Emotional companion cognitive analysis method and system for virtual-real fusion scene

Abstract

The present application relates to the technical field of artificial intelligence, and discloses a method and system for cognitive analysis of emotional companions in a virtual-real fusion scene, comprising: firstly, sensing the emotional state of a user in the virtual-real fusion scene, identifying the emotional demand category in the interaction task, and querying the corresponding emotional response level; then, analyzing the emotional barrier limit triggered by each level, positioning the emotional fluctuation node of the user in the accompanying process, analyzing the interaction frequency and feedback time limit of the emotional companion at the fluctuation node, quantitatively calculating the perceived response amount, identifying the emotional feedback content based on the perceived response amount, analyzing the matching degree of the emotional feedback content with the virtual-real fusion scene, and finally determining the emotional accompanying state of the emotional companion to the user. The present application can improve the accurate judgment and continuous optimization of the emotional accompanying state of the user.

Description

Technical Field

[0001] This invention relates to a method and system for cognitive analysis of emotional companionship in virtual-real fusion scenarios, belonging to the field of artificial intelligence technology. Background Technology

[0002] The emotional companion in a virtual-real fusion scenario is an artificial intelligence system that provides emotional support to users in an environment that integrates virtual and real elements through multimodal perception and interaction technology.

[0003] Currently, emotional companionship systems mainly rely on preset interaction rules and single-modal emotion recognition technology to achieve user state perception through voice dialogue, facial expression recognition, or physiological signal monitoring, and generate emotional feedback based on a fixed response database. However, these existing technologies have a fundamental flaw: they lack a continuous cognition of emotional state and a dynamic response control mechanism, which results in an overly rigid companionship experience and makes it difficult to establish a long-term and continuous emotional connection with users. Summary of the Invention

[0004] This invention provides a method and system for cognitive analysis of emotional companionship in virtual-real integrated scenarios, the main purpose of which is to improve the accuracy of judgment and continuous optimization of the user's emotional companionship status.

[0005] To achieve the above objectives, the present invention provides a cognitive analysis method for emotional companionship in a virtual-real fusion scenario, comprising: After matching the emotional companion corresponding to the perceived user, obtain the description of the perceived user's emotional state in the virtual-real fusion scenario; After identifying the emotional need category corresponding to the interactive task in the emotional state description, query the emotional response level corresponding to the emotional need category; Analyze the emotional barrier boundaries triggered by each of the emotional response levels, and identify the emotional fluctuation nodes of the perceived user during the companionship process; The number of interactions and the feedback time limit generated by the emotional companion at the emotional fluctuation node are analyzed, and the perceived response quantity of the emotional companion during the companionship process is calculated based on the number of interactions and the feedback time limit. Based on the perceived response quantity, after identifying the emotional feedback content output by the emotional companion, the degree of matching of the emotional feedback content in the virtual-real fusion scenario is analyzed to determine the emotional companionship status of the emotional companion for the perceived user.

[0006] Optionally, identifying the emotional feedback content output by the emotional companion based on the perceived response quantity includes: After analyzing the response intensity value and response time value in the perceived response quantity, the basic emotional tone of the response intensity value in the preset emotional intensity range is analyzed; Based on the response time value, a dynamic expression mode that conforms to the basic emotional tone is generated, and the emotional output tendency corresponding to the dynamic expression mode is analyzed. Based on the stated emotional output tendency, the emotional feedback content of the emotional companion is output under the scene adaptation.

[0007] Optionally, generating a dynamic expression that conforms to the basic emotional tone based on the response timeliness value includes: The response timeliness value is classified into timeliness levels, and the speech rate configuration and word complexity corresponding to the timeliness level are queried. After determining the intensity of emotional expression corresponding to the basic emotional tone, the emotional response content corresponding to the emotional companion is output in combination with the speech rate configuration and the word complexity. The dynamic expression of the emotional response content under the emotional expression intensity is detected.

[0008] Optionally, the emotional response content is specific interactive information composed of voice parameters, text statements and image elements, carrying the specific emotional expression generated by the emotional companion based on the perceived response amount, and is used to conduct targeted emotional interaction with the perceived user in the virtual-real fusion scenario.

[0009] Optionally, obtaining the description of the perceived user's emotional state in the virtual-real fusion scenario includes: After collecting the comprehensive interaction information corresponding to the perceived user, the behavioral expression elements in the comprehensive interaction information are extracted; The multidimensional expression vectors of the behavioral expression elements in the virtual-real fusion scenario are analyzed, and an emotional state description corresponding to the perceived user is generated based on the multidimensional expression vectors.

[0010] Optionally, identifying the emotional need category corresponding to the interaction task in the emotional state description includes: After decomposing the emotional state description into a sequence of emotional elements, the distribution details of the key points corresponding to the core emotional points in the sequence of emotional elements are generated. Extract the high-weight sentiment points from the key point distribution details; The high-weighted emotional points are matched with a preset emotional need category library to obtain emotional need categories.

[0011] Optionally, the analysis of the emotional barrier threshold triggered by each of the emotional response levels includes: Query the number of regular interactions and the depth of emotional topics corresponding to the emotional response level, and obtain the emotional resistance index of the perceived user at the current interaction moment; Based on the number of regular interactions, determine the optimal interaction threshold corresponding to the emotional resistance index; Based on the depth of the emotional topic and the optimal interaction threshold, the emotional barrier boundary triggered by the emotional response level is determined.

[0012] Optionally, identifying the emotional fluctuations of the perceived user during the companionship process includes: After obtaining the time sequence of the perceived user's emotional state during the companionship process, a threshold for the perceived user's emotional change in the current interaction scenario is set. Based on the emotional change threshold, abnormal fluctuation points in the emotional state time series are scanned, and the abnormal fluctuation points are marked as emotional fluctuation nodes.

[0013] Optionally, calculating the perceived response of the emotional companion during the companionship process based on the number of interactions and the feedback time limit includes: Query the interaction frequency within a unit of time for the number of interactions, and determine the average feedback delay corresponding to the overall feedback time limit; Analyze the response efficiency of the emotional companion during the companionship process; Based on the interaction frequency, the average feedback delay, and the response efficiency value, the perceived response of the emotional companion during the companionship process is calculated.

