Immersive digital interaction user behavior analysis feedback method

Abstract

The invention discloses a user behavior analysis feedback method for immersive digital interaction, and relates to the technical field of man-machine interaction, and the method comprises the steps: obtaining interaction state data of a target user in an immersive digital interaction environment, and constructing a user state sequence according to the interaction state data; dividing the user state sequence into a plurality of analysis units according to a preset time length, determining a current behavior mode of the target user according to a state density value in each analysis unit and a state transition value between adjacent analysis units, and generating a feedback control signal corresponding to the current behavior mode; and according to the feedback control signal and the current position of the target user in the immersive digital interaction environment, generating spatial positioning feedback content and rendering the spatial positioning feedback content to the immersive digital interaction environment. According to the method and the device, accurate recognition of the user behavior mode is realized, differentiated spatial positioning feedback is generated according to different behavior modes, and the problem that the user behavior state is difficult to perceive and guide in real time in an immersive environment is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of human-computer interaction, in particular to a user behavior analysis feedback method for immersive digital interaction. BACKGROUND

[0002] Immersive digital interaction environments (such as virtual reality, augmented reality, etc.) are widely used in education and training, industrial design, medical rehabilitation, etc. In the immersive environment, users interact with virtual scenes through head-mounted display devices, gesture controllers, and other interaction devices. Conventional immersive interaction systems mainly rely on pre-set interaction logic and fixed feedback modes. When the user performs a specific operation, the system provides corresponding visual, auditory, or tactile feedback according to pre-defined rules.

[0003] However, the existing feedback methods based on pre-set rules have the following problems: first, they cannot be adjusted according to the actual behavior state of the user. When the user focuses too much on a certain area, the system lacks effective guidance measures, which can easily lead to user fatigue or missing important interaction content; second, when the user's attention is distracted or deviates from the main interaction area, it is difficult to identify and provide recall guidance in a timely manner, affecting the completion efficiency of the interaction task; third, the fixed feedback mode cannot adapt to the behavior habits and interaction rhythm of different users, reducing the degree of personalization of user experience. Therefore, the related technology cannot provide differentiated feedback guidance according to the real-time behavior state of the user, resulting in low interaction efficiency and poor user experience. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the present application provides a user behavior analysis feedback method for immersive digital interaction, which can solve the problems mentioned in the background art.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a user behavior analysis feedback method for immersive digital interaction, comprising: acquiring interaction state data of a target user in an immersive digital interaction environment, and constructing a user state sequence according to the interaction state data;

[0008] dividing the user state sequence into multiple analysis units according to a pre-set time length, determining the current behavior pattern of the target user according to the state density value within each analysis unit and the state transition value between adjacent analysis units, and generating a feedback control signal corresponding to the current behavior pattern;

[0009] According to the feedback control signal and the current position of the target user in the immersive digital interactive environment, spatial positioning feedback content is generated and rendered to the immersive digital interactive environment.

[0010] Preferably, the determination of the current behavior pattern of the target user and the generation of the feedback control signal corresponding to the current behavior pattern according to the state density value in each analysis unit and the state transition value between adjacent analysis units comprises:

[0011] The state density value and the state transition value are calculated.

[0012] When the state density value is higher than the density upper limit and the state transition value is lower than the transition lower limit, it is determined that the current behavior pattern is an over-immersion state, and a dispersion-type feedback control signal is generated.

[0013] When the state density value is between the density upper limit and the density lower limit, and the state transition value is higher than the transition upper limit, it is determined that the current behavior pattern is an active exploration state, and a focus-type feedback control signal is generated.

[0014] When the state density value is lower than the density lower limit and the state transition value is lower than the transition lower limit, it is determined that the current behavior pattern is an attention wandering state, and a recall-type feedback control signal is generated.

[0015] Preferably, the calculation of the state density value comprises:

[0016] The gaze focus trajectory and the hand interaction trajectory of the target user are extracted from the analysis unit;

[0017] The spatial coincidence degree of the gaze focus trajectory and the hand interaction trajectory is calculated.

[0018] The state density value is determined according to the spatial coincidence degree and the number of interaction responses of virtual objects in the analysis unit.

[0019] Preferably, the calculation of the state transition value comprises:

[0020] Two adjacent analysis units are selected from the plurality of analysis units as a previous analysis unit and a subsequent analysis unit, respectively;

[0021] The end state vector of the previous analysis unit and the start state vector of the subsequent analysis unit are extracted;

[0022] The difference degree between the end state vector and the start state vector is calculated.

[0023] The state transition value is determined according to the difference degree and the time interval between the previous analysis unit and the subsequent analysis unit.

[0024] As preferred, the generating the dispersion-type feedback control signal comprises:

[0025] counting the staying time of each virtual space region in the user state sequence, and determining a region with a staying time exceeding a preset time limit as an over-focused region;

[0026] setting a visual disturbance point at a boundary position of the over-focused region;

[0027] generating the dispersion-type feedback control signal; the dispersion-type feedback control signal controls a light halo effect expanding outwardly generated at the visual disturbance point.

[0028] As preferred, the generating the focus-type feedback control signal comprises:

[0029] analyzing a space exploration path in the state sequence of the target user, and identifying a virtual object with an unfinished interaction of the target user;

[0030] calculating an importance score of each virtual object with an unfinished interaction according to an interaction history frequency of each virtual object with an unfinished interaction and the target user and a preset priority of virtual objects;

[0031] selecting a virtual object with the highest importance score as a target focus object, and generating the focus-type feedback control signal; the focus-type feedback control signal controls a visual enhancement effect generated around the target focus object.

[0032] As preferred, the generating the recall-type feedback control signal comprises:

[0033] calculating a geometric center of an activity region of the target user according to space position data in the user state sequence;

[0034] extracting a current space position of the target user from the interaction state data, and calculating a deviation distance between the current space position and the geometric center;

[0035] when the deviation distance is less than a first deviation limit, generating a cursor pointing to the geometric center at an edge of a field of view of the target user as the recall-type feedback control signal;

[0036] when the deviation distance is between the first deviation limit and a second deviation limit, generating a virtual path light belt from the current space position to the geometric center as the recall-type feedback control signal;

[0037] when the deviation distance is greater than the second deviation limit, generating an annular light wall converging to the geometric center around the target user as the recall-type feedback control signal.

