5results about How to "Closely related" patented technology

The present application relates to the technical field of cluster resource scheduling, in particular to a dynamic computing resource elastic scheduling method for a heterogeneous server cluster, comprising: receiving an upper-layer application computing task description and analyzing resource demand characteristics and dependency constraints; pre-selecting initial candidate server nodes in a cluster global resource state space through an improved deep reinforcement learning algorithm with a dynamically adjustable reward function; constructing a multi-objective constraint optimization model in combination with node real-time load and task demand; calculating an adaptation score and scheduling the task to an optimal node for execution; and collecting task execution progress in real time and comparing it with resource demand to dynamically adjust reinforcement learning algorithm parameters. This method makes node pre-selection more in line with task demand, keeps scheduling decisions consistent with real-time cluster and task states, optimizes cluster load allocation states, and enhances the dynamic adaptation capability of the scheduling process.

This invention provides a method for constructing and reasoning a ship navigation knowledge graph based on multi-source heterogeneous data, involving the intersection of artificial intelligence and maritime technology. The method includes: Step S1, establishing a ship navigation knowledge model; Step S2, acquiring and preprocessing multi-source heterogeneous data; Step S3, performing named entity recognition on the preprocessed data based on a BiLSTM-CRF hybrid model incorporating domain dictionary features; Step S4, extracting entity relationships from the entities identified in Step S3 using a pre-trained BiLSTM hybrid model incorporating interactive attention mechanisms; Step S5, fusing knowledge from the entities and entity relationships extracted in Steps S3 and S4 to construct a preliminary knowledge graph; Step S6, performing link prediction on the preliminary knowledge graph based on an RGCN model incorporating temporal constraints and rule logic to achieve knowledge completion and reasoning. This invention improves the ability to respond to risks in complex navigation scenarios.

The application discloses an advertisement creative dynamic evaluation and intelligent decision system based on multi-source data fusion, and relates to the technical field of advertisement design. The system comprises a state perception and graphing module, an online decision module, an execution feedback module and a collaborative evolution updating module. The state perception and graphing module is used for acquiring real-time interactive behavior and external environment data, mapping the data to a dynamic creative feature graph and outputting a graph structured state vector. The online decision module takes the state vector as input, calls an incremental learning decision model to calculate expected performance values and decision uncertainty values of each candidate creative combination, and generates a creative selection instruction according to the decision uncertainty values through a strategy function. The execution feedback module outputs the instruction and receives corresponding actual performance data. The collaborative evolution updating module synchronously updates the weights of related nodes and edges in the dynamic creative feature graph and the internal parameters of the decision model according to the performance data, the selection instruction and the state vector. The application realizes real-time evaluation, intelligent decision and collaborative self-evolution of advertisement creatives in a dynamic delivery environment.

The invention discloses a fan blade load prediction method and device, and the method does not need to install real sensors at all required to-be-detected positions of a fan blade, and determines the correlation between a to-be-predicted position and an installed position through obtaining the to-be-predicted position and the installed position. And if the correlation is greater than or equal to a correlation threshold, obtaining a regression model, and performing fitting through the regression model according to the blade load data of the installed position to obtain the blade load data of the to-be-predicted position. And if the correlation is smaller than a correlation threshold value, obtaining a prediction model, and performing prediction through the prediction model according to the environment data of the installed position to obtain blade load data of the position to be predicted. And corresponding to different correlation conditions, the blade load data of the to-be-predicted position under the condition of relatively strong correlation is obtained through the regression model, and the blade load data of the to-be-predicted position under the condition of relatively weak correlation is obtained through the prediction model, so that the accuracy of fan blade load prediction is improved.

The invention provides a consumer financial sales clue network construction method based on a large language model, which belongs to the technical field of data processing, and specifically comprises the following steps: a data access module reads associated data to obtain unstructured text data; the feature enhancement module performs deep analysis on unstructured text data by using a semantic network construction strategy and a large language model, performs intention recognition, sentiment analysis and topic extraction, and generates semantic feature tags, and the network dynamic construction module is responsible for forming a dynamic and multi-dimensional relationship network. The reasoning module generates operable sales clues described by natural languages, and the touch strategy generation module automatically generates or recommends personalized communication verbal skills on the basis of the generated sales clues and constructs strategies and recommendation matching conditions of different marketing activities on the basis of a semantic network. And the user portrait group needing to be optimized in the semantic network construction strategy is determined, so that the matching degree of recommendation processing is improved.