973 results about "Service resource" patented technology

The invention relates to the technical field of information management, in particular to an information scheduling scenarized service agent system. The system comprises an information scheduling acquisition and analysis module, a scene classification and service matching module, a service scheme instruction generation module and a service intelligent execution module, user behavior data, environment perception data and business system data corresponding to information scheduling can be obtained, and an original information set is generated; the original information set is preprocessed, an information scheduling scene classifier is constructed, the service matching degree is calculated in combination with the real-time service resource state, and a candidate service scheme set is generated; performing optimization sorting on the candidate service scheme set to generate an optimal service scheme, and disassembling the optimal service scheme into an executable service instruction sequence; receiving a service instruction sequence and generating a service evaluation report; and if the display does not reach the preset service quality threshold value, triggering a dynamic adjustment mechanism to realize self-iteration upgrading of the information scheduling scenarized service. According to the invention, intelligent scheduling and service of information in a complex scene can be realized.

The invention provides a customer service method and device and computer equipment, and relates to the technical field of communication, and the customer service method comprises the steps: obtaining corresponding service requests submitted by a plurality of users in a plurality of different communication channels, and analyzing the corresponding intention information in the service requests based on a natural language processing technology; analyzing preset emotion information of each vocabulary or statement in the intention information; determining a service type required by the user according to the intention information, and matching a corresponding service resource based on the service type; determining a priority order of the service request according to the emotion information and the service resources, and allocating a customer service team or customer service staff corresponding to the service resources according to the priority order; after the service request is distributed, continuous monitoring and data analysis are performed to determine the service quality of the customer service team or the customer service staff for processing the service request, and the evaluation and guidance result is fed back to the corresponding customer service team or the corresponding customer service staff based on the service quality, so that the efficiency and the accuracy of the customer service can be improved.

The invention relates to the technical field of computers, and discloses an inference service management method and device, a medium and a computer program product, and the method comprises the steps: receiving a language model dialogue, and forwarding the dialogue to a corresponding inference service copy through a load balancer; when the language model reasoning service runs, carrying out statistics on throughput data of the reasoning service copy in a set period by utilizing a reasoning service scaling component; when the throughput data exceeds a set throughput threshold value, sending a capacity expansion notification signal to a reasoning service resource scheduler through a reasoning service expansion component, so that the reasoning service resource scheduler carries out reasoning service capacity expansion to obtain a capacity expansion copy; and utilizing a load balancer to configure the address of the expansion copy. In this way, the throughput in the language model service serves as the basis for service capacity expansion and load balancing selection, capacity expansion of the language model reasoning service is triggered when the throughput exceeds the threshold value, then resources are optimally distributed and used, the resource utilization rate can be increased, and user experience can be improved.

The invention discloses a fault root cause positioning method and system for a server cluster, and relates to the technical field of network fault diagnosis. According to the method, nanosecond-level synchronous acquisition of micro-service call chains, container indexes, physical nodes and network data is realized through a precise time protocol, and a consistent data set is constructed through entity association and standardized processing; a service-resource topological graph is dynamically constructed, and an inter-service calling edge weight model is innovatively designed: a real-time load factor and a historical fault index attenuation sum processed by a Sigmoid function are fused, and the weight is periodically updated to accurately quantify the inter-node influence intensity; converting the topological graph into a Bayesian network; when a fault occurs, a three-level assembly line compression alarm is adopted, frequent item sets are mined through bitmap indexes and parallel FP-Growth, and strong causal association item sets are screened in combination with topological edge weights and KL divergence; strong causal alarm is taken as evidence, probabilistic root cause sorting is output through reverse random walk sampling, and high-precision positioning of complex distributed system faults is achieved.

The invention provides a wireless communication and computing resource collaborative optimization method and system. The method provided by the invention comprises the following steps: establishing an initialized global model matched with a business demand for each service provider, and matching service resources of each service provider according to the business demand; establishing a service agent corresponding to each service provider; each service agent generates an optimal strategy based on the corresponding service resources and the state information of other service providers; sending the initialized global model to a distribution client according to an optimal strategy; each distribution client trains the received to-be-trained model based on the distributed service resources, and sends the trained model to the corresponding service provider; and aggregating the received trained models by each service agent to obtain a global model of the corresponding service. The method and the system provided by the invention are used for realizing collaborative optimization of wireless communication and computing resources and improving the overall performance and the resource utilization efficiency of federated learning.

