1627 results about "Information exchange" patented technology

A federated distributed computational system enables secure collaboration across multiple institutions for biological data analysis. The system consists of interconnected computational nodes managed by a centralized or decentralized federation manager, depending on the deployment model. Each node contains specialized components that work together to process biological data while preserving privacy. These components include a local computational engine that handles data processing, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships by connecting various data sources, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaborate on complex biological analysis tasks without compromising their sensitive data, enabling breakthrough discoveries through shared computational resources and expertise while maintaining the security, compliance, and confidentiality required in biological research.

A scalable platform for orchestrating networks of collaborative AI agents utilizing modular hybrid computing architecture. The platform integrates classical, quantum, and neuromorphic computing paradigms through hardware-accelerated translation layers and cross-paradigm coordination mechanisms. A central orchestration engine manages interactions between domain-specific AI agents, dynamically distributing workloads across heterogeneous computing cores based on task complexity, computational requirements, and resource availability. The platform employs hardware-accelerated translation between paradigms, enabling efficient cross-paradigm information exchange while maintaining semantic consistency and computation integrity across different architectures. Specialized monitoring and optimization systems continuously adjust resource allocation and fine-tune performance across computing paradigms. Advanced cache management and fault tolerance mechanisms ensure reliable operation, while privacy-preservation techniques enable secure collaboration. The platform's modular architecture supports integration of different computational approaches, enabling complex multi-domain problem solving that leverages the unique advantages of each paradigm while maintaining system-wide efficiency, scalability, and coherence.

A federated distributed computational system enables secure collaboration across multiple institutions for multi-species biological data analysis. The system consists of interconnected computational nodes managed by a central federation manager. Each node contains specialized components that work together to process multi-species biological data while preserving privacy. These components include a local computational engine that handles data processing, a physics-information integration subsystem that combines physical state calculations with information-theoretic optimization, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities and manages resource allocations across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaboratively analyze complex, multi-species biological systems through integrated physics-based modeling and information-theoretic approaches while maintaining security and confidentiality.

This disclosure provides methods, components, devices, and systems that support information exchanges for coordinated access point (CAP) operations. Some aspects more specifically relate to transmitting and receiving CAP-specific parameters to facilitate transmit opportunity (TXOP) sharing between a first access point (AP) and one or more second APs. For example, the first AP (such as a sharing AP) may use CAP-specific parameters provided by the one or more second APs (such as shared APs) to determine a CAP sharing schedule for a TXOP. The CAP-specific parameters may include static capability parameters and dynamic parameters. The static capability parameters may include supported CAP modes, association identifiers (AIDs), and processing times, while the dynamic parameters may include medium access demands, traffic priority information, and service period information. The first AP may advertise the CAP sharing schedule to the one or more second APs via an announcement frame.

The invention discloses a multi-scale hydrodynamic digital twin model based on a coupling algorithm, which ensures the accuracy and consistency of the model on the overall and local levels through effective information exchange and integration among different scales. The consistency of macroscopic and microscopic variables is ensured on a boundary layer through bridging coupling of different scale models and a Lagrangian multiplier method, and physical behaviors of a complex system are simulated more accurately. Besides, through parameter estimation and data fusion, the model can integrate and utilize real-time data, so that dynamic optimization and prediction are realized, and the practicability and flexibility of the model are greatly enhanced. Therefore, the construction of the multi-scale digital twinborn model not only enhances the description capability of the model to a complex system, but also improves the adaptability and prediction precision of the model in practical application, and provides a more efficient and dynamic method for accurately simulating and predicting behaviors of the complex system.

The invention discloses an editable condition printing image generation method, and belongs to the field of image generation. The method comprises the following steps: constructing a printing image generation model, and training the model to obtain a generation model with editable conditions; and generating a printing design image meeting the condition requirements by using the model. According to the method, different advanced features of each element of the printed plane image are extracted through downsampling, element and style information in the printed plane image is captured through multi-scale feature extraction and information exchange, then hidden space vectors corresponding to different printed plane image features are analyzed, fusion of specific styles is achieved in the hidden space, and the image quality is improved. And the decoding of the printing design image is realized through the decoder, the decoupling of the element information is realized, the personalized design and diversified requirements can be met, and the expansibility is realized.

