17 results about "Collaborative group" patented technology

The invention discloses an air-ground cooperative group target formation identification method based on learnable prior, and the method comprises the following steps: 1, generating behavior prior data, and constructing a dynamic group target behavior prior knowledge base in combination with an improved multi-agent depth deterministic strategy gradient algorithm; step 2, ground end group target formation form classification identification and prior feature extraction; and step 3, taking the prior feature vector group as input, and adopting an improved D-S theory to complete no-load end formation identification. In order to give consideration to the real-time processing capability and the recognition precision of a no-load platform, the method adopts an air-ground cooperative processing architecture, completes high-complexity depth feature extraction and behavior modeling at a ground end, obtains prior information and transmits the prior information to a no-load end, realizes quick response through fusion reasoning, and combines an improved D-S theoretical framework to fuse multi-source prior data. The method can effectively improve the recognition robustness and reasoning credibility of a no-load end in a high-conflict environment.

The invention provides a multi-terminal cooperative transmission management method based on a QUIC protocol in intelligent education recording and broadcasting, and relates to the technical field of intelligent education, and the method comprises the steps: dividing all access terminals into a plurality of cooperative groups according to terminal types, network access modes and geographic areas, and distributing an independent bandwidth pool for each cooperative group; an independent bandwidth pool is allocated based on each collaborative group, QUIC connection of each collaborative group is monitored, an aggregation bandwidth demand is calculated and compared with a link bearing upper limit, and a slow start threshold value down-regulation instruction is generated to suppress bandwidth competition so as to obtain an adjustment result; and according to the adjustment result, performing initial transmission priority marking on different service data streams, and executing a differential retransmission control strategy in the QUIC multi-stream transmission channel. According to the invention, a complete collaborative link from access to transmission to synthesis to optimization is formed, and the full-scene demand of intelligent education recording and broadcasting is fully covered.

The disclosure provides organizing the production a multimedia project in a real-time collaborative environment. In some aspects, a method for managing or organizing a multimedia production in real time is provided. The method comprises accessing one or more databases of records, and presenting a collaborative environment using the records. The method also comprises changing the records in the database in response to edit activity in the collaborative environment where all groups or teams working together, possibly in different locations, receive said changes in real time and can manage what is needed to accommodate said changes. The method further comprises changing content in the collaborative environment using the changed records. The method comprises providing an option for initiating an action based on the changed records. The method comprises providing an option for generating a call sheet that is accessible in real time by all users of the method and system.

The application discloses a combined multi-brain electroencephalogram coupling preservation preprocessing method and system, and belongs to the technical field of collaborative brain-computer interface. The method comprises the following steps: presetting an initialization demixing matrix for each subject, and obtaining corresponding independent components according to the synchronous acquisition of electroencephalogram signals; introducing intra-individual independent constraints and inter-brain coupling preservation constraints in a unified optimization process, and iteratively updating the demixing matrix and the independent components of each subject; automatically discriminating and removing artifact components based on the kurtosis and spatial prior of the independent components; and performing inverse transformation reconstruction on the independent components after removing the artifacts to obtain purified electroencephalogram signals. The application can separate individual artifacts such as electrooculogram and electromyogram while preserving the inter-brain coupling characteristics among multiple subjects, reduce the damage of traditional independent preprocessing to cross-subject synchronous information, and improve the reliability of collaborative intention decoding and inter-brain coupling analysis.

Generally, a computer implemented group distance learning system including one more servers which serve a group distance learning computer code executable to provide a graphical user interface which by user interaction allows a plurality of client users to be allocated into a plurality of client user groups each user group having access to a common graphic user interface to collaborate in performance of group assignments. Specifically, a group distance learning computer code which implements a graphical user interface accessed in common by a plurality of client users from remote locations to facilitate collaborative group video production. 20 24 20 37 37 03 J un 2 02 4 1 0 0 3 9 9 7 5 3 8 A B S T R A C T 2 0 2 4 2 0 3 7 3 7 0 3 J u n 2 0 2 4

A blockchain-based group collaborative privacy game mechanism is proposed, applied to blockchain-assisted collaborative LBS query scenarios. In this scenario, all parties adhere to the same smart contract. Users, as entities on the blockchain, perform data storage and verification. Neighboring users spontaneously form collaborative groups to mutually assist in LBS queries; users are members of these groups. For any collaborative group, user profile information is stored on the blockchain. A mobile edge computing (MEC) server customizes a multi-user collaborative privacy game mechanism (CPGM) for each collaborative group based on member profile information. Group members, guided by the CPGM, perform collaborative forwarding and message obfuscation to defend against speculative attacks based on prior knowledge. The CPGM is updated in real-time when a user leaves or joins a collaborative group. This method satisfies users' privacy needs at an acceptable cost.

