A method for supporting website customization modular construction

Abstract

The application relates to the technical field of website building, and discloses a method for supporting website customization modular building. The method comprises the following steps: collecting website customization demand data sets of a module configuration sequence, a user interface preference sequence and content resource input data; performing modular architecture construction processing on the website customization demand data sets, generating module architecture features containing a page navigation level, a component interaction density and a self-adaptive layout factor; calling a preset website generation engine to perform intelligent optimization processing on the module architecture features, obtaining website preview output and core improvement area marking; performing user preference adaptation correction processing on the website preview output according to a mapping relationship between the user interface preference sequence and a design standard template; and finally generating website deployment execution scheme sets containing a component reconstruction path and an interface beautification strategy based on the core improvement area marking. The method can accurately capture user demand, improve website personalization and building efficiency, and optimize user experience.

Description

Technical Field

[0001] This invention relates to the field of website building technology, specifically to a method for supporting customized modular website building. Background Technology

[0002] With the rapid development of internet technology, the needs of various enterprises, organizations, and individuals for websites are becoming increasingly diversified, ranging from simple information display to complex interactive functions. The functions and forms of websites are constantly being innovated. Traditional website building methods often rely on professional developers to manually write code, which is not only time-consuming but also makes it difficult for users without technical backgrounds to participate in the website customization process independently, resulting in a high barrier to entry for website building.

[0003] In existing technologies, some platforms attempt to provide template-based website building services, allowing users to make minor modifications to a fixed template. However, these templates typically have significant limitations, making it difficult for users to deeply adjust the website's structure and components according to their unique needs. This often results in websites lacking personalization and failing to meet users' needs in specific scenarios.

[0004] Existing modular construction methods often struggle to achieve efficient collaboration and adaptation between modules when addressing user needs, easily leading to issues such as component interaction conflicts and unreasonable layouts. Furthermore, the lack of a precise adaptation mechanism for user interface preferences during website generation results in a significant gap between the generated website's visual appeal and user experience and user expectations. Moreover, the absence of effective optimization and improvement solutions for the generated website forces users to spend considerable time and effort on subsequent adjustments, greatly reducing the efficiency of website construction. Summary of the Invention

[0005] The purpose of this invention is to provide a method for supporting the modular construction of customized websites, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for supporting modular website customization, the method comprising:

[0007] Receive a set of website customization requirements submitted by users, the set of website customization requirements includes a module configuration sequence, a user interface preference sequence, and content resource input data;

[0008] The website customization requirement data set is processed to perform modular architecture construction to generate the module architecture features of the target website. The module architecture features include page navigation hierarchy, component interaction density and adaptive layout factor.

[0009] The pre-built website generation engine is invoked to perform intelligent optimization processing on the module architecture features, generating a website preview output and marking of core improvement areas;

[0010] The website preview output is subjected to user preference adaptation correction processing to generate a corrected website preview output. The user preference adaptation correction processing is implemented based on the mapping relationship between the user interface preference sequence and the design standard template.

[0011] Based on the core improved area markers, a set of website deployment and execution schemes is generated, which includes component reconstruction paths and interface beautification strategies.

[0012] Preferably, the modular architecture construction process performed on the website customization requirement data set to generate the modular architecture features of the target website includes the following operations:

[0013] The module configuration sequence is divided into multiple configuration subsets according to the functional grouping rules, and each configuration subset is associated with an independent page unit;

[0014] For each of the aforementioned configuration subsets, the following process is performed:

[0015] The component dependency network of the target website is constructed using the content resource input data, and the component dependency network records the binding relationship information between parent and child components;

[0016] The component dependency network and the user interface preference sequence are fused together and a collaborative computation process is performed to generate the framework construction result of the current page. The framework construction result includes component coordinate distribution, style inheritance logic and device adaptation threshold.

[0017] The framework construction results of all pages are integrated and global coordination processing is performed to derive the page navigation hierarchy, component interaction density and adaptive layout factor;

[0018] Here, the page navigation hierarchy represents the depth of the navigation path between pages, the component interaction density is calculated by multiplying the component call frequency by the weight factor, and the adaptive layout factor measures the layout stability ratio under different screen resolutions.

[0019] Preferably, the step of fusing the component dependency network and the user interface preference sequence to perform collaborative computation processing and generate the framework construction result of the current page includes:

[0020] Based on the parent-child connection relationship in the component dependency network and the layout priority data of the user interface preference sequence, a component position mapping equation is established, and the component position mapping equation is solved to obtain the component spatial distribution function.

[0021] Based on the color theme parameters and font settings of the user interface preference sequence, a style inheritance model is constructed, which integrates brand consistency rules and accessibility standards.

[0022] Combining the device adaptation threshold and responsive design principles, a layout adaptive iteration process is created, which includes a feedback mechanism for size adjustment and style update.

[0023] By integrating the component spatial distribution function, the style inheritance model, and the output of the layout adaptive iteration process, spatial interpolation is performed to generate multi-dimensional framework data that includes component coordinate distribution, style inheritance logic, and device adaptation thresholds.

[0024] Preferably, the step of calling a preset website generation engine to perform intelligent optimization processing on the module architecture features, generating website preview output and core improvement area markers, includes:

[0025] The page navigation hierarchy is input into the navigation analysis unit of the website generation engine, and the coordinates of the navigation bottleneck area and the path optimization curve are determined by the path complexity calculation module.

[0026] The component interaction density is input into the interaction analysis unit of the website generation engine to perform user behavior simulation calculations and generate inter-component collaboration efficiency values ​​and interaction delay prediction values.

[0027] The adaptive layout factor is input into the layout analysis unit of the website generation engine, and the layout break risk value and rendering time evolution sequence are calculated based on the device compatibility model.

