Front-end interface self-repairing method and device based on user operation trajectory

Abstract

The application discloses a front-end interface self-repairing method and device based on user operation track. The method can realize real-time sensing and analysis of the user operation process by listening to the interactive operation of the user on the front-end interface. The interface state of the front-end interface can be self-repaired based on the target behavior judgment result, and the interface self-adaptive repair is realized without invading the business logic. In addition, the target behavior judgment result is accurately and efficiently generated by feature extraction and analysis of the operation track data, so that the interface state of the front-end interface can be accurately and correctly self-repaired based on the target behavior judgment result, and the user can see the interface state after self-repairing in real time.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a front-end interface self-repair method and apparatus based on user operation trajectory. Background Technology

[0002] With the widespread use of web applications and complex front-end interfaces, users frequently need to perform various interactive operations such as clicking, dragging, inputting, and undoing while using the system. Especially in scenarios such as back-end management systems, data analysis platforms, and low-code platforms, the interface structure is complex and the interaction paths are diverse. In actual operation, users are prone to problems such as accidental touches, repeated operations, and getting lost, which affects operational efficiency and user experience.

[0003] Existing front-end systems typically only focus on whether functions are executed correctly, lacking the ability to perceive user actions in real time. Most systems only record user action results through event tracking or logging for post-event analysis or statistics, but cannot dynamically respond to or optimize the interface during user actions.

[0004] However, this "post-analysis" approach cannot promptly correct difficulties encountered by users, nor can it adaptively adjust the front-end interface based on user behavior characteristics. Summary of the Invention

[0005] This application aims to at least solve the technical problems existing in the prior art. To this end, the first aspect of this application proposes a front-end interface self-repair method based on user operation trajectory, the method comprising: Listen to the user's interactive operations on the front-end interface and obtain the user's operation trajectory data on the front-end interface; Feature extraction and analysis are performed on the operation trajectory data to generate target behavior judgment results. The target behavior judgment results are used to characterize the user's operation status or potential intention. The target behavior judgment results include whether interface repair needs to be triggered, operation anomaly type, target interface element identifier and judgment basis. Operation anomaly type includes high-frequency accidental touch behavior, repeated operation path, hovering anomaly, and operation deviation from expectation. Based on the target behavior determination results, the interface state of the front-end interface is self-repaired, and a self-repaired interface state is generated.

[0006] In one possible implementation, the determination criteria include operation anomaly determination rules and a preset judgment model. Feature extraction and analysis are performed on the operation trajectory data to generate a target behavior determination result, including: Feature extraction and analysis are performed on the operation trajectory data to generate operation feature vectors; The operation feature vector is judged based on the predefined operation anomaly judgment rules, and the first row of judgment results is generated. The operation feature vector is processed based on a preset judgment model to generate a second line of judgment results; A comprehensive judgment is made based on the first behavior judgment result and the second behavior judgment result to generate the target behavior judgment result.

[0007] In one possible implementation, if the operation anomaly type is a repetitive operation path, the operation trajectory data includes user mouse movement behavior data. The process of extracting and analyzing features from the operation trajectory data to generate a target behavior determination result also includes: Construct the original trajectory sequence based on continuously collected user mouse movement behavior data; The original trajectory sequence is segmented based on preset operation boundary conditions to obtain multiple trajectory segments; each trajectory segment corresponds to a complete target control search behavior. After standardizing each trajectory segment, the path distance between different trajectory segments is calculated, and the path similarity relationship between each trajectory segment is determined based on the path distance. The number of repetitions is obtained based on the similarity relationship of each path, and the target behavior determination result is generated based on the number of repetitions.

[0008] In one possible implementation, the interface state of the front-end interface is self-repaired based on the target behavior determination result to generate a self-repaired interface state, including: Based on the first preset correspondence, a target interface repair strategy corresponding to the target behavior judgment result is determined; wherein, the target interface repair strategy includes target interface elements and a set of target repair actions corresponding to the target interface elements; The front-end interface is self-repaired based on the target interface repair strategy, generating a self-repaired interface state.

[0009] In one possible implementation, the method further includes: After parsing the nodes of the front-end interface based on the target interface element identifier, a first preset correspondence between operation exception types and interface repair strategies is constructed; wherein, the interface repair strategy includes a second preset correspondence between each interface element and the set of repair actions.

[0010] In one possible implementation, the method further includes: The process of self-repairing the interface state of the front-end interface will be recorded, and the state record information will be saved to the state stack. The state record information includes the self-repair type, the target interface element for self-repair, the interface state before self-repair, and the interface state after self-repair. The interface state before self-repair includes the position information, size information, interactive area parameters, display or hidden state, style identifiers or key visual attributes of the target interface element. If a rollback trigger condition is detected, the current rollback mode is determined based on the rollback trigger condition; Perform a rollback operation based on the current rollback mode and the state record information in the state stack, obtain the interface state after rollback, and update the state stack.

[0011] In one possible implementation, the current rollback mode includes a step-by-step rollback mode and a global rollback mode. The rollback operation is performed based on the current rollback mode and the state record information in the state stack to obtain the rolled-back interface state. The implementation also includes: When the current rollback mode is the step rollback mode, the nearest non-rollback state unit is determined based on the state record information in the state stack, and the target interface element corresponding to the nearest non-rollback state unit is restored to the interface state before self-repair, and the nearest non-rollback state unit is marked as a rolledback state. When the current rollback mode is the overall rollback mode, the state stack is traversed in a preset order, and each unrolled state unit is rolled back in turn to restore the front-end interface to the state before self-repair.

