Iris recognition personalized preference setting method and system in VR environment

Abstract

The present invention relates to the field of preference setting technology, specifically to a method and system for personalized preference setting of iris recognition in a VR environment, comprising the following steps: extracting key iris features based on user iris information collected in a VR environment, performing normalization processing on iris feature data, and verifying data format consistency. In the present invention, by normalizing iris data, a basis is provided for identity authentication and personalized settings, identity authentication is performed using a decision tree algorithm, the accuracy and security of access control are ensured, and the interface and functions are allowed to be dynamically adjusted according to the exact user identity. By dynamically adjusting preference weights, the VR environment settings can be adjusted in real time according to the user's immediate feedback and historical behavior, thereby greatly improving the personalization of the user experience. By analyzing user feedback through a support vector machine, not only can the preferences of new users be predicted and adapted, but also the user experience can be continuously optimized, and the intuitiveness and operation efficiency of the interface are improved.

Description

Technical Field

[0001] This invention relates to the field of preference setting technology, and in particular to a method and system for personalized preference setting using iris recognition in a VR environment. Background Technology

[0002] Preference setting technology involves the personalized configuration of software and hardware systems to adapt to users' specific needs and preferences. It typically includes collecting interaction behavior analysis information, analyzing user habits, and applying machine learning algorithms to predict users' potential preferences. Its purpose is to improve user experience and interaction efficiency by automatically adjusting system settings or recommended content. In highly personalized application scenarios, such as virtual reality (VR), augmented reality (AR), or other smart device environments, preference setting technology becomes particularly important. It can adjust interface layout, function access permissions, and content display methods according to users' specific needs, thereby achieving a more customized user interaction experience.

[0003] Among them, the iris recognition personalized preference setting method in VR environment uses iris recognition technology to realize the personalized settings of user preferences in VR environment. By analyzing iris data to verify the user's identity, and based on the verification results, the user is given the right to set personalized preferences, such as interface layout, content display and operation method, which enhances the personalization and comfort of user experience, and improves the intuitiveness and efficiency of interaction.

[0004] Standardized preference settings in existing technologies cannot quickly adapt to changes in user behavior, requiring users to frequently adjust settings manually to achieve the desired interactive experience, thus reducing efficiency. In terms of personalized security settings, existing technologies lack effective authentication mechanisms, leading to personal data leaks or unauthorized access. These shortcomings are particularly evident in VR environments because user interaction needs are highly variable, and standardized configurations cannot meet the specific needs of all users. The lack of effective user feedback utilization mechanisms also makes it difficult to accurately predict and adapt to new user preferences, limiting the effectiveness of use among new user groups. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method and system for setting personalized preferences for iris recognition in a VR environment.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for setting personalized preferences for iris recognition in a VR environment, comprising the following steps:

[0007] S1: Based on the user iris information collected in the VR environment, extract key iris features, perform normalization processing on the iris feature data, verify the consistency of data format, and generate an iris feature dataset;

[0008] S2: Based on the iris feature dataset, use the decision tree algorithm to verify identity, determine the matching degree between the iris data and the known user, identify iris feature points associated with user preferences, analyze interaction behavior analysis information and dynamically adjust preference weights to generate user identity and preference information;

[0009] S3: Extract user preference data from the user identity and preference information, set the initial configuration of the VR environment, test the applicability of the personalized configuration by simulating user interaction, and adjust the environment configuration parameters according to the test results to obtain the interaction adaptability configuration;

[0010] S4: Implement the aforementioned interactive adaptive configuration in the VR environment, verify identity and permission changes through iris data, monitor user interaction responses and operation data, collect user feedback data, adjust preference settings, and generate user feedback analysis results;

[0011] S5: Based on the user feedback analysis results, support vector machines are used to predict the new users' settings preferences in the VR environment. The prediction parameters are adjusted in combination with the new users' feedback data to optimize the configuration accuracy and generate detailed preference configuration settings.

[0012] S6: Based on the detailed preference configuration settings, adjust the VR environment settings in real time to match the needs of different users, regularly evaluate user satisfaction and configuration effect, optimize preference settings based on evaluation results, and generate optimized environment configuration.

[0013] As a further aspect of the present invention, the iris feature dataset includes iris texture features, iris color features, and iris morphology features; the user identity and preference information includes user identification identifiers, preference weight indices, and behavioral pattern recognition data; the interactive adaptability configuration includes interactive interface layout parameters, response time optimization parameters, and visual feedback elements; the refined preference configuration settings include preference adjustment thresholds, prediction evaluation results, and user profile segmentation results; and the optimized environment configuration includes configuration update cycles, adaptability evaluation criteria, and satisfaction tracking indicators.

[0014] As a further aspect of the present invention, the steps of extracting key iris features based on user iris information collected in a VR environment, performing normalization processing on the iris feature data, and verifying data format consistency to generate an iris feature dataset are as follows:

[0015] S101: Based on the user's iris information collected in the VR environment, refine the analysis of iris structural feature points, record the coordinates and shape details of each feature point, optimize feature recognizability, and generate an iris feature table;

[0016] S102: Based on the iris feature table, perform numerical transformation of feature points, map feature values ​​from the original scale to a unified analysis range, and use linear transformation to adjust the proportion of each feature point to obtain normalized feature data;

[0017] S103: Based on the normalized feature data, organize and arrange the dataset, connect each iris feature with the corresponding user identifier, and verify the consistency and integrity of each data item to obtain the iris feature dataset.

