94 results about "Service optimization" patented technology

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 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 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.

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 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.

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 invention relates to the technical field of intelligent processing of campus operation and maintenance work orders, and discloses an intelligent analysis and service optimization system for campus informatization maintenance work orders. According to the system, standardization processing of multi-source work order data is achieved through a work order information aggregation module; the intelligent priority evaluation module adopts a deep learning model to analyze fault text semantics, generates a dynamic priority coefficient and improves the accuracy of work order grading; the resource scheduling optimization module generates an optimal allocation scheme based on the priority and the personnel state; and the redundant communication management module constructs an active-active architecture by using a software defined network, so that load balancing and rapid fault switching of a data stream and a control signaling are realized, and the communication reliability of the system is effectively guaranteed. According to the invention, the problems of inaccurate priority judgment and insufficient communication guarantee of a traditional work order system are solved.

The application discloses an in-bound tourism risk prediction and dynamic service optimization method based on big data, aiming at solving the problems of heterogeneous risk data perception difficulty, inaccurate spatio-temporal evolution prediction and disconnection between risk and service response in the prior art. The method predicts the risk evolution trend by constructing a unified risk tensor and using a deep spatio-temporal network combining graph convolution and a gated recurrent unit, and realizes dynamic mapping from risk state to service action through a comprehensive adaptation function weighted by reinforcement learning, and then combines the threshold drift and cross-cultural content generation of the tourist cultural portrait to form a whole-process intelligent solution from global perception, trend prediction, strategy mapping, differentiated intervention to closed-loop adaptive optimization, effectively improving the precision of in-bound tourism safety management and the level of personalized service.

The embodiment of the application provides a service optimization method, system, device, computer program product and storage medium. A new decision basis, i.e., stability description information in the unit of cloud resources, is introduced in the service optimization process. The stability description information in the embodiment of the application is in the unit of cloud resources, so that the fine granularity of the stability description information can reach the cloud resource level. Accordingly, in the process of service optimization, the stability of the cloud computing system itself can be improved, and different cloud resources can more accurately avoid stability problems, so that the influence of the stability problems in the cloud computing system on the cloud resources is reduced, and the service stability perceived by the user side can be effectively improved.

The invention relates to a post-loan intelligent service optimization method and system based on multi-dimensional user portraits, and belongs to the field of financial science and technology and artificial intelligence. The method comprises the following steps: acquiring internal and external compliance multi-source data of a bank, and constructing a multi-dimensional user portrait containing basic attributes, credit characteristics, interests and preferences, post-loan interaction and response sensitivity after structured processing; on the basis of an intelligent decision engine and a multi-target service matching model, a personalized service recommendation strategy is generated in combination with portrait features, and the reaching channel and time are accurately controlled; closed-loop self-learning is realized by acquiring explicit and implicit feedback of a client, reversely updating a user portrait and optimizing a decision model. The method solves the problems of lack of pertinence, low efficiency and the like of the traditional post-loan service, and can improve the customer satisfaction and reduce the post-loan risk.

The application provides a tourist flow prediction and service optimization method and system for smart tourism, relates to the technical field of data processing, comprises the following steps: calculating scheduling flexibility according to scenic spot space unit data, constructing a hierarchical prediction architecture to generate multi-granularity prediction results and quantify uncertainty, dividing confidence levels and determining resource allocation schemes according to the prediction results, constructing a dynamic optimization objective function to solve the optimal scheme, and monitoring the flow in real time and distinguishing intervention event data to perform online updating. The application can improve the prediction accuracy of tourist flow, realize accurate resource allocation, and improve the service quality and tourist satisfaction.

This application provides a method and system for optimizing the appointment process based on user feedback. First, it acquires a set of user feedback data for the target service scenario, including text feedback units and corresponding service node identifiers. Then, it performs sentiment analysis and intent recognition processing on the user feedback data to generate an optimization requirement feature set for each text feedback unit's associated service node identifier. Based on this optimization requirement feature set, it constructs a service optimization strategy set containing service node optimization strategies and their triggering conditions. According to the matching results between the real-time operating status data of the current service system and the strategy triggering conditions, it filters and executes the target service node optimization strategies to adjust the resource configuration rules or interaction logic of the corresponding service nodes in the appointment service process. The adjusted service node status is then updated to the service system, achieving real-time optimization of the appointment service process and improving user experience and service efficiency.

