155 results about "Interaction strategy" patented technology

The invention relates to a multi-modal named entity recognition method based on cross-modal guide interactive fusion, which comprises the following steps: constructing a data set, designing a cross-modal contrast aggregation mechanism, respectively extracting image features and text features, and constructing a contrast learning mechanism to screen out image features with high association degree with text semantics for dynamic aggregation; a DINO model is introduced to extract image features, a dynamic similarity matching method is constructed, a dynamic similarity matching weight is generated based on text features and an image feature correlation matrix, a dynamic gating mechanism is utilized to adaptively select image features related to text feature context, and a cross-modal fusion and guide interaction strategy is constructed. Outputting an enhanced semantic representation vector, and mapping the semantic representation vector after multi-modal fusion into a final entity tag sequence by adopting a conditional random field decoder to complete entity recognition; the method has the advantage of remarkably improving the robustness and accuracy of multi-modal entity recognition.

The invention discloses an intelligent cabin interaction method and system combined with digital twinning, and relates to the technical field of man-machine interaction, and the method comprises the steps: collecting the multi-dimensional perception data of a cabin, and constructing a layered cabin digital twinning model; performing scene analysis on the cockpit digital twin model, extracting driver state features, predicting driver potential interaction demands, generating a multi-modal interaction strategy set in combination with the driver potential interaction demands and a current driving scene, and performing simulation evaluation on the multi-modal interaction strategy set to obtain an interaction evaluation index set; performing multi-objective decision optimization under the driving safety constraint based on the interaction evaluation index set, and determining and adaptively executing an optimal interaction strategy; and acquiring actual execution data and driver feedback data of the optimal interaction strategy, and incrementally updating the hierarchical cabin digital twin model. According to the invention, the interaction adaptability and the safety reliability of the intelligent cabin system in a complex driving scene are obviously improved, and an effective technical support is provided for realizing intelligent man-machine cooperative driving.

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 provides a dialogue interaction method and system based on long historical dialogue semantic understanding, and belongs to the technical field of natural language process.The dialogue interaction method comprises the steps that firstly, a historical dialogue information set containing multiple rounds of user consultation and customer service response of an e-commerce platform target user is obtained, and then deep semantic mining is conducted on the historical dialogue information set; dialogue intention features and dialogue theme association features are obtained, a historical dialogue semantic understanding result containing user core demand induction and a dialogue theme development knowledge path is generated according to the two features, an adaptive dialogue interaction strategy is generated according to the historical dialogue semantic understanding result, and the adaptive dialogue interaction strategy comprises response emphasis, guide direction and rhythm control information. And finally, executing the strategy to realize dialogue interaction, outputting a response statement and receiving new input of the user, thereby improving customer service quality and user experience of the e-commerce platform.

The invention relates to the technical field of artificial intelligence, in particular to a multi-modal emotion recognition and interaction adjusting system and method based on uncertainty evaluation, and solves the defects of uncertainty processing, robustness of interaction strategies, complementary information mining degree among modals and the like in the man-machine interaction process in the prior art. Depth application finiteness is caused by lack of modeling for feature uncertainty after fusion. The uncertainty of an emotion recognition result is quantified through technologies such as multi-modal feature fusion and Bayesian neural network / model integration, an interaction strategy is dynamically adjusted according to an uncertainty score, emotion clarification or conservative response is triggered in a high-uncertainty scene, the robustness of human-computer interaction and the user experience are improved, and the user experience is improved. The method is suitable for intelligent customer service, government affair consultation, medical inquiry and other scenes with high requirements for emotion interaction accuracy.

The invention provides an AI role interaction method, interaction device and interaction equipment in a game platform, and relates to the technical field of game interaction.The method comprises the steps that a current game scene, game task information and game character information are obtained, and a first behavior parameter set of AI roles is generated; collecting current operation behavior data and voice interaction data of a user; performing intention recognition processing on the operation behavior data and the voice interaction data to generate a user behavior feature vector, and determining a relation link between the user behavior feature vector and at least one of game scene information, task information and character information to generate a dynamic interaction strategy; a second behavior parameter set is generated according to the dynamic interaction strategy, and multi-modal interaction content matched with the second behavior parameter set is rendered and generated; and displaying the multi-modal interaction content to the user terminal. According to the method, the interaction of the AI role can be more real and natural, and the game immersion and satisfaction of the user can be remarkably improved.

