An AI-enhanced virtual space social interaction engine system

Abstract

The application discloses an AI-enhanced virtual space social interaction engine system, relates to the field of AI enhancement technology, and constructs a multi-level and three-dimensional user portrait network structure, greatly improves the precision and dimension of user feature expression, and through multi-source fusion analysis of path data, physiological feature data and voice chat data, makes the system capable of comprehensively capturing the explicit interest and implicit preference of the user, forming a more rich and accurate user portrait, facilitating subsequent user matching, capable of simultaneously considering positive attraction factors and negative repulsion factors, greatly improving the matching accuracy and user matching experience satisfaction, meanwhile, the application constructs a behavior characteristic layer, makes the system capable of dynamically adjusting matching and interaction strategies according to different social scenes, and based on the adaptive optimization mechanism of user feedback, realizes the continuous optimization of system performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of AI enhancement, in particular to a virtual space social interaction engine system based on AI enhancement. BACKGROUND

[0002] With the rapid development and popularization of virtual reality technology and the concept of metaverse, virtual space has gradually become an important field for people's social interaction. As a core technology platform connecting users and promoting interaction, the performance and experience of the virtual space social system directly affect the social quality and satisfaction of users in the digital world. Therefore, it is necessary to build an intelligent social interaction engine system based on AI enhancement for multi-dimensional user feature capture and accurate matching.

[0003] In the prior art, most virtual space social systems rely on single-dimensional user data collection methods, such as basic personal information or simple behavior tags, which cannot cover multi-level feature recognition of users in virtual environments. Although some existing technologies introduce interest matching algorithms and interaction data analysis, due to the limited data collection dimensions, it is difficult to achieve intelligent judgment of user social preferences and behavior patterns. In complex and variable virtual social scenarios, using only text or voice data for matching is time-consuming, and when facing a large number of users, simple tag matching may result in inefficient matching. At the same time, the existing technology lacks joint collection, analysis and matching linkage capability of multi-modal data such as path behavior, physiological response characteristics and voice emotional characteristics of users in virtual space, lacks recognition mechanism of user aversion factors, and cannot effectively avoid matching that may lead to negative social experience. Moreover, existing systems often need to rely on user active feedback for experience optimization, which has the defects of long feedback delay, insufficient data value mining and low optimization efficiency. In terms of perceiving user emotions and social intentions, the existing technology mainly relies on text analysis or simple emoticons, which cannot capture the potential emotional state and social needs of users, resulting in a significant gap between the matching result and the actual expectation of users. At the same time, it lacks adaptive strategies for different social scenarios, and cannot dynamically adjust the matching mechanism according to the behavior characteristics of users in different virtual environments. SUMMARY

[0004] The purpose of the present application is to provide a virtual space social interaction engine system based on AI enhancement, which solves the problems in the background art.

[0005] To solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0006] A virtual space social interaction engine system based on AI enhancement, comprising: a user portrait construction module, configured to collect path data, physiological feature data and voice chat data of a target user in a virtual space, and construct a portrait network structure of the target user based on the collected data.

[0007] The intelligent interest matching module is used for intelligently matching different users according to the portrait network structure of the target user, and obtaining a matching user group of the target user.

[0008] The user feedback analysis optimization module is used for randomly matching the target user with users in the corresponding matching user group and generating an interest area, and optimizing the portrait network structure of the target user according to the feedback data after matching.

[0009] The present application has the beneficial effect that the present application constructs a multi-level and three-dimensional user portrait network structure, including an interest layer, a behavior aversion layer and a behavior characteristic layer, which greatly improves the precision and dimension of user feature expression compared with the single label portrait in the prior art. Through multi-source fusion analysis of path data, physiological characteristic data and voice chat data, the system can comprehensively capture the explicit interest and implicit preference of the user, form a more rich and accurate user portrait, facilitate subsequent user matching, and realize a two-way consideration matching mechanism by analyzing the interest correlation coefficient and aversion correlation coefficient between users. Compared with the traditional matching method based on common interest, the present application can consider both positive attraction factors and negative repulsion factors, greatly improving the matching accuracy and user matching experience satisfaction. Meanwhile, the present application constructs a behavior characteristic layer through fine analysis of the behavior characteristics and communication methods of users in different areas, so that the system can dynamically adjust the matching and interaction strategy according to different social scenarios, and based on the adaptive optimization mechanism of user feedback, the system can automatically evaluate the satisfaction degree according to the physiological response data after social interaction, and adjust the weight of the interest layer or the fine behavior aversion layer, realizing continuous optimization of system performance. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0011] Figure 1 The figure is a schematic diagram of the system module of the present application. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0013] Referring to Figure 1 As shown in the figure, the application provides an AI-enhanced virtual space social interaction engine system, comprising a user portrait construction module, an intelligent interest matching module and a user feedback analysis optimization module.

