6results about How to "Improve computing performance" patented technology

This invention discloses a joint optimization method and system for task offloading and resource allocation based on SAGIN. It collects task and scheduling information through a SAG-IoT system; utilizes the task data and scheduling information to achieve optimal association between UAVs and IoT devices based on a matching game algorithm, and makes optimal decisions for task offloading to maximize the computational performance of the SAG-IoT system; based on optimal association, optimal task offloading decisions, and maximizing the computational performance of the SAG-IoT system, it constructs an online offloading framework based on deep reinforcement learning, constructing a path from input X(t) to optimal action x. * The low-complexity mapping strategy (t) is based on repeated interactions between different modules and a random environment, iterating and running sequentially to achieve joint optimization of task offloading and resource allocation. This invention achieves optimal computational performance while stabilizing the system queue. In addition to utilizing binary computation offloading, the optimization framework can also be extended to online partial computation offloading strategies consisting of multiple independent subtasks.

The application discloses a kind of multi-micronet robust game optimization scheduling method and system based on dynamic auction official algorithm, and relates to energy system scheduling and optimization technical field.The method constructs system physical model and double-layer robust optimization model based on Stackelberg master-slave game, upper layer maximizes system operator's profit to formulate price signal, lower layer maximizes the worst scenario income of multi-micronet alliance to optimize resource scheduling, combined with multiple constraint conditions, Stackelberg equilibrium is solved iteratively using dynamic auction official algorithm.The application converges to global optimal solution quickly through three-dimensional hybrid driving price updating mechanism, realizes system economic benefit maximization, operation robust and reliable, and has good explainability and practical application value.

The invention discloses an efficient supervised action segmentation method based on frame-level and action-level double-layer time modeling, which relates to the technical field of computer vision, and comprises the steps of learning a frame relationship by using a convolutional neural network, learning an action dependency relationship by using a converter, and carrying out communication between the two relationships through a cross-self-attention mechanism. A frame branch captures temporal information between frames using multiple CNN networks, an action branch initializes action flags using a multi-head cross-attention and self-attention transformer, using cross-attention to allow frame features to utilize high-level action dependencies learned by the action branch while allowing the action flags to access low-level information in the frame branch, and finally, carrying out final prediction through an average value of the features. According to the method, an action mark set with a fixed size can be efficiently learned to clearly represent an action segment, and actions and frame-level time modeling are executed in parallel, so that the method can be superior to a frame-based and two-stage method at lower calculation cost.

The invention relates to the technical field of talent management, and discloses a talent ability evaluation and post matching system based on a knowledge graph, and the system comprises a data processing module which collects individual information and target industry information, converts the individual information into an individual ability vector, and converts the target industry information into an industry demand vector; the graph construction module is used for constructing a cross-industry knowledge graph through multi-source knowledge fusion; the skill migration module is used for calculating skill migration complexity and a capability gap vector; the path planning module is used for generating a plurality of transformation paths and selecting an optimal transformation path based on a transformation path scoring function; according to the method, accurate matching of individual capabilities and post requirements is realized by constructing the cross-industry knowledge graph, and cross-industry skill migration paths can be identified, so that the system can discover potential matching opportunities which are difficult to identify by a traditional method, and the most suitable development paths are recommended for individuals.

This invention relates to a knowledge graph completion method in the field of enterprise credit, belonging to the field of artificial intelligence technology. It includes the following steps: S1: data preprocessing and graph structure construction; S2: construction of an efficient multi-relation graph convolutional neural network; S3: dynamic boundary conditions and loss optimization; S4: multi-constraint optimization; S5: training and prediction. This invention significantly improves the completion accuracy of multi-relation knowledge graphs through innovative methods such as multi-relation modeling, dynamic similarity adjustment, and boundary adaptive optimization, resulting in a complete graph structure. It enables the model to maintain high prediction accuracy even with scarce data and low-frequency relationships. Through feature smoothing and cluster consistency regularization, it possesses temporal and dynamic semantic modeling capabilities. It also has the ability to model the "equity chain—supply chain—risk chain" characteristics of the enterprise credit field.

The invention discloses a computing task scheduling method and device and electronic equipment. The method comprises the following steps: receiving a task processing request initiated by a target object; performing multi-round analysis on task demand information of the target computing task and environment information within a preset time period before the cloud computing platform receives the task processing request by using a multi-target optimization algorithm to obtain an optimal scheduling strategy set, the optimal scheduling strategy set comprises a plurality of optimal scheduling strategies with the lowest calculation cost or the lowest calculation energy consumption or the highest calculation performance; feeding back the optimal scheduling strategy set to the target object; and in response to a strategy selection instruction initiated by a target object, selecting a corresponding optimal scheduling strategy from the optimal scheduling strategy set to schedule the target calculation task. The technical problems that the task response speed is low and the computing resource utilization rate is low due to the fact that the task scheduling strategy cannot be dynamically adjusted according to the real-time state of the network in related technologies are solved.