158 results about "Service optimization" patented technology

The invention discloses a computer system service optimization scheduling method for a photoelectric system, and particularly relates to the technical field of computer service optimization, which comprises the following steps of: sensing and extracting information such as task types and time constraints through task characteristics, and constructing a multi-dimensional resource model such as processing capability and idle degree by combining resource modeling; a weighting mechanism is used for evaluating task priorities and generating a scheduling queue, tasks are dynamically mapped to matched resources, a scheduling strategy is adjusted according to system loads and feedback, model parameters are optimized through service feedback, and efficient, self-adaptive and intelligent optimization of task scheduling of the photoelectric system is achieved; according to the method, a dynamic scheduling mechanism based on task characteristics and resource prediction is realized, and the response speed of the high-optimal task is improved; optimizing the resource utilization rate by adopting resource state modeling and prospective mapping; and reinforcement learning and a feedback closed loop are introduced, a sustainable optimization scheduling system is constructed, and the adaptability and stability of the system in a complex photoelectric task environment are enhanced.

The invention discloses an e-commerce intelligent customer service AI dynamic duty and automatic recovery system, and relates to the technical field of e-commerce service optimization, and the system comprises the steps: calculating a problem emergency degree value based on a multi-dimensional evaluation index through a problem emergency degree judgment module, and achieving the dynamic evaluation of the priority of a problem; through a manual customer service switching module, a dynamic weight distribution mechanism is adopted to distribute problems to manual service seats corresponding to professional skills; analyzing the dialogue content of the customer service and the user in real time through a dialogue context analysis module, and generating a dialogue satisfaction curve; through an AI customer service switching module, when the satisfaction curve tends to be stable, the dialogue is handed over to the AI customer service for continuous service; and service strategy optimization is carried out based on the dialogue historical record and the knowledge base through an optimization module. According to the invention, an efficient and intelligent service mechanism is constructed from the aspects of emergency degree evaluation, manual and AI customer service collaborative service, dynamic optimization and the like, and the response efficiency and user experience of a customer service system are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a dynamic state portrait driven service optimization method, device and equipment and a medium. And constructing a dynamic state portrait of the individual, analyzing the state portrait to generate a state abnormality level, adjusting strategy optimization service parameters according to the state abnormality level and the service parameters, and matching knowledge contents based on the state data and the abnormality level to output guidance information. According to the method, the multi-source state data of the individual is integrated, the state portrait is dynamically constructed, and the state abnormal level is identified, so that the self-adaptive adjustment of the service parameters and the personalized matching of the knowledge content are realized, and the timeliness and pertinence of service customization are improved.

The invention provides a networking scene-oriented multipath service optimization method and system, and relates to the technical field of network scheduling optimization, and the method comprises the steps: obtaining topological structure information and service demand information of a communication network in a networking scene, and employing a depth-first search algorithm in combination with a pruning mechanism to generate a candidate path set; on the basis of a marginal value contribution degree theory, calculating a transmission value factor and a path complementary factor of each candidate path in the candidate path set, calculating marginal value contribution degrees of different candidate path combinations according to the two factors, and taking the path combination with the highest marginal value contribution degree as an optimal path combination; and aiming at each path in the optimal path combination, establishing a multi-objective optimization model, and obtaining a final scheduling scheme of the service flow among the paths in the optimal path combination. According to the invention, the problems of lack of differentiated path selection mechanism, poor fault tolerance of single-path transmission, insufficient resource coordination capability and the like in multi-service transmission in the prior art can be solved.

The invention provides a customer feedback intelligent processing and service optimization system and method. The system comprises a multi-source feedback access module, an NLP semantic analysis module, an intelligent classification matching module, a work order intelligent scheduling module, an AI reply template generation module and a closed-loop tracking management module. The method comprises the following steps: firstly, collecting a multi-channel customer feedback text through the multi-source feedback access module; performing word segmentation, sentiment analysis and entity recognition through an NLP semantic analysis module, extracting key information and determining a feedback category; the intelligent classification matching module matches the feedback to a preset service module; the work order intelligent scheduling system generates and distributes a work order according to the information; the AI reply template generation module generates a personalized reply in combination with the feedback category and the sentiment analysis result; and the closed-loop tracking management module monitors the progress of the work order, triggers AI return visit and collects evaluation after the work order is completed, and re-dispatches the work order if the work order is not satisfied so as to form a processing closed loop. According to the invention, feedback processing automation is realized, efficiency and accuracy are improved, and customer experience is optimized.

