42 results about "Group collaboration" patented technology

The invention discloses a multi-agent cooperation method based on hierarchical reinforcement learning of a structural information theory, and the method comprises the steps: carrying out the hierarchical representation of environment observation through a directed structural entropy optimization technology, mapping a high-dimensional original state into a low-dimensional abstract space, eliminating noise interference, and capturing a direction-sensitive state transition mode; on the basis, a multi-scale skill tree is constructed in combination with time sequence characteristics and a structural information theory, and time-space consistency optimization of a long-range strategy is achieved. And finally, expanding to a multi-agent scene, and improving the group decision-making efficiency through dynamic role division and a cooperation framework. The invention aims to solve the key bottleneck of traditional hierarchical reinforcement learning in a multi-agent cooperation complex dynamic environment through dynamic hierarchical abstraction, multi-scale skill discovery and a group cooperation mechanism, and improve the precision and efficiency of multi-agent cooperation.

The invention discloses an intelligent group collaborative pursuit method and system under an incomplete information condition. The method comprises the following steps: firstly, establishing a kinematic model of an agent, and constructing a multi-agent training environment with barrier distribution to simulate physical interaction of a real pursuit scene; constructing a joint state space and an action space according to the characteristics of the intelligent agent, and designing cooperative exploration space mapping based on local sensing information design; designing a reinforcement learning algorithm architecture with internal excitation, fusing target tracking information and a maneuvering strategy of group cooperation through an Actor control network, and evaluating the cooperation efficiency in a dynamic environment through a Critic evaluation network; and finally, on the basis of a systematically constructed reward function and an Actor-Critic network training process based on internal incentive rewards, realizing efficient collaborative pursuit dynamic control of multiple agents under an incomplete information condition. According to the method, dynamic control of multi-agent collaborative pursuit under the condition of perception-limited incomplete information can be realized.

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 provides a die cutting equipment error optimization method and system based on group collaboration, and relates to the technical field of die cutting machines, and the method comprises the steps: carrying out the spectrum analysis of a frequency characteristic curve of each piece of die cutting equipment, so as to divide an initial interference group; establishing a virtual spring damping network in the initial interference group, regarding each die cutting device in the initial interference group as a mass point, and determining a spring stiffness coefficient and a damping coefficient according to an actual distance between adjacent mass points; determining vibration mode characteristics of each die cutting device according to the spring stiffness coefficient and the damping coefficient, and converting a control signal generated according to the vibration mode characteristics into a parameter adjusting variable by using a preset control algorithm; and determining a vibration coupling relationship according to the collected vibration energy data of the initial interference group after the initial processing parameters are adjusted according to the parameter adjusting quantity, and determining an optimal production takt and a parameter compensation value according to the vibration coupling relationship. The technical problem that in the prior art, due to vibration coupling of a die cutting equipment group, the machining error cooperative control difficulty is large is solved.

The invention discloses a decision-making method for existing parameter hybrid driving, particularly relates to the field of simulated confrontation, and is used for solving the problem of group collaboration failure caused by dynamic conflicts of vision and text parameters, and solving the problem of group collaboration failure through adaptive downsampling and phase compensation when fusion of high-frequency vision parameters and low-frequency text parameters is processed. The feature counteracting effect in nonlinear fusion is reduced; meanwhile, on the basis of conjoint analysis of time-frequency energy distribution and causal association strength, a weight suppression matrix is constructed to weaken strategy contradictions, a strategy causal path is strengthened, and coordination consistency of decision instructions is guaranteed; besides, through cooperation of the hierarchical neural network and a time-space gating mechanism, dynamic fusion of an action sequence and strategy constraints is realized, and response speed and strategy consistency are improved. And finally, nonlinear oscillation of the instruction stream is inhibited in real time by means of damping control, group strategy deviation is prevented, and thus the overall decision quality is optimized.

