Intelligent virtual accompanying robot scene dialogue rhythm control system

Abstract

The invention discloses an intelligent virtual accompanying robot scene dialogue rhythm control system, and belongs to the technical field of robot dialogue. Comprising a data acquisition module used for acquiring voice data of historical speaking; the historical vector extraction module is used for storing the low-dimensional rhythm shape vectors in a vector database; the online clustering module is used for generating a plurality of low-dimensional rhythm clusters and response templates; the current vector extraction module is used for extracting a rhythm shape vector; the similarity acquisition module is used for acquiring the similarity; the rhythm response module is used for calling a rhythm response template; and the new cluster generation module is used for generating a new rhythm cluster on line and creating a new template. According to the invention, the voice rhythm of the user is compressed into the low-dimensional shape vector, the exclusive rhythm cluster is formed through online clustering, and zero-delay matching is carried out in the real-time dialogue in a sliding slicing manner, so that the technical effects of instant response to the dialogue rhythm and long-term memory of the personal style are achieved; the problems of rhythm response lag and lack of context long-term memory in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of robot dialogue technology, and in particular to a situational dialogue rhythm control system for an intelligent virtual companion robot. Background Art

[0002] With the increasing adoption of intelligent virtual companion robots in settings such as homes, education, and healthcare, the naturalness and fluency of human-machine conversations have become crucial indicators of user experience. To meet user demands for efficient responses and personalized interactions with companion robots, precise control and dynamic adaptation of conversational rhythm are essential. Existing intelligent virtual companion robot systems perform full-sentence statistical analysis of user speech rhythm based on voice activity detection and acoustic feature thresholds to determine the timing and pauses for robot responses.

[0003] In the existing technology, there is a significant delay in the system's extraction of rhythm parameters for users' rapid speeches, and there is a lack of long-term memory and personalized processing of users' historical speaking styles, resulting in problems of delayed rhythm parameter response and loss of long-term context memory. Summary of the Invention

[0004] The embodiments of the present application solve the problems of rhythm parameter response lag and long-term context memory loss in the prior art by providing an intelligent virtual companion robot scenario dialogue rhythm control system, and realize the system's millisecond-level prediction and triggering of rhythm responses in multi-user concurrent dialogue scenarios and continuous personalized adaptation to users' long-term communication styles.

[0005] The embodiment of the present application provides a scenario dialogue rhythm control system for an intelligent virtual companion robot, comprising: a data acquisition module: for acquiring voice data of a user's historical speech and a unique user identifier;

[0006] A history vector extraction module is used to extract a low-dimensional rhythm shape vector from the acquired speech data, associate the low-dimensional rhythm shape vector with a unique user identifier, and store it in a vector database;

[0007] Online clustering module: used to perform online clustering of low-dimensional rhythm shape vectors, dynamically generate multiple low-dimensional rhythm clusters and their associated low-dimensional rhythm response templates that refer to the rhythm styles corresponding to the stored low-dimensional rhythm shape vectors;

[0008] Current vector extraction module: used to continuously extract rhythm shape vectors from the user's speech in the current conversation;

[0009] Similarity acquisition module: used to obtain the similarity between the rhythm shape vector and the low-dimensional rhythm cluster center vector, and compare the similarity with a preset dynamically adjustable matching threshold;

[0010] Rhythm response module: when the similarity is not less than the matching threshold, it is determined that the match is successful and the rhythm response template associated with the successfully matched low-dimensional rhythm cluster is called;

[0011] New cluster generation module: When a new unmatched rhythm pattern appears and its frequency reaches a preset frequency threshold, it determines that the matching has failed, enables the default rhythm template, generates a new rhythm cluster online, and creates a new template.

[0012] Furthermore, the step of extracting a low-dimensional rhythm shape vector from the acquired speech data includes:

[0013] Standardizing the acquired voice data and dividing the standardized voice data into frames;

[0014] Segment the continuous speech stream into multiple short-term speech frames and preprocess the short-term speech frames;

[0015] Extracting acoustic features reflecting speech rhythm characteristics based on preprocessed short-term speech frames, including fundamental frequency variation, energy fluctuation, speech rate information, and sound length distribution of speech, and performing quantitative analysis on the acoustic features;

[0016] The extracted acoustic features are integrated and dimensionally reduced, and the high-dimensional acoustic features are mapped to a low-dimensional space to form a low-dimensional rhythm shape vector.

