304 results about "Routing decision" patented technology

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

The invention discloses a multi-dimensional computing power dynamic perception routing decision-making method and system based on SRv6 driving. The method comprises the following steps: firstly, screening an optimal target computing power node based on a service demand and a node real-time load; and then, based on the dynamic network topological graph fusing the service sensitivity, calculating an optimal network path reaching the computing power node by using an improved SPFA algorithm, and realizing a global collaborative decision of selecting the most suitable computing point and searching the most efficient connection path. Meanwhile, based on an SRv6 driving service chain dynamic generation and closed loop execution method, a target computing power node and necessary network functions are abstracted into a programmable SID, an SID sequence (service chain) is dynamically constructed according to a double-stage decision result and is packaged in an SRH head, second-level path issuing and state monitoring are achieved through a programmable controller, and a real-time monitoring result is obtained. According to the method, the problems of poor real-time performance, single dimension, weak cooperative capability and the like in the prior art are solved, and efficient, stable and intelligent development of a future-oriented distributed intelligent computing network system can be promoted.

The invention provides a knowledge base and business system collaboration method under an AI platform, and belongs to the technical field of AI platform digital data processing.The method includes the steps that triple semantic analysis is conducted on query by constructing a multi-level semantic vector representation module, a parallel data retrieval engine is started, vector retrieval, graph reasoning and real-time data pulling are executed at the same time, and the query efficiency is improved; establishing a dynamic confidence evaluation mechanism to evaluate the quality of the data source, executing a weight distribution algorithm based on reinforcement learning, dynamically calculating the weight of the data source according to a query type by adopting a graph convolutional network intelligent routing decision model, and implementing multi-source data fusion and consistency verification to solve data conflicts through a weighted voting mechanism. An intelligent result sorting and filtering system is established, a multi-dimensional evaluation strategy is adopted to output high-quality answers, a continuous learning and feedback optimization loop is constructed, system performance is continuously optimized through active learning and a graph shortest path algorithm, and the technical problem that knowledge base data and a service system cannot effectively and uniformly make decisions is solved.

The invention discloses an intelligent policy question-answering method and system based on retrieval enhancement generation and a medium. The method comprises the following steps: constructing a universal knowledge base and a plurality of mutually independent domain knowledge bases; in response to user input, the following steps are executed: performing routing analysis on the user input through a first large language model to generate a structured routing decision; retrieving the general knowledge base to obtain related general knowledge text segments and vectorization expressions thereof; according to the routing decision, a domain knowledge base corresponding to the at least one policy domain identifier is retrieved in parallel, and related policy text fragments corresponding to the at least one domain knowledge base and vectorization expressions of the related policy text fragments are obtained; obtaining an initial answer set based on retrieval results of the general knowledge base and the domain knowledge base; and performing intelligent fusion processing on the initial answer set through a second large language model, and generating and outputting response content. According to the method, the problem of knowledge updating lag is effectively solved, and the accuracy, timeliness and cross-domain specialty of policy questions and answers are improved.

The invention discloses an eBPF-based kernel optimization method, system, equipment and medium, belongs to the technical field of Linux kernel network optimization, and aims to solve the technical problem of how to improve the transmission performance for a specific data stream and specify the data stream to carry out a special routing and transmission process so as to provide services for a special network adapter and improve the transmission performance of the specific data stream. According to the technical scheme, the method comprises the steps that data packet interception is carried out on a plurality of kernel eBPF loading points and program interception points selected by a user through an eBPF kernel hook program, routing query is carried out with a routing kernel module, and selection of an operation function and routing parameters is completed; the routing kernel module completes a routing decision based on various complex routing requirements, configuration conditions, data packet conditions and environment conditions, and returns a query result to the eBPF kernel hook program, and the query result is executed by the eBPF kernel hook program. The system comprises an eBPF user mode management program module, a user mode control tool, an eBPF kernel hook program module and a routing kernel module.

