Multi-scene virtual language dialogue training method and system

Abstract

The invention discloses a multi-scene virtual language dialogue training method and system, and relates to the technical field of language training, and the system comprises a scene topology management module which is used for constructing a dynamic scene relation graph and comprises a scene node library which stores the semantic vector of each scene, a dialogue strategy template and a trigger rule; the dynamic edge weight calculation unit is used for updating the connection weight between the scenes in real time based on the semantic similarity and the historical behavior data of the user; cross-domain dialogue intelligent migration is realized through the dynamic scene relation graph, and the deployment cost is reduced; capturing emotional fluctuation in real time through multi-modal sensing and optimizing a strategy; efficient data transmission supports immersive interaction; adversarial training enhances logic and emotion processing capabilities.

Description

Technical Field

[0001] The present invention relates to the field of language training, and in particular to a multi-scenario virtual language dialogue training method and system. Background Art

[0002] With the rapid development of artificial intelligence technology, virtual language dialogue systems have been widely used in intelligent customer service, smart home, educational assistance and other fields. Existing technologies are mainly based on large-scale pre-trained models combined with domain data fine-tuning or reinforcement learning strategy optimization, but there are still significant defects in multi-scenario adaptability: traditional models are trained for a single vertical field, resulting in cross-scenario deployment requiring repeated data annotation and re-fine-tuning of parameters. The offline training model is difficult to perceive changes in user intent and scene context evolution in real time. The lack of multimodal information fusion capabilities restricts immersive interactive experience. The generation method based on statistical laws leads to serious dialogue templates and lack of personalized expression. The knowledge base update relies on periodic data iteration and cannot promptly reflect real-time events or dynamic changes in domain knowledge.

[0003] These defects have seriously restricted the practical application of virtual dialogue systems in complex real-life scenarios. There is an urgent need for an intelligent dialogue training method that can dynamically perceive scene features, efficiently utilize multimodal data, support continuous evolution and achieve differentiated expression. Summary of the invention

[0004] In response to the above technical problems, the present invention provides a multi-scenario virtual language dialogue training system, including a scene topology management module, which is used to construct a dynamic scene relationship graph, including: a scene node library, which stores the semantic vectors, dialogue strategy templates and trigger rules of each scene; a dynamic edge weight calculation unit, which updates the connection weights between scenes in real time based on semantic similarity and user historical behavior data.

[0005] The multimodal perception module is used to obtain heart rate variability and facial micro-expression action unit intensity through the biometric acquisition unit; and calculate the topic deviation, logical contradiction coefficient and emotional polarity value in real time through the language analysis unit.

[0006] The dynamic decision engine module includes a stress index calculation unit and a scene switching controller. The stress index calculation unit is used to generate a dynamic stress index by fusing biological features and language features; the scene switching controller selects a target scene through a decision function.

[0007] The dialogue training execution module includes a context mapper for generating scene switching transition statements; the dialogue training execution module includes a knowledge enhancement unit for connecting to an external knowledge base to expand the training content in real time.

[0008] The multimodal data transmission module includes a biometric compression unit and a voice stream priority scheduler, and is used for transmitting multimodal data.

[0009] The adversarial training module is used to generate unconventional dialogue sequences and evaluate the effectiveness of the generated sequences through adversarial generative training.

[0010] Furthermore, the dynamic edge weight calculation unit calculates the connection weight between scenes, and the formula is: ,in , is the scene semantic vector, is the number of historical switching times, is the total number of stays in the current scene, α is the weight coefficient of semantic similarity, and β is the weight coefficient of historical behavior association.

[0011] A multi-scenario virtual language dialogue training method comprises the following steps: Step S1, construct a dynamic scene topology network and configure training parameters, load the scene node library through the scene management module, configure the semantic encoding vector and dialogue strategy template for each scene node, and set the dynamic constraint conditions for scene switching; the dynamic constraint conditions include the semantic similarity weight coefficient, the historical behavior correlation attenuation factor, and the maximum allowable scene switching frequency threshold.

[0012] Step S2, synchronously collect multimodal training data, deploy non-contact biosensor arrays and voice collection equipment, and execute preset parallel collection tasks.