[0014] To address the aforementioned problems, this invention also provides a cognitive analysis system for emotional companionship in a virtual-real fusion scenario, the system comprising: The state description module is used to obtain the emotional state description of the perceived user in the virtual-real fusion scenario after matching the emotional companion corresponding to the perceived user. The emotion response module is used to identify the emotion need category corresponding to the interactive task in the emotion state description, and then query the emotion response level corresponding to the emotion need category. The node identification module is used to analyze the emotional barrier boundary triggered by each of the emotional response levels and identify the emotional fluctuation nodes of the perceived user during the companionship process. The response quantity calculation module is used to analyze the number of interactions and feedback time limits generated by the emotional companion at the emotional fluctuation node, and calculate the perceived response quantity of the emotional companion during the companionship process based on the number of interactions and the feedback time limits. The state determination module is used to identify the emotional feedback content output by the emotional companion based on the perceived response quantity, and then analyze the degree of matching of the emotional feedback content in the virtual-real fusion scene to determine the emotional companionship state of the emotional companion for the perceived user.

[0015] Compared to the problems described in the background art, the embodiments of the present invention can overcome the limitations of single-modal perception, comprehensively capture the user's true emotional tendencies in virtual-real fusion scenarios, and improve the accuracy of emotional state perception. It allows the emotional companion to quickly adapt to scene characteristics and individual user emotional features, making the interaction more aligned with the user's current state. This helps establish more targeted emotional interactions, laying the foundation for long-term and stable emotional connections. Furthermore, the embodiments of the present invention can accurately identify the core emotional needs behind the interaction tasks, avoiding a one-sided interpretation of the user's emotional state and ensuring the accuracy of need positioning. By matching corresponding emotional response levels, the feedback of the emotional companion is made more aligned with the intensity of the user's current needs, reducing response deviations. Finally, the embodiments of the present invention clearly define the interaction scale boundaries corresponding to different emotional responses, preventing emotional feedback from exceeding the user's acceptable range, while accurately capturing changes in the user's emotions. At key junctures, the emotional companion allows for timely detection of user state anomalies, making the companionship process more aligned with the user's emotional changes. Next, this embodiment quantifies the core parameters of interactive behavior at emotional fluctuation points, providing clear quantitative basis for emotional responses, accurately controlling the frequency and timeliness of feedback, and ensuring that the emotional companion's response at key junctures is both timely and not excessive. Simultaneously, it transforms abstract emotional responses into quantitative indicators, making the response during companionship more controllable. Finally, this embodiment anchors emotional feedback content based on perceived response volume, ensuring the correlation between feedback and the user's current state, avoiding a disconnect between feedback content and the virtual-real integrated scenario, making the positioning of the companionship state more aligned with the user's actual needs, strengthening the adaptability of emotional companionship, and making the interaction more suitable for the current scenario and user state. Therefore, this invention can improve the accuracy and continuous optimization of the user's emotional companionship state. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for analyzing the cognitive relationship of an emotional companion in a virtual-real fusion scenario, as provided in an embodiment of the present invention. Figure 2 This is a flowchart of the emotional feedback content in the emotional companion cognitive analysis method for realizing the virtual-real fusion scenario provided by an embodiment of the present invention. Figure 3 This is a schematic diagram of a module for implementing the emotional companion cognitive analysis system in the virtual-real fusion scenario according to an embodiment of the present invention; Figure 4 A schematic diagram of a computer device for a method of cognitive analysis of emotional companionship in a virtual-real fusion scenario provided in an embodiment of the present invention; The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] This application provides a method for analyzing the cognitive perception of emotional companions in a virtual-real integrated scenario. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for analyzing the cognitive perception of emotional companions in a virtual-real integrated scenario can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0019] Reference Figure 1 The diagram shown is a flowchart illustrating a method for analyzing the cognitive perception of emotional companions in a virtual-real fusion scenario according to an embodiment of the present invention. In this embodiment, the method for analyzing the cognitive perception of emotional companions in a virtual-real fusion scenario includes: S1. After matching the emotional companion corresponding to the perceived user, obtain the emotional state description of the perceived user in the virtual-real fusion scene.

[0020] The embodiments of this invention can overcome the limitations of single-modal perception, comprehensively capture the user's true emotional tendencies in virtual-real integrated scenarios, improve the accuracy of emotional state perception, and enable the emotional companion to quickly adapt to the characteristics of the scenario and the user's individual emotional characteristics, making the interaction more in line with the user's current state. This can help establish more targeted emotional interactions and lay the foundation for long-term and stable emotional connections.

[0021] The "perceived user" refers to an individual interacting with an emotional companion in a virtual-real fusion scenario. Their emotional state, behavioral characteristics, and interaction needs are captured by the system through multimodal perception technology. This includes, but is not limited to, natural persons using relevant systems or services or seeking emotional support and companionship. They are the core actors in the emotional companionship interaction process, and their relevant state data provides the basic input for system interaction. The "emotional companion" refers to an artificial intelligence system integrating multimodal perception, interaction, and AI generation technologies to provide emotional support to users in a virtual-real fusion scenario. It possesses human-like voice, facial expressions, and action capabilities and can capture user states and conduct interactions through a self-developed emotional companionship model. It is the core execution carrier for realizing the emotional companionship function; the virtual-real fusion scene refers to an immersive interactive environment constructed by integrating virtual digital elements and real-world environmental features through audiovisual and other multimodal technologies. It retains the realism and interactive rationality of the real scene while incorporating the flexibility and extensibility of virtual elements, providing an adaptive environment to support the multi-dimensional emotional interaction between users and emotional companions; the emotional state description refers to the objective information integration and presentation of the perceived user's emotional state at a specific time point based on the comprehensive analysis results of multi-dimensional expression vectors. It covers the user's current emotional type, emotional intensity, state stability and other core characteristics, and is a concrete description of the user's emotional state.

[0022] Optionally, the matching emotional companions corresponding to the perceived users can construct user profiles by collecting basic information such as the perceived users' interaction preferences, emotional needs, and usage habits in different scenarios. The cosine similarity algorithm is then used to calculate the matching degree between the user profiles and the functional attributes of each emotional companion, and the optimal matching emotional companion is selected.

