807 results about "Service efficiency" patented technology

The invention relates to the technical field of gas station equipment monitoring, in particular to a gas station equipment monitoring system and method based on the Internet of Things. The method specifically comprises the steps of collecting multi-dimensional operation parameters of equipment and business process node dynamics in real time, constructing a bidirectional coupling graph model of the equipment and business, and extracting high-order features of physical association and business logic interaction between the equipment by using a graph convolutional network; predicting equipment fault risks by adopting a dynamic health scoring mechanism, backtracking a risk propagation chain in combination with an attention mechanism, and mapping multi-dimensional data into interpretable fault diagnosis information through an abnormal semantic generation network; a double-layer reinforcement learning game mechanism is introduced, an inner layer coordinates equipment maintenance and a risk suppression strategy, an outer layer overall plans service efficiency and a safety target, optimization actions such as resource scheduling and oil product allocation are generated, and a perception-diagnosis-decision closed loop is formed. According to the method, full-time perception of the equipment state, active fault early warning and cross-domain collaborative optimization are realized, and the operation and maintenance efficiency and safety of the gas station are remarkably improved.

The invention discloses an intelligent management system for a service consultation platform, and the system comprises the steps: processing data through a data collection and processing module; using a data fusion unit to construct a feature matrix for the cleaned multi-source data; based on a demand prediction module, generating a prediction result of the user consultation demand; user input is recognized through a semantic analysis and question and answer module; the analyzed semantic information is transmitted to a knowledge management module, matched content is retrieved from a knowledge base based on a semantic similarity matching algorithm, and answers are generated after ranking according to priorities; information in the service process is collected through the service quality dynamic evaluation module for scoring, an optimization scheme is made through the service optimization strategy generation module, and the optimization scheme is fed back to the evaluation module to achieve closed-loop management. According to the method, the prediction capability and the service efficiency of the platform are remarkably improved, and more efficient and more personalized consultation experience is provided for the user.

The invention provides a battery state of health (SOH) cooperative control method and system of a dynamic reconfigurable energy storage system, and belongs to the field of battery health. The method comprises the following steps: designing a multi-time-scale SOH real-time identification model based on transient characteristic quantification, and realizing second-level updating of an electric core-level health state map; and by adopting power-SOH dynamic matching double-layer reconstruction control, the health and balance of the battery pack are ensured. In addition, the SOH tracking error is less than 1.5% by utilizing a distributed collaborative observer and topology adaptive state estimation; dual-mode control is realized through model predictive control and grouping dynamic optimization; and finally, topology is optimized by using a deep learning strategy and a life loss entropy objective function, series-parallel mode life equalization is realized, and the combined life cycle of the system is prolonged. According to the invention, the service efficiency of the battery can be effectively improved, the service life of the battery is prolonged, and the reliability and stability of the whole energy storage system are greatly enhanced.

The invention provides a customer service processing method and device based on big data, equipment, a storage medium and a product, and belongs to the technical field of natural language process.The method comprises the steps that a user appeal is received, and the user appeal comprises a text or voice; the user appeal is input into a pre-trained deep learning model for semantic analysis, at least one key intention and context semantic information are extracted, and the pre-trained deep learning model is obtained based on BERT model training; matching each analysis result with a pre-constructed intention knowledge base to obtain at least one intention conforming to the user appeal; calling a corresponding agent from a pre-obtained agent group, and performing corresponding processing on the user appeal; and tracking the whole process, and feeding back a processing result to the client. According to the method, efficient customer demand identification and processing are realized, the customer service efficiency and intelligence are improved, and the user experience is improved.

The invention relates to the technical field of road condition intelligent perception and application, in particular to a road condition adaptive topology perception and application system and method. A condition cluster perception module monitors road condition parameters in real time; the data transmission communication module realizes differential data transmission strategies; a road section dynamic risk grade evaluation module calculates a road section risk index and grade according to the road structural state parameters and a risk evaluation model; the node adjustment and resource allocation module adjusts the activation state and sampling frequency of each sensor according to the road section risk level, simulates and verifies the implementation effect of a resource allocation scheme under different road section risk levels by using a constructed road system digital twin model, and optimizes the resource allocation scheme by using a multi-objective optimization algorithm; the application service module converts the risk level into a specific service action; the cooperative operation of the modules realizes the flexible allocation of road condition perception, communication and computing resources, and improves the utilization efficiency of the resources and the service efficiency of the system.