The embodiment of the application provides a kind of based on bus communication protocol's multi-device collaborative control method and system, belong to bus communication protocol's multi-device collaborative control field;The method includes collecting multi-modal data, and extracting event data, parsing the event metadata, get event influence radius and event type, according to the event influence radius and the event type, event is graded, based on the event list after grading, event arbitration result is obtained by dynamic arbitration mechanism;According to the event arbitration result, distributed spatiotemporal collaborative decision-making is carried out, and the collaborative group execution scheme is obtained;Based on the event list after grading, the collaborative group execution scheme is sent to corresponding device, and multi-device collaborative control is completed.The application realizes event grading by three-dimensional evaluation matrix, ensures high-priority event immediate response in combination with dynamic arbitration mechanism, adopts distributed spatiotemporal collaborative decision-making system, significantly improves the real-time performance and reliability of multi-device collaboration.

This invention discloses a multi-perspective collaborative control method, system, device, and medium for power restoration, belonging to the field of power system automation technology. The method includes: acquiring initial state data from multiple monitoring terminals deployed on power lines after a power restoration command is issued; selecting collaborative nodes with communication and sensing capabilities based on the initial state data to construct a multi-perspective collaborative group; utilizing each collaborative node within the multi-perspective collaborative group to collect electrical parameters and environmental image data in a time-division multiplexing manner, and performing spatiotemporal alignment and fusion of the collected data to generate a power restoration situational awareness matrix; inputting the power restoration situational awareness matrix into a power restoration decision model to output power restoration control strategies for different line sections, including immediate power restoration, delayed power restoration, combined switching, and manual intervention maintenance. This invention, through multi-perspective collaborative sensing and fusion decision-making, solves the problem of misjudgment caused by incomplete data from a single point during power restoration, improving the accuracy and safety of power restoration operations.

The invention belongs to the technical field of cooperative group detection, and discloses a cooperative group detection method based on sampling enhanced multi-view comparative learning, and the method employs a graph neural network to capture and fuse semantic features and structural features of various behaviors, effectively captures the basic time sequence and structural features of cooperative behaviors through a node sampling strategy, and improves the detection precision. And training a model through node-level contrast learning and graph-level contrast learning to obtain unified node vector representation, and distinguishing cooperative and non-cooperative groups by using a clustering method. Experiments show that the method is superior to an existing cooperative group detection method based on multiple views in terms of multiple indexes.

This application provides a mobile control device and method for cultural tourism nighttime light shows. The method involves acquiring a state dataset of a group of light devices; determining a 3D point cloud digital model of the performance area; and, based on the 3D point cloud digital model and a preset performance timeline, determining the dynamic priority of each light and shadow performance task in the performance area. Each mobile light show device in the light device group is abstracted as a particle, and a decision population is constructed. Through the state dataset and the dynamic priority of each light and shadow performance task, the decision population is iteratively optimized using an adaptive search mode switching particle swarm optimization to obtain a collaborative control strategy for all mobile light show devices to perform light shows. Based on the collaborative control strategy, each mobile light show device is driven to perform an adaptive and collaborative group light show. Using the scheme of this application, a collaborative control method for mobile light shows integrating real-time state perception, dynamic task scheduling, and swarm intelligence optimization can be realized.

The invention discloses a multispectral inspection robot for a greenhouse. The multispectral inspection robot comprises a plurality of autonomous nodes with moving, multi-modal sensing and precise operation capabilities. The autonomous nodes can spontaneously establish a temporary communication network according to task requirements to form a dynamic collaborative group; through data exchange and negotiation in the network, a collective consensus is achieved for crop stress states, and a consistent operation instruction is generated; each node records a job effect and calculates an efficiency score, and packages the efficiency score into a sharable strategy packet; and the node continuously optimizes a self decision-making model by using a local and shared strategy packet to realize intelligent evolution. According to the invention, the robot group has the capabilities of group perception, collaborative decision, experience sharing and sustainable evolution, and the greenhouse operation efficiency and the system intelligence level are significantly improved.

The invention discloses a dynamic cross-language cooperation method for multilingual large model federal learning, and aims to solve the problem of cooperative training under data privacy, language heterogeneity and disastrous forgetting. The method is cooperatively executed by a server and a plurality of clients: the clients report semantic abstracts; the server dynamically clusters to form a collaboration group with similar semantics; the method comprises the following steps that: firstly, a plurality of groups are selected, then multi-round iteration training in the groups is carried out, the clients update local LoRA parameters by adopting orthogonal gradient projection so as to retain existing knowledge, and the server carries out adaptive aggregation based on collaborative graph weights obtained by meta-learning and dynamically distributes LoRA ranks for the clients so as to optimize communication. And finally, forming a global model through cross-group coordination. According to the method, efficient and privacy-safe cross-language knowledge migration is realized, the low-resource language performance is remarkably improved, and the communication overhead is reduced.

