374 results about "Interactive feedback" patented technology

The invention relates to a multi-modal intelligent robot system and an interaction method, and belongs to the technical field of robot digital data processing, and the method comprises the following steps: S1, multi-modal input collection; s2, carrying out multi-modal input preprocessing; s3, performing multi-modal information fusion, inputting the information into a pre-trained multi-modal feature fusion model, and performing feature fusion on each modal feature through a quantitative feature data fusion mechanism to obtain a user interaction intention vector and a user emotional state vector; s4, interaction response generation: matching a preset interaction response strategy library based on the user interaction intention vector to obtain basic response content, and performing emotion adaptation adjustment on the basic response content in combination with the user emotion state vector to generate multi-modal response content including text information, voice information and action information; s5, interactive feedback is executed; the method has the beneficial effects that the robot is fused with multi-modal information through the basic response content and the user emotional state vector, and interaction is dynamically optimized.

The invention discloses an intelligent auxiliary teacher lesson preparation system and method based on an education large model. The method comprises the following steps: S1, generating a standardized teacher lesson preparation data set; s2, constructing a teaching knowledge graph; s3, generating preliminary intelligent lesson preparation content; s4, presenting the preliminarily generated intelligent lesson preparation content, and providing an interactive interface combining a natural language and voice recognition; s5, generating the adjusted intelligent lesson preparation content; and S6, according to historical lesson preparation data of the teacher and the interactive feedback data, performing adaptive learning and optimization on the intelligent lesson preparation content, adjusting weights of key knowledge points in the teaching knowledge graph, updating a content generation strategy of the large education model, and generating a final intelligent lesson preparation scheme. According to the method, the response capability of the intelligently generated content to the teaching intention of a teacher is remarkably improved, the teaching content and the test question style are supported to dynamically cooperate with classroom feedback along with the teaching rhythm, and an intelligent decision support basis is provided for large-scale personalized teaching.

The invention provides an AI assistance-based landscape garden scheme auxiliary generation method and system, and the method comprises the steps: firstly receiving initial topographic data and a landscape adjustment instruction containing a visual presentation requirement and an ecological function requirement, which are inputted by a user in an interactive interface, and then processing the initial topographic data through a topographic segmentation model; the method comprises the following steps: extracting gradient distribution, altitude interval and land cover features, dynamically correcting the gradient distribution features into stepped topographic features according to visual requirements, matching the land cover features according to ecological requirements to obtain vegetation distribution features, performing spatial superposition on the two features, and performing path connectivity analysis in combination with tourist flow data to determine touring path features. Finally, a landscape garden scheme containing a three-dimensional visualization model and ecological evaluation parameters is generated through multi-dimensional rendering according to the sightseeing path features and sent to a user terminal to achieve interactive feedback, so that various requirements are efficiently integrated, and scientificity and intuition of landscape garden design are improved.

The invention provides a traffic comprehensive query optimization method and system applied to intelligent human-computer interaction, and the method comprises the steps: obtaining a traffic query request input by a target user in an interaction scene, carrying out the multi-modal feature analysis of the traffic query request, generating a semantic understanding feature and a scene correlation feature, and obtaining a semantic understanding feature and a scene correlation feature based on a preset language large model; performing dynamic interaction feature fusion processing on the semantic understanding feature and the scene association feature to generate a multi-dimensional intention feature, and performing service path optimization on the traffic query request of the target user according to the multi-dimensional intention feature to generate a comprehensive response strategy; and converting the comprehensive response strategy into interactive feedback information, dynamically adapting to equipment parameters in the real-time environment association information, and executing feedback operation to complete optimized response of the traffic query request. According to the invention, the response real-time performance, decision reasonability and execution reliability of the intelligent traffic query service can be improved.

The invention discloses an intelligent programming assistance method and system combining a large model and a knowledge graph. The method comprises the steps that the system is initialized, and a programming domain knowledge graph body and an education programming large language model are loaded; constructing a learner initial portrait and associating the learner initial portrait with a knowledge graph; personalized programming learning experience is provided, including real-time programming tutoring, and thinking chain-based heuristic guidance is generated through combination of a large language model and knowledge graph information; and interactive feedback and system iteration are carried out, and a reinforcement learning mechanism based on human feedback is utilized to continuously optimize the large language model. Through deep fusion of structured knowledge of a knowledge graph and understanding generation ability of a large language model, the method aims at solving the problems that an existing programming auxiliary tool is insufficient in individuation and heuristic, heterogeneous knowledge is difficult to fuse, teaching resource modes are single, and an effective evolution mechanism is lacked. More accurate, personalized, heuristic and sustainable-evolution intelligent programming assistance can be provided, and the programming thinking and autonomous learning ability of learners can be effectively improved.

