Intelligent interaction method and system for user behavior data in virtual reality environment

Abstract

The invention discloses an intelligent interaction method and system for user behavior data in a virtual reality environment, and particularly relates to the technical field of virtual reality. Evaluating the matching degree between the interaction intention characteristics of the user and the immersion target of the user by analyzing the interaction intention characteristics of the user in the virtual reality environment; the adaptability of the user to the virtual environment is evaluated by analyzing behavior changes of the user in different virtual scenes. And based on the overall integrating degree index and the adaptability index, comprehensively evaluating the immersion state of the user, and judging whether the immersion depth of the virtual reality system needs to be adjusted or not. When adjustment is needed, the refined interaction demand intensity of the user is analyzed, and the interaction strategy of the virtual reality system is adjusted in combination with the overall integrating degree index, the adaptability index and the demand intensity index, so that the interaction demands of different users in the virtual environment are met, and the overall experience effect of the user is improved.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and more specifically, to an intelligent interaction method and system for user behavior data in a virtual reality environment. Background Art

[0002] Virtual reality technology has been widely used in entertainment, education, and training to provide users with a highly immersive experience. Existing virtual reality interaction systems often lack in-depth analysis of user behavior data and cannot dynamically respond to user behavior changes, adaptability, and interaction needs in virtual environments. When users switch between different virtual scenes or try to perform refined operations, existing technologies cannot adjust interaction strategies in a timely manner, resulting in poor user experience. The user's immersion state in a virtual environment is affected by many factors, including the fit of the interaction intention, environmental adaptability, and the need for refined interaction, but traditional systems have failed to effectively integrate these factors for comprehensive adjustments.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for intelligent interaction of user behavior data in a virtual reality environment to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent interaction method for user behavior data in a virtual reality environment comprises the following steps:

[0007] Analyze the user's interaction intention characteristics in the virtual reality environment and evaluate the overall fit between the user's interaction intention characteristics in the virtual behavior and the user's immersion goals;

[0008] Analyze the changes in user behavior in different virtual scenarios and evaluate the user's adaptability to different virtual environments;

[0009] Based on the degree of fit between the user's interactive intention characteristics in virtual behavior and the user's immersion goal and the user's adaptability to different virtual environments, the user's immersion state is analyzed to determine whether the immersion depth of the virtual reality system needs to be adjusted;

[0010] When the immersion depth of the VR system needs to be adjusted, the interactive behavior characteristics of users in the VR environment are analyzed to assess the intensity of users' demand for refined interaction in the VR environment;

[0011] A comprehensive analysis is conducted on the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goals, the user's adaptability to different virtual environments, and the intensity of the user's demand for refined interaction in the virtual reality environment, so as to adjust the interaction strategy of the virtual reality system.

[0012] In a preferred embodiment, the interaction intention characteristics of the user in the virtual reality environment are analyzed to evaluate the overall fit between the interaction intention characteristics of the user in the virtual behavior and the user's immersion goal, specifically:

[0013] A fit function is established to calculate the fit between each user's interaction intention feature and the corresponding user immersion goal. The expression is: C(I k ,O m )=exp(-γ km *I k ,O m );

[0014] Among them, C(I k ,O m ) is the fit between the kth interaction intention feature and the mth immersion goal; γ km is the sensitivity coefficient between the kth interaction intention feature and the mth immersion target; I k is the intention feature value of the user's kth interaction intention feature; m is the characteristic value of the mth user’s immersion goal;

[0015] In order to evaluate the overall fit between the user's interaction intention characteristics and the user's immersion goals, the overall fit index is defined, and its expression is:

[0016] Among them, FI is the overall fit index; N is the total number of user interaction intention features; P is the total number of user immersion goals.

[0017] In a preferred embodiment, the behavior changes of users in different virtual scenes are analyzed to evaluate the adaptability of users to different virtual environments, specifically:

[0018] In order to evaluate the user's adaptability to different virtual environments, the adaptability index is defined, and its expression is:

[0019]

[0020] Among them, AI is the adaptability index; Q represents the total number of virtual scenes experienced by users; ΔM y,y+1 Indicates the change in the user's behavior pattern between virtual scene y and virtual scene y+1; ΔM max Indicates the maximum value among all behavior pattern change values.