The invention discloses a multi-device cooperative management system based on cloud edge cooperation, and relates to the technical field of Internet of Things communication, and the system comprises a device management module which is used for connecting edge devices, carrying out the centralized management of the edge devices, and carrying out the optimal scheduling based on the functions and distribution positions of different edge devices; and the data acquisition module is connected with the equipment management module. According to the multi-device collaborative management system based on cloud edge collaboration, a comprehensive and accurate data basis is provided for multi-device collaborative management, and information exchange and collaborative work between devices are promoted; the optimization decision-making unit can formulate an equipment optimization management strategy according to the operation scheme generated by the big data analysis unit and the prediction result obtained by the prediction analysis unit, and continuously performs optimization adjustment according to the real-time operation condition of each piece of edge equipment; therefore, the system can adapt to continuously changing equipment operation environments and task requirements, and more efficient multi-equipment collaborative management is realized.

The invention discloses a power grid visual diagnosis system based on multi-source data fusion, and relates to the field of power grid diagnosis systems. The system comprises an information acquisition module, an electrical fault alarm module, a non-electrical quantity fault alarm module, a fault discrimination module, a communication module and a fault recording module. Firstly, information of a power grid is collected through the information collection module, then the information is transmitted to the electrical fault alarm module and the non-electrical fault alarm module for monitoring, corresponding actions are made when abnormity occurs, then the information is transmitted to the fault judgment module, input information is analyzed, and the fault type is obtained; meanwhile, the fault recording module records and broadcasts the power grid; the communication module collects information of other modules in the system and exchanges information with the monitoring system. According to the method, the abnormal information of the power grid is monitored and alarmed in combination with the multi-source data, and the fault type is judged through the data model, so that the omnibearing monitoring of the power grid is realized, and data support is provided for a subsequent maintenance plan.

The invention relates to a dynamic cooperative path planning and autonomous landing control method for an unmanned aerial vehicle and an unmanned ship, and the method comprises the following steps: collecting the state data of the unmanned ship and the unmanned aerial vehicle and the high sea condition environment data, and carrying out the data fusion processing; an improved fast extended random tree algorithm is adopted to plan a safe navigation path for the unmanned ship, the generated path of the unmanned ship is optimized based on an unmanned ship motion model considering environmental interference, and a model prediction control algorithm is adopted to plan a path for the unmanned ship to fly to a predicted landing point of the unmanned ship. Dynamic cooperation between the unmanned ship and the unmanned aerial vehicle is realized through information interaction between the unmanned ship and the unmanned aerial vehicle; the unmanned aerial vehicle recognizes an unmanned ship landing identifier through a visual servo guide system, calculates the position and attitude deviation relative to a landing point, adjusts flight control parameters according to interference conditions by using an adaptive control algorithm, and performs trajectory prediction compensation in combination with an unmanned ship motion model to realize autonomous landing. Compared with the prior art, safe landing of the unmanned aerial vehicle on the unmanned ship under the high sea condition is achieved.

The invention discloses a transformer substation intelligent monitoring system and method based on digital spasm, and relates to the technical field of digital spasm, the transformer substation intelligent monitoring system comprises a transformer substation information exchange hub, the transformer substation information exchange hub is in communication connection with an operation parameter monitoring module, a fault early warning module and a data visualization module; the parameter monitoring module collects temperature and current data of a transformer substation, the fault early warning module combines a simulation model and historical and real-time monitoring data, an LSTM model is used for early warning a fault, the data visualization module displays performance, trend and alarm, an LSTM is called to predict a load, and an algorithm is used for optimizing energy consumption, cost and load distribution. Intelligent monitoring of the transformer substation is achieved through the digital spasm technology, the accuracy and the real-time performance of monitoring are improved, the fault early warning precision and the load prediction capacity are improved through the LSTM model, meanwhile, the energy consumption, the cost and the load distribution are optimized through the algorithm, and the operation efficiency and the economic benefits of the transformer substation are effectively improved.