[0018] As a further aspect of the present invention, based on the iris feature dataset, the steps of using a decision tree algorithm for identity verification, determining the matching degree between the iris data and known users, identifying iris feature points associated with user preferences, analyzing interaction behavior analysis information and dynamically adjusting preference weights to generate user identity and preference information are as follows:

[0019] S201: Based on the iris feature dataset, use the decision tree algorithm to analyze the matching between the iris data and the existing user data, mark the user data item with the best similarity, and generate a preliminary identity verification result;

[0020] S202: Based on the preliminary identity verification results, analyze the correlation between iris features and interaction behavior analysis information, adjust the correlation strength between preferences and behavior data, reconfigure preference settings, and obtain preference adjustment records;

[0021] S203: Based on the preference adjustment record, merge user identity information and preference data, perform information structuring, verify the integrity of the dataset, and obtain user identity and preference information.

[0022] As a further aspect of the present invention, the decision tree algorithm is used to analyze the matching between iris data and existing user data, according to the formula:

[0023]

[0024] Calculate the similarity score GS, where xs i ys represents the feature value in the iris feature dataset. i n represents the corresponding feature value of the existing user data. s The number of representative features.

[0025] As a further aspect of the present invention, the steps of extracting user preference data from the user identity and preference information, setting the initial configuration of the VR environment, testing the applicability of the personalized configuration by simulating user interaction, and adjusting the environment configuration parameters according to the test results to obtain the interaction adaptability configuration are as follows:

[0026] S301: Based on the user's identity and preference information, extract key preference parameters, adjust the volume and brightness control parameters in the VR environment settings, and adjust the display position of objects and the interaction response delay to obtain a preliminary environment configuration;

[0027] S302: Based on the initial environment configuration, conduct real-time interaction tests through virtual user scenarios, record user operation time and selection preferences under multiple configurations, identify bottlenecks in user experience, and obtain interaction test feedback information;

[0028] S303: Based on the interactive test feedback information, refine the interactive elements of the VR environment, including enhancing the visual feedback speed and adjusting the sensitivity of the operation interface, reconfiguring the interactive elements and matching user preference requirements, and obtaining the interactive adaptability configuration.

[0029] As a further aspect of the present invention, the steps of implementing the aforementioned interactive adaptive configuration in a VR environment, verifying identity and permission changes through iris data, monitoring user interaction responses and operation data, collecting user feedback data, adjusting preference settings, and generating user feedback analysis results are as follows:

[0030] S401: Based on the aforementioned interactive adaptive configuration, verify user identity and permissions through iris recognition technology, monitor permission changes and access records in real time, optimize permission log recording, track key access events, and obtain permission verification records;

[0031] S402: Based on the permission verification record, monitor the user interaction mode and operation data, record the user's behavior in different interaction scenarios, collect and analyze operation habits and response time, determine interaction preferences, and obtain interaction behavior analysis information;

[0032] S403: Based on the interaction behavior analysis information, adjust the user interface layout in the interaction settings, collect real-time user feedback information, and re-match user expectations by comparing the feedback with the preference analysis results to obtain user feedback analysis results.

[0033] As a further aspect of the present invention, based on the user feedback analysis results, a support vector machine is used to predict the new user's setting preferences in the VR environment. The prediction parameters are then adjusted in conjunction with the new user's feedback data to optimize configuration accuracy and generate detailed preference configuration settings. The specific steps are as follows:

[0034] S501: Based on the user feedback analysis results, a support vector machine is used to extract feedback data from new users, analyze user behavior and preferences, and perform preliminary optimization of VR settings through parameter adjustment to obtain preliminary preference configuration;

[0035] S502: Based on the initial preference configuration, simulate real-time user scenarios, collect operation data and feedback information of new users in the VR environment, determine the applicability of the preference configuration, and obtain the configuration effect evaluation results;

[0036] S503: Based on the configuration effect evaluation results, refine the visual and audio settings in the VR environment, repeatedly test and combine user feedback to optimize the settings, and obtain refined preference configuration settings.

[0037] As a further aspect of the present invention, the steps of adjusting the VR environment settings in real time according to the refined preference configuration settings to match the needs of different users, periodically evaluating user satisfaction and configuration effectiveness, and optimizing the preference settings based on the evaluation results to generate an optimized environment configuration are as follows:

[0038] S601: Based on the refined preference configuration settings, update the visual and interaction parameters in the VR environment, monitor the impact of changes on user experience in real time, collect user operation data and feedback, match the user's personalized needs, and obtain real-time experience feedback results;

[0039] S602: Based on the real-time experience feedback results, periodically check user satisfaction and configuration effectiveness, collect key user operation data and feedback information, determine optimization directions and adjust VR settings, and obtain user satisfaction data;

[0040] S603: Based on the user satisfaction data, adjust the operation interface and response parameters of the VR environment, monitor the user's real-time feedback and operation performance, continuously optimize preference settings, and generate an optimized environment configuration.