The invention relates to the technical field of natural language processing, and discloses an intelligent customer service optimization method and system based on reinforcement learning, and the method comprises the steps: obtaining a current dialogue between a user and an intelligent customer service and user experience, evaluating the problem difficulty degree of the current dialogue, and obtaining the problem difficulty degree of the current dialogue according to the problem difficulty degree of the current dialogue and a preset difficulty degree threshold value; dividing the current dialogue into simple questions and difficult questions, and constructing a reward mechanism based on user experience; for a simple problem, a service track is generated through intelligent customer service autonomous decision processing, and a decision model is updated by adopting a reinforcement learning strategy according to the service track and a reward mechanism; and processing the difficult problem by using the artificial customer service, recording the decision making process of the artificial customer service as an expert demonstration track, and updating the decision making model by adopting a mixed strategy combining imitation learning and reinforcement learning according to the expert demonstration track and a reward mechanism. A solution of manual customer service intervention is used as a demonstration track, so that the intelligent customer service performs imitation learning.

The invention discloses an intelligent advertisement putting service optimization system and a putting platform. The intelligent advertisement putting service optimization system comprises a Plan module, a Test module, an Optimization module, a Regeneration module and a Scale module, wherein the Plan module, the Test module, the Optimization module, the Regeneration module and the Scale module are connected in sequence and are subjected to loop iteration. The system adopts a multi-agent collaboration architecture, a planning agent, a test agent, an optimization agent, a regeneration agent and an expansion agent corresponding to each module are configured, and each agent shares information and makes a collaborative decision through a data flow pipeline to form a closed-loop iterative optimization mechanism. According to the system, intelligent collaborative management of the whole advertisement putting process is achieved, the problems of strategy making, effect verification, budget optimization, creativity regeneration, scale expansion and the like in advertisement putting are efficiently solved through module circulation and intelligent agent cooperation, the putting efficiency and effect are remarkably improved, and the full-link advertisement putting requirements of small and medium-sized enterprises are met.

The invention discloses a business service quality control method, device and equipment and a storage medium, and the method comprises the steps: collecting multi-source business service data, and carrying out the preprocessing of the multi-source business service data; the multi-source business service data comprises settlement data, code scanning data, card swiping data and input data; performing feature extraction on the preprocessed multi-source business service data to obtain corresponding multi-source business service features; performing index scoring on the multi-source business service features by using a weighted scoring model to obtain a basic score; performing regression prediction on the multi-source business service features by using a machine learning model, and correcting the basic score based on a regression prediction result to obtain a target score; dividing the target score into corresponding service quality grades; and when the service quality grade does not reach a grade threshold value, generating a corresponding service optimization strategy in combination with the target score. The business service quality control effect can be improved.

The application discloses a service optimization system based on big data, which comprises a data acquisition module, a dining monitoring module and an output module, and is used for analyzing the dining situation of customers at a dining table; the dish taking data statistics submodule analyzes the dish taking situation of customers from seats, obtains the dish with the maximum dish taking target probability from the angle of dish taking of the customers, and records the dish eating time through a timing unit, so that the real-time consumption of the dish is accurately expressed; the dining consumption analysis submodule combines the different dining speeds and dish consumption amounts of the customers in different time periods, helps the waiter accurately obtain the dish serving time, controls the dish serving speed, prevents the dish pushing and stacking problem on the dining table, effectively matches the dining process, controls the dish serving speed, and maintains the table surface clean, and the application has the characteristics of high service monitoring precision and high human resource utilization rate.

The application relates to the technical field of artificial intelligence and community demand matching, and discloses a community demand intelligent matching service system based on natural language processing, which comprises a text vector generation module, a multi-source vector fusion module, a knowledge graph analysis module, a candidate service optimization module and a service matching and pushing module. Compared with the community service recommendation mode in the prior art which depends on keyword or fixed template matching, especially under the condition that the expression of residents is unclear or the context changes frequently, the technical problem that service intention accurate identification and intelligent recommendation are difficult to realize is solved. Since the natural language pre-training model is introduced, the community service knowledge graph is constructed, and the multi-layer semantic review sorting mechanism is constructed, semantic understanding of the service demand of residents and personalized service pushing are realized, so that the problems of large recommendation deviation, low service coverage and poor response efficiency are avoided, and the intelligentization and response accuracy of the community service system are improved.

This invention provides a microservice optimization method for industrial edge networks based on dynamic orchestration and migration expansion, comprising: abstracting the industrial edge network into an intelligent industrial network model composed of heterogeneous edge nodes and links; modeling industrial application requests as request chains composed of multiple microservices with data dependencies; calculating microservice instance deployment schemes and request routing schemes; statistically analyzing time-varying request flows; and utilizing a method based on... m / m / c This invention utilizes a multivariate performance analysis model of queue networks and a binary search algorithm to analyze and statistically determine the minimum number of cores that satisfy latency constraints. It then dynamically scales and adjusts the core resources of deployed microservice instances. Based on the preliminary dynamic scaling and adjustment results and migration costs, it solves a mixed-integer nonlinear programming migration decision problem to optimize the optimal collaborative deployment and request routing of microservice instances among edge nodes. This invention improves the service continuity and overall energy efficiency of industrial edge network systems.