A digital human real-time interaction and collaboration system and method based on natural language processing

Abstract

The application discloses a digital human real-time interaction and cooperation system and method based on natural language processing, relates to the technical field of collaborative office, and comprises a natural language processing unit, a digital human interaction unit, a real-time cooperation unit, a decision support unit and a safe operation unit.In the application, the natural language processing unit is combined with the digital human interaction unit to improve interaction efficiency and naturalness, reduce communication cost, the real-time cooperation unit guarantees the stability of multi-person concurrent cooperation and improves cross-department process efficiency through a dynamic process engine, a multi-person real-time editing tool, intelligent resource scheduling and cross-system data processing, the decision support unit realizes knowledge-driven risk prediction and scientific decision-making by relying on knowledge graph construction, reinforcement learning prediction model and man-machine collaborative decision-making display, and the safe operation unit ensures data security and cooperation compliance, so that an efficient, intelligent and safe collaborative office system is finally formed.

Description

Technical Field

[0001] This invention relates to the field of collaborative office technology, specifically to a real-time interactive collaboration system and method for digital humans based on natural language processing. Background Technology

[0002] Collaborative office refers to a modern office model that integrates internal and external resources of an organization through information technology, breaks down barriers of time, space, and systems, and enables information sharing, process collaboration, and decision-making coordination among team members. Its core objective is to improve overall organizational efficiency, reduce collaboration costs, and enhance responsiveness to market changes by optimizing communication efficiency, automating business processes, and integrating data resources. As enterprises increase their requirements for collaborative office, the definition of collaborative office has expanded, elevating it to the category of intelligent office.

[0003] For example, the patent application CN118521272B, titled "An Office Collaboration System and Method Based on Artificial Intelligence," establishes an integrated intelligent collaboration platform. It utilizes technologies such as natural language processing and machine learning to analyze documents and emails, extract key information, and construct an enterprise knowledge graph. Through an intelligent collaborative control system, it pushes task reminders and decision-making suggestions to relevant personnel. It also uses predictive analytics models to predict project risks and resource requirements, and employs VR technology to showcase project progress.

[0004] In existing technologies, collaborative work largely relies on single-mode interaction via text or voice, lacking a deep understanding of semantics, emotion, and multimodal interaction. This results in rigid interaction strategies that are difficult to dynamically adapt to different user needs. Furthermore, most systems provide static data reports, knowledge graphs have long update cycles, and cannot synchronize the latest internal and external data in real time. Entity alignment relies on manual annotation, and prediction models trained on historical data lack online learning capabilities. When the collaborative scenario changes, the prediction error rate increases, and the correction of prediction deviations is passive and cannot be monitored in real time, leading to delayed risk warnings. Summary of the Invention

[0005] The purpose of this invention is to provide a real-time interactive collaboration system and method for digital humans based on natural language processing, in order to solve the problems mentioned in the background art, such as long knowledge graph update cycles, prediction models that rely on historical data for training and lack online learning capabilities, which lead to increased prediction error rates and delayed risk warnings.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a real-time interactive collaboration system and method for digital humans based on natural language processing, comprising a natural language processing unit, a digital human interaction unit, a real-time collaboration unit, a decision support unit, and a security operation and maintenance unit;

[0007] The Natural Language Processing Unit performs semantic parsing through multilingual speech recognition and speech synthesis. The Digital Human Interaction Unit is used to bind multiple system roles, render and generate realistic digital humans, and match different collaborative logics. The Real-Time Collaboration Unit builds a dynamic process engine based on the Natural Language Processing Unit and the Digital Human Interaction Unit to configure cross-system collaborative processes. The Decision Support Unit builds and calls knowledge graphs to predict collaboration trends and provide early warnings.

[0008] The decision support unit is based on a knowledge graph construction module, a predictive analysis module, and a decision display module. The knowledge graph construction module extracts structured and unstructured data from the real-time collaboration unit to build a knowledge graph, which is then called in real time during collaboration. The predictive analysis module can call data from the knowledge graph construction module to predict collaboration trends, issue risk warnings in advance, monitor the warning results, and correct errors. The decision display module breaks down the digital human decision suggestions from the digital human interaction unit into causal chains and displays them through flowcharts. In high-risk decision-making, the digital human provides multi-dimensional analysis reports.

[0009] The security operations and maintenance unit encrypts sensitive data, dynamically adjusts permissions, and monitors and blocks abnormal behavior in the decision support unit in real time.

[0010] Preferably, the real-time collaboration unit includes a multi-terminal synchronization module, an intelligent scheduling module, and a cross-system integration module;

[0011] The multi-terminal synchronization module integrates online documents, whiteboards, and task boards for real-time editing and version management by multiple users; the intelligent scheduling module optimizes computing resources in real time based on reinforcement learning, monitors collaboration anomalies in real time, and automatically triggers retry mechanisms and switches to backup nodes; the cross-system integration module adapts to legacy systems through low-code visualization tools, automates the processing of structured and unstructured data, and sends the processed data to the decision support unit.

