Shenzhen Academy of Inspection and Quarantine

Shenzhen Customs Information Center

Shenzhen Customs Animal and Plant Inspection and Quarantine Technology Center

Shenzhen Customs Animal and Plant Inspection and Quaratine Technology Center

Yina Cai

Zhouxi Ruan

Xianyu Bao

Tikang Lu

Yun Guo

Shaojing Wu

Zhinan Chen

Zhifeng Qin

Jinxue Peng

Wenli Zheng

Lixun Cheng

Heping Li

The present disclosure provides an unbalanced sample classification method and an unbalanced sample classification apparatus. The method includes: obtaining unbalanced sample data; calculating a sample contribution rate based on the sample data and the characteristic data; filtering out a part of the sample data within a preset sample contribution threshold according to the sample contribution rate to determine as target sample data; and inputting the target sample data into a sample classification model to calculate a sample classification result through a classification algorithm. By using two variables of the characteristic value contribution rate and the characteristic contribution rate, the characteristics and samples with low contribution rate for classification are eliminated to effectively reducing the processing of unbalanced sample data, and a machine learning classification algorithm can be used on this basis to adopt the effective characteristics or samples to achieve efficient classification.

Unbalanced sample classification method and apparatus

The present disclosure provides a method, a device and a computer readable storage medium for food risk traceability information classification. The method includes: building a deep learning neural networks model by a self-learning ability of an artificial intelligence model, initializing weights and a bias of the built model, and obtaining an original deep learning neural networks model; obtaining samples of food risk traceability information, dividing the samples, and obtaining factors of the food risk traceability information, converting the factors into vectors of the food risk traceability information; inputting the vectors into the original deep learning neural networks model, and obtaining original classification vectors of current food risk traceability information; and inputting the original classification vectors into a loss function, obtaining a loss rate of the original classification vectors, and determining the original classification vectors as a target classification result in response to the loss rate being within a preset range.

Method and device for food risk traceability information classification, and computer readable storage medium

The present disclosure provides a method for automatically collecting and matching laboratory data, including: obtaining a creation time of experimental data, determining target experimental data corresponding to a target time in accordance with the creation time, segmenting the target experimental data into a plurality data blocks, generating a data block index table, including at least one data block identifier, according to the data blocks, selecting a target matching mode from a plurality of predetermined matching modes according to the data block index table, obtaining the data block identifier upon determining the target experimental data in a storage node is loaded, and extracting data content in the target experimental data corresponding to the data block identifier by the target matching mode. This method may greatly reduce the number of string matching and may reduce the complexity of the algorithm.

Method for automatically collecting and matching of laboratory data

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Shenzhen, China

Yina Cai