41 results about "Virtual intelligence" patented technology

The invention relates to the technical field of intelligent education, in particular to a history self-learning system based on virtual intelligent tutoring, which comprises a screening classification unit, a recommendation learning module, a learning unit, a deepening module, a feedback module and a secondary deepening module. Wherein the screening and classifying unit is used for classifying learners into a function and benefit oriented type and an emotional resonance type by adopting a learner classifying algorithm based on POS (Point of Sale) annotation and a random forest; the recommendation learning module is used for recommending different historical learning contents to the learner according to the classification result and a self-learning mechanism; the learning unit is used for providing various learning modes for the learner; the deepening module is used for deepening the learning of the learner in an intelligent tutoring mode; the feedback module is used for feeding back a learning condition to the learner and optimizing a self-learning mechanism according to a feedback result; and the secondary deepening module is used for reminding the learner to review and customizing review questions for the learner. Therefore, the problems that in the prior art, courses lack personalized adaptation, learning interaction is not deep, feedback lags and the like are solved.

The invention relates to an unmanned cluster virtual-real interactive simulation method based on three-dimensional motion capture, which comprises the following steps of: operating autonomous navigation and autonomous exploration functions of a single real intelligent agent in a real environment, acquiring posture information of the real intelligent agent through a motion capture system, and acquiring image, point cloud and IMU (Inertial Measurement Unit) posture information through a sensor; the autonomous navigation and autonomous exploration functions of a single virtual intelligent body are operated in a simulation environment, and posture information, images, point clouds and IMU posture information of the virtual intelligent body are obtained; sending data stored in a real environment to a simulation platform in real time, comparing and calculating a dynamic error, a kinematics error, a sensor error and an environment error, and correcting a virtual agent model and a simulation environment model; in a real environment and a simulation environment, multi-machine topology flexible formation and multi-machine cluster autonomous navigation are carried out at the same time, whether various errors of each virtual agent and a real agent exceed a set threshold value or not is calculated through comparison, and virtual-real interaction iteration training is carried out until the errors converge.

The invention discloses an agent control method and device, terminal equipment and a medium, and the method comprises the steps: obtaining battlefield situation information which at least comprises our situation information and opposite situation information; performing feature extraction on the battlefield situation information to obtain a feature vector corresponding to the battlefield situation information; performing fusion processing on the feature vectors to obtain fused feature vectors; training a pre-trained agent model according to the fused feature vector to obtain battlefield decision information; according to the battlefield decision information, a control instruction corresponding to the battlefield decision information is generated, through an artificial intelligence algorithm, an intelligent command and control entity capable of supporting single-loading and formation two-level battlefield command training and a virtual agent used for command decision are constructed, intelligent opponents of different levels are provided for battlefield command simulation training, and the intelligent opponents of different levels are provided for battlefield command simulation training. According to different scenes, flexible battlefield situation display can be provided, and the evaluation result of the analysis and evaluation module is visually displayed.

The invention discloses an agent training method and device based on an FTS platform, terminal equipment and a storage medium, and the method comprises the steps: obtaining the physical component information of agents, and determining a first agent and a second agent according to a scene combat demand and the physical component information; determining action space information of the first intelligent agent according to the state space information of the first intelligent agent, and determining action space information of the second intelligent agent according to the state space information of the second intelligent agent; determining a battle strategy according to the action space information of the first intelligent agent and the action space information of the second intelligent agent; the method comprises the following steps: executing a battle strategy to obtain a reward function, training the battle strategy according to the reward function, and continuously improving the long-term adaptive capacity of an intelligent system to a complex environment through interaction among a physical agent, a virtual agent and the environment. And the knowledge migration and generalization ability of the intelligent agent in a complex environment is more emphasized.

The invention discloses a video content automatic auditing method and system based on AI drive, and relates to the technical field of video content analysis, and the method comprises the steps: obtaining video information to be audited, and carrying out the multi-level segmentation of the video information; in the slices, node simulation is carried out according to the content independence of the video information; analyzing associated branches and historical features of each node to generate attribute features of the nodes; content auditing is carried out through an auditing strategy of multiple virtual agents, and illegal space analysis is carried out according to an unstable part in an auditing result; and generating a final video content auditing report according to the auditing result and the analysis result of the illegal space. According to the method, the structured recognition and intelligent auditing efficiency of the multi-modal video content is improved, accurate positioning, label attribution and automatic early warning of complex violation behaviors are realized, and the content security management capability is enhanced.