The invention relates to the technical field of artificial intelligence, and discloses a method for scoring question and answer pairs based on a reward mechanism to realize large model fine tuning, comprising the following steps: S1, screening initial question and answer pairs from historical chatting records of an artificial customer service; s2, scoring the initial question and answer pairs based on a reward mechanism; s3, screening high-score question and answer pairs and updating the business question and answer pair knowledge base; s4, performing fine tuning training on the large model based on the updated business question and answer pair knowledge base; s5, executing an intelligent customer service question and answer task by using the fine-tuned large model, the S1 comprising: performing data cleaning and preprocessing on historical chat records, and eliminating invalid question and answer pairs; using a semantic model to calculate the semantic similarity between the questions and the answers, and screening question and answer pairs with complete semantics; according to the invention, through the reward mechanism-based question and answer pair scoring method and dynamic knowledge base updating, high business adaptability and accurate question and answer ability of a large model are realized, and the service efficiency and individuation level of intelligent customer service are improved.

The invention provides an intelligent financial management business service efficiency improvement method and system and electronic equipment, and the method comprises the steps: S1, collecting customer data, and carrying out the cleaning and preprocessing of the data; s2, performing deep mining on the customer data; s3, according to the customer portrait, market dynamic and customer behavior changes are monitored in real time, and a recommendation strategy is dynamically adjusted; s4, constructing a risk assessment model, performing risk assessment on the recommended financial products, and setting a risk early warning mechanism; s5, introducing an intelligent customer service system to carry out real-time conversation with the customer, solving customer questions, collecting customer feedback, and optimizing a recommendation algorithm and a risk assessment model according to the customer feedback and transaction behavior data; and S6, regularly evaluating system performance and service effect, collecting user feedback and market data, identifying potential problems and improving space, and optimizing and iterating the system according to an evaluation result. According to the invention, accurate capture of deep demands of customers is realized, and more personalized financial management suggestions are provided.

The invention relates to the technical field of intelligent voice interaction, in particular to an intelligent voice interaction system and an electronic student identity card, and the system comprises a voice input processing module, a semantic decomposition module, a task allocation module, a logic execution module, a dynamic response module and a voice interaction adjustment module. According to the invention, by analyzing the frequency domain and time domain features of the voice signal, accurately capturing voice details, improving the recognition accuracy, analyzing and recombining semantic content word by word, supporting accurate extraction and task description of complex semantics, analyzing resource consumption and dependency relationship, optimizing a task sequence, and improving the efficiency and adaptability of the system; task execution is combined with data retrieval and content classification, it is ensured that key tasks and associated tasks are carried out in a coordinated mode, uncompleted tasks are dynamically fed back and marked, supplementary information is assembled, dialogue continuity and response naturalness are enhanced, a voice interaction adjustment module analyzes uncompleted instructions and repeated calling, and adaptability to demand changes and service efficiency are improved.

The invention discloses an unmanned aerial vehicle track and resource scheduling method based on deep reinforcement learning, and belongs to the technical field of resource scheduling, and the method comprises the steps: carrying out the real-time simulation and optimization of a communication interaction process through employing a digital twin network of a physical network; by minimizing the task completion time delay of all user equipment in the average time slot, the QoE target of task offloading service in the multi-UAV edge network is realized, so that the service efficiency and user experience of the whole network are improved. And aiming at the constructed optimization target, a deep reinforcement learning algorithm is combined with a variational auto-encoder module to solve the problem of mixed action space. Continuous and discrete action spaces are effectively processed, and the decision-making ability of an algorithm is improved. Besides, a graph attention network module is also introduced, so that the unmanned aerial vehicle can adaptively capture the state characteristics of other unmanned aerial vehicles, thereby making a more accurate joint decision. The joint decision-making mechanism can significantly improve the task unloading efficiency and the service quality of the multi-unmanned aerial vehicle edge network.

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 discloses an intelligent government affair service platform and a data processing method applied by the intelligent government affair service platform, and relates to the technical field of government affair service management, and the method comprises a public configuration module which is used for carrying out hierarchical configuration on system service capability according to a configuration instruction, and the hierarchical configuration comprises capability application management configuration, public template configuration and service model configuration; the service model configuration comprises a service overall planning model and a service implementation model; the reservation rule engine module comprises multi-level reservation rules of a hall reservation rule, a special area reservation rule and an item reservation rule, rule parameters can be dynamically adjusted, and the multi-level reservation rules comprise a reservation time period, a government affair service number allocation strategy and a conflict detection mechanism; the business handling module is used for realizing automatic material verification and document generation in a certificate-free business scene; and carrying out data interaction with an external system through the capability application management module, and carrying out system service capability authorization. The service efficiency of government affair services is improved, and the government affair handling experience of the masses is improved.

