8278 results about "Collaboration" patented technology

A scalable platform for orchestrating networks of specialized AI multi-agent networks that enables secure collaboration through token-based protocols and real-time result streaming with advanced dynamic chain-of-thought pruning. The central orchestration engine manages domain-specific agents, implementing sophisticated multi-branch reasoning with contribution-estimation layers that evaluate each agent's utility using Shapley value-inspired metrics. The system employs information-theoretic and gradient-based surprise metric to guide memory updates and dynamic reasoning expansion, preventing local minima stagnation while preserving valuable insights through adaptive forgetting mechanisms. The platform unifies Monte Carlo tree search with contribution-aware estimation to detect high-synergy expert combinations while maintaining privacy through partial data approaches. It scales across distributed computing environments, enabling complex collaborative tasks like materials discovery, product engineering and manufacturing process design, biomedical research, and drug development. The system supports multi-party economic rewards through systematic contribution effort, cost and importance tracking, while standardized interfaces manage security, privacy, and policy constraints across heterogeneous agents.

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention belongs to the technical field of multi-agent collaboration, and provides an electric power cross-modal knowledge fusion multi-agent collaboration processing method and system, and the technical scheme is as follows: analyzing a task execution command instruction input by a user; key elements of the task are extracted, the task is decomposed into a plurality of sub-tasks according to the extracted key elements, and logic association and priorities among the sub-tasks are identified; according to the logical association and priority among the subtasks, allocating the subtasks to the corresponding agents; each agent executes the corresponding subtask according to a set execution path, the executed task result is shared to other agents to cooperatively complete the complex task, and finally a cooperative processing result is obtained through integration. Efficient execution of complex tasks such as document generation, review and management is realized, and smooth completion of the tasks is ensured.

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.

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for multi-agent AI collaboration. The system provides a universal multi-modal key-value subsystem for sharing partial computations, implements hybrid placement strategies for dynamic memory management, and incorporates quantum-resistant secure enclaves. The architecture integrates hardware acceleration through GPU-FPGA hybrid caching and neuromorphic processors, applies adaptive energy and thermal management across hardware generations, and implements autonomous flash resource orchestration with multi-dimensional wear management. The system orchestrates tensor workflows using hierarchical scheduling, enables cross-agent collaboration with privacy preservation, and supports continuous learning without catastrophic forgetting. This integration delivers unprecedented computational efficiency and security in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.

The invention provides an industrial park multi-target collaborative optimization scheduling method and system based on artificial intelligence, relates to the technical field of intelligent scheduling of industrial parks, and aims to establish a digital twinborn model and a dynamic topological graph, perform resource flow relationship modeling by using a graph neural network, and perform resource demand analysis in combination with a multi-model prediction framework. A multi-agent reinforcement learning algorithm is adopted to generate a scheduling strategy, migration from a global strategy to a local decision module is realized through a strategy distillation technology, robustness of the scheduling strategy is simulated and verified in a digital twin model, the strategy is adjusted according to a simulation result, and finally efficient scheduling of park resources is realized. The method effectively improves the intelligentization and collaboration level of the operation of the industrial park, reduces the energy consumption and carbon emission, enhances the ability of the park to cope with complex scenes, and is suitable for the efficient management of the modern industrial park.

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

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

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

The invention provides a multi-agent task collaboration method, device and equipment and a storage medium, and the method comprises the steps: carrying out the deep fusion and unified semantic coding of a collected multi-modal data set through a multi-modal large language model, and obtaining a high-dimensional cross-modal feature embedding and semantic representation file, the task requirement mapping module is used for enabling local task requirements of multiple agents to correspond to cross-modal semantics to obtain task requirement semantic mapping, and task division and time arrangement are carried out; when multiple agents execute tasks, key data and operation results are sampled in real time and compared with high-dimensional semantic representation, a concept offset detection result is obtained, and when it is detected that the concept drifts progressively, the multi-modal large language model is dynamically adjusted. According to the method, the communication and cooperation efficiency among multiple agents is enhanced by using a large language model, and task allocation and collaborative decision are optimized through task demand semantic mapping; and concept drift detection and a dynamic adjustment mechanism are introduced, so that the long-term adaptability in a complex dynamic environment is improved.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention discloses a data sharing system and method for realizing multi-agent interaction, and belongs to the technical field of agents. According to the method, production command instructions are labeled, a large language model is trained, and task types are identified; disassembling the production command instruction into a subtask sequence; constructing a knowledge relationship graph through the graph database; establishing a unified data interface, and constructing a data sharing center; dividing agent types according to the power transmission and distribution service scene, and determining a function boundary of each agent; a multi-agent simulation environment is used for testing the cooperation efficiency of the subtasks, and the execution sequence of the subtask sequence is optimized; sub-tasks are dynamically allocated according to the intelligent agent load and the skill matching degree; the intelligent agent accesses the knowledge relation graph through a unified data interface, and real-time pushing of data change is achieved through a message queue; the decision of the large language model is optimized through cue word engineering, specific operation is executed by utilizing a tool library component, and the intelligent agent executes operation according to a subtask sequence.

