1454 results about "Interaction technology" patented technology

The invention discloses an intelligent customer service system based on an AI large model, and belongs to the technical field of intelligent customer service systems. Through the multi-modal interaction technology, the flexibility of the system and the user experience are improved, the data type is intelligently recognized and correspondingly processed, the language style and emotional intonation of the response content are automatically adjusted according to the mood and intention of the user, the knowledge base integrates static data and dynamic interaction data, and the user experience is improved. Real-time updating and comprehensiveness of knowledge content are ensured, rapid retrieval is performed based on a knowledge base, accurate reply is performed in combination with a large language model, high-quality answers and solutions are generated through rule reasoning and model generation, state information of each round of interaction of a user is recorded and managed by constructing a dialogue state tracker, and the user experience is improved. And precise tracking and updating of the context are realized. For a complex task, the system splits a user request into a plurality of sub-tasks, and the sub-tasks are executed in sequence according to the priority and the dependency relationship, so that the high efficiency and continuity of task processing are ensured.

The invention relates to the technical field of building data interaction, in particular to a building engineering interaction method and system based on a BIM model and a medium. The method comprises the following steps: collecting construction site environment parameters and structure response data by using a distributed sensor network; preprocessing the data by an edge computing node; generating standardized environment data and key structure indexes; comparing environment threshold values to identify abnormal events; the method comprises the following steps of: displaying spatial positioning and risk levels through a visual interface, receiving a regulation and control instruction input by a user, dynamically adjusting construction parameters of a BIM model, generating an optimized construction progress scheme, comparing the optimized scheme with an original model to identify a construction conflict area, and generating a conflict resolution report. Through the advanced monitoring technology and construction management technology, the response speed and decision-making efficiency of the building engineering project are improved, and the safety guarantee and resource utilization efficiency in the construction process are improved.

The invention provides a dynamic order making method and device based on multiple rounds of dialogues and electronic equipment, and belongs to the technical field of intelligent interaction. The method comprises the steps of guiding a user to gradually fill in a form through a multi-round dialogue mechanism, generating a question based on a business logic dependency relationship, and mapping a natural language answer of the user into a structured field value in combination with semantic analysis and field dependency management; integrating a business rule engine in a multi-round dialogue mechanism process, executing field-level, logic-level and process-level multi-dimensional verification in real time in a user input process, and dynamically adjusting a verification strategy based on context; constructing and adaptively adjusting a form structure in real time based on a dynamic form generation engine, multi-round dialogue contents and business rules; based on a low-code configuration development platform, in combination with a preset intelligent interaction component and a visual tool, an intelligent filling system supporting natural language interaction is constructed. And the intelligence, flexibility and accuracy of form filling are obviously improved.

The invention discloses a multi-screen voice interaction system and method applied in an automobile cabin, and relates to the technical field of vehicle-mounted intelligent interaction, and the system comprises a voice collection unit which is used for carrying out sound source positioning and collection, carrying out the noise reduction processing of a collected voice signal, extracting features from the processed voice signal, and generating a voice feature vector; the multi-modal sensing unit is used for collecting behavior information and physiological state data of a driver and passengers through multi-sensor fusion, so as to extract multi-modal information; the man-machine interaction unit is used for carrying out space-time modeling on the multi-modal information to generate a scene state vector; and the cooperative scheduling unit is used for realizing multi-screen intelligent distribution and cooperative control. According to the invention, voice instructions of a driver and passengers can be accurately identified, the safety and experience of the driver are improved, changes in different driving environments are dynamically adapted, the operation efficiency and user experience of a vehicle-mounted system are improved, and the intelligence and adaptability of the system are improved.

The invention provides a multi-mode-based man-machine interaction method and system, and relates to the technical field of man-machine interaction. Voice input of a user is received, environment information is collected, facial expressions of the user are captured, and voice features, environment features and facial features are obtained based on the input; utilizing a pre-trained emotion recognition model to generate emotion features of the user, and utilizing a pre-trained user behavior model to generate behavior features according to the current operation information of the user; after the features are obtained, the first weights and the second weights of the features are dynamically adjusted, feature weighted fusion is carried out, and comprehensive features are obtained; the multiple candidate responses are generated based on the comprehensive characteristics, the final response is screened out through a preset scoring mechanism and output, comprehensive understanding of the intention and the emotional state of the user is achieved through comprehensive utilization of multiple data sources, and meanwhile the weight of each characteristic can be dynamically adjusted so that more intelligent and humanized interaction experience can be generated.

