357 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 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 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 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.

The invention relates to the field of power supply work order service management, in particular to a power supply service management method and system based on multi-source data. The method comprises the following steps: collecting a current fault repair work order based on a power supply service platform, carrying out deep work order text semantic analysis and standard fault instance modeling, and constructing a global fault instance map; recognizing a client real-time repair call audio stream, carrying out time window voice analysis one by one, and carrying out intelligent fault work order filling, thereby obtaining a real-time audio intelligent fault work order; performing fault demand decomposition on the real-time audio intelligent fault work order and the global fault instance map, performing power supply service resource dynamic configuration regulation and control, and constructing a service resource regulation and control engine; and performing global fault work order real-time processing based on the service resource regulation and control engine, performing overdue work order early warning, and generating a service timeliness early warning strategy. Through dynamic resource allocation and preventive work order creation, the power supply service efficiency, the power grid reliability and the intelligent level are improved.

The invention discloses a service-based killer chain analysis construction method, which belongs to the technical field of cooperative combat, and comprises the following steps: constructing a service resource pool, and setting a service discovery mechanism; on the basis of the fusion situation set, a situation switching event is recognized so as to perform local updating, and a three-level situation view is constructed; constructing a directed hyperedge, generating a directed hypergraph in combination with function category constraints, generating a dimensionality reduction state space based on multi-dimensional constraints, and generating an initial combat candidate set; configuring a management node, sending task invitation information to the initial combat candidate set in combination with the subtasks, generating a response scheme, performing adaptive screening, generating an initial killing chain, and performing failure verification; constructing a servitization twinborn body, deducing the initial killing chain, and executing delay at a calculation end so as to perform elimination recombination or delay optimization; a service combination template is constructed, an efficiency feedback mechanism is established, the superposition influence of frequent situation switching and state space catastrophe is cooperatively solved, and the real-time performance of generation of the killing chain is remarkably improved.

The invention relates to the technical field of retail supply chain and store resource management, and discloses a service reservation data-based store resource dynamic allocation method, which comprises the following steps of: constructing a coupling association graph of a service item and commodity combination by adopting a graph optimization algorithm fused with sparse perception constraint; according to a core service item determined by the coupling correlation graph, obtaining a reservation fluctuation mode of the core service item by using a time sequence decomposition method of an embedded state feedback mechanism; constructing and training an attention convolutional neural network model combined with local feature self-correction, and predicting service resource demand distribution of the store by using the model; generating a store resource collaborative configuration strategy based on the service resource demand distribution and the associated commodity combination real-time inventory data; according to the invention, accurate coupling prediction and collaborative dynamic allocation of the service reservation demand and the commodity inventory demand are realized, so that the efficiency and accuracy of store resource allocation are remarkably improved.

The invention relates to the technical field of intelligent window scheduling, in particular to a multi-objective optimization intelligent window dynamic scheduling method and system combined with business rules. The method comprises the following steps: acquiring reservation queuing data, customer demand characteristics and window state data associated with each service window in real time; based on real-time collected data, a multi-objective optimization function including global average waiting time minimization, overall service efficiency maximization of all windows and global resource utilization balance degree maximization is constructed; solving the multi-objective optimization function based on a sorting genetic optimization algorithm; selecting an optimal scheduling strategy through a multi-factor fuzzy decision maker; and dynamically allocating window service resources according to the optimal scheduling strategy, and adjusting a window service queue in real time. By constructing the multi-objective optimization function, the resource waste problems of partial window congestion and partial idle windows caused by traditional fixed rule scheduling are effectively avoided, and the overall rationality and efficiency of window scheduling are improved.

The invention relates to the field of information security, discloses a service resource access method and device, computer equipment, a computer readable storage medium and a computer program product, and is applied to a server. The method comprises the following steps: in response to a login verification request sent by a client, obtaining a to-be-verified verification code returned by the client; under the condition that the verification code to be verified passes verification, generating a target encryption token according to token element information of the client, and storing the target encryption token to a target database; in response to a resource access request sent by a client, acquiring authentication information; decrypting the encrypted token to be verified according to the access channel information, and acquiring a stored token under the condition that a plaintext token obtained by decryption passes verification; and under the condition that the stored token is matched with the to-be-verified encryption token, sending a target service resource requested to be accessed by the resource access request to the client. By adopting the method, the technical effect of improving the flexibility of a token verification mechanism can be realized.

