803 results about "Service demand" patented technology

The invention relates to the technical field of satellite communication, in particular to a high-orbit and low-orbit satellite communication resource scheduling method and system, which collects real-time state information of high-orbit and low-orbit satellites, performs digital simulation of a satellite network based on the real-time information, constructs a satellite network management model and predicts satellite trajectory change. The method comprises the following steps: performing multi-dimensional evaluation on a satellite network management model by using a pre-constructed network evaluation framework, generating cross-orbit link evaluation information, analyzing parameters such as link quality and bandwidth, obtaining current service demand information, analyzing a communication resource allocation strategy in combination with a cross-orbit link evaluation result, and formulating a resource scheduling strategy according to strategy analysis. And a high-orbit and low-orbit satellite communication network is constructed through execution of a hierarchical network management mechanism and a cross-orbit data transmission protocol.

The invention provides a smart campus-oriented multi-hyper fusion platform collaborative scheduling system and method, and is applied to the technical field of data processing. Resource sensing and dynamic modeling processing is performed on multi-hyper fusion platform resource pool data to generate target resource model data, and the target resource model data is composed of a resource real-time monitoring index, load prediction model output, a resource isomerism adaptation result and a cross-platform protocol conversion adaptation parameter; the target resource model data is processed, platform collaborative scheduling strategy parameters are generated based on reinforcement learning, and a campus business scene reward and punishment mechanism is introduced in the reinforcement learning process; processing the platform collaborative scheduling strategy parameters to generate a dynamic resource allocation scheme; processing the dynamic resource allocation scheme and the campus service demand data, and generating a service and resource matching agent model based on an intelligent optimization algorithm; and processing the target campus information based on the service and resource matching agent model to generate campus resource scheduling information.

An intention-driven low-orbit satellite network SRv6 routing management and control method comprises the following steps: step 1, receiving and analyzing a user service demand from an application layer, and generating a corresponding intention label according to a service type; step 2, collecting state information of a satellite network in real time, tracking and recording satellite node position change and link state conversion, and establishing a complete network state database; 3, calculating a routing path meeting the service quality requirement by adopting an improved breadth-first search algorithm; 4, monitoring the state change of the inter-satellite link in real time; step 5, continuously monitoring fault events in the network, immediately starting a pre-calculated standby path when a fault is detected, updating a corresponding SRv6SID list, and performing rapid path switching; and step 6, dynamically adjusting the network flow. The method not only can adapt to the dynamic characteristics of the low earth orbit satellite network, but also can effectively meet the differentiated service requirements, and has the rapid fault recovery and load balancing capability.

The invention relates to the technical field of wireless communication, in particular to a wireless communication network system and method for intelligently fusing a multi-mode heterogeneous network. Comprising a multi-mode access unit; a protocol compatibility and conversion unit; the heterogeneous network intelligent fusion unit completes cross-network dynamic flow collaboration and resource optimization decision through cross-layer feature association mining, service-network two-dimensional weight calibration and a double-closed-loop feedback optimization mechanism, generates a resource scheduling instruction and transmits the resource scheduling instruction to the intelligent scheduling and resource control unit; an intelligent scheduling and resource control unit; and a system control and cooperation unit. According to the invention, the service-network two-dimensional dynamic fusion weight is constructed through the intelligent fusion unit of the heterogeneous network, and bandwidth, frequency spectrum and power resources are allocated according to the priority and margin balance principle in combination with the intelligent scheduling unit, so that the problem of insufficient two-dimensional coordination of resource scheduling is solved, the resource scheduling is matched with the service demand and the network load, and the resource scheduling efficiency is improved. And resource waste or incapability of meeting business requirements is avoided.

The invention provides a dynamic configuration computing resource management method and system based on a power wireless terminal, and the method comprises the steps: obtaining a real-time operation state data set and a power business demand data set of the power wireless terminal, and carrying out the multi-dimensional feature fusion processing of the real-time operation state data set and the power business demand data set; the method comprises the steps of generating a resource adaptation feature set, performing real-time strategy matching processing on the resource adaptation feature set based on a preset resource adaptation rule base, generating a computing resource configuration strategy set, performing adjustment operation on computing resources of the power wireless terminal, and generating a resource allocation verification result and terminal operation efficiency feedback data. And continuously comparing the terminal operation efficiency feedback data with a preset efficiency optimization threshold value, and carrying out iterative updating processing on the resource adaptation rule base according to a comparison result. According to the invention, accurate matching between computing resource allocation and power business demand fluctuation can be realized.