[0028] The path optimization curve, the inter-component collaboration efficiency value, and the layout breakage risk value are combined to generate a comprehensive performance index for the target website. The website preview output is determined based on the comparison result between the comprehensive performance index and a preset threshold.

[0029] Based on the spatial overlay analysis of the coordinates of the navigation bottleneck area, the predicted interaction delay value, and the rendering time evolution sequence, the geometric locations of navigation optimization points, interaction hotspots, and layout vulnerability areas are marked.

[0030] Preferably, the step of performing user preference adaptation correction processing on the website preview output to generate a corrected website preview output includes:

[0031] Extract extreme preference values ​​and preference change frequencies from the user interface preference sequence, and calculate the dynamic adjustment amount of design elements;

[0032] Based on the dynamic adjustment amount of the design elements, visual flow compensation calculation is performed on the page navigation hierarchy to generate a corrected page navigation hierarchy.

[0033] Based on the correlation between the frequency of preference changes and user habit patterns, the component interaction density is subjected to behavior pattern adaptation processing to generate a corrected component interaction density.

[0034] Based on the aesthetic constraint data under extreme preference values, the adaptive layout factor is subjected to aesthetic consistency calibration to generate a corrected adaptive layout factor.

[0035] The corrected page navigation hierarchy, the corrected component interaction density, and the corrected adaptive layout factor are input into the website generation engine for reprocessing to generate a website preview output adapted to user preferences.

[0036] Preferably, the step of performing visual flow compensation calculations on the page navigation hierarchy based on the dynamic adjustment amount of the design elements to generate a corrected page navigation hierarchy includes:

[0037] Obtain the initial streamline coefficient of the target website under the baseline design template and the dynamic adjustment amount of the design elements, and construct the streamline coefficient and preference correlation function;

[0038] The visual streamline offset is calculated based on the streamline coefficient and the preference correlation function, and the visual streamline offset is the product of the preference change and the streamline coefficient change.

[0039] The visual streamline offset is superimposed into the calculation process of the page navigation hierarchy to generate a page navigation hierarchy correction value that includes the influence of the visual streamline.

[0040] The page navigation hierarchy correction value is processed by user attention distribution compensation, which integrates eye-tracking mode and dwell time weight factor.

[0041] Preferably, the set of website deployment and execution schemes generated based on the core improved region markers includes:

[0042] For the marked navigation optimization points, the optimal navigation reconstruction path is calculated, which is achieved by adjusting the weight distribution ratio of sibling pages;

[0043] Based on the marking of the interactive hotspots, a component interaction enhancement scheme is constructed, which includes the selection of animation effect areas and the configuration of trigger parameters;

[0044] Based on the marking of the vulnerable areas in the layout, a responsive optimization strategy is generated. The responsive optimization strategy dynamically adjusts the breakpoint settings and media query rules according to the fracture risk prediction value.

[0045] The optimal navigation reconstruction path, the component interaction enhancement scheme, and the responsive optimization strategy are prioritized and sorted to generate a set of deployment schemes containing execution order and configuration parameters.

[0046] Preferably, the component interaction enhancement scheme includes:

[0047] Extract the spatial features of the interaction hotspots and calculate the hotspot coverage and interaction intensity gradient;

[0048] The application density of the animation effect is determined based on the hotspot coverage area, and the application density is directly proportional to the hotspot coverage area.

[0049] The dynamic parameters of the animation effect are adjusted based on the interaction intensity gradient so that the dynamic change rate and the interaction intensity gradient form a preset matching relationship.

[0050] The duration of the animation effect is dynamically set based on the user's device performance data to ensure that the rendering load is below the device's processing capacity threshold.

[0051] Generate an enhancement parameter configuration table that includes application density, dynamic parameters, and duration.

[0052] Preferably, the method further includes:

[0053] Collect actual user interaction data and page loading performance data of the target website within a preset testing period;

[0054] The actual user interaction data is compared with the predicted interaction delay value to calculate the deviation and generate an interaction error correction factor.

[0055] The page loading performance data is compared with the rendering time evolution sequence to generate a rendering error correction factor.

[0056] The internal parameters of the website generation engine are adjusted according to the interaction error correction factor and the rendering error correction factor to generate an optimized website generation engine.

[0057] The optimized website generation engine will be applied to subsequent batches of website customization tasks.

[0058] Preferably, the method further includes:

[0059] The website deployment execution scheme set is simulated and processed to generate deployment verification results;

[0060] The deployment verification results are compared with the preset deployment standards to perform consistency analysis and generate a scheme adjustment instruction.

[0061] Based on the aforementioned scheme adjustment instructions, update the component reconstruction paths and interface beautification strategies in the website deployment execution scheme set;

[0062] The updated website deployment execution plan is output to the website publishing system for final deployment.

[0063] Compared with the prior art, the beneficial effects of the present invention are:

[0064] By receiving a set of website customization requirements data, including module configuration sequences, user interface preference sequences, and content resource input data, it is possible to comprehensively and accurately capture users' personalized needs, providing a sufficient basis for subsequent website construction.

[0065] The website's customized requirements data set is processed using a modular architecture to generate modular architecture features that include page navigation hierarchy, component interaction density, and adaptive layout factors. This makes the website's architecture design more scientific and reasonable, enables efficient collaboration between modules, avoids problems such as component interaction conflicts, and improves the overall stability and reliability of the website.

[0066] The system uses a pre-built website generation engine to perform intelligent optimization on the module architecture features, generating a website preview output and marking key improvement areas. This not only allows users to understand the general effect of the website in advance, but also clearly points out the areas that need improvement, making it easier for users to make targeted adjustments. This reduces the blindness of subsequent modifications and saves a lot of time and effort.

