5results about How to "Optimize weight" patented technology

The application discloses a mechanical drilling speed prediction method based on micro-inclination characteristics, comprising the following steps: acquiring real drilling wellbore logging while drilling and logging data as a first data set; preprocessing the first data set and extracting the best feature and the micro-inclination feature as input features; the preprocessing comprises data cleaning and correlation analysis; training a mechanical drilling speed prediction model by using the input features; and predicting the mechanical drilling speed of a target well by using the trained mechanical drilling speed prediction model. The micro-inclination feature introduced in the application can significantly improve the ROP prediction accuracy of the model. By introducing the micro-inclination feature in the MLP neural network, the model weight is optimized, and the prediction accuracy of the ROP is improved. By introducing the micro-inclination feature in the SVR model and selecting the optimal hyperparameter, the prediction effect is significantly improved. The method provides a new idea for improving drilling parameter prediction and control by using machine learning, and can be applied to the optimization of drilling speed and efficiency.

This invention relates to the field of intelligent inspection and image analysis technology, and discloses an intelligent hazard investigation method and system based on image recognition. The method includes establishing a hazard knowledge network representing the correlation between hazard types, features, and causes. Hazard images are collected during inspections and input into the network. Hazard entities are extracted, and their features are matched with hazard features in the network. Network reasoning is used to obtain the associated hazard types and causes. Hazard probabilities are calculated based on the correlation strength of causes. Intelligent investigation tasks bound to locations are generated and output based on a comparison of the probability and a threshold to guide targeted investigations. Finally, the correlation relationships in the hazard knowledge network are optimized based on feedback from on-site verification of the tasks. This invention achieves intelligent hazard identification and cause reasoning through a knowledge network, and optimizes the network using a closed-loop business feedback mechanism, improving the intelligence depth of hazard investigation and the system's adaptability.

The invention discloses a multi-anomaly detection early warning method and system based on a large model, and relates to the technical field of biological sample storage automation, and the method comprises the steps: collecting and obtaining a frame sequence and shooting parameters; outputting a structured exception list; calculating a unified score and an index; outputting a gating level according to the configurable gating level set and the threshold interval and the decision function thereof; and the recheck closed loop is butted with the visual early warning packet. According to the method, various abnormities can be covered, the method can be expanded to any newly-added exception category through an exception plug-in mechanism, and compared with a scheme which only completes identification or post-event disposal, scene understanding, structured exception, unified gating and correction reinspection are solidified into a standardized interface and time sequence, empty grabbing, falling and sample damage are reduced, and the method is suitable for large-scale popularization and application. The door opening exposure time is shortened, the product stability is enhanced, and the technical problem that low-temperature sample grabbing abnormities are difficult to process in a unified mode is solved.

This invention provides a transformer-based method for foodborne disease analysis, belonging to the field of data analysis and processing. The method includes acquiring foodborne disease case monitoring data, obtaining a standardized sample feature set through preprocessing, extracting features using an improved ReliefF algorithm, constructing a multi-dimensional feature matrix, training a basic prediction model based on a transformer architecture and an associative attention mechanism to obtain a foodborne disease analysis model, inputting the foodborne disease case data to be monitored, outputting the foodborne disease analysis results, and performing adaptive learning on the foodborne disease analysis model to complete iterative optimization. This invention solves the problems of low disease screening efficiency, insufficient predictive analysis accuracy, and limited functionality in existing technologies due to insufficient utilization of multi-dimensional case features and the lack of mechanisms to highlight key case features.

The application discloses an environment perception and adaptive cleaning algorithm field, and discloses an unmanned environmental sanitation vehicle adaptive cleaning method and device based on multi-sensor fusion, aiming at solving the technical problem of insufficient ground material, stain and obstacle recognition in a complex environment. The method comprises the following steps: acquiring multi-source sensor environment data and fusing the multi-source sensor environment data to obtain optimal pose state estimation; constructing an environment state matrix based on the optimal pose state estimation, and fusing visual features to obtain environment pollution structured information; using a deep Q network to learn and decide an adaptive cleaning strategy, and outputting a continuous control quantity; converting the control quantity into a driving signal to adjust a cleaning action; determining a cleaning coverage rate according to an actual pose and environment information, and planning a rescan path; constructing a reward function based on the coverage rate change and actual energy consumption, updating deep Q network weights, and generating an optimal cleaning strategy. The application can realize precise perception of the environment and intelligent and efficient cleaning function of the unmanned environmental sanitation vehicle.