Visual page editing method based on intelligent television terminal

Abstract

The embodiment of the invention provides a visual page editing method based on a smart television terminal. The method comprises the steps of obtaining user behavior data and generating an interaction path analysis result; identifying an operation redundancy link of the page element based on the interaction path analysis result; automatically generating optimized page element interaction logic based on the operation redundancy link; and mapping the optimized page element interaction logic to a visual editing interface for real-time verification. Through the scheme of the embodiment of the invention, the page element interaction logic can be automatically optimized according to the user behavior data analysis result, so that the problem of user loss caused by lengthy operation path is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent television, in particular to a visual editing method for intelligent television terminal page. BACKGROUND

[0002] The visual editing method for intelligent television terminal page refers to a method that allows users to flexibly configure and modify the page layout and interactive logic of the television terminal through a graphical interface without programming, thereby improving the efficiency of content management and user experience. A key problem faced by this method in practical application is how to automatically optimize the interactive logic of page elements based on user behavior data analysis results to solve the problem of user loss caused by long operation paths, that is, how to simplify the operation process of users and improve their satisfaction and retention rate through intelligent means without reducing user experience. SUMMARY

[0003] Therefore, the embodiments of the present disclosure provide a visual editing method for intelligent television terminal page to at least partially solve the problems in the prior art.

[0004] A visual editing method for intelligent television terminal page, comprising:

[0005] Obtaining user behavior data and generating an interactive path analysis result;

[0006] Identifying operation redundant links of page elements based on the interactive path analysis result;

[0007] Generating an optimized page element interactive logic based on the operation redundant links;

[0008] Mapping the optimized page element interactive logic to a visual editing interface for real-time verification.

[0009] In one specific embodiment, the obtaining user behavior data and generating an interactive path analysis result further comprises:

[0010] Obtaining the click position, click frequency and dwell time of the user;

[0011] Counting the number of user interaction behaviors on each page element;

[0012] Calculating the user satisfaction index S based on the following formula: S = (K x T) / D, where K is the user interaction frequency, T is the dwell time (seconds), and D is the number of page operation steps;

[0013] Dividing the user into high / medium / low satisfaction levels according to the size of S.

[0014] In one specific implementation, the redundant step of identifying page elements based on the interaction path analysis results further includes:

[0015] Determine the number N of unclicked buttons on the page;

[0016] Collect user click path records P;

[0017] Construct a node weight model and set a threshold θ;

[0018] If the jump time T_ij between any two consecutive nodes is less than θ, it is determined that there is a redundant link.

[0019] In one specific implementation, the automatic generation of optimized page element interaction logic based on the operational redundancy further includes:

[0020] Extract the element combination F from the redundant steps;

[0021] Establish a dependency matrix M between elements, where M[i][j] = 1 indicates that i and j are related;

[0022] Applying a fuzzy clustering algorithm to the set of elements yields the optimal layout scheme;

[0023] Calculate the optimization coefficient R = α × W + β × C for each solution, where W is the user experience weight and C is the simplification complexity.

[0024] In one specific implementation, the automatic generation of optimized page element interaction logic based on the operational redundancy further includes:

[0025] Identify frequently used button A and inefficient path P;

[0026] Set the operation path compression factor λ;

[0027] The interaction path is reconstructed using the following formula: P = P × λ, where λ ∈ (0, 1) represents the degree of operation reduction;

[0028] If the reconstructed path length is less than the preset minimum threshold L_min, then the default layout scheme will be forcibly enabled.

[0029] In one specific implementation, the step of mapping the optimized page element interaction logic to the visual editing interface for real-time verification further includes:

[0030] Synchronously update the interactive objects and their states in the visual layout;

[0031] Display a comparison chart of operation paths to evaluate the optimization effect;

[0032] Collect user feedback scores (F) for the optimized page;

[0033] If F ≥ the set value, the optimization is confirmed to be effective; otherwise, a rollback mechanism is triggered.

[0034] In one specific implementation, the redundant step of identifying page elements based on the interaction path analysis results further includes:

[0035] Record the average time D_avg required for a user to complete one operation;

[0036] Calculate the current path complexity exponent C = ∑(D_i) / n, where D_i is the time for each step and n is the number of operation steps;

[0037] Redundancy is determined based on the following formula: if C > D_avg × ε, then the path segment is marked as a redundant path, where ε ∈ [2, 5].

[0038] Set a redundancy threshold and include redundant paths in the optimization library.