[0038] In a second aspect, the present application also provides a user behavior analysis feedback system for immersive digital interaction, comprising: a state collection module configured to obtain interaction state data of a target user in an immersive digital interaction environment, and to construct a user state sequence according to the interaction state data;

[0039] a behavior analysis module configured to divide the user state sequence into a plurality of analysis units according to a preset time length, to determine a current behavior pattern of the target user according to a state density value in each analysis unit and a state transition value between adjacent analysis units, and to generate a feedback control signal corresponding to the current behavior pattern;

[0040] a feedback rendering module configured to generate spatial positioning feedback content according to the feedback control signal and a current position of the target user in the immersive digital interaction environment, and to render the spatial positioning feedback content to the immersive digital interaction environment.

[0041] In a third aspect, the present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0042] obtaining interaction state data of a target user in an immersive digital interaction environment, and constructing a user state sequence according to the interaction state data;

[0043] dividing the user state sequence into a plurality of analysis units according to a preset time length, determining a current behavior pattern of the target user according to a state density value in each analysis unit and a state transition value between adjacent analysis units, and generating a feedback control signal corresponding to the current behavior pattern;

[0044] generating spatial positioning feedback content according to the feedback control signal and a current position of the target user in the immersive digital interaction environment, and rendering the spatial positioning feedback content to the immersive digital interaction environment.

[0045] In a fourth aspect, the present application also provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the following steps:

[0046] obtaining interaction state data of a target user in an immersive digital interaction environment, and constructing a user state sequence according to the interaction state data;

[0047] dividing the user state sequence into a plurality of analysis units according to a preset time length, determining a current behavior pattern of the target user according to a state density value in each analysis unit and a state transition value between adjacent analysis units, and generating a feedback control signal corresponding to the current behavior pattern;

[0048] generate spatial positioning feedback content and render to the immersive digital interaction environment according to the feedback control signal and the current position of the target user in the immersive digital interaction environment.

[0049] The application has the following beneficial effects: the application provides a user behavior analysis feedback method for immersive digital interaction, realizes time sequence modeling and segmented analysis of user behavior by constructing a user state sequence and dividing it into multiple analysis units, and can capture the continuity and evolution law of user behavior compared with the instant response mechanism of the prior art. By calculating the state density value in the analysis unit, the spatial coincidence degree of the gaze focus track and the hand interaction track, and the interaction response times of the virtual object are combined to realize quantitative evaluation of the concentration degree of user interaction, and the one-sidedness of single-dimensional evaluation is overcome. At the same time, by calculating the state transition value between adjacent analysis units, the difference degree of the end state vector and the start state vector is extracted and combined with the time interval to accurately reflect the dynamic change trend of user behavior. Based on the two-dimensional judgment of the state density value and the state transition value, the user behavior mode is accurately divided into three states of over-immersion, active exploration, and attention wandering, and corresponding dispersive, focused, and recall feedback control signals are generated to realize precise matching of feedback strategies and actual user needs. Further, the spatial positioning and rendering mode of the feedback content are dynamically adjusted according to the current position of the user, such as setting visual disturbance at the boundary of the over-focused area, generating visual enhancement for incomplete interaction objects, and generating recall guidance according to the distance of deviation, so that the feedback content is closely combined with the user's spatial perception, and the intuitiveness and effectiveness of the guidance effect are improved, fundamentally solving the real-time and pertinence problems of user behavior guidance in the immersive environment. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the application or the related art, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the application or the related art. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0051] Figure 1 is a schematic diagram of a user behavior analysis feedback method for immersive digital interaction related by the application;

[0052] Figure 2 is an application environment diagram of a user behavior analysis feedback method for immersive digital interaction related by the application;

[0053] Figure 3is a whole structure schematic diagram of a user behavior analysis feedback system of immersive digital interaction involved in the present application;

[0054] Figure 4 is a computer device diagram of a user behavior analysis feedback method of immersive digital interaction involved in the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0056] Figure 1 is a flowchart of the user behavior analysis feedback method of immersive digital interaction according to the embodiment of the present application, and the flowchart includes the following steps 202 to 206:

[0057] Step 202: acquiring interaction state data of a target user in an immersive digital interaction environment, and constructing a user state sequence according to the interaction state data.

[0058] Specifically, in the immersive digital interaction environment in the embodiment of the present application, the collection of the interaction state data of the target user can be performed by using hardware devices such as a head-mounted display device, a gesture controller, and a position tracker. Therefore, the interaction state data includes multiple different dimensions, such as gaze focus data, hand operation data, body posture data, spatial position data, and virtual object interaction records.

[0059] Further, the gaze focus data is mainly recorded by an eye movement tracking sensor built in the head-mounted display device, and includes a gaze point coordinate and a gaze duration of the gaze of the target user in a virtual space. The eye movement information is continuously collected at a certain sampling frequency (for example, 90Hz, 120Hz, etc.), the gaze direction is calculated through a pupil center positioning and corneal reflection algorithm, and then the projection point of the gaze in the three-dimensional virtual space is determined. The gaze focus data at each sampling time includes a time stamp, a three-dimensional coordinate value and a gaze object identifier.

[0060] The hand operation data is collected by the gesture controller, and mainly records the position, posture and interaction action of the hands of the target user in the virtual space. An inertial measurement unit built in the gesture controller collects the acceleration, angular velocity and magnetic field intensity of the hands in real time. When the target user performs interaction actions such as grabbing, clicking and dragging, the action type, action object and action parameters are recorded.

[0061] Body posture data is acquired through position trackers or depth cameras, recording the target user's head position, body orientation, and movement trajectory. Position trackers, using external positioning base stations or internal inertial sensors, can determine the target user's position in physical space and map it to a virtual coordinate system. Body posture data includes the head's pitch, yaw, and roll angles, as well as the velocity and acceleration of the body's center of gravity.

[0062] Spatial location data records the target user's current location coordinates and historical movement paths within the virtual environment. Movement in physical space is mapped to virtual space, taking into account the scale of the virtual environment and coordinate transformations. Spatial location data is recorded at fixed time intervals, forming a continuous sequence of location points, each containing three-dimensional coordinates and a corresponding timestamp.

[0063] Virtual object interaction logs include interaction events between the target user and various objects in the virtual environment. When the target user triggers a virtual button, operates a virtual tool, or moves a virtual object, the logs record the identifier of the interacting object, the interaction type, the interaction start time, the interaction end time, and the interaction result. Virtual object interaction logs reflect the target user's task execution within the virtual environment.

[0064] Furthermore, in this embodiment, the process of constructing the user state sequence involves aligning the interaction state data from different sensors according to timestamps to eliminate time discrepancies between different data sources. Since the sampling frequencies of each sensor are different, it is necessary to unify all data to the same time base.

