1006 results about "Information sharing" patented technology

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The invention relates to the technical field of model optimization, in particular to an e-commerce inventory dynamic optimization model system based on real-time big data, which comprises an inventory information sharing unit, an intelligent replenishment algorithm unit and an inventory distribution strategy unit. In combination with edge calculation and block chain technologies, real-time collection, processing and secure sharing of inventory data are realized, an intelligent replenishment algorithm unit obtains data from a block chain platform, a demand prediction model is constructed by using a gating circulation module, and an automatic replenishment module sets a replenishment threshold and adjusts replenishment amount in combination with risk factors. The multi-objective optimization algorithm selects an optimal supplier, and the inventory distribution strategy unit formulates and optimizes an inventory distribution rule by applying a reinforcement learning algorithm based on real-time data, adjusts an inventory distribution proportion and uploads the inventory distribution proportion to the block chain, so that the inventory management efficiency is improved, the cost is reduced, and dynamic inventory optimization is realized.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The invention relates to the technical field of artificial intelligence, in particular to a multi-agent cooperative communication and real-time resource optimization management method and system based on large model driving, and the method comprises the following steps: semantic understanding and task analysis, communication protocol and mechanism design, resource demand prediction and allocation scheduling, information sharing and interaction, and feedback and evaluation mechanism. The method has the beneficial effects that efficient reliability allocation and feedback error correction are realized through a multi-agent reinforcement learning framework by utilizing powerful reasoning and generalization capabilities of a large model, so that the communication and cooperation capabilities among multiple agents are remarkably improved, and meanwhile, the method is combined with a real-time resource optimization management technology, and through reasonable resource allocation and scheduling, the real-time resource optimization management efficiency is improved. And maximum utilization of resources is realized.

The invention relates to the technical field of building engineering automation, and discloses a building robot collaborative operation system, a BIM modeling module is used for establishing a three-dimensional model of building engineering and dividing the three-dimensional model into a plurality of operation areas; the information acquisition module is used for acquiring state information and working environment information of the robot; the central control module is used for performing task allocation and operation planning by using an ant colony algorithm; the information sharing module is used for carrying out real-time communication between the robots through wireless communication in the operation process, and sharing operation progress, state information and environment information; the operation evaluation module is used for monitoring the operation progress and state of each robot in real time, evaluating the operation effect of the robot, adjusting task allocation and planning strategies in time according to the evaluation result, optimizing the collaborative operation process of the robot, and improving the operation efficiency of the robot. According to the invention, the problems of unreasonable task allocation, low communication efficiency, difficult operation coordination and the like in the collaborative operation of the existing building robot are solved.

The invention discloses a self-adaptive management method and system for intelligent terminal equipment of a distributed power distribution network. According to the method, a self-organizing peer-to-peer communication network covering the whole network is constructed, and neighbor discovery and information sharing among intelligent terminal devices are achieved; the terminal independently completes control instruction calculation based on the local electrical parameters and a preset multi-objective function and optimizes an area control effect through a cooperation mechanism; introducing a self-adaptive adjustment strategy of a control algorithm; when abnormal operation is detected, the terminal can autonomously complete fault positioning and isolation, and power supply recovery is realized based on standby path judgment or micro-grid switching; the central management unit periodically carries out acceptance capacity evaluation, and terminal model parameters are aggregated in a federated learning mode; the terminal has an edge autonomous capability, and can maintain local control and data retention when communication is interrupted. The system has the characteristics of quick response, flexible structure, intelligent cooperation, high adaptability and the like, and is suitable for intelligent operation and management and control scenes of various types of power distribution systems.