[0023] In this embodiment of the invention, obtaining the emotional state description of the perceived user in the virtual-real fusion scenario includes: collecting comprehensive interaction information corresponding to the perceived user, extracting behavioral expression elements from the comprehensive interaction information; parsing the multi-dimensional expression vector of the behavioral expression elements in the virtual-real fusion scenario, and generating the emotional state description corresponding to the perceived user based on the multi-dimensional expression vector.

[0024] The comprehensive interactive information refers to the set of all-dimensional interactive data related to the perceived user collected in a virtual-real fusion scenario through multimodal perception technology, including AI vision and AI hearing. This data covers visual expressions such as facial expressions and movements captured by AI vision, auditory information such as tone of voice and dialogue content collected by AI hearing, as well as physiological data and scene interaction behavior records. It is the basic data source for capturing the user's emotional state. The behavioral expression elements refer to the specific feature information extracted from the comprehensive interactive information obtained from multimodal perception that can reflect the perceived user's emotional tendency. This includes facial expression details and body movement amplitude recognized by AI vision, changes in voice tone and semantic tendency of dialogue analyzed by AI hearing, and physiological signal fluctuation characteristics. It is the core basis for analyzing the user's emotional state. The multidimensional expression vector refers to the structured data carrier formed after quantitative analysis of the behavioral expression elements. By converting behavioral features of different dimensions into calculable values ​​or feature vectors, it covers quantitative information of multiple dimensions such as emotional tendency, emotional intensity, and interaction willingness, and realizes the standardized presentation of behavioral expression elements.

[0025] S2. After identifying the emotional need category corresponding to the interactive task in the emotional state description, query the emotional response level corresponding to the emotional need category.

[0026] The embodiments of the present invention can accurately identify the core emotional needs behind interactive tasks, avoid one-sided interpretation of users' emotional states, ensure the accuracy of need positioning, and make the feedback of the emotional companion more in line with the intensity of users' current needs by matching the corresponding emotional response level, thereby reducing response deviation.

[0027] The interactive task refers to the specific behavioral direction or interactive goal carried in the process of emotional interaction between the perceived user and the emotional companion in a virtual-real integrated scenario. It includes behaviors such as dialogue, emotional expression, and demand expression initiated by the user, as well as naturally occurring interactive behaviors between the two parties. It is the specific carrier connecting the user's emotional state and the companion's response. The emotional need category refers to the classification and definition of the core emotional needs based on the perceived user's emotional state description. It includes different types such as comfort, confiding, encouragement, companionship, and answering questions. Each category corresponds to the user's emotional need tendency in a specific scenario, which can distinguish the differences in users' emotional needs. The emotional response level refers to the gradient standard preset for different emotional need categories, corresponding to different feedback intensities and interaction depths. Its classification is based on factors such as the urgency of the emotional need and the amplitude of the user's emotional fluctuations, which clarifies the definition standard for the strength, method, and level of interaction investment when the emotional companion provides feedback.

[0028] In this embodiment of the invention, identifying the emotional need category corresponding to the interactive task in the emotional state description includes: decomposing the emotional state description into an emotional element sequence, generating key point distribution details corresponding to the core emotional key points in the emotional element sequence; extracting high-weight emotional key points from the key point distribution details; and matching the high-weight emotional key points with a preset emotional need category library to obtain the emotional need category.

[0029] The emotional element sequence refers to an ordered set of basic emotional units formed by decomposing the description of the perceived user's emotional state according to preset rules. Each unit corresponds to a specific feature of the emotional state, including core elements related to the user's emotional expression such as emotional tendency, expression intensity, and related scene clues. The core emotional points refer to key information extracted from the emotional element sequence that can centrally reflect the perceived user's core emotional needs. They are not scattered emotional elements, but core content that plays a decisive role in the user's emotional state. The details of the distribution of key points refer to a set of information formed after systematically sorting out the distribution characteristics of the core emotional points in the emotional element sequence, covering the weight ratio, frequency of occurrence, interrelationships, and emotional expression of each core emotional point. The content, including the scenarios in which Da Zhong works, serves as a visual representation of the distribution patterns of core emotional points. High-weight emotional points refer to core emotional points that, in the details of the point distribution, have a significantly higher weight than other points and exert a dominant influence on the determination of the perceived user's emotional needs. Their weighting is based on the degree of relevance and intensity of expression of emotional elements to the user's core demands, and they are key objects for matching with a pre-defined emotional need category library. The pre-defined emotional need category library is a pre-constructed structured database that stores various emotional need categories and corresponding feature identifiers, including definitions, typical characteristics, and matching rules for various emotional needs such as comfort, confiding, encouragement, companionship, and guidance. Each category has clear feature boundaries, providing a standardized reference for matching high-weight emotional points.

[0030] Optionally, the query of the emotional response level corresponding to the emotional need category can be achieved by pre-constructing a "emotional need category - emotional response level" mapping rule library, clarifying the level determination conditions corresponding to each category, using the Drools rule engine tool to load the rule library, and after inputting the emotional need category, the engine matches the corresponding rules and outputs the emotional response level.

[0031] S3. Analyze the emotional barrier boundaries triggered by each of the emotional response levels, and identify the emotional fluctuation nodes of the perceived user during the companionship process.

[0032] The embodiments of the present invention clearly define the interaction scale boundaries corresponding to different emotional responses, so as to avoid emotional feedback exceeding the user's acceptable range. At the same time, they accurately capture key nodes of user emotional changes, allowing the emotional companion to promptly perceive changes in the user's state, making the companionship process more in line with the user's emotional change patterns.

[0033] The emotional barrier boundary refers to the boundary standard that defines the scope and scale of interaction between emotional companions based on different emotional response levels. It covers dimensions such as the depth of emotional expression, the upper limit of interaction frequency, and the boundaries of content. Its setting is based on the user's acceptance in different emotional states, scene adaptability, and the principle of appropriateness of emotional interaction, which clarifies the boundaries of interactive behaviors that companions can carry out at specific response levels. The emotional fluctuation node refers to the specific time point or event trigger point in the process of emotional companionship where the user's emotional state changes significantly. It is characterized by features such as emotional tendency conversion, sudden change in emotional intensity, and obvious change in emotional expression. It is determined by the sudden change or continuous shift of emotional-related features in multimodal perception data. It is a landmark moment reflecting the user's emotional state turning point, so as to present the key node of the user's emotional change.

