User psychological state assessment method and system for meta universe

Abstract

The invention discloses a user psychological state assessment method and system for a meta universe, and particularly relates to the technical field of user psychological state assessment, whether the psychological state of a user has abnormal fluctuation or not is quantified by obtaining virtual scene update data in the meta universe, whether the user is an unstable user or not is judged, and the user psychological state assessment accuracy is improved. And acquiring unstable information of data acquisition, fluctuation information of data transmission and uneven resource allocation calculation information of the user psychological state assessment model to construct an assessment result timeliness monitoring model, identifying and responding to potential problems, ensuring that the timeliness of the assessment result can be continuously monitored, providing basic data for subsequent risk early warning, and improving the risk assessment efficiency. The method comprises the steps of obtaining timeliness monitoring data information, establishing a timeliness monitoring data set, analyzing the timeliness monitoring data set and calculating a moving average value, carrying out risk early warning on the influence depth of a timeliness hidden danger, ensuring that intervention is carried out before a problem occurs or at the initial stage of the problem, and improving the overall performance and the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of user psychological state assessment, and more specifically, to a user psychological state assessment method and system for a metaverse. Background Art

[0002] Metaverse is a digital space that combines virtual reality and augmented reality, in which the psychological state of the user plays a vital role. Evaluating the psychological state of the user in the Metaverse can significantly improve the user experience. The user psychological state evaluation system of the Metaverse collects the user's behavioral data, physiological data, and emotional data in the Metaverse through various devices at the data collection end, builds a user psychological state evaluation model, and evaluates the user's psychological state in the Metaverse in real time. The Metaverse virtual environment control subsystem adjusts the virtual scene in the Metaverse in real time according to the user's psychological state evaluation results.

[0003] The existing user psychological state assessment system of the Metaverse has been running for a long time, and the instability of data collection, the volatility of data transmission, and the uneven distribution of computing resources of the assessment model may all lead to a reduction in the timeliness of the assessment results. For users whose psychological states fluctuate slightly, the reduction in timeliness may have little impact on them. However, for some users whose psychological states fluctuate sharply, the lack of risk warning for reduced timeliness may cause the existing user psychological state assessment system of the Metaverse to be unable to accurately capture the peak points of user psychological changes, resulting in the Metaverse virtual environment control subsystem being unable to timely adjust the virtual scenes in the Metaverse according to the user's psychological state assessment results. 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 user psychological state assessment method and system for a metaverse 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: A method for evaluating the psychological state of a user in a metaverse comprises the following steps: Step S1, obtaining virtual scene update data in the metaverse, and analyzing the obtained virtual scene update data to determine whether the user is an unstable user; Step S2, when the user is in an unstable state, obtain the instability information of data collection, the fluctuation information of data transmission, and the uneven distribution information of computing resources of the user psychological state evaluation model when the Metaverse user psychological state evaluation system is running; Step S3, constructing an evaluation result timeliness monitoring model, analyzing the output timeliness of the user psychological state evaluation model, and generating a timeliness monitoring index; Step S4, comparing the timeliness monitoring index with the timeliness monitoring index threshold, classifying the output timeliness of the user psychological state assessment model, and generating different timeliness signals, including a timeliness hidden danger signal and a timeliness stable signal; Step S5, when a timeliness hidden danger signal is generated, the corresponding timeliness monitoring data information is obtained, a timeliness monitoring data set is established, the timeliness monitoring data in the set is analyzed, and a risk warning is issued for the depth of impact of the timeliness hidden danger.

[0006] In a preferred embodiment, in step S1, the virtual scene update data in the metaverse is obtained, and the obtained virtual scene update data is analyzed to quantify whether there is abnormal fluctuation in the user's psychological state and determine whether the user is an unstable user, as follows: Obtain virtual scene update data in the Metaverse, including but not limited to scene existence duration, scene switching times, and user interaction times with scene objects; Preprocessing the virtual scene update data, including processing missing values, outliers, and data standardization, to obtain the original virtual scene update data time series based on equal intervals; The local regression method is used to extract the trend component from the original virtual scene update data time series. The expression is as follows , where Represents the trend component, Represents the original virtual scene update data, An extraction function representing a local regression method for extracting trend components from the original virtual scene update data time series; Subtract the trend component from the original data and use the local regression method to extract the seasonal component. The expression is as follows , where Indicates seasonal component; The residual component is calculated based on the trend component and seasonal component. The expression is as follows , where represents the residual component; Calculate the standard deviation of the residual component, the expression is as follows , where represents the standard deviation of the residual component, Represents the average value of the residual component, and the expression is as follows , express The residual component at time, is a positive integer; The standard deviation of the residual component is compared with the standard deviation threshold. If the standard deviation of the residual component is greater than the standard deviation threshold, the user is marked as an unstable user; if the standard deviation of the residual component is less than or equal to the standard deviation threshold, the user is marked as a stable user.

