11 results about "Routing model" patented technology

A hybrid inference system for a coding assistant utilizes a routing model to predict whether output generated by a large language model for a given prompt would be accepted by a user of the coding assistant. The routing model routes the prompt when the routing model indicates that the output generated by the large language model is likely to be accepted. The routing model routes the prompt to a local model when the output generated by the large language model is not likely to be accepted. The routing model is trained on the historical output generated by the large language model for various prompts and the acceptance or rejection of the output by users of the coding assistant.

A multi-commodity flow-based synchronous escape routing method and apparatus, and a medium. The method comprises: creating a grid pin array (GPA) routing graph on the basis of a chip GPA, and creating an irregular pin array routing graph on the basis of an array having a miss pin; creating escape boundary nodes in synchronous escape routing on the basis of the created GPA routing graph; creating an overall synchronous escape routing graph on the basis of the created escape boundary nodes; on the basis of the created overall synchronous escape routing graph, creating a multi-commodity flow-based synchronous escape routing model; solving the synchronous escape routing model to obtain a routing result, and designing a constraint for grouping and same-layer arrangement in the synchronous escape routing; and optimizing routing paths of the synchronous escape routing model, and accelerating the synchronous escape routing on the basis of an optimization result. By modeling routing constraints and path optimization methods, the present invention supports the simultaneous action of multiple acceleration methods, thereby improving system efficiency.

Disclosed in the present invention is an SDN-architecture-based routing method for guaranteeing network QoS. The method comprises: on the basis of network topology information, performing abstraction to obtain a directed graph; then, on the basis of an improved K-means algorithm, dividing data into mouse flows, elephant flows and mammoth flows; defining a traffic transmission constraint condition; constructing a DQN neural network; training the DQN neural network by using the maximum Q value, which corresponds to an action a having the maximum reward function R, as an objective, so as to obtain a QoS guarantee routing model; obtaining an optimal path weight value; determining a data packet sending path on the basis a Dijkstra algorithm; and finally issuing path information to a switch. The present invention avoids the situations in which data processing performed by using a conventional K-means algorithm is not stable enough and a classification result is not accurate enough. During the training of a DQN neural network, data is extracted from an experience pool according to the priority of data, such that the neural network pays more attention to important data, thereby improving the learning efficiency of the neural network and the performance of the algorithm.

This invention discloses a power communication routing optimization method considering fault risk. The method establishes a primary and backup routing model for the communication system; correlates the importance of load control services in the power system with the information flow, proposing a communication link importance evaluation index; then, combined with a conditional risk measurement model, it proposes a power communication routing optimization method; it treats communication service losses in each fault scenario as risk values, aiming to minimize the losses of important services across all fault scenarios, and considers constraints such as node constraints, bandwidth constraints, and routing constraints to solve for the primary and backup routing paths of the power communication network. This invention flexibly formulates communication routing paths based on changes in the fault area, and the route selection can proactively avoid fault risk areas, effectively reducing load losses during fault processes; it can be used to formulate power communication routing schemes, improving the resilience of power communication systems in responding to faults.

This invention provides a low-altitude path planning method and system based on hierarchical optimization of sub-blocks after connected component filtering, relating to the field of unmanned aerial vehicles (UAVs). The method includes: constructing a third-order grid tensor map of the flight area; determining the set of wingable grid points using a breadth-first search-based connected wingable region extraction algorithm; constructing a UAV shortest route model based on a single commodity flow; dividing the set of wingable grid points into multiple non-uniform sub-blocks; constructing a global optimization model based on the single commodity flow-based UAV shortest route model and the multiple sub-blocks, solving the model, and determining the minimum combination of sub-blocks participating in UAV commodity transportation; and constructing a path optimization model based on the minimum combination of sub-blocks participating in UAV commodity transportation, the set of wingable grid points, and the single commodity flow-based UAV shortest route model, solving the model, and generating the optimal path. This method has the advantages of improving the efficiency and reliability of UAV path planning.

