Multi-platform embedded graphical user interface development method

Abstract

This invention belongs to the field of embedded interface development technology. It discloses a multi-platform embedded graphical user interface development method, comprising: establishing a framework database storing GUI framework characteristic data and performance record data; collecting target device hardware parameters, acquiring and quantifying graphical interface requirement characteristics; filtering frameworks that meet the requirements based on hardware parameters, and predicting the performance indicators of each candidate framework based on hardware parameters, requirement characteristics, and the framework database; generating a recommended framework list based on the predicted performance indicators and graphical interface requirements; selecting the target framework and generating an optimized configuration. This method overcomes the limitations of traditional experience-based judgment by using a deep neural network model combined with historical performance records to predict framework performance; it reduces configuration time through automated configuration optimization, ensuring that the GUI framework achieves good performance on the target hardware, fully utilizing hardware potential while avoiding resource waste.

Description

Technical Field

[0001] This invention relates to the field of embedded interface development technology, and more specifically, to a method for developing multi-platform embedded graphical user interfaces. Background Technology

[0002] In embedded systems, the graphical user interface (GUI) is a key factor in enhancing user experience and product competitiveness. Embedded GUIs not only need to provide intuitive and aesthetically pleasing visuals, but also need to maintain a smooth interactive experience with limited hardware resources. Therefore, choosing a suitable GUI framework and optimizing its configuration is crucial to the success of embedded products. Currently, there are various embedded GUI frameworks on the market, such as LVGL, emWin, µGFX, TouchGFX, and GUIX, each with its specific technical characteristics, and advantages and disadvantages in terms of memory usage, rendering performance, and feature richness.

[0003] Traditional methods for selecting embedded GUI frameworks rely heavily on developers' experience and subjective judgment. Developers typically choose a framework based on past project experience, personal preferences, or peer recommendations, then adjust the framework configuration through trial and error. This approach often leads to the following problems: First, embedded GUI framework technology iterates rapidly, with each framework constantly releasing new features and optimizing performance, making it difficult for developers to continuously track and fully grasp the latest status of all frameworks. Second, embedded device hardware configurations vary significantly, ranging from low-end MCUs to high-performance MPUs, resulting in noticeable differences in the performance of the same framework on different hardware platforms, making accurate judgment based solely on experience difficult. Furthermore, modern GUI frameworks have numerous configuration parameters, and manually configuring and combining them is time-consuming and laborious, often failing to achieve the desired results, thus impacting the efficiency and effectiveness of the final graphical interface development. Summary of the Invention

[0004] To overcome the problems of existing technologies affecting the efficiency and effectiveness of final graphical user interface development, this invention proposes a multi-platform embedded graphical user interface development method to solve the above problems.

[0005] This invention provides the following technical solution: Multi-platform embedded graphical user interface development methodologies include: Establish a framework database, which stores feature data and performance records of various embedded GUI frameworks; Collect the hardware parameters of the target embedded device, obtain the graphical interface requirements of the target project, and quantify them into requirement characteristics; Based on hardware parameters, embedded GUI frameworks that meet the hardware requirements are selected from the framework database as candidate frameworks. Based on device hardware parameters, requirement characteristics and framework database, various performance indicators of each candidate framework are predicted. A list of recommended frameworks is generated based on predicted performance metrics and graphical interface requirements. Select a target framework from the recommended framework list, generate an optimized configuration for the target framework based on the device hardware parameters and requirements, and develop the interface based on the configured target framework.

[0006] Preferably, the feature data includes the name, version, minimum hardware requirements, list of supported control types, rendering engine type, buffering strategy, memory management mechanism, and event handling mechanism of the embedded GUI framework; The performance data includes various performance metrics measured by each embedded GUI framework when running interfaces with different requirements under different hardware parameters.

