Interface window tree and behavior sequence tree-based interactive interface evaluation method and system

Abstract

This invention proposes an interactive interface evaluation method and system based on Interface Window Tree (IWT) and Behavior Sequence Tree (BST), belonging to the field of human-computer interaction technology. The interactive interface evaluation method includes: constructing an Interface Window Tree (IWT) model, defining the spatial logic and hierarchical relationships of interface elements through multi-level nodes; when a user operates on the front-end interface, collecting the user's input information in real time, generating a user behavior sequence tree based on the input information, and obtaining the interaction operation information of the user's interaction with the front-end interface through the behavior sequence tree; determining whether there are defects in the front-end interface by combining the interaction operation information of the user's interaction with the front-end interface with the misalignment between the IWT static path and the BST dynamic path. This addresses the black-box problem existing in the evaluation of complex human-computer interaction interfaces in current technologies.

Description

Technical Field

[0001] This invention proposes an interactive interface evaluation method and system based on interface window tree and behavior sequence tree, belonging to the field of human-computer interaction (HCI) technology. Background Technology

[0002] In safety-critical fields such as aerospace, industrial monitoring, and medical diagnostics, the complexity of human-computer interaction systems is increasing daily. Massive data display, multi-level control logic, and complex task flow transitions have become the norm. Operators must process massive amounts of information and make high-frequency decisions in a high-pressure, high-dynamic interface environment. Therefore, the design quality of the human-computer interface directly determines the safety and efficiency of the entire system, becoming a core performance indicator. The "Mental Model Difference Theory" is the core of existing interface evaluation systems. This theory points out that the user's mental representation of the system's functions and operational paths (user model) often differs from the actual logic of the system (system model). This discrepancy is the root cause of cognitive resistance, decreased operational efficiency, and even human error. Statistical data shows that the causes of many aviation safety accidents can be traced back to perceptual biases in interface information or inaccurate mental models. Therefore, accurately quantifying mental model differences and locating design flaws has become a key research focus in the field of human-computer interaction.

[0003] Existing interface evaluation methods mainly rely on subjective scoring such as heuristic evaluation and usability testing, or record superficial performance indicators such as task completion time and error rate. In terms of structured modeling, early methods standardized the spatial logic and hierarchical relationship of interface elements through the Interface Window Tree (IWT) model. Subsequent iterations introduced elements such as APIs and controls to achieve functional separation and reuse. User behavior analysis is mostly based on test scripts or automated tools to capture click flows and analyze operation trajectories.

[0004] However, existing technologies have significant limitations when dealing with modern, complex, and highly interactive interface systems: First, the evaluation is black-box, only providing aggregated quantitative scores and failing to break down "why it doesn't work" or pinpoint the root cause of design flaws; second, behavioral analysis is fragmented, breaking down operation sequences into isolated events, failing to capture deep behavioral patterns, and making it difficult to trigger logical conflicts in complex task flows; third, the evaluation dimensions are singular, separating static structure from dynamic behavioral analysis, lacking quantitative models to map behavioral anomalies to specific components, and evaluation intervention lags behind the development cycle, resulting in high costs for later refactoring.

[0005] In summary, existing technologies have serious shortcomings in standardized representation, behavior analysis, and quantitative positioning. Developing an evaluation framework that integrates static architecture and dynamic behavior, quantifies differences in mental models, and supports atomic component-level diagnosis has become a core technical challenge that urgently needs to be solved in the field of human-computer interaction. Summary of the Invention

[0006] This invention provides a method and system for evaluating interactive interfaces based on interface window trees and behavior sequence trees. It relates to a method for performance evaluation, mental model difference quantification diagnosis, and design defect tracing of human-computer interaction interfaces in complex systems, aiming to solve the "black box" problem in existing technologies for evaluating complex human-computer interaction interfaces. The technical solution adopted is as follows:

[0007] An interactive interface evaluation method and system based on interface window tree and behavior sequence tree, wherein the interactive interface evaluation method includes:

[0008] Construct the Interface Window Tree Model (IWT) and define the spatial logic and hierarchical relationship of interface elements through multi-level nodes;

[0009] When a user interacts with the front-end interface, the system collects the user's input information in real time, generates a user behavior sequence tree based on the input information, and obtains the interaction operation information of the user's interaction with the front-end interface through the behavior sequence tree.

[0010] By combining the user's interaction information with the front-end interface with the misalignment between the IWT static path and the BST dynamic path, it can be determined whether there are defects in the front-end interface.

[0011] Furthermore, an Interface Window Tree Model (IWT) is constructed, defining the spatial logic and hierarchical relationships of interface elements through multi-level node definitions, including defining Widgets, Menu, API, BaseInterface, and InterfaceNode respectively.

[0012] Furthermore, when a user interacts with the front-end interface, the system collects the user's input information in real time, generates a user behavior sequence tree based on the input information, and obtains the interaction operation information of the user's interaction with the front-end interface through the behavior sequence tree, including:

[0013] When a user performs an operation on the front-end interface, the input information corresponding to the user's input operation is captured, and the input information is synchronized to the corresponding control node on the front-end interface, and the latest control status value is returned.

[0014] After a user completes a full operation event, the user's input parameters and the API input sequence are validated and filled using a preset mapping table.

[0015] At the moment the API is triggered, the user's ID information, API identifier, and input information are recorded, and the server returns the current system timestamp in response.

[0016] Locate the menu, basic interface, and interface nodes to which the API belongs, and capture the interface interaction data, including input and output information, to form a standardized record of user operation traces.

[0017] Based on the user's ID information, a behavior sequence tree with the user ID as the root node is constructed through a recursive process, and the interaction operation information of the user and the front-end interface is obtained through the behavior sequence tree.

