Enterprise role adaptive cooperative control method and system based on element universe multi-dimensional data

Abstract

The invention discloses an enterprise role adaptive cooperative control method and system based on meta universe multi-dimensional data, and the method comprises the steps: determining a cultural adaptation coefficient between enterprise roles; constructing a culture cognition middle layer composed of a plurality of virtual buffer areas; target virtual buffer area combinations are determined, dynamic connection channels are established among the target virtual buffer area combinations, and a cultural cognition transmission network is formed; cultural resonance detection points are set; when cultural difference fluctuation is detected, calling a culture filtering rule for intervention to obtain corrected interaction data; dividing role activity areas in the meta-cosmic space according to culture feature distribution in the correction interaction data, and setting culture buffer zones at junctions of areas where different culture features are distributed; and on the basis of the distribution state of the culture buffer zone, generating an optimal interaction path and a coordination time sequence which are in one-to-one correspondence for each enterprise role, and sending the optimal interaction path and the coordination time sequence to the corresponding enterprise role. According to the invention, the enterprise cooperation efficiency in the universe environment is improved.

Description

Technical Field

[0001] This application belongs to the field of input devices or input and output combined devices for interaction between users and computers, and particularly relates to a method and system for enterprise role adaptive collaborative control based on metaverse multi-dimensional data. Background Art

[0002] With the rapid development of metaverse technology, collaborative office work and business operations of enterprises in the metaverse environment have become increasingly important. Especially in the real-time digital twin exhibition hall scenarios of multinational enterprises, different roles such as product presentation experts, technical support personnel, and business representatives need to simultaneously process multi-dimensional data such as product demonstration data, customer interaction information, and business negotiation parameters in the metaverse space. However, due to the complex data dimensions and the lack of an effective role positioning mechanism, the collaborative efficiency of enterprises in the metaverse environment is low, and it is difficult to achieve precise task allocation and resource scheduling.

[0003] In related technologies, a neural network model can be established to analyze the behavioral characteristics of users in the metaverse, and combined with enterprise organizational structure information, the identification and classification of different roles can be realized. This technology can solve the role positioning problem of enterprises in the metaverse environment to a certain extent and improve the collaborative efficiency.

[0004] However, in the process of digital exhibition hall interaction across multiple time zones and cultures, when customer groups with different cultural backgrounds are online at the same time, due to the lack of a quantitative modeling mechanism for role cultural cognitive differences, it is difficult to adjust the expression methods and interaction strategies of each role in the metaverse environment, which is likely to cause cultural cognitive biases and further reduce the enterprise collaborative efficiency in the metaverse environment. Summary of the Invention

[0005] This application provides a method and system for enterprise role adaptive collaborative control based on metaverse multi-dimensional data, which is used to improve the enterprise collaborative efficiency in the metaverse environment.

[0006] In the first aspect, this application provides a method for enterprise role adaptive collaborative control based on metaverse multi-dimensional data, which collects the interaction parameters between enterprise roles in the metaverse environment and determines the cultural adaptation coefficients between enterprise roles according to the interaction parameters; Build a cultural cognitive intermediate layer composed of multiple virtual buffers in the metaverse space, and set different cultural filtering rules in each virtual buffer; Determine the target virtual buffer combination based on the cultural adaptation coefficients, and establish a dynamic connection channel between the target virtual buffer combinations to form a cultural cognitive transmission network; Set cultural resonance detection points on each connection channel of the cultural cognitive transmission network; When cultural difference fluctuations are detected at the cultural resonance detection point, call the cultural filtering rules of the virtual buffer adjacent to the combined target virtual buffer for intervention to obtain corrected interaction data; Divide the role activity areas in the metaverse space according to the cultural feature distribution in the corrected interaction data, and set cultural buffer zones at the junctions of areas with different cultural feature distributions; Based on the distribution status of the cultural buffer zones, generate a one-to-one corresponding optimal interaction path and collaboration time sequence for each enterprise role, and send the optimal interaction path and collaboration time sequence to the corresponding enterprise role.

[0007] By adopting the above technical solutions, the cultural adaptation coefficient is determined by collecting the interaction parameters between enterprise roles in the metaverse environment, and a cultural cognition intermediate layer is established by setting up multiple virtual buffers with different cultural filtering rules, which can realize the accurate identification and classification of interaction behaviors under different cultural backgrounds. On this basis, a cultural cognition transmission network is constructed by establishing dynamic connection channels, and cultural resonance detection points are set for real-time monitoring, so as to timely detect interaction barriers caused by cultural differences. When cultural difference fluctuations are detected, the system will automatically call the cultural filtering rules of adjacent virtual buffers for intervention and correction, effectively reducing the negative impact of cultural differences on the interaction quality. By dividing the role activity areas in the metaverse space and setting up cultural buffer zones, combined with the generated optimal interaction path and collaboration time sequence, enterprise roles can achieve smooth cross-cultural interaction while maintaining their respective cultural characteristics, improving the collaboration efficiency and the quality of interaction experience in a multicultural background.

[0008] Combined with some embodiments of the first aspect, in some embodiments, determining the cultural adaptation coefficient between each enterprise role according to the interaction parameters specifically includes: The interaction parameters include the interaction delay time, speech pause frequency and semantic correlation degree between each enterprise role; Perform normalization processing on the interaction parameters to obtain standardized interaction indicators; Calculate the cultural adaptation benchmark value according to the standardized interaction indicators; Perform time-series accumulation on the cultural adaptation benchmark value to obtain the cultural adaptation accumulation value; Generate a cultural adaptation curve based on the cultural adaptation accumulation value, and extract the characteristic points of the cultural adaptation curve; Calculate the cultural adaptation coefficient according to the distribution law of the characteristic points.

[0009] By adopting the above technical solutions, by analyzing specific interaction parameters such as interaction delay time, speech pause frequency, and semantic relevance, normalizing these data to obtain standardized interaction indicators, and then calculating and cumulating the cultural adaptation benchmark value over time, finally obtaining the cultural adaptation coefficient through the distribution law of the characteristic points of the cultural adaptation curve. By quantitatively analyzing the subtle features in the interaction process, the system can accurately capture the differences in interaction patterns under different cultural backgrounds, thereby accurately measuring the cultural adaptation level. This method for calculating the cultural adaptation coefficient based on multi-dimensional interaction parameters improves the accuracy of evaluating cultural fit in cross-cultural interactions.

[0010] Combined with some embodiments of the first aspect, in some embodiments, determining the target virtual buffer combination based on the cultural adaptation coefficient specifically includes: Calculating the matching degree between the cultural filtering rules of each virtual buffer and the cultural adaptation coefficient; Regarding multiple virtual buffers with a matching degree higher than the preset matching degree threshold as candidate buffers; Performing combined optimization on multiple candidate buffers to obtain an initial virtual buffer combination; Performing conflict detection on the initial virtual buffer combination, and deleting the virtual buffer combinations with conflict relationships to obtain the target virtual buffer combination.