[0034] In this embodiment of the invention, the step of analyzing the emotional barrier boundary triggered by each emotional response level includes: querying the number of regular interactions and the depth of emotional topics corresponding to the emotional response level, and obtaining the emotional resistance index of the perceived user at the current interaction moment; determining the optimal interaction threshold corresponding to the emotional resistance index based on the number of regular interactions; and determining the emotional barrier boundary triggered by the emotional response level according to the depth of emotional topics and the optimal interaction threshold.

[0035] The "regular interaction frequency" refers to the standard frequency of emotional interaction between the emotional companion and the perceiving user within a unit of time, based on historical interaction data, scenario adaptability, and general user acceptance, for a specific emotional response level. It is a baseline interaction frequency determined through statistical analysis and applicable to regular scenarios at that response level. The "emotional topic depth" refers to the emotional level and degree of discussion involved in the emotional topics discussed between the emotional companion and the perceiving user at a specific emotional response level. It encompasses dimensions such as detailed exploration of emotional experiences and in-depth communication of inner feelings. Its division is based on the intensity of the emotional response level and the correlation between the topic and the user's emotional state, clearly defining the boundaries of topic discussion depth at that response level. The resistance index is a numerical indicator that reflects the user's degree of rejection or acceptance of the current emotional interaction, calculated through quantitative calculation based on perceived multimodal interaction data of the user, such as physical resistance, perfunctory tone, and dialogue response duration. The value directly corresponds to the strength of the user's resistance to the current interaction and is the core quantitative basis for judging whether the user is willing to continue the current emotional interaction. The optimal interaction threshold refers to a critical value that can balance the interaction effect and the user's acceptance, calculated through data modeling, combining the regular interaction frequency at a specific emotional response level and using the perceived user's current emotional resistance index as the core reference. It clarifies the upper limit of the maximum interaction frequency that the emotional companion can carry out under this resistance index and is a quantitative standard to avoid excessive interaction that causes user resistance.

[0036] In this embodiment of the invention, identifying the emotional fluctuation nodes of the perceived user during the companionship process includes: after obtaining the emotional state time sequence of the perceived user during the companionship process, setting an emotional change threshold for the perceived user in the current interaction scenario; based on the emotional change threshold, scanning abnormal fluctuation points in the emotional state time sequence, and marking the abnormal fluctuation points as emotional fluctuation nodes.

[0037] The emotional state time series refers to the set of perceived user emotional state data continuously recorded chronologically during the emotional companionship process. This data is collected and processed in real-time based on multimodal perception technology, covering core characteristics such as emotional tendency, emotional intensity, and expression at different time points. It fully presents the dynamic trajectory of the user's emotional state as the companionship process unfolds, serving as the foundational time series data for analyzing emotional fluctuations. The current interaction scenario refers to the specific virtual-real fusion environment in which the perceived user interacts with the emotional companion, including scenario types such as a confessional scenario, a leisure scenario, environmental atmosphere characteristics, the specific background of the interaction, and scenario-related constraints. It represents the specific context in which the emotional interaction occurs. The contextual carrier directly defines the scene boundaries for the perception and response to emotional states. The emotional change threshold refers to a pre-set critical standard for determining whether the perceived user's emotional state has changed significantly. It is set comprehensively based on the characteristics of the current interaction scene, the user's historical emotional fluctuation patterns, and the corresponding emotional response level, and is defined in the form of a clear numerical value or range. The abnormal fluctuation point refers to a specific data point in the emotional state time series where the amplitude and rate of change of the perceived user's emotional characteristics exceed the emotional change threshold. It is manifested as a significant deviation between the emotional state and the data of adjacent time nodes, objectively reflecting the sudden change or large shift in the user's emotional state, and is the direct basis for marking emotional fluctuation nodes.

[0038] S4. Analyze the number of interactions and feedback time limits generated by the emotional companion at the emotional fluctuation node, and calculate the perceived response amount of the emotional companion during the companionship process based on the number of interactions and the feedback time limits.

[0039] This invention quantifies the core parameters of interactive behavior at emotional fluctuation nodes, providing clear quantitative basis for emotional response, accurately controlling the frequency and timeliness of feedback, ensuring that the emotional companion's response at key nodes is both timely and not excessive, and transforming abstract emotional response into quantitative indicators, making the response during the companionship process more controllable.

[0040] The interaction count refers to the total number of emotional interactions initiated or responded to by the emotional companion after the user's emotional fluctuation point occurs. This includes various interactive behaviors related to emotional expression, such as voice dialogue, facial expression responses, and topic guidance. The statistical scope is limited to a specific time window after the fluctuation point occurs, and it is a specific indicator for quantifying the frequency of the companion's interactive behaviors. The feedback time limit refers to the time interval from when the system identifies the user's emotional fluctuation point to when the emotional companion outputs targeted emotional feedback. It is the core time definition standard for measuring the timeliness of the companion's response to the user's emotional fluctuation. Its duration is related to the level of emotional response and the characteristics of the scenario, and only reflects the time range attribute of the feedback. The perceived response quantity refers to a comprehensive indicator that quantifies the emotional companion's response performance, calculated by the following algorithm based on the interaction count and feedback time limit at the emotional fluctuation point. It integrates the interaction frequency and feedback time, reflecting the comprehensive situation of the companion's response strength and timeliness to the user's emotional state at that fluctuation point.

[0041] Optionally, the analysis of the number of interactions and feedback time limits generated by the emotional companion at the emotional fluctuation node can be performed by using the ELKStack log collection tool to record the full interaction logs between the emotional companion and the user, including information such as the interaction trigger time and the interaction type "voice / emoji". The logs are then filtered by time sequence using the Python pandas library to extract the interaction records within the time window of the emotional fluctuation node. The number of interactions is counted using the count function, and the difference between the node recognition time and the first feedback time is calculated using the datetime module to finally obtain the number of interactions and the feedback time limit.

[0042] In this embodiment of the invention, calculating the perceived response of the emotional companion during the companionship process based on the number of interactions and the feedback time limit includes: querying the interaction frequency of the number of interactions per unit time and determining the average feedback delay corresponding to the overall feedback time limit; analyzing the response efficiency value of the emotional companion during the companionship process; and calculating the perceived response of the emotional companion during the companionship process based on the interaction frequency, the average feedback delay, and the response efficiency value.