The invention relates to the technical field of robot intelligent control, in particular to a concrete construction digital intelligent robot control system. Comprising a concrete data acquisition module, a construction path planning module, a robot control module, a quality monitoring module and an interactive feedback module. The data acquisition module acquires information such as concrete height and humidity of a construction area through a sensor; the path planning module generates an optimal construction path by using an artificial bee colony algorithm; the robot control module accurately executes a path instruction through sliding mode control and a fuzzy adaptive algorithm; the quality monitoring module adopts a support vector machine to evaluate construction quality and generate a report; and the interaction feedback module realizes path adjustment and information interaction through a visual page. Through combination of digital intelligence and algorithm optimization, integration of intelligent planning of a construction path, precise control of the robot and quality monitoring and evaluation is realized, and the intelligent level and engineering quality of concrete construction are remarkably improved.

The invention relates to the technical field of artificial intelligence and language learning, in particular to a multi-modal language learning auxiliary system and method based on artificial intelligence, and the system comprises a multi-modal input module, a cross-modal feature fusion module, a dynamic adaptive learning module and an interactive feedback generation module. Wherein the multi-mode input module is used for receiving original text data, original image / video data and original voice data; the cross-modal feature fusion module is used for extracting a visual feature vector and a semantic coding vector and generating a cross-modal joint feature vector; the dynamic adaptive learning module is used for generating dynamic scene parameters; and the interactive feedback generation module is used for outputting a multi-mode feedback data packet through a prompt learning engine. According to the method, by fusing multi-modal feature alignment and dynamic context parameter modeling, linkage generation of grammar, culture and pronunciation feedback is achieved, and the context understanding ability and interaction feedback precision in the language learning process are improved.

The invention provides an immersive concert experience system based on virtual reality, and belongs to the technical field of virtual reality, and the system comprises a dynamic adjustment module which is used for predicting the preference of a user for stage arrangement, light effects and music types through a machine learning model according to the historical preference data of the user, and outputting the preference to the stage arrangement, light effects and music types; dynamically adjusting visual elements and sound field parameters of the virtual scene and a refresh rate and a field angle of the virtual scene in combination with the user physiological indexes and the user head motion trail which are acquired in real time; the action capture and feedback module is used for capturing an interaction instruction sent to the dynamically adjusted virtual scene by the limb action of the user in real time; and the interaction feedback module is used for enabling the virtual performer to respond to the interaction instruction of the user in real time based on the expression recognition and voice synthesis technology, and generating personalized interaction feedback content. A user constructs an immersive music interaction field domain, and experience logic of a virtual music meeting is completely reconstructed.

The invention discloses a virtual teaching training method and system based on a large language model and a medium, and the method comprises the following steps: determining preset feature parameters of virtual students according to teaching levels and subject contents; constructing a large language model, generating virtual students based on the large language model according to preset feature parameters of the virtual students, and inputting multi-modal teaching information; the virtual students perform model training and learning on the multi-modal teaching information to generate learning state information and feedback information; the virtual teaching expert proposes a suggestion for dynamically adjusting and optimizing the teaching strategy based on the evaluation result; dynamic learning and interactive feedback are carried out by simulating virtual students with different priori knowledge levels and cognitive competence, and meanwhile, real-time evaluation and personalized improvement suggestions are carried out on the teaching process by utilizing the constructed virtual expert system, so that a user is helped to continuously optimize a teaching strategy, and the teaching quality of a specific subject is improved.

