28 results about "Collaborative intelligence" patented technology

The invention relates to an Internet of Vehicles multi-agent cooperative sensing method and system. The method comprises the following steps: constructing a collaborative sensing network, wherein the collaborative sensing network comprises a self-agent and a plurality of collaborative agents; acquiring and processing sensing data through the collaborative sensing network; performing feature extraction to obtain intermediate features; self-adaptive sparsification is carried out to obtain sparse features, and the sparse features are compressed and transmitted to a self-agent; performing time sequence feature enhancement on the features of all the agents at the self-agent end; fusing the features to obtain fused features; and constructing an Internet of Vehicles perception model, and realizing perception by the detection model according to the fused features. According to the method, the calculation complexity of traditional global attention is reduced from the square level to the linear level through an adaptive sparsification mechanism, the calculation overhead is remarkably reduced while the multi-agent feature interaction precision is kept, and the method is more suitable for real-time operation on the vehicle-mounted edge equipment with limited resources.

The invention discloses an AI-based browser user behavior recording intelligent workflow method and system, and relates to the technical field of workflow management.The method comprises the steps that interaction behaviors are recorded in a browser, and normalized event data are generated; the input recording cooperative agent performs user task intention analysis and abstracts the user task intention into a structured workflow; inputting a recording test agent for verification, and adjusting the structured workflow to obtain a verified target workflow; the browser is driven to automatically execute and monitor the execution state, a self-repairing strategy is triggered for adjustment when the execution state is abnormal, and the target workflow is updated when self-repairing succeeds. Through the technical scheme of the invention, standardized modeling and precipitation of the browser event flow are realized, the workload of establishing an automatic process is reduced, the use threshold of establishing the browser automatic process is reduced, the execution success rate and stability of the target workflow are improved, and the user experience is improved. And the robustness and the long-term maintainability of the automatic process are effectively improved.

The application discloses a wireless federated learning method for large-scale Internet of Things cooperative intelligence, aiming at the problem of heterogeneous device computing capability in a large-scale Internet of Things scene, and integrating centralized learning and federated learning to form a unified architecture, so that devices with weak computing capability can participate in global model training. On the one hand, the application determines the data sample selection strategy by the data importance of the centralized learning user, which can reduce the communication overhead and transmission time of data uploading; on the other hand, the application prunes the model of the federated learning user, which can effectively reduce the local computing time under the premise of ensuring the learning performance. The federated learning method provided by the application can realize data sample selection, model pruning and user scheduling of different types of users, which is helpful to improve the utilization rate of wireless network resources and alleviate the problem of limited resources of the Internet of Things.

The invention discloses an edge terminal-oriented memory efficient model fine tuning method and system, which realize decoupling of a backbone network and a fine tuning process by constructing a parallel side network module, and avoid video memory occupation caused by backbone gradient return. A double-adapter module is arranged, modeling is carried out on same-layer features and cross-layer context information, and the feature expression precision is improved. And constructing a feature fusion module, and performing adaptive weighting on different path outputs through learnable gating. A backbone grouping module is integrated, dynamic grouping is carried out on a backbone network based on interlayer similarity, and redundant modules and calculation overhead are reduced. And through the fine tuning execution module, only back propagation and parameter updating are carried out on the side network, and a trunk freezing state is kept, so that the memory and training cost is reduced. According to the method, the memory efficiency and the training speed of the edge fine adjustment process are improved, high-precision model self-adaption is achieved with extremely low calculation burden, and application in the fields of cloud edge collaborative intelligence and the like of unmanned aerial vehicles, robots, vehicle-mounted terminals and the like is effectively supported.

The invention relates to the technical field of intelligent traffic and automotive electronics, in particular to a projection type road marking method and system based on dynamic traffic environment perception, and the method comprises the steps: collecting and fusing real-time traffic flow, meteorological conditions and event data to form an environment state data set; calculating a traffic state index to evaluate a current road traffic state; triggering a preset road marking rule based on the traffic state index, and generating a marking scheme; constructing a dynamic space mapping mechanism based on a two-dimensional coordinate system, and mapping the marking elements into coordinate data; performing geometric distortion correction and smooth transition processing on the coordinate data to obtain final marking graph data; and dynamically calculating a brightness parameter of a finally projected road marking graph in combination with a real-time environment state to finish dynamic marking projection. According to the invention, a perception-decision-projection integrated network is constructed, collaborative study and judgment are carried out based on the traffic situation of the whole road section, and evolution from isolated projection to whole-network collaborative intelligence is realized.

