130 results about "Service allocation" patented technology

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

The invention relates to the technical field of information processing, in particular to an elastic capacity expansion and contraction method, device and equipment for a container arrangement platform and a storage medium, and solves the problem of insufficient service perception of a traditional HPA by collecting resource indexes and service indexes of containers in real time and performing joint modeling on the service indexes and system indexes. The multi-index joint prediction model is used for predicting resource requirements in a certain time in the future, differentiated service priority weights are distributed for different service services, the number of Deployment copies is dynamically adjusted based on the resource requirements and the service priority weights, capacity expansion and shrinkage adjustment information is generated based on changes of the number of the Deployment copies, and the capacity expansion and shrinkage adjustment information is used for adjusting the capacity expansion and shrinkage of the different service services. According to the method, the response effect on burst flow is improved, the operation state of the capacity expansion and contraction adjustment information is monitored in real time, when the monitoring information is abnormal, the rollback strategy is executed, the abnormal feedback information is generated, response is rapid, and the capacity expansion and contraction requirement of the delay sensitive application container is met.

The invention provides a flow distribution method and related equipment. The method comprises the following steps: in a constructed training environment, according to a preset reward function, training to obtain an intelligent agent based on a deep learning algorithm; deploying an actual network topology according to the training environment; in response to a service distribution demand, inputting a traffic matrix corresponding to the service into the trained agent to obtain a target action; the target action comprises a flow distribution proportion for each segment routing path; and determining a traffic distribution proportion of each segment routing path based on the target action, and performing traffic distribution processing on the service according to the traffic distribution proportion. According to the embodiment of the invention, the intelligent agent capable of quickly and autonomously making a decision is obtained through deep reinforcement learning training, and reasonable path selection and traffic distribution are performed on each traffic request, so that the global network link load is reduced.

A policy determining or resource allocation method includes obtaining, by a policy control network element and a computing service request from a server. The computing service request includes computing service description information of a target computing service of a terminal device, and the computing service request is used for requesting allocation of computing power resources for the target computing service; and determining, by the policy control network element, a target QoS policy for the target computing service based on the computing service description information. The target QoS policy includes a computing power QoS policy.

Method for managing payment transactions associated with a task completion includes receiving a service assignment and a digital coin value associated with the service assignment over a network from a first computing device associated with a first user, and sending the service assignment over the network for display on a second computing device associated with a second user. The method also includes receiving notification from the second user that the assigned service has been completed, and transferring digital coins equal to the digital coin value of the service assignment from a first digital wallet associated with the first user to a second digital wallet associated with the second user. The method further includes recording the transfer of the one or more digital coins in the digital coin ledger, and applying the one or more digital coins in the second digital wallet to acquire a purchase item.

The embodiment of the invention provides a communication method and device based on an MCP protocol, a storage medium and a program product. The method comprises the following steps: acquiring publisher information of the MCP service; distributing a unique identifier for the MCP service according to the publisher information; combining the unique identifier with a tool identifier of an MCP tool provided by the MCP service to obtain a combined tool identifier; based on the combined tool identifier, adding an MCP tool in a tool list; wherein the tool list is used for recording the MCP tool which can be called by the first pre-training language model.

Methods and systems for network slice switching in a fixed wireless access (FWA) system are described herein. In one implementation, a device configured to route outgoing data traffic generated in the FWA system may receive from a first client device on the FWA system, a request to transmit data packets associated with a service to a service provider located external of the FWA system. The device may obtain, from the request, a first tag being indicative of a first quality of service (QOS) category requested by the service. The device may further determine, based on the first tag, a network slice to be assigned for the service, establish, via a wireless network, a session corresponding to the network slice between the first client device and the service provider, and facilitate transmission of the data packets associated with the service on the session.

In general, the present subject matter relates to prioritizing random access channel layer-less (RACH-less) Layer 1 / Layer 2 triggered mobility (LTM). In some implementations, prioritizing RACH-less LTM may include receiving, at a serving distribution unit (DU) of a base station, information indicating RACH-less handover configurations for multiple LTM target cells of the base station or timing advance information for each of the multiple LTM target cells from an aggregation unit control plane of the base station; identifying, at the serving DU, at least one target cell to which a RACH-less handover of a service for a user equipment (UE) may be performed based on the received information; and selecting, at the serving DU, one of the identified at least one target cell for handover from the serving DU of the service for the UE and triggering a handover of the service to the selected target cell.