The invention discloses an entity chain store management system based on the Internet of Things, and the system comprises an Internet of Things sensing module, an intelligent analysis and decision support module, a dynamic resource scheduling module, a customer service optimization module, a safety monitoring and emergency response module, and an innovation marketing module. The Internet of Things sensing module is used for comprehensively sensing data of temperature, humidity and illumination in a chain store, positions and stock of commodities and browsing tracks and purchase records of customers. According to the entity chain store management system based on the Internet of Things, the dynamic resource scheduling module captures subtle changes of the stock quantity, the personnel state and the equipment parameters of each chain store in real time through high-frequency data acquisition once every five minutes, the limitation of data lag in traditional static scheduling is broken through, a virtual mapping model is constructed based on 1000 resource nodes, and the real-time performance of the system is improved. A local resource state of a single store is converted into a global visual digital resource network, so that a scheduling decision can cover resource pools of all chain stores.

The invention discloses a cross-platform member unified management and service optimization system. A data fusion processing module breaks a data island, fuses multi-platform member data, and constructs a complete member portrait; the member value evaluation module comprehensively and accurately evaluates the member value by using a member value evaluation formula based on the comprehensive data, insights the contribution and potential of the member value, and provides a basis for a marketing strategy; the member layering module divides levels according to values and dynamic thresholds, enterprises can formulate personalized management strategies accordingly, resources are reasonably distributed, and the management efficiency and the member satisfaction degree are improved; the service recommendation module calculates and recommends personalized services according to a matching degree formula in combination with the hierarchy and historical behavior data; all the modules are tightly matched, a complete closed loop is formed from data integration, value evaluation, hierarchical division and service recommendation, the member experience is effectively improved, and the recognition sense and stickiness of members to enterprises are enhanced.

The present disclosure relates to a service providing system and method for supporting service optimization operation maintenance and decision-making. In more detail, the present disclosure relates to a service providing system and method for supporting service optimization operation maintenance and decision-making, the system and method being able to create prediction information about the usage and capacity of each of resources optimized through an optimization algorithm on the basis of current status-related status information about the usage and capacity of each of resources that are invested into a service when using an IT service (information communication service), being able to support decision-making for service optimization by providing a result of an optimal service driving environment considering an economic feasibility through comparison analysis using the status information and the prediction information, and being able to support automation of such service optimization.

The invention provides an industrial edge network micro-service optimization method based on dynamic arrangement and migration expansion, which comprises the following steps: abstracting an industrial edge network into an intelligent industrial network model formed by heterogeneous edge nodes and links, modeling an industrial application request into a request chain formed by combining a plurality of micro-services with a data dependency relationship in sequence, calculating a micro-service instance deployment scheme and a request routing scheme; counting a time-varying request stream, analyzing the minimum core number meeting the time delay constraint in a statistical result by using a multivariate performance analysis model based on an m / m / c queue network and a binary search algorithm, and carrying out dynamic expansion and contraction adjustment on core resources of deployed micro-service instances; and solving a mixed integer nonlinear programming migration decision problem according to the initial dynamic scaling adjustment result and the migration cost so as to optimize the optimal collaborative deployment and request routing of the micro-service instance between the edge nodes. According to the invention, the service continuity and the overall energy efficiency of the industrial edge network system are improved.