[0012] Preferably, the predictive analysis module includes a trend prediction module, an error monitoring module, and a data correction module;

[0013] The trend prediction module calls on the data from the graph construction module, builds a prediction model based on historical collaboration data and real-time dynamics, predicts collaboration trends in real time, and issues risk warnings; the error monitoring module is used to monitor the error drift of the prediction model in the trend prediction module in real time. When the feature distribution deviation exceeds the preset threshold, it automatically triggers the prediction model to be retrained and updated; the data correction module cleans abnormal data, corrects the prediction deviation in the trend prediction module, and optimizes the prediction model parameters through feedback loops.

[0014] Preferably, within the knowledge graph construction module, after constructing the knowledge network, a neural network is used to vectorize the knowledge graph, divide the semantics of entity neighborhoods, perform entity alignment, dynamically adjust the matching priority based on the self-attention mechanism, collect user-annotated data, detect duplicate data, and update the knowledge graph content.

[0015] Preferably, in the trend prediction module, the prediction model construction includes the following:

[0016] A1. Collect internal collaboration data and external data, and construct feature engineering, which includes time features, behavioral features, resource features and correlation features;

[0017] A2. Based on deep reinforcement learning, model the dynamic decision-making process in the collaboration process and build a prediction model;

[0018] A3. Set early warning thresholds based on historical data distribution, and optimize the thresholds through reinforcement learning.

[0019] Preferably, in the predictive analysis module, the early warning results are monitored, and the error correction includes the following:

[0020] B1. Monitor real-time error indicators and use the maximum mean difference to detect whether the input feature distribution deviates from the training. When the feature distribution deviation exceeds the preset threshold, trigger the prediction model to retrain and update the prediction model.

[0021] B2. Based on the sliding window statistical filtering of abnormal operation records, abnormal data is cleaned, strategy network parameters are adjusted, samples are screened, and labeled data is supplemented.

[0022] Preferably, in the predictive analysis module, when detecting whether the distribution of input features deviates from the training set, a significance test is performed on the distributions of real-time input features and training set features, using the following formula:

[0023] Let the feature samples of the training set be X. train ={x1,x2,...,x m}, where the real-time feature sample is X real ={x1',x'2,...,x' n The kernel function chosen is the RBF kernel k(x,x') = exp(-γ||x-x'||). 2 );

[0024] The maximum mean difference (MMD) statistic is:

[0025]

[0026] Where φ is the kernel function mapping;

[0027] In MMD>τ MMDWhen this occurs, it is determined to be a characteristic distribution shift, τ MMD This indicates a preset threshold, calibrated using historical distribution offset data.

[0028] Preferably, in the predictive analysis module, the online learning method is used to update the predictive model with the latest incremental data. The specific method is as follows:

[0029] Initialize the model parameters and reuse the historical model parameters W0;

[0030] The incremental update formula is:

[0031]

[0032] Where L is the loss function, Ω(W) is the L1 regularization term, W is the set of parameters to be optimized in the model, and y i Let i be the true label of the i-th sample. Given the current parameters W, this is the model's prediction output for the i-th sample.

[0033] Dynamically adjust the learning rate:

[0034] η t =η0·MMD t ;

[0035] Where η0 represents the basic learning rate;

[0036] Calculate the individual MMD value for each feature dimension, i.e., the distribution offset MMD of the j-th feature. j Press MMD j The features are sorted in descending order, and a subset of drift features is selected based on the feature dimensions. The drift features are given higher weights to strengthen the model's learning of them. Historical data is retained, and data is sampled according to the importance of the samples before being used in training to update the prediction model.

[0037] A real-time interactive collaboration method for digital humans based on natural language processing specifically includes the following steps:

[0038] S1. Natural Language Processing and Digital Human Interaction: Parse the semantics of instructions through multilingual speech recognition, analyze user facial expressions and speech rhythm, identify micro-expressions and tone changes, dynamically adjust the digital human response strategy, and identify user-dominated interaction methods;

[0039] S2. Real-time collaborative workflow construction and execution: Configure cross-system collaborative workflows through a dynamic workflow engine, integrate multiple tools to achieve multi-terminal synchronization and real-time editing by multiple people, adapt to legacy systems, and perform data parsing and processing.

[0040] S3, Decision Support and Knowledge-Driven: Construct and invoke knowledge graphs, build predictive models, predict task progress, resource requirements and risk points, and display collaborative decision-making;

[0041] S4. Security Operations and Access Management: Encrypt sensitive data, dynamically adjust permissions according to collaboration scenarios, monitor behavior logs in real time, and block abnormal behavior.