The invention belongs to the technical field of cloud native and automatic operation and maintenance, and discloses a micro-service resource automatic telescoping system and method based on multi-dimensional state evaluation. Comprising a data collector responsible for acquiring service indexes in real time, a decision engine for performing multi-dimensional state evaluation to generate a resource adjustment strategy, and a decision executor for executing an adjustment decision. In order to realize high intelligence and adaptivity, the core control loop also works cooperatively with three key auxiliary modules: a dynamic threshold management module responsible for dynamically learning and resetting a threshold, an event detection engine used for identifying a load mode, and a genetic algorithm optimizer capable of dynamically optimizing the weight of a decision model. The system disclosed by the invention is a lightweight and responsive micro-service real-time automatic telescopic system with high self-adaptive capability.

The invention provides a service calling method and device, electronic equipment and a computer storage medium, and the method comprises the steps that first equipment receives service quota request information sent by second equipment, and the service quota request information comprises expected service quota information corresponding to the second equipment; querying service capability state information of a target service corresponding to the service quota request information based on the service quota request information; sending quota detail information to the second device based on the service capability state information; and under the condition that the quota detail information indicates that the first equipment has the service capability corresponding to the expected service quota information, responding to received service execution request information sent by the second equipment, and executing corresponding service calling logic based on the service execution request information. According to the embodiment provided by the scheme, the service resource utilization efficiency and the service response efficiency can be improved.

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 relates to the technical field of artificial intelligence, old-age service and quality monitoring, in particular to an artificial intelligence-driven precise old-age service matching and quality monitoring system, which comprises a service demand analysis module, a service resource matching module, a multi-modal data acquisition module, a topological data analysis module, a service quality monitoring module and a user interaction module, the physical health data and daily activity information of the old people are analyzed by adopting a deep learning technology, and personalized service demand evaluation is realized; intelligent scheduling of service resources is carried out based on a multi-agent system; the service quality manifold is constructed through the topological data analysis technology, the topological distance between the real-time service data and the standard service quality manifold is calculated, whole-process monitoring and early warning of the service quality are achieved, the problems that in existing old-age care services, demand evaluation is not accurate, the resource configuration efficiency is low, and service quality monitoring is not comprehensive are solved, and the service quality monitoring efficiency is improved. The service matching accuracy and the resource utilization efficiency are remarkably improved, and the service abnormity is found in advance and accurately positioned.

The embodiment of the invention discloses a dynamic resource isolation method and device in a computing power network, equipment and a medium, and relates to the technical field of computing power networks. The method comprises the following steps: acquiring the priority of a target power service in a computing power network, and performing iterative calculation of an isolation index according to the priority, an initial resource demand vector of the target power service and an available resource vector; in the iterative calculation process of the isolation index, if the isolation index is greater than or equal to an isolation threshold value, issuing configuration to the resource management and control component through dynamic adaptive learning constraint based on the current resource demand vector, and if the isolation index is smaller than the isolation threshold value after iteration is finished, issuing configuration to the resource management and control component through dynamic adaptive learning constraint. If yes, preempting service resources according to the priority, the current resource demand vector, the current available resource vector and the current isolation index; and based on the preempted resource demand vector, configuration is issued to the resource management and control component through dynamic adaptive learning constraint, an end-to-end isolation path is established, and a flow table is updated.

The invention discloses an airport ground service task scheduling method based on collaborative multi-agent reinforcement learning, and relates to the technical field of airport operation management and intelligent scheduling, and the method comprises the steps: S1, obtaining the real-time position of a flight, the occupation state of a machine position, the operation progress of a special vehicle, and the sign-in data of personnel in a rigid business system, s2, inputting the first running state snapshot into a trained multi-agent reinforcement learning model, outputting a first decision instruction set for resource scheduling by the model, and enabling each instruction to carry an identifier for sending an agent; according to the airport ground service task scheduling method based on collaborative multi-agent reinforcement learning, the real-time performance, the self-adaptability and the global optimization capability of ground service resource scheduling are improved, the manual intervention frequency is reduced, and the airport operation efficiency and the overall operation stability are improved.

