CN117129390A - Rainfall particle real-time monitoring system and method based on linear array camera shooting - Google Patents
Rainfall particle real-time monitoring system and method based on linear array camera shooting Download PDFInfo
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
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Abstract
The invention provides a rainfall particle real-time monitoring system and method based on linear array camera shooting, wherein the system comprises a light source, a linear array camera and a data receiving and processing unit; the light source continuously emits visible light to the raindrop measuring area, the emitted light is parallel light, the raindrop measuring area is uniformly illuminated, a single linear array scanning camera is adopted, a rainfall particle image and the corresponding image shooting time are obtained through high-speed linear scanning, the rainfall type can be identified, and parameters such as the particle size, the dropping speed, the rainfall rate, the raindrop size distribution and the like can be calculated. Compared with a laser raindrop spectrometer and a micro-rain radar, the system can acquire raindrop information directly through an image obtained through line scanning, well solves the problem that a plurality of raindrops fall down simultaneously, and the measuring process is not interfered by external microwave signals. In addition, the system only uses one linear array scanning camera, has strong environmental adaptability, few maintenance parts and small volume, and is convenient for field layout.
Description
Technical Field
The invention relates to the field of meteorological observation, in particular to a rainfall particle real-time monitoring system and method based on linear array camera shooting.
Background
Acquisition and analysis of rainfall parameters is important in the field of meteorology, and is usually acquired by equipment such as a rainfall bucket, a laser raindrop spectrometer, a rain radar, and the like. However, each device has its limitations. The rainfall barrel can only acquire rainfall and cannot acquire parameters such as raindrop size, dropping speed and the like which are important for understanding rainfall process, climate model and hydrologic cycle. The laser raindrop spectrometer acquires the size and falling speed of the raindrops by emitting laser beams and measuring the time when the raindrops block the laser beams, but its measurement may be disturbed by simultaneous passage of a plurality of raindrops or scattering and absorption of the laser beams by other particles, resulting in measurement errors. The rain radar uses a radar beam that emits microwaves or millimeter waves upward, then measures the particle falling speed by analyzing echo signals and applying the doppler principle, and estimates the particle size distribution by the reflected beam intensity. However, its measurement may be disturbed by microwave signals from other sources such as communication devices or other radar devices.
Disclosure of Invention
The invention provides a rainfall particle real-time monitoring system and a rainfall particle real-time monitoring method based on linear array shooting aiming at the technical problems existing in the prior art.
According to a first aspect of the invention, there is provided a rainfall particle real-time monitoring system based on linear array camera, comprising a light source, a linear array camera and a data receiving and processing unit;
the light source continuously emits visible light to the raindrop measurement region, and the emitted light is parallel light to uniformly illuminate the raindrop measurement region;
the linear array camera is used for continuously shooting images of the raindrop measurement area, capturing the raindrop images in real time and recording the time for shooting the raindrop images;
the data receiving and processing unit is used for analyzing and processing the raindrop images, identifying the rainfall type, calculating the size, the dropping speed, the rainfall rate and the raindrop size distribution of the raindrops.
On the basis of the technical scheme, the invention can also make the following improvements.
Optionally, the system further comprises a fresnel mirror, and the visible light continuously emitted by the light source is converted into parallel light through the fresnel mirror, and uniformly illuminates the raindrop measurement region through the slit.
According to a second aspect of the present invention, there is provided a method for monitoring rainfall particles in real time based on linear array imaging, comprising:
continuously emitting visible light to the raindrop measurement region through the light source, wherein the emitted light is parallel light, and uniformly illuminating the raindrop measurement region;
continuously shooting images on a raindrop measurement area based on a linear array camera, capturing the raindrop images in real time and recording the time for shooting the raindrop images;
and analyzing and processing the raindrop image, identifying the rainfall type, and calculating the size, the dropping speed, the rainfall rate and the raindrop size distribution of the raindrops.
According to the rainfall particle real-time monitoring system and method based on the linear array camera, a single linear array scanning camera is adopted, rainfall particle images and corresponding image acquisition time are obtained through high-speed linear scanning, the rainfall type can be identified, and parameters such as particle size, dropping speed, rainfall rate, rainfall droplet size distribution and the like can be calculated. Compared with a laser raindrop spectrometer and a micro-rain radar, the system can acquire raindrop information directly through an image obtained through line scanning, well solves the problem that a plurality of raindrops fall down simultaneously, and the measuring process is not interfered by external microwave signals. In addition, the system only uses one linear array scanning camera, has strong environmental adaptability, few maintenance parts and small volume, and is convenient for field layout.