The invention provides a flexible production line adaptive scheduling control method fusing multi-agent reinforcement learning, and relates to the technical field of production line scheduling, and the method comprises the steps: obtaining the state of processing equipment and workpiece demand information in real time, constructing a state and action space, mapping the equipment into agents, achieving information sharing, and then selecting a scheduling action and executing the scheduling action. And the state-action value function is updated through the experience playback pool, and the multi-agent model is optimized. The system monitors the equipment state during operation, dynamically updates the state and action space to deal with faults or material shortage, and generates a compensation scheduling scheme. Finally, in combination with a real-time scheduling instruction, adaptive scheduling control of the flexible production line is realized, and the production efficiency and flexibility are improved.

The invention discloses a high and large formwork engineering supporting system construction method, and belongs to the technical field of building engineering. Comprising the following steps: step 1, arranging intelligent nodes in a high and large template support system to realize information sharing and group cooperative regulation and control among the nodes; step 2, dividing the support system into a plurality of mutually associated load control areas, and setting differentiated load control parameters; step 3, automatically optimizing a pouring path; 4, when the concrete strength reaches a preset threshold value, the supporting rigidity or the reserved deformation amount is automatically adjusted according to a preset program, and the concrete structure bears the dead weight step by step; 5, the temporary load is automatically compensated, when the load exceeds a safety threshold value, a load redistribution mechanism and a reinforcing device are triggered, meanwhile, an alarm is started, and abnormal data are pushed; and step 6, continuously optimizing control parameters, accumulating construction data, constructing a digital construction record and a knowledge base, and realizing intelligent iterative upgrading of the construction method.

The invention provides an adaptive interference suppression carrier communication system based on a complex electromagnetic environment, relates to the technical field of communication systems, and solves the problems that an existing communication system is poor in adaptability, weak in anti-interference capability and lack of collaboration and safety in the complex electromagnetic environment. The system comprises an environment perception module used for deeply perceiving an environment and identifying known and unknown interferences by using AI; the cognitive decision engine is used for generating a multi-domain control instruction by utilizing AI according to environment, internal, cooperation and safety states; the self-adaptive transceiving control module is used for executing cross-multi-domain parameter self-adaptive adjustment; the distributed collaborative module is used for realizing information sharing among nodes, federated learning and collaborative resource management; and the endogenous security enhancement module is used for executing active security mechanisms such as dynamic heterogeneous redundancy, mimicry defense and physical layer authentication. The invention further discloses a corresponding communication method. According to the method, the robustness, adaptability, efficiency and safety of a communication system in a complex electromagnetic environment can be remarkably improved.

The invention discloses an unmanned aerial vehicle intelligent obstacle avoidance system based on a laser radar, and belongs to the technical field of path planning, and the system specifically comprises a parameter collection module which is used for collecting original point cloud data of a flight area; carrying out obstacle geometric structure analysis to generate desensitization characteristic parameters including surface curvature and spatial density distribution; the feature analysis module is used for sending the desensitization feature parameters to an edge computing node through a hierarchical encryption transmission link, and performing primary aggregation on the feature parameters of a plurality of unmanned aerial vehicles in the same region to form a region feature template; the cloud server is used for integrating obstacle features of different areas through a security fusion protocol and constructing a global obstacle feature library; the path planning module is used for acquiring update data of the global obstacle feature library through the safe synchronization channel, and generating a three-dimensional obstacle avoidance path by combining the real-time scanning features with a feature library matching result; according to the invention, multi-machine collaborative obstacle avoidance path optimization based on obstacle avoidance information sharing is realized.

The invention discloses a power distribution terminal control system and method based on multi-data analysis, and relates to the technical field of smart power grids. The system integrates a multi-data acquisition module, a parameter management module, a fault analysis decision module, an execution control module and a communication interaction module; the multi-data acquisition module collects electrical quantity and switch position signals, and the parameter management module provides basic data support; the fault analysis decision-making module analyzes FTU types and fault types in sequence to generate differentiation strategies, switching operation is executed through the execution control module, and information sharing and collaboration are achieved through the communication interaction module; according to the system, through dynamic FTU type identification, a precise fault processing mechanism and multi-source data deep fusion, the problems of data island, single fault processing strategy and insufficient multi-source data fusion in a traditional power distribution automation system are solved, the power supply reliability is improved, the manual configuration workload is reduced, and the equipment damage risk is reduced.