[0012] A second aspect of this application proposes a front-end interface self-repair device based on user operation trajectory, the device comprising: The acquisition module is used to listen to the user's interactive operations on the front-end interface and acquire the user's operation trajectory data on the front-end interface. The first generation module is used to extract and analyze features from operation trajectory data and generate target behavior judgment results. The target behavior judgment results are used to characterize the user's operation status or potential intention. The target behavior judgment results include whether interface repair needs to be triggered, operation anomaly type, target interface element identifier and judgment basis. Operation anomaly type includes high-frequency accidental touch behavior, repeated operation path, hovering anomaly, and operation deviation from expectation. The second generation module is used to self-repair the interface state of the front-end interface based on the target behavior judgment result and generate the self-repaired interface state.

[0013] In one possible implementation, the first generation module is specifically used for: Feature extraction and analysis are performed on the operation trajectory data to generate operation feature vectors; The operation feature vector is judged based on the predefined operation anomaly judgment rules, and the first row of judgment results is generated. The operation feature vector is processed based on a preset judgment model to generate a second line of judgment results; A comprehensive judgment is made based on the first behavior judgment result and the second behavior judgment result to generate the target behavior judgment result.

[0014] In one possible implementation, if the operation exception type is a duplicate operation path, the first generation module is further configured to: Construct the original trajectory sequence based on continuously collected user mouse movement behavior data; The original trajectory sequence is segmented based on preset operation boundary conditions to obtain multiple trajectory segments; each trajectory segment corresponds to a complete target control search behavior. After standardizing each trajectory segment, the path distance between different trajectory segments is calculated, and the path similarity relationship between each trajectory segment is determined based on the path distance. The number of repetitions is obtained based on the similarity relationship of each path, and the target behavior determination result is generated based on the number of repetitions.

[0015] In one possible implementation, the second generation module is specifically used for: Based on the first preset correspondence, a target interface repair strategy corresponding to the target behavior judgment result is determined; wherein, the target interface repair strategy includes target interface elements and a set of target repair actions corresponding to the target interface elements; The front-end interface is self-repaired based on the target interface repair strategy, generating a self-repaired interface state.

[0016] In one possible implementation, the second generation module is further configured to: After parsing the nodes of the front-end interface based on the target interface element identifier, a first preset correspondence between operation exception types and interface repair strategies is constructed; wherein, the interface repair strategy includes a second preset correspondence between each interface element and the set of repair actions.

[0017] In one possible implementation, the aforementioned front-end interface self-healing device based on user operation trajectory is further used for: The process of self-repairing the interface state of the front-end interface will be recorded, and the state record information will be saved to the state stack. The state record information includes the self-repair type, the target interface element for self-repair, the interface state before self-repair, and the interface state after self-repair. The interface state before self-repair includes the position information, size information, interactive area parameters, display or hidden state, style identifiers or key visual attributes of the target interface element. If a rollback trigger condition is detected, the current rollback mode is determined based on the rollback trigger condition; Perform a rollback operation based on the current rollback mode and the state record information in the state stack, obtain the interface state after rollback, and update the state stack.

[0018] In one possible implementation, the current rollback mode includes a gradual rollback mode and a global rollback mode, and the aforementioned front-end interface self-repair device based on user operation trajectory is further used for: When the current rollback mode is the step rollback mode, the nearest non-rollback state unit is determined based on the state record information in the state stack, and the target interface element corresponding to the nearest non-rollback state unit is restored to the interface state before self-repair, and the nearest non-rollback state unit is marked as a rolledback state. When the current rollback mode is the overall rollback mode, the state stack is traversed in a preset order, and each unrolled state unit is rolled back in turn to restore the front-end interface to the state before self-repair.

[0019] A third aspect of this application provides an electronic device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the self-healing method for a front-end interface based on user operation trajectories as described in the first aspect.

[0020] The fourth aspect of this application proposes a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the front-end interface self-healing method based on user operation trajectory as described in the first aspect.

[0021] The embodiments of this application have the following beneficial effects: The front-end interface self-repair method based on user operation trajectory provided in this application includes: listening to the user's interactive operation on the front-end interface and obtaining the user's operation trajectory data on the front-end interface; performing feature extraction and analysis on the operation trajectory data to generate a target behavior judgment result, wherein the target behavior judgment result is used to characterize the user's operation state or potential intention, and the target behavior judgment result includes an indicator of whether interface repair needs to be triggered, operation anomaly type, target interface element identifier and judgment basis, operation anomaly type including high-frequency accidental touch behavior, repeated operation path, hovering anomaly, operation deviation from expectation; and performing self-repair on the interface state of the front-end interface based on the target behavior judgment result to generate a self-repaired interface state. This solution enables real-time perception and analysis of user interactions on the front-end interface. Furthermore, it can self-repair the front-end interface state based on the target behavior determination results, achieving adaptive interface repair without intruding on business logic. Additionally, by extracting and analyzing features from the operation trajectory data, it accurately and efficiently generates target behavior determination results, allowing for accurate self-repair of the front-end interface state based on these results, and enabling users to view the self-repaired interface state in real time. Attached Figure Description

