329 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.

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 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 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 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 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.

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.

Cybercriminals are using advanced techniques to access information not only inside a company network but outside of a company network in the ecosystem of company communications with customers, suppliers, partners, and professional advisors. There are many ways cybercriminals gain access to this information, often by compromising email accounts, archives, and document management systems outside of a company's own control and / or outside of their control to manage security of those external accounts. This invention provides an apparatus that will proactively detect when a cybercriminal has accessed or is attempting to access a document attached to an email, a document stored in a repository, a link to an e-signature transaction, a link to a file download (“Files”), and pre-emptively auto-lock the attempted-to-be-eavesdropped on Files and alert the File owner of a high risk information breach in progress, even if that breach is outside their network or information perimeter.

The invention relates to a hybrid ad hoc network and a safe and efficient communication method in a large-scale wireless sensor network. The method comprises the steps that a network topology construction module is used for designing a heterogeneous communication protocol with high adaptability based on the distribution condition of large-scale sensor nodes, and seamless cooperative work of multiple communication technologies is ensured; the ad-hoc network optimization module adopts a dynamic ad-hoc mechanism to realize autonomous discovery and network connection between nodes and improve the expansibility and flexibility of the network; the safety communication module is combined with an advanced encryption algorithm and an authentication mechanism to ensure the safety and the reliability in a data transmission process; and the efficient communication protocol module improves the data transmission efficiency and the overall performance of the network through the technical means of signal transmission optimization, interference management and the like. Through cooperative work of the modules, efficient, safe and reliable heterogeneous wireless communication can be realized in the large-scale wireless sensor network, and the requirements of the large-scale wireless sensor network in practical application are met.

The invention discloses a data and model heterogeneous federated learning method of global decision boundary distillation learning. Two key problems of data heterogeneity and heterogeneous model performance difference are respectively solved through two sub-methods. According to the first sub-method, aiming at the problem of data heterogeneity, multiple prototypes are locally clustered, private data field distribution information is fully condensed, irrelevant distillation is introduced in a local supervised learning stage, and local knowledge forgetting is relieved. According to the second sub-method, global decision boundary distillation learning is provided for the problem of performance difference of the heterogeneous model, a global decision boundary learner is maintained and updated in a server, the local model is optimized from the perspective of the global decision boundary, and the influence of conflict information on the local model is reduced. The performance of the method is superior to that of various most advanced federal learning methods, the method can better adapt to challenging scenes, and meanwhile, the method has higher communication efficiency and better privacy reliability.

The invention relates to the technical field of server management and cloud computing, in particular to a dynamic load-oriented intelligent identification heterogeneous server resource expansion method. The method aims at optimizing the resource management efficiency, the hardware expansion flexibility and the overall operation stability of the system in a data center and a cloud computing environment by integrating an advanced intelligent identification technology, a real-time data analysis capability and a self-adaptive resource scheduling mechanism. The method is particularly suitable for modern data center scenes requiring high concurrent processing capability, dynamic load adaptability and high reliability, such as a large-scale cloud computing service platform, an enterprise-level distributed storage system and an artificial intelligence computing cluster.

Along with continuous development of technologies such as deep learning and natural language processing, the AI model shows powerful capabilities in the aspects of understanding user intentions, processing complex information, realizing accurate matching and the like. The large star model is a large AI model which is developed by China Telecom and is based on an advanced Transform architecture. The Transformer architecture obtains significant results in the fields of natural language processing, image recognition and the like due to good expandability and strong performance of the Transformer architecture. When the method is applied to a customer service scene, the large star model can deeply understand the appeal of the user. Through natural language processing and semantic understanding technologies, the model can accurately identify and analyze questions of a user and quickly give corresponding answers or suggestions. In addition, the large star model also has strong copywriting generation ability and knowledge question-answering ability, can provide personalized exchange recommendation and shopping preferential information for the user, and further improves the user experience.

The invention discloses an intelligent agricultural big data system, and belongs to the field of agricultural big data, and the system comprises an environment monitoring module, a sensing layer deploys a sensor to collect original data, and a transmission layer is provided with an edge gateway which is connected with the sensor and carries out the transmission of the original data; the equipment layer is used for regulating and controlling irrigation, ventilation and light supplement schemes, and the control layer is used for optimizing MQTT message middleware to control the agricultural Internet of Things system; according to the data management module, a data collection layer obtains original data, a data processing layer processes the original data according to a three-layer data cleaning mechanism, and a data storage layer stores the processed data; according to the system management module, the data application layer constructs an interface covering Web, a mobile terminal and AR equipment, the data display layer generates a soil moisture thermodynamic diagram, a crop growth distribution diagram and an equipment operation state diagram, and intelligent management of agricultural production is ensured. The advanced Internet of Things technology is adopted, and the accuracy and the real-time performance of data acquisition are improved.

The AI-Enhanced Home Storage System of the present invention integrates a combination of physical storage containers and a dedicated mobile application. Leveraging advanced artificial intelligence (AI) and image recognition technology, the system is configured to streamline the process of categorizing, retrieving, and organizing items stored within the household. Through intuitive interfaces and smart categorization, the system offers users an efficient and convenient way to track and locate their belongings within the designated storage spaces.