The invention provides a vehicle-mounted display terminal man-machine interface interaction experience optimization method and system, and relates to the technical field of vehicle-mounted man-machine interaction, and the method comprises the steps: obtaining eyeball watching, hand operation and head rotation data of a driver, and carrying out the fusion to generate an attention distribution thermodynamic diagram; constructing a dynamic scene recognition model in combination with the vehicle running state and the environment information; and calculating a function module optimization index based on the thermodynamic diagram and scene prediction, and dynamically adjusting the position, the response area and the display level of the function module. According to the invention, the interaction efficiency of drivers can be improved, the operation distraction risk is reduced, and the driving safety is enhanced.

The invention discloses a multi-mode interaction method for an accompanying robot, and relates to the technical field of robot interaction.The multi-mode interaction method comprises the steps that voice, visual and tactile signals are converted into quantum states through multi-mode quantum state coding, emotion weights are dynamically fused, and unified quantum representation is constructed; the dynamic quantum decision engine analyzes environmental noise and user emotion intensity in real time based on a quantum measurement theory, generates an adaptive strategy through dynamic modal weight distribution, and solves a multi-modal instruction conflict; holographic reinforcement learning optimization is combined with quantum acceleration calculation and classical reinforcement learning, the reward function weight is dynamically adjusted, cross-modal knowledge migration is achieved through the quantum tunneling effect, and the system strategy is continuously optimized. The problems that a traditional multi-mode interaction system is rigid in mode switching, high in emotion recognition error rate, lack of self-adaptive optimization of a feedback strategy and the like are solved, and the interaction efficiency and the user experience are improved.

The invention relates to the technical field of voice interaction, particularly discloses an AI intelligent robot voice interaction method and system, and aims to solve the problems of low voice interaction accuracy, insufficient reliability and lack of authority control in a complex noise environment. A dynamic noise feature library containing steady-state noise, impact noise and human voice interference features and a pre-stored gesture instruction library are constructed, audio signals are collected in real time, low-frequency-band, middle-frequency-band and high-frequency-band differential noise reduction is executed, Mel-frequency cepstral coefficient features are extracted, noise scenes are matched, corresponding voice recognition models are switched, and voice recognition is achieved. And calculating a confidence value of the voice instruction, outputting multi-modal verification data in combination with a dynamic confidence threshold, and outputting an authority control signal through voiceprint matching, authority verification and instruction consistency judgment. Through multi-modal fusion, dynamic adaptation and authority control, the voice recognition accuracy and interaction safety in a complex noise environment are remarkably improved, and the method is suitable for scenes such as factory intelligent inspection.

The invention relates to the technical field of computer vision and human-computer interaction, and discloses an AI digital human intelligent interaction method, system and equipment based on multiple modalities, and the method comprises the steps: pre-awakening a digital human when a human face is detected, and further thoroughly awakening the digital human based on recognized preset voice information or preset gesture information; voice and video information of a user in the interaction process is obtained, a keyword extraction result, a gesture recognition result and an emotional state tag are generated, a pre-constructed knowledge base is utilized to retrieve related information, a big language generation model module is combined to generate an answer text, and the answer text is input into a preset voice synthesis model to generate emotional voice output. And based on the current emotional state label of the user, driving the digital human animation to be output in an emotional manner. According to the method and the system, the digital human for understanding the emotion of the user, generating personalized answers, providing voices with rich emotions and displaying natural expressions and actions can be created, better interaction with the user can be realized, and more humanized and effective services can be provided.