[0067] By implementing user preference adaptation and correction processing on the website preview output based on the mapping relationship between the user interface preference sequence and the design standard template, the generated website can better meet the user's expectations in terms of visual effects and user experience, enhance the website's personalized features, and improve user satisfaction.

[0068] Based on the core improved area markers, a set of website deployment execution schemes is generated, which includes component reconstruction paths and interface beautification strategies. This provides clear guidance for the final deployment of the website, ensuring that the website can be deployed in the optimal way, further improving the quality and performance of the website, and enabling the website to better meet the user's needs in various scenarios. Attached Figure Description

[0069] Figure 1 This is a schematic diagram illustrating the working principle of the method for supporting customized modular website construction as described in this invention.

[0070] Figure 2 A flowchart for constructing a modular architecture;

[0071] Figure 3 Flowchart for intelligent optimization processing;

[0072] Figure 4 A flowchart for adapting and correcting user preferences;

[0073] Figure 5 A flowchart generated for the website deployment and execution plan. Detailed Implementation

[0074] 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.

[0075] Please see Figure 1 This invention provides a method for supporting modular website customization, the method comprising:

[0076] The system receives a set of website customization requirements submitted by users, which includes module configuration sequences, user interface preference sequences, and content resource input data. It performs modular architecture construction processing on this data set, generating target website module architecture features that include page navigation hierarchy, component interaction density, and adaptive layout factors. A pre-built website generation engine is invoked to perform intelligent optimization processing on the module architecture features, producing a website preview output and markers for core improvement areas. User preference adaptation correction processing is applied to the website preview output, generating a revised website preview output. Based on the markers for core improvement areas, a set of website deployment execution plans is generated, including component refactoring paths and interface beautification strategies.

[0077] Example 1: See Figure 2 The modular architecture construction process begins with receiving a set of website customization requirements submitted by the user. This set includes a sequence of module configurations, a sequence of user interface preferences, and content resource input data. The module configuration sequence is split according to functional grouping rules, forming multiple configuration subsets, each corresponding to an independent page unit. This splitting process is based on the functional relevance of the modules, ensuring that the configuration information of the same functional module is categorized into the same subset, facilitating subsequent page unit construction.

[0078] For each configuration subset, the system first constructs a component dependency network using the content resource input data. This network records the parent-child binding relationships between components in a tree structure, clearly defining the hierarchical distribution of each component on the page. During the construction of the component dependency network, the system analyzes the component reference relationships in the input data, identifies the core components and their subordinate child components, and establishes the corresponding dependency mappings. This step ensures that subsequent layout calculations accurately reflect the actual relationships between components.

[0079] Building upon the component dependency network, the system performs collaborative computation processing in conjunction with user interface preference sequences. These user interface preference sequences include personalized configuration information such as layout priority data, color theme parameters, and font settings. Layout priority data guides the spatial distribution calculation of components, and the system adjusts the relative positions of components on the page according to their priority weights. Color theme parameters and font settings are used to construct a style inheritance model that ensures brand consistency while adhering to accessibility standards, guaranteeing that the visual presentation conforms to industry norms.

[0080] The component position mapping equation is solved using a spatial distribution algorithm, comprehensively considering the component's functional weight, user layout preferences, and screen space utilization. The calculated component spatial distribution function defines the precise coordinates of each component on the page, while reserving some dynamic adjustment space to adapt to different device sizes. The style inheritance model passes visual attributes between component levels; the style settings of a higher-level component can be selectively inherited by lower-level components, forming a unified visual style.

[0081] The system calculates device adaptation thresholds in conjunction with responsive design principles, and presets layout adjustment rules for different screen resolutions. The adaptive layout iteration process continuously monitors the display characteristics of the target device, dynamically adjusting the size and style of components. This process employs a feedback mechanism, evaluating the display effect after each layout adjustment to ensure high stability of the final presentation across different devices.

[0082] The generation process of multidimensional framework data integrates the outputs of component spatial distribution functions, style inheritance models, and layout adaptive iteration processes. The system performs spatial interpolation processing, smoothly transitioning between display differences across different devices while preserving the core layout logic. The generated framework data includes component coordinate distribution, style inheritance logic, and device adaptation thresholds, providing structured guidance for subsequent page construction.

[0083] The framework construction results of all pages undergo global coordination and processing to ultimately derive the modular architecture characteristics of the target website. The calculation of page navigation hierarchy is based on the analysis of jump paths between pages, quantifying the access depth between each level. The calculation of component interaction density combines component call frequency with preset weight factors to reflect the activity level of components within the page. The adaptive layout factor assesses the website's adaptability in multi-device environments by comparing layout stability under different resolutions.

[0084] The generation process of the modular architecture features strictly follows modular design principles. The construction logic of each page maintains independence while ensuring overall consistency through global coordination. The collaborative computation of component dependencies and user interface preference sequences ensures that the final website architecture meets functional requirements while adapting to users' personalized preferences. The adaptive layout mechanism allows the website to present optimal results on different terminal devices, enhancing the user experience.

[0085] The entire modular architecture construction process employs a layered computation strategy, progressively building the website framework from the component layout of individual pages to the overall website architecture coordination. A dynamic adjustment mechanism introduced during the computation process allows the system to flexibly respond to changes in user needs, ensuring that the generated modular architecture features possess high scalability and adaptability. The final output of page navigation hierarchy, component interaction density, and adaptive layout factors provides precise input parameters for subsequent intelligent optimization processing.