[0043] The interaction frequency refers to the average number of emotional interactions between the emotional companion and the perceiving user within a unit of time corresponding to an emotional fluctuation node. It is calculated based on the ratio of the total number of interactions within the time window of that node to the window duration. The unit of time can be preset according to the characteristics of the current interaction scenario. It is a core indicator for quantifying the intensity of interaction by the companion at key nodes, directly reflecting the temporal distribution characteristics of the interaction frequency. The average feedback delay refers to the arithmetic mean of all single feedback durations during the interaction process corresponding to the emotional fluctuation node. The single feedback duration is the time interval from when the node is identified to when the companion outputs the corresponding feedback. It is calculated by dividing the sum of the total feedback durations by the number of interactions, presenting the overall level of the companion's feedback delay at that node. It is a comprehensive time indicator for measuring the timeliness of feedback. The response efficiency value is a quantitative indicator set based on the emotional companion's feedback quality, interaction adaptability, and system operating status. Its judgment criteria include the degree of fit between the feedback content and the user's emotional needs, the smoothness of the interaction process, and the system resource utilization rate. It can be obtained by quantifying and weighting each dimension through a preset scoring model, and is a core parameter for comprehensively evaluating the companion's response performance.

[0044] Furthermore, in this embodiment of the invention, calculating the perceived response of the emotional companion during the companionship process based on the interaction frequency, the average feedback delay, and the response efficiency value includes: ; in, This represents the perceived response quantity. This represents the response efficiency value. Indicates the reference time constant. Indicates the total time spent together. Indicates the total number of interactions. Indicates the number of interactions index. This represents the feedback delay during the i-th interaction.

[0045] In detail, the perceived response quantity can represent the overall response performance of the companion during the companionship process. For example, assuming a response efficiency value REV = 0.8 and a reference time constant... =5 minutes, total companionship time T=30 minutes, total number of interactions N=6, feedback delay for interactions 1-6 The time intervals are 4, 5, 3, 6, 4, and 5 minutes respectively. Substituting these values ​​into the formula, PRQ≈1.17 can be calculated. The response efficiency value can represent a weighted quantification of dimensions such as the relevance of the emotional companion's feedback content, the smoothness of the interaction process, and the stability of the system operation. The value typically ranges from 0 to 1. For example, if the relevance of the companion's feedback to the user's needs is 80%, the smoothness of the interaction is 85%, and the system stability is 90%, calculated with each dimension weighted at 1 / 3, REV=(0.8+0.85+0.9) / 3≈0.85. The reference time constant can represent a preset baseline time value based on a typical single interaction cycle in similar virtual-real integrated emotional companionship scenarios. It is used for calculating the time dimension in the standardized formula. For example, in a leisure-type virtual-real integrated companionship scenario, the typical single interaction cycle is 5 minutes, therefore, the preset baseline time value for this scenario is... =5 minutes, serving as a reference benchmark for the time dimension; the total companionship time can represent the cumulative duration from the establishment of a companionship connection between the emotional companion and the perceiving user to the calculation of the perceiving response. For example: if the user enters the virtual-real fusion comfort scene at 14:00 and establishes a connection with the companion, and the perceiving response is calculated at 14:30, the total companionship time T = 30 minutes; the feedback delay can represent the time interval from the system recognizing the perceiving user's emotional fluctuation point to the emotional companion outputting the corresponding feedback. For example: during the third interaction, the system recognizes the user's emotional fluctuation point at 14:10, and the companion outputs feedback at 14:13, the feedback delay for this interaction is... =3 minutes; during the 5th interaction, node recognition occurred at 14:22, and feedback was output at 14:26. =4 minutes.

[0046] S5. Based on the perceived response quantity, after identifying the emotional feedback content output by the emotional companion, analyze the degree of matching of the emotional feedback content in the virtual-real fusion scenario to determine the emotional companion status of the emotional companion for the perceived user.

[0047] This invention uses perceived response volume as a basis to anchor emotional feedback content, ensuring the relevance of feedback to the user's current state, avoiding the disconnect between feedback content and the virtual-real integrated scenario, making the positioning of the companionship state more in line with the user's actual needs, strengthening the adaptability of emotional companionship, and making the interaction more in line with the current scenario and user state.

[0048] The emotional feedback content refers to the multimodal feedback set output by the emotional companion based on the perceived response quantity, targeting the perceived emotional state of the user. This includes AI-generated multi-tone and multi-emotional voice feedback, such as gentle, comforting words, encouraging expressions, and AI-generated... Facial expressions and basic motor responses, such as smiling emojis, soothing head shakes, and topic guidance, serve as concrete carriers for the companion to convey emotional support to the user through multimodal means. The content type and presentation format are related to the user's emotional state and the characteristics of the scenario. The degree of matching refers to the degree of fit between the emotional feedback content and the environmental characteristics of the virtual-real integrated scenario, as well as the perceived user's emotional state. This encompasses dimensions such as the adaptability of content style to the scene atmosphere, the fit of feedback format to user interaction habits, and the correspondence between content direction and user emotional needs. It describes the adaptation of the feedback content to the current interactive context. The emotional companionship state refers to the actual state of the emotional companion's support for the perceived user during the current companionship process, defined by the degree of matching between the emotional feedback content and the scenario. This includes categories such as good fit, poor fit, and need for adjustment. Its determination is directly related to the matching of the feedback content and is a state-based definition of the companion's current companionship performance.

[0049] In this embodiment of the invention, identifying the emotional feedback content output by the emotional companion based on the perceived response quantity includes: parsing the response intensity value and response time value in the perceived response quantity, analyzing the basic emotional tone of the response intensity value within a preset emotional intensity range; generating a dynamic expression mode that conforms to the basic emotional tone based on the response time value, and analyzing the emotional output tendency corresponding to the dynamic expression mode; and outputting the emotional feedback content of the emotional companion under scene adaptation based on the emotional output tendency.

