269 results about "Service optimization" patented technology

The invention belongs to the technical field of bandwidth access management, and particularly relates to an internet access service optimization method and system for adaptive bandwidth management. Through the distributed probe technology, network state parameters and user terminal metadata can be collected in real time, a deep learning network model such as an LSTM-Transformer hybrid neural network is utilized to process and analyze the collected data, the bandwidth requirement in a period of time in the future is predicted, time periodicity and traffic mutation characteristics are considered, and the bandwidth requirement in a period of time in the future is predicted. Equipment association characteristics are introduced, so that the prediction accuracy and reliability are improved, virtual channels are dynamically divided according to the service emergency degree and predicted bandwidth requirements, bandwidth resources are allocated to the virtual channels, it is ensured that high-priority services can obtain enough bandwidth resources in the network peak period, and the network performance is improved. Therefore, the stability and the quality of network service are ensured, and in addition, a bandwidth expansion mechanism and a bandwidth recovery strategy are introduced to cope with the dynamic change of the network bandwidth.

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 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 discloses a medical and educational informatization comprehensive supervision and diagnosis platform system based on multi-modal data fusion. A data acquisition module acquires multi-modal data from a medical and educational information system and preprocesses the multi-modal data; the data storage module constructs a hybrid architecture to classify and store data and establishes an index. The multi-modal data fusion module deeply fuses medical records, images and physiological monitoring data of medical treatment and teaching resources, learning behaviors and teaching evaluation data features of education by means of a feature extraction technology and a fusion algorithm. The supervision and diagnosis module constructs models of medical service quality evaluation, disease diagnosis assistance, teaching quality monitoring and the like based on fusion features to realize accurate analysis. The user interaction module provides interaction interfaces for different roles and supports operations such as data query and uploading. The platform system breaks the data barrier, fully mines the data value, provides powerful support for informatization management, decision making and service optimization in the medical and education fields, and promotes the innovative development of the two fields.

The invention discloses a big language model reasoning service optimization method, system and device and a medium. The method comprises the steps of obtaining a current reasoning request queue; performing dynamic sorting on each reasoning request in the current reasoning request queue; and performing dynamic batch processing on each reasoning request in the reasoning request queue by using an SLO-BS algorithm to obtain a current batch of reasoning requests, and inputting the current batch of reasoning requests into a large language model, the method, the system, the equipment and the medium can solve the problem of video memory fragmentation occurring during multi-task concurrent reasoning, and reduce the SLO violation rate at the same time.

The invention discloses an insurance customer service optimization system based on artificial intelligence, and relates to the technical field of intelligent customer service, and the system comprises a data collection and analysis module which collects the multi-mode interaction data of a customer in real time, extracts the fundamental frequency change rate and MFCC coefficient of voice voiceprint, and calculates an emotion polarity value; meanwhile, analyzing the semantic content of the text to obtain an intention classification label and an emergency degree score, and detecting the coordinates of a text missing region in the image and the micro-expression action frequency in the video stream; and the closed-loop optimization module is used for pushing an audit conclusion and the revised visual decision graph to the client, synchronously returning feedback data, and dynamically optimizing dynamic weight distribution model parameters and causal knowledge graph clause node connection weights. According to the method, accurate quantification of customer emotion and service emergency degree is realized through multi-modal data fusion and PPO dynamic weight distribution.

The invention relates to the technical field of intelligent management, in particular to a computer intelligent service management system and method based on network big data, and the system comprises a service state monitoring module, an operation state modeling module, a service optimization module, an execution scheme adjustment module and a service task distribution module. According to the invention, through data dynamic analysis of the resource consumption proportion, the task processing duration and the completion time, reliable support is provided for task allocation, the resource occupation relationship between tasks is disclosed, the rationality and efficiency improvement of resource allocation are ensured, the task allocation deviation and resource allocation are optimized, and the resource use is dynamically balanced; the service execution efficiency and accuracy are improved, resource waste and task delay are reduced by analyzing task delay and resource utilization changes, identifying execution bottlenecks and adjusting structures, the execution sequence is dynamically optimized in combination with task completion time and a resource allocation proportion, and the system operation efficiency and response capability are improved. And meanwhile, resource utilization, task scheduling and service quality are optimized.

The invention relates to the technical field of business service dynamic optimization, in particular to a customer demand dynamic analysis and consultation service optimization platform based on deep learning, which comprises a user portrait module, an intention analysis module, a knowledge graph module, a learning engine and a recommendation module, the intention analysis module is connected with the knowledge graph module, the knowledge graph module is connected with the learning engine and the recommendation module, the learning engine is connected with the user portrait module and the recommendation module, and the recommendation module is connected with the user portrait module; according to the method, the accuracy of new user demand understanding is remarkably improved, the system can still provide accurate services in the absence of historical data through dynamic extension of the knowledge graph and an incremental learning mechanism, the problem of insufficient personalization caused by user portrait missing is thoroughly solved, and the user experience is improved. And meanwhile, the security and adaptability of data processing are ensured through multi-modal analysis and federal learning.