[0041] A VR environment iris recognition personalized preference setting system, wherein the VR environment iris recognition personalized preference setting system is used to execute the above-described VR environment iris recognition personalized preference setting method, the system comprising:

[0042] The iris feature generation module extracts key iris feature points based on user iris information collected in the VR environment, normalizes the feature points, and checks the consistency of the data format to generate an iris feature dataset.

[0043] The user authentication analysis module uses the iris feature dataset to perform user authentication by applying a decision tree algorithm, determining the matching degree between the iris data and known users, identifying the correlation between key iris feature points and user preferences, and dynamically adjusting preference weights to generate user identity and preference information.

[0044] The configuration test adjustment module performs preliminary VR environment settings based on the user's identity and preference information. By simulating user interaction, it tests the applicability of personalized configuration and adjusts the environment configuration parameters based on the test results to obtain an interaction adaptability configuration.

[0045] The interaction feedback analysis module implements the aforementioned interaction adaptation configuration in the VR environment, continuously verifies user identity and permission changes using iris data, monitors user interaction responses and operation data, collects and analyzes user feedback, adjusts preference settings, and obtains user feedback analysis results.

[0046] Based on the user feedback analysis results, the preference prediction and optimization module uses support vector machines to predict the new users' preference settings in the VR environment, adjusts the VR environment settings in real time to match the needs of differentiated users, and refines the preference settings based on periodic evaluation results to generate an optimized environment configuration.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] In this invention, iris data is normalized to provide a basis for identity verification and personalized settings. Decision tree algorithms are used for identity verification, ensuring the accuracy and security of access control. The interface and functions are dynamically adjusted based on the precise user identity. Dynamically adjusting preference weights allows for real-time adjustments to VR environment settings based on user feedback and historical behavior, significantly improving the personalization of the user experience. Support vector machine analysis of user feedback not only predicts and adapts to new users' preferences but also continuously optimizes the user experience, improving interface intuitiveness and operational efficiency. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flowchart of S1 of the present invention;

[0051] Figure 3 This is a detailed flowchart of the S2 process of the present invention;

[0052] Figure 4 This is a detailed flowchart of the S3 process of the present invention;

[0053] Figure 5 This is a detailed flowchart of the S4 process of the present invention;

[0054] Figure 6 This is a detailed flowchart of S5 of the present invention;

[0055] Figure 7 This is a detailed flowchart of S6 of the present invention;

[0056] Figure 8 This is a system flowchart of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0058] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0059] Example 1

[0060] Please see Figure 1 This invention provides a technical solution: a method for setting personalized preferences for iris recognition in a VR environment, comprising the following steps:

[0061] S1: Based on the user iris information collected in the VR environment, extract key iris features, perform normalization processing on the iris feature data, verify the consistency of data format, and generate an iris feature dataset;

[0062] S2: Based on the iris feature dataset, use the decision tree algorithm for identity verification, determine the matching degree between iris data and known users, identify iris feature points associated with user preferences, analyze interaction behavior analysis information and dynamically adjust preference weights to generate user identity and preference information;

[0063] S3: Extract user preference data from user identity and preference information, set the initial configuration of the VR environment, test the applicability of personalized configuration by simulating user interaction, and adjust the environment configuration parameters according to the test results to obtain the interaction adaptability configuration;

[0064] S4: Implement interactive adaptive configuration in the VR environment, verify identity and permission changes through iris data, monitor user interaction responses and operation data, collect user feedback data, adjust preference settings, and generate user feedback analysis results;

[0065] S5: Based on user feedback analysis results, support vector machines are used to predict new users' settings preferences in the VR environment. The prediction parameters are adjusted in combination with the feedback data of new users to optimize the configuration accuracy and generate detailed preference configuration settings.

[0066] S6: Based on detailed preference settings, adjust VR environment settings in real time to match the needs of different users, regularly evaluate user satisfaction and configuration effectiveness, and optimize preference settings based on evaluation results to generate optimized environment configurations.

[0067] The iris feature dataset includes iris texture features, iris color features, and iris morphology features. User identity and preference information includes user identification identifiers, preference weight indices, and behavioral pattern recognition data. Interaction adaptation configuration includes interactive interface layout parameters, response time optimization parameters, and visual feedback elements. Detailed preference configuration settings include preference adjustment thresholds, prediction evaluation results, and user profile segmentation results. Optimized environment configuration includes configuration update cycle, adaptation evaluation criteria, and satisfaction tracking indicators.

[0068] Please see Figure 2 The specific steps for generating an iris feature dataset based on user iris information collected in a VR environment are as follows: Key iris features are extracted, the iris feature data is normalized, and the data format consistency is verified.

[0069] S101: Based on the user's iris information collected in the VR environment, refine the analysis of iris structural feature points, record the coordinates and shape details of each feature point, optimize feature recognizability, and generate an iris feature table;

[0070] In a virtual reality environment, user iris information is collected, and image recognition algorithms are used to analyze iris images and accurately identify different iris structural feature points. The coordinates and shape details of the feature points are accurately recorded and optimized to ensure that the recognizability of the feature points is optimized. Recording the coordinates and shape details of each iris feature point is the core. The feature points are classified and their shapes are refined to enhance the overall recognition capability of the system and generate an iris feature table.