[0014] It should be noted that the user portrait construction module is connected to the intelligent interest matching module, and the intelligent interest matching module is connected to the user feedback analysis optimization module.

[0015] The user portrait construction module is configured to collect path data, physiological feature data and voice chat data of a target user in a virtual space, and construct a portrait network structure of the target user based on the collected data.

[0016] In one specific embodiment, the path data, physiological feature data and voice chat data of the target user in the virtual space are collected by an API interface of a VR virtual space device.

[0017] It should be noted that the path data includes spatial coordinates of each region, time points at which each spatial coordinate appears, and the length of stay in each region.

[0018] It should be noted that the physiological feature data includes physiological response data and body feature data.

[0019] It should be further noted that the physiological response data includes blood pressure, skin electrical response, heart rate variability and other physiological response data.

[0020] It should be further noted that the body feature data includes body posture action data points and other body feature data.

[0021] It should be noted that the voice chat data includes text data, text data time points and voice tone.

[0022] In a specific embodiment of the application, the method for constructing the portrait network structure of the target user includes constructing an interest layer of the target user based on the path data and voice chat data of the target user in the virtual space.

[0023] The method for constructing the portrait network structure of the target user further includes constructing a behavior aversion layer of the target user based on the physiological feature data and voice chat data of the target user in the virtual space.

[0024] The method for constructing the portrait network structure of the target user further includes constructing a behavior feature layer of the target user based on the path data, physiological feature data and voice chat data of the target user in the virtual space.

[0025] The interest layer, the behavior aversion layer and the behavior characteristic layer of the target user are taken as the portrait network structure.

[0026] In the embodiment of the application, the interest layer of the target user is constructed, and the specific method is as follows: according to the path data and the voice chat data of the target user in the virtual space, and by using the TF-IDF algorithm, each interest keyword of the target user and the region where each interest keyword appears are extracted.

[0027] It should be noted that the each interest keyword includes the interest keyword of liking playing cards, the interest keyword of loving drinking, and the interest keyword of being enthusiastic about running.

[0028] In one embodiment, the specific extraction method of the each interest keyword of the target user and the region where each interest keyword appears is as follows: according to the existing TF-IDF algorithm, and according to the text data in the voice chat data of the target user in the virtual space, each interest keyword of the target user can be extracted, the occurrence time point of each interest keyword of the target user is obtained according to the text data occurrence time point in the voice chat data of the target user in the virtual space, and the space coordinates where each interest keyword of the target user appears is obtained according to the occurrence time point in the path data of the target user in the virtual space, the coordinate interval of each region in the virtual space is obtained from the local database, if the space coordinates where the certain interest keyword of the target user appears is in the coordinate interval of a certain region, the region is taken as the region where the certain interest keyword of the target user appears, and thus the region where each interest keyword of the target user appears is obtained.

[0029] It should be noted that the local database is used to store the coordinate interval of each region in the virtual space, the characteristic keyword of each interest, the center coordinates of each region in the virtual space, each interest involved by each region in the virtual space, the conventional value of each physiological reaction data of the target user, the physiological characteristic mutation coefficient threshold, the comprehensive matching coefficient threshold, the occurrence proportion threshold, the satisfaction index threshold, and the correlation index threshold.

[0030] According to the each interest keyword of the target user, the first love coefficient of the target user to each interest is analyzed.

[0031] In one embodiment, the first preference coefficient of the target user for each interest is analyzed by obtaining the characteristic keywords of each interest from the local database, comparing the keywords of each interest of the target user with the characteristic keywords of each interest in terms of semantic relevance, obtaining the semantic relevance of the keywords of each interest of the target user with the characteristic keywords of each interest, taking the keyword of each interest of the target user as the target keyword of the interest if the semantic relevance of the keyword of each interest of the target user with the characteristic keyword of the interest is greater than a preset relevance threshold, screening the target keywords of each interest of the target user, and counting the total number of the target keywords of each interest of the target user. wherein n represents the number of each interest, m is a positive integer greater than 2, and the first preference coefficient of the target user for each interest is calculated .

