1992 results about "User satisfaction" patented technology

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

The invention discloses a dynamic load balancing method and system for an AI model intelligent machine, and relates to the technical field of computer science, and the method comprises the steps: constructing an agent behavior strategy model through combining deep reinforcement learning and a multi-agent system MAS; constructing an intelligent scheduling model based on user request data and resource data, inputting the data into the model, performing resource demand prediction and task characteristic analysis, and outputting an optimal resource allocation scheme; constructing a dynamic load balancing function based on the optimal resource allocation scheme, and automatically adjusting the dynamic load balancing function when the network topology and the load mode change; collecting user feedback data, and performing deep analysis on the user feedback data to obtain a user satisfaction score and a service quality score as a basis for constructing a user feedback evaluation function; constructing a resource scheduling optimization model based on the intelligent scheduling model, the dynamic load balancing function and the user feedback evaluation function, and predicting a user experience strategy; and according to a prediction result, carrying out strategy adjustment and resource redistribution decision making.

The invention discloses a large language model knowledge base question answering system based on a multi-path fusion recall retrieval algorithm, and relates to the technical field of artificial intelligence application, and the system comprises the steps of S1, data collection, S2, data preprocessing, S3, knowledge base construction, S4, analysis and clustering, S5, knowledge recall, S6, weight adjustment, and S7, weight fusion. According to the method, a dynamic weight distribution module is arranged, so that various paths of recall weights such as keyword matching, semantic similarity and a knowledge graph can be flexibly adjusted according to different semantic scenes such as a technical scene and a product query scene, a recall result can be effectively optimized, knowledge related to problems can be accurately screened, and the method is high in practicability and high in practicability. And irrelevant information interference is reduced, the answer quality and the system efficiency are improved, and the user satisfaction is improved.

The invention discloses a task processing method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: responding to a to-be-executed task input by a user, inputting the to-be-executed task and a preset cue word into a large language model, and obtaining a task analysis result output by the large language model; distributing each sub-task corresponding to the to-be-executed task to a corresponding service agent based on the task analysis result; a technical agent corresponding to the subtask is scheduled through the service agent to execute the subtask, and subtask execution results are summarized; and determining a task execution result corresponding to the to-be-executed task based on the sub-task execution result. Compared with the mode that an existing automatic robot can only execute the set task according to the fixed processing flow, the method has the advantages that the to-be-executed task can be fully understood, the to-be-executed task can be split, the to-be-executed task is executed in a graded mode through the corresponding service agent scheduling technology agent, human intervention can be reduced, and the user satisfaction degree can be improved.

The invention discloses an airport intelligent service question answering method and system based on AI, relates to the technical field of artificial intelligence, and comprises the steps of performing combined modeling by using a time sequence and a multi-head attention mechanism, extracting key behavior characteristics, fusing intention recognition and behavior prediction, and improving the answer reasoning accuracy. The method comprises the following steps: selecting a semantic knowledge base, constructing deep semantic representation and accurately matching the deep semantic representation with the semantic knowledge base, extracting high-weight phrases in combination with a sliding window mechanism, generating unique semantic fingerprints by utilizing a Karp-Rabin hash function, improving the speed and accuracy of answer matching, and performing fuzzy control and redundant information elimination on candidate answers by adopting a rough set tolerance model. Semantic similarity judgment is carried out according to the intersection and union set proportion between the upper and lower approximate sets, the problems of semantic drift and repeated candidates in traditional matching are effectively relieved, and through reinforcement learning and A / B testing, the question and answer strategy is continuously optimized, and the response effect of the system and the user satisfaction degree are enhanced.