[0071] S102: Based on the iris feature table, perform numerical transformation of feature points to map feature values ​​from the original scale to a unified analysis range, and use linear transformation to adjust the proportion of each feature point to obtain normalized feature data;

[0072] Based on the iris feature table, perform numerical transformation of feature points according to the formula:

[0073]

[0074] Calculate the normalized feature data, where NF represents the normalized feature, FV represents the feature value, and min(FV) and max(FV) represent the minimum and maximum values ​​of the feature value, respectively. The purpose is to map the original feature values ​​to the range of 0 to 1, thereby ensuring that the comparison of different irises is carried out within a unified analytical range. For example, if the feature value FV of a certain iris is 30, the minimum value is 10, and the maximum value is 50, then the normalized feature calculation process is as follows:

[0075]

[0076] This result indicates that the eigenvalue is in the middle of its range.

[0077] S103: Based on normalized feature data, organize and arrange the dataset, connect each iris feature with the corresponding user identifier, and verify the consistency and integrity of each data item to obtain the iris feature dataset.

[0078] In the process of organizing and normalizing the iris feature data and connecting each iris feature with its corresponding user identifier, the first step is data integration. This includes mapping the normalized iris feature data to the user identifier. The data integration process involves verifying the consistency and integrity of the data to ensure that every piece of data in the database is complete and error-free, providing a reliable data source for subsequent data use. The data consistency verification uses a validation algorithm to confirm that the data has not been corrupted or incorrectly entered during the input process. The integrity check ensures that each user's dataset is complete and contains all the necessary feature information. Once completed, this constitutes an iris feature dataset that can be used for further analysis.

[0079] Please see Figure 3 Based on an iris feature dataset, the decision tree algorithm is used for identity verification to determine the matching degree between iris data and known users, identify iris feature points associated with user preferences, analyze interaction behavior analysis information, and dynamically adjust preference weights to generate user identity and preference information. The specific steps are as follows:

[0080] S201: Based on the iris feature dataset, the decision tree algorithm is used to analyze the matching between iris data and existing user data, mark the user data items with the best similarity, and generate preliminary identity verification results;

[0081] Based on the iris feature dataset, the matching between iris data and existing user data is analyzed using a decision tree algorithm, according to the formula:

[0082]

[0083] Calculate the similarity score GS, where xs i ys represents the feature value in the iris feature dataset. in represents the corresponding feature value of the existing user data. s The feature vector represents the number of features and aims to assess the similarity between the iris feature dataset and existing user data by calculating the complement of the mean squared error between them. If there is a feature vector containing three feature values, xs i =[0.2,0.8,0.5], ys i If the similarity is [0.1, 0.85, 0.55], then the similarity calculation process is as follows:

[0084]

[0085] The results show that the iris features of new users are very similar to those of existing user data, with a score close to 1, indicating high similarity, which helps generate preliminary identity verification results.

[0086] S202: Based on the preliminary identity verification results, analyze the correlation between iris features and interaction behavior analysis information, adjust the correlation strength between preferences and behavioral data, reconfigure preference settings, and obtain preference adjustment records;

[0087] Based on the initial identity verification results, the process of analyzing the correlation between iris features and user interaction behavior involves collecting user behavior data and analyzing the correlation between iris features and the data. This analysis helps to understand the mutual influence between user behavioral preferences and their identity characteristics. Furthermore, the correlation strength between preferences and behavioral data is adjusted to better meet the actual needs of users, and preference settings are reconfigured to ensure that each preference adjustment is based on sufficient data analysis. The adjusted preference settings are recorded to form a preference adjustment record, which reflects the dynamic process of user preferences changing over time and behavior.

[0088] S203: Based on preference adjustment records, merge user identity information and preference data, structure the information, verify the integrity of the dataset, and obtain user identity and preference information;

[0089] The process of merging user identity information and preference data involves more than simply storing these two types of data together. It involves structuring the information to make the data more organized and usable, classifying and labeling the data, and then using data integration techniques to ensure that all data items are arranged in a certain structure. Data verification is an important part of this process. By checking the integrity of the dataset, it is ensured that there are no missing or incorrect data items. The integrated dataset contains complete user identity and preference information, making data application and analysis more convenient and accurate, thereby providing users with more personalized services.

[0090] Please see Figure 4The specific steps for extracting user preference data from user identity and preference information, setting the initial configuration of the VR environment, testing the applicability of personalized configurations through simulated user interaction, and adjusting the environment configuration parameters based on the test results to obtain the interaction adaptability configuration are as follows:

[0091] S301: Based on user identity and preference information, extract key preference parameters, adjust volume and brightness control parameters in VR environment settings, and adjust the display position of objects and interaction response delay to obtain preliminary environment configuration;

[0092] Based on user identity and preference information, the process of extracting key preference parameters first involves in-depth analysis of user historical data to determine the volume and brightness settings most frequently used by users in the virtual reality environment. The data is extracted using data mining techniques, and the preference parameters are used to adjust the volume and brightness control parameters of the VR environment. Similarly, the display position and the latency of interactive response are also adjusted according to the user's specific needs to achieve the best user experience. During the adjustment process, the environment settings are dynamically adjusted according to the user's behavior patterns to ensure that each user has a personalized experience in the VR environment. The resulting initial environment configuration is the result optimized for a specific user.