The invention provides a multi-source heterogeneous corpus fusion method and system based on government affair service data, and the method comprises the steps: obtaining an original corpus set of a plurality of data sources in government affair service, carrying out the cross-modal semantic alignment processing of each corpus unit in the original corpus set, generating a normalized data block corresponding to each corpus unit, and carrying out the fusion of the data blocks; carrying out multi-modal semantic coding on the standardized data blocks to obtain semantic feature vectors of all corpus units, carrying out topological structure coding on association attribute sets among the standardized data blocks to generate a global structure relation graph, and carrying out dynamic weight distribution on the semantic feature vectors based on node connection weights in the global structure relation graph to obtain semantic feature vectors of all corpus units; and generating a fusion weight matrix, performing cross-modal feature fusion on the semantic feature vector to obtain a target semantic embedding representation, and generating a standardized corpus associated with the government affair service. According to the method, the semantic aggregation problem of the non-uniformly distributed corpus units is solved, and the government affair data governance efficiency and the cross-department cooperation capability are greatly improved.

The invention discloses an operation and maintenance method and system of a multi-cloud management platform based on cloud edge collaboration, and the method comprises the following steps: S1, deploying cloud service and edge nodes, and initializing a cloud edge collaboration environment; s2, constructing an edge autonomous optimization network, adjusting and optimizing a task strategy, and predicting a task load of an edge node; s3, adjusting task priorities, and classifying task strategies; s4, constructing a task feature matrix, a cloud edge resource matrix and a task and resource matching model; s5, allocating resources to each task to generate a task resource allocation table; and S6, identifying operation abnormity through the time sequence abnormity detection model, and triggering task migration, resource switching or task redistribution when detecting that the edge node is abnormal. According to the method, the edge autonomous optimization network and the intelligent scheduling algorithm are utilized, dynamic task allocation and exception recovery are achieved, and the method has the advantages of being high in resource utilization rate, high in scheduling real-time performance and good in system stability.

The invention provides a source-network-storage integrated power distribution network operation optimization method, which relates to the technical field of power distribution networks and comprises the steps of setting an intelligent sensor, collecting multi-dimensional power distribution operation parameters, performing semantic extraction and automatic labeling, determining logic association after conflict correction, obtaining a power distribution knowledge graph, constructing a dynamic prediction model and performing power distribution index prediction. Obtaining a prediction result and generating a key index prediction map; constructing a multi-temporal-spatial-scale collaborative optimization model, setting an objective function, constructing a physical optimization sub-model and a market game optimization sub-model, carrying out interactive coupling, and carrying out collaboration, interactive feedback and iterative convergence to obtain a multi-scale collaborative optimization scheme; and carrying out distributed evaluation, generating an evaluation result, if the evaluation result does not reach the standard, packaging the multi-temporal-spatial-scale collaborative optimization model into an optimization contract, triggering the optimization contract, generating a candidate optimization scheme, carrying out repeated evaluation until the standard is reached, and taking the candidate optimization scheme as a consensus scheme and executing the candidate optimization scheme.