The invention provides an intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller. The intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller comprises a guidance signal module, a speed optimization module, a parallel path tracking controller module, a data-driven learning predictor module and an actual controller module. The data-driven learning predictor module receives position information, course angle information and speed information from the actual intelligent ship system module, and the data-driven learning predictor module receives parallel path tracking control force and torque from the parallel path tracking controller module; the data driving learning predictor module sends estimation information of the position and course angle of a virtual intelligent ship and estimation information of the speed of the virtual intelligent ship to the guidance signal module and the parallel path tracking controller module. According to the method, the self-adaptive capacity of the ship under the complex sea condition is enhanced, the collision risk is reduced fundamentally, and an effective solution is provided for the navigation scene with the high safety requirement.

Aspects of the subject disclosure may include, for example, instantiating a virtual smartphone in a cloud infrastructure, installing a smartphone application on the virtual smartphone, receiving input sensor data from a physical user device, providing the input sensor data to the smartphone application on the virtual smartphone, receiving output data from the smartphone application on the virtual smartphone, and providing the output data to the physical user device. Other embodiments are disclosed.

The invention discloses an automatic driving test method and system. Belongs to the technical field of automatic driving testing. According to the method, a high-fidelity virtual environment is constructed by collecting real scene data, and a virtual sensor is configured; mapping the real track to a virtual space, and generating a dynamic traffic flow in combination with a car following and lane changing model; after the real vehicle and the virtual intelligent agent are synchronously initialized, the real vehicle runs in the physical field and periodically updates the state; the virtual traffic flow is dynamically evolved according to the state of the main vehicle, and the virtual sensor senses the target in the field of view and generates a virtual sensing result; real sensor data is used as a reference, and virtual and real perception information is fused to form unified input; and circularly executing until the task is completed, wherein each virtual agent only participates in one test. According to the scheme, high-reality and high-efficiency closed-loop testing is achieved, the defects of a traditional method in the aspects of scene richness and testing safety are overcome, and the reliability and repeatability of verification of the automatic driving system are improved.

A method and apparatus to generate and update virtual personification using artificial intelligence comprising a system configured to perform the following. Receive data associated with a person such as text files, audio files, image files, and video files. Render a virtual personification of the person and output the virtual personification to a user, such as on a display screen. Then, receiving and interpreting a user input to generate a user request, and then updating the virtual personification. The update may include generating an audio output using the text files and the audio files of the person and / or generating a video output using the image files and the video files of the person. The audio output and the video output is presented to the user by the virtual personification and it has not previously occurred by the person or thing represented by the virtual personification.

The invention belongs to the technical field of virtual intelligence, and particularly relates to an intelligent manufacturing factory simulation teaching system based on AI + VR and an implementation method. The system comprises a VR display module, an AI learning guiding module, a factory simulation module, an interaction control module and a data storage module, all the modules achieve data interaction and function cooperation through standardized interfaces, the AI learning guiding module obtains an operation instruction from the interaction control module and sends a visual prompt instruction to the VR display module, and the VR display module displays the visual prompt instruction. Interacting trainee ability data with the data storage module; the factory simulation module receives an operation instruction of the interaction control module and provides equipment state data for the AI learning guiding module; the interaction control module sends a control instruction to the factory simulation module; the data storage module interacts with the AI learning guiding module through an API interface. According to the invention, the problems of lack of intelligence and adaptivity in teaching and lack of deep fusion between a virtual environment and a real industrial control protocol are solved.

The present disclosure provides an object identification method and device, equipment and storage medium, relates to the technical field of computers, in particular to the fields of big data, intelligent search and the like. The specific implementation scheme is: according to historical interaction data of a plurality of users, determining a candidate user in the plurality of users; according to the historical interaction data of the candidate user, determining an interaction parameter of the candidate user and a virtual intelligent user; according to the interaction parameter, obtaining interaction data of the virtual intelligent user and the candidate user; according to the interaction data, determining a category of the candidate user, the category of the candidate user indicating whether the candidate user has a preset behavior, and the object being the candidate user having the preset behavior. The identification of the object can be completed for the candidate user suspected of having the preset behavior without affecting other users, and the identification efficiency is also high.

