453 results about "IMT Advanced" patented technology

A scalable platform for orchestrating networks of specialized AI multi-agent networks that enables secure collaboration through token-based protocols and real-time result streaming with advanced dynamic chain-of-thought pruning. The central orchestration engine manages domain-specific agents, implementing sophisticated multi-branch reasoning with contribution-estimation layers that evaluate each agent's utility using Shapley value-inspired metrics. The system employs information-theoretic and gradient-based surprise metric to guide memory updates and dynamic reasoning expansion, preventing local minima stagnation while preserving valuable insights through adaptive forgetting mechanisms. The platform unifies Monte Carlo tree search with contribution-aware estimation to detect high-synergy expert combinations while maintaining privacy through partial data approaches. It scales across distributed computing environments, enabling complex collaborative tasks like materials discovery, product engineering and manufacturing process design, biomedical research, and drug development. The system supports multi-party economic rewards through systematic contribution effort, cost and importance tracking, while standardized interfaces manage security, privacy, and policy constraints across heterogeneous agents.

The present disclosure generally relates to a two-stage system for detecting malicious nodes in Wireless Sensor Networks (WSNs), enhancing network security and resilience. The system employs a distributed approach, leveraging Cluster Heads (CHs) and a central server for efficient and accurate detection. Initially, sensor nodes are monitored for comprehensive node and network metrics, statistically ranked by significance in identifying malicious behavior. CHs perform a resource-aware first-stage detection based on their resource weight, filtering potential threats locally. Results are then aggregated at a server for a second-stage analysis using a hybrid Machine Learning (ML) and Deep Learning (DL) approach. This advanced analysis, combined with statistically relevant metrics, significantly improves detection accuracy. By integrating resource-conscious CH operation with powerful server-side ML / DL, this system offers a scalable, energy-efficient, and highly effective solution for securing WSNs against malicious node attacks, surpassing traditional detection methods in both speed and precision.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent device communication method and system based on the Internet of Things. The method comprises the following steps: acquiring spectrum occupation data, electromagnetic interference mode data and terminal equipment energy state data of a plurality of communication channels in an industrial environment through a preset distributed spectrum sensing network; performing adaptive spectrum analysis processing on the spectrum occupancy data and the electromagnetic interference mode data to obtain channel quality evaluation data including interference source characteristics and channel attenuation characteristics; and according to the channel quality evaluation data and a pre-acquired historical channel performance memory bank, performing priority ranking on the available communication channels to obtain dynamic channel priority data based on time continuity. The spectrum resource allocation condition in the industrial environment is monitored in real time through the distributed spectrum sensing network, the current interference can be identified, and the interference can be pre-judged through an advanced prediction model.

The invention discloses a multi-source heterogeneous data dynamic protocol security exchange adaptation system, which comprises a protocol analysis engine, a data encryption module, a data format standardization engine and a routing strategy engine, and is characterized in that the protocol analysis engine is respectively connected with a data source, the data encryption module and the data format standardization engine; the data format standardization engine is connected with the routing strategy engine, and the routing strategy engine is connected with the nodes. The invention discloses a multi-source heterogeneous data dynamic protocol secure exchange adaptation method. Comprising the following steps of protocol analysis and data conversion, data encryption and sensitive data desensitization, integrity verification and anomaly detection, routing strategy and intelligent scheduling, data distribution and storage, and dynamic extension and protocol adaptation, and the dynamic conversion of different protocol data ensures that various types of data can be smoothly interacted. Meanwhile, an advanced data security enhancement technology is integrated, multiple protections such as encryption and authentication are carried out on transmitted and stored data, and the data security is effectively improved.

The present disclosure describes systems and methods for detecting and preventing data misconfigurations within a security-focused data fabric platform. The system integrates an advanced script migration engine designed to streamline the translation of security rules and scripts across different scripting languages while ensuring alignment with the fabric's unified schema. The method involves receiving inputs from data sources, mapping these inputs to entities of a target schema, monitoring real-time data changes, and simulating impacts on operational dependencies to detect misconfigurations proactively. Leveraging AI-driven mechanisms, including Large Language Models (LLMs), the system dynamically identifies breaking changes in third-party data streams, issues alerts, and provides suggested fixes. The script migration engine further enhances the platform's functionality by automating cross-platform script translations and enabling faster onboarding of security tools. Together, these innovations ensure scalable, accurate, and resilient integration and management of security data across heterogeneous sources, strengthening operational integrity and minimizing security risks.