[0011] By adopting the above technical solutions, by calculating the matching degree between the cultural filtering rules of the virtual buffer and the cultural adaptation coefficient, selecting candidate buffers with a matching degree higher than the preset threshold for combined optimization, and screening out the final target virtual buffer combination through conflict detection. This multi-level screening and optimization mechanism ensures that the selected virtual buffer combination can adapt to the current cultural difference situation to the greatest extent. By performing combined optimization and conflict detection on the candidate buffers, the system avoids possible mutual interference between cultural filtering rules, improves the coherence and coordination of the cultural reconciliation process, enhances the accuracy and adaptability of cultural difference reconciliation, and enables the cultural cognitive middle layer to better serve the cross-cultural interaction process.

[0012] Combined with some embodiments of the first aspect, in some embodiments, after sending the optimal interaction path and collaborative time sequence to the corresponding enterprise roles, the method further includes: Constructing a role interaction intelligent perception layer in the metaverse space to collect the emotional states, cognitive levels, and interaction willingness of each enterprise role; Calculating the emotional state matching degree, cognitive level compatibility, and interaction willingness synchronization between different enterprise roles to obtain the overall matching intensity; When the overall matching intensity is lower than the preset threshold for multiple consecutive preset periods, triggering an interaction enhancement mechanism; Set multiple emotion regulation points in the cultural buffer zone based on the emotional state matching degree, and each emotion regulation point has different emotion reconciliation rules; Arrange cognitive assistance sites on the optimal interaction path according to the cognitive level compatibility; Optimize the rhythm density of the collaborative time sequence according to the interaction willingness synchronization to obtain the optimized collaborative time sequence; Send the interaction enhancement solution including emotion regulation points, cognitive assistance sites, and the optimized collaborative time sequence to each enterprise role.

[0013] By adopting the above technical solution, by constructing an intelligent perception layer for role interaction, the emotional state, cognitive level, and interaction willingness of enterprise roles are collected in real time, and the matching strength between these factors is calculated. When the system detects that the overall matching strength is continuously low, it will automatically trigger the interaction enhancement mechanism, improve the interaction quality by setting emotion regulation points in the cultural buffer zone, arranging cognitive assistance sites on the interaction path, and optimizing the rhythm density of the collaborative time sequence. Considering multiple dimensions such as emotion, cognition, and willingness, interaction barriers are eliminated through dynamic adjustment and auxiliary means. The system can adaptively adjust the enhancement strategy according to real-time monitoring data, enabling enterprise roles to obtain better emotional experiences and cognitive understandings during cross-cultural interactions, thereby improving the stability and sustainability of the cross-cultural collaboration process.

[0014] Combined with some embodiments of the first aspect, in some embodiments, an interaction evaluation function is used to calculate the emotional state matching degree, cognitive level compatibility, and interaction willingness synchronization between different enterprise roles to obtain the overall matching strength, specifically including: Collect the physiological sensing data and behavioral data of each enterprise role, and construct an emotional fluctuation curve based on the physiological sensing data and behavioral data; Calculate the correlation of the emotional fluctuation curve in different time windows to obtain the emotional state matching degree; Construct a cognitive feature matrix for each enterprise role, and the cognitive feature matrix includes professional knowledge, cultural understanding, and interaction experience; Calculate the difference degree of the cognitive feature matrix to obtain the cognitive level compatibility index; Extract the interaction behavior time sequence data of each enterprise role, and the interaction behavior time sequence data includes interaction frequency and response time; Calculate the synchronization degree of the interaction willingness based on the interaction behavior time sequence data; Perform a weighted sum of the emotional state matching degree, cognitive level compatibility index, and synchronization degree to obtain the overall matching strength.

[0015] By adopting the above technical solution, by collecting the physiological sensing data and behavioral data of enterprise roles to construct an emotional fluctuation curve, calculating the correlation of different time windows to obtain the emotional state matching degree, it can accurately reflect the emotional fit degree between enterprise roles. Combining the professional knowledge, cultural understanding and interaction experience in the cognitive feature matrix to calculate the difference degree to obtain the cognitive level compatibility index, which can quantitatively evaluate the cognitive gap between enterprise roles. By analyzing the interaction frequency and response time in the interaction behavior time series data to calculate the synchronization degree of interaction willingness, it can objectively reflect the enthusiasm of enterprise roles to participate in cooperation. Weighted summation of the indicators in these three dimensions to obtain the overall matching strength enables the system to timely discover potential cooperation obstacles between enterprise roles, improves the accuracy and reliability of interaction evaluation, and helps the system maintain a continuous and stable cooperation relationship between enterprise roles.

[0016] Combined with some embodiments of the first aspect, in some embodiments, the interaction enhancement mechanism is as follows: Dynamically adjust the number and distribution positions of emotional regulation points in the cultural buffer zone according to the emotional state matching degree; Set emotional reconciliation rules based on the distribution positions of emotional regulation points; Determine the layout density of cognitive assistance sites on the optimal interaction path according to the cognitive level compatibility; Set the information presentation mode of cognitive assistance sites according to the cognitive differences of each enterprise role; Calculate the optimal rhythm density of the cooperation time sequence based on the interaction willingness synchronization; Form an interaction enhancement plan by combining the emotional reconciliation rules, information presentation mode and optimal rhythm density; Send the interaction enhancement plan to each enterprise role.

[0017] By adopting the above technical solution, the interaction enhancement mechanism can specifically adjust the emotional states between enterprise roles by dynamically adjusting the number and distribution positions of emotional regulation points in the cultural buffer zone and setting corresponding emotional reconciliation rules. Determining the layout density of cognitive assistance sites on the optimal interaction path according to the cognitive level compatibility and setting the information presentation mode based on cognitive differences can narrow the cognitive gap between enterprise roles. Optimizing the interaction rhythm by calculating the optimal rhythm density of the cooperation time sequence makes the cooperation between enterprise roles smoother and more natural. This adaptive interaction enhancement plan can promote effective communication and in-depth understanding between roles while maintaining the personalized characteristics of enterprise roles, improve the quality and efficiency of cross-cultural cooperation in the metaverse environment, and reduce the cooperation costs and risks caused by cultural differences.

[0018] Combined with some embodiments of the first aspect, in some embodiments, divide the role activity areas in the metaverse space according to the cultural feature distribution in the calibrated interaction data, specifically including: Extract the cultural features in the calibrated interaction data; Analyze the distribution law of cultural features in the metaverse space; Determine the position and scope of the cultural buffer zone according to the distribution law; Divide different role activity areas based on cultural features in the area outside the cultural buffer zone.

