425 results about "Personalization service" patented technology

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

The invention provides a smart home management method and system based on the Internet of Things. The method comprises the steps that indoor and outdoor environment parameters, user physiological data, home equipment operation states and energy consumption data are collected; based on the data, learning behavior preferences of the user in different time and environments, and establishing a personalized behavior prediction model; deploying the model at an edge computing node, combining big data analysis of a cloud server to form a hierarchical intelligent decision-making architecture, and outputting a preliminary decision-making result; the real intention of the user is understood and an intention confidence evaluation mechanism is established; when the consistency of the identification results of the multiple modes is lower than a threshold value, confirmation is actively carried out on the user, and a primary intention is obtained; according to the intention, a control strategy is dynamically generated in combination with the environment state and the equipment capacity; and based on the operation data and the energy consumption data, establishing an equipment health degree evaluation model, and predicting a fault risk and a maintenance demand. Through the scheme of the invention, the intention of the user can be accurately identified, and accurate personalized services are provided, so that the user experience is improved.

The invention discloses a property operation decision support system based on digital twinning and generative AI, which relates to the technical field of property operation management and comprises a sensing layer, a data layer, a digital twinning layer, a generative AI layer and a decision execution layer which are connected in sequence. In the invention, the generative AI layer is based on a special Transform architecture model, fuses historical operation data and digital twinning real-time data, can automatically generate an executable optimization strategy in multiple scenes such as facility maintenance, resource allocation, personalized service, emergency disposal and the like, verifies feasibility and effects in a virtual environment, and improves the implementation efficiency. The decision execution layer efficiently implements a strategy subjected to simulation verification into a physical space by using an intelligent scheduling and equipment linkage mechanism, performs quantitative evaluation and strategy iterative optimization based on an execution effect, deeply fuses a high-fidelity digital twin with a generative AI, constructs an AI decision closed-loop system based on simulation verification, and performs decision making on the AI decision closed-loop system. And a high-feasibility and feasible optimization scheme can be generated for a complex and dynamic property operation environment.

The invention relates to the technical field of artificial intelligence customer service, provides an intelligent customer life cycle management AiCRM method and system, and is used for solving the problems of low customer service accuracy and poor response timeliness in the prior art. The method comprises the steps that in the interaction process of a target client and an enterprise, client voice, expression and text data are acquired, multi-modal sentiment analysis is carried out, and a client sentiment feature set is generated; dynamically updating a customer emotion evolution graph based on the customer emotion feature set, and generating an accurate customer emotion score by adopting an emotion calculation model in combination with historical data and real-time interaction information; and when the score is lower than a preset baseline, the system automatically generates a personalized service strategy adjustment scheme matched with the current emotional state and the historical change trend of the customer. According to the application, the customer service accuracy and the response timeliness are improved.

The invention discloses an intelligent class case retrieval method and system based on reinforcement learning self-feedback, and the method comprises the following steps: S1, constructing a multi-level semantic understanding framework, carrying out the semantic understanding of a query case through the multi-level semantic understanding framework, and generating a complete semantic representation; s2, carrying out multi-dimensional similarity calculation and sorting optimization on the query case and historical discriminants in the candidate case library; s3, constructing a dynamic user portrait and optimizing a recommendation strategy; and S4, establishing multiple rounds of dialogues and context awareness, and carrying out visual display and feedback mechanism optimization. According to the intelligent class case retrieval method and class case retrieval system based on reinforcement learning self-feedback, a more accurate, efficient and reliable law intelligent retrieval system is finally achieved, judicial practice requirements are met, and quantitative and measurable technical progress is achieved in the aspects of retrieval accuracy, sorting quality, personalized service, law adaptability and the like.

The invention discloses a knowledge graph recommendation method based on generative denoising and multistage contrast learning, and belongs to the technical field of recommendation system and knowledge graph combination, and the method comprises the steps: firstly constructing a user-project interaction graph, and supplementing project semantic information in combination with an external knowledge base to generate a knowledge graph; thirdly, obtaining decoupled embedded representations, and enhancing the learning of the representations by adopting LightGCN; noise is added to the original knowledge graph, and mask processing is carried out to optimize the anti-noise capability of the model; then extracting a user multi-preference representation from each sub-view and optimizing a final user representation through a contrast learning task; embedding and mapping items under the knowledge graph and the collaborative filtering view into a unified space to realize semantic alignment; and finally, designing a multi-task joint loss function to fuse the knowledge graph and collaborative filtering information so as to improve the personalized recommendation performance. According to the method, the accuracy, the robustness and the personalized service capability of the recommendation system are effectively improved.