The invention relates to the technical field of semantic analysis, can be applied to business scenes in the fields of medical health, financial science and technology, intelligent interactive service and the like, and discloses a business service processing method based on dialogue recognition, which comprises the following steps: performing semantic analysis on current dialogue content, determining a key problem type, and obtaining a business processing record of a dialogue subject; under the condition that the service processing problem type is identified, obtaining a target operation record in combination with problem occurrence time and a current time interval, constructing a user operation behavior track based on an interface interaction event in the target operation record, and identifying a deviation node in the track; and generating a business processing exception report, matching the target service item in the service resource library according to the service demand keyword and the business processing record, and outputting operation guidance information. By fusing dialogue semantic analysis and user behavior trajectory modeling, automatic identification and accurate service recommendation of customer problems are realized, and the business processing efficiency and the service intelligence level are improved.

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

The embodiment of the invention provides a local life service integration method and device and terminal equipment. The method comprises the following steps: integrating a plurality of heterogeneous local life service providers into a gas station service platform to obtain a local life service platform, and collecting real-time service data in real time; non-oil sales fluctuation of the gas station is predicted based on the real-time service data, and the order quantity and the delivery cycle of upstream and downstream suppliers are dynamically adjusted according to a prediction result; constructing a multi-dimensional data analysis model, predicting user traffic and various service demands in different time periods every day, and configuring a corresponding local life service time window based on a prediction result; dynamically updating callable local life service resources and dynamically deploying gas station human resources based on the order quantity, the delivery cycle and the local life service resource configuration; and generating a personalized service list of the user by using a collaborative filtering algorithm and a reinforcement learning algorithm, interacting with the user in an interaction interface, and providing corresponding local life service resources for the user.

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

A software application includes work order resources, each of which defines an atomic operation for the software application, and a construction service resource, which processes the work order resources in response to all interaction requests for the software application. Each interaction request is received from a client and identifies a corresponding work order, which the construction service processes to dynamically construct a set of deliverables, which can include a custom representation of the work order. While processing the interaction request, the construction service, as directed by the work order, can make one or more requests to context resources for context information corresponding to an activity for which the interaction was requested to construct the set of deliverables. The work order resource can comprise a reflective program that enables the construction service to dynamically determine and construct the set of deliverables, including the next appropriate interaction(s) using the context information, thereby directing a set of atomic operations as part of an activity being performed and enabling the dynamic context-based construction of interaction deliverables.

The invention discloses a self-adaptive micro-service resource management method and device, and the method is applied to a resource-limited micro-service system, and comprises the steps: receiving a task, carrying out the modeling of the time sequence data and spatial dependency of a micro-service based on a space-time diagram neural network, and predicting the resource demands of a future time step, including a CPU, a memory and a network bandwidth; classifying the micro-services into resource redundancy, resource insufficiency and resource self-sufficiency according to the predicted resource demand, the current resource allocation and the resource guarantee threshold; according to the resource state and the business importance, the dynamic priority of the micro-services is calculated, the micro-services with redundant resources are sorted in an ascending order, and the micro-services with insufficient resources are sorted in a descending order; and recovering redundant resources from the low-priority micro-services, and allocating the redundant resources to the high-priority micro-services until available resources are exhausted or resource requirements are met, so that the resource utilization rate is optimized, and a service level protocol is guaranteed. According to the method, when resources are limited, the adaptability of the micro-service in different load environments can be improved.

The invention provides an intelligent business resource scheduling method and system based on big data, and relates to the technical field of data processing, and the method comprises the steps: 1, capturing equipment layer sensor data, business layer order data and logistics layer vehicle track data in real time through distributed data collection nodes, and carrying out the real-time acquisition of the data; a dynamic time window division algorithm is adopted to carry out time sequence alignment processing on the heterogeneous data flow, a ternary state vector matrix with a unified timestamp is generated, the row dimension of the matrix represents a collection time sequence, and the column dimension of the matrix corresponds to equipment layer operation parameters, a service layer task queue state and logistics layer transportation node coordinates respectively. According to the invention, through multi-source data fusion, three-dimensional space analysis and an intelligent algorithm, resource gap accurate identification and cross-level scheduling optimization are realized, and the business processing efficiency and the resource utilization rate are improved.

The invention discloses a service node deployment method, system and device, equipment and a storage medium, and can improve the deployment and debugging efficiency of service nodes. The method comprises the following steps: acquiring MCP Server configuration information and resource configuration information of service resources; generating deployment parameters used for operating the MCP Server and the service resources; sending the deployment parameters to a target server, so that the target server deploys an operation environment corresponding to the MCP Server based on the deployment parameters; in response to a debugging instruction for the service resources, performing debugging operation based on the running environment to obtain a debugging result; under the condition that the debugging result indicates that the service resource meets the preset operation condition, a service node construction request is sent to a target server, so that the target server constructs an MCP Server node in a to-be-called state, and node metadata of the MCP Server node is generated; and receiving the node metadata sent by the target server, and registering the node metadata in the MCP Server management platform.

