980 results about "Service demand" patented technology

The invention provides an automatic resource matching method and system based on trust degree dynamic grading, and relates to the technical field of data analysis and decision making, and the method comprises the steps: building a feature vector matrix through obtaining user historical behavior data, generating a trust degree grading model through the training of a deep learning model, and carrying out the trust degree grading of a target user; determining an accessible system resource pool based on the credibility level, collecting real-time service demand information, and generating a target resource configuration scheme through a resource demand prediction model; and the resource use efficiency is monitored in real time, the credibility level is dynamically updated, and resource redistribution is carried out. Accurate matching and dynamic adjustment of system resources are achieved, the resource utilization efficiency is improved, resource waste is reduced, and the safety and stability of system operation are guaranteed.

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

The invention discloses a trusted cloud security confidential privacy level product service method, and relates to the technical field of design of cloud computing security. The method comprises the following steps: acquiring a trusted cloud privacy security level and a hierarchical core security mechanism, establishing a binding relationship with a product, extracting technical features and performing quantitative scoring, and constructing a three-dimensional security matrix; obtaining a privacy grading service demand of the user, and establishing a demand matching model to screen a security preference grade product; obtaining different-dimension technical grade improvement times of the user security preference grade product, and carrying out synergistic effect analysis; if significant positive correlation exists, correlation analysis is carried out to obtain a dynamic correlation coefficient, a product optimization strategy is made, a three-dimensional security matrix is dynamically updated, and the trusted cloud security confidential privacy level product service system comprises a feature classification module, a product optimization module, a collaborative analysis module and an optimization updating module. According to the method, more accurate security product recommendation can be provided for the user, and the trusted cloud security service level is 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 discloses a password resource allocation method and system based on an attention mechanism and a residual network, and relates to the field of password allocation based on a deep Q network. The method and the device aim at meeting high-concurrency and multi-task complex network decision-making requirements. The password service scene covers multiple task types, including encryption and decryption operation, large-scale signature verification, key management and cross-service security calculation. The tasks put forward extremely high real-time performance and accuracy requirements for computing resources, communication bandwidth and task scheduling of the system. According to the method, the deep reinforcement learning method is introduced, the characteristics of different password service tasks are dynamically adapted, intelligent decision making can be carried out based on the real-time system state and service requirements, and therefore the resource utilization efficiency is remarkably improved. Meanwhile, by optimizing task path planning, system time delay is reduced, load balancing is achieved, and the performance stability and task response capacity of the password service in a high-concurrency scene are effectively guaranteed.

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 network resource scheduling, and provides a heterogeneous network intelligent slice resource scheduling method and device, electronic equipment and a medium. The method comprises the following steps: performing three-dimensional joint modeling on heterogeneous network physical resources to obtain a dynamic resource state tensor, and performing a dynamic resource allocation decision by combining a deep reinforcement learning model with a service demand matrix to obtain an initial slice resource allocation matrix; and performing mixed integer linear programming through a preemption decision model in combination with the URLLC service request queue to obtain a resource allocation sequence, and performing occupancy rate updating on the dynamic resource state tensor according to the resource allocation sequence to obtain ground network resource data, and carrying out resource fusion in combination with the satellite beam capacity data to obtain a space-ground integrated resource pool tensor, thereby carrying out traffic prediction in combination with historical traffic data through a long-short memory network to obtain a bandwidth pre-adjustment strategy. Through organic integration of technologies such as mixed integer linear programming and traffic prediction, the overall network resource utilization rate is improved.

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.

A service-level communication and computing collaborative resource allocation device and method for a large-scale satellite network are provided, the device includes: a network information management function module including a service request collection unit configured to collect service requests arriving at each satellite node in the network, and a network resource collection unit configured to collect available resource status information for each satellite node; a service preprocessing function module configured to preprocess service requests; a service feature extraction function module configured to perform feature extraction for preprocessed service requests to generate service demand features; a service order decision submodule configured to determine a service order of service requests; a service strategy decision submodule configured to output a service strategy of the service request. The device and method can effectively ensure that the large-scale satellite network provides high-performance services for service with service demands of communication and computing collaborative resources.