The invention relates to the technical field of network security, in particular to a defense method and device for coping with DDoS attacks, equipment and a medium. According to the method, in response to an adjustment request of a defense strategy for the DDoS attack, a performance evaluation result of each node in the same block chain network is obtained, the performance evaluation result of any node is a performance score determined by the node based on network performance data of the node, and the performance score is used for evaluating the service capability of the node; the performance evaluation result of each node is input into a pre-constructed target function, and in combination with a preset constraint condition, optimal scheduling parameters in the target function are solved by taking maximization of the whole network service quality as an optimization target, and the scheduling parameters comprise priority weights of each node for processing various services and resource weights allocated by each node for various services; and sending the optimal scheduling parameter to the corresponding node, so that each node adjusts a defense strategy of the DDoS attack according to the optimal scheduling parameter to defend the DDoS attack.

The invention discloses a migration service distribution method and device, a storage medium and electronic equipment, and relates to the technical field of computers, a target request is received, and the target request is used for requesting a plurality of migration equipment included in a data migration system to execute a target migration service; detecting the service level of the target migration service according to the service level information; under the condition that the target migration service is the migration service of the target service level, target migration equipment is called to execute the target migration service, and the target migration equipment is screened out from the multiple pieces of migration equipment according to migration resource occupation information of the multiple pieces of migration equipment at the beginning of the current time period. The target migration device is specially used for executing the migration service of the target service level in the current time period, the technical problem that in the related technology, the execution efficiency of a data migration system on the migration service is low is solved, and the technical effect of improving the execution efficiency of the data migration system on the migration service is achieved.

The invention discloses a customer service allocation method and device for a cloud duty retail store, and the method comprises the steps: obtaining user features, and generating an allocation request based on the user features; then, in response to the distribution request, obtaining retail store features, states of all customer service staff and historical service information of all customer service staff; then determining a matching score of each customer service based on the retail store features, the state of each customer service and historical service information of each customer service; and finally, on-duty customer service is allocated to the retail store based on the matching score. The customer service distribution efficiency can be improved, and the matching degree between the distributed customer service and the retail store is improved, so that the customer service response speed and the customer service resource utilization efficiency in the cloud duty retail store are improved.

A method includes, using a mobile device connected to an industrial network of an industrial system, receiving a chainable compute service assignment from a first intelligent industrial device of the industrial system that assigns a chainable compute service to the mobile device or assignment from a cloud-based device or system connected to the industrial network that assigns the chainable compute service to the mobile device; and using the mobile device, performing the chainable compute service.

The invention discloses a government affair hall service distribution system based on big data analysis, and relates to the technical field of government affair services. A government affair hall service distribution system based on big data analysis comprises a service category division module, a service real-time distribution module and a special service distribution module. According to the invention, through the service category division module, to-be-allocated user government affair service requests are accurately divided according to categories of the to-be-allocated user government affair service requests, the efficiency and accuracy of service allocation are greatly improved, and resource waste and allocation conflicts are avoided through shunting processing of special service requests and non-special service requests; the service real-time allocation module is used for ensuring that non-special service requests can be quickly responded when service resources are available by introducing a real-time request queue and a real-time allocation model, so that the user satisfaction is improved; the special service distribution module is specially used for carrying out resource optimization configuration according to the requirements of special users, and finer and more personalized services are realized through an independent window and a distribution mechanism.

The present application discloses a service allocation method, apparatus, product, storage medium and device. The method includes: obtaining device attribute information of an entity heterogeneous device in the physical attribute dimension from the entity heterogeneous device; obtaining service requirement information of the entity heterogeneous device in the service requirement dimension according to historical service requirement conditions in historical requirement conditions of historical executed services; obtaining operation status information of the entity heterogeneous device in the operation status dimension according to the operation status of the entity heterogeneous device; creating a virtual heterogeneous device corresponding to the entity heterogeneous device, and associating the device attribute information, service requirement information and operation status information with the virtual heterogeneous device; determining a target entity heterogeneous device that matches the current requirement conditions of the service to be executed according to the device attribute information, service requirement information and operation status information associated with the virtual heterogeneous device, and allocating the service to be executed to the target entity heterogeneous device for execution. The present application provides better flexibility in service allocation.

A system and method is presented that extracts snippets form web pages according to specially designed logic. The extracted snippets might be made relevant to, i.e., indexed by, a location and time / day applicability. Such snippets may be thought of as apps or services that are defined only when a mobile terminal is in a pre-defined geographical area at a certain time and day (e.g., as defined by a calendar of events). Extracted snippets are stored and made searchable. Methods and a system are described to control the display of snippet search results. Snippets may be selected by user or by programmed logic and executed on the mobile terminal or in remote servers without the need to download the app or source code associated with the snippet.