[0017] Furthermore, the steps of performing online clustering on the low-dimensional rhythm shape vectors and dynamically generating a plurality of low-dimensional rhythm clusters representing rhythm styles corresponding to the stored low-dimensional rhythm shape vectors and their associated low-dimensional rhythm response templates include:

[0018] Calculate the centroid distance of each vector, classify the vectors whose distance is less than the set threshold into the same set, and average the set to obtain the new low-dimensional rhythm cluster center vector;

[0019] Establishing a unique low-dimensional rhythm cluster identifier in each new low-dimensional rhythm cluster center vector, and creating a rhythm response template with the same name as the low-dimensional rhythm cluster identifier;

[0020] The centroid distance of each vector is obtained by the centroid distance calculation formula:

[0021] ;

[0022] Where, is the dynamic time series weighted centroid distance, is the M-dimensional component of the current rhythm shape vector, is the M-dimensional component of the center vector of the corresponding rhythm cluster, For the The weight coefficient of the dimension feature, is the time attenuation coefficient, is the timestamp of the current vector, is the calibration timestamp of the cluster center vector, is the time normalization constant, is a natural constant, is the current rhythm shape vector No. dimensional component, is the rhythm cluster center vector No. dimensional component, is the total number of feature dimensions.

[0023] Furthermore, the step of averaging the set to obtain a new low-dimensional rhythm cluster center vector includes:

[0024] The new low-dimensional rhythm cluster center vector is obtained by calculating the weighted average formula:

[0025] ;

[0026] Where, is the vector number, For the set The first vector dimensional component, is the total number of vectors used to update cluster centers, is the weight based on time decay, where It is timestamps of a vector, is the current time when this cluster update is triggered, is the time attenuation coefficient, is the time normalization constant, For the The weight coefficient of the dimension feature.

[0027] Furthermore, the step of obtaining the similarity between the rhythm shape vector and the low-dimensional rhythm cluster center vector includes:

[0028] Obtain the latest low-dimensional rhythm shape vector continuously extracted from the user's speech in the current conversation as input data for similarity calculation;

[0029] Retrieve the center vectors of all generated low-dimensional rhythm clusters from the vector database;

[0030] The preset similarity calculation formula is used to calculate the similarity between the latest extracted low-dimensional rhythm shape vector and each retrieved low-dimensional rhythm cluster center vector.

[0031] Furthermore, the similarity calculation formula is:

[0032] ;

[0033] Where, is the weighted cosine similarity with temporal decay, is the currently extracted low-dimensional rhythm shape vector, is the low-dimensional shape vector corresponding to the center of the rhythm cluster, For the dimensional feature weights, To form the basis of weighted cosine similarity, is the timing attenuation factor, is the time series decay rate parameter, is the timestamp of the current vector, is the timestamp of the cluster center vector, is the time normalization constant, is the current rhythm shape vector No. dimensional component, is the rhythm cluster center vector No. dimensional component, is the total number of feature dimensions.

[0034] Furthermore, the step of dynamically adjusting the matching threshold includes:

[0035] Continuously monitor the overall performance of the robot's scenario dialogue rhythm control system, including the rhythm pattern matching success rate and matching failure rate;

[0036] According to the preset dynamic adjustment formula and combined with the monitored overall operation performance data, the matching threshold is adjusted in real time;

[0037] The dynamically adjusted matching threshold is applied to the subsequent similarity comparison process between the rhythm shape vector and the low-dimensional rhythm cluster center vector.

[0038] Furthermore, the preset dynamic adjustment formula is:

[0039] ;

[0040] Where, for Dynamic matching threshold updated at all times, is the initial baseline threshold, for Real-time matching success rate at each moment, is the target matching success rate, for Real-time matching failure rate at each moment, is the target matching failure rate, for User feedback ratings at all times, Rating target user feedback, is the success rate sensitivity coefficient, is the failure rate sensitivity coefficient, is the user feedback weight coefficient, The timestamp of the last threshold adjustment. is the time attenuation coefficient, is the time normalization constant.