Drawings
Fig. 1 is a schematic structural diagram of a rainfall particle real-time monitoring system based on linear array camera shooting;
fig. 2 is a schematic flow chart of a method for monitoring rainfall particles in real time based on linear array camera shooting.
In the drawings, the names of the components represented by the reference numerals are as follows:
1. the device comprises a light source, a Fresnel mirror, a slit, a linear array camera and a data receiving and processing unit, wherein the light source, the Fresnel mirror, the slit and the linear array camera are arranged in sequence, and the light source, the Fresnel mirror, the slit, the linear array camera and the data receiving and processing unit are arranged in sequence.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. In addition, the technical features of each embodiment or the single embodiment provided by the invention can be combined with each other at will to form a feasible technical scheme, and the combination is not limited by the sequence of steps and/or the structural composition mode, but is necessarily based on the fact that a person of ordinary skill in the art can realize the combination, and when the technical scheme is contradictory or can not realize, the combination of the technical scheme is not considered to exist and is not within the protection scope of the invention claimed.
In view of the limitations of the devices provided in the background art, the invention provides a rainfall particle real-time monitoring system based on linear array shooting, which comprises a light source 1, a linear array camera 4 and a data receiving and processing unit 5 as shown in fig. 1.
The light source 1 continuously emits visible light to the raindrop measurement region, and the emitted light is parallel light to uniformly illuminate the raindrop measurement region; a line camera 4 for continuously capturing images of the raindrop measurement region, capturing the raindrop images in real time, and recording the time for the line camera to capture the raindrop images; and the data receiving and processing unit 5 is used for analyzing and processing the raindrop image, identifying the rainfall type, calculating the size, the dropping speed, the rainfall rate and the raindrop size distribution of the raindrops.
The monitoring system further comprises a Fresnel mirror 2 and a slit 3, wherein visible light continuously emitted by the light source 1 is converted into parallel light through the Fresnel mirror 2, and a raindrop measuring area is uniformly illuminated through the slit 3. The line camera 4 continuously takes images. The images taken by the line camera 4 are transferred to a data receiving and processing unit 5 which processes the acquired raindrop images, identifies the type of precipitation, calculates the size of the raindrops, the falling speed, the rainfall rate and the distribution of the raindrop size.
Referring to fig. 2, there is provided a method for monitoring rainfall particles in real time using the rainfall particle real-time monitoring system provided in fig. 1, the method comprising:
step 1, continuously emitting visible light to a raindrop measurement region through a light source, changing the emitted light into parallel light after processing, and uniformly illuminating the raindrop measurement region.
And 2, continuously shooting images on the raindrop measurement area based on the linear array camera, capturing the raindrop images in real time and recording the time of shooting the raindrop images by the linear array camera.
It is understood that the light source continuously emits visible light, the emitted light is converted into parallel light, the measuring area is uniformly illuminated, and the line camera continuously shoots raindrop images and records the shooting time.
And 3, analyzing and processing the raindrop image, identifying the rainfall type, and calculating the size, the dropping speed, the rainfall rate and the raindrop size distribution of the raindrops.
It can be understood that the falling raindrop image is continuously shot according to the line camera, and then the raindrop image obtained by shooting is analyzed and processed.
Wherein, discern precipitation type from raindrop image, include: collecting raindrop images of different types of rainfall by using a linear array camera, and marking the rainfall type of each raindrop image to form a sample data set; dividing the sample dataset into a training dataset and a test dataset; and training the convolutional neural network based on the training data set, and obtaining an optimized precipitation type recognition model by adjusting model parameters. The test dataset is then used to evaluate the performance of this model. And inputting the raindrop image to be identified into a tested precipitation type identification model to obtain the precipitation type of the raindrops to be identified.