[0013] Step S3, multi-source data spatiotemporal alignment and feature fusion, preprocessing the original sensor data, the preprocessing includes: physiological signal denoising, using an adaptive Kalman filter to eliminate motion artifacts; micro-expression feature decoupling, separating the cross-interference between facial action units through independent component analysis; speech-text alignment, matching the temporal relationship between the speech stream and the text sequence based on a dynamic time warping algorithm.

[0014] Step S4, dynamic scene switching decision: Generate scene migration instructions based on fusion features, calculate scene switching priority scores, and trigger scene migration protocols when the priority scores exceed the dynamic threshold.

[0015] Step S5, context-aware dialogue generation, is performed during the scene switching process: loading the target scene dialogue strategy template, retrieving the external knowledge base to generate domain-related question-answer pairs; and fusing historical dialogue memories through the gated attention mechanism.

[0016] Step S6, adversarial training and system self-optimization. When the number of consecutive correct responses by the user reaches the set threshold, the adversarial training mode is activated. The generator constructs a dialogue sequence containing logical traps or emotional conflicts; the discriminator evaluates the effectiveness of the generated content based on the scene consistency rules.

[0017] The preset parallel acquisition tasks in step S2 include obtaining the ballistocardiogram signal and analyzing the heart rate variability parameters; using a camera to capture facial muscle micro-movements and extracting the activation intensity of action units; recording the voice stream in real time and converting it into a text sequence, and synchronously marking the timestamp and emotion polarity label.

[0018] Furthermore, step S1 of constructing a dynamic scene topology network includes step S101, loading a predefined scene node library from persistent storage, verifying the integrity and format consistency of node data, triggering alarms for nodes with missing key fields or abnormal data, and isolating and processing them.

[0019] Step S102, semantic coding vector generation and association, generate a high-dimensional semantic vector for each scene node to represent its conversation topic and context features; calculate the similarity between scenes based on the semantic vector, construct the initial association edge and assign the basic weight.

[0020] Step S103, dialogue strategy template allocation, binding a multi-level dialogue strategy template to each scene node, setting strategy triggering conditions, including user response time threshold, emotion deviation and physiological indicator range.

[0021] Furthermore, the multimodal data transmission includes a biometric compression unit configured to perform dynamic layered compression, including lossless mode, wavelet compression, and feature extraction transmission; the feature extraction transmission only sends heart rate variability and action unit intensity; forward error correction coding, using RaptorQ code to achieve packet level protection, , λ is the dynamic redundancy coefficient, (Packet Loss Rate history) is the historical packet loss rate.

[0022] Furthermore, the adversarial training mode of step S6 includes: first-level adversarial, inserting factual contradictory sentences into the dialogue flow; second-level adversarial, simulating network delays to cause loss of dialogue context; and third-level adversarial, generating emotional accusatory words to trigger stress response.

[0023] Compared with the prior art, the present invention has the following advantages: (1) The present invention realizes cross-domain dialogue intelligent migration through dynamic scene relationship graph, reducing deployment costs; multimodal perception captures emotional fluctuations in real time and optimizes strategies; efficient data transmission supports immersive interaction; adversarial training enhances logic and emotion processing capabilities; (2) The present invention constructs a continuously evolving dialogue training system through intelligent perception, multimodal fusion and self-optimization architecture, significantly improving the generalization ability of complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is an overall schematic diagram of the system of the present invention.

[0025] Figure 2 Flow chart of exemplary steps of the training method of the present invention.

[0026] Figure 3 A flowchart of an exemplary step for constructing a dynamic scene topology network according to the present invention. DETAILED DESCRIPTION

[0027] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0028] Example: Figure 1 A multi-scenario virtual language dialogue training system is shown, including a scene topology management module for constructing a dynamic scene relationship graph, including: a scene node library, which stores the semantic vectors, dialogue strategy templates and trigger rules of each scene; a dynamic edge weight calculation unit, which updates the connection weights between scenes in real time based on semantic similarity and user historical behavior data.