The invention relates to the technical field of government and enterprise services, and discloses an intelligent adaptation system and method for government and enterprise services, and the method comprises the following steps: carrying out the structural analysis of an original policy text through a deep learning model, including word segmentation, word vector generation, dominant condition extraction and implicit condition reasoning, and finally constructing a policy knowledge graph; the multi-source heterogeneous data is integrated, dynamic enterprise feature vectors are generated through spatial-temporal feature construction, industrial association diagram modeling and spatial-temporal diagram convolution calculation, the enterprise state change gradient is detected, and the abnormal fluctuation risk is recognized; and mapping the policy condition vector and the enterprise feature to a hyperbolic space, calculating a hyperbolic distance and converting the hyperbolic distance into a matching degree, superposing timeliness attenuation, data freshness punishment and state mutation filtering, and outputting a final matching result. According to the invention, a closed-loop system of policy analysis, accurate matching, resource allocation and feedback optimization is finally constructed, the financial resource waste rate is compressed to a low level, and the government affair service efficiency is improved.

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 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 relates to the technical field of artificial intelligence and natural language processing, in particular to an intelligent customer service method and system based on a large model, and the method comprises the steps: generating a unique session ID based on a user identity, and associating a historical context; multi-modal input of texts, voices, images and the like is carried out, and data standardization is carried out through a unified interface; realizing mixed intention recognition by combining a rule engine and a large model; constructing a dynamic customer portrait through multiple rounds of dialogue records, historical work orders and business data; according to the question emergency degree and customer value grading response, analyzing customer emotion in real time, adjusting a reply style, and outputting an answer; the manual operation process of the traditional customer service is converted into an AI automatic closed loop; the method has the beneficial effects that customer intention deep understanding, multi-mode interaction, emotion perception and service process automation are realized through a large language model (LLM) technology, and the service efficiency and the user satisfaction are remarkably improved.

The invention relates to the technical field of e-commerce, and particularly discloses an intelligent customer service implementation system and method based on a digital human, which utilizes a natural language processing technology based on deep learning to perform semantic analysis on commodity information and interaction content input by a user. And mining semantic feature expressions of the user interaction information and each piece of commodity information, determining the commodity information required by the user based on the semantic similarity between the user interaction information and each piece of commodity information, and returning corresponding commodity introduction information. In this way, more personalized and efficient customer service can be provided for the user, so that the user experience and the service efficiency of the customer service system are improved.

The invention provides a multi-tenant large model reasoning service architecture and an operation method thereof, and the architecture comprises a multi-level cache module which is used for managing the storage of a LoRA model in a video memory, an internal memory, a disk and a remote memory according to the priority; the route registration module is used for recording the model cache position and the load state of each server; the dynamic routing module is used for allocating requests according to the tenant identifiers and the server states; and the parameter fusion module is used for superposing the LoRA model and the basic model to generate a personalized inference model. According to the method, efficient resource utilization and low-delay service are realized through multi-stage cache management, a dynamic routing strategy and a load balancing mechanism. The framework supports on-demand loading and parameter fusion of the tenant personalized LoRA model, and the reasoning service efficiency in a multi-tenant scene is remarkably improved.

The invention relates to the technical field of digital marketing, in particular to a digital advertisement putting method based on big data, and the method comprises the following steps: initializing an advertisement model, importing advertisement request time sequence data, carrying out time sequence analysis, adjusting an enqueue rule and a processing rate, calculating request distribution in a differentiated time period, and generating model parameter configuration. According to the invention, through a queuing theory and time sequence data analysis, advertisement time sequence and processing efficiency optimization, time sequence analysis and parameter adjustment, advertisement timeliness is enhanced, user activity peak synchronization is ensured, user participation degree and advertisement effect are improved, and recalculation of service time and estimation of waiting time support real-time monitoring of service efficiency. Accurate matching of advertisement resources and user requirements is achieved, real-time adjustment of strategies and simulation testing rapidly adapt to market changes, the resource utilization efficiency and ROI are improved, and precision and economic benefits of advertisement putting are remarkably improved through fine management and data driving optimization.

The invention discloses an intelligent management system for clinical department storehouse consumables, relates to the technical field of hospital consumable management, and aims to solve the problems that a traditional consumable management mode often depends on manual recording and manual management, data omission and inaccuracy easily occur, the use condition of the consumables is difficult to track, the replenishment opportunity is likely to be inaccurate, and the use cost is low. According to the system, full-life-cycle tracking of consumables is realized through an RFID tag and a block chain recording module, the authenticity and integrity of data are ensured, the management risk caused by information asymmetry is reduced, the consumable inventory condition and consumable demand prediction are monitored in real time, and the medical service efficiency is improved. According to the technical scheme, the relation between the current inventory level and the safety inventory threshold value is automatically evaluated, early warning is given out, medical service efficiency reduction caused by insufficient inventory is avoided, the replenishment order is automatically generated and transmitted to related departments, the requirement for manual intervention is reduced, and the efficiency and accuracy of the whole replenishment process are improved.