[0050] The response intensity value refers to the core parameter quantifying the emotional support provider's response strength, extracted from the perceived response quantity. It is calculated based on data such as interaction frequency and emotional intensity of feedback content per unit time, directly reflecting the strength of the support provider's response to the user's emotional state. The response timeliness value refers to the parameter characterizing the timeliness of the emotional support provider's feedback, extracted from the perceived response quantity. It is derived from data such as average feedback delay and the time distribution characteristics of feedback output, reflecting the time efficiency of the support provider's feedback output at emotional fluctuation points and in subsequent interactions. The preset emotional intensity range refers to a pre-defined set of graded ranges divided according to the intensity of emotional expression, covering multiple ranges such as low intensity, medium intensity, and high intensity. Each range corresponds to a specific quantitative value range, and its division is based on the characteristics of the emotional support scenario and the general patterns of user emotional responses. The basic emotional tone refers to the core emotional tendency of the emotional support provider's feedback content, determined based on the response intensity value's placement within the preset emotional intensity range. Examples include gentle comfort, moderate encouragement, and strong empathy. It does not involve specific expression forms, only clarifying the core emotional direction of the feedback content, providing a basis for dynamic expression. This provides the basic framework; the dynamic expression method refers to the multimodal feedback presentation form determined based on the response time value and the basic emotional tone, encompassing the speech rate and rhythm of AI multi-timbre and multi-emotional voice feedback in multimodal feedback. For example, fast response time corresponds to a bright timbre and medium-speed rhythm, while slow response time corresponds to a soothing timbre, long pauses, and the amplitude of AI facial expressions and basic action feedback. For example, low-intensity emotion corresponds to a slight smile, while high-intensity emotion corresponds to frowning empathy + patting shoulder action and topic progression rhythm. It is the core carrier for the concrete expression of emotional tone through multimodal forms; the action feedback is a part of multimodal feedback. A key component, in collaboration with AI multi-tone and multi-emotional voice feedback, specifically refers to AI facial expressions and basic movement feedback generated based on the intensity and dynamic expression of emotions. This includes facial expressions such as "smiling, frowning, and eye contact" and basic body movements such as shaking the head. These are output in real time through AI visual presentation technology, forming a complete emotional feedback system together with voice feedback. The emotional output tendency refers to the specific emotional direction of the feedback content output by the emotional companion, further clarified based on dynamic expression, such as a tendency to soothe anxiety or an encouragement tendency for low mood.

[0051] Furthermore, in this embodiment of the invention, generating a dynamic expression mode that conforms to the basic emotional tone based on the response timeliness value includes: classifying the timeliness level corresponding to the response timeliness value, and querying the speech rate configuration and word complexity corresponding to the timeliness level; after determining the emotional expression intensity corresponding to the basic emotional tone, combining the speech rate configuration and the word complexity, outputting the emotional response content corresponding to the emotional companion; and detecting the dynamic expression mode of the emotional response content under the emotional expression intensity.

[0052] The timeliness level refers to a grading standard based on the numerical range of response timeliness values, covering different levels such as fast, medium, and slow. Each level corresponds to a specific response timeliness value range, such as fast corresponding to a timeliness value ≤ 2 minutes and medium corresponding to 2-5 minutes. This division is based on the common need for timely feedback in emotional companionship scenarios and serves as a preliminary classification basis for matching corresponding speech speed configurations, word complexity, and the amplitude of AI facial expressions and basic action feedback. The speech speed configuration refers to the speech speed parameter standards preset for emotional companionship voice feedback corresponding to different timeliness levels, including specific indicators such as the number of words per minute and the duration of pauses between sentences. For example, the fast timeliness level corresponds to 180 words per minute and short pauses, while the medium speed level corresponds to 150 words per minute and regular pauses, which is a standardized setting for the rhythm of emotional feedback voice expression. The word complexity refers to the language complexity standards set for emotional feedback content corresponding to different timeliness levels, covering the frequency of vocabulary use, sentence length, and structural complexity. For example, the fast timeliness level corresponds to 180 words per minute and short pauses, while the medium speed level corresponds to 150 words per minute and regular pauses, which is a standardized setting for the rhythm of emotional feedback voice expression. The effectiveness level uses common monosyllabic words and short sentences, while the medium-speed level can use slightly more complex disyllabic words and subject-verb-object short sentences, serving as the standard for the linguistic form of emotional feedback content. The emotional expression intensity refers to the standard of emotional feedback strength corresponding to the basic emotional tone, encompassing dimensions such as the degree of tone and the emotional concentration of the content. For example, a low intensity corresponds to a gentle and comforting basic emotional tone, while a high intensity corresponds to a strong empathy, serving as the basis for defining the strength of the emotional response content. The emotional response content refers to the multimodal feedback base generated by combining the speech rate configuration, word complexity, and emotional expression intensity corresponding to the basic emotional tone of the effectiveness level. This includes text-based voice scripts, such as those adapted to AI multi-tone and multi-emotion voice synthesis, and action command scripts, such as those adapted to AI facial expressions and basic action generation. For example, for content at the fast effectiveness level with a gentle and comforting tone, the script would be "Don't be too sad, I'm here with you" voice script + "smiling interaction" action expression script, serving as the core basis for multimodal feedback output.

[0053] Furthermore, in this embodiment of the invention, the emotional response content is specific interactive information composed of voice parameters, text statements and image elements, carrying the specific emotional expression generated by the emotional companion based on the perceived response quantity, and is used to conduct targeted emotional interaction with the perceived user in the virtual-real fusion scenario.

[0054] For details, please refer to Figure 2 The diagram shown is a flowchart illustrating a method for generating emotional feedback content based on perceived response quantities, according to an embodiment of the present invention. Figure 2In this process, the process begins with "Input: Perceived Response Volume," first breaking down the core parameters by "analyzing the response intensity value and response timeliness value." Then, it proceeds in two branches: one branch "analyzes the response timeliness value and classifies the timeliness level," and then "queries the corresponding speech rate configuration and word complexity"; the other branch "analyzes the basic emotional tone corresponding to the response intensity value," and then "determines the corresponding emotional expression intensity." Next, "combining speech rate, word complexity, and emotional expression intensity, it generates emotional response content," followed by "detecting its dynamic expression mode" and "analyzing the corresponding emotional output tendency." Finally, it forms "Output: Emotional Feedback Content," constructing a layer-by-layer deductive logic from perceived response volume to emotional feedback content.