The invention relates to the technical field of supply chain collaboration, in particular to a multi-level supply chain service ecological digital twin network collaborative optimization system which comprises a system and is used for building, optimizing and executing a supply chain digital twin network. The data acquisition module is used for acquiring physical entity data and service state data of a whole link of a supply chain in real time through an Internet of Things device and an information system interface; the data fusion and twinning construction layer is in communication connection with the physical perception layer and is used for carrying out fusion and standardization processing on the collected multi-source heterogeneous data and is based on a unified supply chain knowledge graph; according to the method, supply chain whole-process accurate mapping and real-time visualization are realized by constructing a multi-level digital twinborn body, automatic optimization is carried out among multiple targets such as cost aging carbon emission by applying an intelligent optimization algorithm, third-party service resources are dynamically scheduled based on a multi-attribute utility theory, cross-organization credible collaboration is realized by means of a block chain intelligent contract, and the service efficiency is improved. And a complete perception decision execution optimization intelligent closed loop is formed.

The invention provides a service scheme generation method and system, and relates to the technical field of computer application, and the method comprises the steps: obtaining user demand information which comprises a service type, a service scene, a resource constraint condition and an expected effect description; semantic analysis is performed on the user demand information based on the comprehensive domain knowledge graph, core service elements are extracted, and a user demand model is constructed; according to the user demand model, candidate service resources are matched in a service resource library through a heuristic search algorithm, and candidate service schemes are generated; a multi-objective optimization algorithm is adopted, real-time resource state data and user feedback information are combined, the candidate service schemes are dynamically adjusted, and the final service scheme is output, so that user requirements can be accurately analyzed, service resources are efficiently matched, the generated service scheme is optimized, knowledge and resource iteration is realized, and the scheme generation efficiency and quality are improved.

The invention relates to the field of edge computing, in particular to a task migration method and device based on an edge computing system, equipment and a medium. By acquiring the current service resources of each service node of the edge computing system, available resources of the edge nodes and the cloud can be mastered in real time, resource finiteness consideration is met, and a complex and high-consumption algorithm is avoided. Task execution data of a target user terminal is accurately captured, and an initial task migration strategy and initial global time delay are comprehensively analyzed in combination with service resources, which means that a decision can be flexibly made according to the actual situation of a task and the current state of a node. When edge node performance fluctuates and a queuing task changes, a strategy is dynamically generated based on real-time data, a switching decision is enabled to accord with a network working condition, and the problems of lack of dynamic adaptability and mismatching of a node selection algorithm and edge equipment in the prior art are solved.

The invention provides a single packet authentication method, electronic equipment and a storage medium, and the method comprises the steps that an SDP client encrypts to-be-verified service request information by adopting a single packet encryption key pre-distributed by an SPA key distribution center to obtain a service request ciphertext, the service request information is used for requesting to access a target service resource through an SDP gateway, and the service request ciphertext is sent to the SPA key distribution center; the method comprises the following steps: performing HMAC operation on user information and a service request ciphertext corresponding to an SDP client by adopting a single-packet authentication key pre-distributed by an SPA key distribution center to obtain a to-be-verified message authentication code, generating a single-packet authentication knocking message according to the user information, the service request ciphertext and the to-be-verified message authentication code, and sending the single-packet authentication knocking message to an SDP controller. According to the application, the pre-distributed single-packet encryption key and the single-packet authentication key are used for single-packet authentication, and the user does not need to keep the key by himself, so that the problems that the key is easy to leak and the user operation is complicated are solved, and the usability is high.

The embodiment of the invention relates to an API intelligent selection and business process calling method and device based on a large model, equipment and a medium. The method comprises the steps that multiple service resources in a target service scene are obtained and packaged according to a target format API, and an API service resource pool is obtained; calling the recorded data based on the historical business process to construct a business data set corresponding to the API service resource pool; adjusting a pre-trained large model based on the business data set to obtain an adjusted model; adjusting the adjustment model based on a pre-constructed cue word to obtain a target model; and receiving input problem information of the user, processing the input problem information based on the target model to obtain a target business code, calling one or more API service resources in the API service resource pool based on the target business code to obtain a business result, and providing the business result to the user. By adopting the technical scheme, the reliability of the target business code can be enhanced, and the smoothness of the business process is improved.