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 relates to the technical field of wireless communication and edge computing fusion, in particular to a wireless network resource allocation method based on edge intelligence, which comprises the following steps: S1, collecting a multi-dimensional parameter set of network edge nodes in real time; s2, converting node position coordinates into a network topological structure map, and mapping service type labels and bandwidth requirements into service requirement feature vectors; s3, generating a domain division matrix based on weight distribution of the network topological structure atlas; s4, outputting a resource block configuration tensor according to the time-frequency feature of the business demand feature vector; s5, generating a resource allocation mapping relation table; and S6, generating and executing a node-level resource scheduling instruction set. According to the invention, by constructing a resource scheduling mechanism fusing a topological structure and service features, precise resource allocation and dynamic load balancing of a cooperative domain level and a node level are realized, and the efficiency and the resource utilization rate of multi-task concurrent processing in an edge computing environment are improved.

The invention is suitable for the technical field of network scheduling, and provides a network scheduling method and system for hybrid service transmission based on RDMA, and the method comprises the steps: receiving a to-be-transmitted service demand instruction, and determining a corresponding service type according to a service quality parameter in the service demand instruction; matching a corresponding virtual transmission channel for the service demand instruction according to the service type, wherein the virtual transmission channel is used for isolating hardware-level resources of different types of traffic; selecting an optimal transmission path for the flow of each virtual transmission channel according to a preset path evaluation model; analyzing the congestion risk of the optimal transmission path by adopting a prediction model; and dynamically adjusting a flow transmission path according to a congestion risk analysis result, and ensuring the transmission quality of the highest priority service through a hardware-level scheduling strategy. According to the invention, the optimal balance between the service quality and the resource efficiency can be realized in a complex mixed service environment.

The invention relates to the technical field of artificial intelligence, in particular to an AI digital human-based customer service method and system, and the method comprises the steps: receiving a voice stream, a visual stream and auxiliary sensor data inputted by a user, achieving the time-space alignment of multi-modal data through a cross-modal feature synchronization engine, and fusing the multi-modal data into a unified fusion data packet; based on the fused data packet, extracting an emotion intensity value and an emotion type code in real time, generating a dynamic emotion label, and extracting service demand characteristics to generate a demand vector; inputting the dynamic emotion label and the demand vector into a strategy decision tree, and generating a strategy identifier containing an emotion pacifying coefficient and a response emergency degree; the AI digital human is driven to generate the multi-modal response based on the strategy identifier, deep perception and flexible response to the user state can be achieved, the interaction naturalness and the user satisfaction of a digital human service system are effectively improved, and the method is suitable for multiple intelligent service scenes such as online customer service and intelligent terminal assistants.

The invention relates to the technical field of enterprise service intelligent recommendation, and discloses an enterprise service matching method and system based on a large language model, and the method comprises the following steps: S1, receiving an enterprise service demand text input by a user, carrying out the semantic analysis of the service demand text through the large language model, and obtaining a semantic analysis result; constructing a service semantic graph containing service nodes and semantic relationships; s2, establishing a plurality of agents representing different roles in the enterprise, wherein each agent corresponds to one service preference; s3, based on the service semantic graph and the service preference, node embedding is carried out by using a graph neural network, and the service combination potential is estimated; and S4, performing multi-target matching optimization according to the service preferences and the combined potentials of the multiple agents, and outputting a service matching result. A graph neural network is adopted to model service node embedding, so that a combination relationship among services is efficiently captured in a graph structure, and automatic learning of non-uniform importance among the service nodes is realized by introducing a multi-layer graph attention mechanism.