[0018] Furthermore, after detecting that a user has completed a full operation event, the user's input parameters and the API input sequence are validated and filled using a preset mapping table, including:

[0019] Once a complete operation event is detected, the API corresponding to the complete operation event is decomposed into a set of control sequences according to a preset mapping table;

[0020] The sequence of controls is traversed, and the WidgCall function is called.

[0021] The input information generated by the complete operation event corresponding to the user is validated and filled in with the input sequence of the API.

[0022] Furthermore, based on the user's ID information, a behavior sequence tree with the user ID as the root node is constructed recursively. The interaction operation information between the user and the front-end interface is then obtained through the behavior sequence tree, including:

[0023] By setting the starting child node corresponding to the user-triggered behavior in the front-end interface and combining the timestamp interval between adjacent operations, a user behavior sequence tree is constructed.

[0024] Based on the user's behavior sequence tree, the front-end interface during the user's operation process is retrieved and traced back. By combining NodeDecom, NodeQuery and TreeQuery with user ID and timestamp, all interface nodes, API calls and data streams operated by the user are retrieved in reverse in the interface window tree model IWT.

[0025] Furthermore, by setting the starting child node corresponding to the user-triggered behavior on the front-end interface and combining the timestamp intervals between adjacent operations, a user behavior sequence tree is constructed, including:

[0026] Set the first interface node triggered by the user in each independent interaction cycle as the direct child node of the user ID root node, i.e. the starting child node.

[0027] During subsequent node recording, the timestamp interval between two adjacent interface operations is calculated, and the completion of an independent interaction cycle is determined based on the timestamp interval; where, if the timestamp interval is greater than the preset maximum threshold T, max If the condition is not met, it is considered the end of an independent interaction cycle, and a new valid access path is generated again using the root node.

[0028] Furthermore, by combining the user's interaction information with the front-end interface with the misalignment between the IWT static path and the BST dynamic path, it is determined whether there are defects in the front-end interface, including:

[0029] Perform structural alignment and preprocessing on the interactive operation information of the user and the front-end interface.

[0030] The misalignment deviation between the IWT static path and the BST dynamic path is obtained by using the data corresponding to the IWT static path and the BST dynamic path.

[0031] The misalignment deviation between the IWT static path and the BST dynamic path is used to determine whether there are defects in the front-end interface.

[0032] Furthermore, the interactive operation information during the user's interaction with the front-end interface is structurally aligned and preprocessed, including:

[0033] Map the operation behavior Au in BST to the control or API node of the interface window tree model IWT by ID;

[0034] The operation is not a repetitive jump-type operation, and consideration is given to the user's habitual vibration noise;

[0035] The actual path Pu and the expert preset path Pd are segmented according to the task fragment corresponding to the specific operation task when the user operates, so as to ensure consistency in comparison.

[0036] Furthermore, the misalignment deviation between the IWT static path and the BST dynamic path is obtained through the data corresponding to the IWT static path and the BST dynamic path, including:

[0037] The original bias score D is defined using two dimensions: structural missing and behavioral redundancy. raw Wherein, the original deviation score D raw Obtain it using the following formula:

[0038]

[0039] in, This indicates the number of nodes where the actual operation overlaps with the expected path; This indicates a dimensional correction to ensure horizontal comparability between tasks of different complexities. Indicates redundant navigation; This indicates a penalty for skipping critical functional nodes;

[0040] The original bias score D is obtained by using a nonlinear mapping function. raw The constraints are within the interval [0,1]; wherein, the structure of the nonlinear mapping function is as follows:

[0041]

[0042] Where PD represents the original deviation score D raw The corresponding parameters after constraints are defined. Furthermore, a PD approaching 0 indicates that the user behavior is highly consistent with the design expectations, while a PD approaching 1 indicates that the user is in a state of extreme disorientation in the interface.

[0043] Using the original deviation score D raw The mental model discrepancy value (IFI) is determined by the corresponding parameters after constraints; wherein, the mental model discrepancy value (IFI) is obtained by the following formula:

[0044]

[0045] Wherein, IFI represents the mental model variance value; α represents the weighting coefficient of path deviation, used to adjust the influence weight of path logic correctness on the final mental model variance value, reflecting the evaluation model's focus on the logical rationality of the operational path; β represents the weighting coefficient of the time loss term, used to adjust the influence weight of interaction time cost on mental model variance, to correct efficiency losses caused by unreasonable layout, feedback delay, etc.; T actual The actual task completion time refers to the total time consumed from triggering the first API to completing the last API operation when a user performs a specific interactive task on the front-end interface; T expected The expected task completion time refers to the baseline time required to complete the task under the optimal design path preset by experts, with no cognitive resistance and ideal system response.

[0046] Based on the Mental Model Difference Value (IFI), interface performance is divided into four levels: excellent, good, warning, and failure.

[0047] Furthermore, the misalignment deviation between the IWT static path and the BST dynamic path is used to determine whether there are defects in the front-end interface, including:

[0048] Redundant navigation is determined by mapping nodes between IWT static paths and BST dynamic paths. The IWT node with the highest accumulated value accurately identifies the source of design defects.

[0049] If the user interface operation process includes API node T actual If the value is too high, it is determined to be a system delay or lack of feedback;

[0050] If multiple users experience a backtracking loop at the unified basic interface BIF, it is determined that the menu logic or component affiliation of that basic interface does not conform to the mental model.

[0051] An interactive interface evaluation system based on interface window tree and behavior sequence tree, the interactive interface evaluation system includes:

[0052] The interface construction module is used to build the interface window tree model (IWT), defining the spatial logic and hierarchical relationship of interface elements through multi-level nodes;

[0053] The behavior sequence tree and interaction operation information acquisition module is used to collect user input information in real time when the user operates on the front-end interface, generate the user's behavior sequence tree based on the input information, and obtain the interaction operation information of the user's interaction with the front-end interface through the behavior sequence tree.