[0029] The multimodal perception module is used to obtain heart rate variability and facial micro-expression action unit intensity through the biometric acquisition unit; and calculate the topic deviation, logical contradiction coefficient and emotional polarity value in real time through the language analysis unit.

[0030] The dynamic decision engine module includes a stress index calculation unit and a scene switching controller. The stress index calculation unit is used to generate a dynamic stress index by fusing biological features and language features; the scene switching controller selects a target scene through a decision function.

[0031] The dialogue training execution module includes a context mapper for generating scene switching transition statements; the dialogue training execution module includes a knowledge enhancement unit for connecting to an external knowledge base to expand the training content in real time.

[0032] The multimodal data transmission module includes a biometric compression unit and a voice stream priority scheduler, and is used for transmitting multimodal data.

[0033] The adversarial training module is used to generate unconventional dialogue sequences and evaluate the effectiveness of the generated sequences through adversarial generative training.

[0034] The dynamic edge weight calculation unit calculates the connection weight between scenes. The formula is: ,in , is the scene semantic vector, is the number of historical switching times, is the total number of stays in the current scene, α is the weight coefficient of semantic similarity, and β is the weight coefficient of historical behavior association.

[0035] In this embodiment, a "professional skills assessment scenario" and a "career planning discussion scenario" are constructed in the interview virtual dialogue training system. The dynamic edge weight calculation unit first analyzes the semantic vectors of the two scenarios, identifies the relevance of the interview content, and counts the frequency of the user's historical switching between the two scenarios and the total duration of each scene. If the semantic vector indicates that there is a logical connection between professional skills and career planning, and the user frequently switches between the two scenarios, the edge weight value calculated by the formula will increase accordingly.

[0036] like Figure 2 As shown, a multi-scenario virtual language dialogue training method comprises the following steps: Step S1, construct a dynamic scene topology network and configure training parameters, load the scene node library through the scene management module, configure the semantic encoding vector and dialogue strategy template for each scene node, and set the dynamic constraint conditions for scene switching; the dynamic constraint conditions include the semantic similarity weight coefficient, the historical behavior correlation attenuation factor, and the maximum allowable scene switching frequency threshold.

[0037] Step S2, synchronously collect multimodal training data, deploy non-contact biosensor arrays and voice collection equipment, and execute preset parallel collection tasks.

[0038] Step S3, multi-source data spatiotemporal alignment and feature fusion, preprocessing the original sensor data, the preprocessing includes: physiological signal denoising, using an adaptive Kalman filter to eliminate motion artifacts; micro-expression feature decoupling, separating the cross-interference between facial action units through independent component analysis; speech-text alignment, matching the temporal relationship between the speech stream and the text sequence based on the dynamic time warping algorithm.

[0039] Step S4, dynamic scene switching decision: Generate scene migration instructions based on fusion features, calculate scene switching priority scores, and trigger scene migration protocols when the priority scores exceed the dynamic threshold.

[0040] Step S5, context-aware dialogue generation, is performed during the scene switching process: loading the target scene dialogue strategy template, retrieving the external knowledge base to generate domain-related question-answer pairs; and fusing historical dialogue memories through the gated attention mechanism.

[0041] Step S6, adversarial training and system self-optimization. When the number of consecutive correct responses by the user reaches the set threshold, the adversarial training mode is activated. The generator constructs a dialogue sequence containing logical traps or emotional conflicts; the discriminator evaluates the effectiveness of the generated content based on the scene consistency rules.

[0042] In this embodiment, the preset parallel acquisition tasks in step S2 include obtaining the ballistocardiogram signal and analyzing the heart rate variability parameters; using a camera to capture facial muscle micro-movements and extracting the activation intensity of action units; recording the voice stream in real time and converting it into a text sequence, and synchronously marking the timestamp and the emotion polarity label.

[0043] like Figure 3 As shown, the step of building a dynamic scene topology network in step S1 in this embodiment includes: step S101, loading a predefined scene node library from persistent storage, verifying the integrity and format consistency of node data, triggering alarms for nodes with missing key fields or abnormal data, and isolating them.