The invention relates to the technical field of large model service management, and discloses a system for carrying out self-adaptive current limiting on a token number and a QPS called by a large model. A dynamic monitoring module of the system is responsible for collecting a request flow data set comprising a token consumption sequence, a request frequency sequence and a response delay sequence; a traffic feature extraction module performs multi-dimensional feature analysis on the data set to generate a traffic feature matrix containing token consumption rate, request frequency fluctuation coefficient and delay sensitivity index; the adaptive current-limiting decision module dynamically matches a load balancing strategy based on the matrix, and generates a current-limiting control parameter set containing a token quota threshold and a QPS upper limit threshold; the real-time regulation and control module dynamically adjusts the request queue according to the request queue to generate a new request scheduling sequence; and the feedback optimization module monitors the execution effect and iteratively updates the load balancing strategy. According to the invention, through real-time perception and dynamic adjustment, refined and adaptive management of large model service resources is realized.

The application discloses a community service matching method and system of an interactive terminal and a storage medium, relates to the technical field of interaction, and discloses a community service matching method of an interactive terminal, which comprises the following steps: performing local desensitization processing on information collected indoors, combining a public area terminal to acquire outdoor environment information to generate a structured emergency service demand, and blocking indoor image transmission when service resources are matched, so that the problems of easy leakage of resident privacy, low abnormality identification accuracy, service resource mismatch and response delay in indoor monitoring of emergency events in the prior art are solved, the abnormality identification accuracy of a single elderly person or pet can be improved, the service resource matching effect can be optimized, the emergency service response time is shortened, and the reliability and user acceptance of community emergency service are effectively improved.

The present disclosure relates to a remote resource configuration mechanism. A system for facilitating configuration of infrastructure resources is described. The system includes a plurality of on-premise infrastructure devices, each device including a plurality of infrastructure equipment and an on-premise infrastructure controller for controlling the plurality of infrastructure equipment. The system also includes a cloud service resource configuration manager communicatively coupled to each of the on-premise infrastructure controllers to configure each of the on-premise infrastructure devices via the respective on-premise infrastructure controller.

The invention belongs to the technical field of resource scheduling, and provides a cloud manufacturing machine tool service resource scheduling method and system oriented to customer income, and the method comprises the steps: distinguishing customer types based on customer personalized preference demands, carrying out the weighting of machine tool service key indexes according to the customer types, and constructing a cloud manufacturing customer task income function; solving the cloud manufacturing customer task revenue function by adopting an improved genetic algorithm to obtain an optimal machine tool service resource scheduling scheme; according to the improved genetic algorithm, an individual fitness function is reconstructed based on a non-cooperative game, a machine tool service resource scheduling scheme is optimized, and multi-task income Nash equilibrium is achieved. The technical problem that dynamic balance of client task income under the influence of multiple factors is usually difficult to consider by an existing scheduling method is solved.

The invention provides a non-intrusive cluster diagnosis method and device based on AI and electronic equipment, and relates to the technical field of data processing.The method comprises the steps that real-time event monitoring is conducted on a POD cluster through a local network in a POD, cluster operation data are collected, and the cluster operation data comprise POD service operation data and peripheral equipment state data; obtaining a service resource dependency relationship among the POD services through the POD service operation data; obtaining a fault root cause service according to all the service resource dependency relationships; based on multi-model collaborative prediction, predicting the peripheral equipment state data to obtain peripheral equipment prediction data; and when the peripheral equipment prediction data is greater than an equipment threshold value, generating equipment fault early warning. According to the invention, the efficiency and precision of cluster diagnosis are improved.

The invention relates to the field of communication, and provides an embedded universal integrated circuit card data downloading method and device, equipment and a medium. The method comprises the following steps: acquiring equipment identity information of a user terminal from an eUICC card of the user terminal through a non-contact channel under the condition that a non-contact channel connection is established between an operator system and the user terminal; the equipment identity information comprises IMEI and / or EID; determining operator code number data corresponding to the eUICC card according to the equipment identity information and sending the operator code number data to the SM-DP + platform; the SM-DP + platform is indicated to generate an operator code number data configuration file Profile; under the condition that the Profile is generated in the SM-DP + platform, downloading the Profile from the SM-DP + platform through an operator system; and the Profile is written into the eUICC card through the non-contact channel. According to the technical scheme, the service resource consumption is relatively low.

