data; Step S6: Determine whether the preset threshold is met based on the user experience score data. If not, return to step S5 for iterative optimization. If the preset threshold is met, generate final menu rendering instruction data to complete the adaptive menu generation based on the LCD screen.

2. The menu generation method based on the liquid crystal screen according to claim 1, characterized in that: Step S1 includes the following steps: Step S121: performing a processing capability evaluation based on the CPU architecture type, cache size, and instruction set support according to the CPU model and core number, core number, and main frequency data in the device hardware parameters, and calculating the CPU performance score, thereby obtaining CPU processing capability index data; Step S122: Based on the device hardware parameters and the display screen size, resolution, refresh rate data in the system resource data, as well as the GPU model and core number, a graphics rendering capability evaluation based on the memory bandwidth and the supported graphics API is performed to obtain GPU rendering capability index data; Step S123: performing a multi-task processing capability evaluation based on memory speed, number of channels, and available memory size according to the current CPU usage data and the current memory occupancy data, thereby obtaining multi-task processing capability index data; Step S124: Based on the current memory occupancy rate data and the current storage space usage data, data read and write speed evaluation is performed based on the storage medium type, interface speed, and remaining available space, thereby obtaining read and write capacity index data; Step S125: performing display quality evaluation based on pixel density, color range, and touch response speed according to display screen size, resolution, and refresh rate data in the device hardware parameters, thereby obtaining display quality index data; Step S126: Based on the battery capacity data and the current battery power data, a battery life evaluation is performed based on power consumption efficiency, charging speed, and battery health status, thereby obtaining battery life indicator data; Step S127: The CPU processing capability index data, GPU rendering capability index data, multi-tasking capability index data, reading and writing capability index data, display quality index data and battery life index data are weighted and integrated based on the device type and expected usage scenario to obtain device performance index data.

3. The menu generation method based on liquid crystal screen according to claim 2, characterized in that: Step S2 includes the following steps: Step S21: standardizing the device characteristic vector data and performing format unification based on a preset device mode library, thereby generating device characteristic standard data; Step S22: Calculate the similarity between the device characteristic standard data and the templates in the preset device pattern library by using the K nearest neighbor algorithm, so as to obtain device type matching result data; Step S23: determining the most matching device prototype pattern according to the device type matching result data, thereby generating device prototype pattern data, wherein if there are multiple similar matching results when matching the device prototype patterns, the one with the highest similarity is selected as the primary pattern, and the others are selected as secondary patterns; Step S24: determining the performance level of the device according to the device prototype mode data and the device performance indicator data, thereby generating device performance level data, wherein the performance level of the device includes high performance, medium performance and low performance; Step S25: formulating a preliminary menu complexity strategy based on the device performance level data and the device prototype mode data, and adjusting the preliminary menu complexity strategy based on the real-time status of the device, thereby obtaining menu complexity strategy data; Step S26: Map the menu complexity policy data to the complexity level, thereby generating menu complexity level data.

4. The menu generation method based on liquid crystal screen according to claim 3, characterized in that: Step S3 includes the following steps: Step S31: extracting key display parameters of screen size, resolution and pixel density from device hardware parameters to generate screen characteristic data; Step S32: Divide the screen into a number of columns and rows according to the screen characteristic data, determine a basic grid system, and thereby generate basic layout grid data; Step S33: selecting a responsive layout strategy based on flow layout, elastic layout or grid layout according to the basic layout grid data and the device prototype mode data, thereby obtaining layout strategy data; Step S34: identifying key layout breakpoints based on layout change points under different screen widths according to the layout strategy data, thereby obtaining layout breakpoint data; Step S35: designing a corresponding layout structure for each breakpoint interval based on the layout breakpoint data, and generating multi-breakpoint layout structure data, wherein the layout structure includes the arrangement, spacing, and size ratio of the menu items; Step S36: adjusting and optimizing the multi-breakpoint layout structure data based on element density and complexity according to the menu complexity level data, thereby obtaining layout structure optimization data; Step S37: designing an adaptively scalable UI component according to the layout structure optimization data, and combining the layout structure with the UI component to obtain adaptive layout template data; Step S38: Optimizing the menu element layout of the adaptive layout template data, thereby obtaining multi-size screen adaptation solution data.

5. The menu generation method based on liquid crystal screen according to claim 4, characterized in that: Step S38 includes the following steps: Step S381: Evaluate the display effect and interactive convenience of the adaptive layout template data under different screen sizes and directions to generate layout usability evaluation data; Step S382: fine-tuning and optimizing the adaptive layout template data based on element size, position and spacing according to the layout usability evaluation data, thereby obtaining layout template optimization data; Step S383: Generate specific layout solutions corresponding to different screen sizes and directions based on the layout template optimization data, thereby obtaining multi-size screen adaptation solution data, where the specific layout solution includes menu element arrangement and size specifications in horizontal and vertical modes.