The invention relates to the technical field of traffic management, in particular to a road traffic dispersion and emergency command system construction method based on multi-source data fusion, and the method comprises the steps: collecting traffic flow sensor data, video monitoring data, meteorological data, social media user feedback data and the like through a multi-source data collection module; a data fusion and processing module is used, and data-level, information-level and decision-level fusion algorithms are adopted to carry out standardization processing, key information extraction and comprehensive evaluation and prediction on multi-source data. And constructing a traffic flow prediction model based on machine learning, such as a long and short-term memory network model, and accurately predicting the traffic flow. And according to the prediction result and the real-time traffic condition, a traffic dispersion strategy making module is used for adjusting signal lamp timing, issuing traffic guidance information and allocating traffic police resources. In the aspect of emergency command, multi-source data are integrated to evaluate event severity, deploy rescue resources and determine an optimal rescue route, and multi-department information sharing and cooperative work are achieved.

Various systems, computer-readable media, and computer-implemented methods of providing improved data privacy, anonymity and security by enabling subjects to which data pertains to remain “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent that is desired—are disclosed herein. Embodiments include systems that create, access, use, store and / or erase data with increased privacy, anonymity, and security—thereby facilitating the availability of more qualified and accurate information. When personal data is authorized by data subjects to be shared with third parties, embodiments described herein may facilitate the sharing of information in a dynamically-controlled manner that also enables the delivery of temporally-, geographically-, and / or purpose-limited information to the receiving party. In one example, the disclosed techniques may be used to functionally separate geospatial information, such that it remains “dynamically anonymous,” i.e., anonymous for as long as is desired—and to the extent or degree that is desired.

The invention proposes a flood disaster dynamic prediction method based on multi-source remote sensing data and a knowledge graph, and relates to the field of data prediction, and the method comprises the specific steps: firstly, building a space-time disaster dynamic model through a physical drive text generation module, and describing the evolution process of a flood disaster; generating personalized flood disaster description in combination with the static characteristics and the dynamic remote sensing data; then, a multi-scale residual diffusion enhancement module improves sensitivity to dynamic change of disasters through multi-scale trend extraction and residual calculation, noise is removed, and useful information is reserved; then, the multi-modal fusion module enhances interdependence and information sharing among different modals by using adaptive modal mapping, a cross attention mechanism and a weighted fusion strategy; and finally, training through a regression model, dynamically adjusting the feature weight by using an adaptive feature weighting mechanism and an incremental learning mechanism, and finally obtaining a flood disaster prediction value through the prediction set.

The invention provides a fan gearbox dynamic integration fault detection method and system based on feature fusion, and relates to the technical field of data processing, and the method comprises the steps: obtaining each preliminary feature set; performing feature-to-feature and feature-fault nonlinear relation quantization on each preliminary feature set to obtain each preliminary screening feature set; analyzing the contribution degree of each feature in each preliminary screening feature set, executing feature fine screening, and generating each fine screening feature set; and calling a multi-agent integrated fault detection system, executing multi-layer agent information sharing and collaborative decision from bottom to top based on each fine screening feature set, and generating fan gearbox fault detection information. According to the method and the device, the technical problem of low fault detection accuracy caused by lack of deep feature mining and dependence on a single-machine diagnosis system in the prior art is solved, and the technical effect of improving the fault detection accuracy is achieved by constructing the multi-agent integrated fault detection system, so that the false alarm rate and the missing report rate are remarkably reduced.