The invention discloses an intelligent customer service dialogue management method and system fused with multi-layer memory, and belongs to the technical field of computers, and the method obtains a three-dimensional context through multi-mode context awareness, achieves the dynamic scheduling of a memory system through a memory coordinator, and combines a dynamic construction mechanism of a service situation portrait, thereby achieving the intelligent customer service dialogue management. The problem that the response of the intelligent customer service system is not matched with the complex and dynamic service demand of the user due to the single situation awareness dimension and the rigid memory scheduling mechanism is effectively solved, and the adaptive personalized service response can be generated according to the real-time change of the dialogue situation.

This application provides a cloud service test method, including: constructing an application programming interface API knowledge graph of a cloud service, where the API knowledge graph includes a reference relationship between an API parameter and a resource object; then identifying an API dependency relationship based on the reference relationship between an API parameter and a resource object; and then testing the cloud service based on the API dependency relationship, to obtain a test result. In the method, the API dependency relationship is identified based on the reference relationship between an API parameter and a resource object. Even if API parameter names do not match, the API dependency relationship can be accurately identified based on a same pointed resource object, thereby improving accuracy of a cloud service test. The method is universal and can meet a service requirement.

The invention provides a service recommendation decision-making method based on large model retrieval enhancement generation and related equipment. The method comprises the following steps: acquiring an unstructured service demand; calling a large language model to perform semantic analysis and extension on the service demand, and generating an unstructured extended semantic text; encoding the extended semantic text to obtain a semantic query vector; querying a service description of each pre-stored service from a service database, respectively calculating a first similarity score between the semantic query vector and the service description, expanding a second similarity score between each keyword in the semantic text and the service description, and screening out a candidate service set having semantic association with the service demand; all corresponding application programming interfaces in the candidate service set are determined, and a final application programming interface set is screened out and recommended according to the use frequency of the various application programming interfaces, the semantic similarity among the labels and the semantic query vectors. According to the method and the device, the API recommendation accuracy is improved.

The invention discloses a rail transit multi-network integration method based on a digital trunking technology, relates to the technical field of communication, and aims to solve the problem that a traditional narrowband trunking system cannot meet the requirements of voice and data comprehensive bearing and vehicle-ground large-bandwidth service real-time transmission. The method comprises the following steps: constructing a time division duplex long-term evolution broadband digital trunking unified bearing platform; an intermediate media access control layer fusion architecture is deployed, and seamless mobility management of the heterogeneous network is realized through unified authentication and multi-link aggregation; an intelligent vertical switching algorithm based on prediction and multi-criterion decision is introduced, a bidirectional long-short-term memory network model is utilized to predict a network state trend, and an improved entropy weight method and an analytic hierarchy process are combined to dynamically select an optimal access network according to service requirements. According to the invention, the network operation efficiency and the user experience can be obviously improved, and high reliability and high service quality of the rail transit communication system are ensured.

The invention discloses an operation planning optimization method, system and device for resource allocation, a storage medium and a product, and relates to the technical field of resource allocation and optimization decision, and the method comprises the steps: collecting resource related data; through a pre-trained resource allocation large model, based on the constraint condition and the target function, outputting an operation planning optimization formula matched with the current service scene; based on the workflow arrangement mode, performing service logic definition and workflow node visualization processing on the service demand information to obtain a workflow execution model; executing operation planning optimization calculation according to the resource related data and an operation planning optimization formula to obtain an optimal solution set, and fusing a workflow model to generate a decision variable; and calling a decision engine to carry out variable replacement, and generating a resource allocation strategy. According to the scheme, large model modeling and operation planning optimization capabilities are fused, the modeling threshold is reduced, and the intellectualization, data driving and interpretability of a resource allocation decision are improved; and meanwhile, in combination with a workflow dynamic management mechanism, intelligent optimization decision making is realized.