[0007] In a preferred embodiment, in step S2, the unstable information of data collection includes the unstable coefficient of collection, the fluctuation information of data transmission includes the transmission fluctuation coefficient, and the uneven resource allocation information calculated by the user psychological state evaluation model includes the uneven resource allocation coefficient. The unstable coefficient of collection, the transmission fluctuation coefficient, and the uneven resource allocation coefficient are marked as , , .

[0008] In a preferred embodiment, by acquiring the instability information of data acquisition and analyzing the instability factors of the data acquisition process, the acquisition instability coefficient is acquired. The acquisition logic of the acquisition instability coefficient is as follows: Acquire unstable data of data collection including but not limited to data missing rate, equipment failure rate, and data collection interval variance, and construct an unstable data set. Each data point in the unstable data set includes three types of indicators, namely, data missing rate, equipment failure rate, and data collection interval variance; Compute the raw distance matrix between data points in an unstable dataset , the expression is as follows , where , , Represents data points The values ​​of data missing rate, equipment failure rate, and data collection interval variance are , , Represents data points The values ​​of missing data rate, equipment failure rate, and data collection interval variance; Input the original distance matrix Algorithm, maps the original distance matrix to three-dimensional space and sets the objective function , the expression is as follows , where represents the distance in the original distance matrix, Represents the distance in three-dimensional space. The position of data points in three-dimensional space is adjusted through iterative optimization until the objective function converges to the minimum value to obtain the final three-dimensional space. Calculate the variance of all data points in three-dimensional space along different axes. The axes of data points in three-dimensional space include x-axis, y-axis, and z-axis. The acquisition instability coefficient is calculated based on the variance of different axes. The expression is as follows , where represents the variance along the x-axis, represents the variance along the y-axis, Indicates the variance along the z-axis.

[0009] In a preferred embodiment, by acquiring the fluctuation information of data transmission and analyzing the fluctuation of the data transmission process, the transmission fluctuation coefficient is acquired. The acquisition logic of the transmission fluctuation coefficient is as follows: Get the original time series data during data transmission , including but not limited to the arrival time and transmission delay of data packets; For the original time series data Perform wavelet decomposition and select Wavelet is used as the wavelet basis function, and the decomposed scale layer is marked as ; The original time series data Decomposed into approximate coefficients and detail coefficients, the decomposition formula is as follows , where Indicates The approximation coefficient of the layer, Indicates Layer detail factor; The energy value is calculated according to the detail coefficient of each layer scale. The expression is as follows , where Indicates Energy values ​​at the layer scale; Calculate the standard deviation of energy values , where Represents the average value of energy value; Calculate the transmission fluctuation coefficient, the expression is as follows .

[0010] In a preferred embodiment, by obtaining the uneven resource allocation information of the user psychological state evaluation model, the uneven resource allocation situation is analyzed to obtain the uneven resource allocation coefficient. The logic for obtaining the uneven resource allocation coefficient is as follows: Calculate the ratio of resource usage of each user to total resource usage by the user psychological state evaluation model in the same time period; Calculating the actual entropy , the expression is as follows , where Indicates The ratio of resource usage for each user to total resource usage, Indicates the total number of users; Calculate the maximum entropy value , the expression is as follows ; Calculate the resource inequality coefficient, the expression is as follows .

[0011] In a preferred embodiment, in step S3, the acquisition instability coefficient, transmission fluctuation coefficient, and resource allocation unevenness coefficient are normalized, and an evaluation result timeliness monitoring model is constructed based on the normalized acquisition instability coefficient, transmission fluctuation coefficient, and resource allocation unevenness coefficient to generate a timeliness monitoring index. The model is based on the following formula , where , , They represent the preset proportional coefficients of the acquisition instability coefficient, the transmission fluctuation coefficient, and the resource allocation uneven coefficient, respectively, and , , Both are greater than 0.

[0012] In a preferred embodiment, in step S4, the timeliness monitoring index is compared with the timeliness monitoring index threshold, and if the timeliness monitoring index is greater than the timeliness monitoring index threshold, a timeliness hidden danger signal is generated; If the timeliness monitoring index is less than or equal to the timeliness monitoring index threshold, a timeliness stability signal is generated.