[0019] By adopting the above technical solution, by extracting the cultural features in the calibrated interaction data and analyzing their distribution law in the metaverse space, the system can scientifically determine the position and scope of the cultural buffer zone, reasonably divide different role activity areas in the area outside the cultural buffer zone, enable enterprise roles to carry out activities in areas suitable for their own cultural characteristics, and at the same time relieve the conflicts between different cultures through the cultural buffer zone. This spatial layout not only ensures that enterprise roles can interact in a comfortable cultural environment, but also provides a buffer space for cross-cultural communication, reduces the negative impact of cultural differences on the collaboration effect, and improves the harmony degree of the coexistence of diverse cultures in the metaverse environment.

[0020] In a second aspect, an embodiment of the present application provides an enterprise role adaptive collaborative control system based on metaverse multi-dimensional data. The enterprise role adaptive collaborative control system based on metaverse multi-dimensional data includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code. The computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, which when running on the system, enable the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, which when running on the system, enables the system to execute the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application provides a method for enterprise role adaptive collaborative control based on metaverse multi-dimensional data. By collecting the interaction parameters between enterprise roles in the metaverse environment, the cultural adaptation coefficient is determined, and multiple virtual buffers with different cultural filtering rules are established to form a cultural cognition intermediate layer, which can achieve accurate identification and classification of interaction behaviors under different cultural backgrounds. On this basis, a cultural cognition transmission network is constructed by establishing dynamic connection channels, and cultural resonance detection points are set for real-time monitoring, which can timely detect the interaction obstacles caused by cultural differences. When cultural difference fluctuations are detected, the system will automatically call the cultural filtering rules of adjacent virtual buffers for intervention and correction, effectively reducing the negative impact of cultural differences on the interaction quality. By dividing the role activity areas in the metaverse space and setting cultural buffer zones, combined with the generated optimal interaction path and collaboration time sequence, enterprise roles can achieve smooth cross-cultural interaction while maintaining their respective cultural characteristics, improving the collaboration efficiency and interaction experience quality in a multi-cultural background.

[0024] 2. This application provides a method for enterprise role adaptive collaborative control based on metaverse multi-dimensional data. By constructing an intelligent perception layer for role interaction, the emotional state, cognitive level, and interaction willingness of enterprise roles are collected in real-time, and the matching intensity between these factors is calculated. When the system detects that the overall matching intensity remains low, the interaction enhancement mechanism will be automatically triggered. By setting emotional regulation points in the cultural buffer zone, arranging cognitive assistance sites on the interaction path, and optimizing the rhythm density of the collaboration time sequence, the interaction quality can be improved. Considering multiple dimensions such as emotion, cognition, and willingness, interaction obstacles are eliminated through dynamic adjustment and auxiliary means. The system can adaptively adjust the enhancement strategy according to real-time monitoring data, enabling enterprise roles to obtain better emotional experiences and cognitive understandings during cross-cultural interaction, thereby improving the stability and sustainability of the cross-cultural collaboration process.

[0025] 3. This application provides a method for enterprise role adaptive collaborative control based on metaverse multi-dimensional data. The interaction enhancement mechanism can specifically regulate the emotional states between enterprise roles by dynamically adjusting the number and distribution positions of emotional regulation points in the cultural buffer zone and setting corresponding emotional reconciliation rules. The layout density of cognitive assistance sites is determined according to the cognitive level compatibility on the optimal interaction path, and the information presentation method is set based on cognitive differences, which can narrow the cognitive gap between enterprise roles. By calculating the optimal rhythm density of the collaboration time sequence to optimize the interaction rhythm, the collaboration of enterprise roles becomes more smooth and natural. This adaptive interaction enhancement scheme can promote effective communication and in-depth understanding between roles while maintaining the personalized characteristics of enterprise roles, improving the quality and efficiency of cross-cultural collaboration in the metaverse environment, and reducing the collaboration costs and risks caused by cultural differences. Description of the Drawings

[0026] Figure 1 It is a schematic flowchart of a method for enterprise role adaptive collaborative control based on metaverse multi-dimensional data in an embodiment of the present application.

[0027] Figure 2 It is another schematic flowchart of a method for enterprise role adaptive collaborative control based on metaverse multi-dimensional data in an embodiment of the present application.

[0028] Figure 3 It is a schematic diagram of the physical device structure of a system for enterprise role adaptive collaborative control based on metaverse multi-dimensional data provided in an embodiment of the present application. Detailed implementation manners

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term " / and / " used in the present application refers to any and all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as implying or indicating relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0031] Next, an embodiment is used in combination with Figure 1 to describe a method for enterprise role adaptive collaborative control based on metaverse multi-dimensional data in an embodiment of the present application: Please refer to Figure 1 which is a schematic flowchart of a method for enterprise role adaptive collaborative control based on metaverse multi-dimensional data in an embodiment of the present application.

[0032] S101. Collect the interaction parameters between enterprise roles in the metaverse environment, and determine the cultural adaptation coefficients between enterprise roles according to the interaction parameters; The system collects the interaction parameters among various enterprise roles in the metaverse environment, and determines the cultural adaptation coefficients among various enterprise roles according to the interaction parameters. Specifically, the interaction parameters include the interaction delay time, speech pause frequency, and semantic correlation degree among various enterprise roles; the interaction parameters are normalized to obtain standardized interaction indicators; the cultural adaptation benchmark value is calculated according to the standardized interaction indicators; the cultural adaptation benchmark value is cumulated over time to obtain the cultural adaptation cumulative value; a cultural adaptation curve is generated based on the cultural adaptation cumulative value, and the characteristic points of the cultural adaptation curve are extracted; the cultural adaptation coefficient is calculated according to the distribution law of the characteristic points.

[0033] In this step, the system collects the interaction parameters generated during the interaction among various enterprise roles in the metaverse environment. The interaction parameters may include, but are not limited to, indicators such as interaction delay time, speech pause frequency, and semantic correlation degree that can reflect the cultural differences among enterprise roles. The system can collect these interaction parameters in real time through various means such as sensors and monitoring devices set in the metaverse environment.

[0034] After obtaining the interaction parameters, the system normalizes these parameters to convert them into standardized interaction indicators for subsequent analysis and calculation. Then, the system calculates the cultural adaptation benchmark value according to the standardized interaction indicators, and this benchmark value reflects the initial cultural adaptation degree among enterprise roles. Next, the system cumulates the cultural adaptation benchmark value over time to obtain the dynamically changing cultural adaptation cumulative value. Based on the cultural adaptation cumulative value, the system generates a cultural adaptation curve and extracts the characteristic points of the curve. By analyzing the distribution law of the characteristic points, the system finally calculates the cultural adaptation coefficient that reflects the cultural adaptation degree among enterprise roles.