[0021] In a preferred embodiment, based on the degree of fit between the interactive intention characteristics of the user in the virtual behavior and the user's immersion goal and the user's adaptability to different virtual environments, the user's immersion state is analyzed to determine whether the immersion depth of the virtual reality system needs to be adjusted, specifically:

[0022] Preset the overall fit index threshold, and compare the overall fit index with the overall fit index threshold:

[0023] When the overall fit index is greater than or equal to the overall fit index threshold, it indicates that the overall fit between the user's interaction intention characteristics in the virtual behavior and the user's immersion goal is high.

[0024] Preset the adaptability index threshold and compare the adaptability index with the adaptability index threshold:

[0025] When the adaptability index is greater than or equal to the adaptability index threshold, it indicates that the evaluated user has strong adaptability to different virtual environments.

[0026] When the overall fit index is greater than or equal to the overall fit index threshold, and the adaptability index is greater than or equal to the adaptability index threshold, the immersion depth of the virtual reality system does not need to be adjusted.

[0027] In a preferred embodiment, when the immersion depth of the virtual reality system needs to be adjusted, the interactive behavior characteristics of the user in the virtual reality environment are analyzed to evaluate the user's demand intensity for refined interaction in the virtual reality environment, specifically:

[0028] The demand intensity index is defined to quantify the intensity of users' demand for refined interaction in virtual scenes. The expression is:

[0029] Where DII is the demand intensity index; R represents the total number of refined interaction features; W r represents the weight coefficient of the rth refined interaction feature; J r represents the demand value of the rth refined interaction feature; δ is the exponential amplification factor.

[0030] In a preferred embodiment, the interaction intention characteristics of the user in the virtual behavior are comprehensively analyzed with respect to the degree of fit between the user's immersion goal, the user's adaptability to different virtual environments, and the user's demand intensity for refined interaction in the virtual reality environment, and the interaction strategy of the virtual reality system is adjusted, specifically:

[0031] The overall fit index corresponding to the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goal, the adaptability index corresponding to the user's adaptability to different virtual environments, and the demand intensity index corresponding to the user's demand intensity for refined interaction in the virtual reality environment are normalized. The overall fit index, adaptability index, and demand intensity index obtained after normalization are comprehensively analyzed to calculate the interaction optimization index. The expression of the interaction optimization index is:

[0032]

[0033] Among them, IOI is the interactive optimization index; α, β, and η represent the weight coefficients of the overall fit index, adaptability index, and demand intensity index, respectively;

[0034] A preset interaction optimization index threshold is set, and the set interaction optimization index is compared with the set interaction optimization index threshold:

[0035] When the interaction optimization index is greater than or equal to the interaction optimization index threshold, there is no need to adjust the interaction strategy of the virtual reality system;

[0036] When the interaction optimization index is less than the interaction optimization index threshold, the interaction strategy of the virtual reality system needs to be adjusted.

[0037] On the other hand, the present invention provides an intelligent interactive system for user behavior data in a virtual reality environment, including a fit evaluation module, an adaptability evaluation module, an immersion state analysis module, a demand intensity evaluation module, and an interactive strategy adjustment module.

[0038] The fit evaluation module is used to analyze the user's interaction intention characteristics in the virtual reality environment and evaluate the overall fit between the user's interaction intention characteristics in the virtual behavior and the user's immersion goal;

[0039] The adaptability evaluation module is used to analyze the user's behavior changes in different virtual scenarios and evaluate the user's adaptability to different virtual environments;

[0040] An immersion state analysis module is used to analyze the user's immersion state based on the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goal and the user's adaptability to different virtual environments, and determine whether the immersion depth of the virtual reality system needs to be adjusted;

[0041] The demand intensity assessment module is used to adjust the immersion depth of the virtual reality system when necessary, analyze the interactive behavior characteristics of users in the virtual reality environment, and assess the user's demand intensity for refined interaction in the virtual reality environment;

[0042] The interaction strategy adjustment module is used to comprehensively analyze the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goals, the user's adaptability to different virtual environments, and the user's demand for refined interaction in the virtual reality environment, and adjust the interaction strategy of the virtual reality system.