[0022] Figure 1 A block diagram of a computer device provided in an embodiment of this application; Figure 2 A flowchart illustrating the steps of a front-end interface self-repair method based on user operation trajectory provided in this application embodiment; Figure 3 A flowchart illustrating the steps for generating a target behavior determination result is provided in this application embodiment; Figure 4 A flowchart illustrating another step for generating a target behavior determination result provided in an embodiment of this application; Figure 5 A flowchart illustrating the steps for generating a self-healing interface state is provided in this application embodiment. Figure 6 A flowchart illustrating the steps of interface self-repair and rollback provided in this application embodiment; Figure 7 This is a structural block diagram of a front-end interface self-repair device based on user operation trajectory provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0024] Existing front-end systems typically focus only on whether functions are executed correctly, lacking real-time awareness of user actions. Most systems simply record user actions through event tracking or logging for post-event analysis or statistics, failing to dynamically respond to or optimize the interface during user interactions. This "post-event analysis" approach cannot promptly correct difficulties encountered by users, nor can it adaptively adjust the interface based on user behavior characteristics.

[0025] Therefore, there is an urgent need for a front-end interface interpretable and self-healing system based on user operation trajectory, which can perceive user operations in real time, automatically repair the interface, and support reversible control without intruding on business logic or modifying data processing flow, so as to improve the intelligent interaction capability of the front-end system.

[0026] The existing technology has the following main shortcomings: 1. Only records operation results, unable to analyze the process: Most systems only collect the final operation such as user click or submission, lacking process information such as operation path, dwell time, and repeated operations; 2. Lack of real-time feedback mechanism: When users accidentally touch or are confused during operation, the system cannot detect it in real time and optimize the interface.

[0027] 3. Interface optimization relies on manual analysis: Designers often rely on historical statistical data for manual analysis, which cannot automatically and dynamically respond to user behavior.

[0028] 4. Highly invasive or costly: Some solutions require modification of business logic, data models, or backend interfaces, resulting in high implementation costs and risks.

[0029] Based on this, this application proposes a front-end interface self-repair method based on user operation trajectory. The method includes: listening to the user's interactive operation on the front-end interface and obtaining the user's operation trajectory data on the front-end interface; performing feature extraction and analysis on the operation trajectory data to generate a target behavior judgment result. The target behavior judgment result is used to characterize the user's operation state or potential intention. The target behavior judgment result includes an indicator of whether interface repair needs to be triggered, operation anomaly type, target interface element identifier, and judgment basis. Operation anomaly type includes high-frequency accidental touch behavior, repeated operation path, hovering anomaly, and operation deviation from expectation. Based on the target behavior judgment result, the interface state of the front-end interface is self-repaired to generate a self-repaired interface state. This solution enables real-time perception and analysis of user interactions on the front-end interface. Furthermore, it can self-repair the front-end interface state based on the target behavior determination results, achieving adaptive interface repair without intruding on business logic. Additionally, by extracting and analyzing features from the operation trajectory data, it accurately and efficiently generates target behavior determination results, allowing for accurate self-repair of the front-end interface state based on these results, and enabling users to view the self-repaired interface state in real time.

[0030] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more. Furthermore, the use of "based on" or "according to" implies openness and inclusiveness, because processes, steps, calculations, or other actions "based on" or "according to" one or more of the stated conditions or values ​​may in practice be based on additional conditions or beyond the stated values.

[0031] The front-end interface self-repair method based on user operation trajectory provided in this application can be applied to computer devices (electronic devices). The computer device can be a server or a terminal. The server can be a single server or a server cluster composed of multiple servers. This application does not specifically limit this. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets and portable wearable devices.

[0032] Taking a computer device as an example, Figure 1 A block diagram of a server is shown, such as Figure 1As shown, the server may include a processor and memory connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. When the computer program is executed by the processor, it implements a self-healing method for the front-end interface based on user operation trajectories.

[0033] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the server to which the present application is applied. Optionally, the server may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0034] It should be noted that the execution subject of the embodiments of this application can be a computer device or a front-end interface self-repair device based on user operation trajectory. The following method embodiments will be described with computer device as the execution subject.

[0035] Figure 2 This document provides a flowchart of a front-end interface self-repair method based on user operation trajectory, as illustrated in an embodiment of this application. Figure 2 As shown, the method includes the following steps: Step 202: Listen to the user's interactive operations on the front-end interface and obtain the user's operation trajectory data on the front-end interface.

[0036] Specifically, user interactions on the front-end interface can be monitored via event delegation to collect user action trajectory data for behavior analysis. Optionally, this action trajectory data may include, but is not limited to, mouse movement paths, click locations and frequencies, control hover times, undo / back operations, and repetitive operation sequences. For mouse movement paths, the coordinate sequence of mouse movement on the page can be recorded and stored as a trajectory array. For click locations and frequencies, the control ID, coordinates, timestamp, and number of clicks can be recorded. For control hover times, the duration the mouse stays on the control can be recorded. For undo / back operations, the order and time of triggering the undo or back action can be recorded. For repetitive operation sequences, the number of times the user performs the same operation and the interval between them can be recorded to construct a repetitive operation sequence.

[0037] It should be noted that the collection of operation trajectory data only affects the front-end display layer and does not modify the business logic or back-end data. In addition, the operation trajectory data is raw data collection without any analysis or modification of the interface, and only forms an analyzable dataset.

[0038] Step 204: Extract and analyze features from the operation trajectory data to generate target behavior determination results.

[0039] The target behavior determination result is used to characterize the user's operation status or potential intention. The target behavior determination result includes an indicator of whether interface repair needs to be triggered, the type of operation abnormality, the target interface element identifier, and the determination basis.