The invention discloses a remote sensing image change detection method and system based on Transform and graph semantic guidance in the technical field of image processing. In an overall framework, a CNN (Convolutional Neural Network) and a Transform multilayer are connected in series and in parallel, a novel graph semantic module (GSM) is also constructed, and the purpose is to guide the network to carry out more detailed feature extraction through graph semantic features. In the module, each channel of a feature map is regarded as a feature vertex, an adjacent matrix is used for representing the relationship between the channels, and the capturing capability of the model on the long-distance dependency relationship is enhanced through multi-hop neighbor aggregation, so that the map semantic features with richer information are obtained. The graph semantic features are used for guiding trunk features extracted by the network in a subsequent Semantic Fusion Transform Module (SFTM), and the graph semantic features and the trunk features are interactively updated in a self-adaptive mode. Wherein the SFTM is mainly composed of cross attention used for completing information exchange, a local information compensation layer used for supplementing local information loss and an enhanced feedforward network used for optimizing input. According to the method, the performance of the network can be improved, and the change detection precision is improved.

The invention discloses a power monitoring system host security micro-isolation method and system, particularly relates to the technical field of power terminal security isolation, and aims to monitor electromagnetic interference and quantum state changes in real time and improve the stability and security of a quantum key distribution process in combination with quantum error correction and dynamic prediction technologies. A high-confidentiality encryption key is generated through the randomness of a quantum key generator, the interference level is monitored in real time in a high electromagnetic interference environment, a security protocol is automatically triggered, and parameters of a quantum system are adjusted through an active feedback mechanism; a dynamic Bayesian network is utilized to predict entanglement attenuation of a quantum state, an information exchange behavior of a quantum system is analyzed through quantum entanglement entropy, the attenuation degree and interaction intensity of the quantum state are comprehensively analyzed, and a dynamic micro-isolation strategy is implemented according to an analysis result; the reliability of quantum key distribution is improved, and the anti-interference capability of a power monitoring system in a complex electromagnetic environment is enhanced.

The invention discloses a multi-module-based analog simulation calculation method, and relates to the technical field of analog simulation calculation, and the method comprises the following steps: S1, system demand analysis and module division; s2, establishing a module model and setting parameters; s3, constructing a parallel computing environment; s4, module parallel computing task allocation; s5, data exchange and synchronization; s6, summarizing results; and S7, feedback optimization. According to the analog simulation calculation method based on the multiple modules, through modular design, a complex system is decomposed into the multiple relatively simple modules, each module can independently conduct simulation and calculation, and the maintainability and expandability of the system are improved; data and information exchange and cooperative work can be carried out among different modules through interfaces, so that comprehensive simulation and optimization of the whole system are realized; in addition, parallel computing resources are fully utilized, parallel computing is carried out on the multiple modules, the computing time is greatly shortened, and the computing efficiency is improved.

The invention belongs to the technical field of wind power prediction, and particularly relates to a wind power prediction method and system based on multi-source data fusion. According to the method, a cascade prediction architecture composed of a wind speed prediction model and a power prediction model is constructed, and the wind speed prediction module extracts cross-variable association features of multi-dimensional meteorological parameters from numerical weather forecast data by adopting a variable-level attention mechanism and a patch-level attention mechanism; capturing a long and short time dependence mode from the actually measured historical operation data; the power prediction model decomposes a predicted wind speed sequence through one-dimensional average pooling, and constructs a nonlinear power mapping model. Besides, an attention fusion module is provided, a learnable global variable is introduced as an information exchange bridge, information interaction between meteorological data and actually measured data is realized, and the collaborative utilization efficiency of multi-source data is effectively improved. Experiments show that the method is superior to an existing prediction model based on deep learning.

The invention relates to the technical field of image conversion, and discloses a multi-view conversion method based on a multi-view consistency diffusion model, and the multi-view consistency diffusion model is trained through a unified multi-view-to-multi-view conversion framework based on the diffusion model. The conversion framework has a new self-supervised training strategy and a consistency and antagonism supervision strategy, and the strategies utilize an existing 2D single-view image converter and a 3D Gaussian splash technology to generate pseudo-truth values as supervision signals, so that view consistency and high-frequency details are enhanced; the multi-view consistency diffusion model trained by the conversion framework further comprises a potential multi-view consistency module, and the potential 3DGS module is used as a 3D representation of a bottom layer to promote information exchange across multiple views, so that 3D priori is introduced into the diffusion model to ensure view consistency. Finally, a joint optimization strategy is provided, and better balance between consistency and authenticity can be realized.