The invention discloses a database interaction method, device and equipment based on multi-agent cooperation and a medium, and relates to the technical field of database interaction. The method comprises the steps of obtaining a query request, performing semantic partitioning and vectorization, and obtaining a retrieval vector set; and selecting a search source and searching to obtain an information block. And obtaining candidate knowledge through knowledge base retrieval. And integrating the information blocks and the candidate knowledge to obtain a candidate information set. And constructing a dynamic demand understanding model according to the query request and the candidate information set, and obtaining a task state matrix and a tool calling task. And calling a corresponding auxiliary tool and operating, updating the task state matrix according to an operation result and returning the task state matrix to the task planning agent to update the tool calling task until the tool calling task is no longer output, and obtaining a task result. And when the database interaction module is called, generating a chart according to an output result of the database interaction module, and outputting the chart and a task result together. Otherwise, directly outputting a task result.

The invention relates to the technical field of man-machine interaction, in particular to a mixed interaction system based on an AI large model, which comprises a multi-modal question acquisition module, a preprocessing and classification module, a retrieval and analysis module, a mixed processing module, a result integration and output module, an interaction module and an answer display module, the problem preprocessing and classification module performs text cleaning, semantic analysis and classification, and the hybrid processing module distributes tasks to natural language processing or AI large models according to a data complexity threshold value, so that the problem processing flow is optimized, the system performance is improved, and the system performance is improved. According to the system, model parameters are optimized based on a machine learning classification algorithm and transfer learning training, classification accuracy is ensured, the system adapts to problem changes, through cooperation of all the modules, the existing technical problems are effectively solved, the man-machine interaction efficiency and quality are improved, the system has wide application prospects in multiple fields, and the man-machine interaction technology is promoted to be developed to be intelligent and efficient.

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

The invention belongs to the technical field of AI interaction, and discloses an AI interaction method and system based on emotion recognition. The method comprises the following steps that in a cloud data center, an emotion relieving database is built, and an emotion AI interaction engine is built; according to the real-time multi-mode monitoring data, using an emotion AI interaction engine to generate a real-time emotion recognition result and real-time AI interaction data; according to the real-time emotion recognition result, a real-time AI response strategy is generated, and real-time emotion relieving data is extracted; generating real-time AI response data according to the real-time AI response strategy, the real-time AI interaction data and the real-time emotion relieving data; and optimizing the emotion AI interaction engine according to the real-time feedback data to obtain an optimized emotion AI interaction engine. According to the method, the problems of low emotion recognition accuracy, low AI intelligence degree and lack of an evolution function in the prior art are solved.

The invention discloses a voice interaction system and method based on an artificial intelligence technology, and relates to the technical field of voice interaction, and the method comprises the steps: employing the double-layer cooperative recognition of a main channel Transform model and an auxiliary channel CTC-Attention mixed architecture, obtaining a text with a multi-dimensional feature label, carrying out the scene classification and dependency syntactic analysis through a bidirectional recurrent neural network, and obtaining a multi-dimensional feature label text; retrieving associated entities in real time and generating structured intention data in combination with a dynamic knowledge graph, executing three-dimensional analysis through an interpretability engine and generating a multi-dimensional traceability report, and triggering an intelligent decision fusion engine through a confidence threshold to generate structured enhanced interaction data; according to the method, scene classification and dependency syntactic analysis are carried out through the bidirectional recurrent neural network, the associated entities are retrieved in real time in combination with the dynamic knowledge graph, and the structured intention data are generated, so that the intention of the user is accurately understood, and personalized interaction services are provided.

The invention relates to a multi-modal fusion real-time digital human driving method based on unified behavior vector mapping, and belongs to the technical field of human-computer interaction. The method comprises the following steps of: 1) extracting multi-modal features of voice, action and vision, and generating input features by adopting language feature-emotion decoupling, staged action modeling and macroscopic-micro expression flow analysis; (2) cross-modal collaborative alignment and conflict correction are carried out, and high-precision fusion is realized through knowledge graph constraint time sequence decomposition, particle-by-particle interactive fusion and a situation-driven modal dominant strategy; and 3) constructing a three-dimensional behavior vector space, mapping the multi-modal features to a unified coordinate by utilizing honeycomb grid projection, and driving the digital human to output in combination with a coordinate-action mapping table. According to the method, the problems of difficulty in multi-modal time sequence alignment, feature isomerism and out-of-control conflict are solved, real-time interaction of naturalization, emotional and scenarized is realized, and the expressive force and adaptability of the digital human are remarkably improved.

