108 results about "Interaction strategy" patented technology

The embodiment of the invention provides a vehicle-mounted emotion interaction method and device based on multi-dimensional recognition, and the method and device achieve the precise judgment of the emotion of a driver through innovatively constructing an emotion fusion recognition model and integrating the facial expression, voice emotion, driving behavior and physiological state features. And designing a scene-based self-adaptive interaction strategy, and establishing an interaction triggering threshold value for intelligent matching in combination with external environment data and a danger level. An interaction effect evaluation mechanism is introduced, an interaction strategy model is continuously optimized through an online learning module, and dynamic adjustment of personalized interaction content is achieved. According to the method, the defects of the traditional technology in the aspects of emotion recognition, interaction strategies, effect evaluation and the like are effectively overcome, and the intelligent level and the user experience of vehicle-mounted emotion interaction are remarkably improved.

The invention discloses an interaction control method and an interaction control system, and belongs to the technical field of interaction control, and the method comprises the steps: obtaining a current behavior time sequence graph of a vehicle in a current driving process; when the difference degree between the current attention distribution of the first large model and the reference attention distribution of the current driving scene is smaller than a preset value and the duration of the current driving scene exceeds a preset duration, updating the current attention distribution based on the duration; performing user preference modeling based on a behavior time sequence graph by using the first large model after the attention distribution update to obtain user preference information output by the first large model; and determining a target interaction strategy based on the user preference information, and controlling related components in the vehicle to execute interaction operation based on the target interaction strategy. Therefore, the problem of attention collapse of an attention mechanism of dynamic preference modeling in long-term interaction can be effectively avoided, and the adaptability of an interaction strategy is improved.

The invention relates to the technical field of vehicles, in particular to a multi-mode out-of-vehicle interaction method and related equipment based on environment self-adaptation, and the method comprises the steps: obtaining perception information in real time through an environment perception module, the perception information comprising environment conditions, interaction object information and scene semantic understanding information; based on the perception information, utilizing an adaptive decision engine to dynamically analyze an optimized interaction mode and parameters, and generating interaction content; outputting interaction information according to the interaction content generated by the self-adaptive decision engine through a multi-modal output module; the interaction strategy is dynamically adjusted by sensing the environment state in real time, and the safety and the interaction efficiency can be improved.

The invention discloses a personalized interaction method and system based on multi-modal emotion perception, and relates to the technical field of intelligent interaction, and the method comprises the steps: collecting user historical interaction event data, and extracting interaction content features and user behavior features; obtaining interaction emotion data of the user in real time through a multi-mode sensor; fusing the interactive emotion multi-modal data to generate a dynamic emotion quantized value; generating an emotion correction factor; dynamically generating a personalized interaction strategy; and executing the interaction strategy, and collecting user response data to update a historical interaction event library. The method has the advantages that the key problem that a traditional interaction system is difficult to perceive the real-time emotional state of the user is effectively solved, a closed-loop optimization system of perception-decision-execution-feedback is constructed, and the user interaction satisfaction degree and the situation adaptive capacity of the whole system are continuously improved.

The invention provides an advertisement system and method based on human-computer interaction, and the method comprises the steps: generating an emotion semantic map of a user through multi-modal interaction data in the interaction process of the user and an advertisement platform, and extracting an implicit preference intention vector of the user from the multi-modal interaction data; determining the content attribute of the to-be-pushed advertisement, determining a personalized advertisement interaction strategy according to the emotion semantic map and the content attribute, and constructing a pushing attention matrix of the user according to the implicit preference intention vector and the content attribute; based on the personalized advertisement interaction strategy and the push attention matrix, the advertisement platform presents the advertisement content to the user, and human-computer interaction feedback data in the advertisement content presentation process is monitored; and dynamically optimizing the advertisement content of the user in the subsequent interaction process with the advertisement platform on the basis of the human-computer interaction feedback data and emotion semantic map. By adopting the scheme of the invention, the to-be-pushed advertisement content of the user can be dynamically adapted in combination with the user interaction logic change.

The invention discloses a humanoid robot decision interaction method, system and device and a medium. The method comprises the steps of obtaining industrial sensor data; inputting the industrial sensor data into a multi-modal large model for environment evaluation processing to obtain an industrial environment evaluation result; inputting the industrial sensor data into an anomaly recognition model according to the industrial environment evaluation result to obtain an anomaly recognition result; inputting the industrial sensor data and the anomaly recognition result into the multi-modal large model for strategy generation processing to obtain an interaction strategy; and performing interaction control on the humanoid robot according to the interaction strategy. The decision interaction efficiency of the humanoid robot can be improved, and the method can be widely applied to the technical field of intelligent robots.