[0041] Furthermore, when the similarity is not less than the matching threshold, it is determined that the match is successful, and the steps of calling the rhythm response template associated with the successfully matched low-dimensional rhythm cluster include:

[0042] Compare the obtained similarity value with the matching threshold;

[0043] When the similarity value is not less than the matching threshold, it is determined that the current user's rhythm pattern successfully matches the rhythm style of one of the low-dimensional rhythm clusters, and the successfully matched low-dimensional rhythm cluster is identified;

[0044] Call and enable the low-dimensional rhythm response template associated with the rhythm cluster to guide the robot to generate conversation content that conforms to the user's rhythm.

[0045] Furthermore, the steps of determining that the matching fails, enabling the default rhythm template, generating a new rhythm cluster online, and creating a new template include:

[0046] After similarity comparison, if the current rhythm shape vector fails to successfully match all existing low-dimensional rhythm clusters, it is determined that a new unmatched rhythm pattern has appeared;

[0047] Continuously tracking and counting the frequency of occurrence of the new pattern, and when the frequency reaches a preset frequency threshold, confirming it as a new rhythm style worthy of recognition;

[0048] The preset default rhythm template is enabled, and a new low-dimensional rhythm cluster is generated online based on the confirmed features that do not match the new rhythm pattern, and a new low-dimensional rhythm response template is created and associated for the new cluster.

[0049] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0050] 1. By collecting user speeches in the background and compressing multi-dimensional rhythm fluctuations into low-dimensional shape vectors, we can achieve high-speed extraction and matching of user speech rhythm features, and then realize millisecond-level prediction and triggering of rhythm responses in multi-user concurrent conversation scenarios, effectively solving the problems of delayed rhythm parameter response and lack of real-time performance in existing technologies.

[0051] 2. By dynamically adjusting the threshold based on the matching success rate and drift degree, accurate recognition and classification of various rhythm patterns can be achieved, thereby enabling the system to continuously remember the user's long-term communication style and adapt to the personalized rhythm, effectively solving the problem of the lack of long-term contextual memory and rhythm style adaptation capabilities in existing technologies.

[0052] 3. By triggering the generation of new clusters based on the frequency threshold of unmatched new rhythm patterns and creating corresponding response templates online, the system can achieve self-learning and online evolution of emerging or changing rhythm styles, thereby enabling the robot to continuously provide a coherent conversation experience that matches the user's rhythm in different users and conversation environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a structural diagram of the intelligent virtual companion robot scenario dialogue rhythm control system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The embodiments of the present application solve the problems of rhythm parameter response lag and long-term context memory loss in the prior art by providing an intelligent virtual companion robot scenario dialogue rhythm control system. By silently collecting and compressing low-dimensional rhythm shape vectors in the background and combining online clustering to dynamically match rhythm response templates, the system achieves millisecond-level prediction and triggering of rhythm responses in multi-user concurrent dialogue scenarios and continuous personalized adaptation to users' long-term communication styles.

[0055] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0056] like Figure 1 The figure shows a schematic diagram of the structure of the intelligent virtual companion robot scenario dialogue rhythm control system provided by an embodiment of the present application, including: a data acquisition module: used to obtain the voice data of the user's historical speech and a unique user identifier, the voice data including the speech duration, speaking speed and pause information; the unique user identifier is generated during the voiceprint registration stage;

[0057] A history vector extraction module is used to extract a low-dimensional rhythm shape vector from the acquired speech data using Fourier transform technology, and associate the low-dimensional rhythm shape vector with a unique user identifier and store it in a vector database;

[0058] Online clustering module: used to perform online clustering on stored low-dimensional rhythm shape vectors to identify common low-dimensional rhythm patterns, dynamically generate and maintain multiple low-dimensional rhythm clusters that refer to the rhythm styles corresponding to the stored low-dimensional rhythm shape vectors and their associated low-dimensional rhythm response templates;

[0059] Current vector extraction module: used to continuously extract rhythm shape vectors from the user's speech through a sliding window in the current conversation;

[0060] Similarity acquisition module: used to obtain the similarity between the rhythm shape vector and the low-dimensional rhythm cluster center vector, and compare the similarity with the preset dynamically adjustable matching threshold; the matching threshold is adaptively adjusted based on the user's rhythm drift and historical success rate.