[0039] In one specific implementation, the automatic generation of optimized page element interaction logic based on the operational redundancy further includes:

[0040] Use a Markov chain model to predict the node V_i that the user may operate on next;

[0041] Calculate the transition probability P(V_j|V_i) to determine the optimal jump path;

[0042] Introduce a user habit weight α to adjust the prediction results so that they are closer to actual user behavior;

[0043] The new path is compared with the original path, and the path with a similarity greater than β is selected as the optimization scheme.

[0044] In one specific implementation, the step of mapping the optimized page element interaction logic to the visual editing interface for real-time verification further includes:

[0045] Convert the generated page structure into a visual layout tree;

[0046] Perform a simulated click test and record the response time (RT);

[0047] The performance index P is calculated using the following formula: P = k × (1 / RT_max / RT), where RT_max is the expected maximum response time and k is the adjustment coefficient.

[0048] If P ≥ 8, the verification is considered successful, and the release process begins.

[0049] In one specific implementation, the automatic generation of optimized page element interaction logic based on the operational redundancy further includes:

[0050] Establish the correlation matrix R between different operation paths;

[0051] For each redundant path, calculate the redundancy score: Score = Σ(Ci × Wi), where Ci is the number of steps and Wi is the user's attention weight.

[0052] The random forest algorithm is used to filter highly redundant paths and assign them priority.

[0053] By inputting highly redundant paths into the path optimization model, a new logical structure is obtained.

[0054] In one specific implementation, the step of mapping the optimized page element interaction logic to the visual editing interface for real-time verification further includes:

[0055] Load and render the optimized page view;

[0056] Capture operation flow information, such as click sequence and response latency;

[0057] Construct the validation metric I = a × (F1_score) + b × (RT_avg), where F1_score is used to evaluate the accuracy of click behavior recognition, and RT_avg is the average latency time;

[0058] When I>c and the user retention criteria are met, the system will automatically submit an update.

[0059] This disclosure provides a method for visually editing pages on a smart TV terminal, comprising: acquiring user behavior data and generating interaction path analysis results; identifying redundant operational steps of page elements based on the interaction path analysis results; automatically generating optimized page element interaction logic based on the redundant operational steps; and mapping the optimized page element interaction logic to a visual editing interface for real-time verification. The solution provided by this disclosure addresses how to automatically optimize page element interaction logic based on user behavior data analysis results, thereby resolving the problem of user churn caused by lengthy operation paths. Attached Figure Description

[0060] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0061] Figure 1 This is a flowchart of a visual editing method for smart TV terminal pages. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings. The illustrative implementation methods and descriptions of the embodiments of this disclosure are only used to explain the embodiments of this disclosure and are not intended to limit the embodiments of this disclosure.

[0063] Next, refer to Figure 1 This invention describes the specific steps of a visual editing method for smart TV terminal pages. The first step is to acquire user behavior data and generate interaction path analysis results. The system continuously collects user behavior data, such as clicks, navigation, dwell time, and page browsing, by installing a monitoring module on the smart TV terminal. After cleaning and structuring, this data is input into the interaction path analysis algorithm to obtain the user's actual page access path and operating habits, such as high-frequency operation nodes and low-frequency areas.

[0064] Next, the system identifies redundant operational steps in page elements based on interaction path analysis results. Through statistical analysis of large amounts of user behavior data, the system can identify invalid navigation or complex operational chains on the page. For example, some buttons may be clicked too infrequently but are located at too high a level, or multiple buttons with duplicate functions may lead to user misoperation. In this case, the system can automatically generate markers for redundant operational steps, providing a basis for further optimization. For instance, a movie recommendation page might have multiple sub-menus to display movie types, but data analysis reveals that most users directly jump to the top category, making the sub-menu hierarchy cumbersome.

[0065] Then, the system automatically generates optimized page element interaction logic based on redundant operational steps. This step involves a combination of machine learning algorithms and a rule engine. The system can automatically adjust the page layout and interaction methods based on historical data, such as merging duplicate function buttons, moving frequently used elements to more prominent positions, or reducing user operation steps through dynamic layout adjustments. At the same time, the system also retains a certain amount of manual intervention interface to ensure that editors can further fine-tune the interaction logic according to actual needs. Specifically, in one embodiment, when analysis reveals that over 65% of users directly enter the video playback page from the homepage, but the existing design requires at least three clicks, the system automatically generates an optimization solution: displaying recommended movies as quick access points in the center of the homepage, reducing the number of steps required for entry and making the user experience smoother.