[0054] The interface defect determination module is used to determine whether there are defects in the front-end interface by combining the interaction operation information of the user with the front-end interface with the misalignment between the IWT static path and the BST dynamic path.

[0055] Beneficial effects of this invention:

[0056] This invention proposes an interactive interface evaluation method and system based on Interface Window Tree (IWT) and Behavior Sequence Tree (BST). Specifically, it presents an evaluation framework that integrates Interface Window Tree (IWT) and Behavior Sequence Tree (BST) with computational modeling. Relying on an improved IWT model and BST behavior analysis, this invention establishes a quantitative mapping from controls and behavioral paths to performance indicators, supporting atomic component-level problem localization. It can accurately identify specific defect sources causing discrepancies in mental models, such as component response delays, jump path conflicts, and missing feedback. Furthermore, going beyond simple clickstream analysis, this invention utilizes BST to structurally represent users' operation sequences, decision paths, and state transitions under task-driven conditions. This effectively identifies deep behavioral patterns such as hesitation, backtracking, and erroneous attempts, revealing cognitive conflicts caused by navigational disorientation. Moreover, by introducing Path Deviation (PD) and Mental Model Difference (IFI), this invention can objectively quantify the degree of deviation between static design architecture and user dynamic behavior, improving the objectivity and accuracy of the evaluation. Attached Figure Description

[0057] Figure 1 This is an element relationship diagram of the front-end interface described in this invention;

[0058] Figure 2 This is a schematic diagram of the subtree structure of the behavior sequence tree described in this invention;

[0059] Figure 3 This is a schematic diagram of the calculation model for path deviation and interface friction index of the present invention. Detailed Implementation

[0060] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0061] This invention proposes an interactive interface evaluation method and system based on an interface window tree and a behavior sequence tree. The interactive interface evaluation method includes:

[0062] Constructing the Interface Window Tree (IWT) model, such as Figure 1 As shown, the spatial logic and hierarchical relationship of interface elements are defined through multi-level nodes;

[0063] When a user interacts with the front-end interface, the system collects the user's input information in real time, generates a user behavior sequence tree based on the input information, and obtains the interaction operation information of the user's interaction with the front-end interface through the behavior sequence tree.

[0064] By combining the user's interaction information with the front-end interface with the misalignment between the IWT static path and the BST dynamic path, it can be determined whether there are defects in the front-end interface.

[0065] The technical solution described in this embodiment aims to address the "black box" problem in the evaluation of complex human-computer interaction interfaces, providing an evaluation framework capable of quantifying differences in mental models and accurately locating the root causes of design flaws. Specifically, the technical solution described in this embodiment mainly solves the following technical problems:

[0066] (1) The disconnect between evaluation indicators and the root causes of defects: Traditional evaluation methods (such as heuristic assessment or simple usability testing) can usually only give rough aggregate scores such as task completion time and error rate, and cannot distinguish whether the delay is caused by menu complexity, component hierarchy design errors or navigation path conflicts. This invention establishes an explicit calculation model to decompose and map macro performance indicators to specific interface components, thereby making the evaluation process transparent.

[0067] (2) Solving the problem of fragmented behavioral data: Existing user behavior analysis often treats operations as isolated events, which obscures the deep workflow patterns. This invention uses behavioral sequence trees to structurally represent the user's operation sequence, decision path and state transition, and realizes in-depth insight into complex patterns such as backtracking loops and navigational disorientation.

[0068] (3) Quantifying the difference in mental models between user models and system models: In response to the pain point of lacking an explicit model to map behavioral anomalies to specific interface elements, this invention establishes a quantitative mapping of “controls, behavioral paths to performance indicators” through the deep integration of interface window tree and behavior sequence tree, so as to achieve interpretable diagnosis of performance bottlenecks (such as response delay, path conflict, and lack of feedback).

[0069] Meanwhile, the technical solution described above in this embodiment relies on the improved IWT model and BST behavior analysis to complete the quantitative mapping from controls and behavior paths to performance indicators. It supports atomic component-level problem localization and can accurately identify specific defect sources causing differences in mental models, such as component response delays, jump path conflicts, and missing feedback. This enhances the structure and depth of behavior analysis, going beyond simple clickstream analysis. This embodiment uses BST to structurally represent the user's operation sequence, decision path, and state transitions under task-driven conditions, effectively identifying deep behavioral patterns such as hesitation, backtracking, and erroneous attempts, revealing cognitive conflicts caused by navigational disorientation. A scientifically quantified evaluation index system is constructed. By introducing path deviation (PD) and mental model difference value (IFI), this embodiment can objectively quantify the degree of deviation between the static design architecture and the user's dynamic behavior, improving the objectivity and accuracy of the evaluation.

[0070] In one embodiment of the present invention, an Interface Window Tree (IWT) model is constructed, which defines the spatial logic and hierarchical relationship of interface elements through multi-level nodes, including: defining Widgets, Menu, API interface, BaseInterface, and InterfaceNode respectively.

[0071] The specific definitions are as follows:

[0072] (1) Widgets are defined as (id, name, type, value, isVisible, isEditable), where:

[0073] 1) id represents the Widget's ID.

[0074] 2) name represents the name of the Widget.

[0075] 3) Type represents the type of Widget, Type:=Input|Output|getApi

[0076] 4) Value represents the current value of the Widget, which is initially empty.

[0077] 5) isVisible indicates the visibility of a widget; isVisible := True / False

[0078] 6) `isEditable` indicates the modifiability of a Widget; `isEditable := True / False`

[0079] (2) Menu, defined as (id, menuName, APIsequence), where:

[0080] 1) id represents the menu number;

[0081] 2) menuName represents the name of the menu;

[0082] 3) APIsequence indicates the API functions contained in the menu.

[0083] (3) API, defined as (id, APIName, Parmi, Parmo, Function, Widgetssequence), where:

[0084] 1) id represents the API number;

[0085] 2) APIName represents the name of the API.