The invention provides a resource allocation method for an AI (artificial intelligence) generation content service based on integrated perception and communication, which is used for allocating perception and communication resources for the AI generation content service based on an integrated perception and communication technology. Obtaining display capacity of a plurality of pieces of user equipment participating in resource allocation at this time and states related to channels; according to the display capacity of the multiple pieces of user equipment and the states related to the channels, maximizing the average service quality of the users is used as an optimization target, sensing resources and communication resources needing to be allocated to the multiple pieces of user equipment at this time are iteratively adjusted, and an allocation decision is obtained, the service quality of each user is related to the perception precision of the user posture information obtained through the perception resource and the quality of the generated image corresponding to the digital human body driven according to the human body posture obtained through the user equipment.

The invention relates to a system for the secure transmission of initial data packets between a client and a server, wherein the server can execute machine control. The server provides services with assigned port numbers. The port numbers assigned to the services are blocked either on the server or in the data transmission network. An intermediary application is available on the server, which receives encrypted data packets from the client. These data packets are decrypted and transmitted to the services. A method and a computer program product are also provided.

Techniques are described for a system configured to assign one or more services to a first compute node of a set of compute nodes. The system may obtain an alert for a service. The system may determine an alert context for the alert. The system may add the alert to an alert group of a plurality of alert groups. The system may generate an updated alert group context for the alert group. The system may add a second compute node to the set of compute nodes. The system may provide a plurality of alert group contexts including the updated alert group context to the second compute node. The system may reassign at least one service to the second compute node based on an updated set of compute nodes, wherein the updated set of compute nodes is determined based on a change to the set of compute nodes.

The invention discloses a network fault repairing method and device, electronic equipment and a computing program product. The method comprises the following steps: obtaining operation data when a service platform has a fault, the service platform being used for providing a plurality of network services; determining a plurality of candidate services influencing the operation data in the plurality of network services; adopting a high-level decision maker in a preset deep reinforcement learning model to select a repair strategy type according to a service priority configured for each candidate service in advance, and adopting a bottom-layer executor in the deep reinforcement learning model to distribute service resources for each candidate service according to the repair strategy type; and providing services on the business platform according to the service resources allocated for each candidate service. The technical problem that a traditional network fault repairing method is low in repairing accuracy is solved.

This application belongs to the field of artificial intelligence and relates to a service provider allocation method, including: determining whether a service allocation request has been received; if so, parsing target service scenario information from the service allocation request; obtaining a target decision network corresponding to the target service scenario information; receiving a policy expectation value, input parameter values ​​corresponding to condition factors, and service provider parameters corresponding to the service provider; solving the policy on the target decision network based on the input parameter values ​​to obtain the optimal solution of the target policy state node corresponding to the policy expectation value; determining the target service provider based on the optimal solution and the service provider parameters, and allocating the target service provider to the target service scenario corresponding to the target service scenario information. This application also provides a service provider allocation device, computer equipment, and storage medium. Furthermore, this application relates to blockchain technology, and the target decision network can be stored in the blockchain. This application improves the processing efficiency and accuracy of service provider allocation.

The application provides a resource scheduling method and system, a computing device and a storage medium, wherein the method comprises the following steps: a first server determines a first network channel connected with a second server based on service allocation information and resource allocation information, wherein the first network channel comprises a reserved network channel; the first server controls the second server and a third server to receive and process the first service and the second service according to the service allocation information and the resource allocation information; in the case that the third server completes the second service and the second server does not complete the first service, the first server allocates the remaining computing resources of the third server to the second server through the reserved network channel; the dynamic allocation of the computing resources is realized, the network overhead and the processing time of the target service are saved, and the system maintenance difficulty is reduced without changing the network topology structure.

The application discloses a network spectrum fragmentation minimization routing resource allocation method and device, the method comprises two stages of path selection and spectrum allocation, the path selection stage takes maximizing path resource utilization as the target, obtains optical network topology information, filters the optimal path through quantifying spectrum resource availability and concentration degree of the link and the path, and forms a candidate path set; the spectrum allocation stage takes minimizing spectrum fragmentation generation and reducing service blocking rate as the target, converts the IP service into optical layer frequency gap demand, filters the continuous idle spectrum block meeting the spectrum constraint condition in the candidate path set to form a frequency block candidate set, determines the optimal frequency block through quantifying the close degree of the frequency block and the occupied resource and the influence degree of the frequency block deployment on the link resource, completes service allocation, and updates the link frequency gap use state in real time after the allocation. The application effectively reduces spectrum fragmentation, improves spectrum resource utilization and network transmission reliability, and adapts to high dynamic and high burst service demand.

The present disclosure a method for data regulations-aware cloud storage and processing service allocation. Conventional approaches fail to address the technical problem of data placement for storage as well as processing, considering multiple criteria. Further, the conventional approaches fail to address compliance with data regulations, tier pricing policy for multiple Cloud Service Providers (CSPs) which impacts storage and processing center selection and constraint satisfaction. The present disclosure proposes a joint optimization model for the selection of storage and processing services from multiple cloud service providers, taking into practical consideration of data regulations and tiered pricing, which has not been addressed in the prior art. To solve this hard multi-objective combinatorial optimization problem, the present disclosure utilizes a cost-reduction-based algorithm for obtaining optimal solution.