The invention relates to the technical field of intelligent interaction, in particular to an interaction system based on emotion recognition, and the system comprises the steps: collecting multi-modal sensing data and environment context of a user; the feature matching module is used for constructing a first situation feature by utilizing each single-dimensional feature deviation degree obtained by calculating the current multi-modal sensing data of the user and a corresponding coupling feature deviation degree when the first situation is matched, and constructing a second situation feature when the first situation is not matched; constructing a second situational feature by using the deviation degree of each single-dimensional feature obtained by calculating the current multimode sensing data of the user; the emotion risk index calculation module is used for fusing the first situation feature or the second situation feature according to a pre-trained fusion model to obtain an emotion risk index; and the interaction module is used for triggering the interaction strategy of the corresponding level according to the relationship between the emotion risk index and the latest emotion risk threshold. In the emotion recognition process, the current situation type of the user is fused, and the accuracy of emotion recognition is greatly improved.

The invention discloses a ChatBI plug-in construction method and system based on multiple models. The method comprises the steps that a data source is established, and configuration connection is carried out; selecting successfully connected data sources and databases and tables under the data sources to form a database mode; selecting a pre-integrated natural language processing model type and inputting an API-key to complete information registration of the natural language processing model; the pre-integrated natural language processing model comprises a plurality of models; aiming at different SQL dialogue scenes, configuring corresponding prompt word templates; aiming at different SQL dialogue scenes, configuring corresponding dialogue interaction strategies; configuring application basic information, and selecting a corresponding database mode, a model, a cue word template and a dialogue interaction strategy according to the application basic information to form a query scene application; and generating a ChatBI plug-in package according to the query scene application. The problems that a traditional BI system lacks intelligence and is inconvenient to integrate with an original system are solved.

The invention relates to an automatic driving safety decision control method and device for a strong interaction scene, and the method comprises the steps: simulating an environment based on the strong interaction scene; constructing an interactive feature encoder based on a graph attention mechanism, an interactive feature enhanced reinforcement learning behavior decision network and a motion planning control strategy based on interaction strategy guidance to construct an initial graph attention enhanced reinforcement learning decision model; and training the initial model based on a preset trajectory data set and an interaction experience pool until a preset condition is met, and constructing a final graph attention enhanced reinforcement learning decision model to output an automatic driving safety decision control strategy oriented to a strong interaction scene. Therefore, the problems that in the related technology, due to the fact that the mutual influence between behaviors of traffic participants is difficult to accurately model through an automatic driving technology, in a strong interaction scene, decisions are often too conservative, the situation of frequent parking and giving way is likely to occur, the passing efficiency is reduced, and potential safety hazards are possibly caused are solved.

The embodiment of the invention provides an emotion perception robot control method and device based on an LLVM core, and the method and device achieve the effective perception of emotion through multi-modal features and a neural network. A language processing mechanism is constructed, and a reliable interaction strategy is established in combination with emotion adjustment and dialogue understanding. Expression control is introduced, and coordination of emotional expression is ensured through action planning and speech synthesis. According to the method, the defects of a traditional technology in the aspects of emotion recognition, language processing, emotion expression and the like are effectively overcome, and technical guarantee is provided for an emotion perception robot.

The invention belongs to the technical field of artificial intelligence, and relates to a robot-based interaction processing method, which comprises the following steps: acquiring brain wave signal data of a user acquired in advance; preprocessing the brain wave signal data to obtain corresponding target brain wave signal data; performing feature extraction on the target brain wave signal data based on a preset multi-feature extraction strategy to obtain corresponding brain wave features; performing classification processing on the brain wave features based on a preset state classification recognition model to obtain a corresponding brain wave state classification result; determining an interaction strategy corresponding to the brain wave state classification result based on a preset strategy library; and based on the interaction strategy, controlling the robot to execute corresponding interaction processing on the user. The invention further provides an interaction processing device based on the robot, computer equipment and a storage medium. The method can be applied to interactive processing business scenes in the financial science and technology field and the digital medical field, and the accuracy and effectiveness of the robot interaction strategy are improved.