[0007] Preferably, the quantification into demand characteristics includes: Obtain the graphical interface requirements, which include: interface structure information, control configuration information, animation design information, interaction mode information, and resource usage information; The interface complexity index is evaluated based on the number of pages, page hierarchy, and page switching frequency in the interface structure information. The control complexity index is evaluated based on the total number of controls, the number of basic controls, the number of complex controls, and the control density in the control configuration information. The animation complexity index is evaluated based on the number of animations, animation type, animation duration, and frame rate requirements in the animation design information. The interaction complexity index is evaluated based on the frequency of interaction events, touch gesture types, and response time requirements in the interaction mode information. The resource demand index is assessed based on the quantity of image resources, image resolution, font type, and font size in the resource usage information. The interface complexity index, control complexity index, animation complexity index, interaction complexity index, and resource requirement index are combined into standardized requirement characteristics to quantify the requirement characteristics.

[0008] Preferably, the predicted performance metrics for each candidate framework include: for each candidate framework: Extract hardware performance features from hardware parameters, extract framework features from characteristic data, and construct predictive input features based on hardware performance features, requirement features, and framework features; The predicted input features are fed into a pre-trained performance prediction model to predict various performance metrics of the framework. Based on hardware parameters, characteristic data, and demand features, the performance record data is matched to obtain the various performance indicators from the performance record data with the highest matching degree. The final performance indicators are generated by combining the performance indicators predicted by the model and the performance indicators obtained by matching, and by weighting them according to the matching degree.

[0009] Preferably, the training method for the pre-trained performance prediction model includes: Collect training data, which consists of hardware parameters, requirement characteristics, feature data and corresponding performance indicators recorded when running various interface applications implemented with different embedded GUI frameworks on multiple hardware platforms; The feature engineering process is constructed to convert hardware parameters into hardware performance features and feature data into framework features, which together with the requirement features constitute the model input. The collected data is cleaned, including removing outliers, handling missing values, and standardizing features; Design a multi-output performance prediction model structure, which adopts a deep neural network and includes a shared bottom-level feature extraction layer and multiple output branches; The training objective is constructed by taking hardware performance characteristics, requirement characteristics, and framework characteristics as inputs, and predicting multiple performance indicators as outputs. The dataset is divided into training, validation and test sets according to the proportions, and a multi-task loss function is defined to assign weight coefficients to each performance metric prediction task. The training set data is used to update the parameters, the validation set data is used to monitor the training process, and the training of the model is completed when the model's performance on the test set meets the target.

[0010] Preferably, the method for calculating the matching degree includes: Extract hardware performance characteristics, requirement characteristics, and framework characteristics; Calculate the distance between the feature vector and the feature vector of each record in the performance record data, the distance being calculated using the Euclidean distance formula; The distance is normalized and converted into a similarity score between 0 and 1. The higher the similarity score, the higher the matching degree. Weight coefficients are set for hardware performance characteristics, requirement characteristics, and framework characteristics, and a weighted similarity score is calculated as the matching degree.

[0011] Preferably, generating the recommended framework list based on the predicted performance metrics and graphical interface requirements includes: Calculate the coverage of the control types supported by each candidate framework to the control requirements in the project; The candidate frameworks are sorted according to the priority of preset performance indicators and control coverage to obtain a recommended framework list.

[0012] Preferably, the step of generating an optimized configuration for the target framework based on device hardware parameters and requirement characteristics includes: Select the basic configuration template for the target framework from the preset configuration template library; adjust the rendering and memory-related configuration parameters according to the device hardware parameters; adjust the configuration parameters related to functional modules and resource management according to the requirements; generate a configuration file specifically for the target framework to complete the generation of the optimized configuration.

[0013] This invention provides a multi-platform embedded graphical user interface development method, which has the following beneficial effects: By filtering frameworks that meet the requirements from a database based on device hardware parameters, and using a deep neural network model combined with historical performance records to predict the performance of each framework under specific conditions, a recommended framework list is generated based on objective data. This overcomes the limitations of traditional experience-based judgment, enabling developers to choose frameworks after fully understanding the advantages and disadvantages of each framework in the current development context, thus avoiding rework and performance issues caused by inappropriate framework selection.