The invention relates to the technical field of man-machine collaborative interaction, and discloses a resource allocation method and system in a man-machine collaborative decision-making scene, and the method comprises the steps: generating corresponding task features based on the three dimension indexes of task complexity, knowledge density and intelligent adaptation degree of a to-be-processed task; initializing initial weight coefficients of the three-dimensional indexes, and dynamically optimizing the weight coefficients corresponding to the three-dimensional indexes by using a preset strategy network according to the task characteristics and the real-time system resource state; performing weighted normalization on the three-dimensional indexes according to the weight coefficient to generate a task comprehensive score; task priorities are divided according to the task comprehensive scores, and the tasks are dynamically allocated to manual and / or intelligent systems to execute the tasks according to the priorities, so that the flexibility of artificial experience and the high efficiency of the intelligent systems can be brought into full play, man-machine labor division is flexibly adjusted according to task characteristics, and the universality of cross-task scenes is improved. And the dynamic adaptability, decision reliability and task execution efficiency of the man-machine cooperation system are improved.

The invention relates to the technical field of man-machine interaction, and discloses a touch screen control method and device and electronic equipment. The method comprises the steps that S1, it is detected that a user generates a touch starting event in a screen edge area, touch track data are collected, and the track data comprise touch position coordinates, pressure values and corresponding timestamps; s2, constructing the touch track data into a track function with continuous time, and obtaining speed information, acceleration information and track steering change characteristics of a track; and S3, according to the speed information, the acceleration information and the track steering change characteristics, calculating a group of track dynamic characteristic indexes representing track naturalness. By adopting a combined judgment technology of track naturalness and structural complexity, the naturalness and the complexity of the touch track can be accurately recognized, and a high-precision gesture recognition effect is achieved. Compared with the scheme depending on the touch position and speed in the prior art, the method can effectively distinguish mistaken touch and effective touch, and the problem of frequent misoperation is solved.

The invention belongs to the technical field of vehicle-mounted human-computer interaction, and particularly relates to a vehicle-mounted music recommendation system based on context awareness, which covers the technologies of computer vision, recommendation systems, human-computer interaction and the like. The whole system is divided into two main parts: the first part is a context information collection module, and the module realizes real-time recognition of the emotion of a driver by collecting facial images and voice information of the driver; and the second part is a feedback module used for processing interaction between the driver and different emotion analysis results, including mapping rule reasoning and music recommendation. The system generates a personalized music recommendation list by analyzing the facial expression of the driver and the emotional characteristics extracted from the voice information, so as to reduce the music selection burden of the driver and prevent the occurrence of extreme emotions, thereby having important significance for guaranteeing the personal safety of the driver and the road traffic safety.

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention provides a dialogue model training method, a dialogue method, a dialogue system, equipment and a medium, and relates to the technical field of man-machine interaction, the method comprises the following steps: generating current sample dialogue state information according to current sample user input information and historical sample dialogue information in a sample dialogue; performing tree search based on the to-be-trained strategy network and the to-be-trained value network to obtain current sample reply action information and a global reward value of a dialogue track corresponding to the current sample reply action information; according to the next sample dialogue state information, the current sample dialogue state information, the current sample reply action information and the global reward value which are generated by simulating the current sample reply action information, constructing sample data; and performing joint training on the to-be-trained strategy network and the to-be-trained value network according to the sample data to construct a dialogue model. According to the method, long-term return and strategy optimization are comprehensively considered during dialogues, so that the generation quality, the interaction capability and the user experience of multiple rounds of dialogues are improved.

The invention discloses an interactive intention understanding and rapid learning system based on multi-mode information fusion, which aims at semantic understanding of an operation instruction of a robot for picking and placing an article, constructs a key information extraction framework based on a large language model in a task semantic understanding module, and comprises multiple key position and action text information extraction, and multi-mode interactive fusion is carried out on the extracted information, vision and a touch screen, so that correct understanding of the instruction intention is realized. The key information extraction architecture based on the big language model is based on a BERT big language model, and a language processing model is trained through the BERT big language model; the language processing model comprises a long-sequence multi-task instruction and a human instruction data set of mood, scene, positive-sequence and inverse-sequence request instructions, and realizes input of long-sequence multi-task instruction statements and output of a key action sequence; according to the system, efficient and accurate human intention understanding can be realized through a voice, touch screen and vision multi-mode human-computer interaction technology, so that the robot can quickly learn new articles.