[0086] Example 2: See Figure 3 The website generation engine's intelligent optimization process begins with receiving module architecture features, which include three core data points: page navigation hierarchy, component interaction density, and adaptive layout factor. When processing page navigation hierarchy, the navigation analysis unit uses path topology analysis to identify the website's overall navigation structure. The system treats each page as a node and the jump relationships between pages as connecting edges, constructing a complete navigation network graph. By traversing and calculating this network graph, the system quantifies the complexity of each navigation path, identifying bottleneck areas where access paths are too long or have too many branches. The path optimization curve is generated based on simulation of the user's expected access patterns, adjusting the connection weights between key nodes to make access paths for core functions more direct.

[0087] When processing component interaction density, the interaction analysis unit establishes a component behavior relationship model. This model records the trigger frequency and associated responses of each component during user operations. The system simulates typical user operation sequences, recording the call relationships and response latency between components. The calculation of the inter-component collaboration efficiency value comprehensively considers factors such as trigger timing, data transmission efficiency, and visual feedback speed. The interaction latency prediction value is obtained by analyzing the length of the component dependency chain and computational load, marking critical paths that may affect the user experience. A progressive loading strategy is adopted during the simulation to ensure that the initialization of complex components does not affect the overall interaction smoothness.

[0088] When processing adaptive layout factors, the layout analysis unit constructs a multi-device compatibility evaluation system. The system establishes a test matrix based on the screen parameters of mainstream devices, including combinations of different sizes, resolutions, and pixel densities. The calculation of layout breakage risk values ​​is based on the actual rendering effects on each test device, detecting issues such as element misalignment, content truncation, and scaling imbalance. The rendering time evolution sequence records the page loading process on different devices, analyzing the time distribution of style calculations, layout rearrangement, and drawing operations. A dynamic sampling mechanism is employed during the evaluation process to control computational resource consumption while ensuring detection accuracy.

[0089] The generation process of comprehensive performance indicators integrates optimization results from three dimensions: navigation, interaction, and layout. The system establishes a unified scoring system, converting path optimization curves, inter-component collaboration efficiency values, and layout breakage risk values ​​into comparable quantifiable data. The scoring calculation considers the weighted impact of different indicators on user experience, with navigation efficiency, interaction smoothness, and layout stability each occupying an appropriate proportion. The website preview output is generated based on a comparison of the scoring results with preset thresholds; the system automatically selects a design scheme that meets all key indicators. For schemes approaching the threshold, the system marks potential risk points for subsequent optimization reference.

[0090] The marking of core improvement areas employs a spatial overlay analysis method. The system uniformly maps the coordinates of navigation bottleneck areas, the component positions corresponding to predicted interaction delays, and the screen coordinates of layout vulnerability areas onto the website structure diagram. The marking process considers the spatial distribution characteristics of different problems. Navigation optimization points are typically concentrated in the main navigation menu and page navigation links, interaction hotspots often appear around form controls and dynamic content components, and layout vulnerability areas are distributed in groups of elements near responsive breakpoints. The system uses color coding and transparency adjustment to achieve visual differentiation of multi-layered markings, facilitating developers to quickly locate problems.

[0091] The navigation analysis unit employs an incremental optimization strategy, making localized adjustments while maintaining the original navigation structure. After the path complexity calculation module identifies redundant jump steps, the system assesses the feasibility of directly connecting to related pages. Optimization solutions may include adding quick navigation entry points, merging pages with similar functions, or adjusting the menu hierarchy. User habit path analysis data is retained during the calculation process to ensure that the optimized navigation method meets the operational expectations of most users.

[0092] The user behavior simulation in the interaction analysis unit adopts an event-driven model. The system constructs typical user profiles to simulate the operating habits of users with different levels of proficiency. Component call frequency statistics cover various scenarios, including single operation triggers, continuous operation sequences, and abnormal operation recovery. The interaction latency prediction model considers changes in network conditions and differences in device performance, testing the response performance of components under different environments. The analysis results will mark components with large fluctuations in response time, prompting developers to optimize data processing logic or add loading status feedback.

[0093] The layout analysis unit's device compatibility model adopts a progressive enhancement design principle. The system first ensures correct rendering of the website on baseline devices, and then gradually adds enhanced styles for high-end devices. Media query rules are optimized based on actual test data, adjusting breakpoint settings to better match the screen characteristics of mainstream devices. Layout stability testing covers common scenarios such as landscape / portrait switching, font size adjustments, and window scaling, ensuring consistent website performance in dynamic environments.

[0094] The output of intelligent optimization processing includes a directly viewable website preview and structured issue tagging data. The preview version retains full interactive functionality, allowing developers to verify the optimization effects through hands-on testing. Core improvement area tags are stored in a machine-readable format, containing detailed information such as issue type, location coordinates, severity, and remediation suggestions.

[0095] Example 3: See Figure 4 The user preference adaptation and correction process begins by parsing the user interface preference sequence. This system module performs multi-dimensional analysis of the raw preference data, extracting extreme preference values ​​with significant characteristics and the frequency of preference changes reflecting shifts in usage habits. Extreme preference values ​​refer to parameter settings that significantly deviate from conventional design specifications, such as ultra-high contrast color schemes or unconventional font size settings. The frequency of preference changes is derived by statistically analyzing the distribution density of interface adjustment operations in historical operation records, reflecting the frequency with which users adjust specific interface elements. The calculation of dynamic adjustment amounts for design elements employs a dynamic weighted algorithm, combining the degree of deviation of extreme preference values ​​with the statistical characteristics of preference change frequencies to generate correction parameters for different interface elements.

[0096] When optimizing page navigation hierarchy, the visual streamline compensation calculation system establishes a streamline offset model. The core calculation process of this model can be represented as follows:

[0097] ,

[0098] in This represents the total offset of the visual streamline. The function representing the intensity of preference as it changes over time. The function representing the dynamic change of the reference streamline coefficient. arrive It covers the entire user operation cycle. The function implements a nonlinear mapping between preference parameters and streamline coefficients, and internally incorporates regression analysis results from eye-tracking data. During the calculation, the system dynamically tracks the user's visual trajectory on the prototype interface, converting the fixation duration of hotspot areas into spatial weight coefficients.