The invention discloses a multi-dimensional natural resource intelligent monitoring method and system based on big data analysis, and relates to the technical field of resource monitoring, and the method comprises the steps: calculating the information sharing degree between different data streams, constructing an abnormal feature credibility distribution map, and carrying out the calculation of the abnormal feature credibility distribution map; and inputting the abnormal feature credibility distribution graph and the multi-source auxiliary information into an integrated classifier, carrying out joint analysis and comprehensive research and judgment on multi-dimensional information to output a target list, constructing an event response relation graph reflecting a monitoring task priority and an execution logic relation according to the attribute features of each element in the target list, and carrying out event response analysis on the monitoring task priority and the execution logic relation. Wherein each node represents a to-be-monitored target, an edge represents a dependency relationship between tasks, and an optimal monitoring execution path is planned for various intelligent monitoring carriers by using an improved A algorithm integrated with a multi-factor cost function. Through natural resource monitoring of multi-modal data interference correction, intelligent classification and path optimization and dynamic response, the monitoring quality and the execution efficiency are effectively improved.

The invention discloses a contribution degree determination method and device based on secret information sharing, equipment and a medium, and belongs to the field of data processing. The method comprises the steps that in response to each query service, a query result is obtained, first random number masks and first random commitment input parameters of participant platforms are obtained, and the sum of the first random number masks corresponding to the participant platform serving as a sender and a plurality of participant platforms serving as receivers is 0; on the basis of the query result, the first random number mask and the first random commitment input parameter, commitment related information, a first data sum and a second data sum are obtained; and sending the first data sum and the second data sum to the coordinator platform, so that the coordinator platform obtains the contribution degree of the participant platform based on the first data sum and the second data sum, and verifying the contribution degree of the participant platform according to the second data sum and the commitment related information. According to the embodiment of the invention, the real contribution degree of the participant platform can be obtained.

The invention discloses a power transmission and transformation equipment state evaluation method and system based on digital twinning. The method comprises the following steps: acquiring data of power transmission and transformation equipment and preprocessing the data; the LSTM algorithm model predicts the running state of the equipment; the fault tree analysis module evaluates possible fault types and fault probabilities; an interaction mechanism of the LSTM algorithm model and the fault tree analysis module is established, and when the LSTM algorithm model predicts that the operation state of the equipment is abnormal, related data and a prediction result are input into the fault tree analysis module, and a fault tree is guided to locate a fault; when the fault tree analysis module finds a potential fault risk, related information is fed back to the LSTM algorithm model; and performing weighted fusion on the LSTM algorithm prediction result and the fault tree analysis result according to the confidence of the LSTM algorithm prediction result and the fault tree analysis result to obtain an evaluation result. A model interaction mechanism is established, information sharing and cooperative work of the two are realized, and the capability of capturing the abnormal state and the potential fault risk of the equipment is enhanced.

The invention discloses a power grid flood prevention risk early warning and auxiliary decision making system based on flood deduction, and relates to the technical field of flood storage area power grid flood prevention. Compared with a previous flood storage area power grid flood prevention method, the problem that an existing flood deduction model is limited in simulation precision and accuracy is solved; the coupling degree of power grid equipment and the flood model is low; cross-department collaboration and information sharing are difficult; and the problems of system visualization and insufficient interactivity are solved. Accurate flood deduction is carried out through the flood deduction model, information such as the flood development trend, the flood submerging range and the water depth of power equipment can be predicted in advance, and prospective guidance is provided for power grid flood prevention work; meanwhile, a power grid operation department adopts a coping scheme in advance based on a risk early warning strategy; when a flood disaster occurs, the system can quickly provide an emergency decision scheme, and the emergency response speed is improved; and VR and AR technologies are combined, so that the visualization degree is greatly improved, and the first-aid repair accuracy and speed are improved.