[0066] Finally, the optimized page element interaction logic is mapped to a visual editing interface for real-time verification. The system utilizes a visual drag-and-drop design tool to intuitively present the optimized interaction logic to developers and operations personnel. They can directly view the adjusted layout effect in the interface and test user feedback under different operation flows. This mechanism not only speeds up debugging but also helps identify problems promptly, reducing resource waste caused by human error. For example, suppose an optimization attempt moves the settings button to the upper left of the main interface. The system will display this change in the visual environment and simulate user click behavior to confirm its effectiveness. If problems are found during verification, the system can quickly roll back the modification or iterate again for optimization.

[0067] The benefits of this technical solution are multifaceted. First, by automating the analysis and adjustment of user operation paths, it effectively solves the problem of cumbersome operations inherent in traditional page design, significantly improving user efficiency. Second, the introduction of visual editing tools makes the optimization process more intuitive and controllable, reducing manual design costs. Furthermore, the entire system supports continuous data collection and feedback mechanisms, ensuring that page interaction logic constantly adapts to the latest user needs and habits, thereby enhancing long-term user engagement. Through this technical approach, user churn rate can be significantly reduced, enhancing the platform's overall competitiveness.

[0068] Next, the steps of acquiring user behavior data and generating interaction path analysis results according to the present invention are described. First, the user's click locations, click frequency, and dwell time are acquired. This step captures the user's interaction information with page elements by monitoring the user's operational behavior on the smart TV terminal, thereby quantifying the user's actual operation patterns. For example, in one embodiment, the system can record the user's click points in menu options and the length of time spent on each interface. This data provides the basis for subsequent analysis.

[0069] Secondly, the system tracks the number of user interactions on each page. This step categorizes user clicks and touches on different elements on a specific page, helping to identify popular elements or design issues. Specifically, the system can calculate the total number of times various buttons, icons, or content areas are accessed on each page, thus creating a distribution map of interaction frequency.

[0070] The user satisfaction index S is calculated using the formula: S = (K × T) / D, where K is the frequency of user interaction, representing the user's level of engagement on the current page; T is the dwell time (in seconds), reflecting the user's attention span to the content; and D is the number of page operation steps, used to measure task complexity. A higher S value indicates greater user satisfaction. This formula is designed to comprehensively consider user initiative and time investment, avoiding reliance on a single indicator. For example, if a single page requires too many steps to complete a goal, it will lower satisfaction. Ideally, the higher K and T are, and the lower D is, the higher S reaches its maximum value.

[0071] Finally, users are categorized into high, medium, and low satisfaction levels based on the S value. This step uses the calculated S value to classify user behavior in order to optimize page design or service strategies. For example, if a certain type of user clicks multiple times and browses for a long time, but the operation steps are numerous, their satisfaction level can be identified as low, and the interaction process can be streamlined.

[0072] The above steps can accurately assess the user experience on smart TV terminals, provide data support for optimizing interface layout and improving user experience, and enhance user stickiness and ease of operation.

[0073] Next, the steps of identifying redundant operations of page elements based on interaction path analysis results according to the present invention are described. Specifically, these include: determining the number N of unclicked buttons on the page; collecting user click path records P; constructing a node weight model and setting a threshold θ; and determining that a redundant operation exists if the jump time T_ij between any two consecutive nodes is less than θ.

[0074] The purpose of determining the number N of unclicked buttons on a page is to identify interface elements that users may overlook or be unaware of. N represents the functional items on the page that have not been actually triggered by the user; the larger this value, the more likely the interface design has an information overload problem. For example, on the homepage of a TV app, there are multiple function icons, but only some are clicked by the user, while others are ignored.

[0075] The purpose of collecting user click path records P is to obtain real user behavior data for subsequent analysis of interaction patterns. P consists of a series of user operation events, representing the user's interaction trajectory, typically identified by coordinate points or element IDs. In one embodiment, a user may select multiple items consecutively while browsing a video program list; in this case, the system records each click action.

[0076] A node weight model is constructed, and a threshold θ is set to evaluate the interaction frequency and importance between different nodes. θ represents the reasonable jump time range deemed reasonable by the system. Typically, θ is set between 3 and 5 seconds, because within this range, it is normal for a user to complete a jump. If it is less than this value, it may indicate that the user switched without pausing. For example, when watching a TV series, if a user quickly clicks on an adjacent section without pausing to view it, the system will consider this a redundant jump.

