573 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 large model deployment method and system based on a multi-level cache mechanism, and the method comprises the steps: building a first-level dynamic semantic cache, preferentially feeding back a reconstruction answer when a new request is matched, starting a second-level context association cache for a missed request, and starting a second-level context association cache for a missed request; the method comprises the following steps: extracting an implicit semantic trajectory vector and constructing a dynamic association graph to identify a potential context dependency relationship, realizing progressive answer splicing, if coverage is still not performed, activating a third-level intention decision cache, performing scene classification and state marking by utilizing a multi-level analysis structure, and finally performing classification and state marking by utilizing a multi-level analysis structure. Based on a knowledge topology network, a query instruction is generated, multi-modal response elements are extracted to generate a combined response, a system synchronously executes multi-level cache collaborative optimization, a cache strategy is adjusted according to user feedback, cache data are adaptively expanded and eliminated, and a multi-level cache is obtained. According to the technical scheme provided by the embodiment of the invention, the response speed, the context understanding capability and the personalized service level of large model deployment are remarkably improved.

The invention discloses a method for generating and constructing a database test knowledge base based on an open source large language model and retrieval enhancement, and relates to the technical field of database testing, and the method comprises the steps: building a local deployment system on a local computer system or server, and achieving the efficient local operation of the open source large language model and the stable output of language processing service; on a local computer system or a server, a retrieval enhancement generation framework is constructed, and accurate knowledge support and reliable content output are provided for local language processing service by integrating multi-modal knowledge, optimizing retrieval and generation processes and guaranteeing safety and compliance; on a local computer system or a server, a personalized database knowledge base is built, database related information is collected and processed from multiple channels and is output in an adaptive form, and the specified information requirement of local personalized language processing service is met. According to the method, efficient and stable operation of the open source large language model is realized, accurate and reliable language processing output is realized, and personalized services meet specific requirements.

The invention relates to the technical field of pension information management, and discloses a cloud-based pension information management system, which comprises a health data acquisition module, a privacy protection processing module, a risk prediction module, a resource scheduling module and a comprehensive service module. The health data acquisition module synchronously acquires data in a multi-mode manner and encrypts and uploads the data by means of wearable equipment and an Internet of Things sensor; the privacy protection processing module guarantees data security by using federated learning and differential privacy encryption; the risk prediction module accurately predicts health risks based on a graph neural network; the resource scheduling module optimizes resource configuration through deep reinforcement learning; and the comprehensive service module generates a personalized service chain according to the knowledge graph. The system solves many problems of traditional pension information management, realizes multi-dimensional data real-time acquisition and privacy protection, accurately predicts health risks, efficiently schedules resources, provides personalized services, improves the pension service quality and the life quality of old people, and promotes the intelligent development of the pension industry.

The invention relates to the technical field of health management and artificial intelligence, in particular to an old people health analysis system and method based on combination of medicine and nursing. The method comprises the steps of collecting and processing multi-modal health data, and generating a health data vector set; mapping the vector set to a standardized health knowledge ontology, constructing a health map initial structure and generating a map node vector; generating a health portrait and an evolution prediction result based on time sequence modeling; identifying risk features according to the health portrait and the prediction result, and generating a medical care service strategy set; and issuing the strategy set to an execution unit and collecting feedback, and finally updating a graph structure and optimizing model parameters based on feedback data. According to the invention, multi-modal data-driven old people health dynamic evolution modeling and personalized service strategy closed-loop optimization are realized, and the intelligent level and response efficiency of medical and nursing services are improved.

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 provides a personalized recommendation system and method for an intelligent terminal, and belongs to the technical field of artificial intelligence and big data, and the system comprises a five-element multi-mode dynamic perception module, a space-time attention feature fusion unit, a hierarchical federal transfer learning framework, a context perception enhanced recommendation engine and an edge-cloud co-evolution mechanism. The five-element multi-mode dynamic sensing module is used for synchronously collecting physiological features, environmental parameters, behavior data, spatio-temporal information and a social relation graph; and the space-time attention feature fusion unit is used for fusing the multi-modal data by adopting an ST-Transform model. According to the system, on the premise of ensuring the privacy of the user, the recommendation accuracy and real-time performance in a complex scene are remarkably improved, and a new technical normal form is provided for the personalized service of the intelligent terminal; according to the personalized recommendation method, cross-device transfer learning enables the time consumption of new user feature mapping to be greatly shortened, environment-driven brightness adjustment effectively reduces the visual fatigue of the user, and incremental learning obviously reduces the data volume updating demand of the model.