[0035] In this step, when calculating the cultural adaptation coefficient, the system may encounter the situation of a large number of enterprise roles and huge amounts of interaction data, resulting in a sharp increase in the calculation complexity. To address this issue, the system can introduce a distributed computing framework, allocate the calculation tasks to multiple nodes for parallel processing, thereby significantly improving the calculation efficiency of the system and ensuring that the cultural adaptation coefficient can be updated in a timely and accurate manner.

[0036] S102. Construct a cultural cognition intermediate layer composed of multiple virtual buffers in the metaverse space; Construct a cultural cognition intermediate layer composed of multiple virtual buffers in the metaverse space, and different cultural filtering rules are set in each virtual buffer.

[0037] In this step, the system constructs a cultural cognition intermediate layer composed of multiple virtual buffers in the metaverse space. The virtual buffers can be virtual regions divided according to different cultural characteristics, and each virtual buffer is set with cultural filtering rules matching the cultural characteristics. The system can dynamically adjust the number and boundaries of the virtual buffers based on factors such as the cultural attributes and interaction habits of the enterprise roles.

[0038] When constructing the cultural cognition intermediate layer, the system can adopt a space division algorithm to adaptively divide the virtual buffers according to the spatial characteristics of the metaverse environment and the distribution of enterprise roles. At the same time, the system can also assign a unique identifier to each virtual buffer and establish the topological relationship between the virtual buffers for subsequent search and management.

[0039] In this step, due to the dynamics and complexity of the metaverse environment, the boundaries of the virtual buffers may overlap or there may be blank areas, affecting the effect of the cultural cognition intermediate layer. To solve this problem, the system can introduce a fuzzy clustering algorithm to dynamically adjust the boundaries of the virtual buffers by calculating the cultural similarity between enterprise roles, ensuring that the cultural cognition intermediate layer can comprehensively and accurately cover the cultural differences in the metaverse environment.

[0040] S103. Determine the target virtual buffer combination based on the cultural adaptation coefficient, and establish a dynamic connection channel between the target virtual buffer combinations to form a cultural cognition transmission network; Determining the target virtual buffer combination based on the cultural adaptation coefficient specifically means calculating the matching degree between the cultural filtering rules of each virtual buffer and the cultural adaptation coefficient; taking multiple virtual buffers with a matching degree higher than the preset matching degree threshold as candidate buffers; performing combination optimization on the multiple candidate buffers to obtain an initial virtual buffer combination; performing conflict detection on the initial virtual buffer combination, and deleting the virtual buffer combinations with conflict relationships to obtain the target virtual buffer combination. And establish a dynamic connection channel between the target virtual buffer combinations to form a cultural cognition transmission network In this step, based on the cultural adaptation coefficient calculated in step S101, the system selects the virtual buffer combination with the highest cultural adaptation degree to the current enterprise role from the multiple virtual buffers in the cultural cognition intermediate layer as the target virtual buffer combination. Specifically, the system calculates the matching degree between the cultural filtering rules of each virtual buffer and the cultural adaptation coefficient. The higher the matching degree, the more suitable the virtual buffer is for the current enterprise role. The system selects the virtual buffers with a matching degree higher than the preset threshold as candidate buffers, and then performs combination optimization on the candidate buffers to obtain an initial virtual buffer combination.

[0041] Next, the system performs conflict detection on the initial virtual buffer combination, identifies and deletes the virtual buffer combinations with conflict relationships, and finally obtains the target virtual buffer combination with the optimal cultural adaptation degree for the current enterprise role.

[0042] After determining the target virtual buffer combination, the system establishes dynamic connection channels between the virtual buffers within the combination to form a cultural cognition transmission network. The connection channels can be virtual paths calculated based on the cultural similarity between virtual buffers, which are used to transmit cultural cognition information between virtual buffers. The system can dynamically adjust the weights and directions of the connection channels so that cultural cognition information can be transmitted between virtual buffers more efficiently and accurately.

[0043] S104. Set cultural resonance detection points on each connection channel of the cultural cognition transmission network; In this step, the system sets cultural resonance detection points for each connection channel in the cultural cognition transmission network constructed in step S103. The cultural resonance detection points can be virtual sensors deployed on the connection channels, which are used to monitor the cultural cognition information transmitted on the connection channels in real time and identify possible cultural resonance phenomena.

[0044] The system can dynamically adjust the number and positions of the cultural resonance detection points according to factors such as the length of the connection channel and the cultural difference degree between virtual buffers to achieve comprehensive monitoring of the cultural cognition transmission network. At the same time, the system can also set monitoring rules and thresholds for the cultural resonance detection points. When the detected cultural difference fluctuation exceeds the preset threshold, the cultural resonance warning mechanism is triggered.

[0045] S105. When cultural difference fluctuations are detected at the cultural resonance detection points, call the cultural filtering rules of the virtual buffers adjacent to the target virtual buffer combination for intervention to obtain corrected interaction data; In this step, when the system detects cultural difference fluctuations on the connection channel through the cultural resonance detection points, the cultural resonance warning mechanism is triggered. The system automatically calls the cultural filtering rules of the virtual buffers adjacent to the target virtual buffer combination to intervene in and correct the interaction data with cultural difference fluctuations.

[0046] Specifically, the system first identifies the connection channel with cultural difference fluctuations and determines the target virtual buffer combination connected by this connection channel. Then, the system searches for alternative virtual buffers in the neighborhood of the target virtual buffer combination and compares the matching degree between their cultural filtering rules and the interaction data with cultural difference fluctuations. The system selects the neighborhood virtual buffer with the highest matching degree and calls its cultural filtering rules to filter and correct the interaction data, eliminating or alleviating the cultural difference fluctuations to obtain the corrected interaction data.

[0047] The system can dynamically adjust the selection range and filtering intensity of the neighborhood virtual buffer according to the intensity and duration of cultural difference fluctuations, so as to achieve adaptive cultural difference intervention. At the same time, the system can also transmit the corrected interaction data back to the original target virtual buffer combination to update its cultural filtering rules and improve the effect of subsequent interaction data correction.

[0048] S106. Divide the role activity areas in the metaverse space according to the cultural feature distribution in the corrected interaction data, and set cultural buffer zones at the intersections of areas with different cultural feature distributions; Dividing the role activity areas in the metaverse space according to the cultural feature distribution in the corrected interaction data specifically includes: extracting the cultural features in the corrected interaction data; analyzing the distribution law of the cultural features in the metaverse space; determining the position and scope of the cultural buffer zone according to the distribution law; dividing different role activity areas based on the cultural features in the areas outside the cultural buffer zone. And set cultural buffer zones at the intersections of areas with different cultural feature distributions.