[0061] Rhythm response module: when the similarity is not less than the matching threshold, it is determined that the match is successful and the rhythm response template associated with the successfully matched low-dimensional rhythm cluster is called;

[0062] New cluster generation module: When a new unmatched rhythm pattern appears and its frequency reaches a preset frequency threshold, it determines that the matching has failed, enables the default rhythm template, generates a new rhythm cluster online, and creates a new template.

[0063] Furthermore, the step of extracting a low-dimensional rhythm shape vector from the acquired speech data includes:

[0064] Standardize the acquired voice data, including performing noise reduction on the voice signal, removing background noise, and performing frame processing on the standardized voice data;

[0065] Split the continuous speech stream into multiple short-term speech frames and pre-process the short-term speech frames to ensure the accuracy of subsequent feature extraction;

[0066] Extracting acoustic features reflecting speech rhythmic characteristics based on preprocessed short-term speech frames. These acoustic features include fundamental frequency variation, energy fluctuations, speech rate information, and tone length distribution. These acoustic features are then quantitatively analyzed to capture the inherent rhythmic patterns of the user's speech.

[0067] The extracted acoustic features are integrated and dimensionally reduced, and the high-dimensional acoustic features are mapped to a low-dimensional space to form a low-dimensional rhythm shape vector. This vector can effectively represent the user's unique speaking rhythm characteristics and provide basic data for subsequent rhythm pattern recognition.

[0068] Furthermore, the steps of performing online clustering on the low-dimensional rhythm shape vectors and dynamically generating a plurality of low-dimensional rhythm clusters representing rhythm styles corresponding to the stored low-dimensional rhythm shape vectors and their associated low-dimensional rhythm response templates include:

[0069] Calculate the centroid distance of each vector, classify the vectors whose distance is less than the set threshold into the same set, and average the set to obtain the new low-dimensional rhythm cluster center vector;

[0070] Establishing a unique low-dimensional rhythm cluster identifier in each new low-dimensional rhythm cluster center vector, and creating a rhythm response template with the same name as the low-dimensional rhythm cluster identifier, to obtain a low-dimensional rhythm cluster that refers to the rhythm style corresponding to the stored low-dimensional rhythm shape vector and its associated low-dimensional rhythm response template;

[0071] The centroid distance of each vector is obtained by the centroid distance calculation formula:

[0072] ;

[0073] Where, is the dynamic time series weighted centroid distance, which is used to measure the difference between the current vector and the cluster center. is the M-dimensional component of the current rhythm shape vector, is the M-dimensional component of the center vector of the corresponding rhythm cluster, For the The weight coefficient of the dimension feature is automatically assigned by the online clustering module according to the inverse of the variance of the dimension within the cluster, which is used to highlight the importance of low-variance dimensions in distance calculation. is the time attenuation coefficient, is the timestamp of the current vector, is the calibration timestamp of the cluster center vector, is the time normalization constant, is the current rhythm shape vector No. dimensional component, is the rhythm cluster center vector No. dimensional component, is the total number of feature dimensions;

[0074] Using the original rhythm fluctuation sequence corresponding to the center vector of the low-dimensional rhythm cluster as a reference, the position and length of each pause point in the sequence are extracted to obtain the average speaking rate and average pause interval of the sequence. The average speaking rate is mapped to the response delay time, and the pause position sequence is retained as a pause position list. The computer combines the response delay time and the pause position list into a rhythm response template, and the template name is consistent with the low-dimensional rhythm cluster identifier;

[0075] After the computer completes the creation, it immediately writes the low-dimensional rhythm cluster center vector, the low-dimensional rhythm cluster identifier, and the rhythm response template into the cluster partition of the vector database for subsequent real-time call.

[0076] Furthermore, the step of averaging the set to obtain a new low-dimensional rhythm cluster center vector includes:

[0077] The new low-dimensional rhythm cluster center vector is obtained by calculating the weighted average formula:

[0078] ;

[0079] Where, is the vector number, For the set The first vector dimensional component, is the total number of vectors used to update cluster centers, is the weight based on time decay, where It is timestamps of a vector, is the current time when this cluster update is triggered, is the time attenuation coefficient, is the time normalization constant, For the The weight coefficient of the dimension feature is used to emphasize the impact of the dimension with smaller variance within the corresponding cluster on the center update.