An inertia auxiliary service optimization clearing method for network following type wind power virtual inertia compensation comprises the steps that a fan virtual inertia compensation control mechanism of which control parameters are dynamically modulated by an inertia compensation target is determined, and a fan implements a virtual inertia compensation control strategy by means of a regulation and control parameter Kdf according to the real-time wind speed and the corresponding angular frequency omega r0; determining a critical inertia Hmin and a 24-time-period power grid equivalent inertia Hsys according to a synchronous unit power generation plan, wind power prediction data and a power grid frequency safety constraint, and when the power grid equivalent inertia is smaller than the critical inertia, calculating an inertia difference Hreq of each time period; in combination with the obtained power grid inertia difference Hreq, according to different virtual inertia compensation capacities of the wind power plant and the fan, reasonably distributing compensation target quantities of the wind power plant and the fan; and constructing an electric energy and inertia auxiliary service market joint clearing optimization model in which wind power virtual inertia compensation participates. According to the invention, efficient, flexible and economical inertia auxiliary service can be realized.

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 discloses a method and a system for carrying out government affair service interaction by utilizing a large model, and relates to a government affair service optimization technology in the field of government affair. Comprising the steps of 1, directly obtaining OCR data and ASR records from existing communication records of a cloud hall, 2, carrying out RAG updating and timed fine tuning on a large model according to the OCR data and the ASR records, and 3, deploying a client based on a pc end of the cloud hall, obtaining communication information in real time through the client, and sending the communication information to the cloud hall. Wherein screen information in the government affair service handling process is recorded through screen recording of the client side, the screen information comprises key steps and an operation interface, and voice content of a client is converted into text content in real time through a voice recognition method; and 5, automatically arranging and executing a government affair handling process based on a matching result, and carrying out authenticity judgment when the certificate is involved.

The invention discloses a low earth orbit satellite edge computing service optimization method and system based on double time scales, and relates to the technical field of satellite networks and edge computing, and the method comprises the steps: obtaining a plurality of time frames and time slots, the plurality of time frames and time slots are divided based on time, and the time scales of the time frames are larger than the time scales of the time slots; dynamically adjusting satellite selection and request scheduling based on the visibility and resource state of the current satellite in each time slot; according to the method, a service deployment and migration strategy is dynamically adjusted based on long-term service requirements and resource states in each time frame, and the dynamic adjustment method is an optimization algorithm based on double time scales, so that the service quality can be improved, and the system cost can be reduced.

The invention relates to the field of large models, and provides an e-commerce intelligent customer service optimization method, a related device, a storage medium and a program product, which are used for an e-commerce customer service system and comprise a federal query engine and an intelligent customer service. The intelligent customer service integrates an event knowledge graph and a plurality of static knowledge graphs which are federated nodes; the method comprises the steps that if it is determined that a commodity query intention conforms to a multi-hop query mode based on a federated query engine, cross-knowledge-graph joint query is conducted, a first class of results based on all static knowledge graphs and a second class of results based on event knowledge graphs are obtained, and the first class of results and the second class of results are fused and then transmitted into an intelligent customer service; and the intelligent customer service generates a multi-hop reasoning chain based on the fusion result, generates an initial answer according to the combination of the multiple reasoning chains and the fusion result, adjusts the temperature parameter of each hop to enhance the initial answer according to the knowledge graph coverage rate and confidence of the fusion result and the number of reasoning hops, and obtains and outputs a target answer. According to the scheme, the expansibility and accuracy of intelligent customer service and the questioning and answering ability can be balanced.

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 relates to the technical field of after-sales service, and discloses an after-sales service system and method based on an electric tool, the system comprises the electric tool, an information acquisition unit is used for acquiring working parameters and fault information of the electric tool; the control unit is used for sending a control instruction to the electric tool and receiving detection information returned by the electric tool according to the control instruction; the communication unit is used for uploading data acquired by the electric tool to the cloud server; the cloud server is used for carrying out fault classification and health detection on equipment data formed by the working parameters, the fault information and the detection information; performing multi-dimensional division on the equipment data, and generating a product after-sales portrait according to a dimensional division result; and the client is used for obtaining the target data from the cloud server according to the query instruction and visually displaying the target data. According to the invention, the product data can be accurately obtained after the electric tool is sold, the after-sales service optimization of the product is realized based on the product data, and the user experience is improved.

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 software development service data analysis decision system, and belongs to the technical field of software development analysis. The method comprises the following steps: performing problem data monitoring statistics and processing in a software development test stage and a software development pilot run stage to obtain a first development problem processing set and a second development problem processing set in corresponding stages; a first development problem processing set and a second development problem processing set obtained by monitoring in different stages of software development are subjected to problem influence analysis in different dimensions, and corresponding service optimization decision prompts are adaptively generated according to the problem influence analysis results in different dimensions. Various problem influence analysis and data decision prompt in different stages and overall dimensions of software development are realized; the method is used for solving the technical problems that in an existing scheme, the anomaly autonomous supervision analysis effect on different aspects of software development service is poor, and the analysis decision effect on different anomalies is poor.