The invention discloses an intelligent operation and maintenance management and control system for an information communication network, and belongs to the technical field of communication. The system comprises the following modules: an intelligent sensing and data acquisition module which is responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain equipment states and conversion of heterogeneous data into a unified standard; the network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring, and provides comprehensive network operation and maintenance insight; the intelligent resource scheduling module is used for realizing dynamic allocation of cross-domain network resources and ensuring efficient and safe utilization of the resources through multi-dimensional optimization and a real-time load balancing strategy; the intelligent decision support module is used for continuously optimizing an operation and maintenance strategy based on machine learning, analyzing a long-term operation trend and providing suggestions for management decisions; and the open integration and collaboration module provides integration of an open API interface and a third-party system, supports cross-domain collaboration management, and improves the flexibility and expansibility of the system.

A platform for coordinating networks of specialized AI agents that enables secure collaboration through token-based communication and real-time result streaming. The system features a central orchestration engine managing interactions between domain-specific expert agents, with memory management and optional encryption for secure data handling. The platform uses efficient communication protocols for knowledge compression and faster reasoning, while a standardized agent interface system handles security, privacy, and policy requirements. It scales across distributed computing environments to enable complex collaborative tasks like personalized content creation, materials discovery, and drug development while optimizing resource usage and maintaining data privacy.

The invention discloses a BIM-based building electromechanical full life cycle management platform and an optimization strategy thereof, relates to the technical field of building informatization management, and solves the defects of the existing BIM technology in the aspects of processing efficiency, model precision, data synchronization and change management. According to the invention, a data layer is used for data cleaning and standardization through a machine learning model, and distributed storage is realized through a cloud management system; the model layer constructs and updates a high-precision BIM model; the synchronization collaboration layer ensures the consistency and traceability of data by using a real-time synchronization mechanism and a block chain technology; the decision analysis layer performs data analysis and prediction through a deep learning algorithm; the full life cycle management layer integrates historical data and current data and automatically triggers a management process; according to the method, the data processing efficiency and the model precision are greatly improved, and the seamless sharing capability of information and the transparency of data synchronization and change management are remarkably improved, so that the overall collaborative efficiency and the management level of a project are enhanced.

The invention relates to the technical field of tunnel power supply and distribution, in particular to a tunnel modular prefabricated cabin power supply and distribution intelligent substation self-adaptive regulation and control system based on edge calculation. Comprising an edge calculation and AI decision-making unit which is used for realizing rapid acquisition, processing and instant decision-making of tunnel power supply and distribution multi-dimensional data, generating a power supply and distribution adaptive regulation and control strategy by deploying calculation resources and a machine learning algorithm at edge nodes close to a data source, and converting the strategy into an executable regulation and control instruction; a cloud platform collaborative management unit; and an intelligent sensing and internet-of-things unit. According to the invention, hierarchical decision control of the tunnel power supply and distribution system is realized by constructing a hybrid architecture of edge computing and cloud platform collaboration and a priority judgment mechanism; according to the invention, multi-modal data are integrated through the multi-protocol communication link module and the full-scene data fusion analysis module, and data association analysis is realized through Kalman filtering, D-S evidence theory and other algorithms.

The invention provides a supply chain multistage warehousing intelligent scheduling and collaboration method and system, and relates to the technical field of intelligent warehousing, and the method comprises the steps: carrying out the hierarchical modeling prediction of the short-term and long-term demands of each stage of warehouse through employing a long-short-term memory network based on historical order data, and obtaining the inventory demand; setting each warehouse node as an independent intelligent agent, and generating an inventory allocation strategy through strategy iteration optimization among the intelligent agents; converting the inventory allocation strategy into a scheduling instruction containing an allocation object, an allocation quantity and allocation time based on a rule knowledge base; and in the edge computing unit of each warehouse node, receiving a scheduling instruction and performing sorting execution, when inventory abnormity or resource conflict is detected, initiating an emergency cooperation request to an adjacent warehouse node, returning response information by the warehouse node receiving the emergency cooperation request according to the own resource state, and performing scheduling according to the response information. And determining an emergency processing scheme through local negotiation between the nodes.