[0014] Based on the device hardware parameters and quantified requirements, a base template is selected from the configuration template library, and configuration parameters related to rendering, memory, functional modules, and resource management are adjusted accordingly to generate a framework-specific configuration file. This automated configuration optimization not only significantly reduces configuration time but also allows for fine-tuning according to specific project needs, ensuring that the GUI framework achieves good performance on the target hardware, fully leveraging hardware potential while avoiding resource waste. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the multi-platform embedded graphical user interface development method of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1 Please see Figure 1 In this embodiment, the multi-platform embedded graphical user interface development method includes: S1. Establish a framework database, which stores feature data and performance record data of various embedded GUI frameworks; The feature data includes the name, version, minimum hardware requirements, list of supported control types, rendering engine type, buffering strategy, memory management mechanism, and event handling mechanism of the embedded GUI framework. The performance data includes various performance metrics measured by each embedded GUI framework when running interfaces with different requirements under different hardware parameters.

[0018] In this embodiment, it should be noted that the framework database is the foundation of the method of the present invention. It stores the characteristic data and performance record data of various embedded GUI frameworks, providing data support for subsequent framework selection and performance prediction.

[0019] It should be noted that the feature data mainly records the technical characteristics of each embedded GUI framework, including minimum hardware requirements, a list of supported control types, rendering engine type, buffering strategy, memory management mechanism, and event handling mechanism. The minimum hardware requirements are the minimum hardware configuration needed for the framework to function properly, typically including processor type, minimum processor frequency, minimum RAM capacity, and minimum Flash storage space. The list of supported control types includes all UI controls natively supported by the framework, such as basic controls like buttons and text boxes, and complex controls like charts and list views. The rendering engine type refers to the graphics rendering technology used by the framework, such as pure software rendering or hardware-accelerated rendering. The buffering strategy refers to how the framework handles the display buffer, such as single buffering, double buffering, or triple buffering. The memory management mechanism refers to the method the framework uses to allocate and manage memory, such as static allocation, dynamic allocation, or memory pools. The event handling mechanism refers to how the framework handles user input and system events, such as polling, interrupt-driven, or message queues.

[0020] It's important to note that the performance data is collected through actual testing, documenting the performance of each framework under different hardware and application scenarios. This data is obtained by running standardized test applications on various hardware platforms, recording the performance metrics of each framework under specific combinations of hardware parameters and requirements. Performance metrics typically include key indicators such as rendering time, response latency, memory usage, CPU utilization, and power consumption.

[0021] S2. Collect the hardware parameters of the target embedded device, obtain the graphical interface requirements of the target project, and quantify them into requirement features; The quantification of demand characteristics includes: Obtain the graphical interface requirements, which include: interface structure information, control configuration information, animation design information, interaction mode information, and resource usage information; The interface complexity index is evaluated based on the number of pages, page hierarchy, and page switching frequency in the interface structure information. The control complexity index is evaluated based on the total number of controls, the number of basic controls, the number of complex controls, and the control density in the control configuration information. The animation complexity index is evaluated based on the number of animations, animation type, animation duration, and frame rate requirements in the animation design information. The interaction complexity index is evaluated based on the frequency of interaction events, touch gesture types, and response time requirements in the interaction mode information. The resource demand index is assessed based on the quantity of image resources, image resolution, font type, and font size in the resource usage information. The interface complexity index, control complexity index, animation complexity index, interaction complexity index, and resource requirement index are combined into standardized requirement characteristics to quantify the requirement characteristics.

[0022] In this embodiment, it should be noted that collecting the hardware parameters of the target embedded device is to determine the device's performance boundaries and resource limitations. These parameters typically include processor model and frequency, RAM capacity, Flash storage space, display controller type, display resolution and color depth, and whether it has a graphics accelerator. These hardware parameters directly affect the running performance and adaptability of the GUI framework and are important bases for framework selection.