[0077] If the transition time T_ij between any two consecutive nodes is less than θ, it is considered that there is a redundant step. T_ij represents the time interval between two adjacent clicks. If the time is too short, it is considered a duplicate operation or an invalid operation. For example, on a live TV interface, if a user quickly switches between multiple channels without pausing on any channel, it indicates that their selection is aimless and repetitive.

[0078] This technical solution quantifies user interaction behavior, accurately identifies potentially redundant steps in the operation, optimizes the interface structure and user experience, and enhances the intuitiveness and efficiency of the interface design.

[0079] Next, the steps of automatically generating optimized page element interaction logic based on operational redundancy in this invention are described. First, element combinations F from the redundant links are extracted. This step identifies repetitive or meaningless operation sequences during user operations. By analyzing user clicks, swipes, and other behavioral data, the page elements involved in these redundant links are categorized and organized into a set containing related elements. For example, in the homepage editing of a smart TV terminal, if multiple menu items are clicked repeatedly without a valid response, this operation may be redundant, and the menu items involved are elements in F. This step facilitates subsequent systematic processing of redundant operations.

[0080] Next, a dependency matrix M is established between elements, where M[i][j] = 1 indicates that i and j are related. This matrix is ​​used to quantify the interaction relationships between page elements, helping to identify which elements need to be prioritized or whose layout needs to be adjusted. For example, in a TV application, there may be a close relationship between the main menu item and function buttons, in which case the value between them is set to 1. Matrix design can enhance the accuracy of logical analysis and provide a structured foundation for subsequent clustering and optimization.

[0081] Applying fuzzy clustering to a set of elements yields the optimal layout. Fuzzy clustering classifies elements based on their similarity and assigns membership degrees between different groups. For example, in a video playback interface, navigation icons and shortcuts might be grouped into the same group through fuzzy clustering, facilitating layout consistency and optimization. This process makes the interface layout more in line with user habits.

[0082] The optimization coefficient R for each solution is calculated as R = α × W + β × C, where W represents the user experience weight (range [0,1]), C represents the simplification complexity (a larger value indicates greater operational complexity), and α and β are weighting coefficients representing user preference and system simplification requirements, respectively, typically set to around 0.5 to balance their importance. For example, if a solution performs well in improving user experience but has high complexity, the overall score depends on the parameter settings. This formula aims to evaluate the optimization effect from multiple dimensions, ensuring that the solution is both user-friendly and efficient.

[0083] This technical solution can significantly improve the optimization efficiency of the smart TV interface interaction logic, reduce the impact of redundant operations on user perception, and thus enhance the system's usability and stability.

[0084] Next, the steps of automatically generating optimized page element interaction logic based on operational redundancy in this invention are described. First, frequently clicked buttons (A) and inefficient paths (P) are identified. This process involves analyzing user operation logs to filter out frequently clicked buttons and paths that result in excessive operation steps. Here, A represents frequently used interface elements, and P represents complex or lengthy operation sequences encountered by the user. For example, on a smart TV's video playback page, if a user frequently clicks the "Start Play" button and repeatedly swipes to select a program, then "Start Play" is a frequently clicked button, while selecting a program and confirming playback is an inefficient path.

[0085] Next, set the operation path compression factor λ. This parameter controls the degree to which the interaction path is shortened, and its value ranges from 0 to 1. When λ approaches 1, the operation path hardly changes; when λ approaches 0, the path will be greatly simplified. Generally, a value of 0.5 to 0.8 for λ is reasonable, effectively optimizing the interaction path without excessively changing the user's usage habits. The interaction path is reconstructed using the following formula: P = P × λ. This formula means that the original path length is reduced proportionally, making the user's operation steps more concise and efficient.

[0086] If the reconstructed path length is less than the preset minimum threshold L_min, the default layout scheme will be forcibly enabled. L_min is the minimum allowed length for an operation set by the system based on actual interactive experience, typically within 3 steps. This setting avoids interface functionality loss or user experience confusion due to over-assumptions. For example, in smart TV terminals, if the optimized search-selection-playback path length is less than L_min, the system will revert to the default search-playback layout to ensure the availability of basic functions.

[0087] This technical solution improves user operation efficiency and reduces interaction complexity by automatically identifying redundant operations and adjusting paths, while ensuring the stability and predictability of operations.