[0080] Furthermore, the step of obtaining the similarity between the rhythm shape vector and the low-dimensional rhythm cluster center vector includes:

[0081] Obtaining the latest low-dimensional rhythm shape vector continuously extracted from the user's speech in the current conversation, wherein the latest low-dimensional rhythm shape vector represents the user's current speaking rhythm style as input data for similarity calculation;

[0082] Retrieve the center vectors of all generated low-dimensional rhythm clusters from the vector database. These center vectors represent different, identified rhythm style patterns and provide reference points for subsequent similarity comparisons.

[0083] Using the preset similarity calculation formula, the similarity calculation is performed on the latest low-dimensional rhythm shape vector extracted and the center vector of each retrieved low-dimensional rhythm cluster. This calculation process quantifies the degree of match between the current user rhythm and the known rhythm style, and generates a similarity value that reflects the degree of closeness between the two.

[0084] Furthermore, the similarity calculation formula is:

[0085] ;

[0086] Where, is the weighted cosine similarity with time decay, dimensionless, is the currently extracted low-dimensional rhythm shape vector, each component of which is a dimensionless feature. is the low-dimensional shape vector corresponding to the center of the rhythm cluster, and each component is related to Same dimension, For the Dimension feature weight, dimensionless, is automatically assigned by the online clustering module based on the inverse of the variance of the dimension within the cluster. To form the basis of weighted cosine similarity, is the time series attenuation factor, dimensionless, ensuring that the distance similarity varies with time Increases and decreases smoothly, It is the time decay rate parameter, the dimension is "reciprocal per time unit", which is used to adjust the speed of time decay. is the timestamp of the current vector, the dimension is time, is the timestamp of the cluster center vector, the dimension is time, is the time normalization constant, the dimension is time, used to standardize the time difference, is the current rhythm shape vector No. dimensional component, is the rhythm cluster center vector No. dimensional component, is the total number of feature dimensions.

[0087] Furthermore, the step of dynamically adjusting the matching threshold includes:

[0088] Continuously monitor the overall performance of the robot's contextual dialogue rhythm control system, including the rhythm pattern matching success rate, matching failure rate, and user feedback on dialogue rhythm adjustments. This data provides a basis for threshold adjustments.

[0089] Based on a preset dynamic adjustment formula and combined with the monitored overall performance data, the matching threshold is adjusted in real time. For example, when the matching success rate is too high or too low, the system will increase or decrease the matching threshold accordingly to optimize the recognition accuracy and adaptability of rhythm patterns.

[0090] The dynamically adjusted matching threshold is applied to the subsequent similarity comparison between the rhythm shape vector and the low-dimensional rhythm cluster center vector, ensuring that the system can flexibly determine the matching results of the rhythm pattern based on the latest operating status and optimization goals, thereby improving the accuracy of dialogue rhythm control.

[0091] Furthermore, the preset dynamic adjustment formula is:

[0092] ;

[0093] Where, for The dynamic matching threshold after the moment is updated, dimensionless, and the value range is , directly used for subsequent comparison and judgment of similarity and threshold, is the initial baseline threshold, for The real-time matching success rate at each moment is dimensionless and reflects the current system's ability to recognize known rhythm patterns. is the target matching success rate, dimensionless, representing the ideal success rate level expected to be achieved. for Real-time matching failure rate at the moment, dimensionless, is the target matching failure rate, dimensionless, for User feedback rating at the moment, dimensionless, Rating for target user feedback, dimensionless, is the success rate sensitivity coefficient, dimensionless, is the failure rate sensitivity coefficient, dimensionless, is the user feedback weight coefficient, dimensionless, The timestamp of the last threshold adjustment, the dimension is time, is the time decay coefficient, dimensionless, is the time normalization constant, and its dimension is time.

[0094] Furthermore, when the similarity is not less than the matching threshold, it is determined that the match is successful, and the steps of calling the rhythm response template associated with the successfully matched low-dimensional rhythm cluster include:

[0095] Compare the obtained similarity value with the matching threshold to determine whether the current user's rhythm pattern matches the identified rhythm style pattern;

[0096] When the similarity value is not less than the matching threshold, it is determined that the current user's rhythm pattern successfully matches the rhythm style of one of the low-dimensional rhythm clusters, and the successfully matched low-dimensional rhythm cluster is identified;

[0097] The low-dimensional rhythm response template associated with the rhythm cluster is called and enabled. The template contains the dialogue response strategy and rhythm adjustment parameters for the rhythm style corresponding to the stored low-dimensional rhythm shape vector, guiding the robot to generate dialogue content that conforms to the user's rhythm.