[0093] S302: Based on the initial environment configuration, conduct real-time interactive tests through virtual user scenarios, record user operation time and selection preferences under multiple configurations, identify bottlenecks in user experience, and obtain interactive test feedback information;

[0094] Based on the initial environment configuration, record the user's operation time and selection preferences under multiple configurations, according to the formula:

[0095]

[0096] Calculate the average operation time, where TS represents the average operation time, and ts i n represents the time of the i-th operation. s This represents the total number of operations. Calculating the average operation time can help identify bottlenecks in the user experience. For example, if a user's operation time under a specific configuration is much higher than the average, it indicates that the user interface for that configuration needs optimization. If the user's time for five operations is 30 seconds, 45 seconds, 30 seconds, 35 seconds, and 25 seconds respectively, then the average operation time is calculated as follows:

[0097]

[0098] The results show that the average operation time for users under the configuration is 33 seconds, which can be used as a reference for further optimization.

[0099] S303: Based on interactive test feedback information, refine the interactive elements of the VR environment, including enhancing the speed of visual feedback and adjusting the sensitivity of the operation interface, reconfiguring interactive elements and matching user preference needs, and obtaining interactive adaptability configuration;

[0100] The process of refining the interactive elements of the VR environment based on interactive test feedback involves analyzing user responses to visual feedback speed and the sensitivity of the user interface. Data allows for adjustments to the interactive elements to better match user preferences. In this step, the visual feedback speed of the interactive elements is enhanced by increasing the frame rate of image rendering, while the sensitivity of the user interface is achieved by adjusting the response thresholds of the sensors. The adjusted interactive elements are reconfigured and tested to ensure that all modifications significantly improve the user's interactive experience. Through these adjustments, the VR environment becomes more adaptable to each user's specific needs, thereby improving user satisfaction and overall experience quality. The resulting adaptive configuration is developed based on user feedback and predetermined standards.

[0101] Please see Figure 5 The specific steps for implementing interactive adaptive configuration in a VR environment, verifying identity and permission changes through iris data, monitoring user interaction responses and operation data, collecting user feedback data, adjusting preference settings, and generating user feedback analysis results are as follows:

[0102] S401: Based on interactive adaptive configuration, it verifies user identity and permissions through iris recognition technology, monitors permission changes and access records in real time, optimizes permission log recording, tracks key access events, and obtains permission verification records;

[0103] Based on interactive adaptive configuration, user identity and permissions are verified using iris recognition technology, according to the formula:

[0104]

[0105] Calculate the permission verification score, where RD represents the permission verification score, and wd i The weights representing the permissions features, xd i n represents the corresponding iris feature value. d The formula represents the number of features and is used to integrate the importance of each feature point in the iris recognition process with the actual observed values ​​to derive the verification score. For example, if there are three iris features with weights of 0.5, 0.3, and 0.2, and corresponding feature values ​​of 1, 0, and 1, the calculation process is as follows:

[0106] RD=0.5·1+0.3·0+0.2·1=0.7

[0107] The results show that, after comparing the given iris features with the standard, the user's authorization verification score is 0.7, which can be used for further security policy decisions.

[0108] S402: Based on permission verification records, monitor user interaction patterns and operation data, record user behavior in different interaction scenarios, collect and analyze operation habits and response times, determine interaction preferences, and obtain interaction behavior analysis information;

[0109] Based on permission verification records, the process of monitoring user interaction patterns and operation data involves recording specific user behaviors in different interaction scenarios, including detailed records of user selection preferences and response times in each scenario. Analyzing operation habits involves collecting a large amount of user interaction data and using data analysis techniques to identify common behavioral patterns. Response time analysis helps identify problems in interface design, thereby improving the smoothness of user interaction. After preliminary statistical analysis, the collected data is used to determine user interaction preferences, and the generated interaction behavior analysis information provides a basis for subsequent optimization of the interface and functions.

[0110] S403: Based on interactive behavior analysis information, adjust the user interface layout in the interaction settings, collect real-time user feedback information, and re-match user expectations by comparing feedback with preference analysis results to obtain user feedback analysis results;

[0111] The process of adjusting the user interface layout in the interaction settings based on interactive behavior analysis information aims to optimize the interface by analyzing real-time user feedback. First, the interface layout is optimized according to the user's operating habits and preferences to ensure that the position and size of the interface elements can meet the user's actual needs. The response parameters are adjusted according to the user's feedback to adjust the system's response speed and sensitivity. Through these adjustments, the system better matches the user's operating methods and obtains user feedback analysis results.

[0112] Please see Figure 6 Based on user feedback analysis, support vector machines are used to predict new users' settings preferences in the VR environment. The prediction parameters are then adjusted based on the new users' feedback data to optimize configuration accuracy and generate detailed preference settings. The specific steps are as follows:

[0113] S501: Based on user feedback analysis results, support vector machines are used to extract feedback data from new users, analyze user behavior and preferences, and perform preliminary optimization of VR settings through parameter adjustments to obtain preliminary preference configurations;

[0114] Based on user feedback analysis, a support vector machine (SVM) is used to extract feedback data from new users. This data is then trained using an existing dataset to build a model that effectively identifies and analyzes user behavior and preferences. The SVM separates different categories of data using an optimal hyperplane in a high-dimensional space, thus ensuring that the extracted data reflects the user's true preferences. Through analysis and parameter adjustments, preliminary optimizations are made to the VR settings to ensure that each user can have the best experience in the virtual reality environment. These adjustments include changes to volume, brightness, and interface layout, with the aim of achieving a VR environment configuration that matches user preferences.