The invention discloses an Internet of Vehicles service scheduling method, system and device based on a slice private network and a medium, and the method comprises the steps: dividing an Internet of Vehicles service into a plurality of service types according to service demands, and determining the priority of each service type; an eSIM + SoftSIM scheme is adopted to generate a plurality of network slices of different slice types, and slice resource allocation is carried out on the network slices; requesting a network slice through the Internet of Vehicles intelligent terminal, and mapping the Internet of Vehicles services of each service type to the network slices of different slice types according to the priority; and performing data transmission on the corresponding Internet of Vehicles service by using the network slices. According to the invention, refined resource scheduling and multi-service parallel guarantee of the Internet of Vehicles service are realized, the reliability of the Internet of Vehicles service and the network resource utilization rate are improved, the hardware cost and the use power consumption of the Internet of Vehicles intelligent terminal are reduced, and the method can be widely applied to the technical field of Internet of Vehicles.

The invention discloses a shielding space operation communication gateway system based on PLC and 5G multimode fusion, and relates to the technical field of communication gateways, through integrating a task intention recognition module, a link semantic modeling module, an intelligent strategy regulation and control unit, a cross-link redundancy coding and decoding module, a link abnormal game detection module and a cognitive evolution management module; modeling is carried out through task-driven communication service requirements, semantic abstraction is carried out on a link state in combination with a graph neural network, a system constructs a double-input reinforcement learning model, and joint scheduling of link selection, coding configuration and energy consumption control is realized; meanwhile, the abnormal behavior of the link is judged through an adversarial sample recognition and game mechanism, and a main strategy structure is optimized based on a multi-strategy scoring and inheritance mechanism; the system has the capabilities of intelligent decision making of communication strategies, anomaly defense and self-evolution optimization, and is particularly suitable for high-reliability operation communication requirements in a limited space in wireless communication shielding environments such as a hearth, an air pre-heater, an absorption tower, a tunnel, a mine and a subway.

The invention provides an intelligent customer service work order processing method based on a large language model, a medium and electronic equipment. Full-process automatic processing of user service requirements from receiving to feedback is achieved through modular design. The method comprises the following steps: receiving and preprocessing demand information input by a user; performing semantic understanding on the normalized input by using a large language model, identifying a demand category, and extracting key parameters such as a room number and demand details; selecting a corresponding work order generation tool from a preset tool library based on the identified demand category, and matching a required business interface; a required information list is preset according to different requirement categories, whether the extracted key parameters are complete or not is automatically checked, and if not, a prompt is generated to guide a user to supplement information; after the information is complete, calling a corresponding work order template according to the demand category, filling the key parameters into the template to generate a standard work order, and pushing the standard work order to a back-end business processing system; and receiving a back-end processing result, combining the back-end processing result with basic reply content generated by the tool, and performing retouching through a large language model to form a final reply to be fed back to the user. Through the above process, specific demands of users in the vertical field can be accurately understood, the standard work order can be automatically generated and pushed, and the processing result can be acquired in real time and fed back to the user, so that the problems that existing intelligent customer service cannot extract complete demands, work order generation lacks specifications and the processing result cannot be fed back in a closed loop mode are solved; and the service efficiency and the user experience are greatly improved.

The invention relates to the technical field of communication, and provides a service processing method, a service processing device, a computer readable storage medium and electronic equipment, and the service processing method comprises the steps: receiving a data transmission service request from user equipment after establishing a service subscription relationship with the user equipment; the data transmission service request comprises a service demand; analyzing a data transmission type associated with the data transmission service request according to the service demand; and establishing different data transmission channels according to different data transmission types, and processing the data transmission service request based on the data transmission channels. According to the method disclosed by the invention, through AI proxy collaboration and intelligent path selection, refined identification and adaptive transmission channel construction for different service requirements can be realized, and the network resource utilization rate and the end-to-end data transmission efficiency are remarkably improved.

The invention discloses a service-oriented dynamic resource management method and system. According to the method, indexes such as a CPU and a memory on a physical node are directly collected, monitoring errors of a virtualization layer are avoided, it is ensured that resource data truly reflects the hardware occupancy condition, physical resource occupancy is attributed to specific services through service labels, abstract conversion from hardware resources to service requirements is achieved, and a foundation is laid for differentiated management; the threshold value of the resource utilization rate of each service is calculated hierarchically according to the type of the service, the core service is guaranteed preferentially, if the resource utilization rate exceeds the corresponding threshold value, manual intervention is not needed, a standardized control interface is automatically called to execute the resource expansion operation, and the accuracy, high efficiency and adaptability of resource allocation are achieved.