[0055] Optionally, the analysis of the matching degree of the emotional feedback content in the virtual-real fusion scene can be achieved by first extracting the core features of the virtual-real fusion scene, converting the scene features and emotional feedback content into word vectors using the Word2Vec tool in gensim, and then using the cosine similarity algorithm to calculate the similarity value between the two vectors. This value is the matching degree of the emotional feedback content in the scene.

[0056] Specifically, determining the emotional companion's emotional support status for the perceived user can be achieved by using scene matching degree, perceived response amount, and user emotional fluctuation node feedback adaptation degree as input features. The SVM classification algorithm of the Python sklearn library is used to train the model based on "feature-support status" samples in historical interaction data. After inputting the target feature combination, the model directly outputs emotional support statuses such as "good adaptation / adaptation deviation / needs optimization".

[0057] Compared to the problems described in the background art, the embodiments of the present invention can overcome the limitations of single-modal perception, comprehensively capture the user's true emotional tendencies in virtual-real fusion scenarios, and improve the accuracy of emotional state perception. It allows the emotional companion to quickly adapt to scene characteristics and individual user emotional features, making the interaction more aligned with the user's current state. This helps establish more targeted emotional interactions, laying the foundation for long-term and stable emotional connections. Furthermore, the embodiments of the present invention can accurately identify the core emotional needs behind the interaction tasks, avoiding a one-sided interpretation of the user's emotional state and ensuring the accuracy of need positioning. By matching corresponding emotional response levels, the feedback of the emotional companion is made more aligned with the intensity of the user's current needs, reducing response deviations. Finally, the embodiments of the present invention clearly define the interaction scale boundaries corresponding to different emotional responses, preventing emotional feedback from exceeding the user's acceptable range, while accurately capturing changes in the user's emotions. At key junctures, the emotional companion allows for timely detection of user state anomalies, making the companionship process more aligned with the user's emotional changes. Next, this embodiment quantifies the core parameters of interactive behavior at emotional fluctuation points, providing clear quantitative basis for emotional responses, accurately controlling the frequency and timeliness of feedback, and ensuring that the emotional companion's response at key junctures is both timely and not excessive. Simultaneously, it transforms abstract emotional responses into quantitative indicators, making the response during companionship more controllable. Finally, this embodiment anchors emotional feedback content based on perceived response volume, ensuring the correlation between feedback and the user's current state, avoiding a disconnect between feedback content and the virtual-real integrated scenario, making the positioning of the companionship state more aligned with the user's actual needs, strengthening the adaptability of emotional companionship, and making the interaction more suitable for the current scenario and user state. Therefore, this invention can improve the accuracy and continuous optimization of the user's emotional companionship state.

[0058] like Figure 3 The diagram shown is a functional module diagram of the emotional companion cognitive analysis system for virtual-real fusion scenarios of the present invention.

[0059] The emotional companion cognitive analysis system 300 for virtual-real fusion scenarios described in this invention can be installed in an electronic device. Depending on the functions implemented, the emotional companion cognitive analysis system for virtual-real fusion scenarios may include a state description module 301, an emotional response module 302, a node recognition module 303, a response quantity calculation module 304, and a state determination module 305. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0060] In this embodiment of the invention, the functions of each module / unit are as follows: The state description module 301 is used to obtain the emotional state description of the perceived user in the virtual-real fusion scene after matching the emotional companion corresponding to the perceived user. The emotion response module 302 is used to identify the emotion need category corresponding to the interactive task in the emotion state description, and then query the emotion response level corresponding to the emotion need category. The node identification module 303 is used to analyze the emotional barrier boundary triggered by each emotional response level and identify the emotional fluctuation nodes of the perceived user during the companionship process. The response quantity calculation module 304 is used to analyze the number of interactions and feedback time limits generated by the emotional companion at the emotional fluctuation node, and calculate the perceived response quantity of the emotional companion during the companionship process based on the number of interactions and the feedback time limits. The state determination module 305 is used to identify the emotional feedback content output by the emotional companion based on the perceived response quantity, and then analyze the degree of matching of the emotional feedback content in the virtual-real fusion scene to determine the emotional companionship state of the emotional companion for the perceived user.

[0061] In detail, the modules in the emotional companion cognitive analysis system 300 for virtual-real fusion scenarios described in this embodiment of the invention employ the same methods as described above. Figure 1 The method used is the same as the cognitive analysis method for emotional companionship in the virtual-real fusion scenario described above, and can produce the same technical effect, so it will not be elaborated here.

[0062] In one embodiment, a computer device is provided, which may be a server or a client, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements functions or steps on the server or client side of a cognitive analysis method for emotional companionship in a virtual-real fusion scenario.

[0063] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: After matching the emotional companion corresponding to the perceived user, obtain the description of the perceived user's emotional state in the virtual-real fusion scenario; After identifying the emotional need category corresponding to the interactive task in the emotional state description, query the emotional response level corresponding to the emotional need category; Analyze the emotional barrier boundaries triggered by each of the emotional response levels, and identify the emotional fluctuation nodes of the perceived user during the companionship process; The number of interactions and the feedback time limit generated by the emotional companion at the emotional fluctuation node are analyzed, and the perceived response quantity of the emotional companion during the companionship process is calculated based on the number of interactions and the feedback time limit. Based on the perceived response quantity, after identifying the emotional feedback content output by the emotional companion, the degree of matching of the emotional feedback content in the virtual-real fusion scenario is analyzed to determine the emotional companionship status of the emotional companion for the perceived user.

[0064] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: After matching the emotional companion corresponding to the perceived user, obtain the description of the perceived user's emotional state in the virtual-real fusion scenario; After identifying the emotional need category corresponding to the interactive task in the emotional state description, query the emotional response level corresponding to the emotional need category; Analyze the emotional barrier boundaries triggered by each of the emotional response levels, and identify the emotional fluctuation nodes of the perceived user during the companionship process; The number of interactions and the feedback time limit generated by the emotional companion at the emotional fluctuation node are analyzed, and the perceived response quantity of the emotional companion during the companionship process is calculated based on the number of interactions and the feedback time limit. Based on the perceived response quantity, after identifying the emotional feedback content output by the emotional companion, the degree of matching of the emotional feedback content in the virtual-real fusion scenario is analyzed to determine the emotional companionship status of the emotional companion for the perceived user.