[0043] Technical effects and advantages of the intelligent interaction method and system of user behavior data in a virtual reality environment of the present invention:

[0044] 1. Evaluate the user's immersion status by analyzing the degree of fit between the user's interaction intention characteristics and the user's immersion goals, and ensure that the user's behavior meets the design expectations. Combined with the user's adaptability in different virtual scenes, determine whether the immersion depth of the virtual reality system needs to be adjusted. When the immersion depth of the virtual reality system needs to be adjusted, identify the user's needs for high-precision operations by evaluating the intensity of the user's demand for refined interaction.

[0045] 2. By comprehensively considering the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goals, the user's adaptability to different virtual environments, and the user's demand for refined interaction in the virtual reality environment, the interaction strategy of the virtual reality system is intelligently adjusted to enhance the user's immersion, improve the interaction fluency and response accuracy, and enable the virtual reality system to provide users with a more intelligent and personalized virtual reality interaction experience, thereby improving user satisfaction and usage effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of an intelligent interaction method for user behavior data in a virtual reality environment according to the present invention;

[0047] Figure 2 This is a schematic diagram of the structure of an intelligent interactive system for user behavior data in a virtual reality environment according to the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1

[0050] Figure 1 The present invention provides an intelligent interaction method for user behavior data in a virtual reality environment, which includes the following steps:

[0051] Analyze the user's interaction intention characteristics in the virtual reality environment and evaluate the degree of fit between the user's interaction intention characteristics in the virtual behavior and the user's immersion goals;

[0052] Analyze the changes in user behavior in different virtual scenarios and evaluate the user's adaptability to different virtual environments;

[0053] Based on the degree of fit between the user's interactive intention characteristics in virtual behavior and the user's immersion goals and the user's adaptability to different virtual environments, the user's immersion state is determined to determine whether the immersion depth of the virtual reality system needs to be adjusted;

[0054] When the immersion depth of the VR system needs to be adjusted, analyze the user's interactive behavior characteristics in the VR environment, evaluate the user's demand for refined interaction in the VR environment, and determine whether the interaction mode needs to be upgraded;

[0055] A comprehensive analysis is conducted on the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goals, the user's adaptability to different virtual environments, and the intensity of the user's demand for refined interaction in the virtual reality environment, in order to optimize the feedback mode of the virtual reality system and adjust the interaction strategy.

[0056] Specifically, the interactive intention characteristics of users in the virtual reality environment are analyzed to evaluate the overall fit between the interactive intention characteristics of users in virtual behaviors and the user's immersion goals, including:

[0057] In a virtual reality environment, the user's interactive behavior data is collected through sensor devices. The interactive behavior data includes the user's hand movements, eye tracking, head rotation angle, limb movement direction and other interactive behaviors in the virtual environment. The interactive behavior data is used to describe the interaction details and operation tendencies between the user and the virtual environment. For example, the user's hand movements may show that the user is trying to grab or touch a virtual object, and the eye tracking can indicate the area of ​​the user's attention.

[0058] Based on the collected user interaction behavior data, the user's interaction intention features are extracted. The user's interaction intention features reflect the user's operation purpose and behavior tendency in the virtual reality environment. Each interaction intention feature represents the user's behavior tendency in a specific interaction. For example, a longer gaze stay time may indicate that the user is more interested in the target, and frequent gestures may indicate that the user wants to interact more actively.

[0059] In a virtual reality environment, different immersion goals have different requirements for the user's interaction intention characteristics. Therefore, a fit function is established to calculate the fit between each user's interaction intention feature and the corresponding user immersion goal. The expression is: C(I k ,Om )=exp(-γ km *I k ,O m );

[0060] Among them, C(I k ,O m ) is the fit between the kth interaction intention feature and the mth immersion goal; γ km is the sensitivity coefficient between the kth interaction intention feature and the mth immersion target; I k is the intention feature value of the user's kth interaction intention feature; m is the characteristic value of the mth user’s immersion goal.