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 present invention discloses a method for exploring and optimizing the control of a traffic light group collaboration structure based on causal influence, including initialization; converting the original observation of traffic light i into f(oi); making a decision based on the converted f(oi) and the behavior policy model πi to obtain the traffic light action ai, the local reward ri of the traffic light, and the next observation f(f(oi)); performing learning on traffic light i based on (f(oi), traffic light action ai, local reward ri of the traffic light, f(f(oi))); putting the observations of other traffic lights into the global observation sequence set Ui; calculating the causal influence relationship model Gi between traffic light i and other traffic lights in a period of time; appending it to the causal influence graph sample set Ωi of traffic light i; screening to obtain the optimal causal influence graph sample Gi* of traffic light i; clearing the global observation sequence set Ui of traffic light i. Through instant information interaction, the present invention enables traffic lights to have a predictive effect on the possible impacts on other traffic lights, and can take appropriate decisions in advance to relieve regional traffic congestion.

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 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 awareness and crowd-sourcing cooperation. According to the invention, the communication coverage and reliability of the system in a complex marine environment are improved by adopting a low-power wide area network, satellite communication and other technologies, so that data can be transmitted in real time, and instant monitoring and response are realized, thereby ensuring that the signal transmission is stable in a remote sea area or a deep sea environment, and improving the reliability of the system by combining edge calculation and a deep learning algorithm. Multi-dimensional and mass data can be intelligently processed and analyzed, the value of the data is improved, rapid decision making is achieved, meanwhile, a group collaboration mechanism is formed through group perception and collaborative decision making, the system gets rid of application of traditional single equipment or single function module, the functions of multiple sensors can be effectively integrated, and the system has a good application prospect. Information sharing and optimal resource configuration are realized, and the utilization efficiency of ocean resources is improved.

The invention provides a personalized learning content dynamic adjustment method based on a self-adaptive algorithm, and the method comprises the steps: calculating a learner specialty field overlapping degree and a complementary knowledge gap according to individual knowledge vector representation, and measuring the size of the gap according to capability difference to obtain group capability complementarity distribution; evaluating intra-group collaboration efficiency through group collaboration characteristic parameters, combining historical task completion data and an alternating current entropy value, calculating an efficiency score for group collaboration performance, and comparing the efficiency score with a reference value to judge whether a requirement is met or not; if the current grouping does not meet the efficiency requirement, adjusting the grouping according to capability complementarity distribution and task complexity, and redistributing learners to obtain a new grouping scheme; the task complexity matching degree of the new grouping scheme is obtained through task demand analysis, the matching degree of the sub-task difficulty and the learner ability vector is calculated, and a learning task allocation scheme is determined.

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 invention provides a grouping whiteboard control method for automatic split-screen display, and relates to the technical field of split-screen display, and the method comprises the steps: collecting user multi-dimensional information, analyzing data features, and dynamically matching adaptive resources for each group; based on a layout grid algorithm, automatically generating a corresponding number of split-screen windows according to the number of the groups, and establishing an association relationship between the windows and the groups; by means of a data visualization model, resources are visually and efficiently displayed in corresponding windows, user operation is detected in real time, multi-group synchronous operation is achieved, and real-time sharing of information is guaranteed; by identifying the identities of the members, judging the concentration degree and the understanding state of the members on the current teaching resources, combining the difficulty level of the teaching resources, generating the adjustment strategy of the split-screen window, and dynamically adjusting the size and the position of the split-screen window based on the adjustment strategy, the fluency and the visualization efficiency of group collaboration are remarkably improved; and more efficient and intelligent interaction experience is brought to teaching and team cooperation.

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.

The present invention relates to the field of educational interaction technology, and specifically to a method and system for switching teaching interaction modes, including the following steps: collecting classroom behavior data, classifying the time taken for answering questions and error distribution, dividing the teaching task load levels, recording the teaching interaction forms, calling data based on a mapping table, identifying task delays and errors, marking inappropriate interaction forms, matching the teaching structure types, comparing the student speech frequencies, screening group collaboration characteristics, determining the teaching task interaction stage, and writing classroom interaction process instructions. In the present invention, by refining the time taken for students to answer questions and the error frequencies, a more flexible and personalized adaptation between the teaching content and the students' performance is achieved, the specific difficulties encountered by students during the learning process are identified and addressed, the teaching interaction forms are adjusted, the teaching efficiency and learning outcomes are improved, and when students are in the lag period of knowledge mastery, a more suitable teaching structure is recommended to ensure that each student can make progress in the most suitable learning environment.

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.