The invention discloses an intelligent metasurface channel optimization method and system based on a beam forming algorithm, and relates to the technical field of intelligent metasurfaces, and the method comprises the steps: collecting real-time environment data, monitoring channel state data, and evaluating the influence of the environment and the channel state on a signal; evaluating the gain strategy of the intelligent metasurface, and evaluating the performance of the current beam forming strategy by combining the influence of the environment and the channel state on the signal; and calculating an optimal beam forming parameter by using a gradient descent method to realize channel optimization. According to the method, through comprehensive utilization of real-time environment monitoring, channel state evaluation, an advanced data analysis technology and a mathematical optimization method, dynamic adjustment and real-time optimization of intelligent metasurface channel optimization are achieved, efficient and stable operation of a communication system in a complex environment is ensured, the speed and reliability of data transmission are remarkably improved, and the method is suitable for popularization and application. The invention provides a more efficient, flexible and adaptive beam forming strategy optimization method, and the performance of a wireless communication system is remarkably improved.

A new multi-edge smart community system consisting of communication, computing, and energy harvesting models, where the task execution delay and energy consumption are formalized as the optimization objectives under multiple constraints. For single-edge scenarios, we propose an improved twin-delayed DRL-based algorithm. For multi-edge scenarios, we develop a novel personalized FL-based training framework for DRL. Using the real-world settings and testbed, extensive experiments are conducted to validate the effectiveness of the proposed PFR-OA. The results show that the PFR-OA achieves better trade-offs between delay and energy consumption and exhibits higher task execution success rates than benchmark methods under different scenarios. Notably, the PFR-OA reaches a faster convergence speed compared to advanced DRL-based and FRL-based methods. Moreover, we further verify the practicality and superiority of the PFR-OA via real-world testbed experiments.

The invention discloses a power grid communication equipment fault identification method and system based on multi-source heterogeneous data characterization learning. The method comprises the following steps: firstly, collecting various complex multi-source heterogeneous data containing fault power grid communication equipment and preprocessing the complex multi-source heterogeneous data; extracting deep semantic features by using a pre-trained feature extraction network and obtaining a feature vector sequence; capturing time features of the feature vector sequence through a bidirectional long-short-term memory network, and obtaining complete sequence feature representation through a full connection layer; performing low-rank multi-modal fusion on the complete sequence feature representation in combination with cross-modal Transform to obtain three types of output splicing construction joint representation, and obtaining a fusion feature by using a self-attention mechanism; and finally, constructing a fault identification model by using the fusion features. Through innovative data preprocessing and data fusion technologies and in combination with an advanced deep learning model, fault-related characteristics can be automatically learned from multi-source heterogeneous data, and intelligent identification of the fault state of the power grid communication equipment is realized.

The invention discloses an equipment access control method and system based on a security sandbox, which adopts an advanced virtualization technology, constructs a highly isolated operation environment, combines technologies such as deep packet inspection, system call interception and process operation logic analysis, comprehensively monitors access behaviors of processes in the sandbox to virtual equipment, and improves the access performance of the virtual equipment. And the security and credibility of the equipment are evaluated, and illegal processes are prevented from running on the access terminal, so that the security access control of the equipment is realized. The monitoring comprises data packet content, protocol filtering, threat detection, system call interception, process creation, execution and termination behavior monitoring and the like. In combination with a trust evaluation algorithm, behavior data features are collected and extracted, trust scoring is performed by applying a machine learning model, and the equipment trust level and the access strategy are dynamically adjusted according to the score. According to the invention, the access control strategy is managed in a unified manner and deployed quickly based on the security sandbox management program and the authentication server. After identity authentication of the equipment, real-time monitoring of process dynamic behaviors in the running process of the equipment is added, and the safety of the equipment in the data exchange process is further improved.