[0115] S502: Based on the initial preference configuration, simulate real-time user scenarios, collect operation data and feedback information of new users in the VR environment, determine the applicability of the preference configuration, and obtain the configuration effect evaluation results;

[0116] Based on the initial preference configuration, a simulated real-time user scenario was tested. This involved using actual VR devices in a controlled environment to collect and analyze the operation data of new users. During this process, every action of the new user was recorded, including feedback information on different configurations. The data was subsequently used to analyze the applicability of the preference configurations, identify which configurations were most popular and which needed further adjustment. The purpose of the test was to verify the effect of the initial configuration in a real-world scenario and make necessary adjustments accordingly. The obtained configuration effect evaluation results will directly affect the user acceptance and satisfaction of the VR environment.

[0117] S503: Based on the configuration effect evaluation results, refine the visual and audio settings in the VR environment, repeatedly test and combine user feedback to optimize the settings, and obtain detailed preference configuration settings;

[0118] Based on the configuration effect evaluation results, the visual and audio settings in the VR environment are refined according to the formula:

[0119] SQ=α q v q +β q x q

[0120] Calculate the visual and audio setting scores, where SQ represents the setting score, and v q Represents visual settings parameters, x q Represents audio setting parameters, α q and β q These represent the weighting coefficients for visual and audio, respectively. The formula is used to evaluate the contribution of visual and audio settings to the user experience. Assuming the visual setting parameter is 0.8, the audio setting parameter is 0.6, the visual weight is 0.7, and the audio weight is 0.3, the calculation process is as follows:

[0121] SQ=0.7×0.8+0.3×0.6=0.56+0.18=0.74

[0122] The results reflect the contribution of the current visual and audio settings to the overall user experience, guiding subsequent optimization efforts.

[0123] Please see Figure 7 The specific steps for generating an optimized environment configuration are as follows: Based on detailed preference settings, VR environment settings are adjusted in real time to match the needs of different users. User satisfaction and configuration effectiveness are regularly evaluated, and preference settings are optimized based on the evaluation results.

[0124] S601: Based on detailed preference configuration settings, update the visual and interaction parameters in the VR environment, monitor the impact of changes on user experience in real time, collect user operation data and feedback, match the user's personalized needs, and obtain real-time experience feedback results;

[0125] The process of updating visual and interaction parameters in the VR environment based on detailed preference settings involves a comprehensive evaluation of existing settings, including analyzing user feedback during actual use. Through real-time monitoring, the impact of changes can be tracked, and parameters can be adjusted based on real-time user data. For example, if a user expresses dissatisfaction with certain visual effects, the system will immediately adjust the relevant visual parameters to enhance clarity or adjust color balance. At the same time, the adjustment of interaction parameters is also based on user operation data, such as touch response speed and the ease of use of the interactive interface, aiming to match the user's personalized needs to improve the overall user experience. The real-time experience feedback obtained directly reflects the adjustment effect.

[0126] S602: Based on real-time experience feedback, periodically check user satisfaction and configuration effectiveness, collect key user operation data and feedback information, determine optimization directions and adjust VR settings, and obtain user satisfaction data;

[0127] Based on real-time experience feedback, user satisfaction and configuration effectiveness are periodically checked. This includes regularly collecting key user operation data and feedback information through online questionnaires and logs. The information is analyzed to determine the changing trends of user satisfaction and any problem areas. By analyzing user behavior and feedback patterns, it is possible to quickly identify VR settings that need optimization. After determining the optimization direction, relevant parameters will be adjusted to ensure that users can obtain the best experience.

[0128] S603: Based on user satisfaction data, adjust the operation interface and response parameters of the VR environment, monitor real-time user feedback and operation performance, and continuously optimize preference settings to generate an optimized environment configuration;

[0129] Based on user satisfaction data, adjust the VR environment's user interface and response parameters according to the formula:

[0130] EH = γ h ×(rh i ×wh i )

[0131] The optimization effect on the computational interface and response performance is calculated, where EH represents the score of the optimization effect, and rh... i wh represents the adjustment value of each response parameter. i γ represents the importance weight of each parameter. h This represents the adjustment factor. The formula is used to quantify the specific impact of adjusted interface and response parameters on user experience. For example, if the interface response time is reduced from 100ms to 50ms, and the weight of this parameter is 0.3, and the adjustment factor is set to 1.5, then the calculation process is as follows:

[0132] EH = 1.5 × (50 × 0.3) = 22.5

[0133] The results show that adjustments to the interface and response performance contribute to the overall improvement of the user experience, thus guiding further adjustments and optimizations to the user interface and response parameters of the VR environment.