It is understood that the linear array camera is utilized to collect pictures of different types of precipitation, and the precipitation of each picture is labeled with the type. The convolutional neural network is used to automatically extract features and training is performed according to the extracted features and corresponding labels. Data not used in training is used to test the performance of the model. And identifying the precipitation type by using the trained precipitation type identification model.
Calculating the size of the raindrops according to the raindrop images, comprising: extracting the width of raindrops from the raindrop images acquired by the linear array camera; continuously shooting raindrop images by using two linear array cameras which are orthogonal and have a vertical distance which is a set distance value, so as to obtain a large amount of raindrop width data and raindrop height data; training a machine learning model based on the raindrop width data and the raindrop height data, the machine learning model establishing a correspondence between raindrop width and raindrop height; and inputting the width of the raindrops extracted from the raindrop images acquired by the linear array camera into the machine learning model to acquire the height of the raindrops.
It is understood that the image obtained by the line camera may directly obtain the width of the raindrops, but cannot obtain the height thereof, and a machine learning method is adopted to collect a large amount of data of the width and the height of the raindrops, and then use the data to train a machine learning model. This model will learn the relationship between raindrop width and height and can be used to predict the unknown raindrop height. The method comprises the following steps:
(1) The raindrop images are continuously shot by using two orthogonal linear array cameras with the vertical distance of 7mm, and a large amount of raindrop width and height data can be obtained.
(2) The data is cleaned and the missing values and outliers are processed.
(3) And constructing the structure of the neural network.
(4) The collected data is divided into a training set and a validation set. The neural network is trained using the training set data.
(5) After training is completed, the performance of the network is verified by using the verification set data, and model evaluation is performed by comparing the predicted result and the actual result of the verification set by the network.
After the neural network training is completed, the height h meters of the raindrops can be predicted through the input actual measured raindrop width w meters.
After the height of the raindrops is obtained, the falling speed of the raindrops can be calculated. Since the characteristic of the line scanning camera, that is, the raindrop image is repeatedly photographed at specific time intervals, the number of times of co-scanning of the camera when raindrops fall, that is, the time required for raindrops to fall, can be known by stitching the obtained raindrop images.
Assuming that when a complete raindrop image is obtained, the camera scans a rows, the scanning time interval of the camera is t=1/f seconds, f is the scanning frequency of the linear array camera, the raindrop height obtained by predicting the width w m of the input raindrops is h m, and then the calculation formula of the raindrop dropping speed v (m/s) is as follows:
（1）；
after the falling speed of the raindrops is calculated, the rainfall of the rainfall is calculated, and the main steps comprise:
(1) Raindrop volume calculation
The raindrop images obtained through splicing are formed by splicing a plurality of rectangular images with extremely narrow heights, and the width of the raindrop part in each rectangular image is obtained through actual measurement. Therefore, the whole raindrop can be regarded as being composed of innumerable small cylinders, and the bottom edge of the ith small cylinder is the actually measured width w i Meter, the height of which is the product of the scanning interval of the camera and the raindrop dropping speed obtained beforeVolume V of each raindrop n The calculation formula of (2) is as follows:
（2）。
(2) Calculation of total rainfall in one rainfall process
Assuming that m raindrops are monitored in the primary rainfall process, the total rainfall volume V in the primary rainfall process can be obtained by combining the formula (2) total The method comprises the following steps:
（3）；
then, the total rainfall P millimeters in the primary rainfall process is:
（4）。
(3) Rainfall rate calculation for primary rainfall
Let t be 0 From hour, the system observes rainfall, t 1 The rainfall stops at the hour, and the rainfall rate R (millimeters/hr) of the rainfall is:
（5）。
then establishing a raindrop size distribution model, wherein the size of the raindrops is characterized by the diameter of a cylinder, and the establishment of the raindrop size distribution model comprises the following steps:
(1) The volume V of each raindrop in the rainfall of the field is acquired by the raindrop monitoring system n Calculating to obtain the equivalent diameter D of each raindrop n The calculation formula is as follows:
（6）；
using calculated D n The raindrop diameter histogram of the present rainfall can be constructed to show the number of raindrops in each diameter bin (bin 0-0.5mm, 0.5-1mm, 1-1.5mm, etc., bin width 0.5 mm).
