1525 results about "Information sharing" patented technology

The invention provides a cross-domain network security policy automatic generation and protection policy collaboration method and system, and relates to the technical field of network security, and the method comprises the steps: collecting multi-domain security event data; constructing a security threat map; generating a cross-domain security risk score by using a deep learning algorithm; determining an inter-domain access control rule set and an intra-domain protection strategy according to the score; forming a cross-domain collaborative protection strategy; deploying a strategy on the multi-domain security device; establishing an inter-domain information sharing channel; monitoring the strategy effect; optimizing the strategy by using a reinforcement learning algorithm; and updating to each domain security device. According to the method, the effectiveness of cross-domain network security protection is improved.

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 provides an electric power spot market price prediction method and system based on artificial intelligence, and relates to the technical field of electric power price prediction, and the method comprises the steps: calculating an impact factor weight through employing a deep reinforcement learning optimization fuzzy reasoning system based on historical transaction data and external association data; performing wavelet transform decomposition on the data to obtain a feature fluctuation sequence, and performing weighted fusion to generate a market feature fusion sequence; the method comprises the following steps: constructing a bidirectional feedback neural network by using a graph attention network to extract market transaction features and generate feature correction parameters, generating layered early warning signals in combination with a cascade forest algorithm to construct a dynamic market behavior feature map, and inputting an optimized feature map into a prediction model group comprising a plurality of prediction sub-models, and realizing information sharing and complementary learning through a cross-modal feature interaction module, generating an initial prediction result, performing constraint correction according to an intelligent clearing rule, correcting a correction prediction result, and finally outputting a predicted price of the electric power spot market.

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.

Techniques for mobile network information sharing via extended Berkeley Packet Filter (eBPF) for zero trust security are disclosed. In some embodiments, a system / process / computer program product for mobile network information sharing via eBPF for zero trust security includes monitoring network traffic in a core mobile network using an agent executed on a network element in the core mobile network to identify a session associated with a User Equipment (UE) that attached to the core mobile network for mobile network communications; extracting meta information associated with the session using the agent executed on a network element in the core mobile network; sending the extracted meta information to a security platform located outside of the core mobile network; and enforcing a security policy on the session at the security platform based on the extracted meta information to apply granular-based security in the core mobile network based on a security policy.

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.

The invention belongs to the technical field of intelligent electric power inspection and multi-agent cooperative control, and provides an electric power multi-agent dynamic cooperative inspection method and system, and the technical scheme is that a path planning scheme is generated by using a reinforcement learning algorithm and a large language model, so that an agent can dynamically adjust an inspection path according to real-time data and task requirements, and the inspection efficiency is improved. Therefore, the minimization of the path and the maximization of the inspection efficiency are realized, and unnecessary travel and resource consumption are reduced. Meanwhile, a dynamic task redistribution mechanism in the system ensures that the intelligent agent can quickly respond and redistribute tasks under the conditions of equipment failure, environment change or task priority adjustment. And real-time communication and data synchronization among the intelligent agents ensure information sharing, so that the system still keeps task continuity and reliability in emergency.

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 present disclosure relates generally to computer-implemented systems and techniques for leveraging a virtual decentralized database to enable user identity authentication and verification of user activity and associated behaviors on open internet and public web-based and native applications. Techniques are disclosed for user identification profile onboarding using biometrics, frictionless CAPTCHA (Completely Automatic Public Turing Tests To Tell Humans And Computers Apart) processes, passive data capture of physiological and behaviors using device sensors, and cryptographic methods of storing user engagement activity as immutable data files for secure distribution on a decentralized virtual private network. Such technologies operate to establish a high confidence level for user authenticity, communication origin, engagement outcome transparency and clarity of intent for users active on social, commercial, e-commerce and information sharing platforms. More specifically, an unobtrusive passive data capture method of authentication and verification on a secure VPN supports the organic, spontaneous and original experience audiences desire while simultaneously eliminating opportunities for unscrupulous actors to exploit open internet exchanges by inserting fraudulent user accounts, automated bots and other measures that mimic or impersonate live humans, which ultimately undermines user confidence in data safety and erodes mutual trust between people, companies and institutions.

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 general surgery department medical information sharing method and system based on medical big data, and relates to the technical field of medical information sharing, and the system comprises an authority management module which is used for dynamically distributing medical data access authority according to the identity attribute of a user, establishing a multi-level access control model, and storing the access control model; the model is based on user identities and roles, and is combined with sensitivity levels of data. According to the general surgery department medical information sharing method and system based on the medical big data, through dynamic permission adjustment, the permission of the user is dynamically adjusted according to an emergency demand scene and a special situation in an actual medical environment, and medical demands in different scenes are met; the flexibility and adaptability of the system are improved, unauthorized access possibly caused by too loose permission and interference on medical operation efficiency caused by too strict permission in the permission distribution and management process are avoided, and meanwhile it is guaranteed that personal privacy of a patient is not invaded or leaked.