[0040] Operational anomalies include, but are not limited to, high-frequency accidental touches, repetitive operation paths, hovering anomalies, and deviations from expectations. High-frequency accidental touches can be identified by determining if the number of times a user clicks the same control exceeds a threshold or if the accidental click rate exceeds a threshold. Repetitive operation paths can be determined by calculating path similarity between mouse movement sequences to identify if the user is repeatedly searching for the target control; optionally, dynamic time warping algorithms or simplified Euclidean distance can be used for path similarity calculation. Hovering anomalies can be obtained through statistical analysis; excessively long hovering indicates the user is unsure of the operation target, while excessively short hovering indicates the user is ignoring the operation. Deviations from expectations can be determined by comparing the user's operation result with a typical operation path to determine if the operation deviates from expectations.

[0041] In some optional embodiments, if the operation anomaly type is a repetitive operation path, the operation trajectory data includes user mouse movement behavior data, such as... Figure 3 As shown, Figure 3 A flowchart illustrating the steps for generating a target behavior determination result, provided in this application embodiment, includes: Step 302: Construct the original trajectory sequence based on continuously collected user mouse movement behavior data.

[0042] Step 304: Perform search behavior segmentation processing on the original trajectory sequence based on preset operation boundary conditions to obtain multiple trajectory segments.

[0043] Step 306: After standardizing each trajectory segment, calculate the path distance between different trajectory segments, and determine the path similarity relationship between each trajectory segment based on the path distance.

[0044] Step 308: Calculate the number of repetitions based on the similarity relationship of each path, and generate the target behavior determination result based on the number of repetitions.

[0045] During front-end interface interaction, mouse movement events generated by the user are continuously collected. Each collection record includes the mouse's position coordinates on the interface and the corresponding time information. The collected mouse movement events can be sorted in chronological order to form a continuous raw trajectory sequence, providing basic data for subsequent trajectory analysis.

[0046] Next, the original trajectory sequence can be segmented based on preset operation boundary conditions to obtain multiple trajectory segments, where each trajectory segment corresponds to a complete target control search behavior. Specifically, preset operation boundary conditions can be detected in the original trajectory sequence. When a mouse click, mouse pause time exceeding a preset threshold, or a significant directional interruption of the mouse trajectory is detected, the original trajectory sequence is segmented at the corresponding position to generate multiple trajectory segments. Only by distinguishing between "one search" and "the next search" can it be determined whether a duplicate search has occurred.

[0047] When standardizing each trajectory segment, the number of sampling points in each trajectory segment can be unified, the trajectory coordinates can be normalized, and the trajectory path can be smoothed, so that different trajectory segments are comparable in terms of time and space, and the path shape judgment can be avoided due to differences in movement speed or page size.

[0048] After standardizing each trajectory segment, the path distance between different trajectory segments can be calculated, and the path similarity relationship between the trajectory segments can be determined based on the path distance. Specifically, for multiple standardized trajectory segments generated within the same front-end interface, the path distance values ​​between different trajectory segments can be calculated sequentially. By performing time alignment and path matching on each trajectory segment, the consistency of the spatial path shape of different trajectory segments can be evaluated. When the path distance value between trajectory segments is less than or equal to a preset similarity threshold, it is determined that there is a path similarity relationship between the corresponding trajectory segments.

[0049] Ultimately, the number of repetitions can be calculated based on the similarity relationships of each path, and a target behavior determination result can be generated based on the number of repetitions. Specifically, within a preset time window, the trajectory segments that are determined to be similar to the path can be cumulatively counted. When the number of similar trajectory segments reaches a preset threshold, and no valid trigger operation of the target control is detected within the corresponding trajectory segment, a target behavior determination result indicating that the user has repeated operation paths is generated.

[0050] In other alternative embodiments, the determination criteria include operation anomaly determination rules and preset determination models, such as... Figure 4 As shown, Figure 4 Another flowchart for generating a target behavior determination result provided in this application embodiment includes: Step 402: Extract and analyze the operation trajectory data to generate operation feature vectors.

[0051] Step 404: Determine the operation feature vector based on the predefined operation anomaly determination rules, and generate the first row of determination results.

[0052] Step 406: Process the operation feature vector based on the preset judgment model to generate the second line judgment result.

[0053] Step 408: Based on the first behavior determination result and the second behavior determination result, a comprehensive determination is made to generate the target behavior determination result.

[0054] The rules for determining operational anomalies are pre-defined. For example, if a single control is clicked continuously for more than 3 times, it is considered a high-frequency accidental touch; if the hover time is greater than a preset time threshold, it is considered a hovering anomaly; if the mouse path length is greater than 1.5 times the preset normal operation path, it is considered an operation deviating from the expected value; and if the number of repeated operations is greater than or equal to a preset number threshold, it is considered a repeated operation path.

[0055] In addition, each operation exception judgment rule can generate a Boolean flag to record whether an exception has been triggered. Furthermore, the outputs of multiple operation exception judgment rules are uniformly formed into a "rule judgment result set" for subsequent comprehensive analysis.

[0056] The preset judgment model can weight and score the operation feature vector and output the operation status category, including but not limited to normal operation, high frequency accidental touch, repetitive operation, abnormal path, and difficulty in user understanding. Thus, the model can output the contribution of each feature to the judgment result, which is convenient for explaining the reason for the judgment.