[0044] Step S102, semantic coding vector generation and association, generate a high-dimensional semantic vector for each scene node to represent its conversation topic and context features; calculate the similarity between scenes based on the semantic vector, construct the initial association edge and assign the basic weight.

[0045] Step S103, dialogue strategy template allocation, binding a multi-level dialogue strategy template to each scene node, setting strategy triggering conditions, including user response time threshold, emotion deviation and physiological indicator range.

[0046] The multimodal data transmission includes a biometric compression unit configured to perform dynamic layered compression, including lossless mode, wavelet compression, and feature extraction transmission; the feature extraction transmission only sends heart rate variability and action unit intensity; forward error correction coding, using RaptorQ code to achieve packet level protection, , λ is the dynamic redundancy coefficient, (Packet Loss Rate history) is the historical packet loss rate.

[0047] In this embodiment, taking the intelligent medical dialogue scenario as an example, when the patient consults with a virtual medical assistant, the biometric compression unit plays a role: in the hospital LAN environment with excellent network quality, the lossless mode is enabled to fully transmit biometric data such as heart rate variability and facial micro-expression action unit strength to ensure the accuracy of diagnostic data; if it is in a community medical scenario with weak network signal, it switches to wavelet compression to reduce the amount of data while retaining key information of physiological characteristics; when encountering a remote consultation scenario with unstable network, feature extraction transmission is directly used to send only core diagnostic data such as heart rate variability and action unit strength. At the same time, the dynamic redundancy coefficient λ is calculated based on the historical packet loss rate (such as a regional network PLR of 10%), and the packet level protection is added through the RaptorQ code to ensure that the biometric data has improved anti-packet loss capability during transmission, supporting the virtual medical dialogue system to accurately perceive the patient's physiological state and achieve more reliable multimodal interactive diagnosis.

[0048] The adversarial training mode in step S6 includes the first-level adversarial training, which inserts factual contradictory sentences into the dialogue flow; the second-level adversarial training, which simulates network delays and causes loss of dialogue context; and the third-level adversarial training, which generates emotional accusatory words to trigger stress responses.

[0049] In this embodiment, the training scenario of the intelligent customer service system is taken as an example. Level 1 confrontation: When the user inquires about the warranty policy of electronic products, the system inserts a factual contradictory statement in the dialogue flow, "During the warranty period of this product, regardless of whether it is man-made or not, it will be repaired free of charge", and trains the model to identify logical contradictions and output accurate policy interpretations; Level 2 confrontation: Simulate the network instability scenario, deliberately clear the context information of "the newly purchased device has a hardware failure" in the user's previous dialogue, and only pass the user's current question "What should I do now" to the model to test the model's reasoning and completion ability for missing context; Level 3 confrontation: Generate emotional accusatory content such as "Your after-sales service is perfunctory and you don't want to solve the problem at all", forcing the model to respond to pressure, both to appease the user's emotions and to guide the dialogue back to the problem-solving process, and to strengthen the model's ability to respond in complex dialogue scenarios through the three-level confrontation mode.

[0050] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the scope defined by the invention, they shall all fall within the protection scope of the present invention.

Claims

1. A multi-scenario virtual language dialogue training system, characterized by: It includes a scene topology management module, which is used to build a dynamic scene relationship graph, including: a scene node library, which stores the semantic vectors, dialogue strategy templates and trigger rules of each scene; a dynamic edge weight calculation unit, which updates the connection weights between scenes in real time based on semantic similarity and user historical behavior data; The multimodal perception module is used to obtain heart rate variability and facial micro-expression action unit intensity through the biometric acquisition unit; the language analysis unit is used to calculate the topic deviation, logical contradiction coefficient and emotional polarity value in real time; The dynamic decision engine module includes a pressure index calculation unit and a scene switching controller. The pressure index calculation unit is used to generate a dynamic pressure index by fusing biological features and language features; the scene switching controller selects a target scene through a decision function; The dialogue training execution module includes a context mapper for generating scene switching transition statements; the dialogue training execution module includes a knowledge enhancement unit for connecting to an external knowledge base to expand the training content in real time; A multimodal data transmission module, including a biometric compression unit and a voice stream priority scheduler, for transmitting multimodal data; The adversarial training module is used to generate unconventional dialogue sequences and evaluate the effectiveness of the generated sequences through adversarial generative training.