The invention discloses an equipment real-time monitoring and intelligent scheduling system based on IoT and AI fusion, which comprises a sensing layer, a transmission layer, an edge computing layer, a cloud intelligent center layer and an application interaction layer, and is characterized in that the sensing layer maps heterogeneous data collected by a sensor to a unified space-time coordinate system by adopting a space-time encoder technology; the transmission layer is responsible for transmitting the data collected by the sensing layer to the edge computing layer and the cloud; the edge calculation layer calculates a three-dimensional risk field in real time based on the current operation state data of the equipment; the cloud intelligent center layer constructs digital twinborn bodies, and synchronizes the running state of equipment in real time; the application interaction layer presents equipment operation risk distribution through the risk field thermodynamic diagram; by constructing a multi-layer and multi-module collaborative architecture and fully applying advanced IoT and AI technologies, all-around real-time monitoring, accurate fault prediction and intelligent scheduling decision making of industrial equipment are realized, an intelligent, efficient and reliable operation environment is created for enterprises, and industrial production is promoted to be transformed into intelligentization and digitization.

The invention relates to the technical field of agricultural product distribution, and discloses an agricultural product distribution system based on the Internet of Things, and the system comprises an Internet of Things sensing layer which is provided with various sensors, including but not limited to a temperature sensor, a humidity sensor, an infrared sensor and an RFID tag; the data transmission layer is used for transmitting the data acquired by the sensing layer to a cloud server in real time by utilizing a 4G / 5G and Wi-Fi wireless communication technology; and the cloud computing and big data processing layer adopts a high-performance cloud computing platform and integrates a big data analysis engine. According to the invention, the accuracy and real-time performance of original data are ensured by optimizing the sensor configuration and the data acquisition frequency of the Internet of Things sensing layer and adopting an advanced wireless communication technology, so that the accuracy of agricultural product quality prediction, freshness evaluation and transportation path optimization is remarkably improved; mass data can be processed and analyzed by means of a high-performance platform of cloud computing and a big data processing layer.

The invention relates to the technical field of industrial internet data processing, in particular to a cross-platform multi-source data integration analysis method and system based on the industrial internet, and the system comprises a data collection layer, a data preprocessing layer, a data integration layer, a data storage layer, an intelligent analysis framework, an application layer and a data security layer. Through the data adapter and middleware technology, efficient collection and integration of multi-source heterogeneous data are achieved, multiple data sources and communication protocols are supported, the compatibility and expansibility of the system are improved, and the system can rapidly adapt to different industrial scenes and data source changes; according to the method, advanced big data and artificial intelligence technologies are applied, a specific analysis model for industrial production is combined, deep analysis can be performed on the data, potential values in the data are mined, an accurate analysis result is provided for equipment fault prediction and production process optimization, and the intelligent level of industrial production is improved.

The invention relates to the technical field of intelligent power grids, in particular to an anti-error identification and automatic auditing system for intelligent equipment. The system comprises an anti-error identification module and an automatic auditing module, and the anti-error process of the anti-error identification module comprises anti-error analysis in the ticket simulation process, anti-error analysis in auditing, automatic anti-error analysis before execution and anti-error analysis in the execution process. The automatic auditing module comprises an operation order obtaining module, an operation order identification module, an auditing rule base, an auditing processing module and a result feedback module. According to the anti-misoperation identification and automatic auditing system for the intelligent equipment, misoperation identification and content automatic auditing in the operation process of the intelligent equipment are achieved through advanced algorithms and technologies. According to the system, the operation safety and the content auditing efficiency are improved, and powerful technical guarantee is provided for wide application of intelligent equipment.

According to the priority asynchronous processing method and system for network security, multiple data sources are integrated, the threat level of each data packet is evaluated in real time, and the processing priority of each data packet is dynamically adjusted according to the threat level. Different from a traditional static method, the method provided by the invention utilizes an advanced machine learning algorithm, can perform dynamic evaluation in multiple dimensions, and timely identifies and preferentially processes high-risk data packets. The mechanism obviously improves the response capability of the system to novel and complex attacks. Meanwhile, a multi-node cooperative attack response mechanism is introduced, when a certain node detects abnormal traffic, other nodes can be automatically notified, threat situation information can be shared, and therefore rapid cooperative reaction is achieved. The integrated innovation not only enhances the overall security of the network, but also optimizes the data packet processing efficiency, effectively solves the main defects in the traditional network security system, and provides a new thought for constructing a safer and more flexible network environment.