[0098] Furthermore, the steps of determining that the matching fails, enabling the default rhythm template, generating a new rhythm cluster online, and creating a new template include:

[0099] After similarity comparison, if the current rhythm shape vector fails to successfully match all existing low-dimensional rhythm clusters, it is determined that a new unmatched rhythm pattern has appeared;

[0100] Continuously tracking and counting the frequency of occurrence of the new pattern, and when the frequency reaches a preset frequency threshold, confirming it as a new rhythm style worthy of recognition;

[0101] The preset default rhythm template is enabled, and a new low-dimensional rhythm cluster is generated online based on the confirmed features that do not match the new rhythm pattern. A new low-dimensional rhythm response template is created and associated with the new cluster, which will define the corresponding dialogue response strategy and rhythm adjustment parameters.

[0102] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0106] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0107] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. The intelligent virtual companion robot scenario dialogue rhythm control system is characterized by: include: Data acquisition module: used to obtain the user's historical speech data and unique user identification; A history vector extraction module is used to extract a low-dimensional rhythm shape vector from the acquired speech data, associate the low-dimensional rhythm shape vector with a unique user identifier, and store it in a vector database; Online clustering module: used to perform online clustering of low-dimensional rhythm shape vectors, dynamically generate multiple low-dimensional rhythm clusters and their associated low-dimensional rhythm response templates that refer to the rhythm styles corresponding to the stored low-dimensional rhythm shape vectors; Current vector extraction module: used to continuously extract rhythm shape vectors from the user's speech in the current conversation; Similarity acquisition module: used to obtain the similarity between the rhythm shape vector and the low-dimensional rhythm cluster center vector, and compare the similarity with a preset dynamically adjustable matching threshold; Rhythm response module: when the similarity is not less than the matching threshold, it is determined that the match is successful and the rhythm response template associated with the successfully matched low-dimensional rhythm cluster is called; New cluster generation module: When a new unmatched rhythm pattern appears and its frequency reaches a preset frequency threshold, it determines that the matching has failed, enables the default rhythm template, generates a new rhythm cluster online, and creates a new template.

2. The intelligent virtual companion robot scenario dialogue rhythm control system according to claim 1, characterized in that: The steps of extracting a low-dimensional rhythm shape vector from the acquired speech data include: Standardizing the acquired voice data and dividing the standardized voice data into frames; Segment the continuous speech stream into multiple short-term speech frames and preprocess the short-term speech frames; Extracting acoustic features reflecting speech rhythm characteristics based on preprocessed short-term speech frames, including fundamental frequency variation, energy fluctuation, speech rate information, and sound length distribution of speech, and performing quantitative analysis on the acoustic features; The extracted acoustic features are integrated and dimensionally reduced, and the high-dimensional acoustic features are mapped to a low-dimensional space to form a low-dimensional rhythm shape vector.

3. The intelligent virtual companion robot scenario dialogue rhythm control system according to claim 1, characterized in that: The steps of online clustering the low-dimensional rhythm shape vectors to dynamically generate a plurality of low-dimensional rhythm clusters and their associated low-dimensional rhythm response templates referring to rhythm styles corresponding to the stored low-dimensional rhythm shape vectors include: Calculate the centroid distance of each vector, classify the vectors whose distance is less than the set threshold into the same set, and average the set to obtain the new low-dimensional rhythm cluster center vector; Establishing a unique low-dimensional rhythm cluster identifier in each new low-dimensional rhythm cluster center vector, and creating a rhythm response template with the same name as the low-dimensional rhythm cluster identifier; The centroid distance of each vector is obtained by the centroid distance calculation formula: ； Where, is the dynamic time series weighted centroid distance, is the M-dimensional component of the current rhythm shape vector, is the M-dimensional component of the center vector of the corresponding rhythm cluster, For the The weight coefficient of the dimension feature, is the time attenuation coefficient, is the timestamp of the current vector, is the calibration timestamp of the cluster center vector, is the time normalization constant, is a natural constant, is the current rhythm shape vector No. dimensional component, is the rhythm cluster center vector No. dimensional component, is the total number of feature dimensions.