[0013] In a preferred embodiment, in step S5, when a timeliness hidden danger signal is generated, the corresponding timeliness monitoring data information is obtained, and the timeliness monitoring data information includes the timeliness monitoring index difference ; The calculation expression of the timeliness monitoring index difference is as follows: timeliness monitoring index difference = timeliness monitoring index - timeliness monitoring index threshold; A timeliness monitoring data set is established based on the timeliness monitoring index difference obtained each time a timeliness hidden danger signal is generated. The timeliness monitoring index difference in the set is arranged in the order in which the timeliness hidden danger signal is generated. The timeliness monitoring data in the set is analyzed, and a risk warning is issued for the impact depth of the timeliness hidden danger, as follows: Set a moving window size , calculate the moving average of the timeliness monitoring data set, the expression is as follows , where Indicates the calculation Moving averages, Indicates the timeliness monitoring data set The difference in timeliness monitoring index of the numbers; The moving average is compared with the moving average threshold. When the calculated moving average is greater than the moving average threshold, a risk warning signal is generated; when the calculated moving average is less than or equal to the moving average threshold, no immediate corrective action is required.

[0014] In a preferred embodiment, a user psychological state assessment system for a metaverse is used to implement a user psychological state assessment method for a metaverse as described in any one of claims 1 to 9, characterized in that: it includes a user segmentation module, which is used to obtain virtual scene update data in the metaverse, and analyze the obtained virtual scene update data to determine whether the user is an unstable user; The data generation module, when the user is in an unstable state, obtains the instability information of data collection during the operation of the user psychological state evaluation system of the Metaverse, the fluctuation information of data transmission, and the uneven distribution information of computing resources of the user psychological state evaluation model; Model building module, which builds a timeliness monitoring model for evaluation results, analyzes the timeliness of the output of the user psychological state evaluation model, and generates a timeliness monitoring index; The timeliness classification module compares the timeliness monitoring index with the timeliness monitoring index threshold, classifies the output timeliness of the user psychological state evaluation model, and generates different timeliness signals, including timeliness hidden danger signals and timeliness stable signals; The risk warning module, when a timeliness hidden danger signal is generated, obtains the corresponding timeliness monitoring data information, establishes a timeliness monitoring data set, analyzes the timeliness monitoring data in the set, and issues a risk warning on the depth of impact of the timeliness hidden danger.

[0015] Technical effects and advantages of the present invention: 1. The present invention obtains virtual scene update data in the metaverse, and analyzes the obtained virtual scene update data to quantify whether there are abnormal fluctuations in the user's psychological state, and judge whether the user is an unstable user. When the user is an unstable user, the instability information of the data collected during the operation of the user psychological state evaluation system of the metaverse, the fluctuation information of the data transmission, and the uneven distribution of computing resources of the user psychological state evaluation model are obtained to construct an evaluation result timeliness monitoring model, analyze the output timeliness of the user psychological state evaluation model, compare the timeliness monitoring index with the threshold, generate a timeliness hidden danger signal and a timeliness stable signal, identify and respond to potential problems in a timely manner, ensure the real-time nature of the evaluation results, ensure that the timeliness of the evaluation results can be continuously monitored, and provide basic data for subsequent risk warnings. When a timeliness hidden danger signal is generated, the corresponding timeliness monitoring data information is obtained, and a timeliness monitoring data set is established. Through the analysis of the timeliness monitoring data set and the calculation of the moving average, a risk warning is issued for the depth of the impact of the timeliness hidden danger, ensuring intervention before or at the early stage of the problem, and improving the overall performance and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a schematic diagram of the structure of the method of Example 1 of the present invention; Figure 2 Schematic diagram of the structure of the system of Example 2 of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] Embodiment 1: Figure 1 The present invention provides a user psychological state assessment method for a metaverse, comprising the following steps: step S1, obtaining virtual scene update data in the metaverse, and analyzing the obtained virtual scene update data to determine whether the user is an unstable user; When the user is in the Metaverse space, the Metaverse's user psychological state assessment system will collect the user's behavioral data, physiological data, and emotional data in the Metaverse through various devices on the data collection end, build a user psychological state assessment model, and assess the user's psychological state in the Metaverse in real time. The Metaverse virtual environment control subsystem will adjust the virtual scene in the Metaverse in real time according to the user's psychological state assessment results. The changes in the virtual scene in the Metaverse come from the user's psychological state assessment. Therefore, by obtaining the virtual scene update data in the Metaverse and analyzing the obtained virtual scene update data, it is possible to quantify whether there are abnormal fluctuations in the user's psychological state and determine whether the user is an unstable user, as follows: Obtain virtual scene update data in the Metaverse, including but not limited to scene existence duration, scene switching times, and user interaction times with scene objects; Preprocessing the virtual scene update data, including processing missing values, outliers, and data standardization, to obtain the original virtual scene update data time series based on equal intervals; The local regression method is used to extract the trend component from the original virtual scene update data time series. The expression is as follows , where Represents the trend component, Represents the original virtual scene update data, An extraction function representing a local regression method for extracting trend components from the original virtual scene update data time series; Subtract the trend component from the original data and use the local regression method to extract the seasonal component. The expression is as follows , where Indicates seasonal component; The seasonal component is used to identify the periodic fluctuations of virtual scene updates within the metaverse, thereby quantifying the periodic fluctuations of the user's psychological state; The residual component is calculated based on the trend component and seasonal component. The expression is as follows , where represents the standard deviation of the residual component, Represents the average value of the residual component, and the expression is as follows , express The residual component at time, is a positive integer. The standard deviation of the residual component is compared with the standard deviation threshold. If the standard deviation of the residual component is greater than the standard deviation threshold, it indicates that there are abnormal fluctuations in the update of the virtual scene in the metaverse, which means that there are abnormal fluctuations in the user's psychological state, and the user is marked as an unstable user; if the standard deviation of the residual component is less than or equal to the standard deviation threshold, it indicates that the update of the virtual scene in the metaverse is relatively stable, which means that there is no abnormal fluctuation in the user's psychological state, and the user is marked as a stable user.