The invention provides an outbound routing method and system based on multi-dimensional index coupling and large model decision, and the method comprises the steps: carrying out the preprocessing of the historical data of each line provider, predicting the future performance index through an LSTM neural network, and optimizing the prediction result through a Kalman filter, and eliminating the noise. In the route decision-making link, the system calculates the Q value of each line provider based on the deep Q learning network, comprehensively considers the call completing rate, average cost, negative feedback rate and other factors of each line provider, and intelligently selects the optimal line provider in combination with the user portrait, scene recognition and real-time load state. The system also constructs a complete feedback closed loop, continuously optimizes the model by collecting call result data, and automatically adjusts decision parameters. The call completing rate and the service quality of the outbound service are improved, and the communication cost is reduced.

A routing method for supporting differential service requirements in a satellite internet comprises the following steps: step 1, constructing multi-dimensional network state perception as a base layer which is responsible for collecting and quantifying physical states and logic states of a satellite network in real time, and obtaining objective network states and subjective QoS (Quality of Service) requirements of services; 2, generating a nonlinear adaptive weight driven by a service as a strategy layer, mapping an objective network state and a subjective QoS (Quality of Service) demand of the service, dynamically adjusting the value orientation of routing judgment, and generating a dynamic weight; and step 3, taking the dynamic cost routing decision as an execution layer, calculating the comprehensive cost based on the dynamic weight, and executing next hop selection and path maintenance, and through cooperative work of the dynamic cost routing decision, the comprehensive cost and the next hop selection and path maintenance, realizing intelligent path selection and high-reliability transmission for different service flows in a high-dynamic and resource-limited environment of the low-orbit satellite. According to the invention, while the routing computation overhead is effectively reduced, multiple requirements of differentiated services on low time delay and high reliability can be considered.

The invention relates to the technical field of routing context connection, in particular to a zero-code visualization and automatic routing method for large-model servitization orchestration, which comprises the following steps of: analyzing a user session text, extracting a core entity and an intention dependency relationship, constructing an entity intention graph, compressing to generate a state snapshot, and distributing a global session ID (Identity); the method comprises the steps that a snapshot is bound to a distributed routing decision tree node and stored, cross-node state continuity is established, when a new request is received, the snapshot and new session semantics are fused, a routing weight offset correction decision table is calculated, and a routing decision logic flow is generated by dragging an interface configuration rule and strategy. The method improves the service matching degree, reduces the development threshold, and is suitable for agile construction of large-model collaborative application.

The invention discloses a computing power network redundant path construction method based on multi-modal multi-objective optimization and a scheduling system, and belongs to the technical field of routing optimization in a computing power network. According to the method, firstly, for a constructed multi-objective optimization function including the minimum average time delay of all service traffic, the minimum maximum link bandwidth utilization rate and the maximum successful routing rate of all traffic, a transformer-based deep reinforcement learning and truncation selection strategy is adopted to realize multi-modal multi-objective optimization of routing decision; compared with the prior art that an equivalent path cannot be obtained only through an optimal path, the solving method provided by the invention can make an optimal choice for problems such as routing link faults on the premise of ensuring Qos.

The invention discloses a Mesh routing selection method based on a graph neural network and reinforcement learning. The Mesh routing selection method comprises the following steps: step S1, constructing a Mesh network graph structure model; s2, extracting an embedded representation of each router node by using a graph neural network; s3, designing a routing decision mechanism based on Q-learning, and generating a Q value in combination with the current router node state and neighbor information; s4, introducing a Masking mechanism to process the dynamic action space, and shielding illegal next-hop action; and step S5, defining a multi-target reward function which is used for guiding a Q-learning training process. The invention provides a Mesh routing selection method based on a graph neural network and reinforcement learning, and aims to solve the problems of poor adaptive capacity to a dynamic network environment, low routing efficiency and lack of intelligent decision support in the prior art and improve the data transmission performance of a Mesh network.

A computer-implemented system for routing artificial intelligence (AI) queries. The system utilizes a zero-copy data pipeline, which processes prompts in memory-mapped buffers to eliminate at least one memory copy operation, thereby reducing latency relative to conventional serialization pipelines. The system continuously verifies AI provider compliance by injecting synthetic prompts containing invisible, Ed25519-signed Unicode watermarks. Algorithmic bias is detected by generating counterfactual “digital twin” prompts and applying Fisher exact statistical testing.Routing decisions for multi-tier autonomous systems are governed by safety-level requirements (ASIL-D, ASIL-B, QM) and may be constrained by external routing directives received via a meta-identifier. A hash-chained manifest, cryptographically signed using Ed25519 and consumed by downstream gateways, is generated for each routing decision, with its Merkle root asynchronously anchored to a blockchain to create a tamper-evident audit trail for regulatory compliance.

