The application provides a
coal quality detection method, a model construction method and a detection
system. The method comprises the following steps: S1, acquiring LIBS spectrum and NIRS spectrum of a
coal sample, and a standard measured value of a
coal quality index of each sample; S2, extracting a characteristic
peak area of the LIBS spectrum based on a plurality of preset elements to obtain a LIBS characteristic
peak area matrix; performing
noise reduction and normalization on the NIRS spectrum to obtain a NIRS spectrum matrix; S3, respectively constructing a kernel function with an independent bandwidth parameter for the LIBS characteristic
peak area matrix and the NIRS spectrum matrix; S4, weighting and fusing the two kernel functions based on a
weight factor of
adaptive learning to construct a
hybrid kernel function; S5, taking the
hybrid kernel function as a kernel function of any regression model, and optimizing a parameter vector by using a parameter optimization
algorithm to obtain an optimal value of the parameter vector; the parameter vector comprises a
weight factor, a regularization parameter of the regression model and a bandwidth parameter; and S6, inputting the optimal value of the parameter vector into the regression model to perform coal quality detection.