[0023] It should be noted that graphical user interface (GUI) requirements refer to the specific functional and performance requirements of the GUI in a project. These requirements can be extracted from project requirements documents and interface design drafts. Interface structure and control configuration information can be extracted from the interface prototypes provided by the prototype designer, animation design and interaction mode information can be obtained from the interaction design specifications, and resource usage information can be extracted from the visual specification documents. Specifically, interface structure information describes the page organization of the GUI, including the number of pages, page hierarchy, and page switching frequency; control configuration information describes the various controls used in the interface, including the total number of controls, the number and distribution of basic and complex controls; animation design information describes the dynamic effects requirements in the interface, including the number, type, duration, and frame rate requirements of animations; interaction mode information describes the user's interaction with the interface, including the frequency of interaction events, touch gesture types, and response time requirements; and resource usage information describes the resources required by the interface, including the number of image resources, image resolution, font types, and font sizes.

[0024] It should be noted that quantifying requirements into demand characteristics involves converting qualitative graphical interface requirements into quantitative characteristic indicators, which facilitates subsequent performance prediction and framework matching. The interface complexity index reflects the complexity of the interface structure. It is calculated based on the number of pages, page hierarchy, and page switching frequency. The more pages, the deeper the hierarchy, and the higher the switching frequency, the greater the interface complexity index. The control complexity index reflects the complexity of interface controls. It is calculated based on the total number of controls, the number of basic controls, the number of complex controls, and the control density. The more controls, the higher the proportion of complex controls, and the higher the control density, the greater the control complexity index. The animation complexity index reflects the complexity of interface animation effects. It is calculated based on the number of animations, animation types, animation duration, and frame rate requirements. The more animations, the more complex the types, the longer the duration, and the higher the frame rate requirements, the greater the animation complexity index. The interaction complexity index reflects the complexity of interface interactions. It is calculated based on the frequency of interaction events, touch gesture types, and response time requirements. The higher the frequency of interaction events, the more complex the gesture types, and the stricter the response time requirements, the greater the interaction complexity index. The resource requirement index reflects the degree of interface resource requirements. It is calculated based on the number of image resources, image resolution, font types, and font sizes. The more image resources, the higher the resolution, the more font types, and the larger the font size, the greater the resource requirement index.

[0025] It should be noted that combining the above complexity indices into standardized requirement features involves normalizing each index and setting weights according to project characteristics, ultimately forming a multi-dimensional feature vector. This feature vector comprehensively describes the project's GUI requirement characteristics, providing a quantitative basis for subsequent performance prediction and framework matching.

[0026] S3. Based on the hardware parameters, select embedded GUI frameworks that meet the hardware requirements from the framework database as candidate frameworks, and predict the performance indicators of each candidate framework based on the device hardware parameters, requirement characteristics and framework database. The predicted performance metrics for each candidate framework include: For each candidate framework: Extract hardware performance features from hardware parameters, extract framework features from characteristic data, and construct predictive input features based on hardware performance features, requirement features, and framework features; The predicted input features are fed into a pre-trained performance prediction model to predict various performance metrics of the framework. Based on hardware parameters, characteristic data, and demand features, the performance record data is matched to obtain the various performance indicators from the performance record data with the highest matching degree. The final performance indicators are generated by combining the performance indicators predicted by the model and the performance indicators obtained by matching, and by weighting them according to the matching degree.

[0027] The training method for the pre-trained performance prediction model includes: Collect training data, which consists of hardware parameters, requirement characteristics, feature data and corresponding performance indicators recorded when running various interface applications implemented with different embedded GUI frameworks on multiple hardware platforms; The feature engineering process is constructed to convert hardware parameters into hardware performance features and feature data into framework features, which together with the requirement features constitute the model input. The collected data is cleaned, including removing outliers, handling missing values, and standardizing features; Design a multi-output performance prediction model structure, which adopts a deep neural network and includes a shared bottom-level feature extraction layer and multiple output branches; The training objective is constructed by taking hardware performance characteristics, requirement characteristics, and framework characteristics as inputs, and predicting multiple performance indicators as outputs. The dataset is divided into training, validation and test sets according to the proportions, and a multi-task loss function is defined to assign weight coefficients to each performance metric prediction task. The training set data is used to update the parameters, the validation set data is used to monitor the training process, and the training of the model is completed when the model's performance on the test set meets the target.