[0061] Based on the fit function, in order to evaluate the overall fit between the user's interaction intention characteristics and the user's immersion goals, the overall fit index is defined, and its expression is:

[0062] Among them, FI is the overall fit index, which represents the overall fit between the user's interaction intention features and the user's immersion goals; N is the total number of the user's interaction intention features; and P is the total number of the user's immersion goals.

[0063] When the value of the overall fit index is closer to 1, it means that the degree of fit between the user's interaction intention characteristics and the immersion goal is higher. The user's behavior pattern, operation method and focus in the virtual reality environment are highly consistent with the immersion goal set by the virtual reality interaction system. At this time, the user's behavior performance meets the design expectations and can successfully complete the virtual task to achieve the effect of immersion experience. The higher the degree of fit between the user's interaction intention characteristics and the immersion goal, it means that the user can interact with the virtual content in the virtual environment without additional adjustments, and feel a high sense of immersion and reality, which enhances the effectiveness of the virtual reality experience. In addition, the closer the value of the overall fit index is to 1, it also means that the current interaction interface design is reasonable and meets the ideal user experience standards.

[0064] Specifically, analyze the user's behavior changes in different virtual scenarios and evaluate the user's adaptability to different virtual environments, including:

[0065] After the user enters different virtual scenes, the sensor equipment records the user's behavior data in different virtual scenes. Virtual scenes may include exploratory scenes, task-oriented scenes, immersive experience scenes, etc., which represent different scenarios for users to navigate, operate and interact in virtual environments. Set a time period and continuously record the user's behavior data in different virtual scenes during the time period, including data such as eye operations, body movements, and walking paths.

[0066] Based on the recorded user behavior data in different virtual scenes, the user behavior in different virtual scenes is extracted, including the focus area of ​​vision, hand movement type, movement path, etc., reflecting the user's interaction preferences and operation habits in the virtual environment. The behavior extraction in each virtual scene is analyzed to identify the user's behavior pattern, such as frequent gaze in a specific area, fast click operation in a specific time period, etc.

[0067] In order to evaluate the changes in user behavior in different virtual scenarios, it is necessary to quantify the differences in user behavior patterns in different virtual scenarios. Define a behavior pattern change value to indicate the degree of change in the user's behavior pattern in different virtual scenarios. Based on the behavior pattern change value, in order to evaluate the user's adaptability to different virtual environments, define an adaptability index, which is expressed as:

[0068] Among them, AI is the adaptability index, which is used to evaluate the user's adaptability to different virtual environments; Q represents the total number of virtual scenes experienced by the user; ΔM y,y+1 Indicates the change in the user's behavior pattern between virtual scene y and virtual scene y+1; ΔM max Indicates the maximum value among all behavior pattern change values.

[0069] The closer the value of the adaptability index is to 1, the higher the user's adaptability between different virtual scenes, which means that the user can quickly respond to changes in each virtual scene. Specifically, when the value of the adaptability index is close to 1, the user shows continuity in the behavior pattern during the virtual scene switching process, indicating that after entering the new virtual scene, the user can quickly adapt to the interaction requirements and environmental characteristics of the virtual scene, indicating that the user can complete the tasks or interaction requirements in different virtual scenes, reducing the time to adapt to the new virtual environment, and improving the immersion and naturalness of the overall experience. A higher adaptability index not only reflects the user's higher cognitive flexibility and interaction ability, but also means that the current virtual scene design and interaction method are more user-friendly and enable users to understand the intention and functional layout of the scene.

[0070] The closer the value of the adaptability index is to 0, the more it indicates that the user's behavior patterns change between different virtual scenes, the user cannot maintain stable operating habits, and the adaptability is weak. Users may need extra time to familiarize themselves with the interaction methods and environmental characteristics of each new virtual scene. When the new virtual scene is significantly different from the previous virtual scene in terms of vision and operating logic, the user may be confused or slow to respond and need additional guidance or help to complete the task. Therefore, a low adaptability index not only indicates that users have difficulty adapting to the current new virtual environment, but also reflects that the design of the virtual scene may be insufficient in terms of coherence and user guidance.

