40results about How to "Improve analyzability" patented technology

The invention provides an Internet of things log processing method and device based on a Hadoop platform, and aims at improving the analyzability of logs output by a system and meeting the periodic output requirement of report forms of service. The method comprises the steps that the log format of Internet of things logs is configured, and the Internet of things logs are collected according to the configured log format; classification and aggregation are conducted on the collected Internet of things logs according to categories identified by category identification character strings, and storage paths of the Internet of things logs subjected to classification and aggregation are recorded to a log memo sheet; the log memo sheet is periodically updated, or the Internet of things logs, needing to form a data report form, in the Internet of things logs subjected to classification and aggregation are loaded to a date partition of a hive sheet. According to the Internet of things log processing method and device based on the Hadoop platform, the analyzability of the output logs is significantly enhanced, and the service requirement for periodically outputting the analysis results can be met.

A great device (10) processes and visualizes electrical impedance tomography (EIT) data (3) of at least one region of the lungs for determining and visualizing regional properties of the lungs of a living being. The EIT data (3) are obtained from an electrical impedance tomography apparatus (30). The device makes it possible to visualize regional properties of the lungs or of regions of the lungs in terms of hyperdistension or collapse.

The invention provides a method for dividing multiple remote sensing parameter analyzing windows of a spacecraft. Through analyzing a multiple-remote-sensing-parameter association period and selecting a reference parameter acquisition dividing characteristic, accurate dividing for multiple parameter analyzing windows in a manner of alignment in a time scale is realized. According to the method of the invention, analyzing window dividing is performed through a remote sensing parameter reference dividing characteristic instead of a fixed or a slidable window, thereby preventing window drift caused by factors such as space environment and channel transmission, realizing accurate dividing of the remote sensing parameter analyzing windows and improving accuracy of subsequent characteristic extraction and diagnosis detection. According to the method of the invention, synchronous dividing is performed on a plurality of remote sensing parameters through the reference parameter; window alignment in the time scale is realized; analyzability of the remote sensing data is improved; and analyzing and digging of an association relation between multiple parameters are facilitated.

The invention discloses a network map rectifying and drawing method under dynamic map projection. The method comprises the steps of, first of all, according to the comparison of the projection types of clients sides and server sides, carrying out projection transformation calculation of geographical range values, then calculating reversely to acquire the rank numbers and the map levels of all the tiles to be loaded by using cutting principles of a tile map and requested parameters, and carrying out error correction to the geographical range values of each tile through the error deviation values of the tile map acquired by using a plurality of feature points. When each tile is downloaded, encryption is carried out by using a data encryption standard (DES) encryption algorithm, and the tiles are saved in an ini format file which has a same name with the map tiles in sequence. When the downloading of the needed map is completed, the ini file can be deciphered, the geographical range values and the like after correction are read, and the drawing of each map tile is achieved. The network map rectifying and drawing method under dynamic map projection achieves drawing of the network tiles after the error correction, greatly improves the accuracy of data, and enhances the analyzability of the data.