The invention provides an electric power tower state real-time monitoring and common fault identification system based on a digital twinborn technology. According to the system, a high-precision digital twinborn model module is constructed, and a sensor network module is integrated so as to acquire key data of a tower in real time. The data processing center module utilizes an advanced clock synchronization technology to ensure data precision, cooperates with the common fault identification system module and the health state evaluation module, and adopts finite element analysis and Paris laws to accurately evaluate the health condition of the tower. The system further comprises an intelligent maintenance decision support module which provides a scientific maintenance strategy. In addition, the data visualization platform module and the user interaction interface module enhance the user experience, so that the system is not limited to monitoring and evaluation, and optimization of maintenance decision and individuation of user interaction are also realized. The overall system design pays attention to real-time performance, accuracy and intelligence, and aims at improving the operation and maintenance efficiency and safety of the electric power tower.

The invention relates to the field of power distribution network cable main insulation fault detection and management, and discloses a power distribution network cable main insulation fault detection and management integrated test platform construction method comprising the following steps: a) data acquisition: installing a plurality of sensors including a partial discharge sensor, a current and voltage sensor, a temperature sensor and a vibration sensor; acquiring real-time operation data of the power distribution network cable; b) data transmission: transmitting the acquired cable monitoring data to a data processing system in a wired or wireless communication mode; and c) data preprocessing: denoising, missing value interpolation, standardization or normalization processing is carried out on the transmitted original data so as to improve the quality of the data. The real-time operation state of the power distribution network cable is monitored through various sensors, and various advanced data processing methods are combined, so that the accuracy of fault detection is effectively improved, and identification deviation caused by data noise or sensor errors in a traditional fault detection method is avoided.

The invention relates to a self-adaptive network environment parameter adjusting method and device and electronic equipment, and the method comprises the steps: obtaining the real-time data of a network environment, and carrying out the standardization processing and instant analysis of the data, so as to capture the key features of a network state; identifying a network state based on the preprocessed network environment data, and outputting a network state trend prediction result in a short time in the future; and adaptively adjusting network parameters by using an SAC model according to a prediction result. According to the parameter adjustment method and device adaptive to the network environment and the electronic equipment, the network state is predicted through the network environment data collected in real time and by adopting an advanced data analysis technology, so that a network state trend prediction result in a short time in the future is obtained; therefore, the network environment parameters can be conveniently and accurately adjusted in real time according to the dynamic change of the network state, so that the network performance is optimized, and the stability and efficiency of the network are improved.

The invention discloses an intelligent electromagnetic induction heating system and method based on edge calculation, and aims to realize efficient and accurate heating control through advanced technical means. According to the system, a multi-source data real-time acquisition module is integrated, and various key parameters in the heating process can be comprehensively obtained. Through the data preprocessing and feature extraction technology, the system can accurately identify the heating state, and a reliable basis is provided for subsequent control. The edge computing node serves as the core of the system, has strong real-time analysis capability, can quickly respond to changes in the heating process, and dynamically adjusts a control strategy. In addition, through an edge-cloud collaborative optimization technology, cloud resources are fully utilized, and the intelligent level of the system is further improved. The problems of feedback lag, high cloud dependence and the like in the prior art are effectively solved, the heating uniformity, the energy efficiency ratio and the abnormity recovery capability are remarkably improved, and a reliable solution is provided for the fields of industrial heating, household induction cookers and the like.

The invention relates to the technical field of intelligent construction, and particularly discloses a construction scheme generation method and system fusing a large language model and a knowledge graph, and the method comprises key links such as intelligent directory generation, intelligent content generation and deep content quality analysis, achieves the intelligent generation of a building engineering construction scheme, and improves the construction efficiency. The technical blank in the field is effectively filled, the automation and intelligence process of the building industry is promoted, the advanced large language model technology and the knowledge graph technology are combined, a whole set of technical framework and intelligent processing system are constructed, the time and energy required in the manual compiling process are remarkably saved, the working efficiency is improved, and the construction cost is reduced. The project engineering information and the building standard specification are organically combined by utilizing the knowledge graph technology, so that the professionality and the standardization of intelligent generation of the construction scheme are ensured, and the accuracy and the reliability of the construction scheme are greatly improved.

The present invention pertains to an advanced workforce management system that integrates multiple cutting-edge technologies to optimize human capital management and enhance employee engagement. The system comprises an AI-enabled performance dashboard, a blockchain-based gamification module issuing Non-Fungible Tokens (NFTs), a shared human resources service platform, and an augmented reality / virtual reality (AR / VR)-based training platform. These components are interconnected through a novel data processing framework that synchronizes real-time performance data, reward mechanisms, resource allocation, and personalized training. Proprietary machine learning algorithms deliver predictive analytics and actionable insights, while advanced security protocols ensure data integrity and privacy. The blockchain module facilitates transparent and secure NFT rewards, fostering a culture of meritocracy and continuous improvement. Additionally, the AR / VR training platform provides immersive and adaptive learning experiences, enhancing employee skills and preparedness. This integrated system offers a scalable and flexible solution applicable across various industries, significantly improving organizational efficiency and employee satisfaction.