The invention provides a virtual agent long-time behavior generation system based on personality driving, which is used for guiding a virtual agent to continuously and dynamically carry out a series of activities in a three-dimensional environment by fully utilizing the reasoning capability of a large language model and combining a customized multi-layer behavior structure. According to the method, self-adaptive behavior planning of a complex environment is achieved through modeling of personality elements and real-time monitoring of scenes (including space, time, objects and historical activities), and finally the virtual intelligent agent shows vivid and lifelike personalized long-time behaviors; that is to say, multi-round iterative analysis is performed on the environment state by using the decision-making ability of the large language model in conditional reasoning and behavior sampling, and a corresponding behavior sequence is generated at each time step, so that the intelligent agent is ensured to maintain high sensitivity and elastic adaptation to scene change in a long time period.

The invention belongs to the technical field of virtual agents, and discloses a virtual agent driven hidden data collection method and system, and the method comprises the steps: constructing a virtual agent with environment perception and autonomous decision-making capabilities, and combining the association mapping of the virtual agent and an entity agent to achieve the intelligent driving of data collection; the method comprises the following steps: dynamically obtaining a burying point position and mode based on environment perception and target analysis, fusing multi-modal burying point data through a four-dimensional data model, and cooperatively collecting through a heterogeneous intelligent agent to form a unified data view, so as to enhance the integrity and consistency of data; according to the method, efficient, accurate and safe hidden data collection and integration are achieved, data values are fully mined, and the method is suitable for multi-scene data collection requirements.

The invention discloses a wind power plant optimal frequency track cooperative control method and system based on comparative clustering and hierarchical reinforcement learning. The method comprises the following steps: firstly, dynamically screening frequency-adjustable fans; a feature extraction network is pre-trained on massive historical data by adopting comparative learning, and the fan state is mapped to a low-dimensional feature space; performing online density clustering in the feature space by using an adaptive density clustering algorithm to automatically form a group in any shape, and enabling undistributed fans to serve as noise points not to participate in control; each group is used as a virtual agent, a theoretical optimal frequency trajectory is used as a tracking target, and an inter-group cooperation strategy is trained through a multi-agent depth deterministic strategy gradient algorithm; and the lower layer dynamically distributes power according to rotor kinetic energy and rotating speed margin. According to the invention, the cooperative tracking of the optimal frequency trajectory is realized, the lowest point of the system frequency is obviously improved, secondary falling is avoided, the structure is simple, the training is efficient, and the engineering practicability is good.

The invention relates to a multi-agent video multi-round question and answer method based on implicit knowledge enhancement, and the method comprises the steps: merging the video features of a real agent and the features of a virtual agent, and generating an extended agent feature set; constructing a multi-agent video question and answer model; training a multi-agent video question and answer model based on the extended agent feature set to obtain a trained multi-agent video question and answer model; and inputting the video features of the plurality of real agents and the to-be-processed question text features into the trained multi-agent video question and answer model to obtain a final video question and answer result. According to the method and the device, support features of historical rounds are expressed as virtual agent features, and unified modeling is carried out on the virtual agent features and real agent features of the current round, so that efficient multiplexing of context information in multi-round questions and answers is realized. The method has remarkable advantages in question and answer accuracy, calculation efficiency and communication overhead, and a new solution is provided for multi-round multi-modal question and answer tasks.

This invention discloses a multi-objective collaborative optimization control method for energy storage systems in microgrids, comprising: constructing a digital twin of the physical microgrid; defining a first virtual agent and a second virtual agent within the digital twin, wherein the first agent optimizes for long-term economic efficiency, and the second agent optimizes for short-term stability; based on real-time collected load data, renewable energy generation forecast curves, and equipment status, the two agents engage in game negotiation through a rolling time window mechanism until the negotiation process satisfies Nash equilibrium constraints; and sending the power command reached through the game to the power conversion unit of the physical energy storage system for execution via an encrypted communication protocol. This invention constructs a virtual model synchronized in real time with the physical microgrid and achieves precise and efficient control of the energy storage system through game negotiation between two virtual agents with different optimization objectives. This solves the problem of balancing economic efficiency and stability in microgrid operation.