The invention discloses a tooth three-dimensional digital model registration and tooth neck line extraction method based on oral scanning and CBCT, and relates to the field of tooth three-dimensional digital model registration. Comprising the following steps: respectively acquiring three-dimensional oral images of oral scanning and CBCT (cone beam computed tomography), and automatically segmenting three-dimensional structures of dental crowns and teeth by utilizing pre-trained V-Net and ToothNet image segmentation models. Alignment and registration of two modal images are realized through a feature extraction network, an information exchange network and a rigid transformation network. The feature extraction network extracts local and global features by using far-point sampling and a dynamic graph convolutional neural network, the self-attention and cross-attention module performs feature enhancement and information exchange between the same mode and different modes, and based on a registration result, a tooth neck line is further extracted and optimized. According to the method, the tooth three-dimensional digital model can be quickly and accurately registered, a high-precision tooth neck line extraction result is provided, the manpower and time cost of a medical institution can be reduced, and the medical efficiency is improved.

The invention relates to the technical field of green power data monitoring, in particular to a green power data monitoring optimization method and system based on a particle swarm algorithm. The method comprises the following steps: acquiring green power data, namely acquiring generating capacity, power, voltage, current and environmental parameters in a green power system, performing preprocessing based on the acquired green power data, performing feature extraction on the preprocessed green power data, constructing a monitoring model by using the green power data after feature extraction, and optimizing model parameters based on the monitoring model. Comprising the steps of performing model parameter optimization by using a particle swarm optimization algorithm; and setting an early warning threshold according to the characteristics of the green power system and the monitoring target, and comparing the output of the monitoring model with the early warning threshold. According to the method, the particle swarm is divided into a plurality of sub-groups through a multi-group collaborative optimization mechanism, and information exchange is carried out, so that the search diversity can be increased, and the probability of finding a globally optimal solution is further improved.

The invention discloses an intelligent dialogue system for time sequence memory management and task distribution, which is based on CrewAI multi-agent collaboration, combines time sequence memory updating and multi-agent collaboration work, introduces an independent memory unit in order to solve the problem of low memory efficiency management, and uses an undirected graph structure to store learned information. Storing each piece of information as a node in the graph; meanwhile, memory is increased, modified and forgotten according to a time sequence, so that the problem that important information is forgotten is relieved to a certain extent; in order to solve the problem of insufficient task interaction, a multi-agent cooperation mechanism is introduced, user demand understanding, task execution and task verification are respectively submitted to three different agents for independent work, and through independent work and complete information exchange among the three agents, information synchronization and state sharing among modules are realized, information islands are reduced, and the task interaction efficiency is improved. And decision accuracy is improved.

The invention discloses an RGV multi-vehicle cooperative control method and device applied to the field of storage and transportation, and the method comprises a centralized vehicle group management strategy and a distributed inter-vehicle cooperative strategy. The centralized vehicle group management strategy comprises the steps that a vehicle group control center is arranged, and the vehicle group control center is responsible for task RGV selection and task RGV path planning; the distributed inter-vehicle cooperation strategy comprises setting an inter-vehicle communication module and a local control unit for each RGV; each RGV exchanges information with other RGVs through the inter-vehicle communication module so as to obtain real-time states of all the RGVs, and the RGVs are independently controlled through the local control unit to realize speed coordination, anti-collision avoidance and task path avoidance; the vehicle group control center sends information to the inter-vehicle communication module, and the local control units adjust the speed and the driving state of the corresponding RGVs according to the information received by the inter-vehicle communication module. The instantaneity of information exchange between the RGVs is enhanced, and efficient cooperative control of the RGVs is realized.