[0099] User attention distribution compensation processing integrates multi-source perceptual data, including but not limited to visual hotspot distribution recorded by eye-tracking, click density distribution in interface operation logs, and dwell area statistics from page scrolling behavior analysis. Dwell time weighting factors employ differentiated calculation strategies based on different types of interface elements: a reading speed model is used for text content areas, visual complexity assessment is used for image media areas, and operation feedback delay analysis is used for interactive control areas. The compensation algorithm reconstructs the user's visual focus distribution in a three-dimensional coordinate system, generating an attention heatmap with a time dimension.

[0100] When adjusting component interaction density, the behavior pattern adaptation process constructs a user habit feature model. This model analyzes operation sequence patterns in historical interaction data to identify high-frequency operation combinations and typical workflows. The adaptation process preserves the statistical distribution characteristics of the original interaction density, only smoothly adjusting parameters that clearly conflict with user habits. The processing algorithm employs a progressive optimization strategy, gradually migrating component response patterns towards user preferences while maintaining the core functional interaction logic. The system maintains a dynamically updated habit pattern library, recording typical interaction characteristics of different user groups as a reference benchmark for adaptation processing.

[0101] Aesthetic consistency calibration optimizes adaptive layout factors under extreme preference constraints. The system establishes an aesthetic evaluation matrix, quantifying abstract indicators such as color harmony, visual balance, and element rhythm into calculable parameters. The calibration process employs a constraint satisfaction algorithm to maintain basic aesthetic principles while meeting users' extreme preference requirements. During processing, the system generates multiple candidate layout schemes and selects the optimal solution using the aesthetic evaluation matrix. For conflicting parameter settings, the system automatically generates compromise solutions and visually compares the aesthetic differences between the various schemes.

[0102] The integrated application of parameter corrections employs a layered, progressive strategy. First, the system applies visual flow compensation at the layout framework layer to adjust the overall page structure. Then, it implements behavioral pattern adaptation at the component collection layer to optimize interactive response logic. Finally, it performs aesthetic consistency calibration at the visual presentation layer to refine the detailed presentation. Each level of correction retains the key features of the original design, adjusting only those parts that significantly deviate from user preferences. During the correction process, the system monitors changes in various design metrics in real time to prevent excessive adjustment of a single parameter from disrupting the overall balance.

[0103] When the revised module architecture features are re-input into the website generation engine, the system activates a differential processing mode. This mode only recalculates the modules affected by the revisions, retaining the original optimization results for unmodified areas. During processing, the system establishes a model to predict the chain reactions that local changes may trigger and pre-compromises parameters. The regenerated website preview output includes a revision documentation detailing the basis and implementation method for each adjustment.

[0104] A continuous learning mechanism for user preference data runs in the background, automatically collecting user interaction data with the revised interface. The learning algorithm identifies new preference features and updates the user profile model. For recurring adjustments, the system converts them into persistent preference settings; for temporary adjustments, they are recorded as context-dependent parameters. The learning process employs a forgetting curve model to automatically decay long-unused preference data, maintaining the timeliness of the user profile.

[0105] The conflict detection mechanism for extreme preference values ​​runs continuously during the correction process. When contradictory preference settings are detected, the system initiates a negotiation process. This process first attempts to eliminate the conflict through parameter fine-tuning; if consensus cannot be reached, multiple alternative solutions are generated for the user to choose from. The conflict detection algorithm considers the interrelationships between different design dimensions, avoiding the isolation of implicit conflicts caused by a single parameter. The system maintains a design rule knowledge base, storing common conflict patterns and solutions to accelerate the negotiation process.

[0106] The verification process for preference adaptation and correction employs a comparative evaluation method. The system simultaneously displays key interfaces of both the original and corrected outputs, highlighting the adjustment effect through side-by-side comparison. Real-time user feedback is collected during verification, and any unsatisfactory aspects are subject to further correction. The verification algorithm quantifies user satisfaction for each adjustment item, establishing a preference achievement evaluation model to provide a reference for subsequent optimization. The system retains a complete correction history, supporting the rewinding of adjustments at any stage.

[0107] Example 4: See Figure 5The process of generating a set of website deployment execution solutions is based on the specific analysis results of the core improvement area markers. Taking an e-commerce website redesign project as an example, the system identified three key areas that need optimization: the navigation path of the main navigation menu (navigation optimization points), the interactive response of the product filter control (interaction hotspots), and the display issues of the product list on different mobile devices (layout vulnerability areas). For these marked areas, the system constructs specific optimization solutions step by step.

[0108] Navigation optimization began with an analysis of the existing navigation structure. The system detected that users needed three redirects to reach a specific product category page from the homepage, while similar websites typically only required two. By analyzing user access path data, the system proposed upgrading the "Home Appliances" category from a second-level menu to a first-level menu. The weight allocation was adjusted based on actual traffic data for each category page, giving higher menu priority to frequently accessed categories.

[0109] The optimization of interactive hotspots focuses on improving the user experience of the product filtering controls. The system identified a high error rate in user operations on the price range slider control, often requiring multiple adjustments to achieve the desired range. Analysis of user operation patterns revealed that the slider control's touch-sensitive area was small and lacked a fine-tuning mechanism. Solutions included expanding the touch-sensitive area, adding numerical input boxes, and preset frequently used range buttons. For the animation enhancement, a gradual rendering approach was used instead of a full refresh during the loading process of the filtering results, reducing visual abruptness. Dynamic parameter settings considered the performance of different devices, enabling richer transition animations on high-end devices and maintaining a simple effect on low-end devices.