The invention discloses a resource creation method, device and equipment for model service and a medium, and relates to the technical field of computers, and the method comprises the steps: converting a model service deployment demand input by a user into a standardized service template; analyzing the standardized service template to obtain a model service resource scheduling instruction, the model service resource scheduling instruction being used for indicating a target resource pool; and determining a target driver corresponding to a target resource pool indicated by the model service resource scheduling instruction, so as to create cloud resources of the model service in the target resource pool by using the target driver, thereby realizing automatic resource creation of the model service, greatly shortening the deployment period through full-process automatic operation, reducing the labor cost, and improving the deployment efficiency. The heterogeneous resource management process is effectively integrated, the delivery efficiency is remarkably improved, and an enterprise can flexibly and efficiently deploy various AI model services on a private cloud platform and deal with quickly changing business requirements leisurely.

The invention discloses an inference service resource allocation method, electronic equipment and a readable storage medium, and relates to the technical field of computers, the inference service resource allocation method comprises the following steps: obtaining a model file of an inference service, and analyzing the model file to determine a minimum storage resource demand required for running the inference service; determining a resource specification meeting the minimum storage resource requirement, wherein the resource specification is used for representing nodes required for running the reasoning service and storage resource configuration corresponding to the nodes; and according to the storage resource state of the node in the reasoning cluster, determining the resource configuration strategy matched with the resource specification, and configuring the storage resource in the reasoning cluster according to the resource configuration strategy to operate the reasoning service, thereby solving the technical problems of low automation degree and poor utilization rate of the storage resource when resource configuration is performed on the reasoning service, and improving the resource configuration efficiency. The error risk and cost of manual operation are reduced, and the utilization rate of storage resources is also improved.

The invention discloses an electricity customer credit rating management method and system based on a differentiated service strategy, particularly relates to the field of energy service, and solves the problem of cold start user credit evaluation deviation and power service resource dynamic adaptation optimization. Seamless migration of credit scoring parameters and smooth transition of user portraits are realized by introducing multi-dimensional feature similarity analysis of virtual data and real data and a time-driven weight attenuation strategy, and continuity and adaptive optimization capability of an evaluation system are ensured; meanwhile, an intelligent service matching network is constructed based on the credit rating updated in real time, and a closed-loop service management and control system driven by data feedback is formed by dynamically adjusting a prepayment mode threshold value and an electric charge overdraft elastic interval strategy, so that the credit value of a user is deeply associated with the power resource configuration efficiency, and the user experience is improved. The operation risk and the service cost of a power grid enterprise are reduced, and finally collaborative optimization ecology considering energy safety management and user behavior guidance is established.

The invention provides an information processing method and device in a game, electronic equipment and a storage medium, and the method comprises the steps that a first game scene and a first virtual character are displayed in a first graphical user interface, and the first virtual character is a player virtual character controlled by first terminal equipment; controlling the first virtual character to move in the first game scene in a first moving mode; in response to a first interaction instruction initiated for a behavior event of the first virtual character in the first game scene, providing an interaction label through a preset release channel to share the behavior event with a user under the preset release channel, the interaction label comprising an interaction entrance for entering a replica game scene, and a user interface for receiving the interaction entrance; a replica game scene is determined based on the behavioral event. Through the method and the device, interaction modes among players can be increased, game scenes are enriched, the game experience of the players is improved, and the utilization rate of service resources is increased.

The invention discloses a service resource automatic migration method, system and device and a medium, and relates to the technical field of service resource migration, and the method comprises the steps: carrying out the evaluation of service resources of different platforms in a company through a resource evaluation model, determining the use conditions corresponding to different types of service resources, determining whether migration is carried out or not based on the use condition corresponding to any type of service resources in any platform; and when a determination result is yes, determining an optimal migration platform based on the use condition of the same type of service resources of other platforms except the platform, and completing migration processing based on the optimal migration platform. According to the method, all types of service resources in all platforms are evaluated, so that the service resource use condition in a company can be accurately and comprehensively controlled, and the service resources can be distributed more reasonably by carrying out subsequent service resource migration processing on the basis of the service resource use condition.

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

The invention relates to the technical field of computers, and particularly provides an application upgrading method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: dividing an application program into at least two service function modules based on a service function which can be realized by the application program; for each service function module, obtaining a domain name for accessing the service function module; when an upgrading event for the application program is detected, determining a target service function module needing to be upgraded from divided service function modules of the application program, and obtaining upgrading resources corresponding to the target service function module; and based on the resource access path corresponding to the target service function module, updating the service resources stored in the resource access path corresponding to the target service function module by using the upgrade resources. Therefore, the service resource is updated by taking the service function module as the minimum upgrading unit, a tedious file comparison process is not needed, and the time consumed by upgrading is reduced.