[0042] Preferably, in step S3, decision support and knowledge-driven approaches include the following steps:

[0043] S31. Knowledge Graph Construction and Invocation: Real-time synchronization of external data, connection to enterprise knowledge base to extract structured and unstructured data, construction of knowledge graph, dynamic calculation of entity similarity using entity alignment and self-attention mechanism, regular updating of graph content, and invocation of knowledge graph in collaboration.

[0044] S32. Collaboration Trend Prediction and Risk Management: Based on historical data and real-time dynamics, build a prediction model to predict task progress, resource requirements and risk points, issue early warnings, monitor prediction errors in real time, and optimize model parameters.

[0045] S33. Human-Machine Collaborative Decision-Making Demonstration: Provides multi-dimensional analysis reports for high-risk decision-making while retaining access for human intervention, combining human-machine collaborative decision-making.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] In this invention, the natural language processing unit and the digital human interaction unit are combined to improve interaction efficiency and naturalness and reduce communication costs through speech semantic analysis, facial expression prosody recognition, and multimodal collaboration. The real-time collaboration unit ensures stable concurrent collaboration among multiple users and improves cross-departmental process efficiency through a dynamic process engine, multi-user real-time editing tools, intelligent resource scheduling, and cross-system data processing. The decision support unit relies on knowledge graph construction, reinforcement learning prediction models, and human-machine collaborative decision display to achieve knowledge-driven risk prediction and scientific decision-making. It tracks prediction deviations and data distribution changes in real time, automatically triggers model retraining, and forms a "prediction-monitoring-correction" closed loop by combining a data correction module to clean and optimize abnormal data. This allows the prediction accuracy to gradually improve over time, reduces misjudgments caused by model lag, and assists in formulating long-term process optimization strategies. The security operation and maintenance unit ensures data security and collaborative compliance through data encryption, operation evidence storage, dynamic access control, and anomaly blocking, ultimately forming an efficient, intelligent, and secure collaborative office system. Attached Figure Description

[0048] Figure 1 This is a flowchart of a real-time interactive collaboration method for digital humans based on natural language processing, according to the present invention.

[0049] Figure 2 This is a system block diagram of a real-time interactive collaboration system for digital humans based on natural language processing, according to the present invention.

[0050] In the diagram: 1. Natural Language Processing Unit; 2. Digital Human Interaction Unit; 3. Real-time Collaboration Unit; 31. Multi-terminal Synchronization Module; 32. Intelligent Scheduling Module; 33. Cross-system Integration Module; 4. Decision Support Unit; 41. Graph Construction Module; 42. Predictive Analysis Module; 421. Trend Prediction Module; 422. Error Monitoring Module; 423. Data Correction Module; 43. Decision Display Module; 5. Security Operation and Maintenance Unit. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: Refer to Figures 1-2 As shown: This application proposes a real-time interactive collaboration system for digital humans based on natural language processing, aiming to solve problems such as the single interaction mode, lagging knowledge graph updates, and lack of online learning capabilities in existing collaborative office work. The specific content is as follows:

[0053] I. Overview of Technical Solution

[0054] 1. The Natural Language Processing Unit 1 performs speech-to-text conversion through multilingual speech recognition / synthesis, and completes semantic parsing by combining intent recognition, entity extraction, and multi-turn dialogue management to understand user commands;

[0055] Digital Human Interaction Unit 2 renders a realistic digital human by binding multiple system roles; it dynamically adjusts the interaction strategy by recognizing user facial expressions and voice rhythm to adapt to different user needs.

[0056] The real-time collaboration unit 3 integrates online documents, whiteboards, task boards, and other tools through the multi-terminal synchronization module 31, supporting real-time editing and version management by multiple users; the intelligent scheduling module 32 optimizes computing resources, monitors collaboration anomalies, and automatically triggers retries / switchover to backup nodes to achieve self-healing; the cross-system integration module 33 adapts to legacy systems through low-code tools, automates the processing of structured and unstructured data, facilitates cross-system data flow, and sends structured and unstructured data into the decision support unit 4.

[0057] The decision support unit 4 works in conjunction with the graph construction module 41, the predictive analysis module 42, and the decision display module 43. The graph construction module 41 synchronizes internal and external data in real time, builds a knowledge graph, and updates the graph to solve the problem of knowledge update lag. Trend prediction: The predictive analysis module 42 uses deep reinforcement learning to build a model based on historical and real-time data to predict task progress, resource requirements, and risks (with three levels of early warning), detect feature distribution shifts, and optimize model parameters to solve the problem of the prediction model lacking online learning capabilities. The decision display module 43 breaks down the digital human decision suggestions into causal chains, provides multi-dimensional analysis reports for high-risk decisions, retains the human intervention entry point, and realizes human-machine collaboration.

[0058] Security Operation and Maintenance Unit 5 encrypts sensitive data, stores key operations on the blockchain for evidence, dynamically adjusts permissions, monitors and blocks abnormal behavior in real time, and ensures data security.