[0032] According to the path data of the target user in the virtual space, the spatial coordinates of each region of the target user in the virtual space are obtained, and the like degree of the target user for each region is analyzed according to the spatial coordinates, the region where each interest keyword of the target user appears is weighted, and the second preference coefficient of the target user for each interest is mapped.

[0033] In one embodiment, the like degree of the target user for each region is analyzed by obtaining the center coordinates of each region in the virtual space from the local database , extracting the staying time of the target user in each region of the virtual space according to the path data of the target user in the virtual space , and calculating the like degree of the target user for each region according to the spatial coordinates of each region of the target user in the virtual space wherein p represents each spatial coordinate, q is a positive integer greater than 2 .

[0034] In one embodiment, the region where each interest keyword of the target user appears is weighted, and the second preference coefficient of the target user for each interest is mapped by obtaining each interest involved in each region in the virtual space from the local database, mapping the target keywords of each interest involved in each region in the virtual space according to the region where each interest keyword of the target user appears and in combination with the target keywords of each interest of the target user, and counting the total number of the target keywords of each interest involved in each region in the virtual space . .

[0035] Based on the first and second preference coefficients of the target user for each interest, an interest layer of the target user is generated.

[0036] In one specific embodiment, the interest layer of the target user is generated by calculating a comprehensive love coefficient of each interest of the target user according to the first love coefficient and the second love coefficient of each interest of the target user , and taking the comprehensive love coefficient of each interest of the target user as the interest layer of the target user.

[0037] In one specific embodiment of the present application, the behavior aversion layer of the target user is constructed by extracting measured values of each physiological reaction data of the target user when communicating with other users in the virtual space according to the physiological feature data of the target user in the virtual space, analyzing each abnormal physiological reaction time point of the target user, and extracting the body feature data and voice chat data of other users at each abnormal physiological reaction time point of the target user.

[0038] In one specific embodiment, the analysis of each abnormal physiological reaction time point of the target user is performed by obtaining the regular value of each physiological reaction data of the target user from the local database , wherein t represents the number of each physiological reaction data, , w is a positive integer greater than 2, the measured value of each physiological reaction data of the target user and other users at each communication time point is extracted according to the measured value of each physiological reaction data of the target user when communicating with other users in the virtual space , wherein r represents each communication time point, , s is a positive integer greater than 2, the physiological feature mutation coefficient of each communication time point of the target user and other users is calculated , the physiological feature mutation coefficient threshold is obtained from the local database, if the physiological feature mutation coefficient of a communication time point of the target user and other users is greater than the physiological feature mutation coefficient threshold, the communication time point is marked as an abnormal physiological reaction time point, thereby obtaining each abnormal physiological reaction time point of the target user.

[0039] According to the body feature data and voice chat data of other users at each abnormal physiological reaction time point of the target user, the behavior data correlation analysis is performed to obtain each aversion action, each aversion tone and each aversion vocabulary of the target user, and the behavior aversion layer of the target user is generated.

[0040] In one specific embodiment, the method for obtaining the aversion actions, the aversion tones and the aversion words of the target user and generating the behavior aversion layer of the target user comprises: extracting the body posture action data points of the other users at the physiological abnormal reaction time points of the target user according to the body posture feature data of the other users at the physiological abnormal reaction time points of the target user, and obtaining the actions of the other users at the physiological abnormal reaction time points of the target user by using the existing action analysis method; classifying the actions, and classifying similar actions into one category and counting the number of the actions of the other users at each type of action of the target user; calculating the occurrence proportion of the actions of the other users at each type of action of the target user; obtaining the occurrence proportion threshold from the local database; and regarding the action of the target user as an aversion action if the occurrence proportion of the action of the target user is greater than the occurrence proportion threshold, so as to obtain the aversion actions of the target user.

[0041] According to the speech chat data of the other users at the physiological abnormal reaction time points of the target user, the text data and the tone data of the other users at the physiological abnormal reaction time points of the target user are extracted, and the aversion actions, the aversion tones and the aversion words of the target user are obtained by using the method for obtaining the aversion actions of the target user, so as to obtain the behavior aversion layer of the target user.

[0042] In the specific embodiment of the application, the method for constructing the behavior feature layer of the target user comprises: mapping the physiological feature data and the speech chat data of the target user in each region of the virtual space according to the path data, the physiological feature data and the speech chat data of the target user in the virtual space.

