Automatic Acquisition Device of Precipitation Particle Shadow Image

Abstract

The invention discloses an automatic collection device for shadow images of precipitation particles, comprising: a light source end box with a first opening; a camera end box with a second opening, and the second opening is horizontal to the first opening. The direction is opposite to each other, and a sampling area is formed between the second opening and the first opening; the surface light source is arranged in the light source end box, and the light emitted by the surface light source directly enters the first opening through the first opening. The second opening; the camera device whose shooting focal length is aligned with the sampling area is located in the camera end box and can directly image the shadow of the precipitation particles illuminated by the surface light source. The steering device, located on the equipment support, adjusts the direction of the sampling area according to the wind direction and wind speed. The device of the invention can collect clear shadow images of precipitation particles in a state of natural motion, facilitates analysis of physical characteristics such as size and shape of precipitation particles, and identifies various types of precipitation.

Description

technical field [0001] The invention relates to an image collection device for weather phenomena, in particular to an automatic collection device for precipitation particle shadow images. Background technique [0002] Precipitation refers to a weather phenomenon in which liquid or solid water in clouds falls to the ground. Precipitation observation data play an important role in the study of global water vapor transport, climate change and hydrology. Therefore, the observation of precipitation phenomena has always been one of the most basic observation items of meteorological and hydrological stations. The traditional observation methods for precipitation phenomena mainly use rain gauges, tipping bucket rain gauges, etc. to measure the intensity and amount of precipitation. The identification of precipitation types still mainly relies on manual visual inspection, with a low level of automation. At present, there are generally three types of instruments used in the automatic...

AI Technical Summary

Problems solved by technology

The traditional observation methods for precipitation phenomena mainly use rain gauges, tipping bucket rain gauges, etc. to measure the intensity and amount of precipitation. The identification of precipitation types still mainly relies on manual visual inspection, and the level of automation is low.

At present, there are generally three types of instruments used in the automatic observation and identification test of precipitation phenomena: sensors based on the observation of mechanical effects of particle motion, including acoustic sensors, piezoelectric sensors, and freezing rain sensors. It belongs to a kind of auxiliary equipment, limited by the detection principle, it can only meet the requirements of precipitation monitoring and identification in specific occasions; microwave particle spectrometer, a miniaturized, continuous wave, low power Doppler radar, also known as multi The Puler raindrop spectrometer, the system can generally distinguish between rainfall and snowfall, but since the particle phase state, size, shape and spatial attitude cannot be obtained directly, it is difficult to avoid the uncertainty in the identification; optical sensor refers to the particle-based A type of precipitation particle detection technology based on the measurement of scattered light and transmitted light. Currently, the types of solid precipitation that can be effectively identified by such instruments are still relatively limited, and the measurement of the microstructure and dynamic parameters of precipitation particles is not comprehensive.

However, in order to realize the automatic observation and recognition of precipitation phenomena, the difficulty lies in the complex and diverse shape discrimination of solid precipitation particles.

Most of the sensors currently used in operational application tests are difficult to reliably measure the microstructure and dynamic parameters of precipitation particles such as phase state, scale, shape, spatial orientation and falling velocity, so the ability to discriminate precipitation types is insufficient

Existing precipitation phenomenon observation schemes based on digital photography cannot avoid the influence of non-uniform reflection and refraction of light on liquid transparent precipitation particles on imaging, and existing automatic shooting schemes for two-dimensional projected shadow images of precipitation particles cannot avoid imaging panel stains This will make it difficult to correctly identify the phase state of subsequent precipitation particles, and it is difficult to accurately extract characteristic parameters

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