By combining a federated learning model and a low earth orbit (LEO) satellite network, the present invention provides a general federated learning framework over an LEO satellite network (FedSN) for achieving federated learning. The FedSN is composed of two main components: a sub-structure scheme and pseudo-synchronous model aggregation. The sub-structure scheme comprises sub-structure customization, distribution and aggregation methods, and respectively solves problems such as resource limitations, training imbalance, staleness of intra-group models. According to a pseudo-synchronous model aggregation strategy, the difference between weights of models is brought into a weight function, and a buffer-based aggregation method is developed, so that the staleness of inter-group models is reduced. Extensive experimental results show that a FedSN framework is superior to a state-of-the-art baseline. The present invention shows and improves the potential of deploying a FedSN on an LEO satellite network.

The AI-Enhanced Home Storage System of the present invention integrates a combination of physical storage containers and a dedicated mobile application. Leveraging advanced artificial intelligence (AI) and image recognition technology, the system is configured to streamline the process of categorizing, retrieving, and organizing items stored within the household. Through intuitive interfaces and smart categorization, the system offers users an efficient and convenient way to track and locate their belongings within the designated storage spaces.

Quantum teleportation is essential for many quantum information technologies, including long-distance quantum networks. Using fiber-coupled devices, including state-of-the-art low-noise superconducting nanowire single-photon detectors and off-the-shelf optics, we achieve conditional quantum teleportation of time-bin qubits at the telecommunication wavelength of 1536.5 nm. We measure teleportation fidelities of ≥90% that are consistent with an analytical model of our system, which includes realistic imperfections. To demonstrate the compatibility of our setup with deployed quantum networks, we teleport qubits over 22 km of single-mode fiber while transmitting qubits over an additional 22 km of fiber. Our systems, which are compatible with emerging solid-state quantum devices, provide a realistic foundation for a high-fidelity quantum Internet with practical devices.

The invention discloses an intelligent scheduling and management system for multiple communication devices, and the system comprises a multi-modal data fusion module, a deep learning intelligent analysis engine, an AR auxiliary real-time monitoring interface, a self-adaptive scheduling algorithm, a predictive maintenance module, and an intelligent energy management module. The multi-modal data fusion module is used for collecting and processing heterogeneous data from various data sources and executing data preprocessing operation, and the deep learning intelligent analysis engine is used for receiving the data preprocessed by the multi-modal data fusion module for mode recognition and anomaly detection and outputting an analysis result. The AR auxiliary real-time monitoring interface is used for three-dimensionally and visually displaying an analysis result of the deep learning intelligent analysis engine and receiving a user interaction instruction, and the like. The system supports real-time collection and processing of multiple data sources, ensures the integrity and consistency of the data, and can deeply mine mass data by using an advanced machine learning algorithm, so that the system has a wide application prospect. And complex modes and abnormal conditions are identified.

The AI-Enhanced Home Storage System of the present invention integrates a combination of physical storage containers and a dedicated mobile application. Leveraging advanced artificial intelligence (AI) and image recognition technology, the system is configured to streamline the process of categorizing, retrieving, and organizing items stored within the household. Through intuitive interfaces and smart categorization, the system offers users an efficient and convenient way to track and locate their belongings within the designated storage spaces.

The invention discloses a remote attestation method of a cloud server, electronic equipment and a storage medium, and relates to the technical field of computers and cloud computing. The method comprises the steps that a loading message from a virtualization component in a cloud server is received, a preset security architecture is deployed in the cloud server, and a secure world operation environment and a non-secure world operation environment are created on the preset security architecture; generating a remote attestation report based on the loaded message; the remote certification report is output to a remote certification verification assembly in the cloud server, the remote certification verification assembly runs in the non-secure world operation environment, and the remote certification verification assembly is used for conducting remote certification on the target trusted kernel system according to the remote certification report. The technical problem that an advanced reduced instruction set computer server lacks a scheme for supporting remote certification of a plurality of trusted execution environment operating systems in the prior art is solved.

The AI-Enhanced Home Storage System of the present invention integrates a combination of physical storage containers and a dedicated mobile application. Leveraging advanced artificial intelligence (AI) and image recognition technology, the system is configured to streamline the process of categorizing, retrieving, and organizing items stored within the household. Through intuitive interfaces and smart categorization, the system offers users an efficient and convenient way to track and locate their belongings within the designated storage spaces.

The invention relates to the technical field of artificial intelligence, and discloses an intelligent job-seeking assistant system based on artificial intelligence, and the system comprises a data collection and preprocessing module which carries out the comprehensive information collection of all mainstream recruitment websites, enterprise official websites and emerging industry vertical recruitment platforms through employing a network crawler technology, and carries out the processing of the information, meanwhile, information such as education background, work experience, skill specialty, occupational interest, project achievement details, special skill mastering degree and occupational development planning of job seekers is collected through an intelligent guide type input interface, and repeated, wrong and incomplete data are recognized and eliminated through an advanced data cleaning algorithm. According to the intelligent job-seeking assistant system, the intelligent job-seeking assistant system integrating multi-module cooperative work is constructed, all-around and personalized support for job seekers is achieved, the obvious technical effects and advantages are achieved, and in the aspect of post screening, the data collecting and preprocessing module widely collects multi-channel information and conducts deep processing, and a solid foundation is provided for follow-up analysis.