[0110] Taking the product list page as an example, the handling of layout vulnerabilities addresses this issue. The system detected image distortion and text truncation on product cards on small-screen devices. Analysis of layout performance at different breakpoints revealed display defects in the original media query rules within the two key resolution ranges of 320px and 375px. Optimization strategies included adjusting the aspect ratio calculation method for product cards, changing fixed pixel values ​​to relative units; reconstructing the floating rules of the grid layout to ensure correct alignment during line breaks; and optimizing the scaling strategy for image containers, prioritizing the complete display of key visual elements. The breakpoint settings also incorporated special rules for full-screen devices, better adapting to irregularly shaped screen display areas.

[0111] The implementation of the component interaction enhancement solution adopts a phased strategy. Taking the "Add to Cart" button on the product details page as an example, the system first analyzes the user's behavioral characteristics when operating this control. The heatmap shows that users tend to click rapidly and continuously, indicating that the current feedback is not obvious enough. The enhancement solution designs improvements at three levels: visually, it adds color changes and subtle displacement effects to the pressed state; interactively, it adds click vibration feedback on supported devices; and functionally, it optimizes response speed and displays progress indicators during request processing. Controlling application density ensures that animation effects do not excessively consume system resources, automatically switching to a simplified version in low-power mode.

[0112] The responsive optimization strategy employs a dynamic adjustment mechanism. The system continuously monitors actual user device data and automatically updates the device feature database. For newly emerging screen sizes or resolutions, the system generates temporary adaptation solutions, which are then converted into official rules after thorough testing. The overall style of the design system is preserved during layout optimization; all adjustments adhere to consistent spacing, rounded corners, and shadow parameters to ensure visual consistency. Media query rule optimization pays special attention to mobile device screen orientation switching scenarios, ensuring smooth layout transitions without flickering or reflow delays.

[0113] The deployment schemes were prioritized using a multi-dimensional evaluation model. Each optimization scheme was scored based on three dimensions: implementation difficulty, expected benefits, and scope of impact. Navigation structure adjustments were given the highest priority due to their involvement with the global menu; optimizations of interactive hotspots were prioritized based on user frequency, with product filtering controls preceding less important functions; layout optimizations were prioritized based on device coverage, addressing display issues affecting a large number of users first. The execution order was arranged considering the dependencies between schemes, ensuring that basic layout optimizations preceded detailed interaction enhancements.

[0114] Configuration parameters are stored using version control. Each optimization scheme records complete parameter settings and applicable conditions, facilitating subsequent adjustments and rollbacks. The system generates detailed deployment documentation, explaining the operation methods and verification standards for each step. For complex adjustments requiring manual intervention, the documentation provides specific code modification examples and test cases. The configuration management system supports phased releases of schemes, allowing for canary testing with specific user groups, collecting feedback before full rollout.

[0115] The solution verification phase employed real-world user scenario testing. Navigation optimization was assessed through task completion rate evaluation; given a specific product search task, success rate and time were recorded. Interaction optimization was assessed through operation smoothness evaluation, analyzing the number of attempts and satisfaction ratings of users to complete the target operation. Layout optimization was assessed through cross-device display consistency evaluation, checking the presentation effect of key pages in the test device matrix. All test results were converted into quantitative indicators and compared with baseline data before optimization.

[0116] An exception handling mechanism is implemented throughout the entire deployment process. The system anticipates various potential compatibility issues and prepares corresponding fallback plans. For browser feature differences, a progressive enhancement strategy is adopted; for device performance limitations, multi-level degradation plans are set up; and for network fluctuations, resource loading priorities are optimized. The monitoring system tracks post-deployment performance metrics in real time and automatically triggers pre-set contingency plans when anomalies are detected, ensuring the website's normal operation.

[0117] Example 5: The continuous optimization mechanism after website deployment begins with data collection from the actual operating environment. Within a preset testing period, the system comprehensively collects real user behavior data and performance metrics of the target website through a combination of event tracking and log analysis. User interaction data collection covers dimensions such as interface operation events, navigation path trajectories, and component trigger sequences, recording complete behavioral context information. Page loading performance data collection employs resource loading timeline recording technology to accurately measure the time consumption at each stage from initial request to full interactivity. The data collection process follows the principle of minimum necessity, minimizing interference with user privacy while meeting analytical requirements.

[0118] The collected raw data undergoes standardization before entering the analysis process. The comparison between user interaction data and the prediction model employs event flow alignment technology, mapping the actual operation sequence to the expected interaction pattern item by item. A deviation detection algorithm identifies actual operations that significantly deviate from the prediction, including unexpected shortcut paths, ignored recommended function entry points, and repeatedly attempted erroneous operations. The interaction error correction factor is calculated by comprehensively considering factors such as deviation frequency, duration, and scope of impact, resulting in quantitative adjustment suggestions for the prediction model.

[0119] The analysis of page load performance data employs a time-series matching method. The system decomposes the actual measured rendering process into multiple key stages and compares these stages with the predicted rendering time evolution sequence. Difference detection focuses on the relative time changes of each stage, identifying performance bottlenecks unique to the real-world environment. Rendering error correction factors are calculated separately for different device types, reflecting the accuracy differences of the prediction model under different hardware configurations. The analysis process pays particular attention to the performance difference patterns between mobile and desktop devices, ensuring that the corrected model can adapt to multi-platform environments.