[0134] Please see Figure 8 A personalized preference setting system for iris recognition in a VR environment, the system includes:

[0135] The iris feature generation module extracts key iris feature points based on user iris information collected in the VR environment, normalizes the feature points, and checks the consistency of the data format to generate an iris feature dataset.

[0136] The user authentication analysis module uses the iris feature dataset and a decision tree algorithm to authenticate users, determine the matching degree between iris data and known users, identify the correlation between key iris feature points and user preferences, dynamically adjust preference weights, and generate user identity and preference information.

[0137] The configuration test adjustment module performs initial VR environment settings based on user identity and preference information. By simulating user interaction, it tests the applicability of personalized configuration and adjusts the environment configuration parameters based on the test results to obtain an interactive adaptability configuration.

[0138] The interactive feedback analysis module implements interactive adaptive configuration in the VR environment, continuously verifies user identity and permission changes using iris data, monitors user interaction responses and operation data, collects and analyzes user feedback, adjusts preference settings, and obtains user feedback analysis results.

[0139] The preference prediction and optimization module uses support vector machines to predict new users' preferences in the VR environment based on user feedback analysis results. It then adjusts the VR environment settings in real time to match the needs of different users and refines the preference settings based on periodic evaluation results to generate an optimized environment configuration.

[0140] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for setting personalized preferences for iris recognition in a VR environment, characterized in that: The following steps are involved: Based on the user iris information collected in the VR environment, key iris features are extracted, iris feature data is normalized, and data format consistency is verified to generate an iris feature data set; Based on the iris feature data set, a decision tree algorithm is used to perform identity authentication, determine the matching degree between the iris data and known users, identify iris feature points associated with user preferences, analyze interactive behavior analysis information and dynamically adjust preference weights to generate user identity and preference information; Extracting user preference data from the user identity and preference information, setting a preliminary configuration of the VR environment, testing the applicability of the personalized configuration by simulating user interaction, and adjusting the environment configuration parameters according to the test results to obtain an interactively adaptive configuration; Implementing the interactive adaptive configuration in the VR environment, verifying identity and authority changes through iris data, monitoring user interaction reactions and operation data, collecting user feedback data, and adjusting preference settings to generate user feedback analysis results; Based on the user feedback analysis results, a support vector machine is used to predict the setting preferences of new users in the VR environment, and the prediction parameters are adjusted in combination with the feedback data of the new users to optimize the configuration accuracy and generate detailed preference configuration settings; According to the detailed preference configuration settings, the VR environment settings are adjusted in real time to match the needs of differentiated users, user satisfaction and configuration effects are regularly evaluated, and the preference settings are optimized based on the evaluation results to generate an optimized environment configuration.

2. The method for setting personalized preferences for iris recognition in a VR environment according to claim 1, characterized in that: The iris feature data set includes iris texture features, iris color features, and iris morphological features; the user identity and preference information includes user identification identifier, preference weight index, and behavior pattern recognition data; the interactive adaptability configuration includes interactive interface layout parameters, response time optimization parameters, and visual feedback elements; the refined preference configuration settings include preference adjustment thresholds, prediction evaluation results, and user portrait segmentation results; the optimized environment configuration includes configuration update cycle, adaptability evaluation standards, and satisfaction tracking indicators.

3. The method for setting personalized preferences for iris recognition in a VR environment according to claim 1, characterized in that: Based on the user iris information collected in the VR environment, key iris features are extracted, iris feature data is normalized, and data format consistency is verified. The specific steps for generating an iris feature data set are as follows: Based on the user iris information collected in the VR environment, the iris structure feature points are refined and analyzed, the coordinates and shape details of each feature point are recorded, the feature recognizability is optimized, and an iris feature table is generated; Based on the iris feature table, perform numerical conversion of feature points, map feature values ​​from the original scale to a unified analysis range, use linear conversion to adjust the scale of each feature point, and obtain normalized feature data; Based on the normalized feature data, the data set is sorted and compiled, each iris feature is connected with the corresponding user identifier, and the consistency and integrity of each data item are verified to obtain the iris feature data set.

4. The method for setting personalized preferences for iris recognition in a VR environment according to claim 1, characterized in that: Based on the iris feature data set, the decision tree algorithm is used to perform identity authentication, determine the matching degree between the iris data and the known user, identify the iris feature points associated with the user preference, analyze the interactive behavior analysis information and dynamically adjust the preference weight, and generate the user identity and preference information in the following steps: Based on the iris feature data set, a decision tree algorithm is used to analyze the matching between the iris data and the stored user data, and the user data items with the best similarity are marked to generate a preliminary identity authentication result; Based on the preliminary identity authentication result, analyzing the correlation between the iris feature and the interactive behavior analysis information, adjusting the correlation strength between the preference and the behavior data, reconfiguring the preference settings, and obtaining the preference adjustment record; Based on the preference adjustment record, the user identity information and preference data are merged, the information is structured, and the integrity of the data set is verified to obtain the user identity and preference information.

5. The method for setting personalized preferences for iris recognition in a VR environment according to claim 4, characterized in that: The decision tree algorithm is used to analyze the matching between iris data and stored user data, according to the formula: Calculate the similarity score GS, where xs i Represents the feature value in the iris feature dataset, ys i Represents the corresponding characteristic value of the stored user data, n s Represents the number of features.