[0086] 3) Parm_in = {parm1, parm2, ..., parmm}, m ≥ 0, represents the sequence of input parameters for the operation, null indicates that the input parameter sequence is empty.

[0087] 4) Parm_out = {parm1, parm2, ..., parmn}, n ≥ 0, represents the output parameter sequence of this operation; null indicates that the output parameter sequence is empty.

[0088] 5) Function represents the function body corresponding to the API;

[0089] 6) The Widgetssequence represents a set of Widgets corresponding to an API.

[0090] (4) The basic interface BaseInterface(BIF) is defined as (id, name, Menu, Widgetssequence), where:

[0091] 1) id represents the BIF number;

[0092] 2) `name` represents the name of the BIF;

[0093] 3) Menu represents the menu contained in the BIF; Menu can be empty.

[0094] 4) Widgetssequence represents the Widgets contained in the BIF, usually at least one or more.

[0095] (5) Interface node (NIF), defined as (id, name, BIF, (Parent, Children)), where:

[0096] 1) id represents the NIF number;

[0097] 2) `name` represents the name of the NIF;

[0098] 3) The BIF sequence represents all the BIFs contained in the current interface node, that is, the NIF can be decomposed into a superposition of several BIFs;

[0099] 4) (Parent,Childs) represents the direct parent and direct child nodes of NIF; where the direct child nodes Children = {NIF1, NIF2, ..., NIFm} (m ≥ 0)

[0100] The technical solution described in this embodiment constructs an Interface Window Tree (IWT) model, clearly defining and standardizing the definitions and core attributes of controls, menus, API interfaces, basic interfaces, and interface nodes. It clearly defines the spatial logic and hierarchical relationships of various interface elements, achieving standardized and structured management of interface elements. Simultaneously, the precise definition of various interface elements unifies the standards for interface design and development, reduces development deviations caused by chaotic element definitions, and improves the standardization and efficiency of interface development. Furthermore, through hierarchical node design, it clarifies the parent-child relationships of interface nodes and the composition logic of basic interfaces, streamlining the relationships between interface elements and reducing the difficulty of interface maintenance and iteration. This model provides a unified standard and support for interface design, development, and maintenance, optimizes the management logic of interface elements, improves the consistency and efficiency of interface development, and provides a solid structured foundation for subsequent interface-related operations.

[0101] In one embodiment of the present invention, when a user operates on the front-end interface, the user's input information is collected in real time, and a user behavior sequence tree is generated based on the input information. The user's interaction operation information during the interaction process with the front-end interface is obtained through the behavior sequence tree, including:

[0102] When a user performs an operation on the front-end interface, the input information corresponding to the user's input operation is captured, and the input information is synchronized to the corresponding control node on the front-end interface, and the latest control status value is returned.

[0103] After a user completes a full operation event, the user's input parameters and the API input sequence are validated and filled using a preset mapping table.

[0104] At the moment the API is triggered, the user's ID information, API identifier, and input information are recorded, and the server returns the current system timestamp in response.

[0105] Locate the menu, basic interface, and interface nodes to which the API belongs, and capture the interface interaction data, including input and output information, to form a standardized record of user operation traces.

[0106] Based on the user's ID information, a behavior sequence tree with the user ID as the root node is constructed through a recursive process, and the interaction operation information of the user and the front-end interface is obtained through the behavior sequence tree.

[0107] Specifically, after detecting that a user has completed a full operation event, the system uses a preset mapping table to validate and populate the user's input parameters and the API's input sequence, including:

[0108] Once a complete operation event is detected, the API corresponding to the complete operation event is decomposed into a set of control sequences according to a preset mapping table;

[0109] The sequence of controls is traversed, and the WidgCall function is called.

[0110] The input information generated by the complete operation event corresponding to the user is validated and filled in with the input sequence of the API.

[0111] Simultaneously, based on the user's ID information, a behavior sequence tree with the user ID as the root node is constructed recursively. The interaction operation information between the user and the front-end interface is then obtained through this behavior sequence tree, including:

[0112] A user behavior sequence tree is constructed by setting the starting child node corresponding to the user-triggered behavior on the front-end interface and combining the timestamp interval between adjacent operations. Specifically, the first interface node triggered by the user in each independent interaction cycle is set as the direct child node of the user ID root node, i.e., the starting child node. During the recording of subsequent nodes, the timestamp interval between two adjacent interface operations is calculated, and the end of an independent interaction cycle is determined based on the timestamp interval. Among them, if the timestamp interval is greater than the preset maximum threshold T, the user is considered to have completed the interaction cycle. max If the condition is not met, it is considered the end of an independent interaction cycle, and a new valid access path is generated again using the root node.

[0113] In this embodiment, root nodes are used at two stages, as detailed below:

[0114] Step 1: Recursive Initialization of Trees and Parallel Paths

[0115] The system constructs a tree structure recursively. When a user begins to interact, a root node with the userId as its core is first generated, serving as a container for all subsequent subtrees (i.e., interaction paths). This ensures that multiple operation paths of the same user at different times can be unified under the same tree structure.

[0116] Step Two: Segmentation and Redirection of the Interaction Cycle (Key Step)

[0117] This is where the "root node" is used most frequently. The algorithm calculates the timestamp interval between adjacent operations in real time.

[0118] Decision logic: If the time interval \Deltat > T max The current interaction cycle is determined to have ended.

[0119] The role of the root node: Once the cycle ends, the system will perform the "return to the root node" operation and generate new child nodes with the root node as the parent node as the starting point of the next access path.

[0120] Based on the user's behavior sequence tree, the front-end interface during the user's operation process is retrieved and traced back. By combining the user ID and timestamp with NodeDecom (current node decomposition), NodeQuery (current node retrieval) and TreeQuery (behavior sequence tree retrieval), all interface nodes, API calls and data streams operated by the user are retrieved in reverse in the interface window tree model IWT.