The invention discloses an environment space relation reasoning method based on multi-agent debate, a medium and equipment, and belongs to the technical field of large model reasoning. The environment space relation reasoning method comprises the following steps: receiving external instruction information; based on the external instruction information, controlling a plurality of agents to independently analyze, and outputting respective task planning results I; wherein the plurality of intelligent agents are composed of multi-modal large models with different system cues, and are used for representing different roles; controlling the plurality of agents to perform information interaction of respective task planning results based on an information interaction strategy, so that each of the plurality of agents outputs an updated task planning result 2 according to an information interaction result; and inputting the task planning results II output by all the agents into the judgment agent for analysis. The environment space relation reasoning method based on the multi-agent debate is high in three-dimensional space sensing capacity and high in environment space relation reasoning capacity for complex conditions, and illusion can be well resisted.

The invention discloses an intelligent pet robot system and a control method thereof, multi-modal sensing and dynamic character updating are realized, the system comprises a multi-modal sensing module, a character model and dynamic updating module, a data processing and emotion recognition module, an interaction strategy generation and execution module and a data storage and communication module which are connected in sequence; according to the method, the naturalness and individuation of interaction can be enhanced, the character dynamic updating is realized, the emotional resonance and accompanying effect is improved, the application scene is wide, and good social benefits and market prospects are achieved.

The invention discloses an intelligent interaction control method for AI glasses, and relates to the technical field of AI glasses, and the method comprises the following steps: synchronously collecting user input signals and environmental parameters through a multi-source sensor group built in the glasses, the sensor group at least comprising an IMU, a binocular camera, a microphone array and a physiological sensor; carrying out real-time fusion processing on the multi-modal input data by adopting a lightweight neural network to generate an interaction intention feature vector; based on real-time detection results of environmental noise intensity and illumination conditions, dynamically distributing weight coefficients of all interaction modes; generating a hierarchical response instruction according to the weight coefficient and a confidence threshold; and continuously optimizing the personalized interaction strategy of the user through a federal learning framework. According to the AI glasses intelligent interaction control method, through multi-modal weighted fusion, edge AI acceleration and federated learning optimization, the interaction success rate in a complex environment is improved to 93.6%; the end-to-end delay is controlled within 28ms; and a user-defined interaction strategy is supported.

The invention discloses a teaching object interaction method, system and device. The teaching object interaction method comprises the steps of obtaining a plurality of interaction behavior characteristics of a real-time user at a current knowledge point; constructing a plurality of interaction behavior characteristics of the real-time user at the current knowledge point into a real-time interaction vector; inputting the real-time interaction vector into a learning recognition model, performing forward propagation after pre-training in the learning recognition model, and generating a mastering state label of a real-time user at the current knowledge point; according to the mastering state label of the real-time user at the current knowledge point, constructing a teaching interaction strategy of a plurality of to-be-learned knowledge points in a future time axis; wherein the mastering state labels of the plurality of knowledge points to be learned are inherited in a training sample of the learning recognition model; according to the method, the accuracy of learning path deduction and the individuation of learning strategies are improved, and the user learning experience of a teaching system is remarkably improved by combining real-time mastering state prediction and deduction based on a historical behavior mode.

The invention discloses a multi-modal agent interaction method and system under an agent framework, and belongs to the technical field of artificial intelligence, and the method is implemented by the following steps: multi-modal input: supporting input of multiple modals including texts, voices and images, and automatically identifying and converting into a format which can be processed by the system; mode switching: automatically switching an interaction mode according to an input mode of a user so as to ensure the naturalness and fluency of interaction; multi-modal fusion: carrying out fusion processing on the input information of different modals; efficient processing and response to multi-modal input are achieved based on an intelligent agent framework, and the intelligent agent framework dynamically adjusts an interaction strategy according to input of a user and historical interaction records. According to the method, the interaction modes can be automatically switched according to the input mode of the user, richer and more accurate feedback is provided through the multi-mode fusion technology, meanwhile, personalized adaptation is performed according to different application scenes, the requirements of different users are met, and the user experience and the interaction efficiency are improved.

