24 results about "Group collaboration" patented technology

The invention discloses a big data and artificial intelligence teaching practical training platform based on a computer, and relates to the field of practical training teaching, and the platform comprises a teaching unit which is used for teaching students; a teaching interaction unit which is connected with the teaching unit, can provide an experiment scene for students through the teaching interaction unit, and is used for consolidating the learning content of the teaching unit; the data processing unit is connected with the teaching interaction unit and is used for recording the practical training data of the students and analyzing the recorded data; according to the big data and artificial intelligence teaching training platform based on the computer, learning tasks completed by students can be evaluated, the knowledge mastering and applying degree of the students can be known, and meanwhile, according to the performance, including the cooperation ability, the contribution degree, the communication skill and the like, of the students in group cooperation, the knowledge mastering and applying degree of the students can be known. And evaluation and subsequent learning progress are given.

The invention discloses an agent-based instant messaging group cooperation method and device, a medium and a program product, and is applied to a client, and the method comprises the steps: sending a super group creation request to an instant messaging server, and triggering the instant messaging server to automatically bind a default digital intelligent employee for a super group as a super group assistant; when a dialogue message is generated in the group, pushing the dialogue message to the instant messaging server, and triggering the instant messaging server to send message data to the intelligent server; triggering the intelligent server to carry out real-time intention analysis, and assembling service message data when a service intention is recognized; the service message data from the intelligent server is received through the instant messaging server, replied in a super group assistant identity and displayed in a service message card form, and a corresponding service process can be triggered through clicking. According to the invention, the AI intelligent agent and the instant messaging group are deeply combined, the communication-service-intelligence fused cooperation is realized by using group communication, and the enterprise cooperation efficiency and the service processing capability are improved.

The disclosed techniques provide event group collaboration sessions for meeting organizers. An event group collaboration session can include a private chat session that is exclusively reserved for meeting organizers or other users with designated roles, such as presenters. The private chat session is provided to allow organizers to collaborate before a meeting, and the private chat session is not shared with audience members of the meeting. A system also provides tools that allow meeting organizers and other select users to readily access the private chat session from the meeting or from user interfaces that are used in the setup of the meeting. This allows the organizers to readily access a contextually relevant thread directly from a meeting without having to provide a number of manual steps to navigate to a separate thread. A system uses two meeting objects and a data structure relating the objects to control messages threads.

The invention relates to the field of electric power, and particularly discloses a power plant relay protection setting value automatic checking method and system based on edge computing, and the method comprises the steps: carrying out the credibility self-evaluation of an operation state of an edge computing node through the edge computing node, and generating a credibility grade label; the node dynamically selects and executes a strategy of complete checking, simplified checking or calling of a cache conclusion according to the label, and outputs a graded checking result with a mark; the aggregation gateway or the master station performs differentiation processing according to the result mark, and starts collaborative redundancy check for a low-credibility conclusion; performing closed-loop optimization on the node evaluation model and the global algorithm by utilizing arbitration data generated by cooperative verification; the system comprises a node credibility self-evaluation module, a grading check decision and execution module, a constant value processing and collaborative scheduling module and a group collaboration and model evolution module. According to the invention, by introducing an execution main body credibility evaluation and dynamic cooperative verification mechanism, endogenous safety, decision robustness and continuous self-evolution of relay protection constant value verification are realized.

This invention provides a short-term traffic conflict prediction method and early warning system based on RSU group collaboration and spatiotemporal graph neural network. It acquires vehicle trajectory and status data collected collaboratively by roadside unit groups, using the coverage area of ​​each RSU as a spatial analysis unit, and constructs an initial energy field distribution map of traffic conflict based on the spatial density of vehicle distribution and the intensity of speed temporal fluctuations. A pre-constructed spatiotemporal graph neural network is invoked to capture energy transfer paths between units, and a temporal cyclic layer is used to capture the energy change inertia within units, predicting and generating a dynamic evolution map of traffic conflict energy containing an energy evolution path network and peak times. Based on the map, potential conflict outbreak sources and expected affected unit sequences are identified, generating conflict propagation situation prediction information including spatial coordinates and propagation time sequences. Based on this information, differentiated collaborative working mode adjustment instructions are generated and distributed to each RSU. This invention achieves short-term prediction of traffic conflicts and collaborative scheduling of roadside resources.

This application provides a no-code orchestration system based on intelligent agents, belonging to the field of no-code programming and multi-agent collaboration. It addresses the problem that building multi-agent collaborative systems in related technologies requires specialized programming knowledge and has a high technical threshold. The system dynamically organizes intelligent agents through a group collaboration container building module that responds to no-code operations such as visual drag-and-drop. A dialogue-driven intelligent agent collaboration network module parses natural language commands and generates task orchestration plans. A visual rule-protocol conversion engine module converts graphically configured interaction rules into underlying protocols to schedule intelligent agent collaboration. This allows non-technical personnel to quickly build and command complex multi-agent collaborative systems in an intuitive no-code manner, effectively reducing the technical threshold and improving the development and deployment efficiency of intelligent collaborative applications.