[0120] The website generation engine employs an incremental learning strategy for internal parameter adjustments. Guided by interaction error correction factors and rendering error correction factors, the system optimizes the engine's prediction logic. The navigation prediction module adjusts its path weight calculation formula to enhance adaptability to actual user preferences. The interaction simulation module updates its component response time prediction algorithm to better reflect performance changes under different network conditions. The layout rendering module corrects its device feature recognition logic to improve the accuracy of compatibility assessments for new terminal devices. The parameter adjustment process retains the engine's core architecture, only progressively optimizing configurable parameters to ensure system stability.

[0121] The simulated operating environment was built using containerization technology, creating a test space isolated from the production environment but with the same configuration. Deployment verification tests covered normal operation scenarios and boundary condition scenarios, simulating the usage of different user roles under various network environments. Test scripts automatically executed standardized operation sequences, recording interface response and resource consumption changes. The verification results were evaluated using a difference labeling system, visually annotating the deviation between actual performance and expected goals to facilitate rapid problem identification.

[0122] A multi-level inspection system is established through standard consistency analysis. Basic standards verify the achievement of technical indicators, including quantitative parameters such as loading time, response speed, and rendering accuracy. Experience standards assess user-perceived quality, checking subjective indicators such as layout stability, interaction consistency, and visual comfort. Business standards confirm the integrity of core functions, ensuring that all optimization solutions support established business processes. The analysis process generates detailed compliance reports, indicating the pass status and exception details for each inspection item.

[0123] The generation of solution adjustment instructions employs a problem tracing mechanism. For inconsistencies discovered during verification, the system analyzes the root causes and impact paths, proposing targeted corrective suggestions. The instructions include specific parameter modification ranges, optional implementation methods, and relevant constraints. Adjustment strategies are categorized into emergency fixes and long-term optimizations; critical issues affecting core functionality are addressed immediately, while improvements to user experience details are incorporated into the iteration plan. The instruction generation algorithm considers dependencies between solutions and automatically plans a reasonable adjustment sequence.

[0124] The final deployment of the website publishing system adopts a phased release strategy. The system divides updated content into multiple logical units, arranging release batches according to functional importance and impact. An observation period is set after each batch deployment to confirm system stability through real-time monitoring. The release process supports a fast rollback mechanism, automatically reverting to the previous stable version when abnormal indicators are detected. Post-deployment performance tracking continues, collecting operational data in a real-world environment and feeding it back to the optimization system, forming a closed loop of continuous improvement.

[0125] The continuous operation of the optimization mechanism establishes an automated workflow. The system periodically initiates new testing cycles, collects the latest operational data, performs analysis and comparison, generates optimization suggestions, and implements adjustments. Quality checkpoints are set at each stage of the workflow to ensure data accuracy and processing effectiveness. The knowledge base system automatically accumulates historical optimization experience, forming a case study library for reference in subsequent tasks. The entire optimization process is highly automated, retaining only manual confirmation at key decision points, balancing efficiency and reliability.

[0126] An anomaly handling mechanism is implemented throughout the entire optimization lifecycle. The system pre-defines various possible anomaly scenarios, including data acquisition interruptions, analysis timeouts, and optimization result conflicts, and prepares corresponding contingency plans for each situation. The monitoring system tracks the operational status of each stage in real time and triggers pre-defined countermeasures when deviations from the normal range are detected. Anomaly handling employs a tiered response strategy, automatically selecting the appropriate handling intensity based on the severity of the problem, ranging from simple retry operations to complete process rollback.

[0127] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0128] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method of supporting website customization modular building, characterized in that, The method comprises: receiving a user-submitted website customization requirement data set, the website customization requirement data set containing a module configuration sequence, a user interface preference sequence, and content resource input data; performing a modular architecture construction process on the website customization requirement data set to generate module architecture features of a target website, the module architecture features including page navigation hierarchy, component interaction density, and adaptive layout factors; calling a preset website generation engine to perform intelligent optimization processing on the module architecture features to generate a website preview output and a core improvement area marker; implementing user preference adaptation correction processing on the website preview output to generate a corrected website preview output, the user preference adaptation correction processing being implemented according to a mapping relationship between the user interface preference sequence and a design standard template; generating a website deployment execution scheme set based on the core improvement area marker, the website deployment execution scheme set containing component reconstruction paths and interface beautification strategies; the modular architecture construction process performed on the website customization requirement data set to generate the module architecture features of the target website comprises the following operations: The module configuration sequence is split into multiple configuration subsets according to a function grouping rule, and each configuration subset is associated with an independent page unit: For each configuration subset, the following process is performed: Use the content resource input data to construct a component dependency network of the target website, which records the binding relationship information between parent and child components; perform collaborative computing processing on the component dependency network and the user interface preference sequence to generate a framework construction result of the current page, which covers component coordinate distribution, style inheritance logic, and device adaptation threshold; integrate the framework construction results of all pages to perform global coordination processing to derive the page navigation hierarchy, component interaction density, and adaptive layout factors; here, the page navigation hierarchy represents the hierarchical depth measurement of the page jump path, and the component interaction density is calculated by the product of the component call frequency and the weight factor.

2. The method of claim 1, wherein, The collaborative computing processing of the component dependency network and the user interface preference sequence to generate the framework construction result of the current page comprises: Based on the parent-child connection relationship in the component dependency network and the layout priority data of the user interface preference sequence, a component position mapping equation is established, and a component space distribution function is obtained by solving the component position mapping equation, which defines the accurate coordinates of each component in the page while reserving dynamic adjustment space to adapt to different device sizes; According to the color theme parameters and font setting values of the user interface preference sequence, a style inheritance model is constructed, which transfers visual attributes between component levels, and the style settings of upper-level components are selectively inherited by lower-level components to form a unified visual style; Combined with the device adaptation threshold and the responsive design principle, a layout adaptive iteration process is created, which contains a feedback mechanism for size adjustment and style update; The output of the component spatial distribution function, the style inheritance model and the layout adaptive iteration process is subjected to spatial interpolation processing to generate multi-dimensional framework data containing component coordinate distribution, style inheritance logic and device adaptation threshold.