[0057] In some optional embodiments of the smart drum, the target behavior can be determined using the two methods described above, or the operation anomaly determination rule can be used first. When the operation is complex or does not conform to the simple anomaly determination rule, a preset judgment model can be used for further determination.

[0058] In other optional embodiments, the two determination methods can be used separately, and then the two determination results can be combined. That is, the operation feature vector can be determined based on a predefined operation anomaly determination rule to generate a first determination result; then the operation feature vector can be processed based on a preset judgment model to generate a second determination result; finally, the first and second determination results are combined to generate the target behavior determination result. Optionally, the first and second determination results can be weighted for combined determination.

[0059] Step 206: Based on the target behavior determination result, perform self-repair on the interface state of the front-end interface to generate the self-repaired interface state.

[0060] When it is determined that the user has difficulty operating or the interface is not compatible, a self-repair operation will be triggered, including but not limited to expanding or adjusting the clickable area, highlighting or guiding the next operable element, temporarily hiding interfering interface elements, adjusting the interface hierarchy or visual weight, and displaying lightweight operation prompts.

[0061] In some alternative embodiments, such as Figure 5 As shown, Figure 5 A flowchart illustrating the steps for generating a self-healing interface state, provided in this application embodiment, includes: Step 502: Based on the first preset correspondence, determine the target interface repair strategy corresponding to the target behavior judgment result.

[0062] Step 504: Perform self-repair on the front-end interface state based on the target interface repair strategy to generate the self-repaired interface state.

[0063] The target interface repair strategy includes target interface elements and a set of target repair actions corresponding to those elements. Optionally, a first preset correspondence between operation anomaly types and interface repair strategies can be constructed by first parsing the nodes of the front-end interface based on the target interface element identifiers. The interface repair strategy also includes a second preset correspondence between each interface element and the set of repair actions. For example, the interface repair strategy may include at least one of the following: clickable area adjustment strategy, operation guidance highlighting strategy, interference element suppression strategy, interface hierarchy and visual weight adjustment strategy, and lightweight prompt strategy.

[0064] For example, to address frequent accidental touches, a clickable area adjustment strategy can be implemented. Specifically, the original clickable area size of the target element can be read first. Without changing the element's semantics and business logic, the clickable area can be expanded using Cascading Style Sheets (CSS) or a transparent overlay, or the element's click tolerance margin can be increased. The original visual style of the element can be preserved and minimized. By adding inner margins or pseudo-element overlays, the event response area can be expanded without changing the table structure.

[0065] For example, to address deviations between an operation and an expectation, the visual weight of the target element can be increased; controls that can be operated on in the next step can be highlighted or focused; directional guidance markers can be displayed on the interface. Highlighting is achieved through dynamic style injection, without affecting the original functionality of the control.

[0066] For example, when it is determined that there are interfering elements affecting user operation, non-critical functional areas can be temporarily hidden; the transparency of interfering elements can be reduced; and the display of pop-ups or prompts unrelated to the current operation can be delayed. All hiding or suppression operations are temporary and controlled by the state management module.

[0067] For example, when the system adjusts the hierarchical relationship of interface elements according to the operation scenario, it includes: increasing the priority of key controls in the rendering hierarchy; reducing the visual weight of secondary elements; and adjusting the contrast and focus relationship between elements. This is achieved by dynamically modifying style attributes without altering the page structure or business logic.

[0068] For example, when a user needs additional operation prompts, the prompts include: displaying a lightweight prompt related to the current operation context; the prompts do not interrupt the user's operation flow; and the prompt content is consistent with the judgment criteria and is interpretable.

[0069] In some alternative embodiments, such as Figure 6 As shown, Figure 6 A flowchart illustrating the steps of interface self-repair and rollback provided in this application embodiment includes: Step 602: Record the process of self-repairing the interface state of the front-end interface and save the state record information to the state stack.

[0070] Step 604: If a rollback trigger condition is detected, determine the current rollback mode based on the rollback trigger condition.

[0071] Step 606: Perform a rollback operation based on the current rollback mode and the state record information in the state stack to obtain the interface state after rollback and update the state stack.

[0072] The status record information includes the self-repair type, the target interface element for self-repair, the interface state before self-repair, and the interface state after self-repair. The interface state before self-repair includes the position information, size information, interactive area parameters, display or hidden state, style identifiers, or key visual attributes of the target interface element.

[0073] During the self-repair process, it only affects the front-end presentation layer. After the interface repair is completed, the system continuously monitors the rollback trigger conditions. For example, rollback trigger conditions may include, but are not limited to, the user completing the target operation or the operation status changing; the behavior feature analysis module determining that the repair effect is not significant; the interface repair duration exceeding a preset threshold; and multiple consecutive interface repairs triggering a safety threshold.

[0074] Therefore, upon detecting a rollback trigger condition, the current rollback mode is determined based on the rollback trigger condition. Then, based on the current rollback mode and the state record information in the state stack, a rollback operation is performed to obtain the interface state after rollback and the state stack is updated.

[0075] In some optional embodiments, the aforementioned current rollback mode may include a step-by-step rollback mode and a global rollback mode. The step-by-step rollback mode is used to roll back the most recent interface self-repair behavior, and the global rollback mode is used to roll back multiple self-repair behaviors on the current interface. Thus, based on the current rollback mode and the state record information in the state stack, a rollback operation is performed to obtain the rolled-back interface state, which further includes: When the current rollback mode is the step rollback mode, the nearest non-rollback state unit is determined based on the state record information in the state stack, and the target interface element corresponding to the nearest non-rollback state unit is restored to the interface state before self-repair, and the nearest non-rollback state unit is marked as a rolledback state. When the current rollback mode is the overall rollback mode, the state stack is traversed in a preset order, and each unrolled state unit is rolled back in turn to restore the front-end interface to the state before self-repair.

