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Method for rapidly measuring dropping speed of liquid drops of mobile equipment based on deep learning

A mobile device and deep learning technology, applied in the field of neural network transfer learning and image classification, can solve the problems of time-consuming and laborious manual measurement, inconvenient movement, high price of infusion pumps, etc., and achieve the effect of improved portability and strong mobility

Active Publication Date: 2020-07-14
WUHAN UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

[0002] In the intravenous infusion of certain special drugs such as potassium, vasopressors, etc. or special populations such as the elderly and children, or in some chemical reactions that have strict requirements on the drip rate of reactants, the measurement of droplet velocity and Monitoring is very important, especially in hospitals. Intravenous infusion therapy is an extremely common medical method. Manual measurement is time-consuming and laborious, which will bring a great burden to medical workers. The use of infusion pumps is expensive and inconvenient to move, so it is fast and easy. , Accurate detection of drop rate has become an urgent need in the infusion process

Method used

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  • Method for rapidly measuring dropping speed of liquid drops of mobile equipment based on deep learning
  • Method for rapidly measuring dropping speed of liquid drops of mobile equipment based on deep learning
  • Method for rapidly measuring dropping speed of liquid drops of mobile equipment based on deep learning

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Embodiment Construction

[0056] In order to make the purpose, technical solutions and advantages of the present invention clearer, the following technical solutions in the present invention are clearly and completely described. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0057] figure 1 It is a flow chart of the method of the present invention.

[0058] Combine below Figure 1 to Figure 5 , the specific embodiment of the present invention is introduced as a method for rapidly measuring the droplet velocity of a mobile device based on deep learning, which includes the following specific steps:

[0059] Step 1: Collect images of droplets in different scenes, different light conditions, and different colors during the dripping pro...

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Abstract

The invention relates to a method for rapidly measuring the dripping speed of liquid drops of mobile equipment based on deep learning. The method comprises: acquiring an image of a droplet dropping process to construct an image data set, and constructing a droplet image training set and a droplet image test set through image preprocessing and manual marking methods; selecting a binary classification neural network model, and training the binary classification neural network model through the droplet image training set to obtain a trained binary classification neural network model; preprocessing an image acquired by an intelligent terminal to obtain a to-be-detected droplet image; predicting through a trained binary classification neural network model; and if the second dripping state is predicted to be the dripping state, continuously predicting through the trained binary neural network model after a plurality of frames of images are spaced until the next dripping state is predicted tobe the dripping state, calculating to obtain the time length for the interval between two adjacent dripping states, and further calculating the current dripping speed. The drop speed of the liquid drops is measured quickly and accurately, and the measurement efficiency of the drop speed of the liquid drops is greatly improved.

Description

technical field [0001] The invention relates to the fields of neural network transfer learning and image classification, and specifically designs a method for quickly measuring the droplet velocity of a mobile device based on deep learning. Background technique [0002] In the intravenous infusion of certain special drugs such as potassium, vasopressors, etc. or special populations such as the elderly and children, or in some chemical reactions that have strict requirements on the drip rate of reactants, the measurement of droplet velocity and Monitoring is very important, especially in hospitals. Intravenous infusion therapy is an extremely common medical method. Manual measurement is time-consuming and laborious, which will bring a great burden to medical workers. The use of infusion pumps is expensive and inconvenient to move, so it is fast and easy. , Accurate detection of drop rate has become an urgent need in the infusion process. [0003] With the wave of artificial ...

Claims

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Application Information

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IPC IPC(8): G06T7/20G06N3/08G06N3/04
CPCG06T7/20G06N3/08G06T2207/20081G06N3/045
Inventor 李立汪瑞
Owner WUHAN UNIV
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