[0049] In this step, the system analyzes the distribution law of the cultural features contained in the interaction data corrected in step S105 and divides different role activity areas in the metaverse space. Each role activity area corresponds to a dominant cultural feature. When an enterprise role conducts activities in this area, it needs to follow the interaction rules and behavioral guidelines adapted to this cultural feature.

[0050] Specifically, the system first extracts various cultural features from the corrected interaction data, such as language habits, behavior patterns, value orientations, etc. Then, the system analyzes the distribution law of these cultural features in the metaverse space and identifies the aggregation areas and intersection areas of different cultural features. The aggregation area represents the area where a certain cultural feature dominates, and the intersection area represents the area where multiple cultural features are intertwined and mixed.

[0051] Based on the aggregation areas and intersection areas of the cultural features, the system divides the role activity areas in the metaverse space. The system gives priority to ensuring the integrity of each aggregation area and divides it into independent role activity areas. For the intersection area, the system divides it into one or more role activity areas according to the similarity degree and interaction intensity of the cultural features.

[0052] When dividing the role activity areas, the system also needs to set cultural buffer zones at the intersections of different cultural feature areas. The cultural buffer zone is a special role activity area, and its cultural filtering rules take into account the cultural features of adjacent areas, playing a buffering and transitional role.

[0053] S107. Generate a one-to-one corresponding optimal interaction path and coordination timing for each enterprise role based on the distribution status of the cultural buffer zone, and send the optimal interaction path and coordination timing to the corresponding enterprise role.

[0054] In this step, based on the role activity areas and cultural buffer zones divided in step S106, the system plans the optimal interaction paths and coordination timings for each enterprise role. The optimal interaction path represents the movement trajectory and staying areas that an enterprise role should choose when carrying out activities in the metaverse space, enabling it to maximize cultural exchanges with other enterprise roles while avoiding cultural conflicts. The coordination timing represents the action sequence and time arrangement of enterprise roles in different interaction scenarios, enabling the entire group of enterprise roles to achieve efficient coordination.

[0055] Specifically, the system first obtains the distribution status of the cultural buffer zone in the metaverse space, including attributes such as the position, scope, and filtering intensity of the cultural buffer zone. Then, the system calculates an interaction path for each enterprise role that traverses the role activity area and the cultural buffer zone, such that the path can cover the interaction scenarios required by the enterprise role while minimizing the risk of cultural conflicts on the path.

[0056] While generating the interaction path, the system also needs to plan the coordination timing for each enterprise role. The system analyzes the interaction requirements and priorities of enterprise roles in different interaction scenarios, and based on the spatial distribution of the role activity areas and the filtering intensity of the cultural buffer zone, assigns an optimal time window for each enterprise role to enable efficient interaction and coordination with the target enterprise role.

[0057] After generating the optimal interaction path and coordination timing, the system sends them to the corresponding enterprise role. The enterprise role can fine-tune the interaction path and coordination timing according to its actual situation, but generally needs to follow the general direction planned by the system to ensure the cultural coordination effect of the entire group of enterprise roles.

[0058] In the above embodiments, by collecting the interaction parameters between enterprise roles in the metaverse environment to determine the cultural adaptation coefficient, and establishing multiple virtual buffers with different cultural filtering rules to form a cultural cognition intermediate layer, the accurate identification and classification of interaction behaviors under different cultural backgrounds can be achieved. On this basis, by establishing a dynamic connection channel to construct a cultural cognition transmission network and setting cultural resonance detection points for real-time monitoring, the interaction barriers caused by cultural differences can be detected in a timely manner. When detecting cultural difference fluctuations, the system will automatically call the cultural filtering rules of adjacent virtual buffers for intervention and correction, effectively reducing the negative impact of cultural differences on the interaction quality. By dividing the role activity areas in the metaverse space and setting cultural buffer zones, combined with the generated optimal interaction paths and collaborative time sequences, enterprise roles can achieve smooth cross-cultural interactions while maintaining their respective cultural characteristics, improving the collaborative efficiency and interaction experience quality in a multi-cultural background.

[0059] The above embodiments mainly describe the interaction control scheme based on cultural adaptability, which ensures cross-cultural collaboration between enterprise roles by constructing a cultural cognition intermediate layer and cultural buffer zones. However, in practical applications, the collaboration effect between enterprise roles is not only affected by cultural differences, but also closely related to factors such as the emotional state, cognitive level, and interaction willingness of the roles. Therefore, on the basis of ensuring cultural adaptability, it is also necessary to establish an interaction enhancement mechanism for the psychological and behavioral characteristics of enterprise roles to achieve more comprehensive and in-depth collaboration optimization. The following combines Figure 2 , and describes another enterprise role adaptive collaborative control method based on metaverse multi-dimensional data in the embodiments of the present application: Please refer to Figure 2 , which is another process schematic diagram of an enterprise role adaptive collaborative control method based on metaverse multi-dimensional data in the embodiments of the present application.

[0060] S201. Construct a role interaction intelligent perception layer in the metaverse space, and collect the emotional state, cognitive level, and interaction willingness of each enterprise role; In this step, the system constructs a role interaction intelligent perception layer in the metaverse space, which is used to collect and analyze multi-dimensional data such as the emotional state, cognitive level, and interaction willingness of each enterprise role during the interaction process. The role interaction intelligent perception layer can be a distributed virtual sensing network, which can real-time sense and quantify the behavior and state data of enterprise roles through various virtual sensors and analysis modules deployed in the metaverse space.

[0061] The system can adopt various technical means to construct the intelligent perception layer for role interaction, such as facial expression recognition based on computer vision, emotion analysis based on natural language processing, cognitive modeling based on knowledge graphs, and intention prediction based on user behavior sequences. These technical means can be flexibly combined and integrated to form a multi-modal and multi-level intelligent perception mechanism for role interaction.

[0062] When collecting the emotional states of enterprise roles, the system can analyze the overt behavior data of roles in the interaction process, such as facial expressions, speech intonations, and body movements, and at the same time combine the physiological signal data of roles (such as heart rate, breathing rate, etc.) to comprehensively judge the emotional states of roles, such as happy, sad, angry, etc. When collecting the cognitive level, the system can track and analyze the usage frequency and understanding depth of different concepts and knowledge points by roles in the interaction process, dynamically construct the cognitive graph of roles, and quantitatively evaluate the cognitive level of roles in different fields. When collecting the interaction intention, the system can predict the preference degree of roles for different interaction objects and interaction methods, that is, the interaction intention, according to factors such as the historical interaction behavior of roles, the current interaction scenario, and the attention distribution of roles.