The invention provides a passenger cabin service intelligent auxiliary system based on a multi-modal interaction technology. The passenger cabin service intelligent auxiliary system comprises a multi-modal information acquisition module, a passenger intention identification module, an intelligent service scheduling module, a multi-modal feedback module and a data management and self-learning module. According to the passenger cabin service intelligent auxiliary system based on the multi-modal interaction technology, the demand identification accuracy is high, the emergency response speed is extremely high, the workload of a steward is greatly reduced, and the service efficiency is greatly improved; health monitoring and early warning accuracy is extremely high, block chain evidence storage guarantees data integrity and traceability, and safety is enhanced; the federated learning-driven recommendation system greatly improves the satisfaction degree of passengers, the holographic projection interaction naturalness score is high, the personalized experience is optimized, various complex scenes such as day and night, bumping and strong light are supported, the system availability is extremely high, and the environmental adaptability is good; and multi-modal deep fusion is realized, and high-precision interaction is still kept under environmental interference.

The invention belongs to the technical field of information management, and discloses an intelligent management system and method for large model technology government affairs, and the system comprises four layers of architectures of data collection and access, processing and storage, intelligent analysis and decision, and user interaction and application. Through federated learning and large model fusion governance data, a business process is optimized based on a knowledge graph and a large model, and intelligent service interaction is realized by applying multi-modal interaction and the large model. Experiments prove that the system and the method can significantly improve the business handling efficiency, enhance the decision accuracy, improve the user satisfaction, provide effective technical support for intelligent transformation of government affair management, and assist in improving the government service efficiency and decision scientificity.

The invention discloses a telecommunication system service authorization method, and relates to the technical field of optical communication, and the system comprises the following components: S100, a user behavior data acquisition and analysis module, S200, an AI learning engine, S300, a personalized authorization strategy generation module, S400, a dynamic adjustment and feedback mechanism, and S500, a safety guarantee mechanism. According to the method, the diversified behavior data of the user is collected and analyzed in real time, the accurate user behavior portrait is constructed, and the prediction capability of the AI learning engine is combined, so that the future service demand of the user can be intelligently predicted, and a personalized service authorization strategy is generated according to the future service demand; according to the method and the system, telecommunication services which better meet the actual requirements of users can be provided for the users, the user experience is improved, the accuracy of service authorization can be ensured, unnecessary resource waste is reduced, and the service efficiency of operators and the customer satisfaction are improved.

The invention relates to the technical field of elevator group control, in particular to an elevator group control method and system based on deep learning, and the method comprises the steps: building an elevator taking behavior time sequence data set through collecting historical elevator taking behavior data and combining with an elevator calling request and the number of people waiting for an elevator obtained through image recognition; the elevator taking frequency, the elevator calling interval and the passenger number fluctuation are modeled by adopting a long-short-term memory network, passenger flow periodical characteristics in workdays, holidays and peak periods are extracted, the passenger flow of each floor in the future is predicted, and pre-scheduling deployment of idle elevators is completed in advance. In the scheduling stage, state input containing the elevator running state and predicted flow is constructed, a reinforcement learning model is introduced for strategy optimization, a reward function is constructed based on passenger waiting time, response timeout and the service completion number, a scheduling strategy is driven to continuously converge, elevator response distribution is optimized, and position presetting and path adjustment are preset. And the scheduling intelligence and the service efficiency are improved.

The invention relates to the field of data analysis, in particular to a customer service interaction system and method based on natural language processing. The system comprises a multi-channel access module used for supporting interaction of at least one channel and providing a uniform interface; the semantic understanding module is used for carrying out semantic analysis on the input problem through a natural language processing technology so as to determine a problem type and a corresponding intention; the knowledge base retrieval module is used for maintaining a knowledge base containing common questions and answers and quickly retrieving related answers through a semantic similarity matching algorithm; the dialogue management module is used for tracking the dialogue state, understanding the context and managing multiple rounds of dialogue processes; and the natural language generation module is used for converting the answers retrieved by the knowledge base retrieval module into natural languages and replying. And intelligent customer service interaction is realized by utilizing a natural language processing technology, so that efficient and accurate solutions are provided, and the customer service efficiency and satisfaction are improved.