[0088] Next, the steps of the present invention to map the optimized page element interaction logic to a visual editing interface for real-time verification are described. This technical solution includes synchronously updating the interactive objects and their states in the visual layout. This step aims to ensure that the elements on the editing interface are consistent with the backend logic, reducing user confusion caused by delays. For example, in a smart TV terminal, when a user clicks a button, the button's state (such as active or hovered) should be immediately reflected in the visual interface to enhance the user's operating experience. This process enables instant feedback of the interaction.

[0089] Displaying a comparison chart of the user's workflow helps evaluate the effectiveness of optimization. This step analyzes the user's workflow to create a comparison chart of the paths before and after optimization, visually demonstrating the changes brought about by the optimization. For example, before optimization, a user might have needed three clicks to reach a certain function, while after optimization, only one click is required, significantly shortening the workflow and improving efficiency. This comparison method helps designers more accurately evaluate the effectiveness of optimization strategies.

[0090] The system collects user feedback scores, F, for the optimized page. F represents user satisfaction, ranging from 0 to 10, with 10 representing the best user experience. The default value is typically 7; a value higher than this indicates successful optimization, while a rollback mechanism is triggered otherwise. The calculation of F combines user ratings and behavioral analysis data from actual use, ensuring objectivity and accuracy.

[0091] If F ≥ the set value, the optimization is considered effective; otherwise, a rollback mechanism is triggered. This allows for timely adjustments to the optimization strategy while ensuring a good user experience. For example, if the F score is 6.5, lower than the set value, the system automatically reverts to the unoptimized state and notifies the developers to readjust. This mechanism ensures stable system operation and continuous improvement.

[0092] In summary, this technical solution helps improve the accuracy and response speed of interaction design, enhances the user experience on smart TV terminals, and has good fault tolerance and optimization capabilities.

[0093] Next, the steps of identifying redundant operations of page elements based on the interaction path analysis results of this invention are described. First, the average time required for a user to complete one operation, D_avg, is recorded. This value represents the average time a user spends to complete a task in a standard operation flow. Then, the complexity index C of the current path is calculated, where C = ∑(D_i) / n, where D_i represents the time required for each step in the path, and n is the total number of operation steps in the path. This parameter is used to quantify the overall time consumption level of the operation path. Next, redundancy is determined according to the formula C > D_avg × ε, where ε is a coefficient for adjusting the threshold, usually set in the range of [2, 1.5]. When the value of C exceeds this benchmark value, it indicates that there is redundant operation in the path. Subsequently, a reasonable redundancy threshold is set, and the paths marked as redundant are stored in the optimization library for subsequent improvement.

[0094] For example, in a visual editing method for smart TV terminal pages, users need to navigate through multiple menus to select a program. Assuming a normal D_avg of 8 seconds, if a user clicks three times and spends 16 seconds, then C = 16 / 3 ≈ 5.33. If ε is set to 1.8, the calculated D_avg × ε = 14.4, while C = 5.33 is significantly less than the threshold, indicating that this path is not redundant. However, if the path takes 20 seconds, the portion where C = 6.67 is greater than 14.4 may trigger a redundancy flag. This method effectively identifies unnecessary operation steps, improves user interaction efficiency, reduces system resource consumption, and enhances overall page usability.

[0095] Next, the steps of automatically generating optimized page element interaction logic based on operational redundancy in this invention are described. First, a Markov chain model is used to predict the node V_i that the user may operate on next. This model constructs state transition relationships based on historical operation data, where V_i represents the interface element or operation node where the user is currently located, and its value ranges to all possible page elements. By training the model to obtain transition probabilities, user behavior patterns can be effectively identified. For example, in a smart TV, when a user clicks on a movie in the main menu, the system can predict that the user may next enter recently played content or the ranking list. Next, the transition probability P(V_j|V_i) is calculated to determine the optimal jump path. P(V_j|V_i) represents the probability of moving from the current node V_i to the target node V_j, and its range is between 0 and 1, with a larger value indicating a higher probability. For example, if the user has previously selected a movie after a movie multiple times, then P(movie|movie) will be higher. A user habit weight α is introduced to adjust the prediction results to be closer to the user's actual behavior. α is a weighting coefficient, usually set between 0.8 and 1, used to strengthen the influence of the user preference path. For example, for users who frequently choose TV series, the system will increase the probability of related paths, making recommendations more personalized. The new path is compared with the original path in terms of similarity, and the path with a similarity greater than β is selected as the optimization solution. β is set between 0.7 and 0.9 to filter paths with low matching scores and avoid ineffective changes. For example, if the similarity between the new path and the original path reaches 0.8, it indicates that it is highly consistent with user behavior and is suitable as the optimization result.