Disclosed is an information exchange method. In the information exchange method, an electronic device is configured to display a first interface in a first display region of a display. After obtaining recommendation information, the electronic device may reduce the first display region, and display the recommendation information in a second display region. Subsequently, when detecting a first gesture operation of a user, the electronic device may open, in response to the first gesture operation, an interface corresponding to the recommendation information. According to the information exchange method, the user can conveniently view the recommendation information and directly open, based on the first gesture operation, the interface corresponding to the recommendation information.

The invention discloses a cross-scale spatio-temporal feature interactive fusion traffic flow prediction method, which comprises the following steps of: firstly, extracting short-term, periodic and long-term trend features by a spatio-temporal data feature extraction module through multi-level processing on historical time series data, accurately capturing a time trend and spatial dependence in traffic flow data, and extracting a time trend and a spatial dependence in the traffic flow data; and high-quality feature input is provided for subsequent modules. Then, a cross-scale attention mechanism is introduced into the cross-scale feature information exchange module, information interaction between different time scales and hierarchical features is achieved, the expression ability of global features can be enhanced, capturing of local detail information is also considered, and the model can efficiently process a complex space-time dependent structure; and finally, a multi-scale feature fusion module adopts an attention-based progressive fusion strategy to gradually integrate spatial-temporal features of each layer, highlight key features and generate more comprehensive and accurate feature representation, so that a more accurate prediction result is obtained.

Disclosed are various embodiments for using decentralized identity services and generative large language models (LLMs) to facilitate a humanized interactive chatbot session between two entities where information exchanged can be verified for accuracy. An interactive session can be established between a chat wallet service associated with a first user (e.g., a credential holder) and a second user (e.g., inquirer) using decentralized identity services. Inquiries asked by the second user can be directed to the chat wallet service which can obtain a response from a personalized dialog agent. The personalized dialog agent can obtain the answer by executing a language model that has been trained and personalized using information about the first user. The answer to the inquiry can be verified by comparing the answer with a verified credential associated with the first user. The verified response to the inquiry can then be provided to the second user.

The invention provides an Internet of Things platform and AI agent cooperative work method and system, and the method comprises the steps: S1, collecting data information through Internet of Things equipment, and transmitting the collected data information to an Internet of Things platform; s2, receiving and storing the data information by using an Internet of Things platform; and S3, analyzing and processing the data information meeting the preset requirements by using the AI agent to obtain an analysis result, performing abnormality judgment according to the analysis result, and when an abnormality occurs, generating a coping strategy according to the analysis result. Through interaction of multiple AI agents and the IoT platform, the problems of complexity of multi-field data integration and analysis, task allocation and execution and challenges of multi-party information exchange and cooperation in intelligent management are solved.

Disclosed in the present invention is a general-purpose intelligent agent. The general-purpose intelligent agent comprises: an input module, which is configured to acquire a preprocessed input signal; a consciousness module, which is configured to perform perception and cognitive operations in a transparent and interpretable abstract thinking form; a self-awareness module, which is configured for perception and cognition of an intelligent agent body; a subconsciousness module, which is configured to use a data-driven model to implement perception and cognitive functions at a subconscious level; an information exchange module, which is configured to perform mutual exchange between perception, cognition and other information generated by the consciousness module, the self-awareness module and the subconsciousness module, thereby enabling integration and utilization; and an output module, which is configured to output a processing result on demand. In a machine intelligent agent of the present invention, a machine uses a general-purpose language as an underlying language for thinking and interaction, a thinking process and result are completely transparent, and thus autonomous decision-making, autonomous learning and continuous evolution are realized under fully controllable conditions. Further disclosed is a method for constructing a general-purpose intelligent agent and a general-purpose language. Compared with the prior art in which a natural language is used, the machine intelligent agent based on the general-purpose language has wide versatility in application.

The invention provides a safe non-interactive comparison method and device for multi-party set intersection, and relates to the technical field of privacy set intersection comparison methods. The method comprises the following steps: carrying out data collection on software services of a plurality of service providers; determining a communication structure and a multi-party information exchange demand according to the advertisement scene delivery demand; performing offline data storage by adopting a cuckoo hash function to obtain a multi-party hash table; constructing a linear homomorphic hash function according to the multi-party hash table; generating a random number set offline through a trusted third party; according to the multi-party hash table, performing calculation based on one-out-of-two casual transmission to obtain a shared value set; and based on a communication protocol, the random number set, the linear homomorphic hash function and the sharing value set, performing non-interactive comparison according to a multi-party hash table to obtain an advertisement putting sharing data intersection. The non-interactive comparison method is based on non-interaction and is high in processing efficiency and high in universality.