The invention discloses a big data-based government affair service window behavior analysis system and method, and relates to the technical field of government affair service analysis. A government affair service window behavior analysis system based on big data comprises a data acquisition module, an object identification module, a behavior analysis module, a service personnel evaluation module, a feature association module and a service optimization module. A correlation analysis model and a graph neural network technology are utilized to perform multi-dimensional analysis on service features of service personnel, group features of service objects and key features of window behaviors, dynamic correlation between service behaviors and feedback behaviors is accurately identified, scores of service efficiency, service attitude, user satisfaction and the like are generated, and the service efficiency, service attitude, user satisfaction and the like are improved. And the accuracy and depth of behavior analysis are obviously improved.

The invention relates to the technical field of information, and provides a high-value power customer oriented service optimization method and equipment, and the method comprises the steps: judging a power customer feature correlation coefficient through a correlation coefficient matrix, and removing redundant features; marking high-value power customers according to the power customer feature set after dimension reduction; obtaining dynamic behavior data of the high-value power customers from the high-value customer group to obtain dynamic feature vectors of behaviors of the high-value power customers; aiming at the dynamic feature vector, the long-short-term memory network model outputs a behavior prediction result; complaint records and interaction logs of high-value power customers are obtained, and complex appeals are marked; determining a priority service object by adopting a weighted scoring method; and adopting a recommendation algorithm to generate a personalized service strategy for the priority service object. According to the method, accurate identification, behavior prediction and service optimization of high-value power customers are realized, the service quality and satisfaction of the power customers are effectively improved, and technical support is provided for fine operation and differentiated services of power enterprises.

The invention discloses an air-space-ground integrated network cooperative computing service optimization method based on adversarial learning, and belongs to the technical field of artificial intelligence and wireless communication in an air-space-ground integrated network. The method comprises the following steps: (1) in an edge mobile computing communication system in the sky-ground integrated network, proposing a plurality of allocation modes of computing tasks; (2) obtaining total time delay and energy consumption experienced by the wireless equipment in different distribution modes; (3) proposing an objective function and a constraint condition, and forming an optimization problem based on the objective function and the constraint condition; and (4) finding out an optimal result through a plurality of iterations. According to the invention, the total delay of the system is minimized through the space-air-ground integrated network cooperative computing service optimization method; the genetic algorithm driven by the generative adversarial network is provided, the nonlinear high-complexity problem can be solved more efficiently and accurately, the generative adversarial network can learn features and distribution in the data, the generated solution is closer to the actual situation, and the actual problem can be better solved.

The invention provides a service optimization method and system based on data of old people, electronic equipment and a storage medium, and belongs to the field of services. The method comprises the following steps: acquiring basic information and demand information of old people and current service information provided by a community; using a K-means clustering algorithm to classify and label the old people based on the basic information and demand information of the old people; and optimizing the service according to the label identifier and the current service information provided by the community. By collecting and sorting the data of the old people and performing classification management, accurate data management is realized, so that accurate work guidance is provided for service personnel, and diversified requirements of the old people can be efficiently and accurately met.

The invention discloses a self-adaptive price inquiry and comparison service optimization management system based on artificial intelligence, and belongs to the technical field of price inquiry and comparison, and the system specifically comprises the steps: collecting the initial duration of a period, collecting the market price of any article every other collection period, generating a market price sequence every other n collection periods, and obtaining a market price index; calculating an adjustment coefficient alpha of an acquisition period according to a market price index, calculating the adjustment coefficient alpha according to the market price index every n acquisition periods, and adjusting the duration of the next n acquisition periods according to the adjustment coefficient alpha; when the market price of the article acquired in any acquisition period is lower than A% of the current purchase price of the article, A is a set value, and a purchase price abnormity prompt is sent out; according to the invention, fluctuation of market prices can be effectively coped with.

The invention discloses a customer service optimization method and system based on generative artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: reading the retention parcel attribute and retention duration, and setting a reminding short message sending frequency and configuring a sending time zone in combination with the site load and the historical average pickup duration of a user; retrieving a sample data set from a parcel operation database based on the user portrait and a preset parcel taking association factor; training a generative artificial intelligence model by using the sample data, and constructing a pickup probability predictor; and in the short message sending time zone, a predictor is used for optimizing and determining an optimal time node corresponding to the maximum predicted pickup probability, and a reminding short message is sent to a user. Therefore, the technical effects of accurate reminding, improvement of processing efficiency and improvement of user experience are achieved.

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 discloses a service optimization adjustment method and device based on parameter circulation topology. The method comprises the following steps: monitoring parameter circulation information in the operation process of a target service, so as to obtain the parameter circulation information in the operation process of the target service; generating a parameter circulation topological graph of the target service according to the parameter circulation information; obtaining the processing time of each parameter flow link corresponding to the parameter flow information according to the parameter flow topological graph, and obtaining a processing time result set of each parameter flow link; analyzing and processing the processing time result set and the parameter circulation topological graph to obtain a weight coefficient of each node in the parameter circulation topological graph; and when determining that the target service is abnormal, optimizing and adjusting each parameter flow link according to the weight coefficient. According to the method and the device, the technical problems that the optimization and adjustment mode generally depends on experience or trial and error and the reliability is relatively low when a problem occurs in a server system in the prior art are solved.