The embodiment of the invention relates to the field of large language models and multi-agents, and provides a multi-agent collaborative query response method based on a state dependency graph and a related device, and the method comprises the following steps: receiving a to-be-queried text input by a user; performing query semantic analysis on the to-be-queried text to obtain the analyzed to-be-queried text and query complexity; instructing the intention recognition agent to generate a domain index; calling a preference matching agent to generate preference information; calling a resource retrieval agent to query to obtain query use resource information corresponding to the to-be-queried text according to the query complexity; the query response information is generated at least according to the historical context of the to-be-queried text, the to-be-queried text, the analyzed to-be-queried text, the field structure information, the field index, the preference information and the query use resource information, and the personalized service corresponding to the to-be-queried text currently input by the user can be automatically adapted according to the actual interaction scene; and response type and high-efficiency intelligent assistance is realized.

The invention discloses an intelligent situation awareness vehicle-mounted atmosphere lamp control method and system and electronic equipment. The method comprises the following steps: step 1, scene state modeling of multi-source data fusion; step 2, coupling control of the dynamic spectrum and environmental parameters; step 3, generating an emotion-driven light sequence; step 4, dynamically adjusting the noise-coordinated lamplight; step 5, storing and calling personalized parameters; and step 6, optimizing a control strategy of feedback driving. Through multi-source data fusion modeling and comprehensive analysis of the driving scene, the user state and the environmental parameters, the state recognition accuracy is improved, misjudgment is avoided, dynamic spectrum and environmental parameter coupling, emotion driving and noise collaborative light adjustment are achieved, diversified requirements are met, a personalized atmosphere is created, fatigue is relieved, and safety is improved. User parameters can be stored and called, personalized services are enhanced, and more comfortable and convenient driving experience is brought.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an object portrait label set generation method, device and equipment and a medium, and the method comprises the steps: collecting and integrating multi-source data, generating a multi-modal data set, and cleaning and enhancing the data set; extracting a portrait label set based on the historical structured information of the object, obtaining a multi-modal cue word set, and constructing a unified representation sequence; inputting the unified representation sequence into a multi-modal model, obtaining fusion representation and extracting a derivative factor candidate set; and performing consistency verification on the cross-modal data to obtain a consistency factor set, and finally fusing the consistency factor set with the structured portrait label set to generate a unified portrait system and output an object portrait label set. According to the method, the accurate and comprehensive customer portrait is generated by combining multi-modal data and cross-modal consistency verification, the portrait accuracy is improved, and support is provided for accurate marketing and personalized service.

The invention discloses an intelligent customer service dialogue management method and system fused with multi-layer memory, and belongs to the technical field of computers, and the method obtains a three-dimensional context through multi-mode context awareness, achieves the dynamic scheduling of a memory system through a memory coordinator, and combines a dynamic construction mechanism of a service situation portrait, thereby achieving the intelligent customer service dialogue management. The problem that the response of the intelligent customer service system is not matched with the complex and dynamic service demand of the user due to the single situation awareness dimension and the rigid memory scheduling mechanism is effectively solved, and the adaptive personalized service response can be generated according to the real-time change of the dialogue situation.

The invention belongs to the technical field of artificial intelligence personalized services, and particularly relates to a reinforcement learning-fused user portrait dynamic construction and personalized recommendation implementation method, which comprises the following steps of: acquiring multi-dimensional user data; using the multi-dimensional user data to construct a multi-dimensional dynamic user portrait through a preset algorithm; based on the multi-dimensional dynamic user portrait, combining a collaborative filtering algorithm and a deep learning algorithm to generate a scene recommendation strategy; and monitoring user feedback and behavior data in real time, and optimizing the scenarized recommendation strategy by using the real-time monitored user feedback and behavior data. According to the method, the dynamic user portrait is constructed, scene recommendation is generated, and the strategy is optimized in real time by relying on reinforcement learning, so that the individuation degree, recommendation accuracy and user satisfaction of the service are remarkably improved, and the limitation of a traditional service mode is effectively broken through.