The invention discloses an intelligent networking distributed storage interaction system based on multi-cabin cooperation and a method thereof, and belongs to the technical field of camping intelligent cabin interaction, and the method comprises the following steps: taking each intelligent cabin as an independent node, initializing and accessing an intelligent networking distributed system, and enabling the nodes of the intelligent cabins to establish initial connection with other nodes in the system; each intelligent cabin collects user data, environment data and equipment state data through a built-in sensor and stores the collected data in a local storage unit in a classified mode. According to the method, the data synchronization among the nodes of the intelligent cabin is realized through a distributed database consistency algorithm, the data consistency under the condition of network delay or interruption is ensured, the data conflict and damage are prevented, and the system reliability is improved. Meanwhile, resource allocation is dynamically optimized based on an intelligent scheduling algorithm of machine learning, different load requirements are met, resources are saved, and the system efficiency and the intelligent level are improved.

The invention discloses a water ecological environment decision response intelligent interaction system based on AI natural language processing, and the system carries out the data collection and preprocessing, and forms a training set. In combination with the training set, data annotation and feature engineering are carried out, and AI model training including data segmentation, a training method and transfer learning is further carried out; on the basis, continuously collecting monitoring data, and inputting the monitoring data into the trained AI model for real-time prediction; data analysis and decision support are carried out; and once an abnormal trend is detected, an early warning mechanism is triggered. Through an AI large model technology in the vertical field, big data and an artificial intelligence algorithm are utilized to simulate and improve intelligence, convenience and precision of water ecological environment supervision, through combination of an AI large model and an intelligent voice interaction technology, based on analysis and processing capability of the AI large model, deep analysis is performed on water ecological environment data, and a water ecological environment monitoring result is obtained. And managers are helped to quickly make scientific decisions.

The invention discloses an Android system user interface interaction method and device based on a large language model, and relates to the technical field of intelligent man-machine interaction, and the method comprises the steps: receiving a natural language instruction of a user, and synchronously capturing a real-time interface state of a current Android screen to generate a structured context containing semantic information of interactive elements; inputting the instruction and the context into a large language model, and generating a hierarchical task plan formed by a plurality of atomic operations with a logic dependency relationship; based on a dynamic adaptation mechanism, converting the atomic operation sequence into an operation instruction which can be executed by the Android system; receiving interface change information after instruction execution, and feeding back the interface change information as a new round of context to the large language model to determine a task execution state; and finally generating natural language feedback information to the user according to the execution state. By means of the mode, complex cross-application tasks can be understood and executed, and interaction intelligence of the Android system is remarkably improved through real-time interface perception and dynamic planning adjustment.

The invention provides a passenger cabin service intelligent auxiliary system based on a multi-modal interaction technology. The passenger cabin service intelligent auxiliary system comprises a multi-modal information acquisition module, a passenger intention identification module, an intelligent service scheduling module, a multi-modal feedback module and a data management and self-learning module. According to the passenger cabin service intelligent auxiliary system based on the multi-modal interaction technology, the demand identification accuracy is high, the emergency response speed is extremely high, the workload of a steward is greatly reduced, and the service efficiency is greatly improved; health monitoring and early warning accuracy is extremely high, block chain evidence storage guarantees data integrity and traceability, and safety is enhanced; the federated learning-driven recommendation system greatly improves the satisfaction degree of passengers, the holographic projection interaction naturalness score is high, the personalized experience is optimized, various complex scenes such as day and night, bumping and strong light are supported, the system availability is extremely high, and the environmental adaptability is good; and multi-modal deep fusion is realized, and high-precision interaction is still kept under environmental interference.