[0043] The physiological feature data of the target user in each region of the virtual space is analyzed to obtain the behavior feature map of the target user in each region.

[0044] In one specific embodiment, the method for obtaining the behavior feature map of the target user in each region comprises: extracting the body posture action data points of the target user at each monitoring time point in each region of the virtual space according to the physiological feature data of the target user in each region of the virtual space, and obtaining the habitual actions of the target user in each region by using the method for obtaining the aversion actions of the target user according to the data, so as to obtain the behavior feature map of the target user in each region.

[0045] The speech chat data of the target user in each region of the virtual space is analyzed to obtain the communication mode feature set of the target user in each region.

[0046] In one specific embodiment, the target user's communication mode feature set in each area is obtained by extracting the text data and intonation data of the target user at each monitoring time point in each area of the virtual space according to the voice chat data of the target user in each area of the virtual space, and obtaining the usual intonation and usual vocabulary of the target user in each area according to the above data and the method of obtaining each aversion action of the target user.

[0047] The behavior feature map and the communication mode feature set of the target user in each area are unified as data of the behavior feature layer, so as to obtain the behavior feature layer of the target user.

[0048] The intelligent interest matching module is configured to perform intelligent interest matching on different users according to the portrait network structure of the target user, and obtain a matching user group of the target user.

[0049] In the specific embodiment of the present application, the intelligent interest matching is performed to obtain a matching user group of the target user by extracting the interest layer of the target user according to the portrait network structure of the target user, obtaining the interest layer of other users, and analyzing the interest correlation coefficient between the target user and other users.

[0050] According to the portrait network structure of the target user, the behavior aversion layer and the behavior feature layer of the target user are extracted, and the behavior aversion layer and the behavior feature layer of other users are obtained, and the aversion correlation coefficient between the target user and other users is analyzed.

[0051] Based on the interest correlation coefficient and the aversion correlation coefficient between the target user and other users, a comprehensive matching coefficient between the target user and other users is evaluated.

[0052] In one specific embodiment, the comprehensive matching coefficient between the target user and other users is evaluated by calculating the interest correlation coefficient between the target user and other users and the aversion correlation coefficient between the target user and other users to obtain the comprehensive matching coefficient between the target user and other users .

[0053] The comprehensive matching coefficient threshold is obtained from the local database, and based on the comprehensive matching coefficient between the target user and other users, if the comprehensive matching coefficient between the target user and a certain other user is greater than the comprehensive matching coefficient threshold, the other user is divided into a user in the matching user group of the target user, and so on, so as to obtain the matching user group of the target user.

[0054] In the specific embodiment of the present application, the interest correlation coefficient between the target user and other users is analyzed by analyzing the interest of the target user according to the interest layer of the target user.

[0055] In one embodiment, the analysis of the interest weight of the target user is performed by calculating the interest weight of the target user according to the comprehensive interest coefficient of the target user. In one embodiment, the analysis of the interest weight of the other user is performed by calculating the interest weight of the other user according to the interest weight of the target user.

[0056] In one embodiment, the analysis of the interest weight of the other user is performed by calculating the interest weight of the other user according to the interest weight of the target user.

[0057] In one embodiment, the analysis of the interest weight of the other user is performed by calculating the interest weight of the other user according to the interest weight of the target user.

[0058] In one embodiment, the calculation of the correlation index of the interest between the target user and the other user is performed by calculating the correlation index of the interest between the target user and the other user according to the interest weight of the target user and the interest weight of the other user.

[0059] In one embodiment, the calculation of the correlation index of the interest between the target user and the other user is performed by calculating the correlation index of the interest between the target user and the other user according to the interest weight of the target user and the interest weight of the other user. wherein e represents a natural constant.

[0060] In one embodiment, the calculation of the correlation index of the interest between the target user and the other user is performed by calculating the correlation index of the interest between the target user and the other user according to the interest weight of the target user and the interest weight of the other user.

[0061] In one embodiment, the calculation of the correlation index of the interest between the target user and the other user is performed by calculating the correlation index of the interest between the target user and the other user according to the interest weight of the target user and the interest weight of the other user.

[0062] In one embodiment, the analysis of the aversion correlation coefficient between the target user and the other user is performed by performing an aversion action correlation analysis according to the behavior aversion layer and the interest layer of the target user and the behavior characteristic layer and the interest layer of the other user, thereby obtaining a first aversion index between the target user and the other user.

