The invention discloses a pork industry chain key
risk factor traceability method based on multi-kernel metric learning (MKLM for short), and relates to a pork industry chain key
risk factor traceability method based on multi-kernel metric learning (MKLM for short). According to the method, heterogeneous data fusion is realized through multi-kernel metric learning (MKLM for short) according to the propagation characteristics of
African swine fever biological risk factors in a circulation chain. On a pork circulation chain, each link relates to various types of data, generally including numerical data (temperature,
humidity, duration and the like), classified data (whether disinfection is carried out or not, live pig varieties,
hygiene grades, transportation mode types and the like) and picture data. The three types of data fusion are different from traditional data fusion, and data fusion cannot be performed by directly adopting
modes of splicing, weighted average and the like. Therefore, the invention provides a multi-kernel metric learning
algorithm, which comprises the following steps of:
processing various types of data by using different M matrixes,
processing the different types of data by using different kernel functions, mapping the data into a high-dimensional space, better capturing the relationship among the different data, fusing the data, quantifying the contribution of each link to the
infection risk, and obtaining the
risk of infection. And
traceability is realized.