[0059] 2. Core Methodology and Process: This involves four steps to form a complete collaborative loop: 1) Natural Language Processing and Digital Human Interaction: Speech-to-text conversion and semantic analysis, combined with user facial expressions / voice prosody to adjust digital human interaction strategies, generating a collaborative context graph; 2) Real-time Collaborative Process Construction and Execution: Configuration of cross-system processes via a dynamic process engine, integration tools for multi-terminal synchronization, intelligent resource scheduling and anomaly handling, cross-system adaptation, and data processing; 3) Decision Support and Knowledge-Driven Approach: Construction and real-time invocation of the knowledge graph, risk prediction based on predictive models, error monitoring and dynamic model optimization, visualization of decision-making basis, and integration of human-machine judgment; 4) Security Operations and Access Management: Encrypting data, dynamically adjusting permissions, and monitoring and blocking abnormal behavior.

[0060] II. Core Technology Principles

[0061] 1. Multimodal interaction and adaptive strategy principle: The natural language processing unit achieves deep understanding of text / voice commands through "speech recognition + semantic parsing (intent recognition, entity extraction)";

[0062] The digital human interaction unit combines computer vision (facial expression recognition) and speech analysis (tone variation) to build a model of user emotions and needs, dynamically switch interaction modes, and solve the rigidity problem of single-mode interaction.

[0063] 2. Principle of dynamic updating of knowledge graph: It adopts a three-layer model of "entity-relationship-attribute", and uses a neural network to transform entities into computable vectors and divide neighborhood semantics;

[0064] A self-attention mechanism is introduced during entity alignment, which dynamically adjusts the matching priority based on attribute similarity and relationship consistency. Combined with user-annotated data (automatically filtering low-confidence results and pushing annotations) and triple hash deduplication, the graph is updated in real time, solving the problem of long update cycles in traditional knowledge graphs.

[0065] 3. Online Learning and Error Correction Principles of Predictive Models: First, through model construction, based on deep reinforcement learning, four types of features—time, behavior, resources, and association—are integrated to model a dynamic decision-making process. Reinforcement learning is then used to optimize the warning threshold to adapt to different scenarios. Next, error monitoring is performed, using the MMD statistic to detect differences between real-time features and the training set distribution. Finally, online updates are implemented using incremental learning, dynamically adjusting the learning rate, and assigning high weights to drifting features for priority training, thus addressing the problem of increased prediction errors caused by reliance on historical data.

[0066] 4. Cross-system collaboration and resource scheduling principles: Cross-system integration reduces the adaptation cost of legacy systems through low-code tools, and automated processing of structured and unstructured data ensures data consistency; Intelligent scheduling is based on reinforcement learning, which models resource allocation as a dynamic decision problem, and maintains system response stability in high-concurrency scenarios by optimizing the allocation of edge nodes / cloud resources in real time, and the anomaly self-healing mechanism reduces the cost of manual intervention.

[0067] III. Core Innovation Points

[0068] 1. Multimodal deep interaction: Integrates multimodal information such as voice, text, and emoticons to dynamically adapt to user needs and break through the limitations of a single interaction mode;

[0069] 2. Real-time knowledge graph updates: Through self-attention mechanism for entity alignment and user annotation feedback, the knowledge graph is dynamically iterated, solving the problem of update lag;

[0070] 3. Online evolution of the prediction model: Based on MMD detection of distribution shift, the model is optimized in real time by incremental learning, which solves the problems of increased error and delayed early warning caused by the reliance on historical data in traditional models;

[0071] 4. Human-machine collaborative decision-making: Machines provide data analysis and risk prediction, while humans retain experience-based judgments, balancing efficiency and accuracy.

[0072] This application enables intelligent, real-time, and secure collaborative office work, improving collaboration efficiency and risk response capabilities.

[0073] Example 2: Refer to Figure 1 As shown: A real-time interactive collaboration system for digital humans based on natural language processing includes a natural language processing unit 1, a digital human interaction unit 2, a real-time collaboration unit 3, a decision support unit 4, and a security operation and maintenance unit 5.

[0074] Natural Language Processing Unit 1 performs real-time text-to-speech conversion of voice commands and dynamic speech generation of responses through multilingual speech recognition and speech synthesis. It also performs semantic parsing through intent recognition, entity extraction, and multi-turn dialogue management. Digital Human Interaction Unit 2 is used to bind multiple system roles, render and generate realistic digital humans, recognize facial expressions and voice changes, adjust interaction strategies, and match different collaborative logics. Real-time Collaboration Unit 3 is used to build a dynamic process engine and configure cross-system collaborative processes.