The invention discloses an LLM-based scheduling management system, which belongs to the field of industrial and commercial enterprise scheduling management systems, adopts a data acquisition technology driven by bio-electricity signals, enriches data acquisition forms, can acquire various bio-electricity signals such as electroencephalogram, electrocardio, myoelectricity and the like, and improves the scheduling efficiency. And through a multi-channel parallel data transmission architecture and an advanced signal coding technology, the accuracy and stability of data transmission are ensured, key data in a human physiological state or a special environment are reflected, and some application scenes with relatively high requirements on data comprehensiveness and accuracy are met. The LLM-based scheduling module generates an optimal scheduling scheme by means of the powerful analysis and reasoning capability of a large language model in combination with a genetic algorithm and a simulated annealing algorithm, quantitative evaluation is carried out through a multi-objective optimization algorithm, various complex constraint conditions can be fully considered, and efficient scheduling is realized.

The invention discloses a network security detection method based on big data, and relates to the technical field of network security detection, a big data acquisition module is utilized to acquire network flow data, security event data and system security state data of a current detection period, and a network flow anomaly degree measuring unit is utilized to measure the network flow anomaly degree. The method comprises the following steps: firstly, calculating and outputting an abnormal traffic detection index YL, calculating and outputting a threat evaluation value WW, introducing the abnormal traffic detection index YL and the threat evaluation value WW into a unit for comprehensively evaluating the overall security condition of the network, calculating and outputting a security situation index AQ, carrying out detection analysis, and carrying out security response and disposal by a response and disposal module aiming at the security situation index AQ. According to the invention, by collecting and analyzing multi-dimensional data of network traffic, security events and system states and applying advanced algorithms and models, comprehensive security detection is carried out on the network, and comprehensive and dynamic evaluation of network security is realized.

The invention discloses a multi-source data fusion IT operation and maintenance fault root cause analysis method, which comprises the following steps: data acquisition: acquiring data in real time from a plurality of data sources in an IT system, the data sources specifically being system logs, performance index data and network topology data, and the data sources being data of the IT system; the data acquisition is carried out according to a preset time interval or an event triggering mechanism; data preprocessing: preprocessing the multi-source data collected in the step S10, specifically comprising: a data cleaning step: removing noise, repeated data and incomplete data in the data; in the data conversion step, data in different formats are converted into data in a unified structured format, and in the data normalization step, a minimum-maximum normalization method is adopted. The multi-source data fusion IT operation and maintenance fault root cause analysis method and system aim to realize rapid and accurate identification of IT operation and maintenance fault root causes by fusing multiple data sources and utilizing an advanced data analysis technology.

The invention discloses an intelligent data acquisition and analysis method based on big data, and particularly relates to the field of data analysis, comprising intelligent target data, intelligent data acquisition, controller deployment, preprocessing, data analysis and comprehensive analysis. According to the invention, accurate acquisition and analysis of key data of a pipe network system are realized through determination of intelligent target data; a high-precision sensor and an advanced data acquisition technology are adopted, so that the accuracy and reliability of data are ensured; through the comprehensive analysis model, the dynamic response, the multi-dimensional coupling relation and the material degradation process of the pipe network system are comprehensively captured, and a scientific basis is provided for the evaluation of the health state of the pipe network; the established health state evaluation model of the pipe network system realizes quantitative evaluation of the health state, can timely discover potential problems and risks, provides scientific decision support for maintenance and management of the pipe network system, and is helpful for improving the safety of the pipe network system and prolonging the service life of the pipe network system.