The invention discloses a visual shared whiteboard system and method for solving a group collaboration mathematical problem, and belongs to the technical field of computer science and human-computer interaction crossing. The system comprises a multi-modal fusion input module used for converting input such as handwriting and voice into a structured mathematical object; the real-time semantic understanding and knowledge graph engine is used for constructing a collaborative cognitive model; the collaborative process management module based on the directed hypergraph is used for carrying out formalized modeling and navigation on a nonlinear problem solving process; the context-aware intelligent auxiliary module is used for providing verification, recommendation and guiding consensus achievement; and a multi-user cooperation engine ensures stable cooperation experience. According to the method, the problems of professional expression, real-time cooperation splitting, group thinking process management difficulty and the like are solved, and a unified, intelligent and efficient integrated cooperation environment is provided for group solving of complex mathematical problems.

The present application relates to the technical field of Internet of Things communication, in particular to an ocean Internet of Things communication system and method based on group perception and group intelligence cooperation. In the present application, by adopting low-power wide-area network and satellite communication technologies, the communication coverage and reliability of the system in complex marine environments are improved, enabling real-time data transmission and immediate monitoring and response, thereby ensuring stable signal transmission in remote sea areas or deep sea environments. In combination with edge computing and deep learning algorithms, multi-dimensional and massive data can be intelligently processed and analyzed, improving data value and enabling rapid decision-making. Through group perception and collaborative decision-making, a group collaboration mechanism is formed, enabling the system to break away from the application of traditional single devices or single function modules, effectively integrating the functions of multiple sensors, achieving information sharing and optimal resource allocation, and improving the utilization efficiency of marine resources.

This invention discloses an integrated application resource implementation scheme based on architecture diagrams, belonging to the field of computer technology. The system includes an architecture diagram design module, a resource association module, a work order generation module, and multiple collaboration modules. The architecture diagram design module provides a graphical interface for drawing architecture diagrams containing application nodes and connections; the resource association module associates resource information with application nodes; the work order generation module generates resource request work orders based on newly added application nodes and communication access control work orders based on the communication requirements corresponding to the connections; the multiple collaboration modules share the architecture diagram, resource information, resource request work orders, and communication access control work orders with multiple collaboration groups, and receive update feedback on work order status from multiple collaboration groups to dynamically adjust resource configuration. This invention achieves integrated management of architecture, applications, and resources, reduces cross-group collaboration costs, and improves resource configuration efficiency and system management efficiency.

The application discloses a kind of inductive process mining optimization method and system based on multi-agent cooperation.First, the low-frequency trajectory variant in event log is denoised and frequency is counted, and an inductive mining algorithm is used to mine a process tree model, an external knowledge base is retrieved in the context of business process, and structured information input is constructed.Second, a multi-agent group collaboration mechanism of "generation-review-optimization" is introduced, wherein the initial model generation group generates an initial process model based on structured information input, the expert review group reviews the initial process model from three dimensions of process tree operator, model structure robustness and activity event completeness, and the model optimization group refines and optimizes the model based on expert review.The method can effectively handle event logs with high complexity, noise interference and a large number of low-frequency behavior variants, understand the context information of business processes, and generate a process model with reasonable structure and accurate behavior.

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.

This invention discloses a method and system for collaborative fault prediction of IoT devices based on digital twins. By using multi-protocol adaptation and intelligent preprocessing mechanisms in the data acquisition unit, it breaks down data barriers between devices, achieves unified access and standardized processing of heterogeneous sensor data, and provides a highly consistent real-time data source for the digital twin model. The digital twin model construction unit uses a real-time synchronization module and a fault feature extraction module to continuously calibrate the mapping relationship between the virtual model and the physical entity, ensuring that the twin accurately reflects the real-time status and potential fault modes of the device group, laying a high-fidelity foundation for collaborative analysis and prediction.

This invention provides a smart campus task collaboration method and system for multi-group service optimization, belonging to the field of campus service management technology. The method retrieves historical service logs, allocates storage nodes according to service processes to generate pre- and post-collaboration datasets; determines the group collaboration status based on the datasets, and constructs a dedicated task measurement set and group behavior feature set; quantifies cross-group task collaboration priorities through dual-evaluation modality labeling; and generates a group-task collaboration set through iterative computation, using segmentation coefficients (cohesion + separation) for selection and optimization. The system includes modules for data collection and storage, task and feature construction, collaboration priority quantification, and collaboration adaptation analysis and optimization. This invention solves the problems of insufficient precise adaptation and ambiguous priority quantification in existing multi-group collaboration technologies, achieving targeted optimization of service resources, improving task collaboration efficiency and group satisfaction, and providing an efficient solution for differentiated multi-group services in smart campuses.