[0075] The real-time collaboration unit 3 includes a multi-terminal synchronization module 31, an intelligent scheduling module 32, and a cross-system integration module 33. The multi-terminal synchronization module 31 integrates online documents, whiteboards, and task boards, supporting real-time editing and version management by multiple users. The intelligent scheduling module 32 optimizes computing resources in real time based on reinforcement learning, automatically calling edge nodes to offload load during peak meeting periods to ensure stable system response time when multiple users are concurrent. It also monitors collaboration anomalies such as document editing conflicts and audio / video interruptions in real time, automatically triggering retry mechanisms or switching to backup nodes to achieve self-healing and reduce repair time. The cross-system integration module 33 enables rapid adaptation to legacy systems through low-code visualization tools, supporting automated processing of structured and unstructured data. Within the cross-system integration module 33, automated processing of structured and unstructured data includes: data cleaning, format conversion, and field mapping for structured data; text parsing for unstructured data to extract key information; parsing scanned document content; and extracting structured fields.

[0076] Decision Support Unit 4 is used to build and invoke knowledge graphs, predict collaboration trends and provide early warnings, visualize the causal chain of decisions, and provide multi-dimensional analysis reports. It enables knowledge-driven, risk-predictive, and human-machine collaborative decision-making, transforming scattered collaborative data into structured knowledge, uncovering potential risks through predictive models, and ultimately presenting the decision-making basis in an intuitive way. This leverages the data analysis advantages of machines while retaining human experience and judgment, providing intelligent support throughout the entire collaboration lifecycle. Decision Support Unit 4 includes a knowledge graph construction module 41, a predictive analysis module 42, and a decision display module 43. The knowledge graph construction module 41 is used to synchronize external data from policies, regulations, and business processes in real time, connect to the enterprise knowledge base to extract structured and unstructured data, build a knowledge graph, and invoke enterprise knowledge in real time during collaboration. Knowledge graph; within the knowledge graph construction module 41, after constructing the knowledge network, the knowledge graph adopts a three-layer model of entity-relationship-attribute. It uses neural networks to vectorize the knowledge graph, transforming entities into computable vectors, dividing entity neighborhood semantics, reducing feature correlation through regularization methods to avoid overfitting, achieving entity alignment, and solving the problem of synonymy for the same entity in different data sources. Based on the self-attention mechanism, alignment-aware weights are introduced to dynamically adjust matching priorities and periodically collect user-annotated data. When collecting user-annotated data, the system automatically filters low-confidence alignment results, automatically marks potential aligned entities through dual judgment of attribute similarity and relationship consistency, and annotates them after push. It detects duplicate data through triple hash values, skips duplicate data, and updates the knowledge graph content.

[0077] The predictive analysis module 42 is used to predict collaboration trends, issue early risk warnings, monitor the warning results, and correct errors. The predictive analysis module 42 includes a trend prediction module 421, an error monitoring module 422, and a data correction module 423. The trend prediction module 421 constructs a predictive model based on historical collaboration data and real-time dynamics, predicts collaboration trends such as task progress, resource requirements, and risk points in real time, and issues early risk warnings. Risk warnings are divided into three levels according to risk probability: red warning, yellow warning, and blue warning. The warning content includes risk description, scope of impact, historical cases, and preliminary suggestions. The error monitoring module 422 is used to monitor the error drift of the prediction model in real time. When the feature distribution deviation exceeds the preset threshold, it automatically triggers the retraining of the prediction model to update the prediction model and ensure prediction accuracy. The data correction module 423 is used to clean abnormal data, correct prediction deviations, and optimize prediction model parameters through feedback loops to form a closed-loop iteration. The decision display module 43 decomposes the digital human's decision suggestions into causal chains and displays them through flowcharts. When making high-risk decisions, the digital human provides multi-dimensional analysis reports while retaining the human intervention entry point, realizing the integration of machine intelligence and human experience.

[0078] The predictive model is constructed by including the following: A1. Collecting internal and external collaboration data to construct feature engineering, which includes time features, behavioral features, resource features, and association features. Time features include task cycle, node time consumption, and meeting frequency periodicity. Behavioral features include member operation response speed, collaboration conflict frequency, and task flow efficiency. Resource features include edge node load, API call latency, and data transmission volume. Association features are the entity association degree in the knowledge graph. A2. Based on deep reinforcement learning, modeling the dynamic decision-making process in the collaboration process to construct a predictive model. A3. Setting early warning thresholds based on historical data distribution and automatically optimizing the thresholds through reinforcement learning to adapt to different collaboration scenarios.

[0079] Historical data distribution analysis includes fitting the distribution of key indicators of historical collaboration data, such as task delay rate, conflict rate, and resource load overload rate, calculating quantile thresholds, calculating the historical data distribution for each collaboration scenario, and generating scenario-specific initial thresholds. Reinforcement learning-driven threshold optimization focuses on the effectiveness of early warning. The reinforcement learning agent randomly adjusts the threshold in each collaboration cycle, observes the early warning effect, and adds positive rewards when the false alarm rate / false negative rate is lower than the target value, and deducts rewards otherwise. When the threshold adapts to a new scenario, an adaptation reward is added, and the threshold adjustment strategy is updated to make the threshold converge towards the optimal direction.