Systems and methods for processing artificial intelligence user requests including receiving a user input from a user device, performing a routing analysis on the user input on multiple characteristics, making a routing decision based on the routing analysis to send the user input to one or both of an experiential reasoning agent model and an analytical reasoning model, routing the user input to at least one of the experiential reasoning agent model and the analytical reasoning model responsive to the routing decision, receiving one or more outputs from at least one of the experiential reasoning agent model and the analytical reasoning model, generating a final result by performing a result validation procedure on the one or more outputs, and transmitting the final result to the user device.

The invention provides a multi-platform informatization construction system and method based on a computer, and solves the problems that existing multi-platform data synchronization is not timely, formats are not compatible and the like. The system comprises a real-time data capture module, and a data change event is generated in real time through database transaction log monitoring; the intelligent routing decision module optimizes a transmission path in combination with rules and machine learning; the data conversion adaptation module realizes automatic adaptation of a format and an interface by utilizing dynamic template matching; the conflict detection and resolution module guarantees data consistency based on version control and priority judgment; the data synchronization execution module transmits data according to an optimized path, and the monitoring management module monitors the whole process in real time. The method comprises the steps of data capture, routing decision, format conversion, synchronous execution and monitoring feedback. According to the scheme, the data synchronization efficiency and accuracy are improved, the multi-platform integration operation and maintenance cost is reduced, and the method is suitable for multi-scene informatization construction of enterprises, government affairs and the like.

The embodiment of the invention provides an elastic fragment routing and cold and hot data optimization method based on multi-dimensional feature prediction and related equipment, and belongs to the technical field of distributed database storage optimization. The method comprises the following steps: receiving a data stream from Internet of Things equipment; performing analysis and feature extraction on the data stream to obtain multi-dimensional feature data; according to the multi-dimensional feature data and the time sequence prediction model, performing prediction analysis on a fragmentation strategy to obtain an optimal fragmentation strategy; analyzing the routing decision according to the data priority of the data stream and a dual-mode routing engine to obtain routing decision result data; generating a target index name and creating a target index according to the optimal fragmentation strategy and the routing decision result data; according to the target index name, the data stream is routed to the target index according to the fragmentation key for batch writing; the fragmentation keys are from multi-dimensional feature data. According to the embodiment of the invention, intelligent prediction of the fragment number and automatic hierarchical storage of data can be realized, and the problems of resource elastic scaling and cost efficiency optimization are fundamentally solved.

The invention provides a dynamic routing forwarding method and device for an AI large model. Constructing a collaborative architecture of a centralized scheduler and distributed execution nodes; collecting multi-dimensional performance indexes of each model instance in real time, wherein the multi-dimensional performance indexes comprise GPU utilization rate, video memory occupation and reasoning delay; extracting key features of the input request based on a natural language processing technology, wherein the key features comprise an input text length, a Token number and a task type; a load evaluation model based on an entropy weight method is constructed, and an intelligent routing decision is implemented in combination with the real-time load state and request characteristics of the instance; and instance fault detection and adaptive fault-tolerant processing are carried out based on a multi-level threshold mechanism. The invention provides a more intelligent and efficient dynamic routing method for specific requirements of multi-instance deployment of the AI large model. The load state and the performance index of the model instance are monitored in real time, and intelligent scheduling is performed in combination with the request characteristics, so that the resource utilization rate and the service quality are improved, and the operation and maintenance complexity and the management cost are reduced.

