A method for measuring blowout gas flow rate based on image feature recognition

Abstract

The invention provides a blowout gas flow velocity measurement method based on image feature recognition, comprising the following steps: Step 1. Collect a large number of on-site blowout gas high-speed images; Step 2. Perform feature recognition processing on the high-speed images in Step 1; Step 3. Select Obtain two adjacent frames of images with relatively excellent feature points; Step 4. Perform image processing on the two frames of images in Step 3; Step 5. Calculate the blowout gas flow rate through displacement and time. By adopting the method of the invention, the blowout gas flow rate can be obtained under the condition of ensuring the safety of field operators, and economic losses and casualties can be reduced.

Description

technical field [0001] The invention relates to the field of fluid detection, in particular to a method for measuring the flow rate of blowout gas based on image feature recognition. Background technique [0002] In the field of fluid detection, especially the design of blowout gas flow rate measurement based on image feature recognition. In recent years, with the increase in the demand for natural gas, the intensity of natural gas exploitation is increasing. When a blowout accident occurs, due to the lack of grasp of the diffusion speed of toxic and harmful gases after leakage, it will lead to blind evacuation of personnel, delays in evacuation and other problems. Missing the best time for emergency response has caused a large number of casualties, property losses and ecological environment damage. At the same time, due to the lack of sufficient and effective data for decision makers, it will cause delays in emergency response and blind disposal. It can be seen that it is...

AI Technical Summary

Problems solved by technology

[0005] However, the solutions for tracking image feature points provided by traditional technologies are mainly based on manually designed features for analysis, usually only low-order features are used, and they are sensitive to image noise, optical fiber changes and other unfavorable factors. The technical problem of low robustness of feature point tracking. At the same time, this technology requires a large computational burden on computer equipment, which is difficult to meet the application requirements of real-time low power consumption.

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