The invention discloses a pre-configuration protection method based on risk identification, and the method comprises the steps: collecting multi-dimensional data of a target electric power optical communication network, including logic topology information, a service demand set and dynamic environment data; logic links in the target power optical communication network are identified and divided into different shared risk groups, and a comprehensive risk index of each shared risk group is calculated; adding the shared risk groups of which the comprehensive risk indexes are greater than a preset risk index threshold value into a to-be-protected queue, and generating an optimal standby network loop for each shared risk group in the queue to obtain a network protection ring of the to-be-protected queue; when a to-be-processed service request is received, selecting a target protection path for the to-be-processed service request from the network protection ring, so that the to-be-processed service request is switched to the target protection path when a fault occurs; according to the invention, the network can be rapidly switched when a fault occurs, and the reliability and recovery speed of the power optical communication network are improved.

The invention provides a dynamic reconstruction optimization method and system for communication information network topology, and relates to the technical field of communication networks, and the method comprises the steps: firstly obtaining node operation dynamic information, link transmission real-time information and network service demand dynamic data of a communication information network; generating a network topology elastic association body containing a plurality of association rules and logics based on the two; inputting the service demand data into a network topology elastic association body to generate a network topology dynamic demand adaptation factor; generating a network topology reconstruction path self-adaptive sequence containing a specific operation instruction and a time sequence based on the network topology dynamic demand adaptation factor and the network topology elastic correlation body; and adjusting a node connection mode and link transmission configuration in a linkage manner according to a network topology reconstruction path self-adaptive sequence, and synchronously updating an association rule to complete dynamic reconstruction optimization. According to the invention, the network stability, reliability and resource utilization rate are improved.

The invention relates to the technical field of server data access control, and discloses a server data access control method, device and equipment and a storage medium. Distributed server deployment information is queried by estimating bidding document item service requirements of each sub-service area; solving a server data access control strategy by taking the overall processing delay condition of the server executing the corresponding service user bidding document item, the server hardware load and the server resource scheduling limitation as a constraint condition set and taking the server access experience of the service user in the target server access control time period as an optimization target; and resource scheduling and service user matching of the distributed server in each access control period are realized by using a control strategy, so that bidding document project service is executed. According to the method, the rationality of server data access control planning is improved by considering regional differences, time peak differences and strong regional relevance of bidding document application services in different industry categories and hardware resource limitations of distributed servers.

The invention discloses an unmanned aerial vehicle trajectory optimization and power distribution method, system and device based on cost evaluation reinforcement learning, and a medium, and the method comprises the steps: constructing an unmanned aerial vehicle data service scene under safety constraints, modeling a multi-objective optimization problem of unmanned aerial vehicle trajectory optimization and power distribution, and designing an unmanned aerial vehicle data service evaluation index; converting a multi-objective optimization problem into a constrained Markov decision problem; respectively designing a state space, an action space, an instant reward function and a corresponding cost function; constructing a cost evaluation critic network parallel to the reward evaluation critic network, training and updating network parameters of the unmanned aerial vehicle by using a cost evaluation SAC algorithm according to a constrained Markov decision problem, and calculating trajectory optimization and power distribution parameters of the unmanned aerial vehicle; the system, the equipment and the medium are used for implementing the method. According to the invention, ground user data service requirements of a data service scene under random and dynamic security constraints are met.

The invention provides a networking scene-oriented multipath service optimization method and system, and relates to the technical field of network scheduling optimization, and the method comprises the steps: obtaining topological structure information and service demand information of a communication network in a networking scene, and employing a depth-first search algorithm in combination with a pruning mechanism to generate a candidate path set; on the basis of a marginal value contribution degree theory, calculating a transmission value factor and a path complementary factor of each candidate path in the candidate path set, calculating marginal value contribution degrees of different candidate path combinations according to the two factors, and taking the path combination with the highest marginal value contribution degree as an optimal path combination; and aiming at each path in the optimal path combination, establishing a multi-objective optimization model, and obtaining a final scheduling scheme of the service flow among the paths in the optimal path combination. According to the invention, the problems of lack of differentiated path selection mechanism, poor fault tolerance of single-path transmission, insufficient resource coordination capability and the like in multi-service transmission in the prior art can be solved.