The invention relates to the technical field of power grid safety management, in particular to a digital twinning-based power grid control safety system and method.The digital twinning-based power grid control safety system comprises a power grid model unit, an intelligent recognition unit, a strategy making unit and a block chain cooperation unit. The method improves the operation safety and reliability of a power grid, optimizes the resource configuration of the power grid, reduces the energy consumption, provides detailed safety reports and risk assessment for the operation of the power grid, dynamically recognizes potential safety threats and abnormal behaviors, automatically generates a response strategy in real time, improves the overall safety of the system, and improves the safety of the system through the introduction of a block chain technology and the combination with digital twinning. According to the method, transparent information sharing and collaborative management among each level and each region of the power grid are realized, and through decentralized data storage and security guarantee, the power distribution networks in different regions can be ensured to be efficiently collaborative when facing network attacks or faults, and the response efficiency is improved.

The invention relates to a Markov game-based satellite cluster observation resource allocation method and system. Through a two-stage decomposition strategy, a multi-target balance problem of a task integrity rate, a resource utilization rate and a cost-efficiency ratio is effectively solved. In the first stage, a genetic algorithm (GA) is adopted to complete satellite-task-time window three-dimensional matching, and optimal distribution of limited visible windows is achieved. And in the second stage, a distributed decision-making mechanism is implemented based on an improved Improved-MADDPG framework, and after an intelligent agent adopts a random game strategy to execute exploration in a distributed manner, optimal dynamic configuration of observation resources is achieved through global information sharing and local strategy iteration, and key calculation indexes such as an average reward value and the like are remarkably improved.

The invention provides a voice interaction method and device. The method comprises the following steps: receiving voice information input by a user; if it is determined that the intelligent agents need to be switched based on the voice information, determining a target intelligent agent from other intelligent agents except the current intelligent agent in the multiple intelligent agents; processing content corresponding to the voice information by adopting a large model corresponding to the target agent, a skill knowledge base and a shared knowledge base to obtain a reply; and outputting voice corresponding to the reply to the user. As the skill knowledge base queried when the user questions is processed is split into a plurality of sub-knowledge bases with different professional knowledge from a complete knowledge base, the data query amount is all reduced, and the voice interaction efficiency is improved. Moreover, the switched intelligent agent can check the voice information sent by the user to the intelligent agent before switching through the shared knowledge base, user historical voice information transmission between the intelligent agents is not needed, the speed of information sharing between the intelligent agents is increased, and the efficiency and accuracy of information interaction are improved.

The invention discloses a regional medical information sharing method based on artificial intelligence, and relates to the field of information processing, and the method comprises the steps: obtaining regional medical information; extracting text information in the regional medical information; dividing the text information into a key text and a conventional text; text encryption of the key text is completed, and text encryption information is obtained; performing information compression encryption on the non-text information to obtain non-text encrypted information; performing global encryption on the conventional text, the text encryption information and the non-text encryption information to obtain medical encryption information; and uploading the medical encrypted information to a medical information sharing terminal. According to the method and the device, the medical information can be effectively shared in a state that the medical information is not leaked.

The invention discloses a mobile communication network multi-dimensional competition insight method based on DeepSeek large model and multi-agent cooperation, and relates to the technical field of mobile communication network and artificial intelligence crossing, and the method comprises the steps: collecting and preprocessing multi-modal data; carrying out fusion on the preprocessed multi-modal data; constructing a competing network insight agent comprising a special agent and a universal agent; a communication and cooperation framework is constructed, and information sharing and cooperation work of the special intelligent agent and the universal intelligent agent are achieved; carrying out data model design on the special intelligent agent from different dimensions; performing special training on the DeepSeek large model to obtain an enhanced Qwen-32B large model; user requirements are input based on the question and answer interface, the intention recognition module recognizes user intentions, the Qwen-32B large model is called to generate answers, or the Qwen-32B large model and the competition network insight intelligent agent are called to complete professional analysis, thinking and reasoning, and the questions and answers are output and displayed to users for viewing through the question and answer interface. According to the invention, efficient and accurate competition decision support can be provided for mobile communication operators.