6. The method for setting personalized preferences for iris recognition in a VR environment according to claim 1, characterized in that: The steps of extracting user preference data from the user identity and preference information, setting the initial configuration of the VR environment, testing the applicability of the personalized configuration by simulating user interaction, and adjusting the environment configuration parameters according to the test results to obtain the interactive adaptive configuration are as follows: Based on the user identity and preference information, extract key preference parameters, adjust volume and brightness control parameters in VR environment settings, and adjust the display position and interactive response delay of objects to obtain preliminary environment configuration; Based on the preliminary environment configuration, real-time interaction testing is performed through virtual user scenarios to record the user's operation time and selection preferences under multiple configurations, identify bottlenecks in user experience, and obtain interaction test feedback information; Based on the interaction test feedback information, the interaction elements of the VR environment are refined, including enhancing the visual feedback speed and adjusting the sensitivity of the operation interface, reconfiguring the interaction elements and matching the user's preference requirements, and obtaining an interaction adaptive configuration.

7. The method for setting personalized preferences for iris recognition in a VR environment according to claim 1, characterized in that: The steps of implementing the interactive adaptive configuration in the VR environment, verifying identity and permission changes through iris data, monitoring user interaction reactions and operation data, collecting user feedback data, and adjusting preference settings to generate user feedback analysis results are as follows: Based on the interactive adaptive configuration, the user identity and authority are verified through iris recognition technology, authority changes and access records are monitored in real time, and the authority log records are optimized, key access events are tracked, and authority verification records are obtained; Based on the permission verification records, monitor user interaction patterns and operation data, record user behaviors in different interaction scenarios, collect and analyze operation habits and response times, determine interaction preferences, and obtain interaction behavior analysis information; Based on the interaction behavior analysis information, the user interface layout in the interaction setting is adjusted, the user's real-time feedback information is collected, and the user expectations are re-matched by comparing the feedback with the preference analysis results to obtain the user feedback analysis results.

8. The method for setting personalized preferences for iris recognition in a VR environment according to claim 1, characterized in that: Based on the user feedback analysis results, support vector machines are used to predict the setting preferences of new users in the VR environment, and the prediction parameters are adjusted in combination with the feedback data of new users to optimize the configuration accuracy. The specific steps of generating detailed preference configuration settings are as follows: Based on the user feedback analysis results, a support vector machine is used to extract feedback data of new users, analyze user behaviors and preferences, and preliminarily optimize VR settings through parameter adjustment to obtain a preliminary preference configuration; Based on the preliminary preference configuration, simulate and test real-time user scenarios, collect operation data and feedback information of new users in the VR environment, determine the applicability of the preference configuration, and obtain configuration effect evaluation results; Based on the configuration effect evaluation results, the visual and audio settings in the VR environment are refined, and the settings are optimized through repeated testing and combined with user feedback to obtain refined preferred configuration settings.

9. The method for setting personalized preferences for iris recognition in a VR environment according to claim 1, characterized in that: According to the detailed preference configuration settings, the VR environment settings are adjusted in real time to match the needs of differentiated users, user satisfaction and configuration effects are regularly evaluated, and the preference settings are optimized according to the evaluation results. The specific steps for generating the optimized environment configuration are: According to the detailed preference configuration settings, the visual and interactive parameters in the VR environment are updated, the impact of the changes on the user experience is monitored in real time, user operation data and feedback are collected, and the personalized needs of the user are matched to obtain real-time experience feedback results; Based on the real-time experience feedback results, periodically check user satisfaction and configuration effects, collect key user operation data and feedback information, determine optimization directions and adjust VR settings, and obtain user satisfaction data; Based on the user satisfaction data, the operating interface and response parameters of the VR environment are adjusted, the user's real-time feedback and operating performance are monitored, and the preference settings are continuously optimized to generate an optimized environment configuration.

10. An iris recognition personalized preference setting system in a VR environment, characterized in that: According to the method for setting personalized preferences for iris recognition in a VR environment according to any one of claims 1 to 9, the system comprises: The iris feature generation module extracts key iris feature points based on the user iris information collected in the VR environment, normalizes the feature points, and verifies the consistency of the data format to generate an iris feature data set; The user authentication analysis module applies a decision tree algorithm to perform user identity authentication based on the iris feature data set, determines the matching degree between the iris data and known users, identifies the correlation between key iris feature points and user preferences, and dynamically adjusts the preference weights to generate user identity and preference information; The configuration test adjustment module performs preliminary settings of the VR environment according to the user identity and preference information, tests the applicability of the personalized configuration by simulating user interaction, and adjusts the environment configuration parameters according to the test results to obtain an interactive adaptability configuration; The interactive feedback analysis module implements the interactive adaptive configuration in the VR environment, uses iris data to continuously verify user identity and permission changes, monitors user interactive reactions and operation data, collects and analyzes user feedback, adjusts preference settings, and obtains user feedback analysis results; Based on the user feedback analysis results, the preference prediction optimization module uses a support vector machine to predict the preference settings of new users in the VR environment, makes real-time adjustments to the VR environment settings to match differentiated user needs, and refines the preference settings based on regular evaluation results to generate an optimized environment configuration.

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