[0065] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0066] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0067] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0068] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0069] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A cognitive analysis method for emotional companionship in a virtual-real integrated scenario, characterized in that, The system includes: After matching the emotional companion corresponding to the perceived user, obtain the description of the perceived user's emotional state in the virtual-real fusion scenario; After identifying the emotional need category corresponding to the interactive task in the emotional state description, query the emotional response level corresponding to the emotional need category; Analyze the emotional barrier boundaries triggered by each of the emotional response levels, and identify the emotional fluctuation nodes of the perceived user during the companionship process; The number of interactions and the feedback time limit generated by the emotional companion at the emotional fluctuation node are analyzed, and the perceived response quantity of the emotional companion during the companionship process is calculated based on the number of interactions and the feedback time limit. Based on the perceived response quantity, after identifying the emotional feedback content output by the emotional companion, the degree of matching of the emotional feedback content in the virtual-real fusion scenario is analyzed to determine the emotional companionship status of the emotional companion for the perceived user.

2. The method for analyzing the cognitive relationship of emotional companions in a virtual-real fusion scenario as described in claim 1, characterized in that, The step of identifying the emotional feedback content output by the emotional companion based on the perceived response quantity includes: After analyzing the response intensity value and response time value in the perceived response quantity, the basic emotional tone of the response intensity value in the preset emotional intensity range is analyzed; Based on the response time value, a dynamic expression mode that conforms to the basic emotional tone is generated, and the emotional output tendency corresponding to the dynamic expression mode is analyzed. Based on the stated emotional output tendency, the emotional feedback content of the emotional companion is output under the scene adaptation.

3. The cognitive analysis method for emotional companionship in a virtual-real fusion scenario as described in claim 2, characterized in that, The step of generating a dynamic expression that conforms to the basic emotional tone based on the response time value includes: The response timeliness value is classified into timeliness levels, and the speech rate configuration and word complexity corresponding to the timeliness level are queried. After determining the intensity of emotional expression corresponding to the basic emotional tone, the emotional response content corresponding to the emotional companion is output in combination with the speech rate configuration and the word complexity. The dynamic expression of the emotional response content under the emotional expression intensity is detected.

4. The method for analyzing the cognitive relationship of emotional companions in a virtual-real fusion scenario as described in claim 3, characterized in that, The emotional response content is specific interactive information composed of voice parameters, text statements and image elements. It carries the specific emotional expression generated by the emotional companion based on the perceived response amount and is used to conduct targeted emotional interaction with the perceived user in the virtual-real fusion scenario.

5. The method for analyzing the cognitive relationship of emotional companions in a virtual-real fusion scenario as described in claim 1, characterized in that, The step of obtaining the description of the perceived user's emotional state in the virtual-real fusion scenario includes: After collecting the comprehensive interaction information corresponding to the perceived user, the behavioral expression elements in the comprehensive interaction information are extracted; The multidimensional expression vectors of the behavioral expression elements in the virtual-real fusion scenario are analyzed, and an emotional state description corresponding to the perceived user is generated based on the multidimensional expression vectors.

6. The method for analyzing the cognitive relationship of emotional companions in a virtual-real fusion scenario as described in claim 1, characterized in that, The identification of the emotional need category corresponding to the interaction task in the emotional state description includes: After decomposing the emotional state description into a sequence of emotional elements, the distribution details of the key points corresponding to the core emotional points in the sequence of emotional elements are generated. Extract the high-weight sentiment points from the key point distribution details; The high-weighted emotional points are matched with a preset emotional need category library to obtain emotional need categories.

7. The method for analyzing the cognitive relationship of emotional companions in a virtual-real fusion scenario as described in claim 1, characterized in that, The analysis of the emotional barrier limits triggered by each of the emotional response levels includes: Query the number of regular interactions and the depth of emotional topics corresponding to the emotional response level, and obtain the emotional resistance index of the perceived user at the current interaction moment; Based on the number of regular interactions, determine the optimal interaction threshold corresponding to the emotional resistance index; Based on the depth of the emotional topic and the optimal interaction threshold, the emotional barrier boundary triggered by the emotional response level is determined.

8. The method for analyzing the cognitive relationship of emotional companions in a virtual-real fusion scenario as described in claim 1, characterized in that, The identification of the emotional fluctuations of the perceived user during the companionship process includes: After obtaining the time sequence of the perceived user's emotional state during the companionship process, a threshold for the perceived user's emotional change in the current interaction scenario is set. Based on the emotional change threshold, abnormal fluctuation points in the emotional state time series are scanned, and the abnormal fluctuation points are marked as emotional fluctuation nodes.

9. The method for analyzing the cognitive relationship of emotional companions in a virtual-real fusion scenario as described in claim 1, characterized in that, The calculation of the perceived response of the emotional companion during the companionship process, based on the number of interactions and the feedback time limit, includes: Query the interaction frequency within a unit of time for the number of interactions, and determine the average feedback delay corresponding to the overall feedback time limit; Analyze the response efficiency of the emotional companion during the companionship process; Based on the interaction frequency, the average feedback delay, and the response efficiency value, the perceived response of the emotional companion during the companionship process is calculated.

10. A cognitive analysis system for emotional companionship in a virtual-real integrated scenario, characterized in that, The system includes: The state description module is used to obtain the emotional state description of the perceived user in the virtual-real fusion scenario after matching the emotional companion corresponding to the perceived user. The emotion response module is used to identify the emotion need category corresponding to the interactive task in the emotion state description, and then query the emotion response level corresponding to the emotion need category. The node identification module is used to analyze the emotional barrier boundary triggered by each of the emotional response levels and identify the emotional fluctuation nodes of the perceived user during the companionship process. The response quantity calculation module is used to analyze the number of interactions and feedback time limits generated by the emotional companion at the emotional fluctuation node, and calculate the perceived response quantity of the emotional companion during the companionship process based on the number of interactions and the feedback time limits. The state determination module is used to identify the emotional feedback content output by the emotional companion based on the perceived response quantity, and then analyze the degree of matching of the emotional feedback content in the virtual-real fusion scene to determine the emotional companionship state of the emotional companion for the perceived user.

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