[0019] Step S2, when the user is in an unstable state, obtain the instability information of data collection, the fluctuation information of data transmission, and the uneven distribution information of computing resources of the user psychological state evaluation model when the Metaverse user psychological state evaluation system is running; The unstable information of data collection includes the unstable coefficient of data collection, the fluctuation information of data transmission includes the transmission fluctuation coefficient, and the uneven resource allocation information calculated by the user psychological state evaluation model includes the uneven resource allocation coefficient. The unstable coefficient of data collection, the transmission fluctuation coefficient, and the uneven resource allocation coefficient are marked as , , ; The collection instability coefficient is an important indicator used to measure the instability of various devices on the data collection end when collecting user behavior data, physiological data, and emotional data. By analyzing various unstable factors in the data collection process, the reliability and accuracy of the data can be improved, thereby ensuring the accuracy of the user's psychological state assessment and avoiding the user psychological state assessment system of the metaverse spending more time repeatedly processing various unstable factors that appear in the data collection process, resulting in a decrease in the timeliness of the output of the user psychological state assessment model. The larger the collection instability coefficient, the more abnormal situations there are in the data collection process. The specific impacts include: High data missing rate: A high acquisition instability coefficient usually means a high data missing rate. Missing data points require additional filling or processing, which increases the complexity and time of data preprocessing.

[0020] Uneven collection intervals: The unevenness of the collection time intervals will affect the continuity and consistency of the data, further increase the difficulty of data processing, and affect the input quality of the psychological state assessment model.