The invention belongs to the technical field of new energy automobile teaching, and particularly relates to a VR interactive new energy automobile high-voltage electrical principle teaching method, system and equipment, and the method comprises the steps: obtaining the physical parameter data and dynamic circuit simulation data of a new energy automobile high-voltage electrical system; constructing a high-precision 3D model according to the physical parameter data, inputting dynamic circuit simulation data into the model, and generating interactive principle annotations and particle effect animations; based on a gesture recognition model and a new energy automobile VR interaction technology, circuit state changes in the particle effect animation are obtained, a new energy automobile high-voltage system principle and a signal flow are displayed, a practical operation simulation training scene is constructed, student operation data are recorded according to the practical operation simulation training scene, an AI evaluation model is trained, and an evaluation report is obtained. And adjusting interaction feedback parameters of the training scene, and meanwhile, generating a personalized learning report of the trainee. Therefore, the problems of safety risk, teaching lag, abstraction obstacle and the like in the prior art are solved.

The invention provides a design method and system of a touch interaction interface. Wherein user behavior habits are collected, key elements are extracted, a user participation degree evaluation system is constructed, and a behavior habit analysis report is generated; carrying out situation modeling processing by using a situation awareness layout optimization algorithm, carrying out adaptability analysis by adopting an environment adaptation technology, and generating environment adaptation interface configuration; a long-term preference tracking algorithm is used to record and learn personalized preference settings to add personalized components, a progressive interface evolution method is used to carry out progressive evolution processing, an interactive feedback mechanism is introduced step by step, an interactive module is added, and an optimized layout scheme is generated; and constructing a learning library according to the user behavior habits, importing interaction behavior data to perform behavior pre-judgment, adjusting interface elements and a response mode, and generating a touch interaction interface optimization strategy. According to the technical scheme provided by the invention, the man-machine interaction fluency and the user experience of the touch interaction interface are improved.

The invention relates to the field of intelligent interactive design, and particularly discloses a visual intelligent interactive design system which comprises a three-dimensional structure design module, an intelligent color matching module, a visual rendering module and an interactive feedback analysis module. The three-dimensional structure design module can process imported or custom a three-dimensional model, detect and repair topology errors and construct a three-dimensional scene basic framework; the intelligent color matching module generates a color matching scheme based on the instruction, a rule base or user preference and displays a comparison view; the visual rendering module renders a three-dimensional scene in real time, collects rendering pipeline load parameters and quality indexes, and analyzes balance points to adjust rendering parameters; and the interaction feedback analysis module records interaction data, marks over-limit nodes by comparing a standard and analyzes a root cause. According to the system, efficient three-dimensional scene construction, intelligent color matching, rendering optimization and interaction experience improvement are realized through multi-module cooperation, and the system is suitable for multi-field scenes needing visual intelligent interaction design.

The invention provides a human-computer interaction virtual-real simulation method and system of an intestinal nutrition nursing teaching system, and relates to the technical field of virtual interaction teaching. By integrating a force feedback glove, a bioelectric sensor and a pressure detector, multi-mode interaction data acquisition including intubation angle deviation, maximum force application pressure value and step interval time is realized; the operation feedback dimension and immersion are improved; the system is combined with a scene construction and intelligent guide module, when the detection operation goodness of fit is lower than a preset threshold value or the process completion degree is insufficient, force feedback vector correction and cognitive prompt are triggered, and standard attitude and step memory are guided; and the comprehensive evaluation module summarizes key indexes to calculate a total operation score, performs defect positioning and training suggestion matching on low-score behaviors, generates a structured visual learning report, constructs a cognition-operation-feedback closed loop, and solves the problems of single interactive feedback, insufficient teaching structuralization and lack of feedback mechanisms.

The embodiment of the invention relates to the technical field of digital twinning, in particular to a digital twinning multi-modal practical training teaching interaction method and system combined with an industrial scene, and the method comprises the steps: obtaining a real-time operation data set of target industrial equipment and a multi-modal data set of a practical training teaching environment, constructing a virtual twinborn model of the target industrial equipment according to the real-time operation data set, performing dynamic feature fusion processing on the multi-modal data set, generating a multi-modal interaction feature set synchronized with the virtual twinborn model, and training the target industrial equipment on the basis of a preset training teaching strategy network. And carrying out collaborative analysis on the multi-modal interaction feature set and the real-time operation state of the virtual twin model, generating an interaction feedback instruction of the practical teaching environment, and adjusting parameters of the practical teaching strategy network according to the interaction feedback instruction to optimize a teaching path.

