9results about How to "Reduce data noise" patented technology

The invention relates to the technical field of power dispatching, in particular to a power demand real-time sensing and responding system and method based on Internet of Things driving, electronic equipment and a computer readable storage medium, and the method comprises the steps: collecting power utilization power and power utilization time data at a user side, and carrying out the preprocessing of the data; constructing an electricity utilization power curve, and extracting electricity utilization characteristics; using a K-means clustering algorithm to carry out clustering analysis on the user power consumption characteristics, and dividing the users into k power consumption categories; a power consumption demand prediction model is constructed by training a long short-term memory (LSTM) network model, and the future power consumption power curve trend is predicted; and establishing a power utilization scheduling response mechanism based on the prediction result and the clustering-divided power utilization behavior categories in combination with a charging and discharging scheduling strategy of the power energy storage equipment, thereby realizing flexible scheduling response to the power utilization behaviors in different time periods in the future. According to the method, the power consumption management precision and efficiency can be improved, personalized power consumption requirements are met, and energy utilization is optimized.

The invention discloses a high-altitude falling collision test system and method for a child safety seat. The system is characterized in that an experimental test object is fixed in a safety seat to be experimented, a data sensor and a collision marker are arranged in the experimental test object, and the data sensor is connected with a data acquisition and analysis module; the to-be-tested safety seat is installed in a preset seat position of the first test vehicle; the vehicle lifting module lifts the first test vehicle to a preset falling height; a trigger sensor is arranged in the second test vehicle and is connected with the data acquisition and analysis module; at least one in-vehicle high-speed camera is arranged in the first test vehicle, the in-vehicle high-speed camera is used for shooting an experimental test object, and the in-vehicle high-speed camera is connected with the data acquisition and analysis module; according to the invention, comprehensive safety testing can be carried out on the child safety seat in a high-altitude falling mode, and the accuracy of testing the child safety seat is improved by simulating the real motion state of vehicle collision.

The present application relates to the technical field of aero-engine gas path parameter processing, in particular to a kind of aero-engine gas path parameter deviation value preprocessing method, first, using fingerprint map to correct the deviation of gas path parameter caused by non-component performance degradation in gas path parameter deviation value offset;Then, using Grubbs criterion to remove gross error in short-term gas path parameter deviation value that meets the requirement of independent and identically distributed by equal-length overlapping sliding window division;Then, aiming at the problems such as mode coupling and insufficient IMF signal based on correlation coefficient selection that EMD decomposition used by general noise reduction method may encounter, EEMD decomposition is introduced, and single-sample T test is used to distinguish high-frequency and low-frequency IMF signal, by only using SG filter to reduce noise for high-frequency IMF, the problem that long-term trend signal is damaged due to data filtering and noise reduction is avoided;Finally, the superiority of the data preprocessing method proposed in the present application in gas path parameter deviation value preprocessing is verified by comparison experiment.

The present invention discloses a method for identifying and functionally evaluating new genes for Alzheimer's disease driven by rare variants, relates to the field of molecular biology, and solves the problems of strong racial specificity of rare variants and limited research in non-European populations in the genetic research of AD in the prior art; and low statistical power of rare variants; the present invention obtains DNA from peripheral blood samples of target subjects and performs whole genome sequencing on the DNA in the peripheral blood samples to obtain a cohort-level VCF file; obtains rare and harmful genomic variants in the VCF file by designing a variant priority pipeline for screening; performs optimal sequence-nuclear association tests on genomic variants on a gene basis, and performs repeated and meta-analyses in combination with replication cohorts; evaluates the biological plausibility of candidate genes through network analysis, pathway enrichment, and endophenotype association, and explains the functional consequences of top candidate variants through in vitro experiments, thereby solving the above problems.

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.