[0071] Specifically, based on the degree of fit between the user's interactive intention characteristics in virtual behavior and the user's immersion goals and the user's adaptability to different virtual environments, the user's immersion state is determined, and it is judged whether the immersion depth of the virtual reality system needs to be adjusted, including:

[0072] Preset the overall fit index threshold, and compare the overall fit index with the overall fit index threshold:

[0073] When the overall fit index is greater than or equal to the overall fit index threshold, it indicates that the user's interaction intention characteristics in the virtual behavior are highly consistent with the user's immersion goal. At this time, the user's operation behavior has a high degree of match with the task requirements, target objects or interactive areas in the virtual scene, and the user can successfully complete the interactive task. For example, in a task-driven virtual scene, the user's line of sight and gesture operation frequency are consistent with the target area, indicating that the user's understanding of the current environment is accurate, the behavior selection is in line with expectations, and the immersion level required by the design is achieved.

[0074] Preset the adaptability index threshold and compare the adaptability index with the adaptability index threshold:

[0075] When the adaptability index is greater than or equal to the adaptability index threshold, it indicates that the user has strong adaptability to different virtual environments and can cope with changes in the virtual environment. This means that the user's operating habits and behavior patterns in different virtual scenes remain stable, and there is no obvious discomfort or incoherent operation due to the switching of virtual scenes, showing high environmental adaptability.

[0076] When the overall fit index is greater than or equal to the overall fit index threshold, and the adaptability index is greater than or equal to the adaptability index threshold, it means that the user's interactive behavior is highly consistent with the immersion goal, and the user is relatively adaptable to changes in the virtual scene. The immersion state is ideal, and there is no need to adjust the immersion depth of the virtual reality system.

[0077] Except for the case where the overall fit index is greater than or equal to the overall fit index threshold, the immersion depth of the virtual reality system needs to be adjusted, including:

[0078] Add task guidance to help users better understand the task;

[0079] Reduce the difficulty of switching or extend the adaptation time;

[0080] Optimize the depth of immersion, such as simplifying interactive tasks, reducing the frequency of scene changes, and improving the user's immersive experience.

[0081] The setting of the overall fit index threshold needs to comprehensively consider the immersion goal of the virtual scene, the complexity of the user's task, the diversity of the interaction methods, and the sensitivity of the virtual scene to immersion. For scenes or complex tasks with high immersion requirements, such as precision operation training or task-oriented scenes, the virtual system will set a higher overall fit threshold to ensure that the user's behavior is highly consistent with the expected goal and enhance the immersive experience.

[0082] The adaptability index threshold is usually set according to the switching frequency and content change degree of different virtual scenes.

[0083] Specifically, when the immersion depth of the virtual reality system needs to be adjusted, the interactive behavior characteristics of users in the virtual reality environment are analyzed, and the intensity of users' demand for refined interaction in the virtual reality environment is evaluated, including:

[0084] In a virtual reality environment, the user's original interactive behavior data is collected through sensor devices. The user's original interactive behavior data includes the user's gesture operation, gaze point position, line of sight movement, head rotation angle, body movement path, etc. The collected original interactive behavior data of the user is screened to extract the key interactive features of the user in the virtual reality environment. Key interactive features refer to all operational features that have an important impact on the user's interactive behavior, reflecting the user's overall interaction mode and behavioral preferences. Each key interactive feature represents the characteristic value of the user in a specific operational behavior, such as the length of time the line of sight stays, the complexity of the gesture trajectory, the smoothness of the walking path, etc.

[0085] Based on the key interaction features, we screen out features that can reflect the refined interaction needs, which are called refined interaction features. Refined interaction features are a subset selected from the key interaction features, mainly including the user's high-precision, high-frequency or high-attention interaction behaviors in the virtual scene, indicating that the user has more detailed interaction needs for specific operations. For example, if the user stares at the virtual object for a long time or performs multiple precise adjustments, these features are defined as refined interaction features.