The invention discloses a smart home voice interaction control method and system, and relates to the technical field of smart home. The problems that in the prior art, the intention of a user cannot be accurately understood through non-voice information such as emotion and expression of the user, a control strategy is solidified and lacks individuation, and user requirements and emotion cannot be met in real time are solved. By fusing voice and non-voice information, accurate recognition of user intentions is enhanced, recognition accuracy of voice instructions is improved, emotional states and requirements of users are understood more comprehensively, particularly in a noisy environment or a complex situation, recognition and response effects of the system are obviously improved, and user experience is improved. By utilizing the emotion analysis and context understanding technology of the user, the interaction mode is dynamically adjusted according to the emotion state of the user, the emotion demands of different users can be flexibly adapted, more humanized services are provided, the control strategy is continuously optimized according to the feedback of the user, the user satisfaction is improved, and the user experience is improved. Therefore, the intelligent degree and the user experience of the smart home system are improved.

The invention discloses a method for an AI model maternal and infant assistant from question answering to accurate recommendation, belongs to the technical field of artificial intelligence, and comprises the steps of user demand input and analysis, user portrait construction and dynamic updating, commodity recommendation and optimization, feedback optimization and strategy adjustment. According to the method and the system, the change of user demands and interests can be flexibly coped with, the recommendation accuracy and the user satisfaction degree can be improved, accurate understanding and processing of complex demands are ensured, the real-time performance and accuracy of user portraits are ensured, the change of the user demands and interests can be flexibly coped with, and more accurate and personalized recommendation is realized. The recommendation accuracy and the user satisfaction are improved, and the user can understand what a certain commodity is recommended, so that the trust of the user to the recommendation system is enhanced.

The invention discloses an automatic large language model selection method based on multi-dimensional evaluation and dynamic weight adjustment, and aims to solve the problems of low text generation quality, inaccurate dialogue questions and answers, low online assistant task completion rate and the like in an existing large language model by introducing a multi-dimensional evaluation mechanism. According to the method, a plurality of evaluation dimensions such as ROUGE index evaluation, text quality evaluation, semantic similarity evaluation, safety compliance evaluation, front suitability evaluation, question and answer evaluation, dialogue evaluation, online customer service evaluation and online assistant evaluation are comprehensively considered, the performance of the large language model is comprehensively evaluated, and through analysis of operation data and user feedback, the evaluation accuracy of the large language model is improved. And dynamically adjusting the weight of each evaluation dimension to adapt to continuously changing application requirements and user expectations. And finally, according to the comprehensive score, an optimal large language model is automatically selected, and high quality and user satisfaction of Web services are ensured.

The invention provides a distributed storage resource intelligent scheduling method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the feature splicing of feature vectors of different modes, so as to obtain a multi-mode feature vector; predicting the user satisfaction based on the multi-modal feature vector and the resource adjustment parameter; establishing a resource demand priority mapping table under different service scenes according to the user satisfaction; constructing a multi-objective optimization model according to the resource demand priority mapping table; on the basis of the multi-objective optimization model, predicting the resource state and the load condition of each node in the distributed storage system; and according to the resource state and the load condition of each node, lightweight rule scheduling is carried out to obtain a preliminary scheduling scheme. According to the invention, intelligent scheduling management of distributed storage resources is realized.

The invention discloses an e-government affair platform management method and system based on cloud data, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing, extracting keywords, carrying out the multi-scale time coding and multi-scale space coding of the data, and storing the data in a database; constructing a government affair knowledge graph based on the database, mining association relationships of department services and analyzing affair handling habits of users; generating a government affair examination and approval path based on the knowledge graph, optimizing the government affair examination and approval path through reinforcement learning, dynamically adjusting the examination and approval process, performing a simulation test by using a digital twinning technology, and verifying the user satisfaction after the examination and approval process is optimized; and storing the government affair data in a database and performing authority management. According to the method, the electronic government affair data processing capability can be improved, the examination and approval efficiency is improved, the verifiability of an optimization strategy is enhanced, and technical support is provided for digital and intelligent transformation of government affair services.

The invention relates to the technical field of communication networks, and discloses a distributed network resource optimization scheduling method and system under a load balancing strategy. The method comprises the following steps: if first service request information sent by a client is received, determining a health state weight, a response time weight and a bandwidth weight; determining a first candidate node; if the number of the first candidate nodes is at least two, calculating a health detection score of each first candidate node; and calculating the expected revenue of each first candidate node, taking the first candidate node with the highest expected revenue as a first target node, and distributing the first service request information to the first target node. According to the scheme, the weight is dynamically adjusted according to the current load and resource conditions, the allocation strategy can be automatically adjusted in the face of different load conditions, dynamic load balancing is achieved, and the overload problem is avoided. And the node most suitable for the current load is preferentially selected, so that the service quality and the user satisfaction can be improved.

