Blowout gas flow velocity measurement method based on image feature recognition

Abstract

The invention provides a blowout gas flow velocity measuring method based on image feature recognition. The blowout gas flow velocity measuring method comprises the following steps of 1, collecting a large number of field blowout gas high-speed images; 2, performing feature recognition processing on the high-speed image in the step 1; step 3, selecting two adjacent frames of images with excellent feature points; step 4, carrying out image processing on the two frames of images in the step 3; and 5, calculating the flow rate of the blowout gas through the displacement and the time. By adopting the method, the flow rate of the blowout gas can be obtained under the condition of ensuring the safety of field operating personnel, and economic loss and casualties are reduced.

Description

technical field [0001] The invention relates to the field of fluid detection, in particular to a blowout gas flow rate measurement method based on image feature recognition. Background technique [0002] In the field of fluid detection, especially in the design of blowout gas flow velocity measurement based on image feature recognition. In recent years, with the increasing 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 gas after leakage, it will lead to problems such as blind evacuation and delayed evacuation. The best time for emergency response was missed, resulting in a large number of casualties, property losses and ecological environment damage. At the same time, due to the lack of sufficient and effective data provided to decision makers, it will cause delays and blind disposal of emergency response. It can be seen that it is of great...

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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