[0065] Key features are extracted from the original interaction state data, specifically: the spatial distribution range of gaze points, gaze shift frequency and gaze stability index of gaze focus data; the speed and acceleration of hand movements, as well as the type and frequency of interactive actions; the angular velocity of head rotation and the linear velocity of body movement; the movement distance, movement direction and dwell area of ​​spatial position data; and the category distribution of interactive objects and the interaction success rate in virtual object interaction records.

[0066] The extracted features are organized into a user state sequence in chronological order. The user state sequence is represented by a multi-dimensional vector, where each time step's state vector contains all feature values ​​for that time step. The dimension of the state vector is determined by the feature type, for example, it may include gaze coordinates (3D), hand position (6D), body pose (6D), spatial position (3D), and interaction markers (ND, where N is the number of virtual object categories). The user state sequence is represented as S = {s1, s2, ..., s...} t}, where s t Let T represent the state vector at time t, and let T represent the sequence length.

[0067] To ensure the continuity and integrity of the user state sequence, the missing data is processed. When part of the sensor data is missing at a certain time, linear interpolation or nearest neighbor method is used to fill in the missing data. When multiple consecutive time data is missing, the time period is marked as invalid and excluded in subsequent analysis.

[0068] Step 204: The user state sequence is divided into multiple analysis units according to the preset time length, and the current behavior pattern of the target user is determined according to the state density value in each analysis unit and the state transition value between adjacent analysis units, and a feedback control signal corresponding to the current behavior pattern is generated.

[0069] Specifically, the user state sequence in this embodiment is the time series data containing the complete interaction process of the target user constructed in step 202. The user state sequence is divided according to the preset time length to form multiple analysis units. The preset time length needs to be determined according to the application scenario and task characteristics of the immersive interaction environment. For example, the application scenarios of the immersive interaction environment in this application can be set as education and training, game entertainment and industrial training. Therefore, in the education and training scenario, since it is necessary to continuously pay attention to and deeply understand the learning content, the preset time length can be set to 15 seconds, 20 seconds or 30 seconds, and a longer time window is beneficial to capturing the complete behavior pattern in the learning process, which can avoid misjudgment caused by too frequent analysis; in the game entertainment scenario, since the behavior of the player changes quickly and needs to be responded in time, a shorter time window can quickly identify the change of the behavior state, so the preset time length can be set to 5 seconds, 8 seconds or 10 seconds; in the industrial training scenario, since the operation process has fixed steps, the preset time length can be set according to the standard operation time, such as the average completion time of a single operation step.

[0070] Each analysis unit contains all state data within the preset time length, forming a complete behavior segment. The analysis unit can be divided in two ways: non-overlapping division or overlapping division. Non-overlapping division is to sequentially cut the user state sequence according to the preset time length, and each state data belongs to only one analysis unit, which has high calculation efficiency but may miss the behavior change at the boundary; overlapping division adopts sliding window method, and there is part of overlapping state data between adjacent analysis units, for example: the preset time length is 10 seconds, and the sliding step is 5 seconds, then the adjacent two analysis units have 5 seconds of overlap, which improves the time resolution of behavior pattern recognition.

[0071] In step 204, the current behavior pattern of the target user is determined according to the state density value in each analysis unit and the state transition value between adjacent analysis units, and a feedback control signal corresponding to the current behavior pattern is generated, which specifically includes the following steps A1 to A4:

[0072] Step A1: Calculate the state density value and the state transition value.

[0073] It should be noted that state density value and state transition value are two core indicators for determining user behavior patterns. State density value reflects the degree of concentration of interaction of the target user within an analysis unit, while state transition value reflects the degree of change in the target user's behavior between adjacent analysis units. This application combines these two dimensions to overcome the limitations of evaluating a single indicator, thereby enabling a more accurate identification of the user's true behavioral state.

[0074] Specifically, the calculation of the state density value in this embodiment includes the following steps A111 to A113:

[0075] Step A111: Extract the target user's gaze focus trajectory and hand interaction trajectory from the analysis unit.

[0076] Specifically, all state data within the analysis unit is traversed to extract the gaze focus coordinates and hand position coordinates at each sampling moment. The gaze focus coordinates are obtained through the eye-tracking system built into the head-mounted display device. This system uses an infrared camera to capture eye movements and calculates the gaze direction using pupil center localization and corneal reflection algorithms, thereby determining the projection point of the gaze in virtual space. The selection of the sampling frequency needs to balance accuracy and computational load; for example, a sampling rate of 90Hz can capture most eye movements, while 120Hz or higher frequencies can capture rapid saccades. Within a 10-second analysis unit, a sampling rate of 120Hz will yield 1200 gaze focus coordinate points, forming dense trajectory data.

[0077] Hand position coordinates are acquired via a gesture controller or optical tracking system, recording the position and posture of both hands in three-dimensional space. The gesture controller incorporates an inertial measurement unit, including an accelerometer, gyroscope, and magnetometer, which uses sensor fusion algorithms to calculate the six degrees of freedom (6DoF) of the hand in real time. Specifically, it calculates the precise posture (pitch, yaw, and roll angles) and position changes of the hand in three-dimensional space, namely three translational degrees of freedom (position on the X, Y, and Z axes) and three rotational degrees of freedom (rotation about the X, Y, and Z axes). The optical tracking system captures hand markers or directly identifies the hand contour using multiple cameras, and then triangulates and calculates the spatial position.

[0078] Connecting the coordinates of the gaze focus at consecutive sampling moments in chronological order creates a gaze focus trajectory. This trajectory reveals the path of the target user's visual attention movement within the analysis unit's time frame. Densely intertwined trajectories indicate that the user is carefully observing a local area, while sparsely scattered trajectories suggest that the user is scanning a wide area, possibly searching for a target or lacking focus. Similarly, connecting the hand position coordinates at consecutive sampling moments creates a hand interaction trajectory; the smoothness of this trajectory can indicate the target user's operational proficiency.

[0079] Step A112: Calculate the spatial overlap between the gaze focus trajectory and the hand interaction trajectory.

[0080] Specifically, for each sampling moment within the analysis unit, the corresponding gaze focus coordinates and hand position coordinates are extracted, and the Euclidean distance between them in three-dimensional space is calculated. The Euclidean distance is calculated as follows: Let the gaze focus coordinates be (x1, y1, z1) and the hand position coordinates be (x2, y2, z2), then the distance d = √[(x1-x2)]. 2 +(y1-y2) 2 +(z1-z2) 2 When the distance between the focus of the gaze and the hand position is small, it indicates that the target user is looking at the hand operation area, and there is good coordination between vision and hand movements; when the distance is large, it indicates that the gaze and hand operation are separated, indicating distraction, lack of operation skills, or multitasking.