The invention discloses a multi-modal interactive fusion virtual reality emotion computing system, which comprises a multi-modal interactive fusion virtual reality emotion computing device, a multi-modal interactive fusion virtual reality emotion computing device and a multi-modal interactive fusion virtual reality emotion computing device, the virtual reality emotion computing device based on multi-modal interaction fusion further comprises a distributed multi-modal sensing device, a user adaptation interaction module, an interaction feedback module, an emotion extraction module, a multi-modal fusion module and a self-adaptation emotion computing model. The multi-modal fusion module is used for carrying out full-dimensional monitoring and intelligent decision making on a complex scene, the user adaptive interaction module can realize accurate identification and response to user requirements, and the multi-modal fusion module is used for making up for information limitation of a single modal, so that effective interaction and accurate identification can be realized on the whole.

The invention discloses a digital twinning system construction method and system based on flexible job shop scheduling, and relates to the technical field of digital twinning, and the method specifically comprises the steps: constructing a twinning model of a physical workshop according to the production data of the physical workshop; building a workshop scheduling discrete event simulation model based on the twinborn model of the physical workshop; based on an internal and external virtual-real interaction mechanism, virtual-real mapping interaction between the twin model of the physical workshop and the physical workshop and virtual-real mapping interaction between the twin model of the physical workshop and the workshop scheduling discrete event simulation model are realized, and a virtual workshop is generated; data generated in the interaction process is stored in a twin data pool in real time; a workshop service system is constructed based on a three-dimensional visual integration technology, so that a physical workshop and a virtual workshop carry out interactive feedback, and construction of a scheduling digital twin system is completed. According to the method, the self-adaptive capability of the system in the face of complex production tasks is enhanced, and flexible scheduling according to actual conditions is supported.

The invention relates to the related technical field of digital data processing, in particular to an intelligent ancient book information processing method and device based on artificial intelligence, and the method comprises the steps: scanning an ancient book, constructing a knowledge graph, dividing a domain database, and training a vertical processing model; introducing a multi-form historical carrier to form a multi-mode sensing set, establishing a multi-information network, and embedding a processing model; the method comprises the following steps: establishing a dialog box, realizing multi-round interaction and combining threshold optimization output, solving the technical problems of digitization and fragmentation limitation in ancient book information processing and incapability of effectively integrating and utilizing ancient book information due to diversity and complexity of ancient books and literatures, and realizing construction of a knowledge graph and division of a domain database. A knowledge system with a hierarchical structure and logic association is formed, deep learning and optimization are performed according to specific requirements in the field of ancient books by training a vertical processing model, a dialog box is established, multi-round interactive feedback with the user is performed, and the user is assisted to quickly and deeply explore the ancient books.

The present invention discloses a smart digital human control system and method, which relates to the field of educational information technology. The technical solution solves the problems of insufficient interactive intelligence, lack of emotional feedback and limited personalized teaching by dynamically adjusting the teaching mode. The system first generates a learning state index Xlz by collecting students' learning progress data and interactive feedback data in real time, and generates a comprehensive state value Fhxs by combining data such as the emotional change index Ex and the learning task difficulty value Nx, and dynamically adjusts the teaching mode; secondly, the emotion recognition technology provides emotional support by analyzing the emotional fluctuation trend of students, including encouraging feedback and interactive measures to reduce stress; finally, the system calculates the learning demand assessment value Lhx according to the student's learning progress rate Xjz, learning resistance index Zlx and learning interest value Xqx, and automatically adjusts the teaching difficulty to improve the personalized teaching effect, and achieves more accurate learning optimization and interactive experience.

The invention provides a VR scene adaptive rendering method based on AI and multi-modal perceptual interaction, and the method comprises the steps: firstly obtaining a perceptual signal set of a current interaction stage of a user through a multi-modal interaction interface, including user interaction actions, visual attention and scene physical feedback signals, and then analyzing the set to obtain a sensing signal set of the current interaction stage of the user; the method comprises the following steps: generating user concerned area features and scene dynamic sensitive area features, then determining a rendering area priority sequence and a detail retention reference through a rendering resource allocation model based on the user concerned area features and the scene dynamic sensitive area features, and adjusting a resource scheduling rule and a quality control rule of a VR rendering system according to the result. And executing the adjusted rule to generate the current frame of rendering picture, and updating the decision logic of the rendering resource allocation model according to the change trend of the interactive feedback of the user to the current frame of picture and the physical feedback of the scene, thereby realizing the dynamic self-adaption of VR scene rendering, improving the rendering efficiency and quality, and enhancing the user immersion.