This application discloses a model routing method, system, and device based on decoupled model representation, belonging to the field of artificial intelligence technology. The method employs a pre-trained query text encoder to encode user query text into query features, achieving effective representation of the user query text. Furthermore, it introduces prior information and non-prior information at the model representation level to constitute decoupled model representation information, achieving effective representation of large language models. Then, a routing model based on a structured attention mechanism selects a large language model to generate an answer for the input user query text, according to the query features and the decoupled model representation information of each large language model. This enhances the cluster system's responsiveness to different inputs, allowing the model routing process to fully consider the diversity and complexity of inputs. Thus, when processing different types of inputs, it can flexibly adjust routing decisions to accurately select the large language model suitable for processing the current input user query text.

The application relates to a cross-network message pushing method based on a strategy model and belongs to the technical field of computer networks. The application establishes node management, engine management, route management and application management aiming at a cross-network strategy model, establishes a cross-network route model in combination with cross-network, cross-center and cross-system application scenes, shields cross-network link construction in complex scenes only by configuration of the strategy model, solves the interconnection and interworking of various service systems in service and a system application difficulty, and achieves out-of-box use. The application can realize end-to-end message sending only by simple registration and configuration in scenes where different networks, different centers and different systems cannot directly interact.

This invention provides a method and apparatus, electronic device, computer storage medium, and computer program product for model training, model routing, and model service. The model training method includes: acquiring a training dataset, which at least includes query data, first response scoring labels of a portion of the models in an expanded model pool to the query data, and first model description data; acquiring a routing model including a pre-trained encoding module and an adaptive module; inputting the query data and the first model description data into the pre-trained encoding module and the adaptive module, while maintaining the pre-training parameters of the pre-trained encoding module, and training the routing model using at least the first response scoring labels as a supervised condition. This invention reduces the training cost of the routing model while ensuring the routing capability of the expanded model pool.

This invention discloses a method and system for quantitative analysis of defects in offshore wind turbine blades based on dynamic routing and heterogeneous expert models. The method includes: acquiring original images of the blades using a UAV; performing global noise reduction and motion blur removal using an attention-based image enhancement model; segmenting the enhanced image into non-overlapping image blocks; classifying the defect types of each image block using a routing model and dynamically distributing them to the corresponding expert models; processing discrete and continuous defects respectively using parallel instance segmentation expert models and semantic segmentation expert models; reconstructing the local mask into a global mask image and performing connected component labeling, pixel statistics, and morphological skeleton extraction to calculate the number, area, and length of defects and generate a detection report. This invention can stably acquire high-quality images in complex marine environments and can intelligently and accurately perform automated quantitative analysis of multi-morphological defects.

This application relates to the fields of artificial intelligence and data analysis technology, and discloses a task processing method and system. The method includes: acquiring user task text, wherein the user task text describes the target task to be completed; extracting multi-dimensional features from the user task text to obtain task features; using a pre-set link decision rule base and a lightweight routing model to perform link decision on the task features, determining the processing link type corresponding to the task features; processing the task features according to the processing link type to obtain the execution results of multiple atomic tasks, wherein the multiple atomic tasks are execution units for completing the target task; and fusing the execution results of the multiple atomic tasks to obtain the task processing result. This method can improve the adaptability to complex tasks and ensure the processing quality of complex tasks.

This invention relates to the field of edge computing technology, providing a method and system for deploying and routing AI services and microservices in edge scenarios. The method includes: determining a deployment and request routing model for AI services and microservices in a multi-edge collaborative network scenario based on acquired network topology data of multiple edge nodes, service characteristic data of AI services and microservices, and user request flow data; determining a joint optimization function based on the deployment and request routing model, with end-to-end latency, system energy consumption, and resource utilization as optimization objectives; training an agent to learn the optimal deployment strategy using a deep reinforcement learning algorithm based on a potential function and piecewise rewards, guided by the joint optimization function, to obtain a deployment scheme for AI service instances and microservice instances on multiple edge nodes; and calculating the routing path for user requests to reach the network based on the deployment scheme and real-time network status. The method and system provided by this invention improve service quality and resource utilization efficiency.