The invention discloses a multi-agent cooperation system and method based on spatial calculation and multi-modal AI fusion, and the method comprises the steps: collecting all real-time data of a construction site through a multi-modal data collection and fusion unit, and carrying out the processing and integration of all collected data through a multi-modal AI algorithm, and generating a unified semantic association model; a three-dimensional virtual environment of a construction site is constructed through a space calculation unit, real-time synchronous mapping is formed for the construction site, and dynamic changes in the construction process are simulated and predicted; task allocation and behavior planning are carried out on multiple agents through an agent behavior management unit; and the intelligent agents are configured to perform real-time information sharing and task cooperation through a communication protocol through the multi-intelligent-agent cooperation unit. According to the scheme, the problems of difficulty in multi-source data integration, low cross-department cooperation efficiency, insufficient construction dynamic adjustment and the like in intelligent construction in the building industry can be solved.

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

The invention discloses an intelligent supervision management and control method and system, and the method comprises the steps: generating a violation operation recognition result and an equipment abnormality early warning signal according to construction scene multi-mode data collected by an intelligent camera and real-time monitoring information of an Internet of Things sensor; generating a non-tampering block chain evidence storage data chain based on the violation operation identification result and the equipment abnormity early warning signal; according to the engineering change information in the block chain evidence storage data chain, updating a BIM collaboration platform through an incremental three-dimensional model synchronization technology, and generating a multi-party real-time interaction enhanced three-dimensional model; based on an enhanced three-dimensional model and Internet of Things environment monitoring data, a digital twin mirror image space is constructed, a construction process is simulated through a multi-source data fusion deduction engine, and a risk pre-judgment map and a resource configuration optimization strategy are generated. According to the embodiment of the invention, visual supervision, credible tracing and prospective decision making of the construction process can be realized.

The invention provides an integrated logistics management system for cargo transportation. The integrated logistics management system comprises a multi-mode collaborative perception module, a semantic reasoning module, a scene adaptation decision module, an intelligent execution module and a logistics trust chain module. The multi-mode collaborative sensing module collects logistics full-scene state data through a sensor network; the semantic reasoning module is used for constructing a knowledge graph based on a graph attention network and realizing unified modeling of multi-source information; the scene adaptation decision module is combined with a pulse coupled neural network (PCNN) and a swarm intelligence collaboration mechanism to complete routing optimization; the intelligent execution module adopts a hierarchical task management and dynamic adjustment mechanism, and adjusts a task scheme according to execution abnormity and credit evaluation; the logistics trust chain module constructs a logistics trust system through a hierarchical consensus mechanism, a credit scoring model and a trust propagation and early warning mechanism, and realizes risk prevention and control of the whole process. The system solves the technical problems of a traditional logistics system in the aspects of data integration, dynamic supervision, decision support, collaborative scheduling, safety traceability and the like.

The invention discloses a hierarchical multi-agent game confrontation and collaborative decision-making algorithm based on federated learning, and the algorithm comprises the following steps: setting a federated learning framework containing a plurality of agents, enabling the agents to be located in different regions, and carrying out the training of a local model through the data of the regions; in the federated learning framework, deploying a layered reinforcement learning model in each intelligent body; performing confrontation training on a plurality of agents in a game environment by using a hierarchical reinforcement learning model; on the basis of game confrontation, a collaborative decision algorithm is introduced to coordinate behaviors of multiple agents; and when a preset training round is reached or a specific condition is satisfied, uploading local model parameters to a central server, and sharing the model parameters through a federal learning framework to carry out global optimization. According to the method, efficient collaboration and strategy optimization among multiple agents can be realized on the premise that privacy is protected, and high adaptability and robustness are kept in a complex and changeable environment.

The invention provides an asset data collection method based on the Internet of Things, and the method comprises the steps: constructing a directed acyclic graph model based on task decomposition and dependency for each node group, determining a task execution sequence and a data transmission mode between the nodes through employing a critical path analysis algorithm, and obtaining a task-oriented node cooperation process; for the data fusion result, dynamically updating a data model and a decision rule of the node by adopting an incremental learning algorithm, and timely discovering abnormal deviation of node data by adopting an anomaly detection algorithm to trigger dynamic adjustment of node marshalling; and constructing a node cooperation incentive mechanism based on the block chain, dynamically adjusting credit values and resource allocation of the nodes according to contribution degrees and task completion quality of the nodes, and constraining and arbitrating cooperation behaviors among the nodes by adopting an intelligent contract technology.