The invention discloses an intelligent system for customer service quality inspection based on multi-source data, and particularly relates to the technical field of customer service quality inspection. The method comprises the following steps: acquiring session texts generated in a customer service process and associated product purchase and consumption data in a customer account in real time, and performing cleaning and standardization processing; a large-model automatic marking algorithm is constructed to carry out category marking on customer problems, and recognition of problem backgrounds and customer appeals is realized through context correlation analysis; performing double evaluation on customer attitude and customer service quality by using an emotion analysis model and a customer service verbal skill analysis model; and generating a comprehensive evaluation result of the overall quality of the customer service through fusion analysis, finally predicting a customer conversion rate and a contract extension rate based on the overall quality evaluation result, and generating a targeted customer service optimization strategy, thereby realizing accurate evaluation of the customer service quality and intelligent management of the customer relationship.

The invention relates to the technical field of marketing, and particularly discloses a service optimization method and system based on combination of online marketing and offline drainage. By acquiring online marketing and offline service multi-dimensional data in real time, constructing a quantitative evaluation system and dynamically optimizing service collaboration, the method comprises the following steps: firstly, acquiring original data from a user behavior log, an activity configuration library and an offline business system, covering core information such as a user identifier, an activity identifier, a service network state and the like; sequentially calculating a marketing load coefficient, a drainage congestion coefficient, an online marketing behavior coefficient and an offline service bearing coefficient, finally carrying out weighted fusion on the online and offline coefficients to generate a comprehensive service coefficient, verifying an over-limit state by adopting a synchronous non-blocking mechanism and a dynamic threshold value, predicting a resource demand increment in combination with a time sequence, generating a coordination strategy set, and carrying out resource scheduling. Accurate adaptation of online drainage and offline service is realized, the experience is improved, and the system risk is reduced.

The invention discloses a user feedback text emotion recognition method for venue service optimization. The method comprises the steps of 1, collecting data based on online comment information of venue users; 2, carrying out data preprocessing on the collected original data, and carrying out dynamic elimination or weight adjustment on the processed data based on a sample credibility scoring model; 3, performing emotion label assignment on the preprocessed text data to perform data emotion labeling by adopting an automatic labeling process based on combination of an emotion dictionary and a rule algorithm; and 4, based on the semantic features and context structure information of the text data, constructing an ESBA emotion recognition model fusing a pre-training language model ERNIE, a scene perception emotion hierarchical coding mechanism, a bidirectional long-short term memory network BiLSTM and an attention mechanism, and inputting a text sequence into the ESBA emotion recognition model to realize multi-category emotion recognition.

The invention provides a smart campus task collaboration method and system oriented to multi-group service optimization, and relates to the technical field of campus service management. According to the method, historical service logs are called, and storage nodes are distributed according to a service process to generate data sets before and after collaboration; determining a group cooperation state based on the data set, and constructing an exclusive task measurement set and a group behavior feature set; quantifying a cross-group task cooperation priority through a double-evaluation mode mark; generating a group-task collaborative set in combination with iterative operation, and screening and optimizing by using a segmentation coefficient (cohesion + separation degree); the system correspondingly comprises a data acquisition and storage module, a task and feature construction module, a collaborative priority quantification module and a collaborative adaptation analysis and optimization module. According to the method, the problems of lack of precise adaptation, fuzzy priority quantization and the like in multi-group coordination in the prior art are solved, directional optimal configuration of service resources is realized, task coordination efficiency and group satisfaction are improved, and an efficient solution is provided for multi-group differentiated services of the smart campus.

An omni-channel member integration service system relates to the field of information management systems and comprises a multi-source data acquisition module, a data cleaning and standardization module, a member data integration and storage module, a member right and interest unified management module, a personalized marketing and service recommendation module and a cross-channel service collaboration module. According to the method, the members are uniformly collected into a system which is powerful in function and easy to operate, so that an enterprise can clearly insight into complete information of the members from a global perspective, including but not limited to consumption habits, purchase frequency, preference categories and the like, and comprehensively grasp portraits of the members, and meanwhile, the system intelligently identifies and clears repeated data, so that the efficiency of the enterprise is improved. By means of the method, manual repeated input and check work is avoided, the work process is optimized, the overall efficiency of enterprise member management work is remarkably improved, and an enterprise can put more energy into targeted member service optimization and marketing activity planning.