A system and method for providing multi-source, real-time, and context-aware real-world data for machine learning algorithms in artificial intelligence applications, including providing a server and connecting the server and multiple interconnected elements therebetween via a network. Each connected element includes one or more sensing mechanisms. The server includes a memory and a processor. The memory stores a persistent virtual world system, including virtual copies of real-world entities created and edited via a virtual copy editor and updated with multi-source sensing data captured by the sensing mechanisms. Each virtual copy includes data and instructions of the multi-source sensing data. The processor can be set to prepare the data to generate a machine learning data set, then execute a machine learning algorithm on the data set, while generating a trained machine learning model for overall inference of new data and optimizing a system with new world entities.

An autonomous obstacle avoidance flocking control method for distributed convex optimization swarm robots. First, initialize the parameters, and then use the theory of distributed convex optimization to set the cost function and optimization terms of the robots. The cost function can be divided into a total cost function and a local cost function, where the total cost function is the sum of the local cost functions, and we can obtain the minimum value of the cost function if and only if time approaches infinity. The intelligent robot α follows the leader intelligent robot to move. When approaching an obstacle, a virtual intelligent robot β is designed according to the "parallelogram law" of vectors. The virtual intelligent robot β generates a repulsive force to enable the intelligent robot α to smoothly avoid the obstacle. The present invention realizes the adaptive distributed convex optimization and autonomous obstacle avoidance flocking control of swarm robots through the optimization of the speed of swarm intelligent robots and the recognition of obstacles, combined with three sub-control protocols, while avoiding complex obstacles and following a moving target during the energy-saving optimization of swarm robots.

This application relates to a self-optimizing control method and system for the operating state of an AI smart gateway. The method includes: preprocessing the real-time operating data of the gateway to obtain a unified state representation; calculating conflict features based on the covariance of historical multi-objective performance reward data; inputting the unified state representation into a target-specialized virtual agent to generate preference suggestions; extracting dynamic environmental features; inputting these and the conflict features into a meta-coordinator network to output jointly optimized actions; constructing an experience tuple; triggering a rapid adaptive mechanism when the environment changes abruptly; freezing the parameters of each target-specialized virtual agent; and updating the meta-coordinator network. This method, by constructing a hierarchical architecture of target-specialized virtual agents and a meta-coordinator, and introducing conflict feature perception and a meta-learning rapid adaptive mechanism, achieves dynamic modeling of multi-objective conflict relationships and rapid policy transfer under sudden scenarios, thereby improving the efficiency of multi-objective collaborative optimization and adaptive decision-making capabilities of the AI ​​smart gateway in dynamically changing environments.

The invention discloses a hanging basket construction whole process early warning system and method integrating digital twinning and the Internet of Things, and relates to the technical field of bridge engineering and intelligent construction. The method comprises the following steps: constructing a high-fidelity digital twinborn body of a hanging basket structure, calling a preset working condition simulation prediction model to carry out multi-path evolution deduction and risk assessment according to a preset or dynamically generated future construction working condition based on the current state of the digital twinborn body, generating graded early warning information and decision suggestions, and feeding back the graded early warning information and decision suggestions to a physical construction end, according to the working condition simulation and prediction model, a hanging basket structure is discretized into an intelligent cellular network, virtual agents with different objective functions are introduced, and the emergence behavior and risk of a system under the construction working condition are predicted by simulating self-adaptive evolution between cells and a dynamic game between the agents. The fundamental change of hanging basket construction early warning from static monitoring to dynamic deduction, from a single threshold value to a system game, and from post-event alarm to beforehand intervention is realized.