[0021] High frequency of equipment failure: A high acquisition instability coefficient may reflect frequent failures of the acquisition equipment, which not only affects the continuity of data collection and reduces the timeliness of the output of psychological state assessment results, but may also cause data fragmentation and incompleteness, affecting the accuracy of the assessment results; When the acquisition instability coefficient is large, the Metaverse user psychological state assessment system needs more time for data cleaning and preprocessing, resulting in untimely output of psychological state assessment results. For users whose psychological states fluctuate drastically, this delay may cause the Metaverse user psychological state assessment system to be unable to capture the user's psychological change peak in time, affecting the real-time adjustment of the Metaverse virtual scene; Therefore, by obtaining the unstable information of data collection and analyzing the unstable factors of the data collection process, the acquisition instability coefficient is obtained. The acquisition logic of the acquisition instability coefficient is as follows: Acquire unstable data of data collection including but not limited to data missing rate, equipment failure rate, and data collection interval variance, and construct an unstable data set. Each data point in the unstable data set includes three types of indicators, namely, data missing rate, equipment failure rate, and data collection interval variance; Compute the raw distance matrix between data points in an unstable dataset , the expression is as follows , where , , Represents data points The values ​​of data missing rate, equipment failure rate, and data collection interval variance are , , Represents data points The values ​​of missing data rate, equipment failure rate, and data collection interval variance; Input the original distance matrix Algorithm, maps the original distance matrix to three-dimensional space and sets the objective function , the expression is as follows , where represents the distance in the original distance matrix, Represents the distance in three-dimensional space. The position of data points in three-dimensional space is adjusted through iterative optimization until the objective function converges to the minimum value to obtain the final three-dimensional space. Calculate the variance of all data points in three-dimensional space along different axes. The axes of data points in three-dimensional space include x-axis, y-axis, and z-axis. The acquisition instability coefficient is calculated based on the variance of different axes. The expression is as follows , where represents the variance along the x-axis, represents the variance along the y-axis, represents the variance in the z-axis direction; The transmission fluctuation coefficient is used to measure the degree of fluctuation of the user's behavioral data, physiological data, and emotional data during the data transmission process. By analyzing the various fluctuation factors in the data transmission process, the stability and reliability of data transmission can be improved, and the accuracy and timeliness of the user's psychological state assessment can be ensured. The larger the transmission fluctuation coefficient, the greater the degree of fluctuation during the data transmission process, and the greater the impact on the real-time and integrity of the data, which may lead to a reduction in the timeliness and accuracy of the psychological state assessment, affecting the virtual scenes and user experience in the metaverse. Therefore, reducing the transmission fluctuation coefficient is crucial to improving system performance and user experience; By obtaining the fluctuation information of data transmission and analyzing the fluctuation of the data transmission process, the transmission fluctuation coefficient is obtained. The logic for obtaining the transmission fluctuation coefficient is as follows: Get the original time series data during data transmission , including but not limited to the arrival time and transmission delay of data packets; For the original time series data Perform wavelet decomposition and select Wavelet is used as the wavelet basis function, and the decomposed scale layer is marked as ; The original time series data Decomposed into approximate coefficients and detail coefficients, the decomposition formula is as follows , where Indicates The approximation coefficient of the layer, Indicates Layer detail factor; The energy value is calculated according to the detail coefficient of each layer scale. The expression is as follows , where Indicates Energy values ​​at the layer scale; Calculate the standard deviation of energy values , where Represents the average value of energy value; Calculate the transmission fluctuation coefficient, the expression is as follows The resource allocation inequality coefficient is an indicator used to measure the degree of uneven distribution of computing resources when the user psychological state assessment model predicts the user's psychological state. By analyzing the allocation of computing resources when the user psychological state assessment model predicts the user's psychological state, the utilization efficiency of computing resources can be improved, and the accuracy and timeliness of psychological state assessment can be ensured. By obtaining the resource allocation inequality coefficient, the allocation of computing resources of the user psychological state assessment model can be optimized, the system performance and user experience can be improved, and the accuracy and timeliness of user psychological state assessment can be ensured. The larger the resource allocation inequality coefficient, the lower the computing resource utilization efficiency of the user psychological state assessment model when assessing the user's psychological state, and the worse the timeliness and accuracy of the assessment results, which affects the virtual scene and user experience in the metaverse. Therefore, reducing the resource allocation inequality coefficient is crucial to optimizing system performance and improving user experience.

[0022] By obtaining the uneven resource allocation information of the user psychological state evaluation model, the uneven resource allocation situation is analyzed to obtain the uneven resource allocation coefficient. The logic for obtaining the uneven resource allocation coefficient is as follows: Calculate the ratio of resource usage of each user to total resource usage by the user psychological state evaluation model in the same time period; Calculating the actual entropy , the expression is as follows , where Indicates The ratio of resource usage for each user to total resource usage, Indicates the total number of users; Actual entropy value It is a measure of the uncertainty or disorder of resource allocation. Specifically, the actual entropy value represents the actual unevenness of resource allocation among users. It is calculated by considering the proportion of each user's resource usage to the total resource usage; Calculate the maximum entropy value , the expression is as follows ; Calculate the resource inequality coefficient, the expression is as follows ; Step S3, constructing an evaluation result timeliness monitoring model, analyzing the output timeliness of the user psychological state evaluation model, and generating a timeliness monitoring index; Normalize the acquisition instability coefficient, transmission fluctuation coefficient, and resource allocation unevenness coefficient, and build a timeliness monitoring model for the evaluation results based on the normalized acquisition instability coefficient, transmission fluctuation coefficient, and resource allocation unevenness coefficient to generate a timeliness monitoring index. The model is based on the following formula , where , , They represent the preset proportional coefficients of the acquisition instability coefficient, the transmission fluctuation coefficient, and the resource allocation uneven coefficient, respectively, and , , All are greater than 0; From the above calculation expression, it can be seen that the larger the acquisition instability coefficient, the larger the transmission fluctuation coefficient, and the larger the resource allocation unevenness coefficient, the larger the timeliness monitoring index, indicating that the instability of data acquisition, the fluctuation of data transmission, and the uneven resource allocation have a greater impact on the timeliness of the output of the user psychological state evaluation model. On the contrary, the smaller the acquisition instability coefficient, the smaller the transmission fluctuation coefficient, and the smaller the resource allocation unevenness coefficient, the smaller the timeliness monitoring index, indicating that the instability of data acquisition, the fluctuation of data transmission, and the uneven resource allocation have a smaller impact on the timeliness of the output of the user psychological state evaluation model. Step S4, comparing the timeliness monitoring index with the timeliness monitoring index threshold, classifying the output timeliness of the user psychological state assessment model, and generating different timeliness signals, including a timeliness hidden danger signal and a timeliness stable signal; The timeliness monitoring index is compared with the timeliness monitoring index threshold. If the timeliness monitoring index is greater than the timeliness monitoring index threshold, the timeliness output by the user psychological state assessment model has a reduced risk, and a timeliness hidden danger signal is generated; If the timeliness monitoring index is less than or equal to the timeliness monitoring index threshold, it indicates that the timeliness output by the user's psychological state assessment model is relatively normal, and the timeliness has not been significantly reduced, generating a timeliness stability signal; Step S5, when a timeliness hidden danger signal is generated, the corresponding timeliness monitoring data information is obtained, a timeliness monitoring data set is established, the timeliness monitoring data in the set is analyzed, and a risk warning is issued for the depth of impact of the timeliness hidden danger.