[0121] The above technical solution in this embodiment captures user operations in real time and generates a structured behavior sequence tree through the following algorithm flow:

[0122] Atomic-level operation triggering: When a user performs an operation on the front-end interface, the system captures the input value and synchronizes it to the corresponding control node, returning the latest control state value, realizing the transformation of physical operation into digital attribute.

[0123] Functional operation decomposition: After a user completes a full operation event, the system decomposes the corresponding API into a sequence of controls according to a preset mapping table. By traversing the control sequence and calling the WidgCall function, the user's input parameters are validated and filled in against the API's input sequence.

[0124] API Real-Time Response Recording: The system records the user ID, API identifier, and input information the instant the API is triggered, and returns a timestamp of the current system upon the server's response. This algorithm ensures that every interaction is accompanied by accurate time-related information, providing a data foundation for subsequent analysis of hesitation time.

[0125] Dynamic decomposition of window nodes: A mapping table from behavior to static structure is established, as shown in Table 1. When an API is triggered, the system traverses the interface window tree to locate the menu, basic interface, and interface node to which the API belongs. Captured interaction data (including input and output) is written to the distributed storage system in real time, forming a standardized record of user operation traces.

[0126] User behavior path generation: A behavior sequence tree is constructed recursively with the user ID as the root node. The system sets the first interface node triggered by the user in each independent interaction cycle as the direct child node of the user ID root node, i.e., the starting child node. During subsequent recording, the system calculates the timestamp interval between adjacent operations. If the interval exceeds a preset maximum threshold T... max If the condition is not met, it is considered the end of an independent interaction cycle, and a new valid access path is generated again using the root node.

[0127] This process, defined recursively, can fully capture the user's actual operation flow under task-driven conditions, including repeated jumps and abnormal pauses.

[0128] Behavior sequence retrieval and backtracking: Through NodeDecom (current node decomposition), NodeQuery (current node retrieval), and TreeQuery (behavior sequence tree retrieval), the system can reverse-retrieve all the interface nodes, API calls, and data streams operated by the user in IWT based on the user ID and timestamp, realizing a full-scale reconstruction of the interaction process.

[0129] Table 1

[0130]

[0131] The technical solution described in this embodiment achieves multi-dimensional improvements, significantly optimizing the efficiency, accuracy, and traceability of user interface interaction data collection. By capturing user input operations in real time and synchronizing them to control nodes and providing immediate status feedback, the data collection latency is controlled at the millisecond level, ensuring the immediacy and completeness of interactive information and providing a highly timely data source for subsequent analysis. In the API call phase, the parameter validation and filling mechanism based on a preset mapping table effectively minimizes parameter error rates and illegal call interception rates, ensuring the stability and success rate of backend service calls and significantly reducing abnormal overhead during system operation. The behavior sequence tree construction algorithm, relying on timestamp thresholds for intelligent segmentation of interaction cycles, achieves accurate modeling and hierarchical organization of complex user operation logic. Its recursive construction and dynamic expansion characteristics support large-scale concurrent user access while maintaining extremely low memory consumption and query response latency. Meanwhile, in terms of retrieval performance, the integrated collaborative retrieval mechanism of NodeDecom, NodeQuery, and TreeQuery, combined with the indexing advantages of the IWT interface window tree model, significantly improves the efficiency of reverse retrieval of historical operation paths, API call chains, and data flows, greatly reducing the average retrieval time and enabling rapid location of key operation nodes. Overall, this solution comprehensively improves the system's processing performance, retrieval accuracy, and resource utilization in high-concurrency scenarios, from data collection and verification to behavior modeling and end-to-end tracing, providing solid performance support for interactive analysis and operational optimization.

[0132] One embodiment of the present invention determines whether there is a defect in the front-end interface by combining the interaction operation information of the user's interaction with the front-end interface with the misalignment between the IWT static path and the BST dynamic path, including:

[0133] Perform structural alignment and preprocessing on the interactive operation information of the user and the front-end interface.

[0134] The misalignment deviation between the IWT static path and the BST dynamic path is obtained by using the data corresponding to the IWT static path and the BST dynamic path.

[0135] The misalignment deviation between the IWT static path and the BST dynamic path is used to determine whether there are defects in the front-end interface.

[0136] This embodiment's technical solution identifies the misalignment between the IWT static path and the BST dynamic path through a computational model, thereby quantifying the differences in mental models. Simultaneously, by combining user interaction information with the front-end interface, the IWT static path, and the BST dynamic path, a precise and efficient front-end interface defect detection mechanism is constructed. This enables the scientific judgment and accurate identification of interface defects, improving the efficiency and reliability of front-end interface quality control. Structural alignment and preprocessing of the interaction information effectively standardize data formats and eliminate redundant information, ensuring the accuracy of subsequent path comparisons and providing a high-quality data foundation for defect judgment. By acquiring the misalignment deviation between the IWT static path and the BST dynamic path, the difference between the static specifications of the interface design and the dynamic behavior of actual user interaction is accurately captured, clarifying the core basis for defect judgment. Utilizing the misalignment deviation to determine whether a front-end interface has defects achieves automated and intelligent defect detection, replacing traditional manual detection methods, significantly reducing labor costs, improving the efficiency and accuracy of defect detection, and reducing missed and false detections. Meanwhile, this solution focuses on the core issue of path misalignment, which can quickly locate the root cause of defects, provide clear guidance for fixing defects in the front-end interface, shorten the repair cycle, and optimize the efficiency of interface development and iteration.

[0137] One embodiment of the present invention includes structural alignment and preprocessing of interaction operation information during user interaction with the front-end interface, comprising:

[0138] The operation behavior Au in BST is mapped to the control or API node in the interface window tree model IWT through the id; here, id refers to the unique identifier of the interface control or API, not the user ID; its function is to accurately match the user's operation behavior to the specific node in the interface model.