The invention discloses an agent communication cross-domain service quality guarantee method, equipment and a medium, belongs to the technical field of intelligent communication, and aims to solve the technical problem that the existing agent communication protocol uses HTTP / HTTPS in an application layer, and the existing packet filtering and mapping mechanism based on IP quintuple cannot be distinguished. In order to guarantee the service quality of an end-side agent and a virtual agent in a mobile communication network and provide differentiated QoS for different AI services in a 5G-A / 6G network, the adopted technical scheme is as follows: the network issues a cross-domain QoS mapping rule to the end-side agent in an agent session establishment process and issues an N4 mapping rule to a UPF; an inter-domain mapping relation between the 5QI and the DSCP, namely a 5G domain and an IP domain, is embodied; when the QoS flow is mapped in the uplink and downlink directions, if it is detected that an application layer uses an HTTP / HTTPS protocol and indicates an AI service, the DSCP is mapped to 5QI according to a cross-domain QoS mapping rule / N4 mapping rule, and QFI information corresponding to the 5QI is carried in an uplink SDAP packet header and a downlink GTP-U packet header for flow identification.

This invention relates to the field of artificial intelligence interaction technology, and discloses a method and apparatus for establishing intelligent agent friendships based on near-field touch. The method includes: when a first hardware carrier detects a near-field touch event, generating a first temporary token and broadcasting the first temporary token to a second hardware carrier; when the second hardware carrier detects a near-field touch event and receives the first temporary token, generating a second temporary token and a pairing response credential; performing multi-dimensional verification based on the first temporary token, first touch event data, second temporary token, pairing response credential, and second touch event data; when the multi-dimensional verification passes, establishing a friendship relationship between the first and second intelligent agents, sending the binding result to the first hardware carrier, second hardware carrier, client, and corresponding virtual intelligent agent, and updating the interaction state. This invention reduces mispairing and improves pairing success rate and interaction security through bidirectional triggering, one-time tokens, and multi-dimensional verification.

The invention relates to a transformer substation monitoring system based on a master station and substation service interaction mechanism. The transformer substation monitoring system comprises an automatic equipment remote operation and maintenance master station and an automatic equipment remote operation and maintenance substation. The automatic equipment remote operation and maintenance substation communicates with the automatic equipment remote operation and maintenance master station and communicates with intelligent electronic equipment of a transformer substation, the automatic equipment remote operation and maintenance substation comprises an equipment virtual module, and the equipment virtual module comprises a plurality of virtual intelligent electronic equipment. And each virtual intelligent electronic device corresponds to each intelligent electronic device in the substation and is used for storing monitoring information of the intelligent electronic device. According to the invention, a servitization main station and substation data communication mode is adopted, and on-demand retrieval of various data of the substation by various services of the main station is realized.

The application discloses an AI driving-based video content automatic auditing method and system, relates to the technical field of video content analysis, and comprises the following steps: acquiring video information to be audited, and performing multi-level segmentation on the video information; in the slice, node simulation is performed according to the content independence of the video information; the analysis of associated branches and historical characteristics is performed on each node to generate attribute characteristics of the node; content auditing is performed through the auditing strategy of multiple virtual intelligent agents, and the analysis of a violation space is performed according to unstable parts in an auditing result; and a final video content auditing report is generated according to the auditing result and the analysis result of the violation space. The application improves the structured identification and intelligent auditing efficiency of multi-modal video content, realizes accurate positioning, label attribution and automatic early warning of complex violation behaviors, and enhances the content security management capability.

The invention discloses an unmanned aerial vehicle formation adaptive fault-tolerant control and collision avoidance method based on a hierarchical structure, and the method comprises the steps: constructing a trajectory planning layer system based on an all-drive system and an artificial potential energy function method, and employing the projection of the relative speed between virtual intelligent agents on the relative distance and the relative distance as the input of a fuzzy logic system, the intensity of the collision avoidance potential field is determined; a six-degree-of-freedom dynamical model of the fixed-wing unmanned aerial vehicle is divided into a translation dynamical subsystem and a rotation dynamical subsystem, and a control scheme is designed by adopting an all-wheel-drive system method; a novel self-adaptive disturbance observer is designed and is used for compensating adverse effects caused by composite disturbance under the condition of unknown disturbance boundaries; a rotational dynamics subsystem is divided into two channels, and reference signals of the channels are obtained based on a nonlinear guidance law and a coordinated turning principle. According to the method, the robustness, fault tolerance and collision avoidance stability of the fixed-wing unmanned aerial vehicle formation are remarkably improved.