The invention relates to the technical field of man-machine interaction methods and systems (G06F3), in particular to a man-machine system optimization interaction method based on feedback enthusiasm intelligent evaluation, which comprises the following steps: S1, collecting user interaction data; s2, performing quantitative evaluation on the emotion of the user; s3, classifying, identifying and identifying the user; s4, based on the feedback enthusiasm model, performing intelligent evaluation on interaction feedback of different types of users; s5, generating a user emotion evaluation matrix, matching the user emotion evaluation matrix with a historical matrix, and optimizing an interaction strategy; s6, calculating the reference degree of the interactive content through matching analysis; s7, guiding the user to complete the target task in the interaction process; and S8, iteratively optimizing the system performance based on the user feedback data. According to the method, the interaction efficiency of the system and the user experience are improved through sentiment intensity labeling and positive and negative sentiment proportion calculation, the interaction performance of the system is dynamically optimized through multiple interaction feedback weighted analysis, and the method is suitable for a man-machine intelligent interaction system under multiple scenes and has a good application prospect.

The invention discloses an interaction strategy generation method based on user data, cloud equipment and robot equipment, and relates to the technical field of robots, and the method comprises the steps: receiving target user data sent by a robot end, and determining the target user data of the target user data; performing emotion analysis on the target user data through a preset analysis model to obtain an emotion state of the target user; a target portrait is constructed according to historical user data of the target user, and the historical user data comprises historically received target user data; generating a target interaction strategy based on the target portrait and the emotional state; the target interaction strategy is sent to the robot end, and the robot end is used for receiving the target interaction strategy sent by the cloud end and executing the target interaction strategy. The emotional connection and accompanying effect between the user and the robot is enhanced.

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 virtual space social interaction engine system based on AI enhancement, and relates to the technical field of AI enhancement, the virtual space social interaction engine system constructs a multi-level and three-dimensional user portrait network structure, greatly improves the precision and dimension of user feature expression, and improves the user experience through multi-source fusion analysis of path data, physiological feature data and voice chat data. According to the method, the system can comprehensively capture dominant interests and recessive preferences of users, richer and more accurate user portraits are formed, subsequent user matching is facilitated, positive attraction factors and negative rejection factors can be considered at the same time, the matching accuracy and the satisfaction degree of user matching experience are greatly improved, and the user experience is improved. Meanwhile, by constructing a behavior feature layer, the system can dynamically adjust matching and interaction strategies according to different social scenes, and continuous optimization of system performance is achieved based on a self-adaptive optimization mechanism fed back by a user.

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 discloses a rehabilitation nursing robot interaction regulation and control method based on a man-machine trust feedback mechanism. The method comprises the steps that expression, voice, physiology and behavior data of a user are collected through a multi-mode sensor; constructing a trust evaluation model, and predicting a user trust value in real time; according to trust level matching interaction strategy parameters, dynamically adjusting information transparency, control right distribution and interaction frequency; a rehabilitation task is executed, feedback signals are collected, the model is optimized, and self-adaptive learning is achieved. The system forms a closed loop structure of perception-evaluation-regulation-execution-feedback-optimization, and has humanization, individuation and dynamic adaptive capacity. According to the method, the trust degree and participation sense of the user on the rehabilitation nursing robot are improved, the rehabilitation compliance is improved, and the method has good clinical application prospects and popularization value.