The invention relates to the technical field of encrypted transmission, in particular to an encrypted transmission method and system for distributed photovoltaic operation and maintenance data. The method comprises the following steps: installing edge computing equipment at each node of a distributed photovoltaic system, and collecting photovoltaic operation and maintenance data in real time through a photovoltaic sensor; on the edge computing device, photovoltaic operation and maintenance data of the multiple photovoltaic sensors are aggregated and preprocessed; establishing encryption connection through an encryption transmission protocol TLS and a network protocol HTTPS; based on the encrypted connection, the edge computing device encrypts the aggregated photovoltaic operation and maintenance data by using a symmetric encryption AES algorithm and an asymmetric encryption RSA algorithm; and through the SD-WAN technology, the encrypted photovoltaic operation and maintenance data are transmitted through different network paths. According to the design of the invention, an advanced encryption technology (such as TLS, AES and RSA) is adopted to ensure that the photovoltaic operation and maintenance data is not stolen or tampered in the transmission process from the edge computing device to the cloud platform.

An AI-and IoT-driven real-time construction management system continuously acquires and analyzes high-frequency sensor data, including temperature, GPS, and RFID inputs. Utilizing advanced predictive machine learning and reinforcement learning algorithms, the system forecasts schedule deviations and resource conflicts with an accuracy exceeding 90%. Upon identifying deviations, it autonomously triggers corrective actions—such as reallocating resources or adjusting task sequences—typically within five seconds. This integrated, closed-loop management approach seamlessly interfaces with external project management tools, achieving approximately 15-20% improved schedule adherence and notable cost reductions based on preliminary data. The system's modular architecture supports diverse sensor technologies and AI frameworks, ensuring adaptability and sustained performance in large-scale, dynamic construction environments.

The invention belongs to the technical field of multimedia, and particularly relates to a short drama translation and explanation method based on a multi-mode neural network, which realizes full-process automatic processing from subtitle extraction to culture adaptation translation by integrating advanced technical means such as an OCR (optical character recognition) model, optical flow field analysis, an LSTM (long short term memory) network and a graph neural network. Particularly, the subtitles in a complex background can be accurately recognized and processed, subtitle traces are effectively removed, plot conflicts are understood through a deep learning algorithm, and then translation content conforming to target culture is generated. The method not only improves the accuracy of subtitle extraction, but also enhances the cultural adaptability of the translated content, so that the multimedia content can be more naturally and smoothly understood and accepted under different cultural backgrounds, and the watching experience of global users is greatly improved.

The invention discloses a standardized operation and maintenance management method and system based on substation scene dynamic fusion, belongs to the technical field of substation operation and maintenance management, and aims to solve the problems of information islanding, low operation and maintenance efficiency, insufficient decision basis and the like. The Internet of Things technology is utilized, various sensors are deployed in a transformer substation, data such as equipment states and environmental parameters are collected in real time, preliminary processing is carried out through edge computing equipment, noise and redundant information are removed, a big data platform is established, the preprocessed data are stored and managed in a centralized mode, an advanced data fusion algorithm is applied, and the data fusion efficiency is improved. Data from different sources and in different formats are dynamically fused, meanwhile, the fused data are deeply mined and analyzed through an artificial intelligence algorithm, potential faults and risks are recognized, the information island problem is effectively solved, and the real-time performance and accuracy of data processing are improved.

The invention relates to a service interface security configuration automatic evaluation and reinforcement processing method, system and device and a storage medium. The method comprises the steps of receiving a security evaluation request submitted by a user, and generating a service interface behavior portrait in combination with an interface historical access record; identifying potential security risk points of the service interface through an anomaly detection algorithm, and outputting a detailed risk assessment report; based on the detailed risk assessment report, a decision tree algorithm is adopted to monitor the influence of the reinforcement process on the service interface performance, and a reinforced performance monitoring report is generated; and performing security assessment verification on the reinforced service interface to generate an optimized security configuration report. According to the method, by integrating an advanced data analysis technology and a security protection strategy, the accuracy and efficiency of security assessment are improved, automation and performance monitoring of security reinforcement measures and continuous optimization of security configuration are also realized, and an efficient, reliable and intelligent security management solution is provided for a user.

The invention relates to the technical field of digital people, and particularly discloses a 3D digital people stream type audio interaction system based on an end-to-end voice large model, which adopts a stream type audio input interface to receive voice instructions and interaction contents of a user in real time, and uses an advanced feature extraction framework to perform voice feature extraction and semantic analysis. Therefore, the voice interaction content semantics of the user can be more timely and accurately understood, the accurate voice reply is directly generated based on the end-to-end voice large model, and the streaming driving of the 3D digital human is realized based on the voice reply. Thus, the reality sense and immersion sense of interaction are improved, the intelligent level of the 3D digital human system is enhanced, and a new possibility is developed for efficient communication in various application scenes.