The invention provides a TCP long connection server automatic expansion method and system. The method comprises the following steps: acquiring and monitoring various load indexes of a server in real time; receiving and analyzing the real-time load data, and judging whether resources need to be expanded or reduced currently according to a preset threshold value; based on the current load trend and historical data, performing short-term load prediction by adopting an extended decision algorithm; when expansion or reduction conditions are met, expansion or reduction operation is started, and a capacity expansion and reduction decision instruction is output; configuring a plurality of load balancing strategies, and distributing the received TCP long connection request to each service instance; and dynamically adjusting the size of a connection pool in the TCP long connection service cluster according to the system load condition and the capacity expansion and contraction decision instruction, and executing TCP connection load balancing operation after resource expansion or reduction. According to the invention, dynamic adjustment of resources is realized through technical means of load monitoring, automatic expansion, load balancing, dynamic connection management and the like, so as to meet service requirements under different load conditions.

The invention provides a multi-mode satellite terminal power supply adaptive management method. The method comprises the following steps: S1, acquiring an operation state and resource sensing data of a satellite terminal power supply; obtaining business demand data; s2, determining a plurality of working modes corresponding to the satellite terminal power supply based on the operation state and the resource sensing data; s3, based on the plurality of working modes, determining total power consumption corresponding to the plurality of working modes; s4, determining a satellite terminal power management optimization strategy based on the total power consumption, the operation state, the resource sensing data and the service demand data; s5, adjusting a satellite terminal power supply control instruction in a corresponding working mode by adopting a satellite terminal power supply management optimization strategy; judging whether the system operation parameters of the satellite terminal power supply meet preset change conditions or not; and if yes, repeating the steps S1-S5. Signal abrupt change is responded in real time through a closed-loop feedback mechanism, the sensing and decision-making process is cyclically updated, and self-adaptive balance of energy efficiency, communication quality and battery life in a complex scene is achieved.

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 AI intelligent proposition method, and belongs to the technical field of intelligent proposition, and the method comprises the steps that a proposition platform end is established by a platform side, and the proposition platform end comprises an industry large model training module, a professional knowledge base construction module and a user analysis module; the platform side establishes a user side for a corresponding user according to the service demand data, wherein the user side comprises a test question generation module, a user professional library and a test question checking module; the user professional library is used for storing industry knowledge information of service industries corresponding to users; the test question generation module is used for generating corresponding first draft test questions according to proposition requirements of a user and a professional library of the user; the test question checking module is used for automatically checking the generated first draft test question; the method comprises the steps of obtaining proposition requirements of a user, analyzing the proposition requirements through a test question generation module, obtaining first draft test questions, checking the first draft test questions through a test question checking module, marking the first draft test questions passing the checking as application test questions, and displaying the application test questions to the user.

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 discloses a multi-mode fusion data processing method and device, a medium, electronic equipment and a program product, belongs to the technical field of computers, and can reduce the data processing complexity and meet the service requirement of high timeliness when responding to application service to integrate multi-mode data. The multi-mode fusion data processing method comprises the following steps: receiving a multi-mode data retrieval statement, analyzing the multi-mode data retrieval statement, and translating the multi-mode data retrieval statement into single-mode data retrieval statements corresponding to a plurality of modes respectively; according to each single-mode data retrieval statement, performing data retrieval in a corresponding single-mode database in the multi-mode database to obtain target data of different modalities; wherein single-mode databases corresponding to a plurality of modes are maintained in the multi-mode database, and independent data management is carried out on each single-mode database; and performing feature fusion and modal alignment processing on the retrieved target data of different modalities to generate a unified multi-modal data representation, and feeding back the unified multi-modal data representation as a retrieval result.