The invention provides an NLP-based customer service dialogue quality detection method and system, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining an e-commerce platform customer service dialogue text stream in real time; semantic understanding is conducted on the dialogue text flow, a semantic understanding result is output, and the semantic understanding result comprises a user consultation intention, key question elements and customer service inquiry information entity integrity; based on the semantic understanding result, performing sentiment analysis on the dialogue text stream, and outputting a structured sentiment analysis result; setting an initial state point and a termination state point in the dialogue processing path based on the semantic understanding result and the sentiment analysis result; the initial state point is a semantic vector fusing core problem elements fed back by the user for the first time and a current user emotional state value. According to the invention, through full-process intelligent processing, dynamic monitoring and accurate optimization of customer service quality are realized, and user satisfaction, customer service efficiency and business normalization are improved.

The invention provides a historical interaction information processing method based on artificial intelligence. The method comprises the following steps: processing a historical interaction data set associated with a user and generating structured heterogeneous data; performing semantic analysis, emotion recognition and cross-modal association coding on the structured heterogeneous data through a pre-trained multi-modal feature extraction model to generate a multi-modal feature vector containing text semantic features and voice emotion features; based on the multi-modal feature vectors, event association features, appeal evolution paths and risk prediction indexes in historical interaction data are extracted through a time sequence deep learning model; and through a cross-modal attention fusion algorithm, dynamically weighting and integrating the multi-modal feature vector and event association features, an appeal evolution path and a risk prediction index, and constructing a dynamic prompt template adaptive to a real-time scene and a user state. According to the method, the customer service efficiency of the OTA platform is remarkably improved, and the method has remarkable commercial values in the aspects of improving the service efficiency, reducing the operation cost, avoiding legal risks and the like.

The invention discloses a multi-source data fusion method based on a water conservancy rain measuring radar, and belongs to the technical field of meteorological observation. Historical rainfall inversion, short temporary rainfall forecast and short-term rainfall forecast are realized based on multi-source data fusion, and a space-air-ground integrated rainfall observation and prediction system is constructed. According to the method, innovative technologies such as multi-source data, multi-parameter inversion, AI intelligent optimization and manual correction are fused, the limitation of a traditional rainfall inversion method is broken through, and the precision and service efficiency of quantitative rainfall estimation are remarkably improved; through multi-modal fusion and full-link integration, the method has significant technical advancement and business value in application in the fields of water conservancy and meteorology.

The invention provides a customer service information processing method and device, and the method comprises the steps: obtaining historical corpus data, training a natural language understanding initial model, and obtaining a natural language understanding model; training the session management initial model by using the identified intention and entity and the corresponding historical session data to obtain a session management model; and inputting to-be-processed data into the natural language understanding model, identifying a user intention and an entity, and matching historical scene data by the session management model according to the identified user intention and the entity and corresponding historical session data and outputting corresponding operation. According to the invention, the reply time of the customer service staff to a single customer and a single customer message can be reduced, the customer response speed of the customer service staff is improved, the number of customers received in unit time is increased, and the service efficiency is improved.

The invention relates to the technical field of text analysis, in particular to a deep cognitive policy analysis method and device, equipment and a medium. The deep cognitive policy analysis method comprises the following steps: collecting text data, and constructing a multi-source heterogeneous data set; preprocessing, performing data management, and constructing a standardized database; designing a multi-dimensional quantitative index, converting the text data into a numeric feature vector, constructing a multi-dimensional feature matrix, and forming a structured policy database; performing self-supervised pre-training on the DeepSeek-R1 general model and the Qwen3 large language model to obtain a trained intelligent policy model; and performing deep analysis on the policy file to be analyzed, and matching related policies for the user. According to the deep cognitive policy analysis method and device, the equipment and the medium, intelligent disassembly, associated integration and dynamic updating of policy terms are realized, bottom support can be provided for policy intelligent matching, precise pushing and process optimization, and policy service efficiency and reach precision are improved.

The invention discloses a multi-key index fusion service resource scheduling method based on AI large model driving, and relates to the technical field of intelligent resource scheduling, when new service resources are generated, a scheduling process is automatically triggered, and service task information of the service resources is identified and collected; and collecting latest engineer related data, and integrating engineer data and task information to obtain a multi-dimensional task data set. According to the multi-key index fusion service resource scheduling method based on AI large model driving, through comprehensive consideration of factors such as working saturation and distance, overfatigue of engineers and unnecessary path time waste are avoided, resources can be quickly responded, and the average duration of resource processing is reduced, so that the overall service efficiency is improved, and the service quality is improved. And the resources are allocated according to the skills and historical good reputation rates of the engineers, so that the resources are ensured to be processed by the engineers with corresponding capabilities and high service quality, and the success rate of resource processing and the customer satisfaction are improved.