The invention relates to the technical field of project management, in particular to a film and television full-process production management system and method based on industrial big data, and the system comprises an index binding module, a chain of responsibility construction module, a resource scheduling module, a priority adjustment module and a cooperative relationship verification module. According to the method, the file timestamp, the user identifier, the format type and the equipment number combination field are embedded, the unique mapping structure of the material path is constructed, the whole-process tracking and calling accuracy of the material is enhanced, the time sorting based on the material operation behavior is realized, the task responsibility crawler visualization is realized, and the cross screening is carried out in combination with the scene number and the equipment use condition; a scheduling conflict contrast set is established, resource allocation efficiency and scheduling precision are improved, scheduling priority and execution strength are introduced, scenes needing intervention are identified, scheduling contradictions are accurately positioned, member scheduling and scene adjustment windows are matched, cooperation time periods are screened, manpower allocation efficiency and cross-group cooperation capability are improved, and a dynamic closed loop of the whole task process is constructed.

The invention discloses a group collaborative music sharing and emotion synchronization system, which belongs to the technical field of information retrieval and recommendation systems and comprises a multi-source data acquisition module, a group emotion state fusion module, a music sequence collaborative generation module and a feedback learning and adaptive optimization module. Physiological signals and social interaction data of group members are collected, a group emotion influence relation graph is constructed based on a graph neural network, a unified emotion vector is generated through fusion, the collaboration degree of music and group emotion is calculated, an optimal music sequence is selected, real-time monitoring and feedback are conducted, and system parameters are optimized in a closed-loop mode. Accurate recognition, effective fusion, collaborative optimization and continuous improvement of group emotions are achieved, the problem that music recommendation in a group scene is difficult to balance multi-member emotion demands is solved, and the method is suitable for multi-person scenes such as family parties, team cooperation and social activities.

The application provides an AI-based cross-modal cartoon content generation and personalized recommendation method and system, belonging to the field of cartoon generation and generative interactive AI. The method is applied to a user group for multi-person collaboration, comprising: generating first control information by a first user, the first control information instructing a first AI engine to generate first cartoon content based on first modal content loaded by the first control information, and instructing at least one second user to respond to the first control information; the first AI engine acquires response information of the second user to the first control information, and adjusts the first cartoon content based on the response information to generate second cartoon content; the response information comprises second modal content. The system comprises a plurality of user devices, each user device corresponding to an AI engine, for realizing the method. The application can complete cross-modal cartoon content generation and personalized recommendation based on group collaboration.

This invention relates to a method for optimizing the radio frequency front-end of ground-penetrating communication under strong interference, specifically in the field of ground-penetrating communication radio frequency front-end optimization. Through collaborative learning and autonomous optimization among distributed nodes, the overall performance and reliability of ground-penetrating communication links in complex electromagnetic environments are significantly improved. The core of this method is that each node can not only optimize its own radio frequency front-end strategy in real time according to the local environment, but also form efficient cooperation with neighboring nodes through strategy collaboration, selective knowledge transfer, and evolutionary early warning. This effectively suppresses co-channel interference, reduces the overall energy consumption of the network, and extends the endurance of nodes. At the same time, the solution endows the network with strong robustness to continuously adapt to slow environmental changes and resist unknown interference, realizing a leap from individual intelligence to group collaboration, and ensuring the long-term stable operation of large-scale underground Internet of Things.

This application provides a no-code orchestration method based on intelligent agents, belonging to the fields of artificial intelligence and process automation. It addresses the problems of high barriers to entry, rigid processes, and ineffective utilization of dynamic knowledge in related technologies. This method dynamically constructs group collaboration containers to accommodate intelligent agents in response to no-code operations; parses task intents and generates collaborative task sequences in response to natural language instructions; converts graphically configured interaction rules into underlying protocols to drive collaborative operations; and captures and utilizes intermediate information as dynamic context in real time throughout the process. This enables users to intuitively and flexibly organize and drive multi-agent collaboration, achieving real-time knowledge fusion and utilization, significantly reducing the barrier to entry and improving the flexibility and intelligence level of collaboration.