The invention belongs to the technical field of vehicle control, and particularly provides a data interaction method among multiple agents of a cabin, a medium and a vehicle, the agents comprise a system agent and a plurality of ecological agents, and the method comprises the following steps: obtaining capability description information predefined for each agent, the capability description information is used for declaring an executable task type and a data interaction specification of the intelligent agent; in response to generation of a target task, determining at least two cooperative agents for executing the target task based on the capability description information; establishing a secure communication link between the at least two cooperative agents, so that the at least two cooperative agents interact the task data of the target task; and based on an interaction result of the task data, collaborating the at least two collaboration agents to execute the target task. Through the technical scheme provided by the invention, the cooperation capability among multiple agents in the vehicle cabin can be improved.

The invention belongs to the technical field of water conservancy project intelligent operation and maintenance, and discloses a complex environment adaptive collaborative operation intelligent operation and maintenance robot which comprises a vehicle body, and a movable chassis system, a multi-source sensing fusion system, a multi-degree-of-freedom mechanical arm, an intelligent storage platform and an intelligent control system are arranged on the vehicle body. Through the structural design and technical innovation of all the systems, many technical problems existing in a traditional water conservancy pipe network operation and maintenance mode and existing equipment are solved in a targeted mode, and the aspects of environmental adaptability, operation functionality, mechanical stability, collaborative intellectualization, operation and maintenance economical efficiency and the like are remarkably improved; the precise sensing capability in a complex and severe environment is greatly improved, and the detection precision and the anti-interference performance are remarkably enhanced; the robot constructs a triple heterogeneous sensor fusion system of a visual camera, a first laser radar, a second laser radar and a millimeter wave radar, combines a dynamic weight distribution algorithm, gives full play to the technical advantages of each sensor, effectively inhibits the coupling influence of darkness, water mist, dirt and other multi-source interferences in a pipe network, and improves the detection accuracy. The pipe network inner wall defect detection device can accurately capture fine defects such as 0.1 cm-level cracks and corrosion on the inner wall of a pipe network, stabilizes the defect detection accuracy at 92.5% or above, fundamentally solves the problems that a single sensor or simple double sensors are low in detection precision and multiple in missed detection and false alarm in a complex pipe network environment, and is beneficial to actual application and operation.

A collaborative intelligence-based semi-automated semiconductor wafer labeling system according to an embodiment of the present invention comprises: a first database providing at least one set of WBM image data that has been labeled; a second database providing a plurality of test data set control groups composed of a plurality of unlabeled WBM image data; a learning model comprising a first process for receiving WBM image data from the first database and learning defect patterns and normal patterns, a second process for predicting the defect probability of WBM image data received from the second database based on the learned pattern information, a third process for calculating an uncertainty value for each data based on the predicted defect probability value, and a fourth process for determining whether to perform an auto-labeling operation using a labeling model or an engineer labeling operation by a worker on the WBM image data according to the result of comparing the calculated uncertainty value for each data with a preset threshold value; and a first labeling unit that inputs auto-labeled WBM image data into the first database according to the determination result of the learning model and provides it as training data for the learning model. It may include a second labeling unit that inputs engineer-labeled WBM image data into a first database and provides it as training data for the learning model based on the judgment result of the learning model.

Systems and methods for task-driven collaborative smart cluster location reporting are provided. A first wireless communication entity may receive a first message from a second wireless communication entity, the first message including a collaborative smart cluster (CIC) location report configuration. The first wireless communication entity may send a second message to a second wireless communication entity, the second message including CIC location information determined based on the CIC location report configuration.

The invention discloses a man-machine cooperation intelligence agent system and method based on interactive feedback, and relates to the field related to artificial intelligence, and the method comprises the steps: receiving an intelligence analysis demand inputted by a user side, and extracting and analyzing intention elements through intention analysis; generating a task execution tree, and cooperatively adjusting the task execution tree with a user through an interactive interface; performing two-order matching routing decision based on node task characteristics in the agent cluster, determining an agent task cluster and performing parallel tree task analysis, obtaining task analysis results and integrating the task analysis results, determining a structured intelligence report and constructing an evidence tracing chain; and performing terminal interface display on the structured intelligence report and the evidence tracing chain. The technical problem that efficiency and accuracy are unbalanced in the existing intelligence analysis field is solved, and the technical effects that intelligence analysis efficiency is improved, analysis result accuracy and credibility are enhanced, and efficiency and accuracy dynamic balance is achieved are achieved.