The invention belongs to the technical field of intelligent traffic Internet of Vehicles, and discloses an Internet of Vehicles big data real-time monitoring and user service optimization platform for an intelligent traffic system, and the platform comprises a preprocessing unit which carries out the real-time denoising of collected data, unifies the format, and extracts core semantic features. Realizing cross-node semantic feature encryption sharing by relying on a federated learning framework, completing semantic consistency verification by combining a traffic scene knowledge graph, and eliminating conflict abnormal data; the transmission unit identifies the data causal correlation degree based on 5G network slices, and high-correlation data is transmitted through low-delay slices, so that the data correlation is guaranteed; the data fusion unit can dynamically adjust the weight according to the traffic event type, for example, the data weight of the laser radar is improved in an accident scene, high-precision timestamps and dual-mode positioning calibration space-time migration are matched, and finally cross-modal data accurate fusion is achieved; the causal inference unit deploys a lightweight causal Markov model at a roadside edge terminal, and quickly locates traffic anomaly core inducements.

The invention relates to a software development deployment system based on a micro-service architecture. The system comprises a micro-service management module, a load monitoring module, an output evaluation module, a task virtual module and a micro-service optimization module. A real-time resource monitoring system is constructed, and a load monitoring and output evaluation module is combined with a measured data correction mechanism, so that the accuracy of computing resource load and component output data is ensured, and a reliable basis is provided for optimization decision making; a task virtual module is innovatively designed, a virtual task in a complex scene can be simulated through task screening, learning and dynamic generation mechanisms in combination with a replacement ant algorithm and comprehensive reliable vector evaluation, and the adaptability test capability of a micro-service component under different load conditions is improved; and the micro-service management module determines load, value association and substitution logic among the components through component association relationship analysis and substitution feature generation, and provides data support for dynamic deployment and substitution.

The application relates to an observable system based on service context enhancement, which is built based on an observable architecture and comprises a data acquisition layer, a data processing layer, a data storage layer and a data display layer. The data acquisition layer is used for acquiring metadata of service requirements and technical requirements, and generating technical monitoring data under a service context. The data processing layer is used for associating enhanced data including external service data and software life cycle data with the technical monitoring data under the service context to generate derivative data. The data processing layer is also used for performing semantic analysis on the derivative data to generate pragmatic data, wherein the pragmatic data includes service level operation steps in a natural language form and service optimization suggestions. The data storage layer is used for storing the derivative data and the pragmatic data. The data display layer is used for displaying the derivative data and the pragmatic data. Compared with the prior art, the application can realize deep integration and comprehensive observability of technical data and service processes, and reduce communication barriers between technical teams and service teams.

The present application relates to a kind of multi-purpose virtual power plant economy and auxiliary service optimization method, belong to virtual power plant dispatching technical field, including the following steps: considering renewable energy uncertainty, construct virtual power plant economy and auxiliary service joint optimization model;Using the hybrid optimization strategy of fusion improved particle swarm optimization algorithm and snow melting optimizer is solved to joint optimization model, obtains optimal scheduling scheme.The present application realizes the collaborative balance between minimum operating cost and auxiliary service maximization;Optimization method has the advantages of few parameters, convergence fast, high precision, significantly enhances the solving performance of model and the optimization quality of scheduling scheme.

The invention relates to a logistics intelligent customer service optimization method and system based on a large model, and relates to the technical field of logistics intelligent customer service. The method comprises the steps that a vector database for storing logistics QA dialogue vectors is established, user questions are received and vectorized, matched answers are retrieved in the vector database, if matching fails, the answers are generated through an LLM model, new QA pairs are stored in an unprocessed database, non-backflow QA pairs are regularly extracted and cleaned, intentions are extracted to generate standardized QA pairs, and the standardized QA pairs are stored in the vector database. The system further comprises a data processing module, a large language model module, a vector database, an unprocessed database and a data backflow module to achieve corresponding functions. The technical effects of reducing AI time delay and cost, efficiently and accurately processing logistics problems of users and providing reliable answers are achieved.

Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning func-tion based on a combination of the first objective function and the second objective function; executing a tuning opera-tion of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparam-eter-based points along a convex Pareto optimal curve.

The invention discloses a service integration intelligent decision-making method and system based on a credit-driven reinforcement learning closed loop, and the method comprises the following steps: S1, receiving the unstructured service demand input of a user, and generating a structured service task description containing at least one service module and a cooperative constraint condition thereof through mixed analysis processing; s2, initiating multiple rounds of dynamic bidding to a plurality of service providers to obtain dynamic service condition information, fusing the dynamic service condition information, user preference information and collaborative constraint conditions, performing calculation through a multi-target service optimization model of which the optimization weight can be dynamically adjusted, and generating and recommending at least one integrated service scheme to the user; and S3, responding to confirmation of the user to the integrated service scheme, starting and monitoring service execution, and automatically completing expense settlement according to a preset rule after the service execution is completed. Based on a pluggable field knowledge base and the same set of technical architecture taking a credit-driven reinforcement learning closed loop as a core, the system can be quickly adapted to various high-complexity and strong-collaboration service integration fields such as tourism, exhibition, enterprise travel, wedding celebration and medical rehabilitation travel planning by configuring different service modules and collaboration constraint conditions, and has the advantages of being high in practicability and high in practicability. And a set of unified technical architecture and solution which have high adaptability and can be reused is provided.

The invention relates to the technical field of vehicles, in particular to an Internet of Vehicles traffic management method, device and system and a medium, and the method comprises the steps: obtaining group signal quality characteristics and personal behavior characteristics; group signal quality features and personal behavior features are aligned between a vehicle factory server and a user terminal through a federated learning node, and a lightweight decision tree model is adopted to generate a dynamic user portrait with user behavior frequency as a weight; encoding consumption cycle features in the dynamic user portrait into feature vectors, splicing the feature vectors with spatial-temporal features, inputting the spliced feature vectors into an LSTM neural network model containing a portrait feature embedding layer, and outputting a traffic prediction result of the Internet of Vehicles in a period of time in the future; a signal quality map is obtained, and if it is predicted that the vehicle is about to enter a signal blind area through the signal quality map, a corresponding pre-caching strategy is executed according to the Internet of Vehicles flow prediction result and the content preference characteristics of the user; according to the invention, accurate prediction and service optimization of the traffic of the Internet of Vehicles can be realized.

The invention relates to the technical field of enterprise data intelligent management, in particular to a service optimization method and system based on multi-dimensional enterprise data fusion, and the method comprises the following steps: taking a unified social credit code as a main key, and combining an enterprise name and an operation address as auxiliary matching keys; industrial and commercial registration data, tax declaration data, energy consumption monitoring data, social insurance payment data and operation development data are fused; the method has the beneficial effects that a real-time calculation framework and an intelligent algorithm are adopted, the sudden risk of an enterprise, such as sudden energy consumption increase and tax decline, can be captured in time, and an early warning and service intervention mechanism is automatically triggered. Therefore, enterprises can quickly respond to market changes, the operation risk is reduced, and the competitiveness is improved. And by accurately sensing enterprise demands and service states, the system can realize optimal configuration and efficient utilization of the service resources. Therefore, the service cost can be reduced, the service quality can be improved, and the customer satisfaction and loyalty can be enhanced.

The invention provides an intelligent question-answer driven supply chain financial product aggregation and service system based on an artificial intelligence technology, which relates to the field of electric digital data processing and comprises an intelligent question-answer interaction module, a dynamic authority control module, an intelligent business process module, a cross-system data aggregation module and a personalized service optimization module. The intelligent question-answer interaction module is used for carrying out question-answer interaction with a user to identify a service intention, the dynamic permission control module is used for judging user permission in real time and carrying out dynamic adjustment, and the intelligent service process module is used for dynamically generating an operation form driven by metadata and executing a service process. The cross-system data aggregation module is used for realizing data intercommunication and aggregation of a multi-branch external business system, and the personalized service optimization module is used for analyzing user behavior habits and business preferences and optimizing a question and answer process; according to the system, the intelligent level of supply chain financial services can be remarkably improved, the operation complexity is reduced, and personalized financial service experience is provided for users.