The invention discloses a group collaboration-oriented geographic information data visual sharing method and system, and the method comprises the steps: judging whether a target user map coordinate system used by a target user meets a target group collaboration geographic information data visual sharing requirement or not when a target geographic information operation request sent by the target user is received; if not, the target user map coordinate system is converted into a coordinate system meeting the target group cooperation geographic information data visual sharing requirement; judging whether the target geographic information operation request contains a target geographic information editing request or not; if yes, whether the target geographic information editing request meets the target group cooperation geographic information editing requirement or not is judged; and if not, processing the target geographic information editing request according to the target group cooperation geographic information editing requirement. According to the method, the group cooperation efficiency is high, overlapping coverage of layers is not caused when a plurality of users add labels at the same time, and the method can adapt to a dynamic group cooperation scene.

The invention discloses a multi-agent intention recognition method and system based on spatial clustering and time sequence statistical characteristics, and relates to the technical field of multi-agent intention recognition. According to the method, intelligent agents are divided into collaborative formations based on the similarity of spatial distance and motion direction through a clustering algorithm, a formation representative position sequence is utilized to extract time sequence statistical characteristics, intention confidence is obtained through temperature Softmax processing, and a final formation intention label is output. The system comprises five core modules, namely, a data acquisition and preprocessing module, a dynamic clustering formation division module, a time sequence trend analysis module, a confidence coefficient calculation and decision-making module and a collaborative decision-making output module. The multi-agent group behavior intention recognition method solves the problems that a traditional method neglects group cooperation, the formation division effect is poor, and generalization and adaptability are poor, has the advantages of being high in recognition accuracy, robustness and calculation efficiency and good in expandability, and is suitable for multi-agent group behavior intention recognition in the fields of automatic driving, intelligent storage and the like.

This invention discloses a method and system for verifying and arbitrating group ethical consistency in multi-AI agent collaboration, belonging to the field of artificial intelligence ethics and security governance. It addresses the problem that while individual AIs may be ethically compliant, group collaboration can lead to collective ethical vulnerabilities, and existing solutions lack systematic quantitative assessment and tiered arbitration at the group level. The solution involves: extracting ethical profiles from the trusted execution environments of each AI and verifying their authenticity through hardware trust roots; rehearsing group behavior within a secure domain to identify three types of conflicts: individual bias, inter-AI conflicts, and combined effects, and generating a source tracing report; constructing a multi-dimensional weighted scoring model to quantify the ethical status; setting a three-level threshold gradient: level one limits retrying to three times, level two pushes for manual review, and level three freezes permissions through an independently powered hardware isolation mechanism, recoverable only through multi-factor physical co-management; and five-stage time-sharing evidence storage. It is applicable to high-risk multi-AI collaborative scenarios such as healthcare, finance, and the judiciary.

The application provides a building group coordination method of embodied agent and multi-modal large model, comprising the following steps: S1: multi-modal perception access; S2: physical-behavior dual-domain knowledge enhancement reasoning and candidate strategy generation; S3: building group distributed game deduction and equilibrium optimization; and S4: embodied execution and safety verification closed loop. The application provides a building group coordination method and system of embodied agent and multi-modal large model, which completely breaks through the space coverage limitation of fixed sensor network by taking a machine dog as a mobile perception node, and at the same time, the machine dog cross-checks the fixed sensor through the high-precision sensor of the machine dog, discovers sensor drift or failure in time, guarantees that the data basis of energy-saving decision is always reliable, and the embodied agent is not only a mobile sensor but also a near-field actuator, so that the short loop feedback of "perception-reasoning-execution" in seconds to minutes is realized for local incidental energy waste.

This invention discloses a multi-agent collaborative incentive method based on large-model-assisted dynamic grouping, relating to the fields of multi-agent collaboration and artificial intelligence. This invention addresses problems in multi-agent collaboration under complex environments, such as insufficient team cohesion, difficulty in spontaneous alignment of individual goals, and excessively high dimensionality of the joint policy space caused by large-scale agents, leading to low training efficiency and convergence difficulties. It introduces a large language model to achieve global situational awareness and dynamic grouping, decoupling complex global collaboration into local small-group collaboration and global coordination. A follow-consistency reward function is designed to guide member agent actions to align with the leader's behavior. Combined with task rewards, a comprehensive reward is constructed, simplifying collaborative learning into a hierarchical model of leader exploration and member following, improving training efficiency and team collaboration stability.

The invention relates to the technical field of emergency location uploading, and provides a distributed location information aggregation reporting method and system based on group collaboration. The method comprises the following steps: when a trigger device detects a preset emergency event, generating a cooperative discovery beacon frame, and sending first position information and event session information corresponding to the preset emergency event to response devices in an effective cooperative area; the response device generates response data information containing second position information based on the first position information and the event session information, and sends the response data information to the trigger device; and the trigger device generates aggregation position information based on the first position information and the second position information, and sends the aggregation position information and the event session information to a server. According to the method, the problems that position information reporting of single equipment is limited and position information reporting of a fixed group is lack of flexibility are solved.