[0028] The method for calculating the matching degree includes: Extract hardware performance characteristics, requirement characteristics, and framework characteristics; Calculate the distance between the feature vector and the feature vector of each record in the performance record data, the distance being calculated using the Euclidean distance formula; The distance is normalized and converted into a similarity score between 0 and 1. The higher the similarity score, the higher the matching degree. Weight coefficients are set for hardware performance characteristics, requirement characteristics, and framework characteristics, and a weighted similarity score is calculated as the matching degree.

[0029] In this embodiment, it should be noted that the selection of embedded GUI frameworks that meet the hardware requirements from the framework database as candidate frameworks based on hardware parameters is performed by comparing the hardware parameters of the target device with the minimum hardware requirements of each framework. Only when the target device's key hardware parameters, such as processor performance, RAM capacity, and Flash storage space, meet or exceed the minimum requirements of the framework will the framework be selected as a candidate framework. This step can effectively eliminate frameworks that are not suitable for the target hardware and avoid unnecessary subsequent performance prediction calculations.

[0030] It should be noted that predicting the performance metrics of each candidate framework is the core step of this invention, employing a method that combines model prediction with historical data matching. Extracting hardware performance features from hardware parameters involves converting raw hardware parameters into features that characterize device performance, such as converting processor model and frequency into processor performance indices, and RAM capacity and type into memory performance indices. Extracting framework features from feature data involves converting the framework's technical characteristics into quantifiable feature vectors, such as rendering engine type, buffering strategy, and memory management mechanism. Constructing predictive input features involves combining hardware performance features, requirement features, and framework features into a complete feature vector, which serves as the input to the performance prediction model.

[0031] It's important to note that the pre-trained performance prediction model is a deep learning-based multi-output prediction model capable of predicting multiple performance metrics simultaneously. This model employs a shared low-level feature extraction layer and multiple output branches. The low-level feature extraction layer is responsible for extracting general feature representations from the input features, while each output branch predicts different performance metrics based on these general features, such as rendering time, memory usage, and CPU utilization. This structure fully leverages the correlations between different performance metrics, improving prediction accuracy.

[0032] It's important to note that matching within the performance record data aims to find the most similar historical records to the current prediction scenario, serving as a reference for prediction. The matching score is calculated using a similarity metric based on Euclidean distance. First, the Euclidean distance between the feature vector of the current scenario and the feature vector of each record in the historical data is calculated. Then, the distance is converted into a similarity score between 0 and 1, with a higher similarity score indicating a higher matching degree. To reflect the importance of different features in the matching, weight coefficients are assigned to hardware performance features, requirement features, and framework features, and a weighted similarity score is calculated as the final matching degree.

[0033] It should be noted that the process of weighting the various performance metrics based on the matching degree is typically determined by the matching degree itself. A higher matching degree places greater weight on historical performance metrics in the final result, while a lower matching degree places greater weight on model-predicted performance metrics. This approach effectively utilizes historical data while using model predictions to compensate for its shortcomings, thereby improving prediction accuracy and robustness.

[0034] S4. Generate a list of recommended frameworks based on the predicted performance metrics and graphical interface requirements; The process of generating a list of recommended frameworks based on predicted performance metrics and graphical interface requirements includes: Calculate the coverage of the control types supported by each candidate framework to the control requirements in the project; The candidate frameworks are sorted according to the priority of preset performance indicators and control coverage to obtain a recommended framework list.

[0035] In this embodiment, it should be noted that the step of generating a recommendation framework list based on the predicted performance indicators and graphical interface requirements transforms the previously predicted performance data into recommendation results with practical guiding significance.

[0036] It's important to note that calculating the coverage of supported control types by each candidate framework to the control requirements in the project is a crucial metric for evaluating framework functionality adaptability. Control coverage is calculated by comparing the list of controls in the project requirements with the list of control types supported by the framework, calculating the proportion of controls natively supported by the framework to the total number of controls required in the project. For example, if the project requirements include 10 types of controls, and a framework natively supports 8 of them, then that framework's control coverage is 80%. A higher control coverage indicates more comprehensive functional support from the framework for the project requirements, requiring developers to implement fewer controls themselves, thus reducing development difficulty and time costs.