The invention relates to the technical field of semantic recognition, can be applied to business scenes such as financial science and technology and medical health, and discloses a business response method, device and equipment based on communication theme recognition and a medium. And calculating the distribution density of the words in the plurality of text units, generating word weights in combination with word frequencies and the distribution density, identifying concerned topics, generating user feature tags or content classification identifiers, and associating with a service information system to trigger service operation. According to the method, the communication record is converted into the text data, the word frequency and the word distribution density are calculated, and the word weight with the discrimination degree is generated, so that a user concerned theme is automatically identified, and a label or a classification identifier is further constructed; automation of user behavior understanding, standardization of label generation and linkage of an operation process and a business system are realized, and personalized service response capability and operation efficiency are improved.

The invention provides a driver personalized service recommendation method based on scene perception. The method comprises the following steps: S1, collecting multi-dimensional data; s2, scene perception and recognition; s3, constructing a driver portrait and dynamically updating the driver portrait; s4, the intelligent recommendation engine generates personalized services; and S5, real-time decision making and service pushing are carried out. According to the driver behavior, the vehicle state and the external environment, the driver portrait including the behavior characteristics, the preference characteristics and the risk level is formed, and accurate recommendation is carried out.

The invention discloses a multi-modal robot interaction system, and relates to the technical field of robots, the system comprises a robot subsystem and a cloud processing subsystem which are in communication connection with each other, the robot subsystem comprises a multi-modal sensing module and an interaction execution module, and the cloud processing subsystem comprises a cloud processing module. The multi-mode sensing module is used for collecting target data of at least one target user and sending the target data to the cloud processing module, and the target data comprises at least one of touch data, distance data, posture data, position data, action data and voice data; the cloud processing module is used for analyzing the target data, generating an interaction instruction and sending the interaction instruction to the interaction execution module; and the interaction execution module is used for receiving and executing the interaction instruction, so that the higher expectation of the user in the aspects of emotion accompanying and personalized service is effectively met.

Provided are a personalized dynamic intent recognition method and apparatus, and a related device, related to the technical field of computers. The method comprises: acquiring historical dialog data of at least one user with customer service (S202); on the basis of the historical dialog data, constructing empirical tuple data for training a deep reinforcement learning model, the deep reinforcement learning model being used for predicting a value of an agent from one state to one action, the empirical tuple data comprising a current state, an action, a reward value, and a next state of the agent (S204); acquiring personalized weight information of each user, and, in the process of the agent training the deep reinforcement learning model by using the empirical tuple data, using the personalized weight information of each user to update a prediction value of the deep reinforcement learning model, until a personalized intent recognition model satisfying a preset condition is obtained. The present method can dynamically and accurately identify a personalized intent of a user, so as to more conveniently provide the user with a higher-quality personalized service.

The invention relates to the technical field of tourism information data analysis, and discloses a tourism information data analysis method based on multiple data sources. According to the method, multi-source heterogeneous tourism data such as tourist position tracks, social media comments, scenic spot real-time flow and meteorological environments are obtained. Performing spatial-temporal clustering analysis on the tourist position trajectory data to generate a behavior pattern map; extracting an emotional tendency index from the social media comment data; and associating and matching the scenic spot real-time flow with the meteorological environment data to generate an environment bearing pressure coefficient. And inputting the results into a multi-modal fusion model to obtain an analysis decision vector, constructing a personalized recommendation strategy graph through a collaborative filtering algorithm, and outputting a tourism service optimization scheme. According to the invention, deep analysis of multi-source data can be integrated, scenic spots can be assisted to know behaviors and demands of tourists, operation management is optimized, personalized services are provided, and tourism service quality and scenic spot competitiveness are improved.

The invention provides a system and method for enhancing long-term memory of a large language model, and relates to the technical field of artificial intelligence. The system solves the problems of memory loss, information confusion and the like in multiple rounds of dialogues through hierarchical memory storage, time-aware retrieval and an automatic reflection compression mechanism, improves response consistency and credibility, and supports personalized services and complex task scenes.