[0139] The operation is not a repetitive jump-type operation, and consideration is given to the user's habitual vibration noise;

[0140] The actual path Pu and the expert preset path Pd are segmented according to the task fragment corresponding to the specific operation task when the user operates, so as to ensure consistency in comparison.

[0141] Specifically, mapping the operation behavior Au in BST to the control or API node in the Interface Window Tree Model (IWT) via ID is an atomic node mapping process. After mapping, the behavior path is compressed to eliminate repetitive operations that are not jumps (such as multiple modifications within a form) and to filter out user-habitual oscillation noise. Finally, the task is benchmarked by segmenting the actual path Pu and the expert-preset path Pd according to the task fragment corresponding to the specific operation task at the time of user operation, ensuring consistency in comparison. The purpose is to achieve equivalent analysis and evaluation of the actual path and the expert-preset path under the same business objective.

[0142] The technical solution described above in this invention effectively solves the heterogeneous mapping problem between user behavior data and interface models by structuring and aligning interactive operation information, laying a high-precision data foundation for subsequent path comparison and defect detection. By mapping the operation behavior Au in the BST dynamic path to controls or API nodes in the IWT model via ID, a unified semantic association between different data structures is achieved, eliminating data dimensional differences and ensuring logical consistency in subsequent comparative analysis. By merging non-jump-type repetitive operations and filtering out user habitual oscillation noise, the user behavior sequence is effectively purified, eliminating redundant information caused by irrelevant factors such as misoperation and test input, making the behavior data more realistic and representative. The actual path Pu and the expert-preset path Pd are segmented according to the task fragment corresponding to the specific operation task at the time of user operation, achieving refined segmented comparison, ensuring the accuracy of path alignment and difference analysis, and avoiding comparison failure caused by data confusion at different task stages. This preprocessing process significantly improves data quality and comparison effectiveness, providing a solid guarantee for accurately identifying path misalignment and interface defects.

[0143] One embodiment of the present invention obtains the misalignment deviation between the IWT static path and the BST dynamic path through data corresponding to the IWT static path and the BST dynamic path, including:

[0144] The original bias score D is defined using two dimensions: structural missing and behavioral redundancy. raw Wherein, the original deviation score D raw Obtain it using the following formula:

[0145]

[0146] in, This indicates the number of nodes where the actual operation overlaps with the expected path; This indicates a dimensional correction to ensure horizontal comparability between tasks of different complexities (such as a 3-step task and a 50-step task). The missing structure factor is used to measure whether a user has missed a necessary interaction step. Indicating the intensity of behavioral punishment, specifically including: Indicates redundant navigation; This indicates a penalty for skipping critical functional nodes (such as exiting without saving);

[0147] The original bias score D is obtained by using a nonlinear mapping function. raw The constraint is set within the interval [0,1] to address the numerical overflow problem caused by the extreme complexity of the task; the nonlinear mapping function has the following structure:

[0148]

[0149] Where PD represents the original deviation score D raw The corresponding parameters after constraints are defined. Furthermore, a PD approaching 0 indicates that the user behavior is highly consistent with the design expectations, while a PD approaching 1 indicates that the user is in a state of extreme disorientation in the interface.

[0150] Using the original deviation score D raw The mental model discrepancy value (IFI) is determined by the corresponding parameters after constraints; wherein, the mental model discrepancy value (IFI) is obtained by the following formula:

[0151]

[0152] Wherein, IFI represents the mental model discrepancy value; α represents the weighting coefficient of path deviation, used to adjust the influence weight of path logic correctness (PD) on the final mental model discrepancy value (usually recommended to be 0.7), reflecting that the evaluation model focuses on the logical rationality of the operation path; β represents the weighting coefficient of the time loss term, used to adjust the influence weight of interaction time cost on mental model discrepancy (usually recommended to be 0.3), to correct efficiency losses caused by unreasonable layout, feedback delay, etc.; T actual The actual task completion time refers to the total time consumed from triggering the first API to completing the last API operation when a user performs a specific interactive task on the front-end interface; T expected The expected task completion time refers to the baseline time required to complete the task under the optimal design path preset by experts, with no cognitive resistance and ideal system response.

[0153] Based on the Mental Model Difference Value (IFI), interface performance is divided into four levels: excellent, good, warning, and failure.

[0154] Based on the experimental measurements, the grading criteria are as follows:

[0155] 0 ≤ IFI < 0.2 is excellent, indicating a high degree of fit between the mental model and the user's actions: the user completes the task with almost no hesitation or redundant actions.

[0156] 0.2≤IFI<0.5, Good, indicating slight cognitive resistance: the user path is basically correct, but some components have insufficient feedback or the layout has a small room for optimization.

[0157] 0.5≤IFI<0.8, Warning: The interface navigation path is too deep or the hierarchical structure is unclear, causing users to hesitate and backtrack.

[0158] 0.8≤IFI<1, Failure: The interface architecture is completely mismatched with the user's mental model, and an architecture-level refactoring is necessary.

[0159] The Mental Model Difference Value (IFI) integrates the correctness of the path logic with the time cost. The time component reflects the time lost due to unreasonable layout, delayed feedback, or insufficient visual guidance. Based on the IFI results, interface performance can be categorized into four levels: excellent, good, warning, and failure, providing decision support for architecture-level refactoring.