[0080] The system monitors and corrects errors, including the following: B1. Monitoring real-time error metrics, using the maximum mean difference to detect whether the input feature distribution deviates from the training, identifying concept drift and abnormal prediction samples. When the feature distribution shift exceeds a preset threshold, the prediction model is retrained. An online learning method is used to update the prediction model with the latest incremental data, avoiding repeated training on the entire dataset and prioritizing training of feature dimensions with significant drift. B2. Statistical filtering of abnormal operation records based on a sliding window is used to clean abnormal data. The real-time prediction error is mapped to the reward function of the prediction model, adjusting the policy network parameters, automatically filtering high-uncertainty samples, supplementing with high-quality labeled data, selecting the most informative samples for labeling, saving historical versions of the model, and supporting rollback to a stable version.

[0081] When detecting whether the distribution of input features deviates from the training set, a significance test is performed on the distributions of real-time input features and training set features. The specific formula is as follows:

[0082] Let the feature samples of the training set be X. train ={x1,x2,...,x m}, where the real-time feature sample is X real ={x1',x'2,...,x' n The kernel function chosen is the RBF kernel k(x,x') = exp(-γ||x-x'||). 2 );

[0083] The maximum mean difference (MMD) statistic is:

[0084]

[0085] Where φ is the kernel function mapping;

[0086] In MMD>τ MMD When this occurs, it is determined to be a characteristic distribution shift, τ MMD This indicates a preset threshold, calibrated using historical distribution offset data.

[0087] The specific method for updating the prediction model using the latest incremental data through online learning is as follows:

[0088] Initialize the model parameters and reuse the historical model parameters W0;

[0089] The incremental update formula is:

[0090]

[0091] Where L is the loss function, used to measure the difference between the model's predicted results and the true labels; Ω(W) is the L1 regularization term, which constrains the model parameters; W is the set of parameters to be optimized in the current model; and y i Let be the true label of the i-th sample, and be the target that the model predicts to approximate. Given the current parameters W, this is the model's prediction output for the i-th sample. It is a mathematical operation that means finding the parameter W that minimizes the expression in the parentheses, that is, determining the appropriate values ​​of the model parameters in order to minimize the overall objective.

[0092] Dynamically adjust the learning rate: η t =η0·MMD t ;

[0093] Where η0 represents the basic learning rate.

[0094] Calculate the individual MMD value for each feature dimension, i.e., the distribution offset MMD of the j-th feature. j Press MMD j The features are sorted in descending order, and a subset of drift features is selected based on the feature dimensions. The drift features are given higher weights to strengthen the model's learning of them. Historical data from the past three months is retained, and data is sampled according to the importance of the samples before being used in training to update the prediction model.

[0095] Security Operation and Maintenance Unit 5 is used to encrypt sensitive data, store key operations on the blockchain to ensure data immutability, dynamically adjust permissions, monitor and block unauthorized data downloads and other abnormal behaviors in real time, and ensure system data security and collaborative compliance.

[0096] Example 3: Reference Figure 2 As shown: A real-time interactive collaboration method for digital humans based on natural language processing, specifically including the following steps:

[0097] S1. Natural Language Processing and Digital Human Interaction: Through multilingual speech recognition, user speech is converted into text in real time. After intention recognition, entity extraction and semantic parsing of instructions, the user's facial expressions and speech rhythm are analyzed to identify micro-expressions and tone changes. The digital human response strategy is dynamically adjusted (such as switching between professional mode and friendly mode), the user's dominant interaction mode is automatically identified, a collaborative context graph is generated, and similar historical collaborative cases are recommended.

[0098] S2. Real-time Collaborative Workflow Construction and Execution: A dynamic workflow engine configures cross-system collaborative workflows, integrating online documents, whiteboards, task boards, and other tools to achieve multi-device synchronization and real-time editing by multiple users. Reinforcement learning optimizes computing resource allocation and automatically distributes workload during peak meeting times. Real-time monitoring of document editing conflicts, audio / video interruptions, and other anomalies triggers a self-healing mechanism. Low-code visualization tools enable rapid adaptation to legacy systems, completing structured data cleaning and transformation, and intelligent parsing of unstructured data. This improves cross-departmental and cross-system collaboration efficiency and ensures stable system response and smooth workflow execution in concurrent multi-user scenarios.