The application discloses a kind of multi-wheel mobile robot modularization formation collision avoidance control system and method, including online regulator module and trajectory tracking controller module.First, a plurality of virtual intelligent agents are constructed in online regulator module, and formation collision avoidance reference trajectory satisfying the requirements of wheeled mobile robot system is generated;Then, the trajectory tracking controller module of wheeled mobile robot is designed, so that it accurately tracks formation collision avoidance reference trajectory, to realize formation collision avoidance control.The system and method of the application can perform formation collision avoidance task without changing the controller of wheeled mobile robot, enhance the generality and convenience of design, is the expansion of prior art method, and is also a kind of prediction to possible application direction of future multi-wheel mobile robot system.

The application discloses a distributed architecture-based intelligent terminal data synchronization scheduling management system and method, and relates to the technical field of terminal data management; the application establishes a virtual mapping platform to map intelligent terminal ports and build a virtual intelligent terminal node network; a terminal state directory is constructed to record the state of the intelligent terminal; the real-time transmission data of each intelligent terminal is compared and evaluated to obtain the homogenization degree of the real-time transmission data of each intelligent terminal; the transmission data of each intelligent terminal is analyzed in terms of space-time demand characteristics to determine the real-time data transmission demand and complexity distribution of each intelligent terminal; the online duration of each intelligent terminal is called, and the online resource ratio priority of each intelligent terminal is planned in combination with the space-time characteristic demand of data transmission, and the terminal state directory is updated; the application guarantees the real-time demand of the intelligent terminal to adapt to a dynamic network environment and diversified terminal behaviors, and realizes differentiated scheduling of network resources.

The invention relates to a virtual-real fusion asymmetric mapping method based on dual unmanned aerial vehicle cooperative control, and the method comprises the steps: building a dual-vehicle asymmetric mapping model for a single-agent task scene in a virtual-real fusion scene; the pose information of the two real agents is synchronized according to the task scene limitation and the task time, and the two real agents autonomously and alternately receive the information of the virtual agent sensor and alternately complete the specified task. The invention relates to synchronization of a virtual world coordinate system and a real world coordinate system and displacement rotation transformation when two real agent local coordinate systems alternately map the same virtual agent coordinate system. And setting a dual-computer alternative mapping mechanism to realize real-time mapping of a large-scale operation space. According to the method, the scene space scale limitation of the virtual-real fusion experiment when the physical space is insufficient is made up, and distortion caused by simple speed and length scaling transformation in order to realize mapping of a small space to a large space is avoided.

The invention discloses a high-precision map generation system and method based on a large model, and relates to the field of high-precision map generation, and the system comprises a multi-agent simulation verification ring which comprises a high-fidelity simulation environment based on neural rendering and a group of virtual agents with differentiated driving strategies; the multi-agent simulation verification ring is used for receiving initially generated high-precision map data, driving the virtual agent to perform preset mileage driving including an edge use case in the simulation environment, collecting an interaction conflict event and a track smoothness index of the virtual agent and the environment, and generating a targeted optimization instruction; the system is used for solving the technical problems that in the prior art, an active safety verification mechanism is lacked in the high-precision map generation process, elastic supply cannot be achieved according to user requirements, hidden safety defects in the map are difficult to mine and correct, and the overall resource utility is not optimal due to isolated optimization of all links of the system.

This invention relates to the field of virtual-real fusion interaction technology, and discloses a method, apparatus, device, and medium for virtual intelligent agent interaction based on a physical carrier. The method includes: acquiring interaction event data from a physical hardware device; encoding the interaction events in the interaction event data to obtain a unique event identifier; validating the interaction behavior based on the unique event identifier; when the validation passes, uploading the unique event identifier and event proof to a server via a user terminal; upon receiving the unique event identifier and event proof, the server performs a legality verification; if the verification passes, querying the target virtual intelligent agent based on the unique event identifier and a preset binding mapping relationship; and updating the interaction state of the target virtual intelligent agent according to the event type corresponding to the unique event identifier. This invention acquires NFC touch and inertial interaction events through physical hardware, generates unique event identifiers, and drives the virtual intelligent agent to update its state, thereby improving interaction security and preventing interaction misjudgments.