The invention discloses a photovoltaic power generation system optimization management system based on intelligent scheduling, and relates to the technical field of renewable energy sources and intelligent power grids. The system comprises the following components: a distributed cooperative control strategy, an information sharing and transaction platform supported by a block chain technology, an intelligent scheduling and optimization engine, intelligent power grid deep integration and a user interaction and visualization interface. Through an innovative distributed cooperative control strategy, comprehensive operation data of a photovoltaic power generation system, a wind energy system, an energy storage system and a micro-grid peripheral energy system can be accurately collected in real time, and an energy supply and demand trend is analyzed and predicted based on big data; therefore, the system can automatically adjust the power generation plan, the energy storage strategy and the load distribution of each system, and the optimal operation of the whole energy system is realized.

The present disclosure relates to a communication method and device enabling information sharing between multiple SMFs in a wireless communication system for supporting virtual network (VN) group communication. A method performed by a first SMF for managing a session of a terminal in a wireless communication system for supporting VN group communication according to an embodiment of the present disclosure comprises the steps of: receiving, through an AMF that manages the mobility of the terminal, a PDU session establishment request message having been transmitted from the terminal and including identification information indicating the VN group communication; transmitting a request message including the identification information to a UDM that manages subscription information; receiving information about multiple SMFs enabling information sharing for the VN group communication from the UDM; and performing session management for the VN group communication through the information sharing with a second SMF among the multiple SMFs.

The invention discloses a green low-carbon building simulation analysis method based on a three-dimensional model, and the method comprises the steps: 1, carrying out the pre-analysis, constructing a BIM model of an initial building design, obtaining the surrounding environment information of a building, and inputting the information into building simulation software; 2, environment simulation and design analysis are carried out, building design is optimized, and a first design stage is formed; forming a second construction stage; monitoring the energy consumption of the building in real time to form a third operation stage; step 3, simulation optimization; 4, simulating and analyzing data mining; and a fifth step of information sharing and assistance. According to the method, design decision and optimization are assisted through simulated beforehand analysis and simulation, so that energy consumption and carbon emission of the building in each life cycle are reduced, the green performance of the building is improved, efficient and accurate energy consumption evaluation and optimization of a building design scheme are achieved, and generation of a low-carbon and energy-saving building scheme is effectively promoted.

The invention provides a multi-agent collaborative visual completion method and system based on semantic communication, and the method comprises the steps: a first agent collects an occluded overall original image through a visual sensor, generates an occluded region mask through a lightweight semantic segmentation network, extracts mask semantic information, and transmits the mask semantic information to a second agent; the second intelligent agent matches the global semantic feature of the second intelligent agent with the received mask semantic information, locates a sheltered area, then cuts the sheltered area to obtain a semantic feature vector of the area, and returns the semantic feature vector to the first intelligent agent; and after the first intelligent agent receives the image, performing high-fidelity reconstruction on the occlusion area by using a conditional diffusion model to obtain a complemented image with consistent semantics, and performing weighted fusion and local color matching and splicing on the complemented image and the original image to obtain a global perception result. According to the scheme, perceptual information sharing and occlusion area reconstruction with low bit rate, high robustness and high real-time performance can be realized under limited communication resources.

The invention provides a display screen assembly system, method and device and a storage medium, the system comprises a data management analysis module, a multi-agent module, an implementation monitoring module and a communication module which are in communication connection with each other, the data management analysis module is configured in a training framework and is responsible for processing and generating a process database related to display screen assembly, and the multi-agent module is configured in a training framework; intelligent process planning and optimization suggestions are provided; the multi-agent module is configured for assembly process management and comprises a plurality of agents, and each agent is responsible for different assembly tasks and carries out coordination and information sharing through the communication module; the implementation monitoring module is configured on a production site, integrates various sensors, and collects key data in the assembly process in real time. And the communication module is configured to manage communication and information transmission among the intelligent agents. According to the method, by introducing the large language model and the multi-agent module, higher-level intelligent process planning and optimization are realized, so that the assembly process is more efficient and flexible, and the adaptability is higher.