[0063] ​​​In one specific embodiment, the aversion action correlation analysis is performed to obtain the first aversion index of the target user and the other user, and the specific method is as follows: according to the behavior aversion layer of the target user, each aversion action of the target user is extracted, according to the behavior characteristic layer of the other user, each habitual action of the other user in each region is extracted, and according to the interest layer of the target user and the interest layer of the other user, the target interest region between the target user and the other user is evaluated, and each habitual action of the other user in the target interest region is extracted, if a certain aversion action of the target user is consistent with a certain habitual action of the other user in the target interest region, the first aversion index is added by 1, and the first aversion index is initially 0, and the first aversion index of the target user and the other user is obtained in this way.

[0064] In one specific embodiment, the target interest region between the target user and the other user is evaluated, and the specific evaluation method is as follows: a certain other user is randomly selected as a matching user, and a comprehensive love coefficient of the matching user for each interest is extracted, according to each interest involved in each region in the virtual space, a comprehensive love coefficient of the matching user for each interest involved in each region in the virtual space is mapped, a comprehensive love coefficient sum of the matching user for each region is obtained by cumulative statistics , and a comprehensive love coefficient sum of the target user for each region is obtained in the same way , an associated love coefficient of the target user and the matching user for each region is calculated , and the region with the largest associated love coefficient is selected as the target interest region.

[0065] According to the behavior characteristic layer and the interest layer of the target user, and in combination with the behavior aversion layer and the interest layer of the other user, aversion action correlation analysis is performed to obtain the second aversion index of the target user and the other user.

[0066] In one specific embodiment, the aversion action correlation analysis is performed to obtain the second aversion index of the target user and the other user, and the specific method is as follows: according to the method of obtaining the first aversion index of the target user and the other user, the second aversion index of the target user and the other user can be obtained in the same way.

[0067] According to the first aversion index and the second aversion index of the target user and the other user, an aversion association coefficient of the target user and the other user is calculated.

[0068] In one specific embodiment, the aversion association coefficient of the target user and the other user is calculated, and the specific calculation method is as follows: the first aversion index and the second aversion index of the target user and the other user are added, and the aversion association coefficient of the target user and the other user is obtained.

[0069] The user feedback analysis optimization module is configured to randomly match the target user with users in the corresponding matching user group and generate an interest area, and optimize the portrait network structure of the target user according to the matched feedback data.

[0070] In one specific embodiment, the interest area generation operation specifically comprises: obtaining the interest area according to the method of evaluating the target interest area between the target user and other users, and generating the interest area in the virtual space.

[0071] In one specific embodiment, the method of optimizing the portrait network structure of the target user according to the matched feedback data specifically comprises: collecting measured values of physiological response data in the interaction process between the target user and the users in the matching user group, and analyzing the satisfaction index of the target user with respect to the matching user group according to the measured values.

[0072] In one specific embodiment, the method of analyzing the satisfaction index of the target user with respect to the matching user group specifically comprises: calculating the physiological feature mutation coefficients of the target user and the users in the matching user group at each communication time point according to the measured values of the physiological response data in the interaction process between the target user and the users in the matching user group, and calculating the physiological feature mutation coefficients of the target user and the users in the matching user group at each communication time point according to the method of calculating the physiological feature mutation coefficients of the target user and other users at each communication time point, and then performing accumulation statistics to obtain the physiological feature mutation coefficient sum value in the interaction process between the target user and the users in the matching user group, and taking the physiological feature mutation coefficient sum value as the satisfaction index of the target user with respect to the matching user group.

[0073] Based on the satisfaction index of the target user with respect to the matching user group, the interest layer and the behavior aversion layer in the portrait network structure are adjusted correspondingly.

[0074] In one specific embodiment, the method of adjusting the interest layer and the behavior aversion layer in the portrait network structure correspondingly specifically comprises: obtaining a satisfaction index threshold value from a local database, and adjusting the interest layer in the portrait network structure positively if the satisfaction index of the target user with respect to the matching user group is greater than the satisfaction index threshold value, or adjusting the behavior aversion layer in the portrait network structure in a feature refinement manner if the satisfaction index of the target user with respect to the matching user group is less than the satisfaction index threshold value.