The invention discloses a personalized interaction enhancement method and system based on a big language model, and the method and system can accurately capture and understand the demands, interests and preferences of a user through the personalized interaction enhancement based on the big language model, thereby providing a more personalized interaction service, and greatly improving the user experience. Particularly, a self-iterative user portrait mechanism is innovatively introduced, and the behavior pattern and interest change of the user are tracked in real time by dynamically analyzing historical dialogue information, so that the dialogue strategy can be adjusted at any time, and the response content is ensured to be highly consistent with the current demand of the user. Besides, by utilizing a topic segmentation and keyword matching technology, the system can quickly identify the intention of the user, ensure the context correlation of the dialogue, and provide deep domain knowledge support through knowledge graph extension. Generally speaking, the interactive quality is improved, the user portrait is more accurate and dynamic, the personalized service capability is effectively enhanced, and the intelligent response level of the system 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 provides a customer group dynamic division method and system, and relates to the technical field of customer group management.The method comprises the steps that firstly, commodity related data are collected in real time through a multi-channel data collection module, commodity behavior analysis is conducted in combination with service requirements, features are extracted according to analysis results, and commodities are divided into multiple groups; and then, dynamically dividing customers by combining service requirements, generating initial customer group information, and optimizing and adjusting the customer group information according to the updated user portrait information. And finally, performing preference calculation according to the optimized customer group information, and generating a personalized push strategy. According to the method, through dynamic division and optimization of customer group information, unique requirements and behavior characteristics of each customer can be better met, and personalized services are provided.

The embodiment of the invention provides a local life service integration method and device and terminal equipment. The method comprises the following steps: integrating a plurality of heterogeneous local life service providers into a gas station service platform to obtain a local life service platform, and collecting real-time service data in real time; non-oil sales fluctuation of the gas station is predicted based on the real-time service data, and the order quantity and the delivery cycle of upstream and downstream suppliers are dynamically adjusted according to a prediction result; constructing a multi-dimensional data analysis model, predicting user traffic and various service demands in different time periods every day, and configuring a corresponding local life service time window based on a prediction result; dynamically updating callable local life service resources and dynamically deploying gas station human resources based on the order quantity, the delivery cycle and the local life service resource configuration; and generating a personalized service list of the user by using a collaborative filtering algorithm and a reinforcement learning algorithm, interacting with the user in an interaction interface, and providing corresponding local life service resources for the user.

The invention provides a road traffic space-time service platform and method based on multi-source heterogeneous data fusion. The platform comprises a data acquisition module; a data fusion management module; the base map making module is used for generating a road traffic space-time service base map taking a road intersection as a key node; the traffic situation analysis module is used for forming a traffic situation prediction map; and the decision support and personalized service module is used for providing real-time and visual traffic situation display, traffic guidance strategies and personalized travel services for traffic managers and traffic participating users based on the traffic situation prediction map. According to the invention, real-time and visual decision support is provided for traffic managers, and personalized travel schemes can be customized for traffic participating users, so that comprehensive perception and accurate regulation and control of road traffic operation states are realized, and the intelligent level of traffic operation management is greatly improved.

The invention discloses an artificial intelligence offline retail system and method, and relates to the technical field of artificial intelligence. According to the invention, through the integrated artificial intelligence offline retail system, the key technical problem of accurate recognition and tracking of customer behavior tracks by a computer vision technology in an entity retail scene is effectively solved; the customer behavior recognition and tracking capability in a complex shopping mall environment is improved, the system adapts to the diversity of customer behavior modes, and the system can obtain the position and moving track information of customers in real time and construct a preliminary portrait in combination with the appearance characteristics of the customers; in addition, the graph neural network and the knowledge graph technology are used for correlation analysis of multi-modal data, a comprehensive and accurate customer portrait is constructed, semantic user behavior understanding is realized, personalized service and accurate marketing capabilities are remarkably improved, and retailers can better understand and meet customer demands.

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 invention discloses an intelligent integrated control platform for an energy storage system, and relates to the technical field of energy storage and control, and the intelligent integrated control platform comprises an intelligent dispatching center which is in communication connection with a sensing data collection module, a sensing data processing module and an intelligent analysis module; the sensing data acquisition module collects data by using a digital temperature and Hall effect sensor and monitors the charging and discharging rate; the sensing data processing module constructs a real-time pipeline, evaluates the health of the energy storage equipment, performs early warning and makes a plan; and the intelligent analysis module tracks system performance, generates a report, analyzes user behaviors and provides personalized services. According to the intelligent integrated control platform of the energy storage system disclosed by the invention, through cooperation of the intelligent dispatching center and the sensing data acquisition, processing and intelligent analysis module, comprehensive monitoring and health assessment of energy storage equipment, effective early warning of faults and optimization of energy management are realized, and meanwhile, system performance visualization and user behavior analysis are provided; and the operation and maintenance efficiency and the personalized service level can be improved.