[0096] This technical solution can significantly improve the smoothness of user operation, reduce unnecessary jumps, optimize the page interaction experience, enhance personalized recommendation capabilities, and improve user satisfaction and operation efficiency.

[0097] Next, the steps of the present invention to map the optimized page element interaction logic to a visual editing interface for real-time verification are described. The technical solution includes: converting the generated page structure into a visual layout tree; performing a simulated click test and recording the response time RT; calculating the performance index P = k × (1 / RT_max / RT) using the following formula, where RT_max is the expected maximum response time and k is an adjustment coefficient; if P ≥ 8, the verification is considered successful and the release process begins.

[0098] First, converting the generated page structure into a visual layout tree refers to transforming the front-end code or graphical interface definition file into a structured tree data format for rendering and manipulating elements in a visual editing interface. For example, in a visual editing system for smart TV terminals, after a designer modifies the layout of interface elements, the system converts the changed HTML or XML structure into a visual layout tree, facilitating subsequent interactive testing.

[0099] Secondly, performing simulated click tests and recording the response time (RT) is to quantify the responsiveness of page element interactions. In the context of smart TVs, users interact via remote control, thus requiring high responsiveness. For example, after clicking a channel selection button, the system records the interval from the click action to the completion of the interface transition as RT.

[0100] Then, the performance metric P is calculated as P = k × (1 / RT_max / RT). The typical value of RT_max is usually set between 500 and 1000 milliseconds, adjusted according to the hardware configuration of the smart TV terminal; k is an adjustment coefficient ranging from 1 to 3 to accommodate different system performance requirements; when RT is close to or lower than RT_max, the P value will increase significantly. This formula is based on ensuring a sufficiently fast response speed to provide a smooth user experience and avoid lag.

[0101] Finally, if P ≥ 8, the verification is considered successful, indicating that the interaction performance meets the standard. For example, in a test of the program list loading function, if the actual RT is 300 milliseconds, and RT_max is set to 500 milliseconds, and k is set to 2, then P = 2 × (1 / 500 ÷ 300) = 2 × (1 / 1.67) = 2 × 0.6 ≈ 1.2. In this case, the code needs to be adjusted to improve the response speed. However, if the test result is P = 8, it indicates that the system has excellent response capabilities and can proceed normally into the release process.

[0102] This technical solution can effectively verify whether the interactive logic of the visual interface meets the expected performance, improve the accuracy of design work and development efficiency, and ensure that end users have a good user experience.

[0103] Next, the steps of automatically generating optimized page element interaction logic based on operational redundancy in this invention are described. First, a correlation matrix R is established between different operation paths. This step constructs a matrix describing the degree of correlation between operation paths by analyzing the user's operation behavior between different page elements. In R, the element Rij represents the similarity or correlation between path i and path j, with a value range of [0,1]. A higher value indicates a more similar path. For example, in a smart TV terminal, when a user navigates from the homepage to the application list page and then clicks play, there is an overlap in the operation path compared to directly entering the playback page from the homepage. This path can be mapped using matrix R.

[0104] Next, for each redundant path, the redundancy score is calculated as Score = Σ(Ci × Wi), where Ci is the number of steps in the path, and Wi is the user attention weight for the corresponding step. The value of Wi typically ranges from [0.5, 1], representing the user's level of attention; a higher value indicates a more critical step. The formula measures the overall redundancy of the path; a higher Score means greater redundancy and requires optimization. For example, in TV interface editing, if a user repeatedly navigates to the same path, it may create redundancy due to operational inconvenience; the system identifies and marks this redundancy.

[0105] Then, a random forest algorithm is used to filter highly redundant paths and assign them priority. This step uses a machine learning model to evaluate the importance of each redundant path and select paths for priority processing. For example, in a TV program navigation interface, a path that is repeated and unpopular is identified as a high-priority optimization target.

[0106] Finally, the highly redundant paths are input into the path optimization model to obtain a new logical structure. This model adjusts the path design according to the optimization objective, simplifying the operation process. For example, in the program recommendation interface of a smart TV, the optimized path can reduce unnecessary steps and improve the user experience.

[0107] The beneficial effects of this technical solution include improving the efficiency and rationality of page interaction logic, enhancing the user experience, and reducing the operational burden caused by redundant operations.