[0081] The spatial distances at all sampling times within the statistical analysis unit are used to establish a distance distribution histogram. The spatial overlap degree is determined based on the distance distribution. For example, the number of sampling points with a distance less than 0.5 meters is counted, and the ratio of this number to the total number of sampling points is calculated to obtain the overlap ratio. When more than 80% of the sampling points are less than 0.5 meters apart, the spatial overlap degree is 0.8, indicating high coordination; when only 30% of the sampling points are less than 0.5 meters apart, the spatial overlap degree is 0.3, indicating poor coordination. Therefore, this application uses the calculation of spatial overlap degree to integrate the visual and action dimensions of user behavior analysis, thereby improving the accuracy of user behavior pattern recognition.

[0082] Step A113: Determine the state density value based on the spatial overlap and the number of interaction responses of virtual objects within the analysis unit.

[0083] Specifically, this involves statistically analyzing all interactive responses triggered by the target user within a given time period. Interactive responses directly reflect the validity of user behavior and include: color changes or sound effects after clicking a virtual button (indicating command received), positional movement or shape changes after grabbing a virtual object (indicating operation effective), scene switching or function activation after triggering a virtual switch (indicating process progress), and voice responses or action feedback after interacting with a virtual character (indicating communication established). For example, if the target user clicked a button 3 times, grabbed 2 objects, and triggered 1 switch within a 10-second analysis unit, the number of interactive responses would be 6.

[0084] The state density value is calculated by weighting and fusing spatial overlap and interaction response count. First, the interaction response count is normalized: a standard number of interactions per unit time is set (e.g., 5 interactions per 10 seconds is the standard value), and the normalized interaction frequency is obtained by dividing the actual number of interactions by the standard value. Then, the fusion weights are determined based on the application scenario. For example, in medical surgery training scenarios, each operation requires extremely high precision, so the coordination between vision and action is highly emphasized; the weight of spatial overlap is set to 0.7, and the weight of interaction response count is set to 0.3. In fast-paced shooting game scenarios, response speed and operation frequency are more important; the weight of spatial overlap is set to 0.3, and the weight of interaction response count is set to 0.7. In educational learning scenarios, understanding and exploration are equally important; both are weighted at 0.5.

[0085] Preferably, this application uses state density values ​​to quantitatively evaluate user interaction behavior in order to reflect the user's level of focus, operational accuracy, and task participation within the analysis unit.

[0086] Specifically, in this embodiment, the calculation of the state transition value includes the following steps A121 to A124:

[0087] Step A121: Select two adjacent analysis units from multiple analysis units, and use them as the front analysis unit and the back analysis unit, respectively.

[0088] Specifically, the sequence of pre-defined analysis units is traversed chronologically. For any position i in the sequence, the i-th analysis unit is designated as the preceding analysis unit, and the (i+1)-th analysis unit is designated as the following analysis unit. For example, in the case of no overlapping divisions, the i-th analysis unit corresponds to the time period [t1, t2], and the (i+1)-th analysis unit corresponds to the time period [t2, t3], indicating that they are closely connected in time. In the case of overlapping divisions, if the overlap duration is δ, then the i-th analysis unit corresponds to [t1, t2], and the (i+1)-th analysis unit corresponds to [t2-δ, t3-δ], indicating that they share data for a duration of δ. The selection of adjacent analysis units ensures the continuity of behavioral change analysis.

[0089] Step A122: Extract the end state vector of the pre-analytical unit and the start state vector of the post-analytical unit.

[0090] Specifically, the complete state data of the last sampling moment is extracted from the previous analysis unit to form the end-state vector. The end-state vector is a high-dimensional vector containing all state features at that moment: the three-dimensional coordinates of the gaze focus (3 dimensions), the six degrees of freedom data of the left and right hand positions (6 dimensions each, totaling 12 dimensions), the Euler angles of the head posture (pitch, yaw, roll, 3 dimensions), the body position coordinates (3 dimensions), the identifier of the current interactive object (discrete value), and interaction state markers (such as whether it is grasping, whether it is moving, etc., Boolean values). Similarly, the complete state data of the first sampling moment is extracted from the subsequent analysis unit to form the start-state vector, containing features of the same dimensions. These two vectors represent the end state of the previous action segment and the start state of the next action segment, respectively.

[0091] Step A123: Calculate the difference between the terminal state vector and the initial state vector.

[0092] Specifically, the numerical changes of each dimension of features in the terminal state vector and the initial state vector are compared. For continuous features such as spatial coordinates, the absolute value of the numerical difference is calculated; for angular features, the minimum angular difference is calculated periodically; for discrete features such as object identifiers, Hamming distance is used to determine whether a change has occurred. Since different features have different dimensions and value ranges, normalization is required. For example, the ratio of position change to scene scale (e.g., 10 meters) and the ratio of angle change to π are calculated to map the changes of all features to the [0,1] interval. After normalization, the difference between the terminal state vector and the initial state vector can be obtained by weighted summation or by calculating the L2 norm of the vector. Therefore, this application quantifies the degree of abrupt change in user behavior at the intersection of two analysis units by calculating the difference between the terminal state vector and the initial state vector, thereby facilitating the evaluation of the stability of behavior patterns.

[0093] Step A124: Determine the state transition value based on the difference degree and the time interval between the pre-analysis unit and the post-analysis unit.

[0094] Specifically, the end time of the preceding analysis unit and the start time of the following analysis unit are obtained, and the time interval between them is calculated. For non-overlapping partitions, the time interval is zero; for overlapping partitions, the time interval is negative, and its absolute value equals the overlap duration. It should be understood that large changes in a short period indicate a rapid shift in behavior, while changes over a long period represent normal behavioral evolution. Therefore, the state transition value is directly proportional to the degree of difference and inversely proportional to the absolute value of the time interval. The calculation of the state transition value enables a quantitative assessment of the dynamic changes in user behavior, which can be used to capture the transition characteristics of user behavior patterns. This complements the state density value, and together they are used for the evaluation of multi-dimensional indicators in user behavior pattern recognition.

[0095] Step A2: When the state density value is higher than the density upper limit and the state transition value is lower than the transition lower limit, the current behavior mode is determined to be an over-immersion state, and a decentralized feedback control signal is generated.