[0086] The demand intensity reflects the user's demand level for high-precision operation and detailed feedback in the virtual scene. The demand intensity index is defined to quantify the user's demand intensity for refined interaction in the virtual scene. The expression is:

[0087] Where DII is the demand intensity index, which is used to quantify the intensity of users’ demand for refined interaction in the virtual environment; R represents the total number of refined interaction features; W r represents the weight coefficient of the rth refined interaction feature. The weight coefficient is used to reflect the influence of each refined interaction feature. The larger the weight coefficient, the more important the refined interaction feature is to the demand intensity. rIt represents the demand value of the r-th refined interaction feature. The higher the demand value, the stronger the user's demand for refined interaction of the specific feature. δ is the exponential amplification factor, which is used to adjust the sensitivity of the demand value and is usually set to a value greater than 1. By adjusting the exponential amplification factor, the responsiveness of the demand intensity index to each refined interaction feature can be controlled.

[0088] The demand value of the refined interaction feature is obtained by weighted calculation of the user's operation frequency, operation duration, operation refinement, and operation repetition, which will not be repeated here.

[0089] Specifically, the interaction intention characteristics of users in virtual behaviors are comprehensively analyzed in terms of the degree of fit between the user's immersion goals, the user's adaptability to different virtual environments, and the user's demand for refined interaction in the virtual reality environment, and the interaction strategy of the virtual reality system is adjusted, including:

[0090] The overall fit index corresponding to the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goal, the adaptability index corresponding to the user's adaptability to different virtual environments, and the demand intensity index corresponding to the user's demand intensity for refined interaction in the virtual reality environment are normalized. The overall fit index, adaptability index, and demand intensity index obtained after normalization are comprehensively analyzed to calculate the interaction optimization index. The expression of the interaction optimization index is:

[0091]

[0092] Among them, IOI is the interaction optimization index; α, β, and η represent the weight coefficients of the overall fit index, adaptability index, and demand intensity index, respectively, and represent the relative influence of the overall fit index, adaptability index, and demand intensity index on the interaction optimization index, respectively.

[0093] The interaction optimization index threshold is preset. The interaction optimization index threshold is usually set based on the user's expected interaction experience standard in the virtual reality environment. The preset interaction optimization index is compared with the preset interaction optimization index threshold:

[0094] When the interaction optimization index is greater than or equal to the interaction optimization index threshold, it means that the user's current interaction experience has met the design standards of the virtual reality system, and the user's overall fit, adaptability, and refinement needs are all in an ideal state. The user has a high sense of immersion in the virtual environment, can smoothly adapt to the switching of different scenes, and the demand for refined interaction is responded to. At this time, there is no need to adjust the interaction strategy of the virtual reality system.

[0095] When the interaction optimization index is less than the interaction optimization index threshold, it indicates that the user's experience in the virtual reality system may be insufficient and the interaction effect does not meet the design expectations. At this time, the interaction strategy of the virtual reality system needs to be adjusted, including:

[0096] Enhance the fineness of visual and tactile feedback to improve the user's sense of immersion and control. In a virtual reality environment, the quality of visual feedback directly affects the user's experience. For example, the resolution of the rendering can be increased, the clarity of scene details can be improved, or more precise shadows and lighting effects can be introduced to make the virtual environment more realistic. More realistic feedback can be provided through force feedback devices or tactile gloves. For example, when a user touches or operates an object in a virtual environment, corresponding vibration or pressure is provided according to the strength and position of the operation.

[0097] Enhance task guidance and scene transition guidance to help users adapt to different virtual scenes. In a complex virtual environment, users may need to complete multiple tasks or experience multiple scene switches, so clear task guidance and smooth scene transitions are very important. Provide users with task navigation and task instructions through prompt information, virtual signs, sound effects, etc. During the scene switching process, you can add transition animations or sound effects to relieve the user's adaptation pressure.

[0098] Adjust the interaction accuracy and sensitivity according to the user's demand for refined interaction to ensure that it meets the user's operation intention. When the user has a high demand for refined interaction, improve the perception accuracy of subtle movements, such as more accurately capturing subtle operations of the hand or fingertips, so that users can perform high-precision tasks. For interactive scenarios that require fast feedback, improve the response speed of operations so that each user's action can be reflected instantly in the virtual environment.