[0023] Timeliness monitoring data information includes timeliness monitoring index difference zsc; The timeliness monitoring index difference refers to the degree of deviation between the timeliness monitoring index and the timeliness monitoring index threshold when a timeliness hidden danger signal is generated; the calculation expression of the timeliness monitoring index difference is as follows: timeliness monitoring index difference = timeliness monitoring index - timeliness monitoring index threshold; A timeliness monitoring data set is established based on the timeliness monitoring index difference obtained each time a timeliness hidden danger signal is generated. The timeliness monitoring index difference in the set is arranged in the order in which the timeliness hidden danger signal is generated. The timeliness monitoring data in the set is analyzed to provide a risk warning for the depth of impact of the timeliness hidden danger. The details are as follows: Set a moving window size , calculate the moving average of the timeliness monitoring data set, the expression is as follows , where Indicates the calculation Moving averages, Indicates the timeliness monitoring data set The difference in timeliness monitoring index of the numbers; Compare the moving average with the moving average threshold. When the calculated moving average is greater than the moving average threshold, it indicates that the impact depth of timeliness risks continues to deepen, and a risk warning signal is generated to warn of potential risks, thereby improving the performance and user experience of the Metaverse User Psychological State Assessment System. When the calculated moving average is less than or equal to the moving average threshold, it indicates that the timeliness of the output of the user psychological state assessment model at this stage is relatively stable, and the impact depth of timeliness risks is within an acceptable range, and no immediate corrective measures are required. The present invention obtains virtual scene update data in a metaverse, and analyzes the obtained virtual scene update data to quantify whether there are abnormal fluctuations in the user's psychological state, and judge whether the user is an unstable user. When the user is an unstable user, the instability information of data collection during the operation of the user psychological state evaluation system of the metaverse, the fluctuation information of data transmission, and the uneven distribution of computing resources of the user psychological state evaluation model are obtained to construct an evaluation result timeliness monitoring model, analyze the output timeliness of the user psychological state evaluation model, compare the timeliness monitoring index with the threshold, generate a timeliness hidden danger signal and a timeliness stable signal, identify and respond to potential problems in a timely manner, ensure the real-time nature of the evaluation results, ensure that the timeliness of the evaluation results can be continuously monitored, and provide basic data for subsequent risk warnings. When a timeliness hidden danger signal is generated, the corresponding timeliness monitoring data information is obtained, and a timeliness monitoring data set is established. Through the analysis of the timeliness monitoring data set and the calculation of the moving average, a risk warning is issued for the impact depth of the timeliness hidden danger, ensuring intervention before or at the early stage of the problem, and improving the overall performance and user experience.

[0024] Embodiment 2: This embodiment is an introduction to a user psychological state assessment system for the Metaverse, such as Figure 2 As shown, it includes a user classification module, which is used to obtain virtual scene update data in the metaverse, and analyze the obtained virtual scene update data to determine whether the user is an unstable user; The data generation module, when the user is in an unstable state, obtains the instability information of data collection during the operation of the user psychological state evaluation system of the Metaverse, the fluctuation information of data transmission, and the uneven distribution information of computing resources of the user psychological state evaluation model; Model building module, which builds a timeliness monitoring model for evaluation results, analyzes the timeliness of the output of the user psychological state evaluation model, and generates a timeliness monitoring index; The timeliness classification module compares the timeliness monitoring index with the timeliness monitoring index threshold, classifies the output timeliness of the user psychological state evaluation model, and generates different timeliness signals, including timeliness hidden danger signals and timeliness stable signals; The risk warning module, when a timeliness hidden danger signal is generated, obtains the corresponding timeliness monitoring data information, establishes a timeliness monitoring data set, analyzes the timeliness monitoring data in the set, and issues a risk warning on the impact depth of the timeliness hidden danger; 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 in the formula are set by technicians in this field according to actual conditions.

[0025] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 device. 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 or wireless (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.

[0026] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0027] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and method described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0028] In several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways.