6. The menu generation method based on liquid crystal screen according to claim 5, characterized in that: Step S4 includes the following steps: Step S41: obtaining the current real-time power consumption data of the device, including the overall power consumption and the sub-item power consumption of each major hardware component; Step S42: predicting the remaining usage time according to the battery capacity data and the current battery power data, thereby generating power prediction data; Step S43: determining the current power state level of the device according to the power prediction data and the real-time power consumption data, thereby generating power state level data, wherein the power state levels include sufficient power, normal power, low power and extremely low power; Step S44: formulating a preliminary power management strategy including CPU frequency adjustment, GPU usage restriction and screen brightness adjustment based on the power state level data and the device performance level data, thereby generating preliminary power management strategy data; Step S45: obtaining the user's current usage scenario data; and optimizing the preliminary power management strategy data to balance the energy saving requirements and the user experience according to the usage scenario data, thereby obtaining the power management strategy data; Step S46: generating dynamic power consumption control parameter data including CPU maximum frequency limit, GPU performance mode selection and display refresh rate adjustment according to the power management policy data; Step S47: Selecting a menu rendering mode based on the dynamic power consumption control parameter data to obtain energy-saving rendering solution data.

7. The menu generation method based on liquid crystal screen according to claim 6, characterized in that: Step S47 includes the following steps: Step S471: determining a menu rendering mode according to the dynamic power consumption control parameter data and the menu complexity level data, thereby generating menu rendering mode selection data, wherein the menu rendering mode includes a full-function mode, a standard mode and a power saving mode; Step S472: Formulate a specific rendering strategy according to the menu rendering mode selection data, including whether to enable animation effects, graphics detail level, and color depth selection, thereby generating rendering strategy data; Step S473: Based on the rendering strategy data and the multi-size screen adaptation solution data, the menu elements are optimized by simplifying complex graphics, adjusting animation frame rates, and optimizing text rendering, thereby obtaining menu element rendering data; Step S474: Generate energy-saving rendering solution data including rendering instructions, resource loading strategies, and cache usage solutions according to the menu element rendering data.

8. The menu generation method based on liquid crystal screen according to claim 7, characterized in that: Step S5 includes the following steps: Step S51: determining the menu structure and element type according to the menu complexity level data to obtain preliminary menu structure solution data; and adjusting the layout of the preliminary menu structure solution data based on screen size adaptation optimization according to the multi-size screen adaptation solution data to obtain menu structure data; Step S52: optimizing the visual effects of menu elements in the menu structure data according to the energy-saving rendering solution data, thereby obtaining initial menu structure data; Step S53: performing resource occupancy evaluation on the initial menu structure data based on memory occupancy, CPU usage rate and GPU load, thereby obtaining resource occupancy index data; Step S54: performing menu optimization processing on the initial menu structure according to the resource occupancy index data, and performing rendering effect simulation, thereby obtaining rendering effect simulation data; Step S55: Perform a user experience evaluation based on visual appeal, operation fluency, and information readability based on the rendering effect simulation data, thereby obtaining user experience score data.

9. A menu generation system based on a liquid crystal screen, characterized in that: Used to execute the menu generation method based on the liquid crystal screen as claimed in claim 1, the menu generation system based on the liquid crystal screen comprises: The device performance analysis module is used to obtain device hardware parameters and system resource data; perform device capability evaluation based on the device hardware parameters and system resource data to generate device performance index data; perform multi-dimensional device characteristic analysis based on the device performance index data to obtain device characteristic vector data; The menu complexity adaptation module is used to match the device type according to the preset device mode library and the device characteristic vector data, so as to obtain the device prototype mode data; and to perform adaptive menu complexity division according to the device prototype mode data, so as to generate menu complexity level data; The multi-size screen layout module is used to perform responsive layout calculations based on the screen size and resolution information in the device hardware parameters, thereby generating adaptive layout template data; optimizing the menu element layout of the adaptive layout template data, thereby obtaining multi-size screen adaptation solution data; Dynamic power consumption optimization module, used to obtain the current real-time power consumption data of the device; formulate power management strategy based on the real-time power consumption data, generate dynamic power consumption control parameter data; select menu rendering mode based on the dynamic power consumption control parameter data, and obtain energy-saving rendering solution data; The menu generation and evaluation module is used to generate menu elements according to the menu complexity level data, the multi-size screen adaptation solution data and the energy-saving rendering solution data, and generate initial menu structure data; perform resource occupancy evaluation on the initial menu structure data to obtain resource occupancy index data; perform menu optimization based on the resource occupancy index data, and perform rendering effect simulation and user experience evaluation to obtain user experience score data; The iterative optimization decision module is used to determine whether the preset threshold is met based on the user experience score data. If not, it returns to step S5 for iterative optimization; if the preset threshold is met, it generates the final menu rendering instruction data to complete the adaptive menu generation based on the LCD screen.

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