3. The method of claim 2, wherein, The preset website generation engine is called to perform intelligent optimization processing on the module architecture features to generate website preview output and core improvement area markers, including: The page navigation hierarchy is input into a navigation analysis unit of the website generation engine to determine the coordinates and path optimization curve of the navigation bottleneck area through a path complexity calculation module, the navigation bottleneck area being an area with excessively long access paths or excessively many branches; The component interaction density is input into an interaction analysis unit of the website generation engine to perform user behavior simulation calculation to generate inter-component cooperation efficiency values and interaction delay prediction values; The adaptive layout factor is input into a layout analysis unit of the website generation engine to calculate layout fracture risk values and rendering time evolution sequences based on a device compatibility model; The path optimization curve, the inter-component cooperation efficiency values and the layout fracture risk values are combined to generate comprehensive performance indicators of the target website, and the website preview output is determined according to a comparison result of the comprehensive performance indicators and a preset threshold; Based on spatial superposition analysis of the coordinates of the navigation bottleneck area, the interaction delay prediction values and the rendering time evolution sequences, the geometric positions of navigation optimization points, interaction hotspots and layout vulnerable areas are marked.

4. The method of claim 3, wherein, The user preference adaptation correction processing is performed on the website preview output to generate a corrected website preview output, including: Extreme preference values and preference change frequencies in the user interface preference sequence are extracted to calculate design element dynamic adjustment amounts; Visual flow line compensation calculation is performed on the page navigation hierarchy according to the design element dynamic adjustment amounts to generate a corrected page navigation hierarchy; Behavior pattern adaptation processing is performed on the component interaction density based on the association characteristics of the preference change frequencies and user habit patterns to generate a corrected component interaction density; Aesthetic consistency calibration processing is performed on the adaptive layout factor according to aesthetic constraint data under extreme preference values to generate a corrected adaptive layout factor; The corrected page navigation hierarchy, the corrected component interaction density and the corrected adaptive layout factor are input into the website generation engine for reprocessing to generate a website preview output adapted to user preferences.

5. The method of claim 4, wherein, The visual flow line compensation calculation is performed on the page navigation hierarchy according to the design element dynamic adjustment amounts to generate a corrected page navigation hierarchy, including: Initial flow line coefficients of the target website under a benchmark design template and the design element dynamic adjustment amounts are obtained to construct a flow line coefficient and preference association function; Visual flow line offset amounts are calculated according to the flow line coefficient and preference association function, the visual flow line offset amounts being products of preference change amounts and flow line coefficient change amounts; The visual flow line offset amounts are superimposed into a calculation process of the page navigation hierarchy to generate page navigation hierarchy correction values containing visual flow line influences; and The website preview output adapted to user preferences is generated by inputting the page navigation hierarchy correction values, the component interaction density and the adaptive layout factor into the website generation engine for reprocessing. The user attention distribution compensation processing is performed on the page navigation hierarchy correction value, and the user attention distribution compensation processing integrates an eye movement tracking mode and a dwell time weight factor.

6. The method of claim 5, wherein, The website deployment execution scheme set is generated based on the core improvement area label, and the website deployment execution scheme set includes: For the label of the navigation optimization point, an optimal navigation reconstruction path is calculated, and the optimal navigation reconstruction path is realized by adjusting the weight distribution proportion of sibling pages. According to the label of the interaction hot spot, a component interaction enhancement scheme is constructed, and the component interaction enhancement scheme includes selection and trigger parameter configuration of an animation effect area. Based on the label of the layout fragile area, a responsive optimization strategy is generated, and the responsive optimization strategy dynamically adjusts breakpoint settings and media query rules according to a fracture risk prediction value; The optimal navigation reconstruction path, the component interaction enhancement scheme and the responsive optimization strategy are subjected to execution priority sorting processing to generate a deployment scheme set including execution order and configuration parameters.

7. The method of claim 6, wherein, The component interaction enhancement scheme includes: Extract the spatial features of the interaction hot spot, calculate the hot spot coverage range and the interaction intensity gradient; According to the hot spot coverage range, the application density of the animation effect is determined, and the application density is in a positive correlation relationship with the hot spot coverage range; Based on the interaction intensity gradient, the dynamic parameters of the animation effect are adjusted, so that the dynamic change rate and the interaction intensity gradient form a preset matching relationship; According to the user equipment performance data, the duration of the animation effect is dynamically set to ensure that the rendering load is lower than the device processing capability threshold; An enhancement parameter configuration table including the application density, the dynamic parameters and the duration is generated.

8. The method of claim 7, wherein, The method further includes: In a preset test period, actual user interaction data and page loading performance data of the target website are collected; The actual user interaction data and the predicted interaction delay prediction value are subjected to deviation calculation processing to generate an interaction error correction factor; The page loading performance data and the rendering time evolution sequence are subjected to time sequence comparison processing to generate a rendering error correction factor; According to the interaction error correction factor and the rendering error correction factor, the internal parameters of the website generation engine are adjusted to generate an optimized website generation engine; The optimized website generation engine is applied to subsequent batches of website customization tasks.

9. The method of claim 8, wherein, The method further includes: The website deployment execution scheme set is subjected to simulation running processing to generate a deployment verification result; The deployment verification result and a preset deployment standard are subjected to consistency analysis processing to generate a scheme adjustment instruction; Based on the scheme adjustment instruction, the component reconstruction path and the interface beautification strategy in the website deployment execution scheme set are updated; The updated website deployment execution scheme set is output to a website publishing system for final deployment.

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