The invention discloses an accurate analysis method for Internet user behaviors based on artificial intelligence, and the method belongs to the technical field of Internet, and comprises the steps: collecting user behaviors and basic attribute information through a multi-source data collection technology, and integrating data through a data fusion algorithm after data cleaning and preprocessing; designing and extracting a plurality of behavior features, screening key features by using a feature selection algorithm, and performing feature transformation processing at the same time; constructing a deep learning model fusing a recurrent neural network (such as LSTM and GRU), a convolutional neural network and the like, training the model by using a large-scale data set, and optimizing parameters; establishing a real-time analysis system, inputting new data into the model in real time, providing personalized service and recommendation according to an analysis result, and collecting feedback data to update the model online; and finally, evaluating the model by adopting multiple evaluation indexes, verifying the generalization ability through methods such as cross validation and the like, and further optimizing and adjusting the model.

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 provides a man-machine interaction method and device, electronic equipment and a storage medium, relates to the technical field of self-service, and is applied to a consulting and guiding robot, and the consulting and guiding robot comprises an intelligent consulting mode and an intelligent guiding mode. Responding to a dialogue identifier triggered by a user, and obtaining cache data of the user; determining whether to enter a file handling guiding process according to the cache data, and if yes, switching to an intelligent guiding mode; if not, switching to an intelligent consultation mode, analyzing the obtained dialogue information based on a large language model (LLM), identifying a user behavior intention from the dialogue information, selecting a question and answer tool, and identifying a consultation intention and a named entity; and switching to a corresponding working mode according to the identified user behavior intention and item name. Therefore, the user intention is accurately recognized through multiple rounds of dialogues based on the large language model LLM, and personalized services are provided.

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 a camera zoom control system and method based on an optical zoom technology, and relates to the field of camera zoom control, and the system comprises a zoom instruction receiving module, a lens parameter calculation module, a drive control module, a lens movement module, an image quality evaluation module, an ambient light detection module, an anti-shake compensation module and an intelligent learning module. The receiving module receives a zooming instruction of a user to obtain a target focal length. The calculation module calculates a focal length variation and a lens moving distance. The driving module generates a signal to control the lens to move. The lens module moves to realize zooming. The evaluation module evaluates the image quality. The detection module detects the light intensity of a shooting environment. The learning module collects a data learning algorithm to establish a mapping model. According to the invention, the zoom precision and the shooting quality are improved, the influence of light on precise zoom is considered, the anti-shake function reduces shake interference, the image quality is comprehensively evaluated, the high-precision motor ensures accurate mirror moving, the zoom effect is optimized through closed-loop control and intelligent learning, and personalized services are provided.

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 discloses a camera monitoring system based on wireless communication and a communication protocol optimization method, and relates to the technical field of communication optimization, the camera monitoring system is composed of a monitoring camera group, a wireless communication network, an edge computing unit, a cloud data center and a user interaction terminal; the monitoring camera group simulates human eye vision, adjusts a visual angle and a resolution ratio according to needs, and collects data; a wireless communication network is combined with quantum encryption and a traditional technology, and a communication mode is intelligently selected; the edge calculation unit processes data with low power consumption by using a neuromorphic chip; the cloud data center fuses the block chain and federal learning analysis data; the user interaction terminal has holographic projection and emotion perception functions and provides personalized services. According to the invention, the modules are collaboratively innovated, the pertinence of collected data is strong, the communication is efficient and safe, the processing is fast and accurate, and the interaction experience is good; the system has the functions of intelligent analysis, energy-saving management and the like, the intelligence and reliability of the system are improved, and the system has wide application prospects and market value.

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.

The invention discloses a multi-scene adaptive image quality control method and system, and particularly relates to the technical field of image control, and the system comprises an intelligent sensing module, an image quality evaluation module, an adaptive adjustment module, a user feedback module and a data storage and analysis module. The method specifically comprises the steps of S1, environment information and image data acquisition and preprocessing, S2, image quality evaluation, S3, image quality adaptive adjustment, S4, user feedback and personalized service, and S5, data storage, analysis and cloud collaboration. By integrating intelligent perception, image quality evaluation, self-adaptive adjustment strategy, user feedback and big data analysis technologies, the image quality can be remarkably improved, different shooting environments can be adapted, and personalized requirements of users can be met, so that the user experience and the applicability of the system are enhanced.