The invention discloses an intelligent agent cooperation intelligent decision-making method, system, equipment and medium for transformer substation environment control, and belongs to the technical field of intelligent operation and maintenance, and the method comprises the steps: collecting transformer substation historical data, building an intelligent agent interaction environment, carrying out the modeling of a decision-making problem, and carrying out the state estimation according to local observation and the transformer substation historical data, the method comprises the following steps: constructing a global objective function, predicting an individual contribution degree through a neural network model, defining an individual reward signal according to the individual contribution degree, calculating a return index through a primary algorithm, carrying out strategy training and optimization according to the return index, executing a decision, carrying out normalization processing on the individual reward signal, and introducing a regularization item into the global objective function. And calculating a convergence index and monitoring the convergence. According to the method, state sensing and decision making are carried out, the problems of low operation and maintenance efficiency and poor collaboration in substation control are solved, automatic decomposition and efficient decision making of a global optimization target are achieved, and the self-adaptive capacity and the overall energy efficiency of the system are improved.

The application discloses a kind of based on collaborative intelligence's offshore rocket self-lifting platform dynamic scheduling method, comprising the following steps: multiple platforms are constructed as intelligent unit that can be autonomously responded to task, and the adaptive matching between task and platform is realized by dynamic grouping through collaborative mechanism.In the method, multiple indicators are introduced as scheduling targets, and the weight parameters are dynamically adjusted according to real-time task, resource and environmental information to improve scheduling flexibility.Through learning optimization mechanism to generate scheduling strategy, control platform task allocation, path planning and execution timing.In the execution process, scheduling strategy and platform grouping structure can be updated in time according to environmental disturbance and task state change, to ensure smooth execution of task and stable cooperation of platform.The application is suitable for multi-task, high-frequency launch scene.

The invention belongs to the technical field of energy management, and particularly relates to a source network load storage energy management system which comprises a new energy power generation prediction module, a distributed distribution network intelligent protection module and an optimal scheduling module. The power generation prediction module is used for realizing high-precision power generation output prediction by collecting environment type and equipment type power generation influence parameters and utilizing a deep learning algorithm fused with a space-time attention mechanism; the intelligent protection module calculates an output fluctuation coefficient and a voltage deviation ratio by collecting voltage, current and power flow direction data of key nodes of a distribution network, dynamically identifies bidirectional power flow impact and voltage out-of-limit risks and executes hierarchical pre-control; and the optimal scheduling module performs fitting and supply-demand relationship analysis on the source network load storage time sequence data based on an LSTM and a multi-model fusion algorithm, and realizes adaptive dynamic scheduling of energy flow. According to the method, new energy output prediction accuracy, distribution network risk prevention and control initialization and source network load storage collaborative intelligence are realized, and the safety and economical efficiency of system operation are remarkably improved.

A system may be configured for collaboration between language model agents. The agent may be, for example, a computer system, or a software component executing on a computer system that may accept textual and / or natural language inputs, process the inputs with LM and perform functions, and respond via textual and / or natural language outputs. An agent may act as an intermediary to interact with a user, identify a task requested by the user, and delegate one or more sub-tasks to another agent or other resource. The agent may act as a recipient to handle a task or sub-task delegated by the mediator. The agents may communicate with each other using a combination of a structured language and an unstructured language; for example, one or more parameters and natural language messages are used.

Described herein relates to a system and method that supports the creation and execution of collaborative intelligent agents to process machine-readable data into dynamic and / or related outputs. As such, the collaborative intelligent agent system and / or methods thereof may include a compiler to generate an executable expression tree, an executor to manage intelligent agent execution using the expression tree, and / or a processor to manage intelligent agent collaboration during execution. Additionally, using machine-readable inputs, the collaborative intelligent agent system and / or methods thereof may automatically execute the collaborative intelligent agents to dynamically induce outputs, without requiring user interaction. As such, the collaborative intelligent agent system and / or methods thereof may also be configured to realize the second pillar of the Semantic Web and / or may improve upon HTML or machine-readable code only, providing a mechanism to automatically produce dynamic outputs based on input data.

The application discloses a two-dimensional magnetic material use driving reverse recommendation method and system, belongs to the cross application field of material informatics and artificial intelligence technology, and the method comprises the following steps: acquiring patent data and attribute data corresponding to two-dimensional magnetic materials by using a multi-source data fusion module, and obtaining a multi-source fusion database through an ETL data stream integration processing mechanism; receiving a use requirement input by a user by using a multi-AI collaborative intelligent reasoning module, intelligently reasoning the use requirement, and generating a recommendation result; realizing a multi-key polling technology by using an API polling mechanism of a high-availability system architecture module, and constructing a multi-level fault-tolerant processing system; providing a responsive Web interface to support the user to input the use requirement by using an intelligent user interaction module, determining the use correlation, and displaying attribute combination prediction results and intelligent recommendation results. The application solves the problems of data fragmentation, unstructured semantic barriers and single recommendation mode in the existing material screening method.