The invention relates to the technical field of robot control and somatosensory interaction, and particularly discloses a knee joint exoskeleton robot control system based on somatosensory interaction. The system comprises a somatosensory signal acquisition and fusion module, a personalized dynamic model construction module, an adaptive intention decision module, a variable impedance compliance control module and a rehabilitation process evaluation and parameter self-tuning module. Through cooperative work of the modules, precise recognition of the motion intention of a wearer, online construction of an individualized biomechanical model and flexible and adaptive motion control are achieved, control parameters can be automatically adjusted according to the rehabilitation process, and therefore the flexibility and individuation level of man-machine interaction and the effectiveness of rehabilitation training are improved.

The invention relates to the technical field of man-machine interaction, in particular to a dialogue Agent interaction method based on multi-modal intention understanding, which comprises the following steps: S1, collecting multi-modal data in a user interaction process in real time, and calculating a time synchronization deviation value of each modal data source; s2, constructing a space-time fusion feature vector; s3, analyzing a dominant action instruction and a recessive behavior clue in the space-time fusion feature vector; s4, generating a multi-level intention analysis tree; s5, when the corrected confidence coefficient of any node in the intention analysis tree is lower than a set threshold value, activating a targeted sensor to complementarily collect data; and S6, analyzing a tree drive response decision according to the finally confirmed intention. According to the method, high-precision identification and response control of the dialogue Agent on the user intention in a complex scene are realized by constructing a multi-modal interaction method with space-time consistency fusion capability, an explicit and implicit intention analysis mechanism and an adaptive modal clarification strategy.

The invention provides a low-altitude typical scene-oriented aircraft cockpit simulation method, system and device and a storage medium, and the method comprises the steps: constructing a dynamic extensible low-altitude scene library through fusing mixed reality (MR) and a high-precision digital twin model; in combination with a man-machine in-the-loop multimode interaction technology (eye movement-gesture-voice cooperation), the operation naturalness is improved; and an intelligent optimization algorithm driven by reinforcement learning is introduced, adaptive adjustment of flight control parameters is realized, and finally a'perception-decision-evaluation 'closed-loop training system is formed. According to the invention, digital twinning, mixed reality (MR), multi-modal sensing and adaptive control technologies are fused, pilot low-altitude flight skill training, man-machine work efficiency evaluation and intelligent aircraft control system verification are carried out, and the method can be expanded to UAM operator training and unmanned aircraft simulation testing. The technical barrier of low-altitude flight skill training at present is solved, and the technical blank of low-altitude dynamic scene simulation is filled up.

The invention relates to the technical field of intelligent equipment interaction, in particular to an intelligent equipment intention rapid insight and intelligent response method, which comprises the following steps: acquiring multi-modal interaction data input into intelligent equipment by a user, and generating a standardized intention input vector; performing hierarchical feature extraction on the standardized intention input vector, and outputting a fusion intention feature matrix; inputting the fused intention feature matrix into an intention classifier to obtain initial intention probability distribution data of the user; dynamically correcting the initial intention probability distribution data based on the historical intention record to generate a final intention judgment label; the optimal response action set is matched from the intention response strategy library on the basis of the final intention judgment label, the characteristic representation capacity is improved through multi-modal fusion, intention continuity understanding is enhanced through dynamic context perception, response self-adaptive optimization is achieved through reinforcement learning, and therefore the intention recognition accuracy can be improved in the noise environment, and the intention recognition accuracy is improved. And response delay is reduced.

The invention provides an AI interaction method and device, wearable equipment and a storage medium, and belongs to the technical field of man-machine interaction, and the method comprises the following steps: constructing a user portrait based on multi-source data; the multi-source data is data of a target user, and the target user is a user wearing the wearable device. And in response to the received first voice of the target user, determining a target intention type based on the first voice. And determining multi-modal sensing data of the target user based on the target intention type. And determining associated information corresponding to the first voice based on the multi-modal perception data. And determining recommendation information corresponding to the first voice based on the associated information and the user portrait. And generating a second voice based on the association information and the recommendation information. The second voice is voice played by AI in the wearable device. According to the AI interaction method and device, the wearable device and the storage medium provided by the invention, the user experience can be improved, and diversified requirements of users can be met.