The invention relates to the technical field of attribute graph collection and release, in particular to an attribute graph collection and release method, system and device based on local differential privacy and a medium. Receiving disturbed data from each client; based on all disturbed data, estimating community division of the original graph structure and triangular counting of each node; obtaining a target graph structure based on community division and triangular counting; and on the basis of the target graph structure and the disturbed data, node attributes are optimized, so that node attribute distribution and community division are kept consistent, and a final publishable target attribute graph is obtained. The method has the advantages that strict requirements of local differential privacy are met on the privacy protection level, and multi-dimensional features such as a community structure, triangular counting and node attribute homogeneity are reserved in the published attribute graph at the same time; and the practical value of the synthetic data in applications such as downstream graph analysis, community discovery and personalized service is greatly improved.

The invention discloses a hotel check-in information management method and system based on a database, and belongs to the technical field of information management. Nodes and edges are defined based on the extracted data, node embedding is generated through a random walk method, a customer classification model is constructed, and classification labels of customers are generated; generating a room distribution result by constructing a linear programming model based on the classified customers in combination with the real-time room related information of the hotel; through short-time Fourier transform and BERT model extraction emotion features and text embedding, constructing an emotion analysis model and a demand recognition model, and generating an emotion score and a demand recognition result; and based on the customer classification, the room allocation result, the emotion analysis result and the demand identification result, using a logic rule to generate a personalized service recommendation list. An intelligent management mode from efficient data extraction, accurate customer classification and dynamic room allocation to real-time service response and recommendation is realized.

The invention provides a vehicle mode control method and device, a medium and a vehicle, and can respond to a vehicle starting signal to obtain current environment information; under the condition that the current environment information meets the preset commuting condition, the vehicle is controlled to enter a commuting mode; and controlling the intelligent cabin of the vehicle based on the personalized service parameters in the commuting mode. Therefore, by setting the commuting mode and actively triggering the commuting function according to the use scene, the corresponding personalized service is actively provided for the user, the convenience and comfort of the user in the commuting process are improved, and then the vehicle use experience of the user is improved.

The invention relates to the technical field of intelligent property customer service, in particular to an intelligent property customer service system based on an AI large model, which is used for a back-end server for interaction between front ends, each front end comprises an owner end, a customer service end and a management end, and the back-end server comprises an intelligent customer service module, a big data analysis module, a personalized service module and an information management module. According to the application, through the arrangement of the intelligent customer service module, the demands of the owners can be automatically identified and classified, the rapid response to the demands of the owners is realized, and the application range of the system is expanded by adopting a mode of combining manual customer service and intelligent customer service; through the arrangement of the big data analysis module and the utilization of big data analysis and artificial intelligence technologies, the work condition of customer service staff can be known more comprehensively and accurately, and more valuable decision support is provided for a management layer. By arranging the personalized service module, personalized service schemes can be provided for the owners according to historical appeal information and preferences of the owners, so that the satisfaction degree of the owners is improved.

The present disclosure discloses systems and methods for managing behaviour-based personalized services The system (102) obtains multi-modal data associated with user (116) from data sources. The system (102) determines user emotions and behavioural attributes associated with user (116) based on multi-modal data using artificial-intelligence model. The system (102) further generates user persona model based on user emotions and behavioural attributes. Further, the system (102) computes behavioural score for user based on user persona model. The system (102) predicts probable interactions of the user with applications based on behavioural score using generative artificial intelligence (Gen-AI) models. The system(102) determines o modifications to be made to service configurations of the applications (108A-N) by simulating the behavioural score and the predicted future interactions onto virtual applications. The system (102) modifies the service configurations based on the modifications. Furthermore, the system (102) outputs behavioural score as a token to the applications (108A- N).

The invention discloses a whole-house customized smart home personalized interaction system based on artificial intelligence, and relates to the technical field of smart furniture control. The system comprises a multi-modal perception module, a user portrait construction module, a personalized recommendation module, a natural language processing module, an intelligent control module, a data storage and management module and a safety protection module, adopts quantum and other technologies, perceives users in multiple dimensions, accurately constructs portraits, provides personalized services, and guarantees system safety. The system is accurate in multi-mode perception, comprehensive in portrait construction, high in recommendation individuation, intelligent in language interaction, safe and flexible in control, safe in data storage, strict in protection, capable of paying attention to health and environment, capable of achieving self-evolution and capable of improving home experience.

