Abstract: A fingerprint spectrum construction method for a new compound aloe capsule and a fingerprint spectrum includes: (1) taking powder of a new compound aloe capsule, carrying out reflux extraction, adding methanol, carrying out ultrasonic treatment, filtering, and taking a subsequent filtrate as a test solution; dissolving barbaloin, aloe-emodin, indirubin, tryptanthrin, aloesin, and ?-sitosterol in methanol to obtain reference solutions; (2) injecting the test solution and the reference solutions into a high performance liquid chromatograph for gradient elution to obtain a chromatogram of the new compound aloe capsule and chromatograms of the reference solutions, respectively; and (3) labeling chemical components of peaks on the chromatogram of the new compound aloe capsule according to the chromatogram of the new compound aloe capsule and the chromatograms of the reference solutions to obtain a fingerprint spectrum of the new compound aloe capsule.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving first and second data sets, both the first and second data sets including structured data in a plurality of columns, for each of the first data set and the second data set, inputting each column into an encoder specific to a column type of a respective column, the encoder providing encoded data for the first data set, and the second data set, respectively, providing a first multi-dimensional vector based on encoded data of the first data set, providing a second multi-dimensional vector based on encoded data of the second data set, and outputting the first multi-dimensional vector and the second multi-dimensional vector to a loss-function, the loss-function processing the first multi-dimensional vector and the second multi-dimensional vector to provide an output, the output representing matched data points between the first and second data sets.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving first and second data sets, both the first and second data sets including structured data in a plurality of columns, for each of the first data set and the second data set, inputting each column into an encoder specific to a column type of a respective column, the encoder providing encoded data for the first data set, and the second data set, respectively, providing a first multi-dimensional vector based on encoded data of the first data set, providing a second multi-dimensional vector based on encoded data of the second data set, and outputting the first multi-dimensional vector and the second multi-dimensional vector to a loss-function, the loss-function processing the first multi-dimensional vector and the second multi-dimensional vector to provide an output, the output representing matched data points between the first and second data sets.

Abstract: Embodiments of the application disclose a road marking recognition method, a map generation method, and related products. The road marking recognition method includes that: a base map of a road is determined according to acquired point cloud data of the road, pixels in the base map being determined according to reflectivity information of an acquired point cloud and position information of the point cloud; a pixel set composed of the pixels in the base map that road markings include is determined according to the base map; and at least one road marking is determined according to the determined pixel.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving first and second data sets, both the first and second data sets including structured data in a plurality of columns, for each of the first data set and the second data set, inputting each column into an encoder specific to a column type of a respective column, the encoder providing encoded data for the first data set, and the second data set, respectively, providing a first multi-dimensional vector based on encoded data of the first data set, providing a second multi-dimensional vector based on encoded data of the second data set, and outputting the first multi-dimensional vector and the second multi-dimensional vector to a loss-function, the loss-function processing the first multi-dimensional vector and the second multi-dimensional vector to provide an output, the output representing matched data points between the first and second data sets.