[0076] After completing the rollback operation, the system performs a consistency check between the current interface state and the state stack record, and updates the marking information of the corresponding state unit in the state stack to prevent repeated rollbacks or state conflicts.

[0077] In some optional embodiments, enhanced functionality can also be achieved through front-end script integration, utilizing event listening and node parsing without modifying the interface structure, business code, or back-end interface. Its advantage lies in its strong versatility and ease of deployment in existing systems. Specifically, the system loads the front-end enhancement script during interface runtime via interface script injection or module loading, without affecting the original interface initialization process. The enhancement script initializes event delegates on the interface root node or preset container node, uniformly receiving user interaction events and forwarding event information to the behavior collection module. After the interface loads, the system parses the table structure of the current interface, identifies interactive nodes and their attributes, and establishes a mapping relationship between interface elements. During user operation, the system collects user operation trajectory data based on the event delegate and node mapping relationship and transmits it to the behavior feature analysis module. Based on the collected user operation behavior, the system determines whether interface self-repair is needed and generates a corresponding repair decision. According to the repair decision, the system adjusts the display layer state of the target interface elements without modifying the original business logic. After the interface is adjusted, users can continue with the original operation process, and any new operations will be re-entered into the data collection and analysis process, forming a complete interactive feedback loop.

[0078] This application provides a front-end interface self-repair method based on user operation trajectory. The method includes: monitoring user interactions on the front-end interface and acquiring user operation trajectory data; extracting and analyzing features from the operation trajectory data to generate a target behavior judgment result. The target behavior judgment result characterizes the user's operation state or potential intent, and includes an indicator of whether interface repair needs to be triggered, operation anomaly type, target interface element identifier, and judgment criteria. Operation anomaly types include high-frequency accidental touches, repeated operation paths, hovering anomalies, and deviations from expectations. Based on the target behavior judgment result, the front-end interface state is self-repaired to generate a self-repaired interface state. This solution, by monitoring user interactions on the front-end interface, enables real-time perception and analysis of the user operation process. Furthermore, it can self-repair the front-end interface state based on the target behavior judgment result, achieving adaptive interface repair without intruding on business logic. Additionally, by extracting and analyzing features from the operation trajectory data, the target behavior judgment result is accurately and efficiently generated, allowing for accurate self-repair of the front-end interface state based on the target behavior judgment result, and enabling users to see the self-repaired interface state in real time.

[0079] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0080] Figure 7 This is a structural block diagram of a front-end interface self-repair device based on user operation trajectory provided in an embodiment of this application.

[0081] like Figure 7 As shown, the front-end interface self-healing device 700 based on user operation trajectory includes: The acquisition module 702 is used to listen to the user's interactive operations on the front-end interface and acquire the user's operation trajectory data on the front-end interface.

[0082] The first generation module 704 is used to extract and analyze features from operation trajectory data and generate target behavior judgment results. The target behavior judgment results are used to characterize the user's operation status or potential intention. The target behavior judgment results include whether interface repair needs to be triggered, operation anomaly type, target interface element identifier and judgment basis. Operation anomaly type includes high-frequency accidental touch behavior, repeated operation path, hovering anomaly, and operation deviation from expectation.

[0083] The second generation module 706 is used to self-repair the interface state of the front-end interface based on the target behavior judgment result and generate the self-repaired interface state.

[0084] Regarding the apparatus in the above embodiments, the specific methods by which each module performs its operations have been described in detail in the embodiments related to the method, and will not be elaborated upon here. Each module in the above-described self-repairing front-end interface device based on user operation trajectories can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations of each module.

[0085] In one embodiment of this application, a computer device is provided, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps: Listen to the user's interactive operations on the front-end interface and obtain the user's operation trajectory data on the front-end interface; Feature extraction and analysis are performed on the operation trajectory data to generate target behavior judgment results. The target behavior judgment results are used to characterize the user's operation status or potential intention. The target behavior judgment results include whether interface repair needs to be triggered, operation anomaly type, target interface element identifier and judgment basis. Operation anomaly type includes high-frequency accidental touch behavior, repeated operation path, hovering anomaly, and operation deviation from expectation. Based on the target behavior determination results, the interface state of the front-end interface is self-repaired, and a self-repaired interface state is generated.

[0086] In one embodiment of this application, the processor further performs the following steps when executing the computer program: Feature extraction and analysis are performed on the operation trajectory data to generate operation feature vectors; The operation feature vector is judged based on the predefined operation anomaly judgment rules, and the first row of judgment results is generated. The operation feature vector is processed based on a preset judgment model to generate a second line of judgment results; A comprehensive judgment is made based on the first behavior judgment result and the second behavior judgment result to generate the target behavior judgment result.

[0087] In one embodiment of this application, the processor further performs the following steps when executing the computer program: Construct the original trajectory sequence based on continuously collected user mouse movement behavior data; The original trajectory sequence is segmented based on preset operation boundary conditions to obtain multiple trajectory segments; each trajectory segment corresponds to a complete target control search behavior. After standardizing each trajectory segment, the path distance between different trajectory segments is calculated, and the path similarity relationship between each trajectory segment is determined based on the path distance. The number of repetitions is obtained based on the similarity relationship of each path, and the target behavior determination result is generated based on the number of repetitions.