[0037] It should be noted that different projects have different priorities for performance metrics and functional support. For example, for battery-powered portable devices, power consumption may have a higher priority; for real-time interactive systems, response latency may be more important; and for complex interface applications, control coverage may be the primary consideration. The method of this invention allows for the pre-setting of priorities for each metric based on project characteristics, and then a comprehensive ranking of candidate frameworks based on these priorities. The ranking method can employ a weighted ranking algorithm, that is, assigning weights to each metric and calculating a weighted score, or a multi-level ranking strategy, first ranking by high-priority metrics, then by second-priority metrics, and so on.

[0038] It's important to note that the recommended framework list includes not only the sorted framework names but also key performance indicators and control coverage for each framework, as well as comparative analysis between frameworks. This information helps developers understand the rationale behind the recommendations and adjust their choices based on the specific needs of their projects.

[0039] S5. Select the target framework from the recommended framework list, generate an optimized configuration for the target framework based on the device hardware parameters and requirements, and develop the interface based on the configured target framework.

[0040] The process of generating an optimized configuration for the target framework based on device hardware parameters and requirement characteristics includes: Select the basic configuration template for the target framework from the preset configuration template library; adjust the rendering and memory-related configuration parameters according to the device hardware parameters; adjust the configuration parameters related to functional modules and resource management according to the requirements; generate a configuration file specifically for the target framework to complete the generation of the optimized configuration.

[0041] In this embodiment, it should be noted that selecting a target framework from the recommended framework list can be done by directly selecting the framework ranked first, or the developers can determine the final framework to be used from the recommended framework list based on the specific circumstances of the project.

[0042] It should be noted that the purpose of generating optimized configurations for the target framework is to improve GUI performance and resource utilization efficiency. The configuration template library stores the basic configuration templates for the target framework, which are usually configuration files containing default settings.

[0043] It's important to note that adjusting rendering and memory-related configuration parameters based on device hardware specifications is an optimization tailored to the device's hardware characteristics. For example, adjusting buffer configuration based on available memory size involves reducing the buffer size or selecting a single-buffering strategy on devices with memory below a certain threshold. These adjustments directly impact GUI rendering performance and memory usage, requiring fine-tuning based on device hardware parameters.

[0044] It's important to note that adjusting the configuration parameters of functional modules and resource management based on the requirements is an optimization tailored to the application's needs. The decision to enable the advanced layout engine is based on the interface complexity index; for example, complex layout algorithms can be disabled to save resources for interfaces with complexity below a certain threshold. Control caching strategies are selected based on the control complexity index; for example, control caching is increased to improve redraw efficiency for controls with complexity indices above a certain threshold. Animation engine parameters are configured based on the animation complexity index; for example, the animation frame rate is capped at 30fps and the easing function is configured as a linear function for high complexity, while the animation frame rate is capped at 60fps and the easing function is configured as a Bézier curve for low complexity. These adjustments ensure that the GUI framework configuration matches the requirements, preventing performance issues due to insufficient configuration and resource waste due to over-configuration.

[0045] It should be noted that generating a configuration file specific to the target framework involves converting the above adjustments into modifications to the corresponding parameters in the configuration file, thereby generating an optimized configuration. This optimized configuration provides a solid foundation for interface development, enabling developers to fully leverage the performance potential of the chosen framework and create smooth, responsive, and resource-efficient graphical user interfaces.

[0046] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0047] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