[0096] Specifically, the density upper limit and transition lower limit are thresholds derived from statistical analysis of a large amount of user behavior data. The density upper limit, for example, is 0.8, 0.85, 0.9, or other higher values ​​determined based on task characteristics, representing the critical point of highly concentrated interaction. The transition lower limit, for example, is 0.2, 0.25, 0.3, or other lower values ​​determined based on desired behavioral activity, representing the critical point of stagnant behavioral change. When the state density value is higher than the density upper limit, it indicates that the user has invested excessive attention and operation in a local area or on a specific object; while when the state transition value is lower than the transition lower limit, it indicates that the user's behavioral pattern rarely changes, suggesting a possible trap in repetitive operations or a fixed mindset. When both conditions are met simultaneously, it is determined to be a state of over-immersion.

[0097] It should be noted that while excessive immersion demonstrates high concentration, maintaining this state for extended periods can lead to cognitive fatigue, narrowed field of vision, and decreased task completion efficiency. Therefore, it is necessary to promptly identify and generate distraction-based feedback control signals to guide users to appropriately shift their attention, thereby preventing excessive fatigue and ultimately improving overall interaction efficiency.

[0098] It should be noted that, in the embodiments of this application, the generation of the distributed feedback control signal in step A2 specifically includes the following steps A21 to A23:

[0099] Step A21: Calculate the dwell time of each virtual space region in the user state sequence, and identify the regions whose dwell time exceeds the preset time limit as over-focused regions.

[0100] Specifically, the virtual space is divided into areas based on scene function or spatial grid. Functional division is determined by the scene's logical structure; for example, a virtual classroom might be divided into a lectern area (teacher's explanation area), seating area (students' listening area), display area (multimedia display area), and interactive area (experimental operation area); a virtual factory might be divided into an operating table area (equipment operation area), storage area (material storage area), testing area (quality inspection area), and control room area (monitoring and management area). Spatial grid division divides the scene into cubic grids of fixed dimensions (e.g., 2m x 2m x 2m), suitable for open scenes.

[0101] The system iterates through the complete user state sequence and calculates the cumulative dwell time of the target user in each area. The criterion for determining dwell time is that the user's spatial coordinates fall within the area boundary. The setting of preset time limits needs to consider task design and user behavior patterns. For example, by statistically analyzing the average dwell time of a large number of users, if the standard interaction time for a single area is set to 30 seconds, then the preset time limit can be set to 90 seconds (3 times the standard time) or 120 seconds (4 times the standard time). When the dwell time in an area exceeds the preset time limit, that area is marked as an over-focused area. The identification of over-focused areas provides spatial positioning data for subsequent visual guidance.

[0102] Step A22: Set visual disturbance points at the boundary of the over-focused area.

[0103] Specifically, identify the boundary contours of the over-focused area. These boundaries may be regular geometric shapes (such as rectangles or circles) or irregular polygons. Visual disturbance points are then placed on the boundaries according to certain rules: for regular boundaries, they are evenly distributed at fixed intervals, such as one point every 1 or 2 meters; for irregular boundaries, they are preferentially placed at corners, midpoints, and other characteristic locations to ensure comprehensive visual guidance. The three-dimensional coordinates and normal direction (pointing outwards from the area) of each visual disturbance point are recorded. The normal direction is used to determine the initial direction of halo diffusion. The number and density of visual disturbance points affect the intensity of the guidance effect; too few may fail to attract attention, while too many may cause visual interference.

[0104] Step A23: Generate a distributed feedback control signal; the distributed feedback control signal controls the generation of an outward halo effect at the visual disturbance point.

[0105] Specifically, a distributed feedback control signal is constructed, which includes a list of visual disturbance point coordinates, halo rendering parameters, and animation control parameters. The halo effect spreads outward from each visual disturbance point, forming a wavy visual guide. The initial radius of the halo is set small, such as 0.2 meters or 0.3 meters, to avoid sudden appearances that may startle the viewer; the diffusion speed is moderate, such as 0.5 to 1 meter per second, which attracts attention without being too abrupt; the maximum diffusion radius is limited to 2 to 3 meters to prevent excessive overlap between adjacent halos. The halo color uses soft warm tones, such as pale yellow (RGB:255,255,200) or pale orange (RGB:255,230,200), colors that are well-visible and not glaring against different backgrounds. The transparency decreases from the center outward, for example, about 50% transparency at the center and 10% transparency at the edges, creating a natural gradient effect.

[0106] By periodically triggering a halo diffusion animation, a breathing light-like effect is created, gently drawing the user's visual attention away from the area. This avoids the discomfort caused by forced intervention and achieves natural and smooth attention guidance.

[0107] Step A3: When the state density value is between the upper and lower density limits and the state transition value is higher than the upper transition limit, the current behavior mode is determined to be an active exploration state, and a focused feedback control signal is generated.

[0108] Specifically, the lower density limit, such as 0.3, 0.35, 0.4, or other lower values ​​determined based on task characteristics, represents the critical point of sparse interaction. The upper transition limit, such as 0.7, 0.75, 0.8, or other higher values ​​determined based on desired behavioral stability, represents the critical point of excessive behavioral variation. When the state density value is at a moderate level, it indicates that the user's interaction behavior is moderately distributed; when the state transition value is higher than the upper transition limit, it indicates that the user's behavior pattern changes frequently, possibly switching quickly between different objects or areas. When both conditions are met simultaneously, it is determined to be an active exploration state.

[0109] An active exploration state indicates that the user is actively exploring the environment but lacks a clear goal, which may cause them to miss important interactions or fail to complete the task in depth. By generating focused feedback control signals, users can be guided to focus on key objects, improving the efficiency and quality of task completion.

[0110] It should be noted that, in the embodiments of this application, the generation of the focused feedback control signal in step A3 specifically includes the following steps A31 to A33:

[0111] Step A31: Analyze the spatial exploration path in the target user's state sequence and identify the virtual objects of the target user's incomplete interactions.

[0112] Specifically, temporal data of spatial locations are extracted from the user's state sequence, and consecutive location points are connected to form a spatial exploration path. The path analysis algorithm identifies virtual objects that the user has approached but has not fully interacted with. The determination of incomplete interaction includes multiple dimensions: spatial proximity but no trigger (the user enters the object's 1-meter interaction range and stays for more than 2 seconds but does not perform any operation), interaction started but interrupted (the user starts dragging the object but releases it midway, or starts inputting but does not confirm submission), interaction failed and not retried (the first operation was unsuccessful and no second attempt was made), and partial interaction not completed (only the first few steps of a multi-step task were completed).

[0113] Create a list of virtual objects representing incomplete interactions, recording each object's identifier, location, type, reason for incompleteness, and timestamp. These objects may represent important task points missed by the user, or areas where the user encounters difficulties and needs assistance. Identifying these objects is crucial for providing targeted guidance.

