766 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 an elevator control system based on artificial intelligence, which belongs to the technical field of elevator control, and comprises a plurality of layers of architectures such as data acquisition, transmission, processing, intelligent decision, execution control and the like. The data acquisition layer collects multi-source data through a car inside and outside and operation state sensor group; the data processing layer provides support for intelligent decision-making through preprocessing, analysis mining and model training optimization. An elevator dispatching module of the intelligent decision-making layer carries out peak dynamic zoning and flat peak energy-saving dispatching according to information such as passenger flow; the safety protection module monitors and processes hidden dangers in real time; the personalized service module provides customized services based on passenger behaviors and demands. And driving of the control layer, a door machine and a car environment control system are executed, and stable lifting of the car, safe operation of a door and intelligent environment adjustment are achieved correspondingly. Experiments show that the system can remarkably improve the elevator operation efficiency, safety and passenger satisfaction and reduce energy consumption, and has important application value.

The invention relates to a multi-modal digital employee reception system and method based on edge calculation, and relates to the technical field of artificial intelligence, and the system comprises a multi-modal data collection module, an edge calculation module, a data fusion and decision module, a trigger activation module, an emotion recognition module, an interaction response module and a behavior prediction module. The method comprises the following steps: acquiring voice, image and action information of a user through a high-resolution camera, a microphone array and an infrared sensor; locally processing data by using an edge calculation module, and extracting voice, vision and thermal imaging features; a Transformer model is dynamically optimized through Test Time Training (TTT), multi-modal data are dynamically fused in combination with a multi-head attention mechanism, a comprehensive decision vector is generated, the system can accurately judge the emotional state of a user, user behaviors are predicted based on a fused Token sequence and historical records, personalized services are actively provided, and by dynamically adjusting a trigger threshold value and a weight distribution mechanism, the emotion state of the user can be accurately judged. The false triggering rate and the missed triggering rate are reduced, and the stability and the robustness of the system are enhanced.

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 airport intelligent management, in particular to an airport intelligent operating system, which comprises a multi-modal data acquisition layer for acquiring multi-source data such as flights, passengers and luggage in real time through a heterogeneous sensor network; the intelligent analysis layer processes data by using deep learning and a unique algorithm, and generates information such as flight delay prediction and the like; the collaborative scheduling layer realizes cross-domain dynamic optimization of flight-boarding gate-luggage system-ground service based on digital twinning and multi-agent reinforcement learning; the man-machine interaction layer is equipped with AR navigation and voice interaction equipment and provides personalized services; in addition, a block chain evidence storage module is arranged to ensure that key operation records are safe and traceable, and a federal learning framework realizes multi-airport knowledge sharing and privacy protection; through multi-module cooperation, the airport operation efficiency is improved, the safety is enhanced, the passenger service experience is optimized, many problems faced by traditional airport operation are effectively solved, and the system has remarkable innovativeness and practicability.

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 relates to the technical field of meta universe and knowledge graph, and discloses a multi-dimensional fusion meta universe and vertical AI model collaborative innovation platform, which comprises a data generation module, an interaction intention knowledge graph construction module, a vertical domain knowledge graph construction module, a knowledge graph fusion module and an intelligent interaction module. The method comprises the following steps: collecting user interaction behavior data, identifying dominant and implicit intentions, and constructing an interaction intention knowledge graph; generating a fused heterogeneous knowledge graph in combination with the vertical domain knowledge graph; constructing a vertical AI model based on the fused heterogeneous knowledge graph; according to the invention, deep understanding and prediction of user intentions are realized, so that intelligent personalized services are provided.

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 belongs to the technical field of power grids, and discloses a user behavior data mining and prediction analysis system in a smart power grid environment, which comprises a data acquisition module, a multi-dimensional feature extraction module, a behavior pattern recognition module, a demand prediction and trend analysis module, a personalized service recommendation module and a data storage and security module. According to the method, the accuracy of user behavior pattern recognition is improved, complex features can be extracted from the power consumption behaviors of the users through combination of multi-dimensional feature extraction and a deep learning algorithm, and therefore the behavior patterns of the users are recognized more accurately. Compared with simple clustering of traditional single-dimensional features, the system can recognize multi-level behavior features such as power utilization peak and valley periods and seasonal preference, and the comprehensiveness of user behavior portraits is improved. Through a demand prediction technology combining time sequence analysis and an integrated learning model, accurate short-term and long-term power consumption prediction can be carried out, and a demand change trend is judged.