The invention discloses a satellite-based routing decision making method and device and a storage medium. The method comprises the following steps: determining a plurality of reference curves in each first sampling period; determining a first distribution curve in the current time period, and broadcasting the reference curve of the current time period, the first distribution curve and the channel capacity of each area in the current time period to each satellite; determining a reference curve of the next time period, calculating a deformation relation between the reference curve of the second sampling time period and the reference curve of the third sampling time period, and performing deformation processing on the first distribution curve to obtain a plurality of second distribution curves of the next time period; predicting the satellite congestion degree of each region in the next time period according to the channel capacity of each region in the current time period on the basis of the plurality of second distribution curves; and based on each region, constructing a plurality of corresponding routing paths, and determining an optimal routing path in the plurality of routing paths by using a genetic algorithm. Therefore, the technical effect of reliably transmitting data is achieved.

The invention provides a real-time data processing method and system based on edge computing, and relates to the technical field of data processing.The data validity period is calculated through a composite decision model of terminal equipment physical sampling frequency constraint and preset service rules, the signal restoration accuracy requirement and the service scene emergency degree are effectively balanced, and the real-time data processing efficiency is improved. Overtime failure of key data or occupation of non-key data resources is avoided; the data transmission integrity in a bandwidth fluctuation environment is improved through a dynamic window cutting mechanism driven by a real-time network state; optimizing edge computing power load distribution in combination with a routing decision mapping strategy and a processing priority resource ratio; the terminal instruction reliability is improved based on hierarchical control of historical data abnormal value purification and geometric distance measurement; a complete technical chain from data acquisition, self-adaptive transmission cutting, aging precise arbitration and routing resource collaboration to closed-loop control feedback is formed, and the transmission efficiency, the resource utilization precision and the control stability of high-concurrency real-time data processing are enhanced.

The invention discloses a vehicle diagnosis service method and system based on dynamic protocol adaptation and context awareness, and the method comprises the steps: analyzing a diagnosis request, and determining a service range and a dependency relationship required by the diagnosis request; obtaining multi-dimensional context information, and based on the obtained information and the dependency relationship, executing a routing decision to select an optimal service instance and a protocol path; dynamically calling a protocol adapter through a protocol adaptation matrix based on the service instance and the protocol path, converting the diagnosis request into a protocol message, and sending the protocol message to a vehicle network; and receiving the original diagnosis data returned by the vehicle network to generate a complete diagnosis report. According to the method, flexible calling of the protocol adapter is realized through the dynamic protocol adaptation matrix, the service range and the dependency relationship are analyzed in combination with the service governance engine, and the routing decision is optimized by utilizing the multi-dimensional context information, so that the method has the advantages of dynamic and flexible protocol adaptation, accurate and intelligent service analysis and global optimization of the routing decision.

The invention discloses a routing method and device for converged communication messages, electronic equipment and a storage medium, and relates to the technical field of converged communication or other related fields, and the method comprises the following steps: receiving a call request from a communication terminal, and preprocessing the call request to obtain a target call request, the preprocessing comprises heterogeneous protocol unified processing and semantic enhancement information adding; fusing the network state data and the semantic enhancement information of the current network environment to obtain routing decision data; the routing decision data are input to a target decision model, a routing strategy is output, and the target decision model comprises a reinforcement learning model formed by training based on historical routing data; and forwarding the target call request according to the routing policy. According to the method and the device, the technical problem that the communication quality is reduced due to inaccurate message analysis and untimely forwarding caused by defects in protocol adaptability of a traditional routing mechanism in the related technology is solved.

The invention provides a storage device, a storage method and a storage access method based on lake and warehouse integration. The storage device comprises a storage management unit, a unified metadata management unit and a federal query engine unit, the storage management unit is used for realizing hybrid storage and dynamic hierarchical storage; the unified metadata management unit is used for metadata atlas construction, concurrency control and GDPR compliance support; the federal query engine is used for intelligent routing decision, SQL dialect unification and intelligent push-down optimization; based on a lake and warehouse integrated storage device and an access technology thereof, unified management of structured and unstructured data is supported, the problem of data islands is avoided, and cross-engine data visibility is realized through unified metadata management; according to the lake and warehouse integrated storage device and the access method, technologies such as a dynamic hierarchical storage strategy, intelligent routing decision, intelligent push-down optimization and the like are fused, and the storage and access efficiency and the robustness of the system are greatly improved.