The invention discloses a communication resource processing method and device under multi-orbit satellite access, equipment and a medium, which can be applied to the technical field of communication. According to the invention, after the data transmission request is obtained, the current orbit satellite resource detection is carried out according to the data transmission request to obtain the current orbit satellite resource state data, and after the service quality demand information is obtained, the priority allocation is carried out on the service demand according to the service quality demand information to obtain the priority allocation result; then, according to the current orbit satellite resource state data and the priority allocation result, data transmission orbit resource allocation is carried out to obtain a data transmission orbit resource allocation result, and data transmission control is carried out according to the data transmission orbit resource allocation result, so that real-time and intelligent resource scheduling can be carried out; therefore, the bandwidth resource allocation balance degree is improved, and the probability of track overload is reduced.

The invention discloses a network resource allocation method and system based on a network load, and relates to the technical field of network resource allocation, and the method comprises the following steps: 1, determining a service demand satisfaction degree; step 2, calculating a network load balancing degree; 3, predicting future business demand data; and 4, defining constraint conditions, maximizing the service demand satisfaction degree and the network balancing load degree, and formulating a network resource allocation strategy. According to the method, the situation that only the overall average level is concerned and individual key indexes are ignored to be seriously substandard when the business requirements are evaluated in the prior art can be effectively improved.

The invention relates to the technical field of micro-service architecture and intelligent operation and maintenance, and discloses a micro-service dynamic heterogeneous routing system and method based on a large model, and the system comprises a semantic control layer, a protocol adaptation module, a path optimization module, a path scheduling module, and an execution layer. The method comprises the following steps: analyzing a micro-service API document through a large language model, extracting a semantic dependency relationship between services, and generating a service dependency graph; analyzing a business semantic chain; selecting a path with the minimum cost by applying a path optimization algorithm; optimizing path scheduling by using reinforcement learning according to real-time network conditions and service requirements; and finally executing path optimization and performing protocol adaptation conversion. According to the method, the API document is analyzed through the large language model to automatically construct the service dependency graph, the path is dynamically adjusted in combination with the path optimization and reinforcement learning algorithm, and the optimization problem of the service calling path is solved. Through the protocol adaptation execution unit, automatic adaptation of the cross-protocol service is realized, and the efficiency and maintainability of the system are improved.

The invention discloses a network slice dynamic scheduling system and method based on power business differences, and belongs to the technical field of power wireless communication. The scheduling system comprises a slice layer, a service layer and a network layer, the slice layer calculates a network slice resource reservation proportion according to a service layer power service type, service delay and a service arrival rate, and performs weight calculation and sorting on network slices; the service layer calculates priorities of to-be-processed power services according to resource demands of different power service requests and service request delays, sorts the priorities, and distributes the to-be-processed power services to network slices according to network slice weights and the priorities of the to-be-processed power services; and the network layer is used for carrying out network slice link configuration by taking physical link load balancing as a constraint and taking the minimum virtual link time delay of the network slice as a target, and outputting an optimal link configuration result of the network slice. According to the invention, link time delay and load balancing are comprehensively considered, and optimal allocation of resources and service requirements of businesses are guaranteed.

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 present invention discloses an employee service management method and system for a human resources intelligent terminal, which relates to the field of computer technology, including collecting employee static data and employee dynamic data through a human resources intelligent terminal, and generating an employee data set; generating an employee portrait containing employee characteristic information according to the employee data set; triggering a service linkage mechanism to call corresponding resources to generate personalized service content according to predicted service needs, and sending the personalized service content to the employee's human resources intelligent terminal; collecting employee feedback data on pushed personalized services, analyzing the feedback results, and performing parameter optimization on the matching logic of a dynamic rule engine; updating employee portraits and service rules according to the optimized dynamic rule engine; the present invention dynamically generates a rule candidate set through a dynamic rule engine, and selects the optimal rule in combination with reinforcement learning, so as to respond to changes in employee needs in real time and ensure the personalization and timeliness of service content.

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.