The invention relates to the technical field of unmanned aerial vehicle communication and group collaboration, in particular to an integrated framework based on a group collaboration routing algorithm, the integrated framework is deployed in an unmanned aerial vehicle cluster, and the integrated framework comprises a first middleware deployed in a first unmanned aerial vehicle, and a second middleware deployed in a second unmanned aerial vehicle when the first unmanned aerial vehicle transmits information to a second unmanned aerial vehicle; processing the transmitted information, splitting the transmitted information into a plurality of data packets by adopting a mode corresponding to the transmitted information, and sequentially transmitting the data packets to second middleware deployed on a second unmanned aerial vehicle; the second middleware is used for adopting a first mode or a second mode or a third mode or a fourth mode according to the size of the received data packet and / or the change rate of the data packet and / or pre-received indication information of the master control unmanned aerial vehicle in the unmanned aerial vehicle cluster for information transmitted to the second unmanned aerial vehicle by the first unmanned aerial vehicle; and sending the received data packet to the control module.

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 relates to the technical field of electric digital data processing, in particular to a group cognition three-level screening input method and system based on a micro-layout cursor, and the method comprises the following steps: constructing a regional closed ecological medium code; constructing a micro-layout DMT static and dynamic data structure; performing primary screening to obtain a primary screening result; performing secondary screening to obtain a micro-layout with collaborative, emerging and aligned screening type characteristics; performing three-stage screening to obtain a page group with group cognitive features; performing quantitative evaluation on the group cognition effect to obtain a micro-layout group cognition evaluation result; and structured storage is carried out, and the whole process of group cognition based on the micro layout is completed. According to the method and the system provided by the invention, closed-loop design of cognition improvement is realized, the technical limitation of the existing input method is broken through from multiple aspects of group cooperation, cognition optimization, static and dynamic data integration, closed domain adaptation and cognition productivity improvement, and the dimension of group cognition of education and commercial organizations is improved.

Embodiments of the disclosure provide a method and an apparatus for sharing data across groups. In one embodiment, the method comprises creating, by a cross-group collaboration platform, a collaboration project; determining, by the cross-group collaboration platform, groups participating in the collaboration project to designate the groups as project collaborators for the collaboration project; and sharing, by the cross-group collaboration platform, collaboration data amongst the project collaborators of the collaboration project.

The present invention discloses a defect assignment method and system based on the joint optimization of personal preferences and group collaboration; the method includes: obtaining a software defect report to be assigned; extracting a vector representation of the metadata of the software defect report to be assigned; determining an optimal set of k developer candidates based on the vector representation of the software defect report to be assigned; extracting k vectors related to the optimal k developers from the developer ability matrix; performing an interaction operation on the k vectors and the vector representation of the software defect report to be assigned, and outputting the most suitable developer for assignment. It can accurately simulate the real environment of crowdsourcing development, form an accurate construction of developer ability characteristics, and thus can more accurately assign defect reports to suitable developers.

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 invention relates to an intelligent agent decision optimization method and system based on reinforcement learning, and the method comprises the steps: obtaining current state data from a plurality of agents through environment interaction, and constructing an agent group initial decision model; simulating dynamic environment change according to the agent group initial decision model, updating individual strategy parameters by adopting reinforcement learning, and determining group collaboration parameters; based on the group collaboration parameters, strategy evolution is carried out, and decision features after evolution are obtained; based on the evolved decision-making features, optimizing an initial decision-making model of the agent group; and deploying and testing the optimized agent group initial decision model in a dynamic environment, obtaining feedback data, and determining a final adaptive decision strategy. Through adaptive feature extraction and collaborative optimization, the decision-making efficiency and robustness of the multi-intelligent agent in a complex dynamic environment are remarkably improved, and an efficient solution is provided for group intelligent decision-making.

The present application relates to the technical field of drone communication and group collaboration, and in particular to an integrated framework based on a group collaborative routing algorithm. The integrated framework is deployed in a drone cluster and includes: a first middleware deployed in a first drone, for processing the transmitted information when the first drone transmits information to the second drone, and splitting the transmitted information into multiple data packets in a manner corresponding to the transmitted information and transmitting them in sequence to a second middleware deployed in the second drone; a second middleware, for sending the received data packet to a control module in a first manner, a second manner, a third manner, or a fourth manner based on the size of the received data packet and / or the data packet change rate and / or the pre-received indication information of the master drone in the drone cluster regarding the information transmitted from the first drone to the second drone.