[0088] In one embodiment of this application, the processor further performs the following steps when executing the computer program: Based on the first preset correspondence, a target interface repair strategy corresponding to the target behavior judgment result is determined; wherein, the target interface repair strategy includes target interface elements and a set of target repair actions corresponding to the target interface elements; The front-end interface is self-repaired based on the target interface repair strategy, generating a self-repaired interface state.

[0089] In one embodiment of this application, the processor further performs the following steps when executing the computer program: After parsing the nodes of the front-end interface based on the target interface element identifier, a first preset correspondence between operation exception types and interface repair strategies is constructed; wherein, the interface repair strategy includes a second preset correspondence between each interface element and the set of repair actions.

[0090] In one embodiment of this application, the processor further performs the following steps when executing the computer program: The process of self-repairing the interface state of the front-end interface will be recorded, and the state record information will be saved to the state stack. The state record information includes the self-repair type, the target interface element for self-repair, the interface state before self-repair, and the interface state after self-repair. The interface state before self-repair includes the position information, size information, interactive area parameters, display or hidden state, style identifiers or key visual attributes of the target interface element. If a rollback trigger condition is detected, the current rollback mode is determined based on the rollback trigger condition; Perform a rollback operation based on the current rollback mode and the state record information in the state stack, obtain the interface state after rollback, and update the state stack.

[0091] In one embodiment of this application, the processor further performs the following steps when executing the computer program: When the current rollback mode is the step rollback mode, the nearest non-rollback state unit is determined based on the state record information in the state stack, and the target interface element corresponding to the nearest non-rollback state unit is restored to the interface state before self-repair, and the nearest non-rollback state unit is marked as a rolledback state. When the current rollback mode is the overall rollback mode, the state stack is traversed in a preset order, and each unrolled state unit is rolled back in turn to restore the front-end interface to the state before self-repair.

[0092] The computer device provided in this application embodiment has a similar implementation principle and technical effect to the above method embodiment, and will not be described again here.

[0093] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps: Listen to the user's interactive operations on the front-end interface and obtain the user's operation trajectory data on the front-end interface; Feature extraction and analysis are performed on the operation trajectory data to generate target behavior judgment results. The target behavior judgment results are used to characterize the user's operation status or potential intention. The target behavior judgment results include whether interface repair needs to be triggered, operation anomaly type, target interface element identifier and judgment basis. Operation anomaly type includes high-frequency accidental touch behavior, repeated operation path, hovering anomaly, and operation deviation from expectation. Based on the target behavior determination results, the interface state of the front-end interface is self-repaired, and a self-repaired interface state is generated.

[0094] In one embodiment of this application, the computer program, when executed by a processor, further performs the following steps: Feature extraction and analysis are performed on the operation trajectory data to generate operation feature vectors; The operation feature vector is judged based on the predefined operation anomaly judgment rules, and the first row of judgment results is generated. The operation feature vector is processed based on a preset judgment model to generate a second line of judgment results; A comprehensive judgment is made based on the first behavior judgment result and the second behavior judgment result to generate the target behavior judgment result.

[0095] In one embodiment of this application, the computer program, when executed by a processor, further performs the following steps: Construct the original trajectory sequence based on continuously collected user mouse movement behavior data; The original trajectory sequence is segmented based on preset operation boundary conditions to obtain multiple trajectory segments; each trajectory segment corresponds to a complete target control search behavior. After standardizing each trajectory segment, the path distance between different trajectory segments is calculated, and the path similarity relationship between each trajectory segment is determined based on the path distance. The number of repetitions is obtained based on the similarity relationship of each path, and the target behavior determination result is generated based on the number of repetitions.

[0096] In one embodiment of this application, the computer program, when executed by a processor, further performs the following steps: Based on the first preset correspondence, a target interface repair strategy corresponding to the target behavior judgment result is determined; wherein, the target interface repair strategy includes target interface elements and a set of target repair actions corresponding to the target interface elements; The front-end interface is self-repaired based on the target interface repair strategy, generating a self-repaired interface state.

[0097] In one embodiment of this application, the computer program, when executed by a processor, further performs the following steps: After parsing the nodes of the front-end interface based on the target interface element identifier, a first preset correspondence between operation exception types and interface repair strategies is constructed; wherein, the interface repair strategy includes a second preset correspondence between each interface element and the set of repair actions.

[0098] In one embodiment of this application, the computer program, when executed by a processor, further performs the following steps: The process of self-repairing the interface state of the front-end interface will be recorded, and the state record information will be saved to the state stack. The state record information includes the self-repair type, the target interface element for self-repair, the interface state before self-repair, and the interface state after self-repair. The interface state before self-repair includes the position information, size information, interactive area parameters, display or hidden state, style identifiers or key visual attributes of the target interface element. If a rollback trigger condition is detected, the current rollback mode is determined based on the rollback trigger condition; Perform a rollback operation based on the current rollback mode and the state record information in the state stack, obtain the interface state after rollback, and update the state stack.