The invention provides an internet-based information intelligent directional accurate delivery system, which is characterized by comprising an analysis platform, the analysis platform comprises an intelligent matching module and an analysis engine, the intelligent matching module captures user key data and makes a mixed feature set, and the analysis engine is used for analyzing the mixed feature set; the analysis engine reads data of the mixed feature set and constructs a superficial portrait of the user; the fusion platform generates a user deep portrait according to an algorithm of the fusion platform and in combination with the user interest vector; the method relates to the technical field of Internet information accurate release. Through cooperative work of the analysis platform, the fusion platform and the delivery platform, deep mining, portrait construction and accurate content delivery of user data are realized; and the analysis platform, the fusion platform and the delivery platform realize a dynamic optimized pushing strategy of data by adopting a distributed crawling technology, time sequence prediction, transfer learning, reinforcement learning and intelligent routing decision, so that the information delivery accuracy and the user experience are effectively improved.

The invention relates to an ocean communication method based on self-adaptive Q-learning factor setting. In the method, each node periodically collects own state information and broadcasts the own state information to surrounding nodes, and after each node receives a message and stores the message in a cache region, routing decision and parameter adjustment processes are circularly executed until the message is completely transmitted to a target node to complete communication; wherein the routing decision and parameter adjustment process comprises the following steps of: calling a message from a corresponding buffer area by a current node, selecting a next forwarding node of the message based on a current Q value table, updating a learning rate and a discount factor based on the current state information of the node after sending the message, and performing routing decision and parameter adjustment by combining a reward function, the updated learning rate and the discount factor. And a new Q value table is obtained through asynchronous updating of the double-Q network. Compared with the prior art, the method has the advantages that the delivery rate is higher, the method can better adapt to changeable environments, and the problem of real-time changes of topology, load and channel quality can be well solved.

The invention relates to the technical field of artificial intelligence, and discloses a cross-modal agent base deployment system and method based on an AI large model, and the system comprises a data layer which comprises a plug-in multi-source heterogeneous database and a multi-modal dynamic knowledge graph, and builds a cross-modal knowledge base through unified coding and joint retrieval; the processing layer comprises a multi-modal feature analyzer, an intelligent routing decision engine and a three-level dynamic model matrix, the multi-modal feature analyzer is used for analyzing data, and the intelligent routing decision engine selects a target model from the three-level dynamic model matrix for processing to generate a routing decision; the optimization layer comprises a hierarchical retrieval enhancement module, a dynamic feedback optimization module, an optimization data layer and a processing layer, data of different modes are effectively fused, model processing is selected through an intelligent routing decision engine, the response speed is increased on the premise that precision is not lost, the overall performance is improved through the optimization layer, and the reliability of the system is improved. The cross-modal intelligent agent base can efficiently process multi-modal data, and intelligent decision making is achieved.

The invention discloses a reinforcement learning routing optimization method considering a dynamic underwater sound environment and node mobility. The method comprises the following steps: performing parameter initialization on an underwater sensor network; monitoring and updating underwater environment parameters and node positions in real time, and dynamically updating a network topology connection state; during data transmission, constructing a multi-dimensional state vector, selecting a routing action by adopting an epsilon-greedy strategy, and generating a routing decision; if the data packet transmission fails when the routing action is executed, ending the iteration, otherwise, calculating a multi-target reward value; updating the Q value table to optimize the routing decision; and monitoring network performance indexes in real time, adaptively adjusting reinforcement learning parameters of the intelligent agent for next iteration, and outputting an adaptive routing selection model until an iteration termination condition is met. According to the method, time-varying channel and node mobility challenges can be intelligently dealt with, and multi-objective optimization balance of energy consumption, delay and stability is realized while the data delivery rate is remarkably improved.