The invention discloses a method and a system for generating a digital human video, and relates to the technical field of digital humans, and the method and the system for generating the digital human video can be used for obtaining user behavior data, preprocessing the user behavior data, generating a user feature vector, and improving the accuracy of personalized recognition of a user. And inputting the user feature vector into the adversarial network, generating an initial digital human image, analyzing real-time interaction data of the user, obtaining a user interaction matching degree index, realizing dynamic matching of the digital human image and the user emotion, and enhancing the interaction experience. A feedback mechanism is established through a fuzzy logic control algorithm, expression details, intonations and body languages of a digital person are adjusted, a personalized digital person image is generated, personalized dialogue content is generated through a natural language generation model, and a digital person video is generated in combination with distributed rendering, so that high-quality and personalized digital person video output is realized, and the user experience is improved. Therefore, the satisfaction degree and participation sense of the user are improved.

The invention provides an intelligent recommendation method and system for an e-commerce platform, and the method comprises the steps: collecting user interaction behaviors and time-space context data in real time, and constructing a user behavior multi-modal feature matrix; extracting commodity multi-level features, and generating a commodity comprehensive feature matrix; identifying and predicting a user intention based on the user behavior feature matrix, and generating an intention distribution vector; a recommendation candidate set is obtained by combining the commodity feature matrix and utilizing a context awareness collaborative filtering enhancement technology; a multi-objective optimization function is constructed, and after the user intention vector is input, a personalized recommendation sequence is generated in combination with an optimization result and the candidate set; and user feedback is monitored in real time, online learning and reinforcement learning algorithms are adopted, and a recommendation strategy is continuously optimized based on user instant feedback and long-term satisfaction. According to the scheme, the recommendation accuracy, the diversity of recommendation results and the user experience can be improved.

The invention relates to the technical field of information retrieval, in particular to an intelligent RAG knowledge base system fused with a dynamic knowledge graph. Comprising a data processing module for preprocessing original data to obtain a standardized data set; the graph construction and updating module is used for carrying out knowledge extraction on the standardized data set by utilizing a large model, constructing a knowledge graph and carrying out dynamic updating; the dialogue management and understanding module is used for recording complete information construction of a current dialogue, updating a dialogue state and calculating a retrieval weight adjustment amount; the mixed retrieval module is used for performing mixed retrieval in combination with DPR and PPR retrieval methods to obtain a first retrieval result and a second retrieval result, setting an initial retrieval weight according to the question type, and obtaining a comprehensive retrieval result in combination with the retrieval weight adjustment amount; and the response optimization module is used for outputting an optimal response according to the comprehensive retrieval result and user feedback by utilizing reinforcement learning based on strategy gradient. According to the method, the retrieval and response generation effect can be optimized, and the user satisfaction is improved.

The invention relates to the technical field of shopping recommendation, in particular to an intelligent shopping system with a ranking recommendation function, and the system comprises a user price sensitivity modeling module which is used for building a price sensitivity hierarchical model and extracting price sensitivity features of a user in different time periods through time sequence analysis; performing price sensitivity layering on the users by using a clustering algorithm; the commodity price feature analysis module is used for extracting the price interval, discount information, promotion frequency and historical price fluctuation data of the commodity; constructing a commodity price feature matrix, and generating a commodity price attraction factor; and the recommendation sorting decision module is used for generating a personalized recommendation score R through weighted calculation of the price sensitivity factor and the commodity price attraction factor, and carrying out commodity sorting based on the personalized recommendation score R. According to the method, the matching degree of the recommended content and the user demand is improved through multi-dimensional dynamic modeling, and the recommendation accuracy and the user satisfaction degree are improved.