[0108] Next, the steps of mapping the optimized page element interaction logic to a visual editing interface for real-time verification according to the present invention are described, including loading and rendering the optimized page view. This step involves displaying the adjusted page elements and their interaction logic in a visual editing environment to ensure that the design meets the expected results. For example, on a smart TV terminal, if the position of the menu button on a playback page is adjusted, the page will be reloaded and displayed. Specifically, this process involves parsing the editing instructions provided by the user and converting them into a visual layout and event bindings.

[0109] Capture operation flow information, such as click sequences and response latency. This step records the user's actual actions and system response times on the optimized page for subsequent analysis. For example, in one embodiment, when a user clicks play, volume control, and other buttons sequentially during navigation on a smart TV, the system automatically records these click actions and their corresponding response times.

[0110] The validation metric I is constructed as I = a × (F1_score) + b × (RT_avg), where F1_score evaluates the accuracy of click behavior recognition, ranging from 0 to 1, with an optimal value close to 1; RT_avg is the average latency in milliseconds, typically expected to be below 200ms; a and b are weighting coefficients, reflecting the degree of emphasis on accuracy and response speed, respectively. For example, if a = 0.6 and b = 0.4, then recognition accuracy is prioritized over response speed. This formula is used to comprehensively evaluate the optimization effect, combining user behavior recognition capabilities and system performance.

[0111] When I > c and the user retention criteria are met, the system automatically submits an update. c is a preset threshold, set based on experimental data, typically above 0.85. For example, if tests show that users did not churn after using the page, and the calculated I value is higher than the threshold, the system will deploy the updated page to the production environment.

[0112] The above steps make the optimization process more precise and controllable, improve the efficiency of smart TV terminal page editing and user experience, and also reduce the cost and time of manual testing.

[0113] This invention provides a method for visually editing pages on a smart TV terminal, comprising the following steps: First, the system collects user interaction data, such as clicks, swipes, and dwell time, through the smart TV terminal. Then, this data is analyzed and processed to generate corresponding interaction path analysis results. Next, based on these analysis results, the system identifies redundant operational steps in page elements, such as repetitive steps or unnecessary page jumps. After identifying redundant steps, the system further automatically generates optimized page element interaction logic based on this redundant information, making the user operation path simpler and more efficient. The optimized interaction logic is mapped to the visual editing interface, allowing developers to view and verify the optimization effect in real time, ensuring that the optimized logic meets actual user needs and operating habits.

[0114] To address the issue of user churn caused by lengthy operation paths by automatically optimizing page element interaction logic based on user behavior data analysis, this invention introduces a data-driven intelligent optimization mechanism. Through in-depth mining of user behavior data, the system can accurately identify redundant operations generated by users during use, and then deduce more reasonable page layouts and interaction logic through algorithms, reducing unnecessary user operation steps. This intelligent optimization method effectively shortens the user's operation path, improves user experience, and reduces user churn caused by complex operations. Simultaneously, by feeding the optimization logic back to the visual editing interface in real time, developers can intuitively adjust and verify the optimization effects, improving the efficiency and accuracy of page optimization work. In summary, this invention, through data-driven automated analysis and optimization methods, solves the problems of reliance on manual experience and low optimization efficiency in traditional page design, achieving an intelligent and personalized page interaction experience.

[0115] The methods, programs, systems, apparatuses, etc., in embodiments of the present invention can be executed or implemented in one or more networked computers, or practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks can be performed by remote processing devices connected via a communication network.

[0116] Those skilled in the art will understand that the embodiments described in this specification can be provided as methods, systems, or computer program products. Therefore, those skilled in the art will realize that the functional modules / units or controllers and related method steps described in the above embodiments can be implemented in software, hardware, or a combination of both.

[0117] Unless explicitly stated otherwise, the actions or steps of the methods and procedures described in the embodiments of the present invention do not necessarily have to be performed in a specific order and can still achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] This document describes several embodiments of the present invention; however, for the sake of brevity, the descriptions of the embodiments are not exhaustive, and identical or similar features or parts between the embodiments may be omitted. In this document, "one embodiment," "some embodiments," "example," "specific example," or "some examples" refers to embodiments applicable to at least one, but not all, of the present invention. The above terms do not necessarily refer to the same embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described herein, as well as the features of the different embodiments or examples.