The invention provides a multi-modal fusion-driven agent interaction method and system, and relates to the technical field of intelligent interaction, and the method comprises the steps: extracting image, text and audio feature vectors through a deep neural network, carrying out the self-adaptive fusion through a cross-modal self-attention sub-module and an inter-modal interaction attention sub-module, and carrying out the self-adaptive fusion. And updating node features of the agent knowledge graph based on the difference degree, performing relation reasoning, and generating an interaction strategy to control the agent to execute an interaction action. According to the method, effective fusion of multi-modal information and dynamic updating of the knowledge graph are realized, and the accuracy and adaptability of agent interaction are 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 discloses a user deep interaction system and method based on a multi-source data platform, and relates to the technical field of user deep interaction, and the method comprises the steps: obtaining a historical online interaction record and a historical offline interaction record of a user in a shopping mall, evaluating the interaction tendency of the user to commodities in the shopping mall, and obtaining interaction tendency data; obtaining interaction tendency data of each user in the shopping mall, and analyzing the overall interaction tendency of each user to commodities in the shopping mall to obtain target interaction tendency data of the shopping mall; acquiring historical consumption records of the user in the shopping mall, and analyzing consumption collocation of commodities of the user in the shopping mall to obtain consumption collocation data of the user; and acquiring consumption matching data of each user in the shopping mall, analyzing consumption matching degrees among different commodities in the shopping mall, and optimizing and adjusting an interaction strategy of the shopping mall in combination with the target interaction tendency data.

The embodiment of the invention provides a live broadcast interaction method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. Obtaining a first interaction parameter corresponding to each watching user in the current live broadcast; inputting the first interaction parameter corresponding to each watching user into a pre-trained user value model for processing to obtain a conversion value corresponding to each watching user; the conversion value represents the promotion influence of the interaction behavior between the anchor and the watching user on the conversion of the watching user; determining a first target user whose conversion value reaches a preset value from the plurality of watching users according to the conversion value corresponding to each watching user; and for each first target user, determining an interaction strategy corresponding to the first target user through a pre-trained reply model, and sending the interaction strategy to the anchor, so that the anchor interacts with the first target user according to the interaction strategy. Therefore, user conversion can be promoted, and the live broadcast effect is improved.

The invention discloses an AI intelligent training platform and method supporting meta universe multi-mode interaction, and relates to the technical field of artificial intelligence, and the method comprises the following steps: converting user behavior information into multi-module interaction data; performing real-time processing and analysis on the user behavior data based on a deep learning model, performing interaction between a user and a platform, and analyzing model performance in an interaction process; predicting a user demand according to the user behavior information, automatically adjusting an interaction strategy of the platform through a continuous optimization algorithm, and obtaining user feedback in an interaction process based on a Moger model; the multi-modal interaction effect is evaluated based on fuzzy reasoning, the response mode of the platform is adjusted in time according to the multi-modal interaction effect, and continuous optimization of the platform performance is ensured. The problems of single interaction mode and lack of an effective intelligent training mechanism in the prior art are solved, and personalized and intelligent universe experience is provided for users through combination of multi-modal interaction and AI intelligent training.

The invention discloses a multi-mode interaction method and device for users with operation obstacles and a medium, and belongs to the technical field of charging interaction. The method comprises the following steps: acquiring label signal data carried by a user; analyzing the label signal data, and comparing the label signal data with a preset interaction strategy library to extract a feedback mode parameter set; analyzing the feedback mode parameter set to generate dynamic guide data, so that the electronic equipment is connected with a preset charging interface; receiving a user operation input data stream and processing the user operation input data stream according to a preset real-time feedback verification algorithm to generate an operation confirmation code; equipment charging state data is collected, and the equipment charging state data is analyzed to determine multi-mode state indication data. By means of the method, the effects of dynamically responding to environment changes and supporting multi-mode cooperative guiding charging operation assistance are achieved.

The invention discloses an artificial intelligence type interaction method based on a multi-modal large model, and the method comprises the steps: carrying out the real-time processing and analysis of multi-dimensional data collected from a sensor network, carrying out the fusion of the data from different sensors through a weighted summation method, and introducing a weight updating mechanism based on the feedback of audiences; identifying the emotional state of the audience, and performing emotion classification by using a multi-modal fusion model based on a transform architecture; the plot and the interaction strategy are dynamically adjusted, the user emotional state and the personalized user portrait are combined, and a multi-arm bandit problem is solved by using an emotional enhanced Thompson sampling algorithm so as to optimize plot selection; and constructing a structured prompt according to the emotional state, behavior data and plot options of the audience, and integrating the structured prompt with a large language model to generate a more personalized and situation-related response.

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.