Particle detection statistical method based on embedded system

Abstract

The invention discloses a particle detection statistical method based on an embedded system, which classifies signals according to signal waveform shapes. The concrete implementing process is to carry out differentiation on the data in a memory; whether the signal is an M signal, a normal signal or an interference signal is judged according to differentiation; the advantages thereof are as follows: the interference signal and the normal signal can be differentiated, the M signal generated due to the close distance of two signals can be identified, and the problem existing in analog signal identification does not exist as the embedded system is adopted. The method of the invention can greatly reduce the complex degree and the cost of an actual circuit, and can improve the counting accuracy of the identification rate of particle signals.

Description

technical field [0001] The invention relates to a particle detection and statistics method, in particular to a particle detection and statistics method based on an embedded system. Background technique [0002] Electrical impedance method (coulter method) and photoresistance method have been used to detect and count particles for more than 50 years. The Coulter principle was first invented by American scientist Coulter in 1947, and it was successfully applied in blood cell analysis. At present, most hematology analyzers at home and abroad are still using the Coulter method. The photoresistance method instrument was first produced by the HIAC company of the United States, and was introduced to my country in the 1980s. It is mainly used in aerospace gasoline, medicine and other fields, and plays an important role in particle detection. [0003] Regardless of whether the electrical impedance method or the photoresistance method is used to detect particles, it is necessary to ...

AI Technical Summary

Problems solved by technology

[0005] In recent years, methods such as visual image method and artificial neural network have emerged in the process of particle detection methods, and have achieved certain results in actual use. However, these methods are expensive in hardware resources, so few of these methods can The actual transplantation to embedded systems such as ARM has played a certain role in hindering the promotion of these methods.

Images