[0075] In one specific embodiment, the method of adjusting the interest layer in the portrait network structure positively specifically comprises: extracting each interest involved in the interest area in the virtual space according to each interest involved in each area in the virtual space, mapping the comprehensive love coefficient of the target user with respect to each interest involved in the interest area in the virtual space according to the comprehensive love coefficient of the target user with respect to each interest, and calculating the comprehensive love coefficient improvement ratio of each interest involved in the interest area in the virtual space according to the satisfaction index threshold value G and the satisfaction index H of the target user with respect to the matching user group. The comprehensive love coefficient of each target user of interest related to the interest area in the virtual space is multiplied by the comprehensive love coefficient promotion ratio, so as to be positively reinforced.

[0076] In one specific embodiment, the behavior aversion layer in the portrait network structure is finely adjusted in features, and the specific method is as follows: the behavior aversion layer of the target user is reanalyzed, and the behavior features of the users in the matching user group are added together for analysis.

[0077] The present application constructs a multi-level and three-dimensional user portrait network structure, including an interest layer, a behavior aversion layer and a behavior feature layer, which greatly improves the accuracy and dimension of user feature expression compared with the single label portrait in the prior art. Through multi-source fusion analysis of path data, physiological feature data and voice chat data, the system can comprehensively capture the explicit interest and implicit preference of the user, form a more rich and accurate user portrait, facilitate subsequent user matching, and realize a two-way matching mechanism by analyzing the interest correlation coefficient and aversion correlation coefficient between users. Compared with the traditional matching method based on common interest, the present application can consider both positive attraction factors and negative repulsion factors, greatly improving the accuracy of matching and the satisfaction of user matching experience. Meanwhile, the present application constructs a behavior feature layer by finely analyzing the behavior features and communication methods of users in different areas, so that the system can dynamically adjust the matching and interaction strategy according to different social scenarios, and based on the adaptive optimization mechanism of user feedback, the system can automatically evaluate the satisfaction degree according to the physiological response data after social interaction, and adjust the weight of the interest layer or the fine behavior aversion layer, so as to realize the continuous optimization of system performance.

[0078] The formulas in the specification are dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation. The preset parameters and threshold values in the formula are set by a person skilled in the art according to the actual situation.

[0079] The above content is only an example and description of the concept of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace them, as long as they do not deviate from the concept of the present application or exceed the scope defined by the present application.

Claims

1. An AI-augmented virtual space social interaction engine system, comprising: The method comprises the following steps: The user portrait construction module is used to collect path data, physiological feature data and voice chat data of a target user in a virtual space, and construct a portrait network structure of the target user according to the data, and the specific method is as follows: An interest layer of the target user is constructed according to the path data and voice chat data of the target user in the virtual space; A behavior aversion layer of the target user is constructed according to the physiological feature data and voice chat data of the target user in the virtual space; A behavior feature layer of the target user is constructed according to the path data, physiological feature data and voice chat data of the target user in the virtual space; The interest layer, behavior aversion layer and behavior feature layer of the target user are taken as the portrait network structure; The intelligent interest matching module is used to intelligently match different users according to the portrait network structure of the target user, and obtain a matching user group of the target user, and the specific method is as follows: The interest layer of the target user is extracted according to the portrait network structure of the target user, and the interest layer of other users is obtained, and the interest correlation coefficient of the target user and other users is analyzed; The behavior aversion layer and behavior feature layer of the target user are extracted according to the portrait network structure of the target user, and the behavior aversion layer and behavior feature layer of other users are obtained, and the aversion correlation coefficient of the target user and other users is analyzed; Based on the interest correlation coefficient and aversion correlation coefficient of the target user and other users, the comprehensive matching coefficient of the target user and other users is evaluated; The comprehensive matching coefficient threshold is obtained from a local database, and based on the comprehensive matching coefficient of the target user and other users, if the comprehensive matching coefficient of the target user and a certain other user is greater than the comprehensive matching coefficient threshold, the other user is divided into a user in the matching user group of the target user, and the same operation is performed on other users, so as to obtain the matching user of the target user. The user feedback analysis and optimization module is used to randomly match the target user with the users in the corresponding matching user group and generate an interest area, and optimize the portrait network structure of the target user according to the feedback data after the matching.