The application relates to the field of intelligent recommendation, and is used for recommending a seat to a user to improve the efficiency and accuracy of a recommendation process. Specifically, a seat recommendation method is provided. The method comprises the following steps: receiving a service request sent by a terminal device of a target user, obtaining user information of the target user in response to the service request; obtaining historical user information in a first preset time period and obtaining seat information of a seat; generating user score data according to the user information of the target user and the historical user information, and generating seat score data according to the seat information; generating a service allocation table according to the user score data and the seat score data, wherein the service allocation table comprises a corresponding relationship between each seat and the target user; determining a target seat corresponding to the target user according to the service allocation table, and sending a service access instruction to a terminal device of the target seat, so that the target seat can access the service request of the target user.

The present invention discloses a method and system for allocating elevator group control services based on predicted long-wait calls. When a new hall call is generated, the predicted long-wait hall call is determined based on the current operating status of each elevator in the group, rather than relying on historical data. The comprehensive waiting time is obtained by calculating the waiting time of each elevator in response to the new call and the predicted long-wait call, and the elevator with the shortest comprehensive waiting time is selected to serve the new call. The present invention does not rely on historical data, but only makes predictions based on the real-time operating status of the elevator, avoiding prediction errors caused by inaccurate or incomplete historical data, and has greater flexibility and adaptability. It comprehensively considers the impact of new calls and potential long-wait calls, effectively reducing the overall waiting time of passengers and improving the operating efficiency of the elevator group control system.

The invention discloses a customer service allocation method and device, equipment, a medium and a program product, and belongs to the field of customer service allocation. The method comprises the following steps: acquiring a consultation question of a service object and respective working state information of a plurality of candidate customer services; performing classification operation on the consultation questions through a first artificial intelligence model to obtain a first question category; screening out a first number of candidate customer services carrying a first question category label from the plurality of candidate customer services, wherein the first question category label corresponds to the first question category; based on the respective working state information of the plurality of candidate customer service staff, screening out a second number of candidate customer service staff of which the working states meet the working state condition from the plurality of candidate customer service staff; and based on the intersection of the first number of candidate customer service staff and the second number of candidate customer service staff, determining the customer service staff for processing the consultation question. According to the method, question classification is carried out through the first artificial intelligence model, and compared with keyword matching of related technologies, the classification result is more accurate.

An example computer-implemented method includes accessing service request data indicative of a service request, the service request having a service type and one or more service parameters. The method includes computing a complexity score associated with the service request based on the service type and the one or more service parameters. The method includes selecting a first courier of a plurality of candidate couriers based on the complexity score associated with the service request. The method includes outputting first instructions to a first courier device associated with the first courier. The first instructions are executable by the first courier device to cause a service assignment to be provided for display via a user interface of the first courier device, the service assignment indicating the first courier is to perform the service request.

The application discloses a service deployment method and device and electronic equipment, wherein the method comprises the following steps: receiving an access request of a user, and dispatching the access request to a to-be-deployed service via routing; determining a video memory requirement of the to-be-deployed service based on the access request, and generating a configuration file based on the video memory requirement and a graphics card located on a server; allocating video memory for the to-be-deployed service according to the configuration file, deploying an instance in the video memory through the to-be-deployed service, wherein the video memory is located in a target graphics card of a target server, the target server is a server used for deploying the to-be-deployed service, and the target graphics card is a graphics card in the target server which provides video memory resources for the to-be-deployed service; and allocating the access request to the deployed instance and running the instance, so as to deploy the to-be-deployed service to the target server. The application automatically allocates the video memory in the target graphics card of the target server for the to-be-deployed service through the configuration file, realizes the automatic deployment of the service, and does not need to deploy the service to the target server by manpower.

The application provides a large-scale optical network resource allocation method based on iterative learning, comprising: initializing an optical network topology and a state of spectrum resources of each link; initializing a node feature code of each node and a link feature code of each link; dynamically updating v i , e j , and a current service request according to a current network state, and simultaneously generating an input state S I of a corresponding Agent; the input state S I is sent into the Agent as a basis for decision-making; a feasible solution conforming to an optical path constraint is gradually constructed; the decision-making is reinforced; a reward value R of an action is calculated; the updated reward value R is used for further optimization of the decision-making of the Agent; an EGAT model is trained and optimized; the EGAT model is evaluated and simulated; and a service allocation strategy is gradually constructed in combination with iterative learning, which can effectively cope with large-scale network topology changes and significantly improve the global performance and efficiency of scheduling.