The invention discloses a cross-number user portrait association and intelligent dialogue optimization method based on voiceprint recognition, and the method comprises the steps: receiving and processing an incoming call voice stream of a user, carrying out the detection of an adversarial sample after the voice preprocessing, and extracting a voiceprint vector from compliant voice through an ECAPA-TDNN model; the user is retrieved and matched through FAISS / Milvus to obtain the user, the portrait, the work order and the historical record of the user are inquired, and intelligent customer service personalized service guidance is achieved. Wherein noise reduction adopts an RNNoise model of a GRU and a dynamic threshold strategy for user matching, a user portrait is based on a hierarchical label system, a block chain and a private cloud are combined for storage, and the cross-number user identification efficiency and the service personalization level are improved through micro-service architecture deployment.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a data processing method, device, equipment and medium based on man-machine interaction. Performing basic intention classification on the standardized data to obtain classified data; performing context memory processing on the classified data to obtain memory data; performing fine-grained analysis on the memory data to obtain analysis data; constructing a multi-dimensional query vector for the analysis data, and combining the multi-dimensional query vector with a preset external knowledge base to generate an intermediate reply; and performing optimization adjustment on the intermediate reply according to a pre-obtained feedback mechanism to obtain an optimization result. By deeply understanding the intention of the user and providing personalized services, the satisfaction and convenience of the user are remarkably improved, the user can obtain more accurate, more timely and more demand-meeting response in the use process, and the interaction experience is enhanced.

The invention provides an AI-enabled intelligent teaching personalized service method and system, and relates to the technical field of intelligent teaching, and the method comprises the steps: periodically collecting the multi-modal data of a current student; performing correlation analysis on learning data and behavior data in the multi-modal data to generate an analysis result; constructing a weight matrix according to a group label and an ability prediction model in an analysis result, and performing hierarchical course division on all knowledge nodes according to the weight matrix; respectively determining a core knowledge proportion and an expansion knowledge proportion based on the capability prediction model and the academic wind vector, and fusing the core knowledge proportion and the expansion knowledge proportion to generate a fusion proportion; according to the method, a core module and an expansion module are integrated, an intensified training sequence and an expansion content sequence are screened out from the core module and the expansion module respectively, the intensified training sequence and the expansion content sequence are arranged in a crossed mode according to the fusion proportion, and a dynamic learning path is generated. And precision and individuation of teaching services are realized.

The invention relates to the technical field of data processing, and discloses a customer management method, device and equipment and a storage medium, and the method is used for establishing a dynamic customer portrait and managing a customer according to the dynamic customer portrait. The method comprises the following steps: acquiring multi-source heterogeneous logistics data, and preprocessing the multi-source heterogeneous logistics data to obtain preprocessed data; fusing the preprocessed data by using a trained multi-modal data fusion model to obtain fused data; based on a preset customer label system, performing feature extraction on the fused data through a machine learning algorithm, and generating a dynamic customer portrait according to the extracted features; generating a personalized service strategy through a multi-objective optimization algorithm according to the dynamic customer portrait and the real-time logistics state, and storing the personalized service strategy in a preset personalized service strategy library; and monitoring customer logistics data in real time, and when a preset abnormal condition occurs, triggering an early warning mechanism and matching a corresponding personalized service strategy from the personalized service strategy library to start a corresponding customer service response process.

The invention relates to the technical field of cleaning robot task management, and discloses an LLM-based cleaning robot task management method, which comprises the steps of analyzing a user voice instruction in real time through an LLM-driven natural language interaction engine, generating a structured task description, dynamically optimizing task priorities and resource allocation in combination with multi-modal perception data, and carrying out task management on the user voice instruction. The method achieves the autonomous decision-making of environment abnormity, supports the real-time change of tasks and the closed-loop learning of historical data in the cleaning process, and forms a personalized service closed-loop mechanism. According to the method, the man-machine interaction naturalness, the task execution efficiency and the system self-adaptive capacity are remarkably improved, and the technical problems that in the traditional technology, semantic understanding is lacked, environmental perception is insufficient, resource distribution is rigid and personalized services are lacked are solved.