[0099] S3, Decision Support and Knowledge-Driven: Construct and invoke knowledge graphs, build predictive models, predict task progress, resource requirements and risk points, and display collaborative decision-making;

[0100] In step S3, decision support and knowledge-driven approaches include the following steps: S31, Knowledge Graph Construction and Invocation: Real-time synchronization of external data such as policies, regulations, and business processes; extraction of structured and unstructured data from the enterprise knowledge base; construction of a knowledge graph; dynamic calculation of entity similarity using entity alignment and self-attention mechanisms; regular updating of the graph content; and support for real-time knowledge invocation during collaboration; S32, Collaboration Trend Prediction and Risk Management: Based on historical data and real-time dynamics, a prediction model is constructed to predict task progress, resource requirements, and risk points, issuing early warnings, monitoring prediction errors in real time, and automatically retraining the model when the feature distribution deviation exceeds a threshold to optimize model parameters and form a prediction closed loop; S33, Human-Machine Collaborative Decision Demonstration: Providing multi-dimensional analysis reports during high-risk decisions while retaining access for human intervention, integrating machine intelligence and human experience.

[0101] S4. Security Operations and Access Management: Encrypt sensitive data, dynamically adjust permissions according to collaboration scenarios, monitor behavior logs in real time, and block abnormal behavior.

[0102] This invention first completes the conversion of voice commands to text and semantic parsing in the natural language processing unit 1 through multilingual speech recognition and intent recognition. The digital human interaction unit 2 combines facial expression, speech prosody recognition and multimodal priority decision-making to adjust the digital human interaction strategy and match collaborative logic to generate a collaborative context graph. Then, the real-time collaboration unit 3 uses a dynamic process engine to configure cross-system processes, integrates online tools to enable real-time editing by multiple users, optimizes computing resources based on reinforcement learning through the intelligent scheduling module 32, monitors anomalies and triggers self-healing, and adapts to legacy systems to process structured and unstructured data with the help of the cross-system integration module 33.

[0103] Then, the decision support unit 4 constructs and calls the knowledge graph through the graph construction module 41, updates the graph through entity alignment and other operations, and constructs a predictive model based on historical and real-time data through the predictive analysis module 42 to predict collaboration trends and monitor and correct errors. In high-risk decision-making, the decision display module 43 displays the causal chain and analysis report, retaining human intervention. The security operation and maintenance unit 5 encrypts sensitive data, stores key operations on the blockchain for evidence, dynamically adjusts permissions, monitors and blocks abnormal behavior, and ensures data security and collaboration compliance. It realizes the digitization of voice commands through natural language processing, creates a realistic collaboration partner based on digital human interaction, builds an intelligent decision-making center with the help of dynamic process engine and knowledge graph, and strengthens the security defense with blockchain and permission management. Finally, a closed loop of perception-collaboration-decision-protection is formed, promoting the upgrade of enterprise office from manual driving to human-machine collaborative driving.

[0104] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time interactive collaboration system for digital humans based on natural language processing, characterized in that, It includes a natural language processing unit (1), a digital human interaction unit (2), a real-time collaboration unit (3), a decision support unit (4), and a security operation and maintenance unit (5). The natural language processing unit (1) performs semantic analysis through multilingual speech recognition and speech synthesis; The digital human interaction unit (2) is used to bind multiple system roles, render and generate realistic digital humans, recognize facial expressions and voice changes, adjust interaction strategies, and match different collaborative logics. The digital human interaction unit (2) includes an emotion computing module (21) and a multimodal collaboration module (22); The emotion computing module (21) is used to integrate facial expression recognition and prosodic analysis to identify micro-expressions and tone changes and dynamically adjust interaction strategies. The multimodal collaboration module (22) is used to establish a modal priority decision model, automatically identify the user-dominated interaction modality, and the digital human synchronously adjusts the response strategy, matches the collaboration logic, and generates a collaboration context graph. The real-time collaboration unit (3) is used to build a dynamic process engine and configure cross-system collaboration processes; The real-time collaboration unit (3) includes a multi-terminal synchronization module (31), an intelligent scheduling module (32), and a cross-system integration module (33). The multi-terminal synchronization module (31) is used to integrate online documents, whiteboards and task boards for real-time editing and version management by multiple users; The intelligent scheduling module (32) optimizes computing resources in real time based on reinforcement learning, monitors collaboration anomalies in real time, and automatically triggers retry mechanisms and switches backup nodes. The cross-system integration module (33) adapts to legacy systems through low-code visualization tools to automate the processing of structured and unstructured data; The decision support unit (4) is used to build and call knowledge graphs, predict collaboration trends and provide early warnings, and provide multi-dimensional analysis reports; The decision support unit (4) includes a map construction module (41), a predictive analysis module (42), and a decision display module (43). The knowledge graph construction module (41) is used to synchronize external data of policies, regulations and business processes in real time, connect with internal data of enterprise knowledge base, extract structured and unstructured data, construct knowledge graphs, and call knowledge graphs in real time during collaboration. The predictive analysis module (42) is used to predict collaboration trends, issue risk warnings in advance, monitor the warning results, and correct errors. The predictive analysis module (42) includes a trend prediction module (421), an error monitoring module (422), and a data correction module (423). The trend prediction module (421) builds a prediction model based on historical collaboration data and real-time dynamics, predicts collaboration trends in real time, and issues risk warnings. The error monitoring module (422) is used to monitor the error drift of the prediction model in real time. When the feature distribution shift exceeds the preset threshold, the prediction model is automatically retrained and updated. The data correction module (423) is used to clean abnormal data, correct prediction bias, and optimize prediction model parameters through feedback loop; The decision display module (43) breaks down the digital human's decision-making suggestions into a causal chain and displays it through a flowchart. The digital human provides multi-dimensional analysis reports when making high-risk decisions. The security operation and maintenance unit (5) is used to encrypt sensitive data, dynamically adjust permissions, monitor in real time and block abnormal behavior.