[0119] The exemplary systems and methods of the present invention have been specifically shown and described with reference to the above embodiments, which are merely examples of the best mode for implementing the systems and methods. Those skilled in the art will understand that various changes can be made to the embodiments of the systems and methods described herein without departing from the spirit and scope of the invention as defined in the appended claims when implementing the systems and / or methods.

Claims

1. A method for visually editing web pages on a smart TV terminal, characterized in that, include: Acquire user behavior data and generate interaction path analysis results; Based on the interaction path analysis results, identify redundant operational steps for page elements; Based on the aforementioned redundant operational steps, optimized page element interaction logic is automatically generated. The optimized page element interaction logic is mapped to a visual editing interface for real-time verification.

2. The method for visually editing pages on a smart TV terminal according to claim 1, characterized in that, The process of acquiring user behavior data and generating interaction path analysis results further includes: Obtain the user's click location, click frequency, and dwell time; Count the number of user interactions with elements on each page; The user satisfaction index S is calculated based on the following formula: S = (K × T) / D, where K is the frequency of user interaction, T is the dwell time, and D is the number of page operation steps. Users are categorized into high / medium / low satisfaction levels based on the size of S.

3. The method for visual editing of pages on a smart TV terminal according to claim 2, characterized in that, The redundant steps in identifying page elements based on the interaction path analysis results further include: Determine the number N of unclicked buttons on the page; Collect user click path records P; Construct a node weight model and set a threshold θ; If the jump time T_ij between any two consecutive nodes is less than θ, it is determined that there is a redundant link.

4. The method for visual editing of pages on a smart TV terminal according to claim 3, characterized in that, The optimized page element interaction logic automatically generated based on the aforementioned operational redundancy steps further includes: Extract the element combination F from the redundant steps; Establish a dependency matrix M between elements, where M[i][j] = 1 indicates that i and j are related; Applying a fuzzy clustering algorithm to the set of elements yields the optimal layout scheme; Calculate the optimization coefficient R = α × W + β × C for each solution, where W is the user experience weight and C is the simplification complexity.

5. The method for visually editing pages on a smart TV terminal according to claim 4, characterized in that, The optimized page element interaction logic automatically generated based on the aforementioned operational redundancy steps further includes: Identify frequently used button A and inefficient path P; Set the operation path compression factor λ; The interaction path is reconstructed using the following formula: P = P × λ, where λ ∈ (0, 1) represents the degree of operation reduction; If the reconstructed path length is less than the preset minimum threshold L_min, then the default layout scheme will be forcibly enabled.

6. The method for visually editing pages on a smart TV terminal according to claim 5, characterized in that, The step of mapping the optimized page element interaction logic to a visual editing interface for real-time verification further includes: Synchronously update the interactive objects and their states in the visual layout; Display a comparison chart of operation paths to evaluate the optimization effect; Collect user feedback scores (F) for the optimized page; If F ≥ the set value, the optimization is confirmed to be effective; otherwise, a rollback mechanism is triggered.

7. The method for visual editing of a smart TV terminal page according to claim 6, characterized in that, The redundant steps in identifying page elements based on the interaction path analysis results further include: Record the average time D_avg required for a user to complete one operation; Calculate the current path complexity exponent C = ∑(D_i) / n, where D_i is the time for each step and n is the number of operation steps; Redundancy is determined based on the following formula: if C > D_avg × ε, then the path segment is marked as a redundant path, where ε ∈ [2, 1.5]. Set a redundancy threshold and include redundant paths in the optimization library.

8. The method for visually editing pages on a smart TV terminal according to claim 7, characterized in that, The optimized page element interaction logic automatically generated based on the aforementioned operational redundancy steps further includes: Use a Markov chain model to predict the node V_i that the user may operate on next; Calculate the transition probability P(V_j|V_i) to determine the optimal jump path; Introduce a user habit weight α to adjust the prediction results so that they are closer to actual user behavior; The new path is compared with the original path, and the path with a similarity greater than β is selected as the optimization scheme.

9. The method for visually editing pages on a smart TV terminal according to claim 8, characterized in that, The step of mapping the optimized page element interaction logic to a visual editing interface for real-time verification further includes: Convert the generated page structure into a visual layout tree; Perform a simulated click test and record the response time (RT); The performance index P is calculated using the following formula: P = k × (1 / RT_max / RT), where RT_max is the expected maximum response time and k is the adjustment coefficient. If P ≥ 8, the verification is considered successful, and the release process begins.

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