The invention discloses an art teaching management system and method based on AI. The system comprises an intelligent user management module, a course generation module, a real-time interaction module, an evaluation module, an interaction control module and a data security module. Through a multi-modal deep integration learning algorithm MMDILA and a reinforcement learning algorithm, multi-modal data of a user is analyzed, dynamic generation and adjustment of personalized course content are realized, and real-time learning feedback and personalized suggestions are provided. And a block chain technology and a differential privacy technology are adopted to guarantee the security and privacy of user data. According to the method, the personalized teaching effect and learning efficiency are remarkably improved, the learning time is shortened, the user satisfaction is enhanced, the data security and the privacy protection capability are greatly improved, and the defects in the aspects of personalization, real-time feedback and data security in the prior art are overcome. The method is obviously superior to the prior art in the aspects of personalized teaching and data security, and can meet the requirements of different users.

The invention discloses a transaction platform management system based on cloud service, and relates to the technical field of cloud computing, and the system comprises the following components: a user behavior analysis module which collects the browsing track, purchase record and evaluation information of a user through an e-commerce platform, collects the attribute data and sales data of a commodity, and sends the collected data to a server; the user behavior analysis module accurately extracts key features of user activeness and commodity popularity to provide powerful support for personalized recommendation, the intelligent recommendation module generates a personalized recommendation list based on real-time behavior data and interests of the user, and the intelligent customer service module realizes personalized recommendation through natural language processing and machine learning technologies. According to the system, the user problem is quickly and accurately responded, the customer service efficiency and the user satisfaction degree are remarkably improved, the predictive maintenance module identifies potential risks and abnormities in advance through real-time data analysis, an early warning and solution scheme is provided for an administrator, and the system failure rate and the maintenance cost are effectively reduced.

The invention relates to the technical field of directional delivery, in particular to a media promotion directional precise delivery system and method based on big data, and the system comprises a user interest mining module, a content correlation analysis module, a heterogeneous network optimization module, a user behavior insight module, an advertisement matching strategy module and a precise delivery effect monitoring module. According to the method, the advertisement content matching accuracy is further improved by analyzing user browsing contents and records through a big data technology, effectively extracting keywords, identifying interest points and predicting the behavior trend of the user, it is ensured that advertisements can be accurately put to directional target users every time, and the advertisement effect is monitored in real time, so that the advertisement quality is improved. The advertising strategy is adjusted according to the user feedback, so that the advertising efficiency and the user satisfaction degree are remarkably improved, accurate matching and advertising of the advertising content are achieved through deep analysis of user behaviors and interests, the pertinence and effectiveness of advertisements are greatly enhanced, meanwhile, the user experience is optimized, and interference of irrelevant advertisements is avoided.

The invention discloses a homogeneous real estate space intelligent search method and system based on space partition, and relates to the technical field of space data processing and intelligent search. According to the homogeneous real estate space intelligent search method and system based on space partition, the data of the target real estate area is obtained and preprocessed, so that the accuracy and integrity of the data are ensured; dividing the pre-processed area into a plurality of space subareas through a geographic information system, extracting homogeneous features through a clustering algorithm, and analyzing a peripheral matching adaptation degree index of each subarea in combination with geographic position preference of a user to form a homogeneous house resource set; and searching and matching the homogeneous house resource set through a collaborative filtering algorithm, generating a user recommended house resource list, calculating a matching degree index of house resources in each partition, setting a dynamic feedback mechanism, and sorting the recommended house resource list in real time, so as to improve the user satisfaction. Personalized housing resource recommendation is provided for the user, and the convenience and accuracy of real estate transaction are improved.

The invention relates to the technical field of artificial intelligence, and particularly discloses a data query method and device based on a large language model and a medium. By training the large language model in advance, the large language model can be better guided to accurately generate the language expression meeting the user requirement in the database field table range, the understanding ability of the model for the field table in the specific database range is remarkably improved, meanwhile, training of internal data is avoided in the aspect of data query, and the user experience is improved. According to the method, the SQL statements are generated through a large model, data privacy of different scenes and different enterprises is effectively considered, a personalized answer mode suitable for the user can be assembled according to problems and context information of the user, the satisfaction degree of the user is improved, and interactivity and friendliness in the data query process are enhanced.