The invention discloses a software and hardware cooperation system and method for satellite network routing simulation, and mainly solves the problem that an existing simulation scheme is difficult to consider high fidelity of protocol behaviors and high performance of data forwarding in a high dynamic satellite network. The system comprises a software control plane unit, a hardware data plane unit, a satellite network simulation upper computer and a switch, and the switch is bidirectionally connected with the software control plane unit, the hardware data plane unit, the satellite network simulation upper computer and the switch and serves as a data exchange platform of the system. The satellite network simulation upper computer is used for system configuration management, operation state monitoring and result visualization; the software control plane is used for executing simulation calculation and routing decision of a satellite network routing protocol and generating a standardized forwarding table item; and the hardware data plane unit performs high-speed and deterministic table lookup forwarding according to the standardized forwarding table item, and simulates a satellite resource constraint environment. According to the method, the integrity of protocol simulation details is guaranteed, meanwhile, high-efficiency forwarding performance and non-interruption updating in a high-dynamic environment are achieved, and the method can be used for routing protocol verification, network performance testing and satellite-borne algorithm evaluation of large-scale low-orbit satellite constellations.

The invention relates to the technical field of automatic driving decisions, in particular to an automatic driving perception method and device based on dynamic visual angle selection, electronic equipment and a storage medium. According to the method, the key view angle is intelligently selected for processing through a dynamic routing decision-making mechanism, full-view-angle calculation redundancy is avoided, the system calculation load and power consumption are directly and greatly reduced, and the processing efficiency is improved; meanwhile, the mechanism adjusts an expert network activation strategy in real time based on multi-modal information fusion, so that the complex scene adaptability and perception accuracy are enhanced, and the problem of unsmooth control caused by view angle switching oscillation is improved; in addition, by introducing a multi-level security bottom-taking strategy formed by dual real-time verification and periodic global perception, the risk of wrong decision making is effectively reduced, and the reliability and security of system operation are comprehensively improved.

The invention relates to the technical field of communication, in particular to an aviation airborne miniature 5G ad hoc network multimode communication terminal, which comprises a flight state prediction module, a state vector and time step length linear transformation relation is constructed by adopting a kinematics equation based on real-time GPS position, velocity vector, acceleration and attitude angle data of the terminal and neighbor nodes, and the state vector and time step length linear transformation relation is obtained; and by using the residual error between the newly received kinematics data and the predicted value at the previous moment, weighting the correction state vector and the predicted covariance, circulating a recursive correction process, outputting three-dimensional space coordinates of multiple nodes under a plurality of time steps in the future, and obtaining a future position set of the nodes. According to the method, the basis of routing decision is changed by directly processing real-time GPS position, velocity vector and attitude angle data and adopting three-dimensional space coordinates of continuous nodes of a kinematics equation in a future time window. It no longer relies on instantaneous snapshots of the current network state, but is based on future predictions.

The invention relates to the technical field of smart power grids and communication, and discloses a power utilization information acquisition method and system based on dual-mode communication, and the method comprises the steps: 1, initializing a configuration network, building the neighborhood of each acquisition terminal node, carrying out the probability distribution parameterization of the channel states of a power line carrier channel and a wireless radio frequency channel, and carrying out the network configuration; according to the probability distribution parameterization, calculating a Fishi information matrix used for quantifying the information distance between different channel states; and step 2, defining a channel state geometrical relationship based on the Fishi information matrix. The probability distribution parameter space of the channel state is constructed into the information geometric manifold, the manifold Kalman filtering algorithm is adopted for state prediction, and compared with an existing channel prediction scheme adopting standard Kalman filtering or a simple time sequence model, the problem that the statistical characteristics of the channel cannot be accurately described due to model mismatch is solved, and the channel prediction accuracy is improved. Therefore, the problems of low prediction precision and incapability of effectively supporting a prospective routing decision are solved.

The invention discloses a routing decision model training method, a routing request method, electronic equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a pre-constructed training data set and a to-be-trained routing decision model; if the small model answer result meets a preset minimum expected condition, a label corresponding to the training interaction data is set to be a first preset value indicating to route the request to the edge small model, and the preset minimum expected condition includes that the result satisfaction degree corresponding to the small model answer result is larger than or equal to a preset minimum expected satisfaction degree; if the answer result of the small model does not meet the preset lowest expected condition, setting a label corresponding to the training interaction data as a second preset value for indicating to route the request to the cloud large model; and training a routing decision model based on the training interaction data and the label corresponding to the training interaction data. According to the invention, the utilization rate of end-side resources is improved, and the balance degree of cloud resources and end-side resource allocation is further improved.