The embodiment of the invention provides a low-altitude aircraft control method and device, an electronic device and a storage medium, through fusion of prediction analysis of artificial intelligence (AI), man-machine collaborative decision of collaborative intelligence (CI) and autonomous distributed control of swarm intelligence (SI), a human-machine collaborative group intelligence (ACSINtelence) system can realize dynamic, efficient and safe unified management of a low-altitude airspace, and can be applied to control of a low-altitude aircraft. The airspace congestion is effectively relieved, and the operation efficiency is remarkably improved.

The invention discloses a multi-Agent cooperation intelligent number asking method and system for a shipping database based on RAG enhancement. The method comprises the following steps: training a relationship mining model based on a historical shipping business data table of a shipping enterprise; determining ERD relation description among the target shipping service data tables by utilizing a relation mining model and combining with an automatic script; constructing a special shipping business knowledge base and an SQL corpus knowledge base; the query analysis Agent is utilized to analyze the user question intention, and query intention description is obtained; relevant ERD relation description, SQL corpora and the like are recalled in the two knowledge bases through the RAG technology, and an enhanced retrieval result is generated after rewriting and duplicate removal; based on the enhanced retrieval result, utilizing a plurality of SQL generation Agents to generate SQL statements in parallel, and utilizing a decision tree to score the generated SQL statements to determine the SQL statement with the highest score; and executing the SQL statement with the highest score, if execution fails, reasoning an error reason according to the SQL corpus knowledge base, feeding back the error reason to the SQL generation Agent, and regenerating the SQL statement.

The invention provides a periodical publishing management method and system based on multi-agent collaborative decision, and relates to the technical field of electric digital data processing. Comprising the following steps: pre-loading a task scheduling general control agent, a manuscript analysis agent, an expert matching agent, a resource scheduling agent, a compilation cooperation agent and a credible evidence storage agent into a distributed server cluster comprising a plurality of nodes; carrying out preprocessing and duplicate checking on the manuscript by utilizing a manuscript analysis agent; constructing a game model and a decision model by utilizing an expert matching agent to generate a manuscript reviewer list; monitoring a manuscript review state by using a resource scheduling agent to generate a scheduling scheme; semantic error correction and standardization processing are achieved through an editing and correcting cooperation agent; all data generated in a periodical publishing process are encrypted and stored in a block chain network by using a trusted evidence storage agent, and the privacy security of participants is protected in combination with federal learning. According to the method, collaborative optimization of the whole periodical publishing process can be realized, and the manuscript reviewing efficiency and the editing quality are remarkably improved.

The invention relates to the technical field of artificial intelligence, and provides a task subgroup grouping method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the semantic analysis of a task instruction, obtaining a task target, and determining an intention atlas composed of task nodes and a dependency relationship based on a preset task ontology atlas and the task target; constructing an agent capability map by structurally organizing capability information of a plurality of available agents; and calculating the capability matching degree and intention consistency between the task node and the intelligent agent in the intention map, and forming a task sub-group adaptive to the task node based on the capability matching degree and the intention consistency. According to the method, a unified cognitive structure for a task target is provided for all agents by constructing an intention map, and the problem of collaboration obstacle caused by lack of a shared semantic framework is solved; based on the capability matching degree and the intention consistency, the task subgroup used for executing the task nodes in the intention graph is obtained, and the overall collaborative intelligence and task execution robustness of the multi-agent system are remarkably improved.

The invention relates to the technical field of agent control, in particular to an industrial multi-agent cooperation system and method oriented to a complex process, and the method comprises the steps: generating a plurality of agent dispatching strategies through combining a multi-agent knowledge base and the standby real conditions of agents; generating a process flow feature library according to multi-source working condition data of an industrial site, and delimiting process flow dynamic partitions according to the process flow feature library; according to a plurality of agent dispatching strategies and process flow dynamic partitions, determining a current agent distribution strategy of each partition; according to the actual operation condition of the partition, the conflict relation between the intelligent agents is determined, so that the scheduling characteristic labels of the intelligent agents are identified; and comparing the process flow execution vacancy features of other partitions adjacent to the partition with the scheduling feature tags of the agents of the partitions, and changing a part of the agents as shared cooperative agents. By balancing the dedicated and conditional scheduling of the intelligent agent in the process flow, the transport capacity of the intelligent agent cluster is fully utilized, and the requirements of all the process flows for the intelligent agent are efficiently met.