Techniques are provided for discerning energy consumption and carbon footprint data associated with hardware-as-a-service resources, and managing workloads based on carbon cost metrics. A management interface collects energy consumption information associated with a plurality of servers processing workloads in a cloud computing environment. The management interface monitors energy consumption in the plurality of servers. An energy consumption manager calculates carbon cost metrics for the plurality of servers based on the energy consumption information; identifies a subset of the servers as being underutilized based on the carbon cost metrics; and calculates an overall carbon cost of the plurality of servers. A workload manager generates container platform operation labels for the plurality of servers when the overall carbon cost is below a threshold value, such that a workload scheduled on the container platform migrates to one of the servers based on the container platform operation labels.

This invention provides an enterprise-level gateway authentication method and system, comprising: Step S1: managing and authorizing published APIs; Step S2: the gateway uniformly calls the APIs to determine whether authentication is required for each API access request; if authentication is required for each API access request, proceed to Step S3; if authentication is not required for each API access request, proceed to Step S4; Step S3: if authentication is required for each API access request, perform pass-through authentication or TOKEN authentication; Step S4: if authentication is not required for each API access request, perform WEB authentication, and the gateway forwards the request to the backend microservice, where the microservice performs authentication. This invention, by designing three authentication modes, satisfies the needs of different scenarios, solves the problem of unified authentication and authorization at the gateway, and effectively protects backend service resources.

The invention relates to the technical field of computer data processing, in particular to a micro-service resource isolation scheduling method and device, equipment and a storage medium. The micro-service resource isolation scheduling method comprises the following steps: acquiring historical index data related to a micro-service, judging the SLA level of the micro-service according to the historical index data, and judging the resource sensitivity of the micro-service; constructing a performance attenuation coefficient model according to the real-time load and the node resource state, and calculating the influence of the low-SLA-level micro-service on the high-SLA-level micro-service by adopting the performance attenuation coefficient model to obtain an influence result; dynamically allocating resource quotas according to the influence result and the SLA level and resource sensitivity of the micro-service by adopting an auction algorithm; and a cgroup v2 and kernel-level isolation technology is adopted to limit the interference of the low-SLA-level micro-service to the high-SLA-level micro-service. According to the method, computing resources can be reasonably allocated through multiple mechanisms, and the processing of the high-SLA-level micro-service is preferentially ensured.

The invention discloses a catering store customer behavior recognition and intelligent service response system, which belongs to the technical field of artificial intelligence and computer vision and comprises a data acquisition module, a data preprocessing module, a target detection module, a posture estimation module, a behavior feature calculation module, a keyword feature recognition module and an initial service demand triggering module. And a final service demand triggering module and a service response module. According to camera behavior recognition or / and voice keyword recognition, after it is recognized that the user has the service requirement, the initial service requirement is triggered, the judgment time is set, and within the judgment time, recognition is conducted again according to camera behaviors or / and voice keywords, so that the service requirement of the user is met. After the fact that the user still has the service demand is recognized, the final service demand is triggered and responded, the situation that the user service demand is self-solved is screened out through a mechanism of initial triggering-secondary verification-screening decision making, the probability of invalid turn-back occurrence of service personnel is reduced, and waste of service resources is avoided.

This application discloses a service resource allocation method, electronic device, storage medium, and product, relating to the field of computer technology. The method includes: responding to receiving a service resource allocation request sent by a terminal device, determining a target service region based on the regional ranking results of at least two service regions; if no preset server cluster exists among the N server clusters contained in each service region, determining a target server cluster from the N server clusters contained in the target service region based on the historical traffic information corresponding to each server cluster in the target service region; and controlling the target server cluster to allocate service resources to the terminal device according to the service resource allocation request. By determining and prioritizing the use of service resources in idle target server clusters, this application can balance the service resource load of each service region as a whole, thereby improving the overall utilization efficiency of service resources.

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.

Techniques are disclosed for reconfiguring resources (e.g., software resources) within the scalable footprint data center at various stages during the prefab process. A service executing on a computing device of a scalable footprint data center can receive, from a reconfiguration service, stage information for a reconfiguration process of services in the scalable footprint data center. The stage information can include a status of the reconfiguration process. The service can execute a reconfiguration operation of the reconfiguration process to update a service resource of the service. Responsive to executing the reconfiguration operation, the service can send to the reconfiguration service a status of the reconfiguration operation.