[0099] In one embodiment of this application, the computer program, when executed by a processor, further performs the following steps: When the current rollback mode is the step rollback mode, the nearest non-rollback state unit is determined based on the state record information in the state stack, and the target interface element corresponding to the nearest non-rollback state unit is restored to the interface state before self-repair, and the nearest non-rollback state unit is marked as a rolledback state. When the current rollback mode is the overall rollback mode, the state stack is traversed in a preset order, and each unrolled state unit is rolled back in turn to restore the front-end interface to the state before self-repair.

[0100] The computer-readable storage medium provided in this embodiment is similar in principle and technical effect to the method embodiment described above, and will not be repeated here.

[0101] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0102] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0103] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A self-healing method for a front-end interface based on user operation trajectory, characterized in that, The method includes: Listen to the user's interactive operations on the front-end interface and obtain the user's operation trajectory data on the front-end interface; The operation trajectory data is subjected to feature extraction and analysis to generate target behavior determination results; wherein, the target behavior determination results are used to characterize the user's operation state or potential intention, and the target behavior determination results include whether interface repair needs to be triggered, operation anomaly type, target interface element identifier and determination basis, and the operation anomaly type includes high-frequency accidental touch behavior, repeated operation path, hovering anomaly, and operation deviation from expectation; Based on the target behavior determination result, the interface state of the front-end interface is self-repaired to generate a self-repaired interface state.

2. The method according to claim 1, characterized in that, The judgment criteria include operation anomaly judgment rules and preset judgment models. The step of extracting and analyzing features from the operation trajectory data to generate target behavior judgment results includes: The operation trajectory data is subjected to feature extraction and analysis to generate operation feature vectors; The operation feature vector is judged based on the predefined operation anomaly judgment rules to generate the first line of judgment result; The operation feature vector is processed based on a preset judgment model to generate a second behavior judgment result; The target behavior determination result is generated by comprehensively judging the first behavior determination result and the second behavior determination result.

3. The method according to claim 1, characterized in that, If the operation anomaly type is a repeated operation path, and the operation trajectory data includes user mouse movement behavior data, the step of extracting and analyzing features from the operation trajectory data to generate a target behavior determination result further includes: Construct the original trajectory sequence based on continuously collected user mouse movement behavior data; The original trajectory sequence is segmented based on preset operation boundary conditions to obtain multiple trajectory segments; wherein, one trajectory segment corresponds to one complete target control search behavior; After standardizing each trajectory segment, the path distance between different trajectory segments is calculated, and the path similarity relationship between each trajectory segment is determined based on the path distance. The number of repetitions is obtained based on the statistical analysis of the similarity relationships of each path, and the target behavior determination result is generated based on the number of repetitions.

4. The method according to any one of claims 1-3, characterized in that, The step of self-repairing the interface state of the front-end interface based on the target behavior determination result to generate a self-repaired interface state includes: Based on a first preset correspondence, a target interface repair strategy corresponding to the target behavior determination result is determined; wherein, the target interface repair strategy includes target interface elements and a set of target repair actions corresponding to the target interface elements; Based on the target interface repair strategy, the interface state of the front-end interface is self-repaired to generate the self-repaired interface state.

5. The method according to claim 4, characterized in that, The method further includes: After parsing the nodes of the front-end interface based on the target interface element identifier, a first preset correspondence between the operation exception type and the interface repair strategy is constructed; wherein, the interface repair strategy includes a second preset correspondence between each interface element and the set of repair actions.

6. The method according to any one of claims 1-3, characterized in that, The method further includes: The process of self-repairing the interface state of the front-end interface is recorded, and the state recording information is saved to the state stack. The state recording information includes the self-repair type, the target interface element for self-repair, the interface state before self-repair, and the interface state after self-repair. The interface state before self-repair includes the position information, size information, interactive area parameters, display or hidden state, style identifiers or key visual attributes of the target interface element. If a rollback trigger condition is detected, the current rollback mode is determined based on the rollback trigger condition; Based on the current rollback mode and the state record information in the state stack, a rollback operation is performed to obtain the interface state after rollback and update the state stack.

7. The method according to claim 6, characterized in that, The current rollback mode includes a step-by-step rollback mode and a full rollback mode. The step of performing a rollback operation based on the current rollback mode and the state record information in the state stack to obtain the rolled-back interface state also includes: When the current rollback mode is the step rollback mode, the nearest non-rollback state unit is determined based on the state record information in the state stack, and the target interface element corresponding to the nearest non-rollback state unit is restored to the interface state before self-repair, and the nearest non-rollback state unit is marked as a rolledback state. When the current rollback mode is the overall rollback mode, the state stack is traversed in a preset order, and each unrolled state unit is rolled back in turn to restore the front-end interface to the state before self-repair.

8. A front-end interface self-repair device based on user operation trajectory, characterized in that, The device includes: The acquisition module is used to listen to the user's interactive operations on the front-end interface and acquire the user's operation trajectory data on the front-end interface. The first generation module is used to extract and analyze features from the operation trajectory data and generate a target behavior determination result. The target behavior determination result is used to characterize the user's operation state or potential intention. The target behavior determination result includes an indicator of whether interface repair needs to be triggered, operation anomaly type, target interface element identifier, and determination basis. The operation anomaly type includes high-frequency accidental touch behavior, repeated operation path, hovering anomaly, and operation deviation from expectation. The second generation module is used to perform self-repair on the interface state of the front-end interface based on the target behavior determination result, and generate the self-repaired interface state.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the front-end interface self-repair method based on user operation trajectory as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the front-end interface self-repair method based on user operation trajectory as described in any one of claims 1-7.

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