The invention relates to the technical field of medical rehabilitation, in particular to a remote rehabilitation intelligent evaluation system. The system comprises a rehabilitation monitoring module, a remote service module, an interaction feedback module and an intelligent evaluation module. The rehabilitation monitoring module comprises an emotional signal monitoring module, a physiological signal monitoring module and a gamification performance monitoring module and is used for monitoring the emotional condition, the physiological condition and the response condition of a patient; the remote service module is connected with the rehabilitation monitoring module, the interactive feedback module and the intelligent evaluation module; the interactive feedback module is used for information interactive feedback among the patient, the rehabilitatrist and the remote service module; and the intelligent evaluation module is used for analyzing and evaluating the rehabilitation index data and the optimized rehabilitation information which are processed by the remote service module. By comprehensively monitoring the emotional condition, the physiological condition and the reaction condition of the patient, remote, comprehensive and refined intelligent assessment of the rehabilitation condition of the patient is achieved.

The invention discloses a large language model knowledge base self-adaptive updating method combining documents and interactive feedback, and relates to the technical field of knowledge base updating, and the method comprises the steps that a real-time change detection module monitors streaming documents and interactive feedback, and captures and marks a single fact change event; the decision module calculates a shunting decision coefficient according to the heat density index and the linkage sweep index, and the event is shunted to a Micro-Edit or Incremental-Tune path; if shunting to the Micro-Edit path, the positioning module executes attention backtracking and gradient sensitivity analysis, and determines a to-be-updated weight address; the rank-one editing module implements rank-one updating and records an audit log; the verification module generates an online token or a rollback instruction through consistency comparison; the version management module manages a version, activates a new version route and cleans out expired branches. Through event granularity identification, intelligent shunting, local updating and multi-version management, the timeliness of knowledge base updating is remarkably improved, and resource consumption and maintenance cost are reduced.

In an approach for providing an immersive interview system with real-time interactive feedback, a processor presents a simulated environment of an interview based on an interview type and an interview format selected by a user. A processor collects a set of data regarding at least one performance related factor and nonperformance related factor for a length of the interview. A processor evaluates the set of data collected to detect a deficiency associated with the at least one performance related factor of the user. A processor detects the deficiency associated with the at least one performance related factor of the user. A processor outputs a notification to the user, wherein the notification is a recommendation explaining to the user a suggested change to improve the interview skill during the interview.

The invention provides an emotion evaluation method and device, equipment and a medium. The method comprises the following steps: receiving interaction feedback information of a user for an emotion evaluation problem of a current round; generating an interactive input text of the current round based on the interactive feedback information, historical information interacting with the user and prompt information of emotion evaluation; inputting the interaction input text of the current round into the large language model to obtain an emotion evaluation problem of the next round output by the large language model, and returning and receiving interaction feedback information of the user for the emotion evaluation problem of the next round until interaction is finished; and performing emotion evaluation based on the interaction feedback information of each round. According to the emotion evaluation method provided by the invention, the emotion evaluation problem is automatically generated through the large language model, man-machine interaction is carried out, and the dependence on professional evaluation personnel is reduced. And through an interactive feedback mechanism, the questioning content can be adjusted in real time according to the emotion of the user, and personalized requirements are met.

The invention discloses a government affair service digital human intelligent interaction method and system based on deep learning, and the method comprises the steps: firstly obtaining government affair service specification policy data and interaction behavior data, including voice, text, facial expression, limb movement and the like, of a user; performing spatio-temporal feature extraction and fusion on the original interaction data to generate a user behavior semantic vector, performing feature extraction on the policy data to generate a standard vector, analyzing the user behavior semantic vector based on a government affair policy cognition map to obtain a government affair handling intention vector, and optimizing an intention generation service execution vector in combination with the standard vector; and multi-modal behavior decoding and cross-modal alignment loss function optimization are carried out to obtain an interaction behavior sequence conforming to the specification, and finally, the digital human is controlled to carry out interaction feedback with the user according to the sequence. According to the invention, pertinence and normalization of interactive feedback of the government affair service digital human and the integrating degree with the actual demand of the user can be improved.