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 relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses a service content generation method based on emotion recognition, and the method comprises the steps: collecting voice data, extracting voiceprint features, and building a user voiceprint file; extracting Mel frequency cepstrum coefficient features and Mel energy difference coefficient features, and inputting the extracted features into an emotion classification model for emotion classification recognition to obtain an emotion state of the user; the current emotional state is compared with a historical normal emotional state, emotional fluctuation is analyzed, and whether the emotional state of the user is abnormal or not is judged; and when it is detected that the emotional state is abnormal, target service content is determined from the personalized service library in combination with the emotional state and the user voiceprint file. According to the method, the voice features are extracted and the user voiceprint file is established, so that the emotion fluctuation of the user can be accurately analyzed, whether abnormity exists or not is judged, accurate recommendation service is provided in combination with the personalized features and historical behavior data of the user, and the intelligent and personalized degree of vehicle-mounted voice interaction 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 relates to the technical field of automobile marketing, in particular to an owner right marketing management system and platform based on big data. A data acquisition subsystem acquires owner consumption data from multiple sources and cleans and arranges the data; the data storage subsystem adopts a distributed architecture to store different types of data; the data analysis subsystem applies an algorithm to deeply mine and analyze data so as to construct a vehicle owner portrait, predict a vehicle condition, mine a marketing association rule and analyze a market trend; the marketing decision-making subsystem formulates a personalized marketing scheme according to the scheme; the marketing execution subsystem is responsible for right distribution management and activity promotion interaction; and the monitoring and evaluation subsystem monitors and evaluates the marketing process in real time. According to the invention, vehicle owner demands can be accurately positioned, personalized services are provided, and the satisfaction and loyalty of vehicle owners are enhanced; resource configuration is optimized, and cost is controlled; based on data-driven decision making, enterprise decision making scientificity and market competitiveness are improved, and a comprehensive, efficient and accurate solution is provided for owner rights and interests marketing management of automobile enterprises.

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 discloses an enterprise micro-marketing auxiliary method and system based on a large language model. The efficiency and effect of marketing are improved by using the large language model. According to the method, by integrating a vector database, an embedded model and a context learning technology, the capabilities of real-time understanding of user intentions, personalized reply and quick service change adaptation are realized. The method has the core advantages of accurate user demand analysis, personalized service provision and efficient knowledge retrieval system. By analyzing user behaviors and dialogue records, product recommendation and marketing strategies according with user preferences can be provided. In addition, the method also has a real-time verbal skill updating capability, so that the timeliness and accuracy of information are ensured; the automatic order information extraction technology improves the information processing efficiency, and reduces the time and cost of manual operation. According to the invention, the marketing efficiency is improved, the interactive experience between the user and the customer service is enhanced, and competitive advantages are provided for enterprises in the market with intense competition.

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 provides an archive retrieval method and system based on AI images and talent key information, and the method comprises the steps: collecting archive data, carrying out the preprocessing and feature extraction, carrying out the multi-modal fusion of features, obtaining multi-modal features, and mapping the multi-modal features to a unified feature space; generating high-order features by using the archive features, and combining the high-order features of different modals to generate global multi-modal feature representation; repairing a defect part in the archive image, outputting a repaired image, and then performing dynamic enhancement; converting the final multi-mode feature representation into a quantum state, performing amplitude amplification and search, extracting the most matched feature and decoding; extracting user preferences, optimizing retrieval result sorting, optimizing a model and recommending personalized services to the user; and dynamically adjusting the system state, optimizing the state space model to obtain an optimal behavior strategy, constraining the optimal behavior strategy, dynamically adjusting the multi-modal features, and performing incremental updating of the system.

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 a hotel operation management system and method based on big data, and belongs to the technical field of hotel operation management. An artificial intelligence model is adopted to construct a customer portrait recognition model, and a customer portrait label corresponding to preprocessed customer feature big data is adopted to train the customer portrait recognition model; according to the method, the trained customer portrait recognition model is obtained, and then the target customer portrait of the customer to be checked in is recognized through the customer portrait recognition model, so that personalized service recommendation is realized according to the target customer portrait, the hotel competitiveness can be effectively improved, and meanwhile, the hotel supply consumption can be predicted according to the personalized recommendation service. Therefore, the dynamic configuration of hotel resources is realized, and the customer experience is improved.

The invention provides an information pushing method and system based on big data matching and GPS positioning, and the method comprises the steps: receiving position data, processing the position data through a Kalman filtering algorithm, predicting a movement trend through combining a timestamp and velocity vector information, and employing a trajectory prediction algorithm, and generating a potential region of interest list; using a spatial clustering analysis algorithm to analyze historical position data of the user, identifying a behavior mode and a preference location type of the user, and constructing a user interest model according to the behavior mode and the preference location type; utilizing the user interest model to configure a dynamic adaptive geo-fencing system; sequencing the service providing points by adopting a weighted K nearest neighbor algorithm, and combining geographic position information in the service recommendation list with other sensor data by utilizing a multi-modal fusion algorithm to generate a customized information packet; according to the technical scheme provided by the invention, the accuracy of the position information is improved, the prediction precision and timeliness are enhanced, and personalized service recommendation is enhanced.

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 invention provides an intelligent temperature control method for a heating kettle. The method comprises the steps that various sensors are integrated inside and outside the kettle to collect various data of the inside and the environment of the kettle in real time; constructing and training a hybrid deep learning model based on the combination of a convolutional neural network and a long-short-term memory network, and deploying the trained hybrid deep learning model into a control system of the kettle; carrying out data fusion and synchronization and real-time denoising, and carrying out real-time feature extraction on the denoised sensor data; dynamically adjusting heating power, and detecting water temperature abnormity; and a user-friendly interface design and a remote control function are designed. According to the intelligent temperature control kettle system combining deep learning, fuzzy logic control and sound wave sensing technologies, high-precision temperature control, intelligent analysis and personalized service are achieved through comprehensive application of multiple advanced technologies, and therefore the user experience is remarkably improved.