The invention provides a tourist flow prediction and service optimization method and system for a smart travel, and relates to the technical field of data processing, and the method comprises the steps: calculating the scheduling flexibility according to the spatial unit data of a scenic spot, constructing a hierarchical prediction architecture, generating a multi-granularity prediction result, quantifying the uncertainty, dividing the confidence level according to the prediction result, and determining a resource configuration scheme. And constructing a dynamic optimization objective function to solve an optimal scheme, monitoring flow in real time, and distinguishing intervention event data for online updating. According to the invention, tourist flow prediction accuracy can be improved, accurate resource configuration is realized, and service quality and tourist satisfaction are improved.

Aspects are described for a user equipment (UE) comprising a transceiver configured to enable wireless communication with a base station, and a processor communicatively coupled to the transceiver. The processor is configured to enter a radio resource control (RRC) inactive mode and camp on a first cell. The processor is further configured to detect an out of service (OOS) status and set a RAN-based notification area (RNA) parameter, wherein the UE refrains from discarding an access stratum (AS) inactive context based on the RNA parameter. The processor is further configured to camp on a second cell and transmit a message to the base station to report an RNA update responsive to camping on the second cell.

The present application provides a service optimization scheduling method and device, equipment and storage medium, wherein the method comprises: distributing a user service request to a corresponding service provider according to the current weight of each service provider, so that the service provider responds to the user service request, wherein the service provider with a current weight less than a predetermined weight runs an original service model, and the rest of the service providers run an optimized service model; calculating the new weight of each service provider according to the cumulative single service evaluation value of each service provider, the continuous service evaluation value of each service provider and the continuous non-response value of each service provider, and updating the current weight of each service provider with the new weight of each service provider; and adjusting the service model running in the service provider according to the weight change information of the service provider running the optimized service model. The present application can improve the response speed and accuracy of the user service request, optimize the service model in time, and improve the user experience.

The invention relates to the technical field of community data management, in particular to an aged community health management radar map generation system based on a neural network. The data acquisition module is used for acquiring facility configuration data, population structure data and facility use condition data when the elderly take activities; the community clustering analysis module is used for clustering communities according to population structure differences, so that the pertinence of a subsequent analysis process is improved; the activity behavior analysis module compares facility configuration data with facility use condition data in the cluster, judges activity deviation (generality / personality), quantifies a personalized activity influence index corresponding to each old person for a community with the activity deviation being personalized activity, and reflects association between personalized preference and facilities; and the radar map adjustment module dynamically optimizes the radar map based on a neural network and personalized indexes, and visually presents real activity characteristics of the old. According to the system, the accuracy and efficiency of community health management decision making can be improved, and a quantitative basis is provided for aging-suitable service optimization.

The application discloses a computing service optimization method for machine learning tasks, and particularly relates to the technical field of distributed computing. The method comprises the following steps: collecting multi-dimensional behavior data of a training terminal, constructing an original data set, generating a delay offset index and a training deviation index, judging whether a disturbance level evaluation process is triggered, and inputting an influence evaluation model to generate a multi-level disturbance level. According to the level result, interpolation compensation, recovery reconnection or task priority adjustment matching behaviors are performed to realize accurate identification and dynamic response to upload failure or training abnormalities, and the stability of model aggregation and the adaptability of computing service are improved. Through the construction of a double-index evaluation mechanism, the application realizes the pre-identification of upload and training disturbances, improves the stability of model aggregation by combining a multi-level response strategy, dynamically selects an evaluation model according to behavior time sequence characteristics, takes into account evaluation accuracy and execution efficiency, and enhances the adaptability and robustness of computing service in a complex terminal machine learning task environment.