201 results about "Routing decision" patented technology

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 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.

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 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 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 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 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 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 provides a micro-service unified access control and governance system in a hybrid cloud environment, and the system comprises a unified entrance layer which takes an Istio gateway as a unique access point of external flow, and receives all business requests from the outside; according to the authentication and authorization intermediate layer, the Istio Gateway forwards the service request to the Oathkeeper, and Kratos authentication and SpiceDB authentication are carried out. The rule routing layer is used for performing routing decision on the service request passing the Kratos authentication and the SpiceDB authentication based on a predefined dynamic rule set, and generating a routing instruction; and the heterogeneous service layer comprises a VM environment and a K8S environment, and forwards the request to a corresponding target service instance according to the routing instruction. According to the method, the problems of flow entrance dispersion, authentication splitting and non-uniform routing rules in a mixed cloud environment are solved, and uniform, safe and efficient access control and management of heterogeneous micro-services are realized.

The invention relates to the technical field of network communication, and provides a hierarchical reinforcement learning scheduling and routing method for a multi-domain TSN, and the technical scheme comprises the steps: collecting the global state information of the multi-domain TSN, and carrying out the coding processing of the global state information to generate comprehensive state representation; performing a cross-domain routing decision, and outputting an inter-domain path and a time delay budget of a cross-domain flow; executing intra-domain scheduling, determining a sending sequence and a specific path of a domain flow, and generating gating list configuration; through hierarchical coordination and strategy optimization, a cross-domain routing decision and intra-domain scheduling are updated based on reward feedback of an intra-domain scheduling result; and deploying the finally updated intra-domain scheduling to a switch of the multi-domain TSN network for execution, thereby realizing deterministic transmission of the time-triggered flow. According to the invention, through a layered agent architecture, a hybrid neural network coding mechanism and a cross-domain collaborative optimization strategy, challenges of complexity, expandability, dynamic adaptability and the like of a joint routing and scheduling problem in a multi-domain TSN environment are effectively solved.

Existing session routing strategies tend to primarily focus on performance and cost efficiency, with less emphasis on energy consumption or sustainability. To address this, devices, systems, methods, and processes that facilitate sustainability-aware collaboration session activity routing are described herein. A session control logic receives attributes of a plurality of network devices and determines sustainability scores for the plurality of network devices. The session control logic further selects a target network device from the plurality of network devices based on the sustainability scores, and directs a collaboration session activity to the selected target network device. Thus, the target network device receives the collaboration session activity in response to its sustainability score being optimal in comparison to respective sustainability scores of peer network devices. As a result, the session control logic explicitly considers sustainability impact of a routing decision when determining routes, leading to improved energy efficiency or reduced environmental impact.

The embodiment of the invention provides a multi-modal data retrieval method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a natural language problem of a user, carrying out the intention understanding of the natural language problem, and obtaining the query intention of the natural language problem; performing retrieval path selection on the query intention, and determining at least one target modal type matched with the natural language question; the natural language question is distributed to vector databases corresponding to the target modal type to be retrieved, and candidate multi-modal retrieval results of the natural language question are obtained; and verifying the candidate multi-modal retrieval result according to a pre-constructed multi-modal business knowledge graph to obtain a target multi-modal retrieval result of the natural language problem. By utilizing the method, a collaborative mechanism of semantic comprehension, routing decision and knowledge enhancement is introduced, so that the cross-modal semantic comprehension capability and the information retrieval performance are remarkably improved.

The embodiment of the invention provides a gray release and flow control method and device for large model service, and the method and device achieve the effective monitoring of a state through the innovative design of a version evaluation system, index analysis and stability scoring. And a flow control mechanism is constructed, and a reliable publishing strategy is established by combining a routing decision and resource scheduling. And self-adaptive adjustment is introduced, and through state monitoring and rule optimization, the publishing stability is ensured. According to the method, the defects of the traditional technology in the aspects of version evaluation, flow control, dynamic adjustment and the like are effectively overcome, and technical support is provided for large model service.

The invention discloses a model-based micro-service multi-source heterogeneous unified query system and method, and the system comprises a unified data access layer abstract interface which provides a standardized API for a developer to call; according to the model abstraction layer driven by metadata, a three-level metadata structure comprising a BizModel, a BizDataTable and a BizField is adopted for decoupling service logic and physical storage, and multi-source heterogeneous data association crossing MySQL / MongoDB / Elasticsearch / NoSQL is supported through RelativeSource metadata; the declarative query interface is combined with the dynamic SQL generator, provides a chain type API to construct a cross-service query condition, and automatically generates a query statement meeting a target database standard according to a preset data model; and the routing decision module distributes a query request to local execution and remote calling, and supports a plug-in expansion data source dialect generator.