[0114] Step A32: Calculate the importance score of each virtual object that has not completed interaction with the target user based on the interaction history frequency of each virtual object and the preset priority of the virtual object.

[0115] Specifically, the historical interaction database is queried to count the number of interactions between the target user and each virtual object with incomplete interactions in historical sessions. The frequency of interaction history reflects the importance of the object to the user: frequently interacted objects may be familiar and important tasks that need to be completed first; objects that have never been interacted with may be new content that the user is unfamiliar with and requires appropriate guidance.

[0116] The preset priority of each virtual object is read, and the priority is set according to the scenario. For example, in a training scenario, the priority of objects for core operation steps is set to 10 (must be completed), the priority of objects for auxiliary operation steps is set to 5 (recommended to be completed), and the priority of decorative or optional objects is set to 1 (can be skipped). The interaction history frequency and preset priority are normalized to the [0,1] range respectively, and then a fusion calculation (e.g., weighted fusion) is performed to calculate the importance score. The fusion weight can be adjusted according to the application goal. For example, when emphasizing personalization, the weight of historical frequency is increased, and when emphasizing standard processes, the weight of preset priority is increased.

[0117] Step A33: Select the virtual object with the highest importance score as the target focus object and generate a focus feedback control signal; the focus feedback control signal controls the generation of visual enhancement effects around the target focus object.

[0118] Specifically, the importance scores of all virtual objects that have not yet completed interaction are ranked, and the object with the highest score is selected as the target focus object. If multiple objects with similar scores exist, the object closer to the user's current position is prioritized to reduce the guidance path length. A focus-oriented feedback control signal is constructed, including the identifier, location coordinates, and visual enhancement parameters of the target focus object.

[0119] Visual enhancement effects can include glowing borders, pulsating halo effects, and particle effects. Specifically, a glowing border effect renders a bright border a certain width (e.g., 2-3 pixels) around the object's outline, using a blue or green color that contrasts sharply with the environment; a pulsating halo effect generates a ring of light around the object, pulsating in brightness at a certain frequency (e.g., 1-2 Hz) to simulate a breathing rhythm; pointing arrows display dynamic arrows above or around the object, with the arrow direction automatically adjusted according to the user's viewing angle to always maintain the best visibility angle; and particle effects generate rising light particles around the object to create a lively visual atmosphere.

[0120] Step A4: When the state density value is lower than the density lower limit and the state transition value is lower than the transition lower limit, the current behavior pattern is determined to be an attention-free state and a recall-type feedback control signal is generated.

[0121] Specifically, when the state density value is below the lower density limit and the state transition value is below the lower transition limit, it indicates that the user's interaction is sparse and lacks activity, and they may have lost focus on the task or lost interest in the environment. If this state of attention wandering persists, it can lead to task interruption or user churn, requiring timely recall and guidance.

[0122] It should be noted that, in the embodiments of this application, generating the recall-type feedback control signal in step A4 specifically includes the following steps A41 to A45:

[0123] Step A41: Calculate the geometric center of the target user's activity area based on the spatial location data in the user state sequence.

[0124] Specifically, the complete user state sequence is traversed to extract spatial location data recorded at all times. Each location data point contains three coordinate components: x, y, and z, representing the user's position in virtual space. The x-coordinates of all location points are summed and divided by the total number of location points to obtain the x-coordinate of the geometric center. The same calculation is performed on the y and z coordinates to obtain the complete three-dimensional coordinates of the geometric center. The geometric center is not a simple midpoint in space, but rather the statistical center of the user's actual activity trajectory, representing the core location of the user's main activity area. This center point is usually the main execution area of ​​the task or the location that the user should focus on most.

[0125] Step A42: Extract the target user's current spatial location from the interaction state data and calculate the deviation distance between the current spatial location and the geometric center.

[0126] Specifically, the target user's current spatial coordinates are read from the latest interaction status data, which is updated in real time by location tracking devices. The straight-line distance between the current location and the geometric center is calculated using the three-dimensional Euclidean distance formula. The deviation distance intuitively reflects the degree to which the user deviates from the main activity area, providing a quantitative basis for the tiered recall strategy.

[0127] Step A43: When the deviation distance is less than the first deviation limit, a cursor pointing to the geometric center is generated at the edge of the target user's field of vision as a recall feedback control signal.

[0128] Specifically, the first deviation limit is preset based on the spatial scale of the virtual environment. For example, it is set to 5 meters for small indoor scenes (such as a 10m x 10m room), 10 meters for medium-sized scenes (such as a 50m x 50m exhibition hall), and 20 meters for large open scenes (such as a 200m x 200m plaza). When the deviation distance is less than the first deviation limit, it indicates that the user has slightly deviated and only gentle directional prompts are needed.

[0129] The cursor is rendered at the edge of the user's field of vision, its position determined by the geometric center's orientation relative to the user's head: when the geometric center is on the left, the cursor appears on the left edge; when on the right, it appears on the right edge; and when behind, it can appear on the top or bottom edge, accompanied by the text prompt "Please turn around." The cursor is a semi-transparent arrow graphic, occupying approximately 2% to 3% of the field of vision, with a soft white or light blue color and an opacity of 60% to 70%, ensuring it is visible but does not obscure the main content. A slow blink (0.5Hz) can be used to increase attention, but rapid blinking should be avoided to prevent discomfort.

[0130] The subtle edge prompts serve as guidance without interrupting the user's current operation, achieving non-intrusive recall and guidance.

[0131] Step A44: When the deviation distance is between the first deviation limit and the second deviation limit, a virtual path light strip from the current spatial position to the geometric center is generated as a recall feedback control signal.

[0132] Specifically, the second deviation limit is set to 2-3 times the first deviation limit, for example: 15 meters for small scenes, 30 meters for medium scenes, and 60 meters for large scenes. When the deviation distance is between the two limits, it indicates that the user has deviated moderately and needs clear path guidance.

[0133] It should be noted that when constructing path guidance, a straight path can be attempted first. If obstacles exist, an A* algorithm or RRT algorithm should be used to generate a detour path. The path light strip is rendered along the generated path, with its width set to ensure clear visibility from different angles. The path light strip can use a semi-transparent material, and the color can use a gradient effect: light blue at the starting point (current position) and light green at the ending point (geometric center) to help users understand the direction. Add a flowing animation effect: light spots or textures flow along the path from the current position to the geometric center, forming dynamic guidance. The height of the path light strip should be carefully adjusted to avoid confusion with the ground texture (for example, it can be set 0.1 to 0.2 meters above the ground).

[0134] Clear path guidance eliminates users' disorientation, and visual routes help users quickly return to the main activity area.

