5results about How to "Reduce data noise" patented technology

The application discloses a picture labeling and quality checking method and system based on multi-dimensional safety behavior, relates to the technical field of image data labeling, and comprises the following steps: S1, collecting pictures to be labeled and performing pretreatment; S2, constructing a multi-dimensional labeling model and a dynamic behavior baseline model to obtain a picture labeling framework; S3, multi-dimensional collaborative labeling and dangerous behavior identification; S4, performing quality evaluation on the dangerous behavior labeling result through a hierarchical quality inspection model; and S5, calculating a comprehensive quality score, and optimizing and storing the labeling result. The picture labeling and quality checking method and system based on multi-dimensional safety behavior solve the problem of difficult control of the quality of dangerous behavior labeling data in the existing technology in the construction operation scene.

The utility model relates to cantilever beam impact testing machine technical field discloses a new -type cantilever beam impact testing machine, including shell one, the fixed plate is fixedly connected with the shell one inner wall, the fixed plate outer wall is fixedly connected with the pivot, the pivot one end is fixedly connected with the swing bar, the buckle no.

The invention provides a model training method and device for a text open task, on one hand, a reward model is trained through a training text pair marked with a relative quality advantage relation, original complex absolute quality scores of the reward model are converted into simpler and more stable relative advantage judgment, the model training cost and subjective influence are reduced, and the training efficiency is improved. The accuracy and confidence of the reward signal are improved, one first reply in the output replies of the existing model is set as a reference text reply, and it is ensured that the output quality of the trained text model is superior to the output quality of the existing model; on the other hand, in the training process of the to-be-trained model, a second reply output by the to-be-trained model is monitored, and the target reward model is adjusted according to the monitoring result; and / or, the reward value output by the target reward model for the second reply is monitored, and the reference text reply is adjusted according to the monitoring result, so that the optimization direction of the text model is ensured, and the training effect of the text model is improved.

The invention relates to the technical field of network security, in particular to an intelligent security vulnerability monitoring method and system, and the method comprises the steps: deploying distributed monitoring nodes, collecting network asset data, traffic data and behavior log data in real time, and transmitting the data to a vulnerability analysis unit; the vulnerability analysis unit preprocesses the collected data and eliminates data noise and deviation; based on a mode of combining a deep learning model and a rule engine, performing vulnerability feature extraction and abnormal behavior identification; and performing risk level judgment on the identified vulnerabilities through the vulnerability risk assessment model, generating a vulnerability monitoring report, pushing the vulnerability monitoring report to a security management platform, and triggering a corresponding emergency response mechanism. According to the invention, through distributed monitoring, intelligent analysis and risk assessment, all-around, rapid and precise monitoring of network security vulnerabilities is realized, and the security protection capability of a network system is improved.

This invention discloses a method, apparatus, and electronic device for determining the quality model of a transmission link. The method includes: acquiring training index data corresponding to the transmission link of a power optical fiber, wherein the training index data is used to characterize the quality status of the transmission link; obtaining a classification result corresponding to the training index data based on a clustering algorithm; determining a quality level label corresponding to the training index data based on the classification result; and training a predetermined initial quality model using a deep residual network algorithm based on the training index data and the corresponding quality level label to obtain a target quality model for the transmission link. This invention solves the technical problems of low model accuracy, poor robustness, and low convergence caused by data noise interference in the quality model of the transmission link.