[0160] The technical solution described in this embodiment achieves accurate quantification and standardized evaluation of path misalignment deviations, significantly improving the accuracy and reliability of front-end interface defect detection. By defining the original deviation score from both structural missing and behavioral redundancy dimensions and introducing dimensional correction, the evaluation bias between tasks of different complexities is effectively eliminated, ensuring the horizontal comparability of deviation scores and improving the objectivity and universality of the evaluation. By using a nonlinear mapping function to constrain the original deviation score within the [0,1] interval, the standardization and quantification of the deviation degree are achieved, making the deviation evaluation more intuitive and unified, and reducing human interpretation errors. The deviation between user behavior and design expectations is further quantified through mental model difference values, and combined with multi-parameter optimization evaluation logic, the accuracy of deviation evaluation is improved. At the same time, the interface performance is divided into four levels according to the mental model difference values, realizing the hierarchical quantitative evaluation of interface performance, greatly improving the efficiency of interface defect warning and judgment, providing accurate and quantifiable performance references for subsequent interface optimization, and improving the standardization and efficiency of interface quality control as a whole.

[0161] One embodiment of the present invention uses the misalignment deviation between the IWT static path and the BST dynamic path to determine whether there is a defect in the front-end interface, including:

[0162] Redundant navigation is determined by mapping nodes between IWT static paths and BST dynamic paths. The IWT node with the highest accumulated value accurately identifies the source of design defects.

[0163] If the user interface operation process includes API node T actual If the value is too high, it is determined to be a system delay or lack of feedback;

[0164] If multiple users experience a backtracking loop at the unified basic interface BIF, it is determined that the menu logic or component affiliation of that basic interface does not conform to the mental model.

[0165] The technical solution described in this embodiment achieves accurate localization, efficient judgment, and targeted identification of front-end interface defects, significantly improving the accuracy, efficiency, and reliability of interface defect detection and optimizing the performance of interface quality control. By mapping the nodes of the IWT static path and the BST dynamic path, the IWT node with the highest redundant navigation accumulation value is accurately located, greatly improving the accuracy and efficiency of design defect source identification, effectively reducing the time cost of defect localization, avoiding efficiency losses caused by blind investigation, and ensuring that the root cause of defects can be quickly identified. For the judgment of API node anomalies, rapid identification of performance-related defects such as system latency and missing feedback is achieved, improving the detection rate of such hidden defects, reducing user experience degradation and system malfunctions caused by hidden defects, and ensuring the smoothness of interface interaction. By monitoring the operational behavior of multiple users at the same basic interface BIF, defects such as mismatch between menu logic, component affiliation, and user mental models are accurately identified, improving the targeting and objectivity of defect judgment, avoiding misjudgments caused by single user operation deviations, and ensuring the accuracy and universality of defect judgment. Overall, the solution achieves accurate classification and rapid root cause location of defects, significantly improving the efficiency and accuracy of defect detection, reducing false positives and false negatives, providing clear guidance for interface defect repair, shortening the repair cycle, and improving the standardization and efficiency of interface quality control, thus providing users with a smoother and more expected interactive experience.

[0166] An interactive interface evaluation system based on interface window tree and behavior sequence tree, the interactive interface evaluation system includes:

[0167] The interface construction module is used to build the interface window tree model (IWT), defining the spatial logic and hierarchical relationship of interface elements through multi-level nodes;

[0168] The behavior sequence tree and interaction operation information acquisition module is used to collect user input information in real time when the user operates on the front-end interface, generate the user's behavior sequence tree based on the input information, and obtain the interaction operation information of the user's interaction with the front-end interface through the behavior sequence tree.

[0169] The interface defect determination module is used to determine whether there are defects in the front-end interface by combining the interaction operation information of the user with the front-end interface with the misalignment between the IWT static path and the BST dynamic path.

[0170] This embodiment's technical solution relies on an improved IWT model and BST behavior analysis to complete the quantitative mapping from controls and behavioral paths to performance indicators. It supports atomic component-level problem localization and can accurately identify specific defect sources causing discrepancies in mental models, such as component response delays, jump path conflicts, and missing feedback. It enhances the structure and depth of behavioral analysis, going beyond simple clickstream analysis. This embodiment uses BST to structurally represent users' operation sequences, decision paths, and state transitions under task-driven conditions, effectively identifying deep behavioral patterns such as hesitation, backtracking, and erroneous attempts, revealing cognitive conflicts caused by navigational disorientation. A scientifically quantified evaluation index system is constructed. By introducing Path Deviation (PD) and Mental Model Difference (IFI), this embodiment can objectively quantify the degree of deviation between the static design architecture and user dynamic behavior, improving the objectivity and accuracy of the evaluation.

[0171] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An interactive interface evaluation method based on interface window tree and behavior sequence tree, characterized in that, The interactive interface evaluation method includes: Construct the Interface Window Tree Model (IWT) and define the spatial logic and hierarchical relationship of interface elements through multi-level nodes; When a user interacts with the front-end interface, the system collects the user's input information in real time, generates a user behavior sequence tree based on the input information, and obtains the interaction operation information of the user's interaction with the front-end interface through the behavior sequence tree. By combining the user's interaction information with the front-end interface with the misalignment between the IWT static path and the BST dynamic path, it can be determined whether there are defects in the front-end interface.

2. The interactive interface evaluation method according to claim 1, characterized in that, Construct the Interface Window Tree Model (IWT) and define the spatial logic and hierarchical relationships of interface elements through multi-level nodes, including defining Widgets, Menu, API, BaseInterface, and InterfaceNode respectively.

3. The interactive interface evaluation method according to claim 1, characterized in that, When a user interacts with the front-end interface, the system collects the user's input information in real time, generates a user behavior sequence tree based on the input information, and obtains the user's interaction operation information during the interaction process with the front-end interface through the behavior sequence tree, including: When a user performs an operation on the front-end interface, the input information corresponding to the user's input operation is captured, and the input information is synchronized to the corresponding control node on the front-end interface, and the latest control status value is returned. After a user completes a full operation event, the user's input parameters and the API input sequence are validated and filled using a preset mapping table. At the moment the API is triggered, the user's ID information, API identifier, and input information are recorded, and the server returns the current system timestamp in response. Locate the menu, basic interface, and interface nodes to which the API belongs, and capture the interface interaction data, including input and output information, to form a standardized record of user operation traces. Based on the user's ID information, a behavior sequence tree with the user ID as the root node is constructed through a recursive process, and the interaction operation information of the user and the front-end interface is obtained through the behavior sequence tree.