The invention discloses a dance movement digital acquisition method based on motion capture and a teaching system, and solves the problems of subjective evaluation of traditional teaching, neglect of posture difference in the prior art and the like. The motion capture module collects posture and motion data of a dance actor, decomposes the data into basic motions and extracts features to form a standard sequence, generates a personalized teaching evaluation sequence in combination with a student posture feature difference, collects student motions in real time and compares the student motions with the standard, and generates a targeted teaching strategy according to the difference. The system comprises a cloud data server, a motion capture module and the like, and can store standard data, analyze motion characteristics and perform interactive feedback. According to the invention, through accurate capture, personalized evaluation and real-time feedback, the teaching accuracy and efficiency are improved; and data support is provided for dance innovation.

The invention relates to the technical field of intelligent questioning and answering, in particular to an intelligent questioning and answering system implementation method and system.The intelligent questioning and answering system implementation method comprises the following steps of document collection and preprocessing, document dicing, document layout analysis and table layout analysis; the method has the beneficial effects that a semantic association network and a multi-modal index system are formed through offline document collection, preprocessing, slicing, layout analysis, data extraction and knowledge graph construction; in the online part, the capabilities of Embedding vectorization, multi-index joint retrieval, tensor reordering, AI database integration and large language model generation are combined, accurate semantic understanding and rapid knowledge matching of user questions are realized, high-quality answers are generated, and interactive feedback optimization is supported.

The invention relates to the technical field of interface interaction design, in particular to a GUI page element layout and typesetting optimization method and system based on artificial intelligence. The technical problems that a traditional GUI design mode is insufficient in GUI design flexibility, high in dynamic layout technical threshold, high in manual typesetting cost, limited in experience and unreasonable in resource allocation are solved. According to the technical scheme, the GUI page element layout and typesetting optimization system based on artificial intelligence comprises a natural language processing module, a context sensing module, an intelligent agent module, an interactive feedback optimization module and a cross-device adaptability module. Through the natural language processing technology, the user can describe the demand through the natural language, the system automatically identifies the natural language instruction, the layout and typesetting of the page elements are automatically completed by using the large language model and the intelligent agent, the GUI design efficiency is improved, the personalized display of the user interface is realized, and the user experience is improved.

The invention provides a robot interaction processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: in response to a character configuration operation for a robot, configuring the character of the robot as a target character; in response to interaction operation for the robot, the robot is controlled to interact in a multi-modal mode matched with the target character, and the multi-modal mode comprises at least two of voice, expression and action; in the interaction process of the robot, the target character is updated based on interaction feedback data, and the robot is controlled to interact in a multi-mode mode matched with the updated target character. By means of the robot interaction method and device, the naturalness and vividness of the robot interaction process can be improved.

The invention provides a multi-modal content generation AI model construction method and device based on a diffusion model, and relates to the technical field of content generation. The method comprises a multi-source data processing module, a modal priority analysis module, a serialized modal processing module, a diffusion parameter adaptive adjustment module, an interactive feedback integration module and a collaborative output adjustment module. According to the method, multi-modal data input is integrated, model parameters are dynamically adjusted, information entropy is used for evaluating the information amount of the data, the processing priority is set according to the information amount, the recognition capacity of the model for data importance is enhanced, the model parameters are adjusted in real time to adapt to the characteristics of different data modalities, and the accuracy of data processing is improved. And the generation parameters are dynamically adjusted through user feedback, so that the generated content better fits the actual demand and preference of the user, and the consistency and integration of the content are ensured, the attraction of the content is improved and the propagation effect of the content is enhanced by coordinating the style, theme and emotion of the output content.

The invention relates to the technical field of information flow intelligent recommendation, and discloses a real-time interactive feedback information flow content adaptive optimization method and system.The real-time interactive feedback information flow content adaptive optimization method comprises the steps that user interaction data are collected and subjected to robust preprocessing to obtain standardized interaction feature vectors; constructing a user personalized interaction baseline model to obtain a multi-level behavior reference basis, identifying a real-time interest signal of a user and updating an interest state, dynamically adjusting a recall strategy and executing multi-path recall to obtain a candidate content set, adaptively adjusting a sorting feature weight and executing sorting, and dynamically controlling diversity to execute rearrangement to obtain a final recommendation list, and meanwhile, obtaining an interest prediction score in a sorting stage by adopting a hierarchical reasoning architecture and a model distillation technology. The interest change of the user can be captured in real time, the matching degree of the recommended content and the instant interest of the user is improved, and the user experience and the recommendation effect are improved.