[0063] S202. Calculate the emotional state matching degree, cognitive level compatibility, and interaction intention synchronization degree between different enterprise roles to obtain the overall matching strength; The system calculates the emotional state matching degree, cognitive level compatibility, and interaction intention synchronization degree between different enterprise roles to obtain the overall matching strength. Specifically, it collects the physiological sensing data and behavior data of each enterprise role, and constructs an emotional fluctuation curve based on the physiological sensing data and behavior data; calculates the correlation of the emotional fluctuation curve in different time windows to obtain the emotional state matching degree; constructs the cognitive feature matrix of each enterprise role, and the cognitive feature matrix includes professional knowledge, cultural understanding, and interaction experience; calculates the difference degree of the cognitive feature matrix to obtain the cognitive level compatibility index; extracts the sequential data of the interaction behavior of each enterprise role, and the sequential data of the interaction behavior includes the interaction frequency and response time; calculates the synchronization degree of the interaction intention based on the sequential data of the interaction behavior; performs weighted summation on the emotional state matching degree, cognitive level compatibility index, and synchronization degree to obtain the overall matching strength.

[0064] In this step, after the system collects the data such as the emotional states, cognitive levels, and interaction intentions of each enterprise role, it further calculates the matching degree between different enterprise roles in these dimensions to obtain an overall matching strength index reflecting the overall cooperation state of enterprise roles. Specifically, the system collects the physiological sensing data (such as electroencephalogram, electrocardiogram, etc.) and overt behavior data (such as facial expressions, speech intonations, etc.) of each enterprise role in the interaction process, and constructs an emotional curve reflecting the emotional fluctuations of roles based on these data. Then, the system calculates the correlation of the emotional curves of different enterprise roles in different time windows to obtain the matching degree of the emotional states.

[0065] When calculating the cognitive level compatibility, the system constructs a cognitive feature matrix including dimensions such as professional knowledge, cultural understanding, and interaction experience for each enterprise role. By calculating the difference degree between the cognitive feature matrices of different roles, the compatibility index of the cognitive level is obtained. The smaller the difference degree, the closer the enterprise roles are in terms of cognitive level and the higher the compatibility.

[0066] When calculating the synchronization of interaction willingness, the system extracts the sequential data of interaction behaviors of each enterprise role during the interaction process, such as interaction frequency and response time. By analyzing the synchronization degree of these sequential data, the consistency of the interaction willingness of the enterprise roles is judged. The higher the synchronization degree, the more consistent the interaction rhythm and interaction willingness of the enterprise roles.

[0067] Finally, the system performs a weighted sum of the emotional state matching degree, the cognitive level compatibility index, and the synchronization degree of interaction willingness to obtain a matching strength index reflecting the overall collaboration state of the enterprise roles. The higher the matching strength, the more coordinated the enterprise roles are in terms of emotion, cognition, and willingness, and the more conducive to carrying out in-depth and efficient collaboration.

[0068] S203. When the overall matching strength is lower than the preset threshold for multiple consecutive preset cycles, trigger the interaction enhancement mechanism; When the overall matching strength is lower than the preset threshold for multiple consecutive preset cycles, trigger the interaction enhancement mechanism. The interaction enhancement mechanism is as follows: Dynamically adjust the number and distribution positions of emotion regulation points in the cultural buffer zone according to the emotional state matching degree; Set emotion reconciliation rules based on the distribution positions of the emotion regulation points; Determine the layout density of cognitive assistance sites on the optimal interaction path according to the cognitive level compatibility; Set the information presentation mode of the cognitive assistance sites according to the cognitive differences of each enterprise role; Calculate the optimal rhythm density of the collaborative time sequence based on the synchronization of interaction willingness; Compose the emotion reconciliation rules, information presentation mode, and optimal rhythm density into an interaction enhancement plan; Send the interaction enhancement plan to each enterprise role.

[0069] In this step, the system monitors in real time the overall matching strength index of the enterprise roles calculated in step S202. When this index is lower than the preset threshold for multiple consecutive preset cycles, the interaction enhancement mechanism is triggered in a timely manner. The interaction enhancement mechanism is a collaborative optimization strategy that comprehensively utilizes various means such as emotion regulation, cognitive assistance, and willingness guidance. Its basic idea is to appropriately guide and regulate the emotional state, cognitive level, and interaction willingness of the enterprise roles to eliminate or alleviate problems such as emotional disharmony, cognitive imbalance, and willingness deviation that may occur during the collaboration process of the enterprise roles, thereby enhancing the overall matching strength of the enterprise roles and achieving higher-quality collaboration.

[0070] Specifically, the interaction enhancement mechanism includes the following key aspects: First, the system dynamically adjusts the number and distribution positions of emotion regulation nodes in the previously constructed cultural buffer zone according to the change in the emotion state matching degree, and simultaneously sets different emotion reconciliation rules in a targeted manner. Second, on the optimal interaction path of the enterprise roles, the system reasonably sets the layout density of cognitive assistance sites according to the level of cognitive compatibility, and provides personalized information presentation methods for the cognitive differences of different roles. Third, based on the change trend of the synchronization of interaction willingness, the system dynamically optimizes the rhythm density of the collaborative time sequence to guide the enterprise roles to form a synchronous interaction rhythm. Finally, the system forms a complete interaction enhancement plan with the above adjustment means and sends it to each enterprise role to guide its targeted collaborative optimization.

[0071] S204. Set multiple emotion regulation points in the cultural buffer zone based on the emotion state matching degree; The system sets multiple emotion regulation points in the cultural buffer zone based on the emotion state matching degree, and each emotion regulation point has different emotion reconciliation rules.

[0072] In this step, the system dynamically sets multiple emotion regulation nodes in the previously constructed cultural buffer zone according to the emotion state matching degree calculated in step S202. The emotion regulation nodes are essentially a type of virtualized emotion intervention trigger, which can relieve or eliminate possible emotion disharmony problems between enterprise roles, such as emotion conflicts and emotion indifference, by applying specific emotion reconciliation rules to enterprise roles.

[0073] The system dynamically adjusts the number and distribution positions of emotion regulation nodes according to the real-time changing emotion state matching degree. Specifically, when it is detected that the emotion state matching degree of a pair of enterprise roles is low, the system will appropriately increase the layout density of emotion regulation nodes in the area of the cultural buffer zone covered by their interaction path. On the contrary, when the emotion state matching degree is high, the system will correspondingly reduce the layout density of emotion regulation nodes in this area. By dynamically adjusting the distribution of emotion regulation nodes, the system can more accurately and efficiently guide and regulate the emotion states of enterprise roles.