4. The interactive interface evaluation method according to claim 3, characterized in that, After a user completes a full operation, the system uses a pre-defined mapping table to validate and populate the user's input parameters against the API's input sequence, including: Once a complete operation event is detected, the API corresponding to the complete operation event is decomposed into a set of control sequences according to a preset mapping table; The sequence of controls is traversed, and the WidgCall function is called. The input information generated by the complete operation event corresponding to the user is validated and filled in with the input sequence of the API.

5. The interactive interface evaluation method according to claim 3, characterized in that, Based on the user's ID information, a behavior sequence tree is recursively constructed with the user ID as the root node. The interaction information between the user and the front-end interface is then obtained from the behavior sequence tree, including: By setting the starting child node corresponding to the user-triggered behavior in the front-end interface and combining the timestamp interval between adjacent operations, a user behavior sequence tree is constructed. Based on the user's behavior sequence tree, the front-end interface during the user's operation process is retrieved and traced back. By combining NodeDecom, NodeQuery and TreeQuery with user ID and timestamp, all interface nodes, API calls and data streams operated by the user are retrieved in reverse in the interface window tree model IWT.

6. The interactive interface evaluation method according to claim 5, characterized in that, By setting the starting child node corresponding to the user-triggered behavior in the front-end interface and combining the timestamp intervals between adjacent operations, a user behavior sequence tree is constructed, including: Set the first interface node triggered by the user in each independent interaction cycle as the direct child node of the user ID root node, i.e. the starting child node. During subsequent node recording, the timestamp interval between two adjacent interface operations is calculated, and the end of an independent interaction cycle is determined based on the timestamp interval; where, if the timestamp interval is greater than the preset maximum threshold T, max If the condition is not met, it is considered the end of an independent interaction cycle, and a new valid access path is generated again using the root node.

7. The interactive interface evaluation method according to claim 1, characterized in that, By combining user interaction information with the front-end interface and the misalignment between the IWT static path and the BST dynamic path, we can determine whether there are defects in the front-end interface, including: Perform structural alignment and preprocessing on the interactive operation information of the user and the front-end interface. The misalignment deviation between the IWT static path and the BST dynamic path is obtained by using the data corresponding to the IWT static path and the BST dynamic path. The misalignment deviation between the IWT static path and the BST dynamic path is used to determine whether there are defects in the front-end interface.

8. The interactive interface evaluation method according to claim 7, characterized in that, The interaction information between the user and the front-end interface is structurally aligned and preprocessed, including: Map the operation behavior Au in BST to the control or API node of the interface window tree model IWT by ID; The operation is not a repetitive jump-type operation, and consideration is given to the user's habitual vibration noise; The actual path Pu and the expert preset path Pd are segmented according to the task fragment corresponding to the specific operation task when the user operates, so as to ensure consistency in comparison.

9. The interactive interface evaluation method according to claim 7, characterized in that, The misalignment deviation between the IWT static path and the BST dynamic path is obtained by using the data corresponding to the IWT static path and the BST dynamic path, including: The original bias score D is defined using two dimensions: structural missing and behavioral redundancy. raw Wherein, the original deviation score D raw Obtain it using the following formula: in, This indicates the number of nodes where the actual operation overlaps with the expected path; This indicates a dimensional correction to ensure horizontal comparability between tasks of different complexities. Indicates redundant navigation; This indicates a penalty for skipping critical functional nodes; The original bias score D is obtained by using a nonlinear mapping function. raw The constraints are within the interval [0,1]; wherein, the structure of the nonlinear mapping function is as follows: Where PD represents the original deviation score D raw The corresponding parameters after constraints are defined. Furthermore, a PD approaching 0 indicates that the user behavior is highly consistent with the design expectations, while a PD approaching 1 indicates that the user is in a state of extreme disorientation in the interface. Using the original deviation score D raw The mental model discrepancy value (IFI) is determined by the corresponding parameters after constraints; wherein, the mental model discrepancy value (IFI) is obtained by the following formula: Wherein, IFI represents the mental model variance value; α represents the weighting coefficient of path deviation, used to adjust the influence weight of path logic correctness on the final mental model variance value, reflecting the evaluation model's focus on the logical rationality of the operation path; β represents the weighting coefficient of the time loss term, used to adjust the influence weight of interaction time cost on mental model variance, to correct efficiency losses caused by unreasonable layout, feedback delay, etc.; T actual The actual task completion time refers to the total time consumed from triggering the first API to completing the last API operation when a user performs a specific interactive task on the front-end interface; T expected The expected task completion time refers to the baseline time required to complete the task under the optimal design path preset by experts, with no cognitive resistance and ideal system response. Based on the Mental Model Difference Value (IFI), interface performance is divided into four levels: excellent, good, warning, and failure.

10. An interactive interface evaluation system based on interface window tree and behavior sequence tree, characterized in that, The interactive interface evaluation system includes: The interface construction module is used to build the interface window tree model (IWT), defining the spatial logic and hierarchical relationship of interface elements through multi-level nodes; The behavior sequence tree and interaction operation information acquisition module is used to collect user input information in real time when the user operates on the front-end interface, generate the user's behavior sequence tree based on the input information, and obtain the interaction operation information of the user's interaction with the front-end interface through the behavior sequence tree. The interface defect determination module is used to determine whether there are defects in the front-end interface by combining the interaction operation information of the user with the front-end interface with the misalignment between the IWT static path and the BST dynamic path.

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