The invention discloses a digital intelligent service system for enterprise services, which belongs to the field of enterprise digital services and comprises a user demand analysis module, an intelligent data adaptation module, a service resource matching module, a service efficiency evaluation module and a user-friendly interface. The user demand analysis module is used for semantic analysis and classification decision making; the intelligent data adaptation module identifies and analyzes the protocol type, and establishes an intelligent data transmission channel; the service resource matching module carries out resource pre-screening and accurate matching; the service efficiency evaluation module tracks an identifier and captures a four-dimensional index system in real time, and the dynamic evaluation unit realizes service state grading early warning through a sliding window algorithm; according to the invention, accurate demand identification is realized, the response speed and the data transmission efficiency are improved, the user satisfaction is improved, the service matching accuracy is enhanced, the resource utilization efficiency is optimized, the service capability and the customer experience are improved, and maximization of economic benefits and social values is realized.

The invention relates to the technical field of retrieval enhancement generation, in particular to a method for automatically adjusting retrieval enhancement generation parameters based on content feature modeling. The method comprises the following steps: receiving an original query text of a user, performing component analysis, identifying terminologies, general vocabularies and question entities, and quantifying to form query fingerprints; acquiring a historical behavior sequence of the user, and constructing a score reflecting the level of the user by combining the query fingerprints and adopting a time decay weighting algorithm; the user level score is converted into specific retrieval parameter configuration, and a retrieval strategy blueprint is formed; guiding document library retrieval according to the retrieval strategy blueprint, and screening out a candidate knowledge set which is most matched with the professional level of the user; and according to the user level score, a preset instruction template is intelligently filled, and a situational generation instruction is constructed. According to the method, the problem of non-uniform cognitive load caused by a traditional system is solved through a retrieval enhancement generation technology, and the technical knowledge transmission efficiency and the user satisfaction are remarkably improved.

Devices and techniques are generally described for generating user satisfaction data in a natural language processing system. In various examples, first user input data may be received by a natural language processing system. Behavioral data related to the first user input data may be determined. Natural language processing error data related to the first user input data may be determined. First response data corresponding to the first user input data may be determined. First response characteristic data related to the first response data may be determined. First user satisfaction data may be determined based at least in part on the first response characteristic data, the behavioral data, and the natural language processing error data.

The invention discloses a multi-elevator linkage dynamic response intelligent scheduling system and a scheduling method thereof. The scheduling method comprises the following steps: acquiring an operation rule of an elevator, constructing a historical operation database and updating the historical operation database in real time; capturing a field state in real time through a monitoring system, and generating a real-time data stream according to a time sequence; a digital twinborn model is constructed through a historical database and real-time data flow, and the elevator calling probability and the congestion risk of each floor are predicted; the elevator calling probability of each floor in the next five minutes is predicted based on a digital twinborn model, the congestion risk is calculated, a real-time demand thermodynamic diagram fusing the space-time sequence dimension is generated, and intelligent scheduling is conducted on the elevator through the thermodynamic diagram; by reducing the waiting time, the overall operation efficiency is improved, the user satisfaction is enhanced, and smoother vertical traffic experience is provided for high-rise buildings.

The invention discloses a configuration system and method for dynamically adjusting a business process, particularly relates to the technical field of business adjustment, and aims to dynamically group users accurately according to the degree of dependence of the users on different network services so as to enable resource allocation to be more accurate, and dynamically adjust a bucket dividing strategy according to the change of dependence of the users on the services so as to improve the efficiency of business process adjustment. According to the method, changes of markets and user behaviors can be quickly responded, and a network service provider can more accurately adjust priorities of the user bucket groups by comprehensively analyzing the hidden danger degree of abnormal user traffic distribution and network resource demands in peak periods; the preventive analysis and adjustment method not only can effectively predict and relieve the potential service interruption risk, but also optimizes the resource allocation, and through the dynamic and comprehensive adjustment strategy, the network operator can improve the service reliability and the user satisfaction, and reduce the service quality problem caused by improper resource allocation at the same time.