The invention provides a content awareness and intelligent routing document analysis method and device, equipment and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the steps of performing content perception on a to-be-recognized multi-modal document, and extracting a multi-dimensional feature vector of each page of the to-be-recognized multi-modal document; a preset routing decision model is adopted to route the multi-dimensional feature vectors, and a document analysis tool of each page of document is determined; carrying out statistics on document analysis tools of multi-page documents of the to-be-identified multi-modal document to obtain analysis page numbers of the document analysis tools; according to the analysis page number of each document analysis tool, calling each document analysis tool to analyze a document corresponding to the analysis page number to obtain a local analysis result; and integrating all the local analysis results to generate a target analysis result of the to-be-identified multi-modal document. According to the method, the selection and use threshold of a document analysis tool can be reduced, and the efficiency, quality and automation degree of document analysis are improved.

The invention discloses a data communication transmission method and device in a wireless communication network and a medium, and relates to the technical field of resource scheduling, and the method comprises the steps: obtaining multi-source heterogeneous security data, calculating a comprehensive risk value of each coordinate point in a coverage area of the wireless communication network, and constructing a dynamic risk topology field based on the comprehensive risk value; performing risk-aware trajectory prediction analysis on the real-time trajectory data by using the dynamic risk topology field, and generating a preset communication demand entry; executing a routing decision of risk gradient perception on a preset communication data packet corresponding to the preset communication demand entry to obtain a forwarding path; and according to the preset communication demand entry and the forwarding path, performing non-signaling implicit resource scheduling on the wireless communication network to complete transmission of the preset communication data packet. By constructing the dynamic risk topology field, refined description of the comprehensive risk value of each coordinate point in the coverage area of the wireless communication network is realized, and the security of data transmission and the resource utilization efficiency are remarkably improved.

The invention discloses an industrial manufacturing system gray release method based on dynamic routing and flow scheduling. According to the method, the configuration takes effect in real time by loading a routing rule and monitoring configuration change; client requests are received, and the client requests are preliminarily classified based on the request feature attributes and the global gray switch state; dynamically executing a routing decision based on the preliminary classification result and a factory identifier obtained by JWT analysis; a client request routed to a gray environment is marked, the marked client request is routed to a target service instance to complete request forwarding, accurate isolation of core service traffic and gray traffic is ensured based on a traffic pre-classification mechanism, and accurate scheduling of service granularity is realized in combination with intelligent routing decision of a factory identifier. And the closure and observability of the gray environment are guaranteed through full-link mark tracking, the controllability and safety of the gray release process are improved, and meanwhile efficient and stable iteration of the industrial manufacturing system is achieved.

The invention discloses a large model cluster shunting system and method based on performance perception. The system comprises a routing decision module, a resource pooling module, a KV cache perception module and a performance perception module. Through dynamic resource pool division, KV cache index matching, instance health state monitoring and load evaluation, and in combination with a hierarchical scheduling strategy and a hit rate load scoring mechanism, intelligent routing of requests and efficient allocation of resources are realized. The method effectively solves the problems of non-uniform resource allocation, KV cache reuse failure, single-point bottleneck, resource waste and the like in a large model reasoning task, remarkably improves the system throughput, reduces the response delay, and has good fault-tolerant and self-recovery capabilities.

In one embodiment, a device obtains data regarding routing decisions made by a machine learning-based predictive routing engine for a network. The device determines, based on the data regarding the routing decisions, a behavior of the machine learning-based predictive routing engine. The device compares the behavior of the machine learning-based predictive routing engine to a behavioral policy for the machine learning-based predictive routing engine. The device adjusts operation of the machine learning-based predictive routing engine, when the behavior of the machine learning-based predictive routing engine violates the behavioral policy.

The embodiment of the invention provides a multi-round dialogue complexity assessment method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of artificial intelligence. According to the method, by fusing the trend of the session state, the semantic turning of the abstract and the correlation of the historical dialogues, the evaluation accuracy of the real complexity of the request in multiple rounds of dialogues is improved, and misjudgment caused by only depending on the original text length or keywords is avoided; and meanwhile, the context length transmitted to the large language model is effectively compressed by using the abstract containing intention transformation and the related dialogue, so that the Token cost is directly saved, the calculation overhead brought by redundant context transmission is reduced, and a more efficient and more intelligent model routing decision is supported. Accurate context compression and routing decision reduce the average duration of request processing, and the user experience can be improved.

The invention discloses a channel routing optimization system based on a graph neural network, and the system comprises the following modules: a dynamic network graph construction module which is used for collecting network topology and link state data, and constructing a dynamic network graph model; the node embedding generation module is used for inputting the data into an improved MTGNN model and generating a high-quality node embedding vector by introducing a contrast learning mechanism; the candidate path discovery module adopts a greedy neural network algorithm based on beam search, carries out parallel extension and retains high-score candidate paths according to node embedding vector scores, and generates a candidate path set; the optimal path selection module is used for calculating the comprehensive utility value of each path, sorting the comprehensive utility values and outputting an optimal path; and the routing configuration and optimization module is used for generating and issuing a routing configuration instruction according to the optimal path. According to the invention, the accuracy and efficiency of routing decision and the utilization rate of network resources are improved, and intelligent path optimization in a complex dynamic network environment is realized.