[0135] Step A45: When the deviation distance is greater than the second deviation limit, generate a ring-shaped light wall that shrinks towards the geometric center around the target user as a recall-type feedback control signal.

[0136] Specifically, when the deviation distance exceeds the second deviation limit, it indicates that the user has seriously deviated, such as being completely disoriented or forgetting the main task, and needs to be recalled through strong visual cues.

[0137] Therefore, a ring of light is generated around the user, with an initial radius set according to the target user's comfort space (mainly to avoid creating a sense of oppression). The ring of light is rendered as a semi-transparent cylindrical shape surrounding the target user, and warm colors such as orange or light red are used to indicate a warning. The ring of light contracts towards the geometric center at a constant speed, and the speed is adjusted according to the degree of deviation: the farther the deviation, the faster the contraction speed, but care is taken to avoid creating a sense of urgency for the target user.

[0138] It should be noted that the ring light wall can also employ a gradient effect. For example, the bottom can be brighter, the top darker, or it can gradually become transparent from the inside out, increasing the sense of three-dimensionality and visual depth. Furthermore, sound cues can be used during the shrinking of the ring light wall, such as using low-frequency ambient sounds to enhance the recall effect. When the target user begins to move towards the geometric center, the shrinking speed of the ring light wall slows down or pauses to provide positive feedback.

[0139] This application uses a gradually shrinking ring-shaped light wall to create a gentle yet clear spatial pressure, effectively guiding users who have strayed far from the main activity area back to the main activity area, while avoiding methods that disrupt the user's immersion, such as forced teleportation (e.g., the ring-shaped light wall's search speed is so fast that users feel oppressed).

[0140] Step 206: Based on the feedback control signal and the target user's current position in the immersive digital interactive environment, generate spatial positioning feedback content and render it to the immersive digital interactive environment.

[0141] Step 206: Based on the feedback control signal and the target user's current position in the immersive digital interactive environment, generate spatial positioning feedback content and render it to the immersive digital interactive environment.

[0142] Step 206 in this application specifically includes steps B1 to B3:

[0143] Step B1: Receive feedback control signals and parse their type and parameters.

[0144] Specifically, the feedback control signal output by the behavior analysis module is received and parsed. In this embodiment, the feedback control signal is presented as a structured data packet, which includes a feedback type identifier, target spatial coordinates, and rendering parameters. The feedback type identifier explicitly indicates one of three types of feedback control signals: dispersed, focused, or recall. The target spatial coordinates provide corresponding three-dimensional coordinates based on the feedback type. For example, for dispersed feedback control signals, the coordinates of one or more visual disturbance points can be provided; for focused feedback control signals, the center coordinates of the target focused object are provided; and for recall signals, the coordinates of the geometric center are provided. The rendering parameters include color, transparency, size, animation speed, and duration, used to control the visual effects of the feedback content.

[0145] Step B2: Based on the user's current location, dynamically calculate the spatial rendering attributes of the feedback content.

[0146] Furthermore, the system acquires the target user's current location coordinates and head orientation in real time, and calculates the spatial relationship between these coordinates and the parsed target spatial coordinates to adjust the rendering attributes of the feedback content, thereby optimizing its presentation in the user's field of vision. For dispersed feedback, the system maps the coordinates of the "visual disturbance point" to the user's current field of vision and dynamically adjusts the visual size of the halo effect based on the distance between the user and that point. For focused feedback, the system calculates the relative orientation of the target focused object to the user's current location, allowing the visual enhancement effect to be directly superimposed on that object, and adjusts the display direction of the pointing arrow based on the user's head orientation to avoid it being obscured by the object itself. For recall feedback, the system adjusts the display direction of the pointing arrow based on the user's current location and the distance between the target focused object and the user's current location. The relative position of the geometric center is used to generate and adjust the recall guidance: the specific location where the cursor appears at the edge of the field of vision is determined by the azimuth angle of the geometric center relative to the user's head; the virtual path light strip starts from the user's current position and ends at the geometric center, and its path avoids obstacles in the virtual environment in real time, and uses path planning algorithms such as A* to generate the guidance path; the initial center of the ring light wall is located at a certain height directly above or below the user's current position, and its contraction direction is calculated as the vector direction from the user's current position to the geometric center, and the contraction speed is adjusted according to the deviation distance between the user and the geometric center. The greater the deviation distance, the faster the initial contraction speed, but it is limited to a preset comfortable speed range to avoid causing a sense of oppression.

[0147] Step B3: Generate and render spatial positioning feedback content in real time.

[0148] Furthermore, based on the rendering attributes obtained during the spatial calculation phase, the graphics rendering engine of the immersive digital interactive environment is invoked to generate corresponding visual elements, which are then rendered to the user's display device in real time at a high frame rate. All feedback content uses semi-transparent materials and soft colors (such as pale yellow, pale blue, pale green, and orange) to ensure sufficient visibility without excessively obscuring the virtual scene background, thus maintaining immersion. The feedback content has animation effects, such as halo diffusion, aura pulsation, light spot flow, and light wall contraction. These animations are continuous until the user's behavior pattern changes and a new feedback control signal is generated. When the user responds to the guidance (such as starting to move towards the geometric center), the system can detect the change in behavior and adjust the feedback intensity accordingly (such as slowing down the contraction speed of the light wall or gradually fading out the light band), providing positive interactive feedback.

[0149] In summary, this invention constructs a user state sequence and divides it into multiple analysis units, achieving temporal modeling and segmented analysis of user behavior. Compared to the immediate response mechanisms of existing technologies, it can capture the continuity and evolution patterns of user behavior. By calculating the state density value within the analysis unit, combining the spatial overlap between the gaze focus trajectory and the hand interaction trajectory, as well as the number of interaction responses from virtual objects, a quantitative assessment of the concentration of user interaction is achieved, overcoming the one-sidedness of single-dimensional evaluation. Simultaneously, by calculating the state transition values ​​between adjacent analysis units, extracting the difference between the final state vector and the initial state vector, and combining this with the time interval, the dynamic trend of user behavior is accurately reflected. Based on the dual-dimensional determination of state density value and state transition value, user behavior patterns are precisely divided into three states: over-immersion, active exploration, and attention wandering, generating corresponding dispersed, focused, and recalled feedback control signals, achieving precise matching between feedback strategies and actual user needs. Furthermore, the spatial positioning and rendering of feedback content are dynamically adjusted based on the user's current location. For example, visual disturbances are set at the boundaries of over-focused areas, visual enhancements are generated for incomplete interactive objects, and recall guidance is generated in stages based on the deviation distance. This makes the feedback content closely integrated with the user's spatial perception, improves the intuitiveness and effectiveness of the guidance effect, and fundamentally solves the problems of real-time and targeted user behavior guidance in immersive environments.