[0029] 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.

Claims

1. A method for evaluating user psychological state in Metaverse, characterized in that: The method comprises the following steps: Step S1, obtaining virtual scene update data in the metaverse, and analyzing the obtained virtual scene update data to determine whether the user is an unstable user; Step S2, when the user is in an unstable state, obtain the instability information of data collection, the fluctuation information of data transmission, and the uneven distribution information of computing resources of the user psychological state evaluation model when the Metaverse user psychological state evaluation system is running; Step S3, constructing an evaluation result timeliness monitoring model, analyzing the output timeliness of the user psychological state evaluation model, and generating a timeliness monitoring index; Step S4, comparing the timeliness monitoring index with the timeliness monitoring index threshold, classifying the output timeliness of the user psychological state assessment model, and generating different timeliness signals, including a timeliness hidden danger signal and a timeliness stable signal; Step S5, when a timeliness hidden danger signal is generated, the corresponding timeliness monitoring data information is obtained, a timeliness monitoring data set is established, the timeliness monitoring data in the set is analyzed, and a risk warning is issued for the depth of impact of the timeliness hidden danger.

2. A user psychological state assessment method for a metaverse according to claim 1, characterized in that: In step S1, the virtual scene update data in the metaverse is obtained, and the obtained virtual scene update data is analyzed to quantify whether there are abnormal fluctuations in the user's psychological state and determine whether the user is an unstable user, as follows: Obtain virtual scene update data in the Metaverse, including but not limited to scene existence duration, scene switching times, and user interaction times with scene objects; Preprocessing the virtual scene update data, including processing missing values, outliers, and data standardization, to obtain the original virtual scene update data time series based on equal intervals; The local regression method is used to extract the trend component from the original virtual scene update data time series. The expression is as follows , where Represents the trend component, Represents the original virtual scene update data, An extraction function representing a local regression method for extracting trend components from the original virtual scene update data time series; Subtract the trend component from the original data and use the local regression method to extract the seasonal component. The expression is as follows , where Indicates seasonal component; The residual component is calculated based on the trend component and seasonal component. The expression is as follows , where represents the residual component; Calculate the standard deviation of the residual component, the expression is as follows , where represents the standard deviation of the residual component, Represents the average value of the residual component, and the expression is as follows , express The residual component at time, is a positive integer; The standard deviation of the residual component is compared with the standard deviation threshold. If the standard deviation of the residual component is greater than the standard deviation threshold, the user is marked as an unstable user; if the standard deviation of the residual component is less than or equal to the standard deviation threshold, the user is marked as a stable user.

3. A user psychological state assessment method for a metaverse according to claim 2, characterized in that: In step S2, the unstable information of data collection includes the unstable coefficient of data collection, the fluctuation information of data transmission includes the transmission fluctuation coefficient, and the uneven resource allocation information calculated by the user psychological state evaluation model includes the uneven resource allocation coefficient. The unstable coefficient of data collection, the transmission fluctuation coefficient, and the uneven resource allocation coefficient are marked as , , .

4. A user psychological state assessment method for a metaverse according to claim 2, characterized in that: By obtaining the unstable information of data collection and analyzing the unstable factors of the data collection process, the acquisition instability coefficient is obtained. The acquisition logic of the acquisition instability coefficient is as follows: Acquire unstable data of data collection including but not limited to data missing rate, equipment failure rate, and data collection interval variance, and construct an unstable data set. Each data point in the unstable data set includes three types of indicators, namely, data missing rate, equipment failure rate, and data collection interval variance; Compute the raw distance matrix between data points in an unstable dataset , the expression is as follows , where , , Represents data points The values ​​of data missing rate, equipment failure rate, and data collection interval variance are , , Represents data points The values ​​of missing data rate, equipment failure rate, and data collection interval variance; Input the original distance matrix Algorithm, maps the original distance matrix to three-dimensional space and sets the objective function , the expression is as follows , where represents the distance in the original distance matrix, Represents the distance in three-dimensional space. The position of data points in three-dimensional space is adjusted through iterative optimization until the objective function converges to the minimum value to obtain the final three-dimensional space. Calculate the variance of all data points in three-dimensional space along different axes. The axes of data points in three-dimensional space include x-axis, y-axis, and z-axis. The acquisition instability coefficient is calculated based on the variance of different axes. The expression is as follows , where represents the variance along the x-axis, represents the variance along the y-axis, Indicates the variance along the z-axis.