4. The intelligent virtual companion robot scenario dialogue rhythm control system according to claim 3, characterized in that: The steps of averaging the set to obtain a new low-dimensional rhythm cluster center vector include: The new low-dimensional rhythm cluster center vector is obtained by calculating the weighted average formula: ； Where, is the vector number, For the set The first vector dimensional component, is the total number of vectors used to update cluster centers, is the weight based on time decay, where It is timestamps of a vector, is the current time when this cluster update is triggered, is the time attenuation coefficient, is the time normalization constant, For the The weight coefficient of the dimension feature.

5. The intelligent virtual companion robot scenario dialogue rhythm control system according to claim 1, characterized in that: The steps of obtaining the similarity between the rhythm shape vector and the low-dimensional rhythm cluster center vector include: Obtain the latest low-dimensional rhythm shape vector continuously extracted from the user's speech in the current conversation as input data for similarity calculation; Retrieve the center vectors of all generated low-dimensional rhythm clusters from the vector database; The preset similarity calculation formula is used to calculate the similarity between the latest extracted low-dimensional rhythm shape vector and each retrieved low-dimensional rhythm cluster center vector.

6. The intelligent virtual companion robot scenario dialogue rhythm control system according to claim 5, characterized in that: The similarity calculation formula is: ； Where, is the weighted cosine similarity with temporal decay, is the currently extracted low-dimensional rhythm shape vector, is the low-dimensional shape vector corresponding to the center of the rhythm cluster, For the dimensional feature weights, To form the basis of weighted cosine similarity, is the timing attenuation factor, is the time series decay rate parameter, is the timestamp of the current vector, is the timestamp of the cluster center vector, is the time normalization constant, is the current rhythm shape vector No. dimensional component, is the rhythm cluster center vector No. dimensional component, is the total number of feature dimensions.

7. The intelligent virtual companion robot scenario dialogue rhythm control system according to claim 1, characterized in that: The steps for dynamically adjusting the matching threshold include: Continuously monitor the overall performance of the robot's scenario dialogue rhythm control system, including the rhythm pattern matching success rate and matching failure rate; According to the preset dynamic adjustment formula and combined with the monitored overall operation performance data, the matching threshold is adjusted in real time; The dynamically adjusted matching threshold is applied to the subsequent similarity comparison process between the rhythm shape vector and the low-dimensional rhythm cluster center vector.

8. The intelligent virtual companion robot scenario dialogue rhythm control system according to claim 7, characterized in that: The default dynamic adjustment formula is: ； Where, for Dynamic matching threshold updated at all times, is the initial baseline threshold, for Real-time matching success rate at each moment, is the target matching success rate, for Real-time matching failure rate at each moment, is the target matching failure rate, for User feedback ratings at all times, Rating target user feedback, is the success rate sensitivity coefficient, is the failure rate sensitivity coefficient, is the user feedback weight coefficient, The timestamp of the last threshold adjustment. is the time attenuation coefficient, is the time normalization constant.

9. The intelligent virtual companion robot scenario dialogue rhythm control system according to claim 1, characterized in that: When the similarity is not less than the matching threshold, it is determined that the match is successful, and the steps of calling the rhythm response template associated with the successfully matched low-dimensional rhythm cluster include: Compare the obtained similarity value with the matching threshold; When the similarity value is not less than the matching threshold, it is determined that the current user's rhythm pattern successfully matches the rhythm style of one of the low-dimensional rhythm clusters, and the successfully matched low-dimensional rhythm cluster is identified; Call and enable the low-dimensional rhythm response template associated with the rhythm cluster to guide the robot to generate conversation content that conforms to the user's rhythm.

10. The intelligent virtual companion robot scenario dialogue rhythm control system according to claim 1, characterized in that: If the match fails, the steps to enable the default rhythm template and generate a new rhythm cluster online and create a new template include: After similarity comparison, if the current rhythm shape vector fails to successfully match all existing low-dimensional rhythm clusters, it is determined that a new unmatched rhythm pattern has appeared; Continuously tracking and counting the frequency of occurrence of the new pattern, and when the frequency reaches a preset frequency threshold, confirming it as a new rhythm style worthy of recognition; The preset default rhythm template is enabled, and a new low-dimensional rhythm cluster is generated online based on the confirmed features that do not match the new rhythm pattern, and a new low-dimensional rhythm response template is created and associated for the new cluster.

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