2. The real-time interactive collaboration system for digital humans based on natural language processing according to claim 1, characterized in that: Within the knowledge graph construction module (41), after constructing the knowledge network, the knowledge graph is vectorized using a neural network, the semantics of the entity neighborhood are divided, entity alignment is performed, the matching priority is dynamically adjusted based on the self-attention mechanism, user-annotated data is collected, duplicate data is detected, and the knowledge graph content is updated.

3. The real-time interactive collaboration system for digital humans based on natural language processing according to claim 2, characterized in that, In the trend prediction module (421), the prediction model is constructed by including the following: A1. Collect internal collaboration data and external data, and construct feature engineering, which includes time features, behavioral features, resource features and correlation features; A2. Based on deep reinforcement learning, model the dynamic decision-making process in the collaboration process and build a prediction model; A3. Set early warning thresholds based on historical data distribution, and optimize the thresholds through reinforcement learning.

4. A real-time interactive collaboration system for digital humans based on natural language processing according to claim 3, characterized in that, In the predictive analysis module (42), the early warning results are monitored, and the error correction includes the following: B1. Monitor real-time error indicators and use the maximum mean difference to detect whether the input feature distribution deviates from the training. When the feature distribution deviation exceeds the preset threshold, trigger the prediction model to retrain and update the prediction model. B2. Based on the sliding window statistical filtering of abnormal operation records, abnormal data is cleaned, strategy network parameters are adjusted, samples are screened, and labeled data is supplemented.

5. A real-time interactive collaboration system for digital humans based on natural language processing according to claim 4, characterized in that: The security operation and maintenance unit (5) includes a data isolation module (51) and an anomaly blocking module (52). The data isolation module (51) is used to encrypt sensitive data before performing protocol operations, and to record timestamps, operators, and content hash values; The anomaly blocking module (52) is used to dynamically adjust permissions according to the collaboration scenario, monitor collaboration behavior in real time, and block abnormal situations such as unauthorized data downloads.

6. A real-time interactive collaboration method for digital humans based on natural language processing, characterized in that, The real-time interactive collaboration system for digital humans based on natural language processing, as described in any one of claims 1-5, specifically includes the following steps: S1. Natural Language Processing and Digital Human Interaction: Parse the semantics of instructions through multilingual speech recognition, analyze user facial expressions and speech rhythm, identify micro-expressions and tone changes, dynamically adjust the digital human response strategy, and identify user-dominated interaction methods; S2. Real-time collaborative workflow construction and execution: Configure cross-system collaborative workflows through a dynamic workflow engine, integrate multiple tools to achieve multi-terminal synchronization and real-time editing by multiple people, adapt to legacy systems, and perform data parsing and processing. S3, Decision Support and Knowledge-Driven: Construct and invoke knowledge graphs, build predictive models, predict task progress, resource requirements and risk points, and display collaborative decision-making; S4. Security Operations and Access Management: Encrypt sensitive data, dynamically adjust permissions according to collaboration scenarios, monitor behavior logs in real time, and block abnormal behavior.

7. A real-time interactive collaboration method for digital humans based on natural language processing according to claim 6, characterized in that: In step S3, decision support and knowledge-driven approaches include the following steps: S31. Knowledge Graph Construction and Invocation: Real-time synchronization of external data, connection to enterprise knowledge base to extract structured and unstructured data, construction of knowledge graph, dynamic calculation of entity similarity using entity alignment and self-attention mechanism, regular updating of graph content, and invocation of knowledge graph in collaboration. S32. Collaboration Trend Prediction and Risk Management: Based on historical data and real-time dynamics, build a prediction model to predict task progress, resource requirements and risk points, issue early warnings, monitor prediction errors in real time, and optimize model parameters. S33. Human-Machine Collaborative Decision-Making Demonstration: Provides multi-dimensional analysis reports for high-risk decision-making while retaining access for human intervention, combining human-machine collaborative decision-making.

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