The invention discloses a virtual power plant and resource matching method and system based on cooperation preference in the field of virtual power plant resource matching, and the method comprises the steps: building a photovoltaic, energy storage and air conditioning load multi-mode cooperation preference scoring model, and quantifying the economic effectiveness, equipment loss, user comfort and other multi-dimensional features; a double-tower representation and contrast learning technology is adopted, and resource attributes and service requirements are mapped to a unified semantic space to calculate the matching degree; in combination with an entropy regular optimal transmission algorithm, many-to-many flexible matching is realized on the premise of considering grid power flow safety constraints; and a rolling multi-cycle optimization mechanism is introduced, and a Bayesian update and reinforcement learning dynamic adjustment strategy is adopted, so that the technical problems of insufficient cooperation intention quantification, complex cross-time constraint and difficult security risk control in distributed resource scheduling under high-proportion new energy access can be solved, and the economy and security of virtual power plant scheduling can be effectively improved. And the performance stability of the system under the new energy fluctuation is enhanced.

The invention discloses a multi-unmanned aerial vehicle low-altitude service scheduling result generation method and device, equipment and a medium, and relates to the technical field of unmanned aerial vehicle scheduling, and the method comprises the steps: constructing a low-altitude service semantic grid, converting the service demand characteristics of each region of a city into a region semantic vector, and mapping an unmanned aerial vehicle task to the grid to generate a task semantic vector; a semantic enhancement mechanism is utilized to calculate the semantic association strength of a task and a region, a multi-dimensional fairness index system is constructed, and a dynamic fairness preference weight is generated through self-adaptive correction. And finally, task allocation and path planning are performed based on the dynamic weight and the semantic association strength, so that the dynamic balance service efficiency and fairness of multi-unmanned aerial vehicle scheduling are improved, regional service deviation is dynamically corrected, the service accessibility of vulnerable regions and crowds is ensured, and the method is adaptive to complex urban environments and abnormal scenes.

There is disclosed an apparatus. The apparatus comprises means for performing: in response to determining a lack of resource or an impending lack of resource in a communication network, determining whether an agreed level of service for one or more service requirements of one or more services will be affected; and when it is determined that the agreed level of service for one or more service requirements of one or more services will be affected, determining whether one or more customers can tolerate non-compliance with the agreed level of service.

The invention discloses a resource allocation and configuration method and device and electronic equipment. The method comprises the following steps: converting network resources in a private network into programmable objects, and constructing a resource label model according to attribute information of the programmable objects and the network resources; receiving service demand information, extracting a service intention in the service demand information, and converting the service intention into a standardized intermediate instruction; generating a resource allocation scheme according to the standardized intermediate instruction and the network resource use state information, and evaluating the resource allocation scheme in a digital twin environment of the private network; and under the condition that the evaluation result of the resource allocation scheme in the digital twin environment meets a preset requirement, converting the resource allocation scheme into a control instruction, and sending the control instruction to the network resource. The technical problems of low network resource allocation efficiency and slow service response speed caused by non-uniform resource description and high service demand analysis difficulty in a multi-manufacturer heterogeneous network environment are solved.

The invention discloses a self-adaptive communication protocol switching method and device. The method comprises the following steps: acquiring network environment data corresponding to a target network environment, terminal equipment data corresponding to terminal equipment in the target network environment and service demand data; fusing the three kinds of data to obtain fused feature data; inputting the fused feature data into a network environment prediction model, and processing the fused feature data by using the network environment prediction model to obtain performance prediction data of each candidate network in the target network environment on a future preset time window; according to the adaptation capability value of each communication protocol in the protocol library under the performance prediction data; and after a target communication protocol is selected according to the adaptation capability value and the service demand data, switching to the target communication protocol. According to the invention, the technical problem that a conventional communication system adopts a fixed communication protocol and cannot be dynamically adjusted according to a network environment and service requirements in the prior art, which has great limitation, is solved.