2. The AI-augmented virtual space social interaction engine system of claim 1, wherein, The specific method for constructing the interest layer of the target user is as follows: According to the path data and voice chat data of the target user in the virtual space, and using the TF-IDF algorithm, each interest keyword of the target user and the region where each interest keyword appears are extracted; According to each interest keyword of the target user, the first love coefficient of the target user to each interest is analyzed; According to the path data of the target user in the virtual space, each space coordinate of each region in the virtual space of the target user is obtained, and the likeability of the target user to each region is analyzed according to the space coordinates, and the region where each interest keyword of the target user appears is weighted, so as to map the second love coefficient of the target user to each interest. The interest layer of the target user is generated based on the first love coefficient and the second love coefficient of the target user to each interest.

3. The AI-augmented virtual space social interaction engine system of claim 1, wherein, The specific method for constructing the behavior aversion layer of the target user is as follows: According to the physiological characteristic data of the target user in the virtual space, the measured values of the physiological reaction data of the target user in the virtual space when communicating with other users are extracted, the physiological abnormal reaction time points of the target user are analyzed, and the body posture characteristic data and voice chat data of other users at the physiological abnormal reaction time points of the target user are extracted; According to the body posture characteristic data and voice chat data of other users at the physiological abnormal reaction time points of the target user, behavior data correlation analysis is performed to obtain the each aversion action, each aversion tone and each aversion vocabulary of the target user, and generate the behavior aversion layer of the target user.

4. The AI-augmented virtual space social interaction engine system of claim 2, wherein, The behavior characteristic layer of the target user is constructed, and the specific method is: According to the path data, physiological characteristic data and voice chat data of the target user in the virtual space, the physiological characteristic data and voice chat data of the target user in each region of the virtual space are mapped; The behavior analysis is performed on the physiological characteristic data of the target user in each region of the virtual space to obtain the behavior characteristic map of the target user in each region; The voice communication characteristic analysis is performed on the voice chat data of the target user in each region of the virtual space to obtain the communication mode characteristic set of the target user in each region; The behavior characteristic map and the communication mode characteristic set of the target user in each region are unified as the data of the behavior characteristic layer, so as to obtain the behavior characteristic layer of the target user.

5. The AI-augmented virtual space social interaction engine system of claim 1, wherein, The interest correlation coefficient between the target user and other users is analyzed, and the specific analysis method is: According to the interest layer of the target user, the interest weight of the target user for each interest is analyzed; According to the interest layer of other users, the interest weight of other users for each interest is analyzed; Based on the interest weight of the target user for each interest, the interest weight of other users for each interest is combined to calculate the correlation index of each interest between the target user and other users; The correlation index threshold value is obtained from the local database, if the correlation index of the target user and other users for a certain interest is greater than the correlation index threshold value, the interest is marked as a correlation interest, so as to obtain each correlation interest between the target user and other users, extract the correlation index between the target user and other users for each correlation interest, and calculate the interest correlation coefficient between the target user and other users according to the correlation index.

6. The AI-augmented virtual space social interaction engine system of claim 1, wherein, The aversion correlation coefficient between the target user and other users is analyzed, and the specific analysis method is: According to the behavior aversion layer and the interest layer of the target user, and combining the behavior characteristic layer and the interest layer of other users, the aversion action correlation analysis is performed, so as to obtain the first aversion index between the target user and other users; According to the behavior characteristic layer and the interest layer of the target user, and combining the behavior aversion layer and the interest layer of other users, the aversion action correlation analysis is performed, so as to obtain the second aversion index between the target user and other users; According to the first aversion index and the second aversion index between the target user and other users, the aversion correlation coefficient between the target user and other users is calculated.

7. The AI-augmented virtual space social interaction engine system of claim 1, wherein, According to the matched feedback data, the portrait network structure of the target user is optimized, and the specific method is: The measured values of the physiological reaction data of the target user and the users in the matching user group in the interaction process are collected, and the satisfaction index of the target user to the matching user group is analyzed according to the measured values. Based on the satisfaction index of the target user to the matching user group, the interest layer and the behavior aversion layer in the portrait network structure are adjusted correspondingly.

8. The AI-augmented virtual space social interaction engine system of claim 7, wherein, The adjustment of the interest layer and the behavior aversion layer in the portrait network structure is specifically as follows: The satisfaction index threshold is obtained from the local database, and according to the satisfaction index of the target user to the matching user group, if the satisfaction index of the target user to the matching user group is greater than the satisfaction index threshold, the interest layer in the portrait network structure is positively reinforced, otherwise, the feature refinement adjustment is performed on the behavior aversion layer in the portrait network structure.

Images