[0150] 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.

[0151] Based on the same inventive concept, this application also provides a user behavior analysis and feedback system for immersive digital interaction. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the user behavior analysis and feedback system for immersive digital interaction provided below can be found in the limitations of the user behavior analysis and feedback method for immersive digital interaction described above, and will not be repeated here.

[0152] In one exemplary embodiment, such as Figure 3 As shown, an immersive digital interaction user behavior analysis and feedback system is provided, including:

[0153] The status acquisition module acquires the interaction status data of the target user in the immersive digital interactive environment and constructs a user status sequence based on the interaction status data.

[0154] The behavior analysis module divides the user state sequence into multiple analysis units according to a preset duration. Based on the state density value within each analysis unit and the state transition value between adjacent analysis units, it determines the current behavior pattern of the target user and generates a feedback control signal corresponding to the current behavior pattern.

[0155] The feedback rendering module generates spatial positioning feedback content and renders it onto the immersive digital interactive environment based on the feedback control signals and the target user's current position in the immersive digital interactive environment.

[0156] The modules in the aforementioned immersive digital interactive user behavior analysis and feedback system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can invoke and execute the corresponding operations of each module.

[0157] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements an immersive digital interactive user behavior analysis and feedback method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0158] Those skilled in the art will understand that Figure 4 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 computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0159] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0160] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0161] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0162] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0163] 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. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0164] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0165] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A user behavior analysis and feedback method for immersive digital interaction, characterized in that, include: Acquire the interaction state data of the target user in the immersive digital interactive environment, and construct a user state sequence based on the interaction state data; The user state sequence is divided into multiple analysis units according to a preset duration. Based on the state density value within each analysis unit and the state transition value between adjacent analysis units, the current behavior pattern of the target user is determined, and a feedback control signal corresponding to the current behavior pattern is generated. Based on the feedback control signal and the target user's current position in the immersive digital interactive environment, spatial positioning feedback content is generated and rendered into the immersive digital interactive environment.

2. The user behavior analysis and feedback method for immersive digital interaction as described in claim 1, characterized in that: The step of determining the current behavior pattern of the target user based on the state density value within each analysis unit and the state transition value between adjacent analysis units, and generating a feedback control signal corresponding to the current behavior pattern, includes: Calculate the state density value and the state transition value; When the state density value is higher than the density upper limit and the state transition value is lower than the transition lower limit, the current behavior mode is determined to be an over-immersion state, and a decentralized feedback control signal is generated. When the state density value is between the upper and lower density limits and the state transition value is higher than the upper transition limit, the current behavior mode is determined to be an active exploration state, and a focused feedback control signal is generated. When the state density value is lower than the density lower limit and the state transition value is lower than the transition lower limit, the current behavior pattern is determined to be an attention-free state and a recall-type feedback control signal is generated.

3. The user behavior analysis and feedback method for immersive digital interaction as described in claim 2, characterized in that: Calculating the state density value includes: The analysis unit extracts the target user's gaze focus trajectory and hand interaction trajectory. Calculate the spatial overlap between the gaze focus trajectory and the hand interaction trajectory; The state density value is determined based on the spatial overlap and the number of interaction responses of virtual objects within the analysis unit.

4. The user behavior analysis and feedback method for immersive digital interaction as described in claim 3, characterized in that: Calculating the state transition value includes: Two adjacent analysis units are selected from the plurality of analysis units and used as the front analysis unit and the back analysis unit, respectively. Extract the end state vector of the pre-analysis unit and the start state vector of the post-analysis unit; Calculate the difference between the terminal state vector and the initial state vector; The state transition value is determined based on the difference degree and the time interval between the pre-analysis unit and the post-analysis unit.

5. The user behavior analysis and feedback method for immersive digital interaction as described in claim 4, characterized in that: The generation of the distributed feedback control signal includes: The dwell time of each virtual space region in the user state sequence is statistically analyzed, and the regions whose dwell time exceeds a preset time limit are identified as over-focused regions. Visual disturbance points are set at the boundary positions of the over-focused region; The distributed feedback control signal is generated; the distributed feedback control signal controls the generation of an outward-expanding halo effect at the visual disturbance point.

6. The user behavior analysis and feedback method for immersive digital interaction as described in claim 5, characterized in that: The generation of the focused feedback control signal includes: Analyze the spatial exploration path in the state sequence of the target user to identify the virtual objects of the target user's incomplete interactions; Based on the frequency of interaction history between each virtual object that has not completed interaction and the target user, and the preset priority of the virtual object, calculate the importance score of each virtual object that has not completed interaction. The virtual object with the highest importance score is selected as the target focus object, and the focus feedback control signal is generated; the focus feedback control signal controls the generation of visual enhancement effects around the target focus object.

7. The user behavior analysis and feedback method for immersive digital interaction as described in claim 6, characterized in that: The generation of the recall-type feedback control signal includes: Calculate the geometric center of the target user's activity area based on the spatial location data in the user state sequence; Extract the current spatial location of the target user from the interaction state data, and calculate the deviation distance between the current spatial location and the geometric center; When the deviation distance is less than the first deviation limit, a cursor pointing to the geometric center is generated at the edge of the target user's field of vision as the recall feedback control signal. When the deviation distance is between the first deviation limit and the second deviation limit, a virtual path light strip is generated from the current spatial position to the geometric center as the recall feedback control signal. When the deviation distance is greater than the second deviation limit, a ring-shaped light wall that shrinks towards the geometric center is generated around the target user as the recall feedback control signal.

8. A user behavior analysis and feedback system for immersive digital interaction, employing the user behavior analysis and feedback method for immersive digital interaction as described in any one of claims 1 to 7, characterized in that, include: The status acquisition module acquires the interaction status data of the target user in the immersive digital interactive environment, and constructs a user status sequence based on the interaction status data. The behavior analysis module divides the user state sequence into multiple analysis units according to a preset duration, determines the current behavior pattern of the target user based on the state density value within each analysis unit and the state transition value between adjacent analysis units, and generates a feedback control signal corresponding to the current behavior pattern. The feedback rendering module generates spatial positioning feedback content and renders it to the immersive digital interactive environment based on the feedback control signal and the current position of the target user in the immersive digital interactive environment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the user behavior analysis and feedback method for immersive digital interaction as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the user behavior analysis and feedback method for immersive digital interaction as described in any one of claims 1 to 7.

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