The invention discloses a multi-dimensional battle field interactive digital simulation engine construction method, which belongs to the technical field of physical simulation, and comprises the following steps: constructing a distributed engine; designing an intelligent agent distributed dynamic deployment mechanism; designing battle field partition management; designing a distributed interaction mechanism; the battle domain is a medium for information exchange and state updating among different intelligent agents. According to the method for constructing the interactive digital simulation engine in the multi-dimensional battle domain, the problem of collaboration between tens of thousands of agents and complex physical field battle domain calculation is solved, calculation precision and real-time performance are both considered, intelligent scheduling of calculation resources and dynamic agent load balancing are achieved, the resource utilization efficiency is improved, and the application prospect is wide. And the accuracy and continuity of the simulation process are ensured.

The invention relates to a method for identifying and counting cables in a cable trench based on an improved YOLO model, and belongs to the technical field of power cables. The method comprises the following steps: firstly, acquiring image information in a cable trench by using a camera, and then inputting the image information into an improved YOLOv8 model for cable identification. The improved YOLOv8 model introduces an EFFVM module into a backbone network part, so that the recognition capability of the model on a shielded target is enhanced; an FGM module is introduced into a neck network part, so that information exchange of deep and shallow layer features of the neck of the model is enhanced; and a prediction output head for the angle is newly added in a head network part, and a ProbIoU loss function is used to predict the rotating rectangular frame. And finally, counting and outputting the number of cables according to a cable identification result output by the model. According to the method, the number of the cables in the cable trench can be quickly and accurately identified, and technical support is provided for operation and maintenance of the power cables.

The invention relates to the technical field of energy storage systems, and provides a self-adaptive power distribution method and system for multiple energy storage units of an island microgrid. According to the invention, a discrete consistency algorithm is adopted to realize coordination control of multiple units, each unit only exchanges information with adjacent units, and power distribution targets of all the units are converged to a consistent value through iterative computation, so that the communication bottleneck and the single-point fault risk of centralized control are avoided; according to the invention, a complete fault-tolerant processing mechanism is designed, the operation state of each energy storage unit is monitored in real time, topology updating and power redistribution are automatically carried out when a unit fault is detected, and the self-healing operation of the system is realized. Experiments show that compared with a traditional method, the method has remarkable advantages in key performance indexes such as capacity balance among energy storage units, system operation efficiency, communication overhead and fault recovery time, and the operation efficiency and reliability of the island microgrid system are remarkably improved.

The invention discloses a double-branch pyramid attention time sequence prediction method, which belongs to the field of time sequence prediction, and comprises the following steps of: supplementing and standardizing input sequence deletion, and embedding and aligning time covariables; a horizontal branch coding time domain and a vertical branch coding variable are dynamically fused to obtain a unified feature; multi-scale representation is formed through multi-layer convolution down-sampling, and information interaction is completed through local-cross-scale mask attention. The result is subjected to pyramid residual normalization and feedforward network updating, a global context is generated through multilayer stacking, and a linear head outputs future multi-step prediction. The complexity of the method is O (L log L), long-range dependence and a fine-grained mode are captured at the same time, the method is remarkably superior to thirteen existing models on five data sets including ECL, Exchange and Trafic, and the method has the advantages of being high in precision, high in speed and easy to expand.

A first network node measures cross link interference (CLI) from a transmission of a second network node for a set of one or more candidate beam pairs, each candidate beam pair of the set of one or more candidate beam pairs including an uplink beam of the first network node and a downlink beam of the second network node. The first network node provides CLI information indicating at least one of one or more preferred beam pairs or one or more non-preferred beam pairs based on a CLI measurement from the transmission, the CLI information further indicating at least a first Quality of Service (QoS) or a first priority level for uplink traffic of the first network node.