The application discloses a multi-modal-based digital human interaction control method and device, and relates to the technical field of artificial intelligence digital human, and specifically comprises the following steps: collecting multi-source heterogeneous multi-modal data of user interaction with a digital human in real time; constructing a dynamic adaptive multi-modal space-time hypergraph Transformer network, deeply fusing the multi-source heterogeneous multi-modal data, and obtaining a fused interaction feature vector; generating a multi-dimensional interaction situation tensor based on the fused interaction feature vector; generating a digital human response action sequence, voice content, expression posture and interaction strategy according to the multi-dimensional interaction situation tensor, and driving the digital human to execute; acquiring feedback data of the digital human after executing the response in real time, and optimizing parameters of the dynamic adaptive multi-modal space-time hypergraph Transformer network based on the feedback data; and effectively improving the naturalness and response accuracy of digital human interaction, and reducing the interaction misunderstanding rate and user cognitive load.

Provided in the embodiments of the present disclosure are a method and apparatus for interaction, and a device and a storage medium. The method comprises: during the process of playing a video, acquiring related data of the video on the basis of a specified time interval, wherein the related data comprises audio-visual data of the video and data detected by a target device that plays the video; on the basis of a predetermined model, executing matching on the related data and at least one predetermined scene, so as to obtain a matching result; and in response to the matching result indicating that the related data matches the at least one predetermined scene, determining an interaction strategy corresponding to the at least one predetermined scene.

The application discloses an interaction method, smart glasses and a storage medium, and relates to the technical field of smart wearable devices. The method is applied to the smart glasses and comprises the following steps: acquiring identification information of each function module currently assembled, and determining a current module assembly combination; when it is identified that the current module assembly combination contains a first identification module group, determining that a target user is a child and entering a child mode; in the child mode, when it is identified that the current module assembly combination contains any second identification module group, determining a target child application scenario corresponding to the second identification module group according to a preset mapping relationship; and generating and executing a target interaction strategy according to the target child application scenario. The application can solve the problem of poor flexibility of children's smart glasses caused by the fact that the fixed design cannot adapt to the requirements of multiple scenarios.

The embodiment of the application provides a kind of based on multi-dimension recognition's vehicle emotion interaction method and device, through innovatively constructing emotion fusion identification model, by integrating facial expression, speech emotion, driving behavior and physiological state characteristics, the accurate judgment of driver emotion is realized.Design scene-based adaptive interaction strategy, combined with external environment data and danger level, establish interaction trigger threshold for intelligent matching.Introduce interaction effect evaluation mechanism, through online learning module, continuously optimize interaction strategy model, realize the dynamic adjustment of personalized interaction content.The method effectively solves the deficiency of traditional technology in emotion recognition, interaction strategy and effect evaluation, significantly improves the intelligent level and user experience of vehicle emotion interaction.

This invention discloses a multimodal perception-based immersive digital interaction system and method, relating to the field of digital interaction technology. The system includes generating user state representations; constructing a dynamic contextual framework; and optimizing interaction strategies through a multi-channel feedback mechanism. This invention achieves deep fusion and dynamic understanding of the user's multidimensional human state and environmental context, significantly improving the system's accuracy in recognizing user intentions and its real-time response; enhancing the naturalness and immersion of the interaction; and enabling the system to continuously adapt and optimize, generating highly personalized, smooth, and context-consistent immersive interactive experiences. This comprehensively solves the key problems of mechanical interaction, delayed feedback, and insufficient immersion in existing technologies.

The invention discloses an interaction method and device based on a virtual environment, equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: displaying a virtual environment, wherein at least one movable object in the virtual environment is bound with a message; under the condition that the first movable object meets the message viewing condition, the identification information of the message bound with the first movable object is displayed in the peripheral area of the first movable object, and the first movable object is any one of the at least one movable object; the peripheral area is determined based on the position of the first movable object in the virtual environment, and the identification information of the message is used for displaying the message to the interaction object. In this way, the interaction strategy of the interaction object in the virtual environment can be adjusted in time through the message, and the man-machine interaction rate is increased.

This disclosure relates to a method, apparatus, system, computer device, and storage medium for human-vehicle interaction. It pertains to human-machine interaction technology for intelligent vehicles, addressing the problems of poor interactivity, fixed and monotonous implementation methods, and negative impacts on user experience associated with interactive systems such as welcome lights. The method includes: acquiring and verifying the identity information of a person approaching the vehicle; if the identity information is verified, acquiring the person's interaction strategy; and executing the interaction strategy. The technical solution provided by this disclosure is applicable to intelligent vehicle interaction systems, enabling flexible and personalized human-vehicle interaction.