[0074] Each emotion regulation node is built-in with targeted emotion reconciliation rules. These rules can help enterprise roles timely adjust their emotion states and return to a rational and peaceful interaction state by real-time identifying and analyzing the emotion states of enterprise roles and selecting specific emotion intervention strategies, such as emotion catharsis, emotion transfer, and emotion empathy. The system can preset multiple sets of emotion reconciliation rule libraries for different emotion imbalance scenarios and role characteristics to achieve more personalized and refined emotion regulation.

[0075] S205. Arrange cognitive assistance sites on the optimal interaction path according to the cognitive level compatibility; In this step, based on the cognitive level compatibility index between enterprise roles calculated in step S202, the system deploys multiple cognitive assistance sites on the optimized interaction path. A cognitive assistance site is a virtual cognitive guidance tool that helps enterprise roles build a cognitive bridge between each other by providing personalized information presentation and knowledge services, eliminating or alleviating interaction barriers that may be caused by mismatched cognitive levels.

[0076] The system dynamically determines the deployment density of cognitive assistance sites based on the real-time evaluated cognitive level compatibility. Specifically, when it detects that the cognitive level compatibility of a certain pair of enterprise roles in a specific field is low, the system will increase the deployment density of cognitive assistance sites on their optimal interaction path. In this way, when enterprise roles pass through these areas, they can timely obtain key information and knowledge support that helps bridge the cognitive gap. On the contrary, when the cognitive level compatibility of enterprise roles in some fields is high, the system will appropriately reduce the deployment density of cognitive assistance sites in these areas to avoid excessive information redundancy and cognitive load.

[0077] Each cognitive assistance site is equipped with a personalized information presentation mode for different enterprise roles. By analyzing the cognitive feature matrix of enterprise roles, the site adaptively adjusts the perspective, granularity, style, etc. of information presentation to make it more in line with the cognitive habits and preferences of the roles. For example, for a role that prefers graphical and context-based information presentation, the cognitive assistance site can automatically convert text information into forms such as mind maps and relational networks. For a role that prefers case-based and contextualized cognition, the cognitive assistance site will timely supplement rich and vivid case information to help the role achieve cognitive transfer and integration.

[0078] S206. Optimize the rhythm density of the collaborative timing according to the synchronization of interaction willingness to obtain the optimized collaborative timing; In this step, based on the synchronization of interaction willingness between enterprise roles calculated in step S202, the system dynamically optimizes the rhythm density of the previously generated collaborative timing to make it more in line with the interaction willingness and collaborative demands of enterprise roles. The rhythm density of the collaborative timing essentially reflects the interaction frequency and response speed of enterprise roles in the collaborative process and is one of the key factors affecting collaborative efficiency and quality.

[0079] Specifically, when the system detects that the interaction willingness of a pair of enterprise roles is highly synchronized, it means that these roles are highly consistent in the interaction rhythm and interaction willingness, and are eager to collaborate at a higher frequency and speed. At this time, the system will correspondingly increase the rhythm density of the interaction links involving these roles in the collaborative sequence, such as shortening the activity interval time, simplifying the interaction process, etc., to make the collaborative process more compact and smooth. Conversely, when the system finds that some enterprise roles are obviously out of sync in their interaction willingness, it may mean that these roles do not have sufficient motivation and willingness for collaboration at the current stage. At this time, the system will appropriately reduce the rhythm density of the interaction links involving these roles, such as increasing the buffer time, setting collaborative checkpoints, etc., to avoid blindly accelerating the collaborative rhythm and causing new conflicts.

[0080] When optimizing the rhythm density of the collaborative sequence, the system comprehensively considers the synchronization of interaction intentions and the collaborative work characteristics of enterprise roles, and adopts a multi-objective optimization algorithm to improve the overall collaborative efficiency while taking into account the collaborative experience and sustainability of enterprise roles. The optimized collaborative sequence can not only maximize the stimulation and mobilization of the collaborative intentions of enterprise roles and form collaborative resonance, but also dynamically adjust the collaborative rhythm according to the phased changes of collaborative tasks to achieve sustainable high-quality collaboration.

[0081] S207: Send the interaction enhancement plan including the emotion regulation point, cognitive assistance site and optimized collaborative timing to each enterprise role.

[0082] In this step, the system integrates the emotion regulation nodes, cognitive auxiliary sites and optimized collaboration timing generated in the previous steps into a complete set of interaction enhancement solutions, and sends the solution to each enterprise role participating in the collaboration. The interaction enhancement solution is an intelligent collaboration optimization tool built by comprehensively utilizing a variety of computer interaction technologies. Its core goal is to achieve sustainable optimization of cross-role collaboration through systematic guidance of the emotional state, cognitive level and collaboration willingness of enterprise roles.

[0083] Specifically, the interaction enhancement solution, on the one hand, provides targeted emotional intervention and reconciliation services to corporate roles through emotional regulation nodes, eliminates or alleviates possible emotional imbalances between roles, and creates a benign collaborative emotional atmosphere. On the other hand, the interaction enhancement solution uses cognitive auxiliary sites arranged on the role interaction path to provide personalized information presentation and knowledge services for roles, helping roles build cognitive bridges between each other and improve collaborative efficiency. At the same time, the interaction enhancement solution also includes collaborative timing that has been dynamically optimized through rhythm density, so that the collaborative process is more in line with the interactive intentions and demands of corporate roles, stimulating collaborative resonance.

[0084] When sending an interactive enhancement solution to an enterprise role, the system adopts a contextualized and digital twin interactive form, enabling the enterprise role to intuitively perceive and experience the mechanism of action and optimization effect of the interactive enhancement solution. For example, the system can utilize technologies such as virtual reality and augmented reality to construct an immersive interactive scenario, allowing the enterprise role to simulate the entire process of solution execution in advance in a realistic collaboration scenario. The system can also intelligently analyze the acceptance degree and feedback opinions of the enterprise role towards the interactive enhancement solution, continuously optimize and update the solution, and enhance the usage experience and satisfaction of the enterprise role.

[0085] In the above embodiments, by constructing a role interaction intelligent perception layer, the emotional state, cognitive level, and interaction willingness of the enterprise role are collected in real time, and the matching intensity between these factors is calculated. When the system detects that the overall matching intensity remains low, it will automatically trigger an interactive enhancement mechanism to improve the interaction quality by setting emotional regulation points in the cultural buffer zone, arranging cognitive assistance sites on the interaction path, and optimizing the rhythm density of the collaboration time sequence. Considering multiple dimensions such as emotion, cognition, and willingness, interactive barriers are eliminated through dynamic adjustment and auxiliary means. The system can adaptively adjust the enhancement strategy according to the real-time monitoring data, enabling the enterprise role to obtain a better emotional experience and cognitive understanding during the cross-cultural interaction process, thereby improving the stability and sustainability of the cross-cultural collaboration process.