The invention relates to the technical field of mixed reality, in particular to a mixed reality multi-user interaction large-space positioning, aligning and collaborating system and method, and aims to solve the problems that in the prior art, the user interaction experience is improved only by collecting multi-aspect data for processing, efficient collaboration work of multiple users in a large space cannot be achieved, and the user experience is poor. Information sharing, task distribution and real-time interaction among users cannot be realized; information sharing, task distribution and real-time interaction among users are realized through the multi-user cooperation module, cross-device operation consistency is ensured through a multi-device sharing coordinate system, configuration is simplified, cooperation efficiency is improved, real-time data transmission is realized through multi-device network synchronization, delay is reduced, cooperation fluency is enhanced, and user experience is improved. The interaction reality sense is enhanced through physical interaction logic synchronization, the working process is optimized, and the cooperation experience is improved.

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 collaborative decision-making system based on multi-agent reinforcement learning, and the system comprises a global decision-making coordination module, an agent local decision-making module, a state information sharing module, a conflict detection and solution module, and a security and privacy protection module. Each agent realizes autonomous decision making and real-time learning in a local environment, the dependence of the system on centralized control is reduced, the communication burden of the system during data interaction is effectively reduced in a distributed manner, the agents can directly respond based on local data, and the data interaction efficiency is improved. The state information of each agent is collected and analyzed in real time through a global decision coordination module, and the overall strategy of the system is adjusted based on a reinforcement learning algorithm, so that global target implementation in a complex environment is ensured, and the limitation that a traditional system cannot be adaptively adjusted in a dynamic environment is effectively avoided through global optimization; and the global optimization of the multi-agent cooperative system on the basis of each agent target is ensured.

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 discloses an intelligent equipment remote monitoring and management system based on the Internet of Things, which relates to the technical field of equipment monitoring and management and comprises a multi-dimensional intelligent cooperation module, a self-adaptive dynamic optimization module and a real-time intelligent decision module based on edge calculation. The system is characterized in that the multi-dimensional intelligent cooperation module and the adaptive dynamic optimization module share data through a cloud database, and an optimization result of the adaptive dynamic optimization module is provided for the real-time intelligent decision module based on edge calculation. According to the invention, by designing the multi-dimensional intelligent cooperation module and the group intelligence optimization module based on equipment group behavior analysis, the functions of data sharing cooperation and load balancing resource optimization among equipment are realized, and the problems of equipment operation data isolation and incapability of information sharing cooperation response in a traditional system are solved; the cooperation and information sharing capability of the system is improved, the resource and load balance is optimized, and the overall stability and reliability of the system are improved.

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 provides a power grid infrastructure project material collaborative management method, which comprises the following steps: analyzing historical material use, power grid construction progress and external environment data through a reinforcement learning algorithm, dynamically adjusting model parameters, and predicting material demands; a low-power-consumption wide area network and an edge computing technology are used for monitoring and analyzing material states in a warehouse and a transportation process in real time, and a result is transmitted to the central management system; based on the prediction data, an optimized material purchasing plan is automatically generated, and the purchasing strategy is dynamically adjusted by considering the arrival time, the inventory state and the supplier capability; using an improved ant colony algorithm to optimize the transportation route and time of the materials from the warehouse to the construction site; using an intelligent sensor to record the material use condition, and carrying out preliminary data processing and analysis through edge calculation; and an information management platform is established to realize information sharing and cooperative work. According to the method, the intelligence and efficiency of material management are improved by integrating multiple technologies.

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 surgical anesthesia information intelligent management system, and relates to the technical field of medical information, and the system comprises a medical data collection module which is used for collecting first metadata in real time; the intelligent analysis module analyzes the first metadata by using a deep learning algorithm to obtain a first analysis result; the knowledge base management module is used for maintaining a surgical anesthesia case knowledge base; the association verification module verifies and optimizes the first analysis result in combination with historical cases to obtain a second analysis result; the monitoring early warning module is used for generating early warning information based on the second analysis result; the information sharing module visually displays the early warning information and updates the electronic medical record; and the system fault tolerance and backup module ensures system availability and data integrity. The invention further discloses a corresponding management method. Through cooperative work of multiple modules, intelligent monitoring, timely early warning and information sharing in the operation anesthesia process are achieved, and operation safety and medical efficiency are 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 belongs to the technical field of mobile communication, and particularly relates to a multi-agent joint edge caching method based on a federated learning architecture, which is suitable for a macro-micro heterogeneous network environment. According to the method, the content popularity is predicted, and a cache decision is optimized by adopting a deep reinforcement learning method; according to the method, distributed prediction is carried out on content popularity through a federated learning architecture and a denoising auto-encoder long-short term memory network, and dynamic optimization of content cache is realized. And optimizing a caching strategy and resource allocation by combining a game model between the micro base station and the macro base station so as to maximize the effectiveness of the infrastructure provider. According to the method, a multi-agent deep reinforcement learning algorithm without information sharing is introduced to optimize the system efficiency. The system reduces transmission delay and improves network performance and user experience by calculating a dynamic adjustment strategy of terminal user content popularity and combining a communication resource allocation strategy. The method effectively solves the problems of high delay, low cache resource utilization rate and the like in the existing system, and is suitable for a complex network environment.