The application discloses a multi-agent distributed situation interaction device and method based on a multi-modal large model, and relates to the technical field of computer model application. The device comprises the following steps: constructing a double-layer distributed interaction network; setting an environment monitoring time window according to a preset feedback adjustment period, collecting the number of node survival, the available communication bandwidth value and the task urgency level as the environment state parameters; inputting the environment state parameters into a pre-constructed interaction strategy adjustment model to output the adaptive local node interaction strategy and the adaptive agent node interaction strategy in the next feedback adjustment period; and inputting the adaptive local node interaction strategy and the adaptive agent node interaction strategy into the double-layer distributed interaction network in the next feedback adjustment period to control the local consensus node and the agent node to respectively perform distributed situation interaction and output a unified task situation cognition benchmark. The application realizes high reliability, low delay and consistency guarantee of the distributed situation interaction of the unmanned cluster in a complex battlefield or dynamic environment.

The technical scheme of the invention discloses a method for automatically applying high voltage after fault recovery of an energy storage BMS (Battery Management System). According to the BMS and EMS interaction strategy provided by the invention, high voltage can be automatically increased through the interaction process of the EMS and the BMS after the fault is recovered, and operation and maintenance personnel do not need to go to the field for operation, so that the operation and maintenance efficiency of the energy storage system is improved.

This invention discloses an adaptive interaction method, device, equipment, and medium for intelligent vehicle cockpits. The method includes: collecting multi-source perception data, including visual perception data, voice perception data, physiological perception data, and vehicle state perception data from drivers and passengers; cleaning, time-synchronizing, and feature-level fusion processing of the collected multi-source perception data to obtain a feature vector to be identified; analyzing the feature vector based on a trained emotion recognition model to identify the behavioral and emotional state labels of drivers and passengers, generating a comprehensive risk label; matching a corresponding cockpit interaction strategy from a preset strategy library based on the comprehensive risk label, and adjusting the cockpit state based on the cockpit interaction strategy. This technical solution can achieve the goals of improving driving safety, alleviating negative emotions, and enhancing the driving experience.

The embodiment of the invention provides an interaction method and device, equipment, a storage medium and a program product, and relates to the field of artificial intelligence or the field of financial science and technology. The method comprises the following steps: firstly, acquiring multi-modal data and label information of a target object; performing fusion processing on the multi-modal data to generate an initial behavior feature result of the target object; determining culture parameters of the target object according to the label information; carrying out correction processing on the initial behavior characteristic result based on the culture parameters to obtain a corrected behavior characteristic result; and finally, according to the corrected behavior feature result, adjusting an interaction strategy with the target object. According to the method, the problem that an existing interaction mode only depends on multi-modal data and is difficult to cope with diversified scenes is solved, the interaction strategy which adapts to different culture scenes and better meets user requirements is formulated in combination with the culture background of the target object, and the accuracy and adaptability of interaction are effectively improved.

The invention discloses a rehabilitation interaction strategy optimization method of a gamification digital human and an intelligent chip, and relates to the field of artificial intelligence, and the method comprises the steps: collecting rehabilitation features and user features of a target user, and configuring an interaction standard of the target user according to the rehabilitation features; carrying out standard weight and interaction weight distribution according to the rehabilitation features and the user features; calculating the difference among the first standard weight, the first interaction weight, the second standard weight and the second interaction weight to obtain a weight difference coefficient; according to the weight difference coefficient, the first standard weight, the first interaction weight, the second standard weight and the second interaction weight, in combination with the weight difference coefficient, rehabilitation interaction strategy optimization of the gamification digital human is carried out, an optimal rehabilitation interaction strategy is obtained, and interaction processing is carried out in combination with an interaction standard. The problems of insufficient individuation depth, inaccurate strategy adjustment and poor dynamic adaptability in a rehabilitation interaction strategy are solved.

This application provides a simulation testing method, electronic device, and computer storage medium. The simulation testing method includes: simulation playback testing based on road test data to identify key obstacles, wherein the key obstacles are traffic objects that affect the driving in the autonomous driving simulation and are the responsible party in the collision; determining multiple interaction strategies for the interaction between the key obstacles and the autonomous driving simulation device based on scene information corresponding to the driving impact; using the multiple interaction strategies to perform interaction deduction between the key obstacles and the autonomous driving simulation device; and obtaining simulation test results based on the interaction deduction results. According to this application embodiment, the overall efficiency of simulation testing using road test data is improved, and the participation of complex prediction models is not required, thus reducing the implementation cost of the solution.