This invention provides an edge computing method and system for routers, gateways, and cameras in the field of Internet of Things (IoT) and edge computing technology. The method includes steps S1: the edge computing device collects multimodal data and preprocesses it to obtain preprocessed data; step S2: the input computing task is acquired, multidimensional indicators are collected in real time, a heuristic algorithm is used to solve the constructed multi-objective decision function, and an optimal task allocation strategy is dynamically generated based on the multidimensional indicators; step S3: the preprocessed data is synchronized based on the task type to execute the computing task locally or in the cloud; simultaneously, a two-level collaborative caching mechanism between the edge and cloud is established to cache frequently accessed preprocessed data locally on the edge computing device and synchronize all preprocessed data to the cloud when idle. The advantages of this invention are: it greatly improves the overall system performance, resource utilization efficiency, and collaborative intelligence level.

Systems and methods for task-driven collaborative smart cluster management are presented. A first wireless communication entity may send a first message to a second wireless communication entity, the first message including a collaborative smart cluster configuration indicating that the second wireless communication entity may be configured as a collaborative smart cluster head node. The first wireless communication entity may receive a second message from a third wireless communication entity, the second message including a collaborative smart cluster head node configuration.

This invention discloses a regional energy management system and method based on collaborative intelligence and dynamic adaptation. The system operates in a two-level optimization architecture consisting of a regional command layer and a single-building model layer. Privacy-preserving collaborative intelligence sharing is achieved through a federated learning mechanism. The regional command layer receives and aggregates model updates to refine a global energy optimization model, and adaptively adjusts the regional optimization strategy using this model. The output of the single-building model layer continuously performs closed-loop calibration and updates to the digital twin network. Based on real-time detected trigger events or predicted conditions, the regional command layer dynamically adjusts the control granularity of the single-building model layer or temporarily authorizes intermediate cluster-level models. This aims to address the challenges of data privacy, computational complexity, and insufficient system resilience in the management of large-scale distributed energy systems.

The invention discloses a construction operation collaborative scheduling method and system based on multi-source data fusion, and relates to the technical field of operation scheduling. The method comprises the following steps: executing multi-source data acquisition, and establishing a multi-source time sequence state tensor; inputting the state tensor into a BIM model, and updating and generating a digital twinborn body synchronous with the construction state according to parameters of the BIM model; abstracting the operation unit into a plurality of cooperative agents; after the twinborn state and resource constraint of the intelligent agent are configured by using the digital twinborn body, game analysis is executed through a revenue function, and a local strategy is established; and aggregating the local strategies and carrying out construction operation collaborative scheduling. The technical problems that the construction state is difficult to simulate accurately in real time, cooperation among operation units lacks scientific strategy guidance, and conflicts and resource waste are prone to occurring are solved, and the purposes of providing scientific strategies for cooperation of the operation units, reducing conflicts, optimizing resource allocation and improving the working efficiency are achieved by constructing the digital twinborn bodies synchronously evolved with the construction state. And the construction cooperation efficiency is improved.

A system for collaborative execution of a task includes a plurality of edge units in communication with a principal processor. Each edge unit includes a collaborative intelligence module configured to establish a communication link amongst at least two of the edge units. The communication intelligence module is further configured to communicate task attributes of the task with at least one other edge unit, and to exchange sensor data from the set of sensors and a plan with the at least one other edge unit to create a joint workspace followed by execution of the task in collaboration with the at least one other edge unit.

The application discloses an AI-based browser user behavior recording intelligent workflow method and system, relates to the technical field of workflow management, and comprises the following steps: recording interactive behaviors in a browser and generating normalized event data; inputting a recording collaboration agent to analyze user task intentions and abstracting the intentions into a structured workflow; inputting a recording test agent to verify the structured workflow, adjusting the structured workflow, and obtaining a verified target workflow; automatically executing the browser and monitoring the execution state, triggering a self-repair strategy to adjust when an exception occurs, and updating to the target workflow when the self-repair succeeds. Through the technical scheme of the application, the standardization modeling and sedimentation of the browser event flow are realized, the workload of building an automated process is reduced, the use threshold of building a browser automated process is reduced, the execution success rate and stability of the target workflow are improved, and the robustness and long-term maintainability of the automated process are effectively improved.