4 results about "Curve matching" patented technology

The present application relates to the technical field of statistical analysis, in particular to a bridge monitoring method and system based on big data analysis, which collects multi-component monitoring sequences and compares differences in time sequence, generates a response grading baseline according to wave threshold identification of abnormalities, constructs a jump distribution map based on amplitude grading and space-time law, identifies dense inflection points and generates a consistency curve by comparison and linkage, matches the trend of pier support to determine the stable response area, merges sequences to extract statistical characteristics and mark, and generates a bridge monitoring operation state characteristic set. The present application establishes a grading baseline based on the frequency and direction of monitoring data, forms a jump inflection point system in combination with multi-dimensional time sequence, draws a structure jump map, uses linkage identification to depict component consistency, dynamically captures local stable response, integrates trend law to complete characteristic merging, constructs state characteristic layered mapping, establishes a cross-component aggregated response chain, realizes information space-time high correlation extraction, and clearly presents the structure evolution law.

The application discloses a waveform curve matching method and device, which cuts a first waveform curve into a plurality of first waveform segments, cuts a second waveform curve into a plurality of second waveform segments, and calculates matching degrees between the plurality of first waveform segments and the plurality of second waveform segments to obtain the matching degrees of the first waveform curve and the second waveform curve; that is, the first waveform curve and the second waveform curve are both cut into waveform segments, and then each waveform segment is compared to exclude matching of redundant waveform segments and missing segments, so that an accurate matching result can be found, the matching accuracy is improved, and the precision of calculating the similarity of the first waveform curve and the second waveform curve is improved.

This invention relates to the field of deep learning technology, specifically disclosing a method for optimizing the energy efficiency of AI computing devices based on deep learning. The method includes the following steps: allocating a dedicated hardware computing partition for the AI ​​computing task to be executed and initiating the acquisition of real-time power consumption sensor data streams; acquiring the processor frequency sequence and the performance throughput sequence reflecting the task progress of the hardware computing partition; calculating a real-time energy efficiency ratio sequence based on the real-time power consumption sensor data stream and the performance throughput sequence; matching the processor frequency sequence and the real-time energy efficiency ratio sequence to construct a coordinate point sequence; retrieving a benchmark energy efficiency curve matching the current computing task and the current running stage from a pre-built task energy efficiency benchmark library; determining the target frequency adjustment value based on the coordinate point sequence and the benchmark energy efficiency curve, and generating a frequency adjustment command. This invention can achieve a better balance between performance and energy consumption.

The application discloses a kind of v optimal reference iteration-based blade repair area surface reconstruction algorithm.For the cross section of preprocessed point cloud data, the reference cross section point cloud set is obtained, and the reference cross section curve is obtained by fitting the reference cross section point cloud set;After that, the reference cross section curve group is adopted Gaussian weighting discrete, and the v direction curve group is obtained by fitting in v direction;Based on the optimal principle in v direction, the optimal cross section of repair area is selected, the optimal cross section is intersected with the v direction curve group, the cross section curve of repair area is obtained, the cross section curve of repair area is added to the reference cross section curve group after ICP matching, then the v direction curve group is recalculated, all repair area cross sections are fitted in the form of step-by-step iteration, and the reference cross section curve group is added to the reference cross section curve group, finally, the reference cross section curve group is lofted to obtain the blade repair surface.The accuracy of the repair surface is greatly improved by the iterative method.