[0099] Example 2

[0100] The difference between Example 2 of the present invention and Example 1 is that this example introduces an intelligent interactive system for user behavior data in a virtual reality environment.

[0101] Figure 2 The present invention provides a structural schematic diagram of an intelligent interactive system for user behavior data in a virtual reality environment, an intelligent interactive system for user behavior data in a virtual reality environment, including a fit evaluation module, an adaptability evaluation module, an immersion state analysis module, a demand intensity evaluation module, and an interaction strategy adjustment module.

[0102] The fit evaluation module is used to analyze the user's interaction intention characteristics in the virtual reality environment and evaluate the overall fit between the user's interaction intention characteristics in the virtual behavior and the user's immersion goal;

[0103] The adaptability evaluation module is used to analyze the user's behavior changes in different virtual scenarios and evaluate the user's adaptability to different virtual environments;

[0104] An immersion state analysis module is used to analyze the user's immersion state based on the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goal and the user's adaptability to different virtual environments, and determine whether the immersion depth of the virtual reality system needs to be adjusted;

[0105] The demand intensity assessment module is used to adjust the immersion depth of the virtual reality system when necessary, analyze the interactive behavior characteristics of users in the virtual reality environment, and assess the user's demand intensity for refined interaction in the virtual reality environment;

[0106] The interaction strategy adjustment module is used to comprehensively analyze the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goals, the user's adaptability to different virtual environments, and the user's demand for refined interaction in the virtual reality environment, and adjust the interaction strategy of the virtual reality system.

[0107] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0108] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0109] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0111] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0112] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0114] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0115] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0116] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent interaction method for user behavior data in a virtual reality environment, characterized in that: The steps include: Analyze the user's interaction intention characteristics in the virtual reality environment and evaluate the overall fit between the user's interaction intention characteristics in the virtual behavior and the user's immersion goals; Analyze the changes in user behavior in different virtual scenarios and evaluate the user's adaptability to different virtual environments; Based on the degree of fit between the user's interactive intention characteristics in virtual behavior and the user's immersion goal and the user's adaptability to different virtual environments, the user's immersion state is analyzed to determine whether the immersion depth of the virtual reality system needs to be adjusted; When the immersion depth of the VR system needs to be adjusted, the interactive behavior characteristics of users in the VR environment are analyzed to assess the intensity of users' demand for refined interaction in the VR environment; A comprehensive analysis is conducted on the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goals, the user's adaptability to different virtual environments, and the intensity of the user's demand for refined interaction in the virtual reality environment, so as to adjust the interaction strategy of the virtual reality system.

2. The intelligent interaction method of user behavior data in a virtual reality environment according to claim 1, characterized in that: Analyze the user's interaction intention characteristics in the virtual reality environment and evaluate the overall fit between the user's interaction intention characteristics in the virtual behavior and the user's immersion goals, specifically: A fit function is established to calculate the fit between each user's interaction intention feature and the corresponding user immersion goal. The expression is: C(I k ,O m )=exp(-γ km *I k ,O m ); Among them, C(I k ,O m ) is the fit between the kth interaction intention feature and the mth immersion goal; γ km is the sensitivity coefficient between the kth interaction intention feature and the mth immersion target; I k is the intention feature value of the user's kth interaction intention feature; m is the characteristic value of the mth user’s immersion goal; In order to evaluate the overall fit between the user's interaction intention characteristics and the user's immersion goals, the overall fit index is defined, and its expression is: Among them, FI is the overall fit index; N is the total number of user interaction intention features; P is the total number of user immersion goals.

3. The intelligent interaction method of user behavior data in a virtual reality environment according to claim 2, characterized in that: Analyze the changes in user behavior in different virtual scenarios and evaluate the user's adaptability to different virtual environments, specifically: In order to evaluate the user's adaptability to different virtual environments, the adaptability index is defined, and its expression is: Among them, AI is the adaptability index; Q represents the total number of virtual scenes experienced by users; ΔM y,y+1 Indicates the change in the user's behavior pattern between virtual scene y and virtual scene y+1; ΔM max Indicates the maximum value among all behavior pattern change values.