2. A multi-scenario virtual language dialogue training system according to claim 1, characterized in that: The dynamic edge weight calculation unit calculates the connection weight between scenes, and the formula is: ,in , is the scene semantic vector, is the number of historical switching times, is the total number of stays in the current scene, α is the weight coefficient of semantic similarity, and β is the weight coefficient of historical behavior association.

3. A multi-scenario virtual language dialogue training method, characterized in that: The following steps are involved: Step S1, constructing a dynamic scene topology network and configuring training parameters, loading a scene node library through a scene management module, configuring a semantic encoding vector and a dialogue strategy template for each scene node, and setting dynamic constraints for scene switching; the dynamic constraints include a semantic similarity weight coefficient, a historical behavior correlation attenuation factor, and a maximum allowable scene switching frequency threshold; Step S2, synchronously collect multimodal training data, deploy non-contact biosensor arrays and voice collection equipment, and execute preset parallel collection tasks; Step S3, time-space alignment and feature fusion of multi-source data, preprocessing of the original sensor data, the preprocessing includes physiological signal noise reduction, and using an adaptive Kalman filter to eliminate motion artifacts; Decoupling of micro-expression features, separating cross-interference between facial action units through independent component analysis; Speech-text alignment, matching the temporal relationship between speech stream and text sequence based on dynamic time warping algorithm; Step S4, dynamic scene switching decision: Generate scene migration instructions based on fusion features, calculate scene switching priority scores, and trigger scene migration protocols when the priority scores exceed the dynamic threshold; Step S5, context-aware dialogue generation, is performed during the scene switching process: loading the target scene dialogue strategy template, retrieving the external knowledge base to generate domain-related question-answer pairs; fusing historical dialogue memories through the gated attention mechanism; Step S6, adversarial training and system self-optimization. When the number of consecutive correct responses by the user reaches a set threshold, the adversarial training mode is activated. The generator constructs a dialogue sequence containing logical traps or emotional conflicts. The discriminator evaluates the effectiveness of the generated content based on the scene consistency rule. The preset parallel acquisition tasks in step S2 include obtaining a ballistocardiogram signal and analyzing heart rate variability parameters; The camera is used to capture facial muscle micro-movements and extract the activation intensity of action units; Record speech streams in real time and convert them into text sequences, synchronously marking timestamps and sentiment polarity labels.

4. A multi-scenario virtual language dialogue training method according to claim 3, characterized in that: Step S1: The steps of constructing a dynamic scene topology network include: Step S101, loading a predefined scene node library from persistent storage, verifying the integrity and format consistency of node data, triggering an alarm for nodes with missing key fields or abnormal data, and isolating and processing them; Step S102, semantic coding vector generation and association, generating a high-dimensional semantic vector for each scene node to represent its conversation topic and context features; Calculate the similarity between scenes based on semantic vectors, construct initial association edges and assign basic weights; Step S103, dialogue strategy template allocation, binds a multi-level dialogue strategy template to each scene node, and sets strategy trigger conditions, including user response time threshold, emotion deviation, and physiological indicator range.

5. The multi-scenario virtual language dialogue training method according to claim 3, characterized in that: The multimodal data transmission includes a biometric compression unit configured to perform dynamic layered compression, including lossless mode, wavelet compression, and feature extraction transmission; The feature extraction transmission only sends heart rate variability and action unit intensity; forward error correction coding uses RaptorQ code to achieve packet level protection, , λ is the dynamic redundancy coefficient, (Packet Loss Rate history) is the historical packet loss rate.

6. A multi-scenario virtual language dialogue training method according to claim 3, characterized in that: The adversarial training mode in step S6 includes: first-level adversarial, inserting factual contradictory sentences into the dialogue flow; second-level adversarial, simulating network delay to cause dialogue context loss; The third level of confrontation generates emotional accusatory rhetoric that triggers a stress response.

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