[0048] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-platform embedded graphical user interface development method, characterized in that, include: Establish a framework database, which stores feature data and performance records of various embedded GUI frameworks; The feature data includes the name, version, minimum hardware requirements, list of supported control types, rendering engine type, buffering strategy, memory management mechanism, and event handling mechanism of the embedded GUI framework. The performance data includes various performance metrics measured by each embedded GUI framework when running interfaces with different requirements under different hardware parameters. Collect the hardware parameters of the target embedded device, obtain the graphical interface requirements of the target project, and quantify them into requirement characteristics; The quantification of demand characteristics includes: Obtain the graphical interface requirements, which include: interface structure information, control configuration information, animation design information, interaction mode information, and resource usage information; The interface complexity index is evaluated based on the number of pages, page hierarchy, and page switching frequency in the interface structure information. The control complexity index is evaluated based on the total number of controls, the number of basic controls, the number of complex controls, and the control density in the control configuration information. The animation complexity index is evaluated based on the number of animations, animation type, animation duration, and frame rate requirements in the animation design information. The interaction complexity index is evaluated based on the frequency of interaction events, touch gesture types, and response time requirements in the interaction mode information. The resource demand index is assessed based on the quantity of image resources, image resolution, font type, and font size in the resource usage information. The interface complexity index, control complexity index, animation complexity index, interaction complexity index, and resource requirement index are combined into standardized requirement characteristics to complete the quantification of requirement characteristics; Based on hardware parameters, embedded GUI frameworks that meet the hardware requirements are selected from the framework database as candidate frameworks. Based on device hardware parameters, requirement characteristics and framework database, various performance indicators of each candidate framework are predicted. The predicted performance metrics for each candidate framework include: For each candidate framework: Extract hardware performance features from hardware parameters, extract framework features from characteristic data, and construct predictive input features based on hardware performance features, requirement features, and framework features; The predicted input features are fed into a pre-trained performance prediction model to predict various performance metrics of the framework. Based on hardware parameters, characteristic data, and demand features, the performance record data is matched to obtain the various performance indicators from the performance record data with the highest matching degree. The final performance indicators are generated by combining the performance indicators predicted by the model and the performance indicators obtained by matching, and by weighting them according to the matching degree. The training method for the pre-trained performance prediction model includes: Collect training data, which consists of hardware parameters, requirement characteristics, feature data and corresponding performance indicators recorded when running various interface applications implemented with different embedded GUI frameworks on multiple hardware platforms; The feature engineering process is constructed to convert hardware parameters into hardware performance features and feature data into framework features, which together with the requirement features constitute the model input. The collected data is cleaned, including removing outliers, handling missing values, and standardizing features; Design a multi-output performance prediction model structure, which adopts a deep neural network and includes a shared bottom-level feature extraction layer and multiple output branches; The training objective is constructed by taking hardware performance characteristics, requirement characteristics, and framework characteristics as inputs, and predicting multiple performance indicators as outputs. The dataset is divided into training, validation and test sets according to the proportions, and a multi-task loss function is defined to assign weight coefficients to each performance metric prediction task. The training set data is used to update the parameters, the validation set data is used to monitor the training process, and the training of the model is completed when the model's performance on the test set meets the target. A list of recommended frameworks is generated based on predicted performance metrics and graphical interface requirements. Select a target framework from the recommended framework list, generate an optimized configuration for the target framework based on the device hardware parameters and requirements, and develop the interface based on the configured target framework.

2. The multi-platform embedded graphical user interface development method according to claim 1, characterized in that, The method for calculating the matching degree includes: Extract hardware performance characteristics, requirement characteristics, and framework characteristics; Calculate the distance between the feature vector and the feature vector of each record in the performance record data, the distance being calculated using the Euclidean distance formula; The distance is normalized and converted into a similarity score between 0 and 1. The higher the similarity score, the higher the matching degree. Weight coefficients are set for hardware performance characteristics, requirement characteristics, and framework characteristics, and a weighted similarity score is calculated as the matching degree.

3. The multi-platform embedded graphical user interface development method according to claim 1, characterized in that, The process of generating a list of recommended frameworks based on predicted performance metrics and graphical interface requirements includes: Calculate the coverage of the control types supported by each candidate framework to the control requirements in the project; The candidate frameworks are sorted according to the priority of preset performance indicators and control coverage to obtain a recommended framework list.

4. The multi-platform embedded graphical user interface development method according to claim 1, characterized in that, The process of generating an optimized configuration for the target framework based on device hardware parameters and requirement characteristics includes: Select the basic configuration template for the target framework from the preset configuration template library; adjust the rendering and memory-related configuration parameters according to the device hardware parameters; adjust the configuration parameters related to functional modules and resource management according to the requirements; generate a configuration file specifically for the target framework to complete the generation of the optimized configuration.

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