[0086] The system in the embodiments of the present invention application will be described from the perspective of hardware processing. Please refer to Figure 3 which is a schematic structural diagram of an entity device of an enterprise role adaptive collaboration control system based on metaverse multi-dimensional data provided by the embodiments of the present application.

[0087] It should be noted that Figure 3 the structure of the system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0088] As Figure 3 shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage section 308 into the random access memory (RAM) 303, such as executing the methods in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0089] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD), a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0090] Specifically, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by a central processing unit (CPU) 301, various functions defined in the present invention are executed.

[0091] It should be noted that the computer-readable medium shown in the embodiments of the present invention may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0093] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or may exist alone without being assembled into the system. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiments.

[0094] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present application.

[0095] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted as "if determining...", "in response to determining...", "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0096] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it 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. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) manner. 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 includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0097] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The aforementioned storage medium includes various media that can store program codes, such as ROM, random access memory (RAM), magnetic disks, or optical discs.

Claims

1. A method for enterprise role adaptive collaborative control based on metaverse multi-dimensional data, characterized in that Including: Collect the interaction parameters among enterprise roles in the metaverse environment, and determine the cultural adaptation coefficients among the enterprise roles according to the interaction parameters; Construct a cultural cognition intermediate layer composed of multiple virtual buffers in the metaverse space, and different cultural filtering rules are set in each virtual buffer; Determine a target virtual buffer combination based on the cultural adaptation coefficient, and establish a dynamic connection channel between the target virtual buffer combinations to form a cultural cognition transmission network; Set cultural resonance detection points on each connection channel of the cultural cognition transmission network; When cultural difference fluctuations are detected at the cultural resonance detection points, call the cultural filtering rules of the virtual buffers adjacent to the target virtual buffer combination for intervention to obtain corrected interaction data; Divide the role activity areas in the metaverse space according to the cultural feature distribution in the corrected interaction data, and set cultural buffer zones at the intersections of different cultural feature distribution areas; Based on the distribution state of the cultural buffer zones, generate a corresponding optimal interaction path and coordination time sequence for each enterprise role, and send the optimal interaction path and coordination time sequence to the corresponding enterprise role.

2. The method according to claim 1, characterized in that The determining the cultural adaptation coefficients among the enterprise roles according to the interaction parameters specifically includes: The interaction parameters include the interaction delay time, speech pause frequency, and semantic association degree among the enterprise roles; Perform normalization processing on the interaction parameters to obtain standardized interaction indicators; Calculate the cultural adaptation benchmark value according to the standardized interaction indicators; Perform time series accumulation on the cultural adaptation benchmark value to obtain the cultural adaptation accumulation value; Generate a cultural adaptation curve based on the cultural adaptation accumulation value, and extract the characteristic points of the cultural adaptation curve; Calculate the cultural adaptation coefficient according to the distribution law of the characteristic points.

3. The method according to claim 1, wherein The determining the target virtual buffer combination based on the cultural adaptation coefficient specifically includes: Calculate the matching degree between the cultural filtering rules of each virtual buffer and the cultural adaptation coefficient; Use multiple virtual buffers with a matching degree higher than a preset matching degree threshold as candidate buffers; Perform combination optimization on the multiple candidate buffers to obtain an initial virtual buffer combination; Perform conflict detection on the initial virtual buffer combination, and delete the virtual buffer combinations with conflict relationships to obtain the target virtual buffer combination.

4. The method according to claim 1, characterized in that After sending the optimal interaction path and coordination time sequence to the corresponding enterprise role, the method further includes: Construct a role interaction intelligent perception layer in the metaverse space, and collect the emotional states, cognitive levels, and interaction willingness of each enterprise role; Calculate the emotional state matching degree, cognitive level compatibility, and interaction willingness synchronization among different enterprise roles to obtain the overall matching strength; When the overall matching strength is lower than a preset threshold for multiple consecutive preset periods, trigger an interaction enhancement mechanism; Set multiple emotion regulation points in the cultural buffer zone based on the emotional state matching degree, and each emotion regulation point has different emotion reconciliation rules; Arrange cognitive assistance stations on the optimal interaction path according to the cognitive level compatibility; Optimize the rhythm density of the collaborative time sequence according to the synchronization of the interaction willingness to obtain an optimized collaborative time sequence; Send the interaction enhancement plan including the emotion regulation points, the cognitive assistance stations, and the optimized collaborative time sequence to each of the enterprise roles.

5. The method according to claim 4, characterized in that, Calculating the emotional state matching degree, cognitive level compatibility, and interaction willingness synchronization among different enterprise roles to obtain an overall matching strength, specifically including: Collect the physiological sensing data and behavior data of each enterprise role, and construct an emotional fluctuation curve based on the physiological sensing data and the behavior data; Calculate the correlation of the emotional fluctuation curve in different time windows to obtain the emotional state matching degree; Construct a cognitive feature matrix for each enterprise role, where the cognitive feature matrix includes professional knowledge, cultural understanding, and interaction experience; Calculate the difference degree of the cognitive feature matrix to obtain a cognitive level compatibility index; Extract the interaction behavior time sequence data of each enterprise role, where the interaction behavior time sequence data includes interaction frequency and response time; Calculate the synchronization degree of the interaction willingness based on the interaction behavior time sequence data; Perform a weighted sum of the emotional state matching degree, the cognitive level compatibility index, and the synchronization degree to obtain an overall matching strength.

6. The method according to claim 4, wherein The interaction enhancement mechanism is: Dynamically adjust the number and distribution positions of the emotion regulation points in the cultural buffer zone according to the emotional state matching degree; Set emotion reconciliation rules based on the distribution positions of the emotion regulation points; Determine the layout density of the cognitive assistance stations on the optimal interaction path according to the cognitive level compatibility; Set the information presentation mode of the cognitive assistance stations according to the cognitive differences of each enterprise role; Calculate the optimal rhythm density of the collaborative time sequence based on the interaction willingness synchronization; Form an interaction enhancement plan with the emotion reconciliation rules, the information presentation mode, and the optimal rhythm density; Send the interaction enhancement plan to each of the enterprise roles.

7. The method according to claim 1, wherein Dividing the role activity areas in the metaverse space according to the cultural feature distribution in the corrected interaction data, specifically including: Extract the cultural features in the corrected interaction data; Analyze the distribution law of the cultural features in the metaverse space; Determine the position and scope of the cultural buffer zone according to the distribution law; Divide different role activity areas outside the cultural buffer zone based on the cultural features.

8. An enterprise role adaptive collaborative control system based on metaverse multi-dimensional data, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on the system, enable the system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the system, enable the system to execute the method according to any one of claims 1-7.

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