The coordination control method and system of comprehensive energy multi-agent collaborative group control and autonomous decision-making comprises: establishing a relaxation strategy of an agent corresponding to a distributed power supply; a main coordinator determines a global coordination signal by iteratively solving a global optimization target of relaxation according to global operation data of a comprehensive energy system and operation data of each agent, so as to minimize total cost, maximize system energy efficiency, minimize carbon emissions and minimize a global penalty coefficient under global relaxation constraints; each agent takes the minimum operation cost, the minimum response deviation to the global coordination signal and the minimum deviation between the actual state and the reference state as control targets; an electricity price autonomous decision-making model and a response power boundary autonomous decision-making model of the global coordination signal are established; and the control target values of each agent are predicted based on the control targets, the electricity price autonomous decision-making model and the response power boundary autonomous decision-making model of each agent, so as to realize the trade-off between the global optimization target and local autonomy.

This invention relates to the fields of computer simulation and smart elderly care technology, and discloses a method, terminal, and storage medium for simulating the collaborative behavior of intelligent agents in elderly care institutions. The method generates intelligent agents in a three-dimensional virtual simulation environment and assigns a multi-level identity tag system to each agent; it identifies elderly intelligent agents in a state of inability to move independently and sets them as anchor points for nursing task interactions; intelligent agents with mobility generate dynamic perception domains centered on themselves in real time and perform multi-dimensional state perception based on spatial proximity; when a target intelligent agent is detected within the perception domain of a service personnel intelligent agent and preset matching conditions are met, the corresponding collaborative mode is activated; the method calculates the joint path of the collaborative group or the individual service station, drives the intelligent agents to execute corresponding interactive animations, and, during path movement, executes dynamic avoidance logic under group conflict based on preset social behavior rule priorities. This invention realizes the simulation of the entire process from bedridden to out-of-bed transfer.

This invention discloses a federated image classification system and method based on historical state backtracking and probabilistic sampling grouping, comprising: an aggregation server communicating with multiple image acquisition terminals; the aggregation server maintaining a first-in-first-out model buffer for each terminal; each terminal uploading behavioral feature vectors; the server calculating the similarity between vectors, sampling dynamic thresholds from a Beta distribution probability, and constructing collaborative groups for each terminal based on the comparison results; the server using the current model parameters of a terminal as an anchor point, selecting the historical model version with the smallest Euclidean distance from the anchor point in the buffers of each member terminal within its collaborative group, and weighting and aggregating them to generate personalized model parameters which are then distributed to the terminals for classifying the acquired images. This invention achieves flexible collaborative grouping through probabilistic sampling, breaking through the bottleneck of hard clustering collaboration, and suppresses overfitting and model drift through historical state backtracking, thereby improving the personalized performance and convergence stability of the image classification model.

Current state-of-the-art technologies for resource allocation suffer from various problems, including computation time, lack of remedies (e.g., when a solution is infeasible), and so on. The disclosed embodiments utilize parallel processing, incorporating information exchange and / or integrating machine learning and / or heuristics to reduce computation time, conserve computational resources, and achieve scalability. For example, in one embodiment, one or more primal mixed-integer programming (MIP) models are solved in parallel with one or more restricted MIP models of the same unit combinatorial (UC) problem. Solving can be stopped when the upper and lower bounds of the solution converge, and resource allocation schedules can be generated based on the results.

This invention discloses a method and related equipment for providing rendering resources for a metaverse scene. When a user device moves to a new location in a virtual scene, the system first determines the set of distant rendering resources to be pre-fetched and the dynamic latency constraints based on the movement trajectory. It then checks whether the resources are cached on the user device's local machine and within its collaborative group. If not cached, the edge server, acting as the auctioneer, initiates a multi-dimensional auction request to one or more candidate service providers. The system receives bidding information from each candidate, including the provided latency, rendering quality, and price. Based on this information, the dynamic latency constraints, and a user experience quality value model, a social welfare score is calculated. A target service provider is selected based on the score, and the payment price is determined. Resources are then obtained, provided to the user device, and the actual effect is verified. The purpose of this invention is to establish a complementary collaboration mechanism between local, edge, and cloud environments, construct a multi-dimensional resource allocation mechanism, design an honesty incentive mechanism, achieve reliable collaborative rendering, and meet the needs of immersive virtual reality experiences.