4results about How to "Reduce detection workload" patented technology

The application provides a kind of foodborne microorganism identification method based on metal omics coupling XGBoost algorithm, belongs to microorganism identification technical field, the foodborne microorganism identification method includes the following steps: 1) with foodborne microorganism strain as research object, ICP-MS is used to analyze each standard strain metal group characteristics, combined with XGBoost algorithm to construct foodborne microorganism classification model;2) the ICP-MS full quantitative analysis is used to detect the bacterial species, and the detection data of the metal elements of the bacterial species to be detected are obtained;3) the obtained detection data is substituted into the obtained foodborne microorganism classification model for analysis, and the bacterial species to be detected is identified.The foodborne microorganism classification model constructed by the application can quickly and accurately identify 18 kinds of foodborne microorganisms, and lays a good foundation for the broad-spectrum and efficient classification and identification of more kinds of foodborne microorganisms in the future.

The invention provides an electric power equipment intelligent green recovery method and system based on material attribute characteristics, and the method comprises the steps: carrying out the preprocessing of the attribute characteristics of to-be-recovered electric power equipment, so as to correct the distortion characteristics under the environment interference, and complement the characteristic parameters of the lack of novel materials; distributing a dynamic weight for each attribute index in the attribute index system through a dynamic weight distribution algorithm, and calculating a comprehensive score of the power equipment through a weighted summation calculation mode; determining an initial recommendation recovery scheme according to the comprehensive score; and calculating a green recovery index of the power equipment, evaluating whether the power equipment meets a green recovery requirement through the green recovery index, and adjusting the initial recommended recovery scheme according to an evaluation result. According to the method, accurate classification of high-difficulty recovery materials and adaptive recovery scheme recommendation are realized through multi-stage operation processing of attribute feature preprocessing, feature weighted fusion and green evaluation.

This invention discloses a method for identifying the stipe length of Ganoderma lucidum using genetic differences, belonging to the field of Ganoderma lucidum breeding and cultivation technology. This method utilizes specific primer pairs del670 or insertion2441 to perform PCR amplification of Ganoderma lucidum mycelial DNA. Early prediction of the stipe phenotype is achieved by detecting the size of the amplified product fragments: if a 1569 bp fragment is obtained using the del670 primer pair or a 1357 bp fragment is obtained using the insertion2441 primer pair, the strain is identified as short-stalked; if an 899 bp fragment is obtained using the del670 primer pair or a 3798 bp fragment is obtained using the insertion2441 primer pair, the strain is identified as long-stalked. This method enables rapid and accurate identification of Ganoderma lucidum stipe length at the mycelial stage, eliminating the need for traditional fruiting cultivation, significantly shortening the breeding cycle and reducing testing costs.

The application discloses a transformer oil chromatogram false alarm feature extraction and comprehensive identification method, comprising the following steps: screening typical false alarms and real alarm data in the online monitoring historical data of oil chromatogram; setting a sliding time window according to the screened data, and constructing a standardized alarm history library; based on Shapelets transformation, extracting features of each state quantity in the standardized alarm history library; based on the extracted feature data, using a double-layer LightGBM algorithm to comprehensively judge the authenticity of the transformer oil chromatogram alarm of all state quantities. By extracting the time sequence features of the oil chromatogram false alarm data section, combining the double-layer regressor, the real-time and comprehensive discrimination of the false alarms of eight gas components is realized, which is suitable for power companies in the process of transformer equipment management, reduces the number of online chromatogram false alarms, reduces the work burden of the grassroots, and improves the quality and efficiency of maintenance.