The invention provides a tourism information release management method and system based on artificial intelligence and big data. The method comprises the following steps: collecting user emotional states monitored in real time and classifying the user emotional states; recommending suitable tourism activities to the user according to the emotional state of the user; an AR scene is generated according to the personalized activity recommendation list, a user interacts by selecting the AR scene, and the AR scene is adjusted and optimized in real time according to real-time interaction; according to feedback data collected in real time in an AR scene, optimizing recommendation planning and experience contents; the recommendation strategy is dynamically adjusted in combination with historical preference data of the user, emotion feedback of the user and particularity of the tourism scene; and multi-level data integration, real-time resource optimization and dynamic scheduling are realized. According to the invention, real-time feedback and dynamic adjustment of tourism management are realized, so that the user satisfaction is greatly improved, and the requirements of modern tourists on individuation, interactivity and instantaneity are met.

The invention discloses a terminal equipment interface display method and system based on user habits, and belongs to the technical field of digital display, key features such as user intention deviation degree and interface attention stability index are extracted by collecting behavior data of a user for interaction objects and function modules, and a user preference weight table is calculated and dynamically updated; interface module sorting is further carried out in combination with a deep learning model, a fuzzy logic evaluation mechanism is introduced to carry out interpretable scoring on a sorting result, when a scoring result is lower than a threshold value, the system automatically corrects a sorting strategy based on user feedback, and finally optimized interface display content is output. Personalization, dynamics and controllability of interface display are achieved, and the man-machine interaction experience and the user satisfaction degree of the terminal equipment are effectively improved.

According to the large model-based real-time voice interaction method and system provided by the invention, multiple rounds of historical dialogue data of the user are collected, the context can be deeply understood, and a subsequent strategy is adjusted according to the evolution of historical dialogue content. Through accurate construction of the dynamic context and strong semantic understanding capability of the large model, the system can better understand user intention and dialogue logic, the generated reply better conforms to human language habits, and the interaction naturalness is greatly improved. The intelligent decision of the large model is optimized based on a reinforcement learning algorithm and human feedback, so that the large model can continuously learn in real-time interaction, a reply strategy is adjusted according to user feedback and dialogue progress, and the relativity, continuity and user satisfaction of reply are improved. By setting the interruption mechanism, the interruption intention of the user can be effectively processed in the real-time voice interaction process, the effectiveness of the real-time voice interaction is ensured, and the accuracy and smoothness of the real-time voice interaction are improved.

The invention provides an intelligent interaction method, device and system based on a large model, a medium and equipment, and is applied to the technical field of artificial intelligence. The method comprises the steps of obtaining a query intention from user query content, performing knowledge retrieval to generate an item candidate list, generating a model cue word by using the list, and screening out a target business item. After the user confirms, relevant content is retrieved in the database, a new model prompt word is generated, and the large model is input again to generate query reply information and return to the user. According to the method, a large-scale language model and knowledge retrieval are utilized, so that the service efficiency and quality of the intelligent service assistant are improved, and the user satisfaction is improved.

The invention provides a multi-modal emotion recognition software development method for voice interaction of an intelligent robot, and the method achieves the synchronous collection and fusion of multi-modal data through the weighted fusion preprocessing of voice signals, facial expression video streams and text contents. A DCNN and LSTM combined model is adopted to deeply extract emotional features, and an attention mechanism is introduced to strengthen key information capture. And a comprehensive emotion representation vector is calculated by using a cross-modal emotion association network CMAN, so that the emotion distinguishing capability is improved. An emotion classifier combining an SVM and a neural network is constructed, and refined output of emotion categories and intensity is achieved. Through real-time feedback and adaptive optimization steps, the model can continuously learn and adapt to user emotion expression habits, and accuracy and user satisfaction are improved. According to the invention, more humanized and accurate multi-modal emotion recognition technical support is provided for voice interaction of the intelligent robot.