The invention discloses a service-oriented distributed resource automatic extraction and packaging method and a server, and relates to the technical field of micro-services, and the method comprises the following steps: querying to obtain a service packaging task state according to a service identifier; determining whether a packing task being executed exists or not; if not, performing dynamic dependency analysis on the target service corresponding to the service identifier during operation, and constructing a service dependency graph; detecting cyclic dependency based on the service dependency graph, and if the cyclic dependency exists, determining a resource acquisition range; obtaining multi-dimensional service resources from the plurality of distributed data sources; carrying out resource aggregation processing on the multi-dimensional service resources to generate a service basic information file, and packaging the multi-dimensional service resources and the service basic information file to generate a service resource package; and uploading the service resource packet to a distributed service management platform for deployment, and creating an asynchronous task. According to the method, the dependency relationship dynamically generated during operation can be effectively captured, the error rate is reduced, and the efficiency is improved.

The invention belongs to the technical field of wireless network communication, and discloses a dual-time-scale optimization method for multi-unmanned aerial vehicle cooperative auxiliary task unloading and caching, which comprises the following steps: setting an updating time slot of an unmanned aerial vehicle service caching decision as a first time slot, updating time slots of the unmanned aerial vehicle flight path decision, the computing resource allocation decision and the user unloading decision are second time slots; wherein the first time slot is composed of a plurality of second time slots; constructing a first optimization objective function and a second optimization objective function; in each first time slot, obtaining requirements of a user task for various service resources, and updating a service cache decision of the unmanned aerial vehicle according to the first optimization objective function; and in each second time slot, obtaining an unmanned aerial vehicle position, a user task and an unmanned aerial vehicle service cache, and updating an unmanned aerial vehicle flight path decision, a computing resource allocation decision and a user unloading decision according to the second optimization objective function.

The invention belongs to the technical field of hotel operation virtual simulation analysis, and particularly discloses a hotel operation virtual simulation analysis method and system and a storage medium, and the method comprises the steps: integrating construction data, historical operation logs and other multi-source data, and carrying out the multi-dimensional correlation analysis through the simulation initial data; the problem of decision deviation caused by the fact that a traditional method depends on artificial experience is solved, and operation bottleneck problems such as a service efficiency threshold value and resource allocation conflicts can be accurately recognized through a three-dimensional simulation model, so that high-precision data support is provided for optimization decision. Competition pricing data are collected in real time through a web crawler tool, competition influence factors are monitored, the check-in waiting loss probability is analyzed, in combination with environment monitoring data, competition opponent strategy change and market change data can be synchronized in real time, dynamic pricing weight parameters are loaded in a three-dimensional simulation model, the customer waiting duration and the loss rate are reduced, and the user experience is improved. And the technical efficiency of service resource scheduling is improved.

The invention discloses an industry and trade enterprise public health service resource optimization decision support system, and relates to the technical field of public health management, and the system comprises a data perception and access layer, a semantic fusion and modeling layer, a knowledge graph and risk calculation layer, an intelligent early warning and decision layer, and a feedback optimization closed loop. Multi-source heterogeneous data are collected through a protocol adapter, a unified data view is constructed through industry and trade public health field ontology semantic fusion, a space-time knowledge graph is dynamically generated, risks are deduced in combination with an improved SEIR space-time propagation model and a Monte Carlo method, visual early warning and interactive decision making are achieved based on digital twinborn bodies, and the method is applied to the field of industry and trade public health. According to the method, data islands can be broken, accurate advanced early warning of public health risks is realized, resource allocation is optimized, and the method is suitable for industry and trade enterprises with dense personnel such as machine manufacturing and electronic processing.

The invention provides a kernel function trajectory-based deep learning inference service performance analysis method, which comprises the following steps of: based on service indexes and hardware theoretical computing power acquired from a production cluster, defining floating point operation times per request (FPR) index to quantify service resource efficiency, and identifying high FPR hotspot services; positioning a reasoning iteration candidate boundary based on a GPU kernel function trajectory, verifying iteration integrity through fingerprint matching and chi-square test, and calculating a second reasoning iteration number IIPS and a model reasoning efficiency MIE; aiming at calculation-intensive operators on the key path, combining a dynamic Roofline model to estimate an operator theoretical performance upper limit, and based on actual execution time, calculating efficiency and a BottleScore index to identify a key bottleneck operator; and outputting targeted optimization suggestions according to analysis results of service efficiency analysis, model efficiency analysis and operator efficiency analysis. According to the method, the inference behavior pattern can be automatically identified from massive kernel trajectories, and the efficiency loss of each level is quantified.