4. The intelligent interaction method for user behavior data in a virtual reality environment according to claim 3, characterized in that: Based on the degree of fit between the user's interactive intention characteristics in virtual behavior and the user's immersion goal and the user's adaptability to different virtual environments, the user's immersion state is analyzed to determine whether the immersion depth of the virtual reality system needs to be adjusted, specifically: Preset the overall fit index threshold, and compare the overall fit index with the overall fit index threshold: When the overall fit index is greater than or equal to the overall fit index threshold, it indicates that the overall fit between the user's interaction intention characteristics in the virtual behavior and the user's immersion goal is high; Preset the adaptability index threshold and compare the adaptability index with the adaptability index threshold: When the adaptability index is greater than or equal to the adaptability index threshold, it indicates that the evaluated user has strong adaptability to different virtual environments; When the overall fit index is greater than or equal to the overall fit index threshold, and the adaptability index is greater than or equal to the adaptability index threshold, the immersion depth of the virtual reality system does not need to be adjusted.

5. The intelligent interaction method of user behavior data in a virtual reality environment according to claim 4, characterized in that: When the immersion depth of the virtual reality system needs to be adjusted, the interactive behavior characteristics of users in the virtual reality environment are analyzed to evaluate the intensity of users' demand for refined interaction in the virtual reality environment, specifically: The demand intensity index is defined to quantify the intensity of users' demand for refined interaction in virtual scenes. The expression is: Where DII is the demand intensity index; R represents the total number of refined interaction features; W r represents the weight coefficient of the rth refined interaction feature; J r represents the demand value of the rth refined interaction feature; δ is the exponential amplification factor.

6. The intelligent interaction method of user behavior data in a virtual reality environment according to claim 5, characterized in that: The interaction strategy of the virtual reality system is adjusted by comprehensively analyzing the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goal, the user's adaptability to different virtual environments, and the user's demand for refined interaction in the virtual reality environment. Specifically: The overall fit index corresponding to the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goal, the adaptability index corresponding to the user's adaptability to different virtual environments, and the demand intensity index corresponding to the user's demand intensity for refined interaction in the virtual reality environment are normalized. The overall fit index, adaptability index, and demand intensity index obtained after normalization are comprehensively analyzed to calculate the interaction optimization index. The expression of the interaction optimization index is: Among them, IOI is the interactive optimization index; α, β, and η represent the weight coefficients of the overall fit index, adaptability index, and demand intensity index, respectively; A preset interaction optimization index threshold is set, and the set interaction optimization index is compared with the set interaction optimization index threshold: When the interaction optimization index is greater than or equal to the interaction optimization index threshold, there is no need to adjust the interaction strategy of the virtual reality system; When the interaction optimization index is less than the interaction optimization index threshold, the interaction strategy of the virtual reality system needs to be adjusted.

7. An intelligent interaction system for user behavior data in a virtual reality environment, used to implement the intelligent interaction method for user behavior data in a virtual reality environment as claimed in any one of claims 1 to 6, characterized in that: It includes fit evaluation module, adaptability evaluation module, immersion state analysis module, demand intensity evaluation module and interaction strategy adjustment module. The fit evaluation module is used to analyze the user's interaction intention characteristics in the virtual reality environment and evaluate the overall fit between the user's interaction intention characteristics in the virtual behavior and the user's immersion goal; The adaptability evaluation module is used to analyze the user's behavior changes in different virtual scenarios and evaluate the user's adaptability to different virtual environments; An immersion state analysis module is used to analyze the user's immersion state based on the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goal and the user's adaptability to different virtual environments, and determine whether the immersion depth of the virtual reality system needs to be adjusted; The demand intensity assessment module is used to adjust the immersion depth of the virtual reality system when necessary, analyze the interactive behavior characteristics of users in the virtual reality environment, and assess the user's demand intensity for refined interaction in the virtual reality environment; The interaction strategy adjustment module is used to comprehensively analyze the degree of fit between the user's interaction intention characteristics in virtual behavior and the user's immersion goals, the user's adaptability to different virtual environments, and the user's demand for refined interaction in the virtual reality environment, and adjust the interaction strategy of the virtual reality system.

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