The invention provides an industrial large model construction method and a storage medium. The method comprises the steps of obtaining multi-modal fusion data of an industrial field; performing scene classification on the industrial field to obtain industrial subdivided scenes; according to the multi-modal fusion data, determining a multi-modal data set of each type of industrial subdivision scene; according to the multi-modal data set, constructing a knowledge organization structure of each type of industrial subdivision scene; according to a text mode in the multi-mode data set, finely adjusting the small language model to obtain a routing center command body; the routing center command body is used for identifying a received user instruction and determining a target industrial subdivision scene confidence degree set; the target industrial subdivision scene confidence coefficient set is used for screening a target model from a combat model library by adopting an adaptive routing decision algorithm according to the target industrial subdivision scene confidence coefficient set, and generating a target answer by adopting the target model; wherein the combat model library comprises a scene model library, a standby reasoning body and a scene expert agent.

The invention provides a large model routing method and device combined with a reward model, and relates to the technical field of artificial intelligence model dynamic routing, and the method comprises the steps: obtaining a question inputted by a user, and determining a candidate model set according to the user authority and a preset priority rule; performing sensitive information judgment on the question through a sensitive detection model, directly returning a preset answer if sensitive information is detected, and otherwise, entering the next step; carrying out reward evaluation on each model in the candidate model set based on a routing function, training the routing function through a knowledge distillation technology, and optimizing a model selection strategy by taking KL divergence as a loss function; and according to an evaluation result of the routing function, dynamically distributing the problem to a target model with the highest reward value for reasoning, and outputting a processing result of the target model. According to the method, the calculation overhead of large model integration can be effectively reduced, the intelligent level of routing decision is improved, and the system resource utilization rate and the response efficiency are remarkably improved.

The application discloses a multi-model integrated small sample credit score offset adaptive correction system and relates to the technical field of data processing. The system comprises a divergence quantization routing decision module, which acquires target small samples and source domain samples, calculates the JS divergence of each feature dimension, and generates a decoupling mask vector to separate the original feature space into anchor features and drift features; a controlled alignment dynamic demarcation module, which performs an antagonistic feature alignment on the drift features, extracts a deformation vector, and dynamically demarcates the boundary of a decision fuzzy area based on the module length; a partial derivative projection directional correction module, which intercepts samples according to the relative position of the fluctuation boundary and the fuzzy area boundary predicted by a multi-base model, and implements nonlinear directional correction on the intercepted samples based on the dot product of the deformation vector and the partial derivative gradient; and a noise reduction verification closed-loop evaluation module, which generates a final credit score and outputs a verification index of the correction effect. The application realizes adaptive offset correction of credit scores in a small sample scenario and improves the scoring accuracy.

The invention relates to the technical field of artificial intelligence, and discloses a credible RAG-based hybrid expert routing method and system, and the method comprises the steps: obtaining metadata of a document associated with an input text to generate a document-level credible RAG vector; obtaining a hidden representation of each token according to the input text and the text content of the associated document, obtaining a credible vector corresponding to each token based on the document-level credible RAG vector, and splicing the hidden representation of each token and the corresponding credible vector to generate an enhanced input vector of each token; taking the enhanced input vector as an input of a gating network, calculating an expert score and selecting at least one expert network for weighted fusion to generate a final output; through the method, the whole routing decision process of organically fusing the credible hierarchical information of the external knowledge into the hybrid expert model is displayed, the spanning from semantic driving to semantic-credible cooperative driving is realized, and the routing accuracy is remarkably improved.

This invention provides a heterogeneous architecture-based system and method for accelerating large language model inference, aiming to address the limitations of existing acceleration solutions in terms of cost, power consumption, storage capacity, and collaborative efficiency in meeting the deployment requirements of ultra-large-scale MoE models. The system primarily comprises a CPU, an FPGA accelerator card, and a high-speed communication interface. This method, targeting large language models with a hybrid expert architecture, analyzes the computational characteristics and storage requirements of different modules. It deploys computationally intensive attention calculations and shared expert calculations with relatively fixed parameter sizes on the FPGA accelerator card; while storing routing expert weights with large parameter sizes, sparse access, and strong data dependencies in the CPU memory, which then performs routing decisions and routing expert calculations. This invention effectively overcomes the storage capacity limitations of programmable logic accelerator cards, supports the deployment of ultra-large-scale sparse parameter models, and significantly reduces system cost and power consumption, improving the overall energy efficiency and practicality of heterogeneous collaborative inference.