The application discloses a content recommendation system cold start security risk detection method and device, comprising: generating a user portrait according to the registration rules of the measured recommendation system, and constructing a corresponding user in the local system according to the generated user portrait; for any user, executing interaction in the local system based on the configured interaction strategy; constructing training data according to the interaction result; inputting the training data into a potential feature learning model to perform training; inputting the content data to be detected into the trained potential feature learning model; counting and inversely sorting the reconstruction error of the output result of the potential feature learning model; taking the specified number of output data after sorting as abnormal data and performing clustering; and judging whether the content recommendation system cold start exists a security risk according to the clustering result. The method can be used to judge whether the measured recommendation system is maliciously manipulated in the cold start stage.

The invention relates to a man-machine interaction control method and device, security and protection monitoring equipment and a medium, and the method comprises the steps: collecting a preview video stream through a camera unit, and carrying out the character activity process recognition of the preview video stream, so as to obtain the identity of a user to which the character activity of the current scene of the preview video stream belongs, and the activity information of the user; selecting a target interaction strategy matched with the user identity and the activity information from a plurality of preset personalized interaction strategies; generating a corresponding interaction control instruction according to the target interaction strategy and the activity information; and responding to the interaction control instruction, and executing an interaction response mode corresponding to the character activity. According to the method, the intelligent interaction level of the security and protection monitoring equipment is improved by actively understanding the character activities and matching the personalized interaction strategy.

The embodiment of the invention provides a method and device for interaction, equipment and a storage medium. The method comprises the steps that in the video playing process, related data of a video are acquired based on a specified time interval, and the related data comprise audio and picture data of the video and data detected by target equipment playing the video; and based on a predetermined model, matching the related data with at least one predetermined scene to obtain a matching result. And determining an interaction strategy corresponding to the at least one predetermined scene in response to the matching result indicating that the related data is matched with the at least one predetermined scene. Therefore, the interaction quality and efficiency can be improved.

The invention discloses an active interaction method and device and a computer program product, and the method comprises the steps: firstly generating a situation feature vector corresponding to the information of an external situation where a target user is located, and generating a state feature vector corresponding to the information of an internal state of the target user; inputting to-be-initiated target interaction information and the situation feature vector into a multi-dimensional interaction decision model to obtain a decision vector representing the value, time urgency and interference of the target interaction information to a target user; and inputting the state feature vector into a user attention evaluation model to obtain an evaluation scalar representing the cognitive load state and the attention level of the target user, and then inputting the decision vector and the evaluation scalar into an interaction strategy prediction model so as to predict the attention level of the target user on the basis of quantitative tradeoff of the value of the target interaction information and the attention cost of the target user. The interaction strategy is obtained through prediction and used for determining how to initiate active interaction with the target user, interference on the target user is avoided, and the interaction experience of the target user is improved.

The embodiment of the invention provides an intelligent interaction processing system based on facial recognition, and the intelligent interaction processing system based on facial recognition effectively overcomes the defects in the prior art by constructing a closed-loop processing system integrating multi-modal perception, emotion understanding, state judgment and strategy decision and execution. According to the system, the robustness and accuracy of emotion recognition are improved through deep fusion of time-space aligned multi-modal data, a highly-personalized interaction strategy is generated based on accurate judgment of the user emotion state and the change trend of the user emotion state, and natural, dynamic and similar man-machine interaction is achieved. Meanwhile, the system can continuously optimize the self recognition and decision-making capability through a closed-loop feedback learning mechanism, so that the interaction effect and the user satisfaction are kept and continuously improved under diversified application scenes and user states.

The invention discloses a collaborative application method and device of a multi-mode intelligent interaction system and a medium, belongs to the technical field of industrial internet, and is used for solving the technical problems that an existing digital transformation innovation experience center is difficult to meet diversified requirements of customers, the interaction mode is single, and the manual dependence degree is high. The method comprises the steps of performing full-contact data acquisition processing on key behavior tracks of enterprise users on various platforms through enterprise user IDs subjected to code scanning identification to obtain multi-dimensional behavior data; constructing and updating the portrait of the enterprise user to obtain the portrait of the enterprise user; carrying out matching processing on the enterprise user portrait and a content library; generating an exclusive two-dimensional code for code scanning processing of an enterprise user, and determining an enterprise diagnosis report; performing question and answer response control on agents in different areas in the digital transformation innovation experience center to obtain an intelligent interaction strategy; and based on the intelligent interaction strategy, generating a guide visiting route for guiding exhibition of the enterprise user.