5. A user psychological state assessment method for Metaverse according to claim 2, characterized in that: By obtaining the fluctuation information of data transmission and analyzing the fluctuation of the data transmission process, the transmission fluctuation coefficient is obtained. The logic for obtaining the transmission fluctuation coefficient is as follows: Get the original time series data during data transmission , including but not limited to the arrival time of data packets and transmission delay; For the original time series data Perform wavelet decomposition and select Wavelet is used as the wavelet basis function, and the decomposed scale layer is marked as ; The original time series data Decomposed into approximate coefficients and detail coefficients, the decomposition formula is as follows , where Indicates The approximation coefficient of the layer, Indicates Layer detail factor; The energy value is calculated according to the detail coefficient of each layer scale. The expression is as follows , where Indicates Energy values ​​at the layer scale; Calculate the standard deviation of energy values , where Indicates the average value of energy value; Calculate the transmission fluctuation coefficient, the expression is as follows .

6. A method for evaluating user psychological state in a metaverse according to claim 2, characterized in that: By obtaining the uneven resource allocation information of the user psychological state evaluation model, the uneven resource allocation situation is analyzed to obtain the uneven resource allocation coefficient. The logic for obtaining the uneven resource allocation coefficient is as follows: Calculate the ratio of resource usage of each user to total resource usage by the user psychological state evaluation model in the same time period; Calculating the actual entropy , the expression is as follows , where Indicates The ratio of resource usage for each user to total resource usage, Indicates the total number of users; Calculate the maximum entropy value , the expression is as follows ; Calculate the resource inequality coefficient, the expression is as follows .

7. A method for evaluating user psychological state in a metaverse according to claim 2, characterized in that: In step S3, the acquisition instability coefficient, transmission fluctuation coefficient, and resource allocation unevenness coefficient are normalized, and an evaluation result timeliness monitoring model is constructed based on the normalized acquisition instability coefficient, transmission fluctuation coefficient, and resource allocation unevenness coefficient to generate a timeliness monitoring index. The model is based on the following formula , where , , They represent the preset proportional coefficients of the acquisition instability coefficient, the transmission fluctuation coefficient, and the resource allocation uneven coefficient, respectively, and , , Both are greater than 0.

8. A method for evaluating user psychological state in a metaverse according to claim 7, characterized in that: In step S4, the timeliness monitoring index is compared with the timeliness monitoring index threshold, and if the timeliness monitoring index is greater than the timeliness monitoring index threshold, a timeliness hidden danger signal is generated; If the timeliness monitoring index is less than or equal to the timeliness monitoring index threshold, a timeliness stability signal is generated.

9. A method for evaluating user psychological state in a metaverse according to claim 8, characterized in that: In step S5, when a timeliness hidden danger signal is generated, the corresponding timeliness monitoring data information is obtained, and the timeliness monitoring data information includes the timeliness monitoring index difference ; The calculation expression of the timeliness monitoring index difference is as follows: timeliness monitoring index difference = timeliness monitoring index - timeliness monitoring index threshold; A timeliness monitoring data set is established based on the timeliness monitoring index difference obtained each time a timeliness hidden danger signal is generated. The timeliness monitoring index difference in the set is arranged in the order in which the timeliness hidden danger signal is generated. The timeliness monitoring data in the set is analyzed, and a risk warning is issued for the impact depth of the timeliness hidden danger, as follows: Set a moving window size , calculate the moving average of the timeliness monitoring data set, the expression is as follows , where Indicates the calculation Moving averages, Indicates the timeliness monitoring data set The difference in timeliness monitoring index of the numbers; The moving average is compared with the moving average threshold. When the calculated moving average is greater than the moving average threshold, a risk warning signal is generated; when the calculated moving average is less than or equal to the moving average threshold, no immediate corrective action is required.

10. A user psychological state assessment system for a metaverse, used to implement a user psychological state assessment method for a metaverse as claimed in any one of claims 1 to 9, characterized in that: It includes a user classification module, which is used to obtain virtual scene update data in the metaverse, and analyze the obtained virtual scene update data to determine whether the user is an unstable user; The data generation module, when the user is in an unstable state, obtains the instability information of data collection during the operation of the user psychological state evaluation system of the Metaverse, the fluctuation information of data transmission, and the uneven distribution information of computing resources of the user psychological state evaluation model; Model building module, which builds a timeliness monitoring model for evaluation results, analyzes the timeliness of the output of the user psychological state evaluation model, and generates a timeliness monitoring index; The timeliness classification module compares the timeliness monitoring index with the timeliness monitoring index threshold, classifies the output timeliness of the user psychological state evaluation model, and generates different timeliness signals, including timeliness hidden danger signals and timeliness stable signals; The risk warning module, when a timeliness hidden danger signal is generated, obtains the corresponding timeliness monitoring data information, establishes a timeliness monitoring data set, analyzes the timeliness monitoring data in the set, and issues a risk warning on the depth of impact of the timeliness hidden danger.

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