(2) The invention adopts a Marshall-Palmer distribution model to describe the raindrop size distribution (DSD) and has the formula:
（7）；
where N (D) is the number of raindrops per cubic meter per millimeter, i.e. the number of raindrops with a raindrop diameter between D and d+dd per cubic meter of space (D is a small diameter interval). The unit is 1/m.multidot.mm. N (N) 0 Is a normalization parameter, also called cut-off parameter. It is a constant that indicates the number of drops as the diameter of the drops approaches 0. D is the diameter of the raindrop in millimeters. And [ mu ] is a shape parameter for describing the shape of the raindrop size distribution. λ is a scale parameter describing the width of the raindrop size distribution.
The raindrop diameter histogram obtained in the step (1) is combined with a least square method to obtain a model parameter N 0 And [ mu ] and [ lambda ]. The method comprises the following steps:
1) Is N 0 And [ mu ] and [ lambda ] select a set of initial values.
2) And calculating the predicted raindrop quantity corresponding to each raindrop diameter D by using the current parameter value and the DSD model.
3) The predicted number of raindrops is compared with the number of raindrops actually observed (i.e., histogram), and the residual (i.e., square of difference) is calculated.
4) The parameter values are updated using a gradient descent method such that the sum of squares of the residuals is minimized.
5) Repeating the steps 2) to 4) until the parameter value converges (namely the change is very small) to obtain the DSD parameter N of the rainfall 0 And [ mu ] and [ lambda ].
And (3) carrying the parameters into a formula (6) to obtain the number N (D) of the raindrops with the inner diameter D in unit volume in the current rainfall.
Finally, the data receiving and processing unit can send parameters such as the rainfall type, the size of raindrops, the dropping speed, the rainfall rate, the size distribution of raindrops and the like obtained by analysis and calculation to the background server in a wireless transmission mode,
according to the invention, a set of rainfall particle real-time monitoring system and method are developed based on a linear array camera shooting principle, a real-time image of raindrops can be obtained under the condition that only one linear array camera is used, the rainfall type is identified and parameters such as the size, the dropping speed, the rainfall rate and the size distribution of the raindrops are calculated based on a self-grinding raindrop characteristic automatic extraction algorithm, so that important input data are provided for applications such as climate models, flood early warning and agricultural irrigation. In addition, the parameters can also improve the understanding of the atmospheric physical process (such as a micro-physical process and a rainfall process) and further improve the accuracy of weather forecast and weather change prediction.
The system adopts a wireless transmission mode to transmit data, and can transmit real-time data to a background server (monitoring terminal). Compared with the existing rainfall monitoring equipment, the system can acquire the raindrop information directly through the image obtained by line scanning, so that the problem that a plurality of raindrops fall simultaneously is well solved, and the measuring process is not interfered by external microwave signals. In addition, the system only uses one linear array scanning camera, has strong environmental adaptability, few maintenance parts and small volume, is convenient for field layout, and provides a novel, efficient and accurate raindrop measurement and analysis method.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The rainfall particle real-time monitoring system based on linear array camera shooting is characterized by comprising a light source, a linear array camera and a data receiving and processing unit;
the light source continuously emits visible light to the raindrop measurement region, and the emitted light is parallel light to uniformly illuminate the raindrop measurement region;
the linear array camera is used for continuously shooting images of the raindrop measurement area, capturing the raindrop images in real time and recording the time for shooting the raindrop images;
the data receiving and processing unit is used for analyzing and processing the raindrop images, identifying the rainfall type, calculating the size, the dropping speed, the rainfall rate and the raindrop size distribution of the raindrops.
2. The system of claim 1, further comprising a fresnel mirror through which visible light continuously emitted from the light source is converted into parallel light and uniformly illuminates the raindrop measurement region through the slit.
3. A method for monitoring rainfall particles in real time based on the rainfall particle real-time monitoring system as claimed in claim 1, characterized by comprising:
continuously emitting visible light to the raindrop measurement region through the light source, wherein the emitted light is parallel light, and uniformly illuminating the raindrop measurement region;
continuously shooting images on a raindrop measurement area based on a linear array camera, capturing the raindrop images in real time and recording the time for shooting the raindrop images;
and analyzing and processing the raindrop image, identifying the rainfall type, and calculating the size, the dropping speed, the rainfall rate and the raindrop size distribution of the raindrops.
4. A method for monitoring rainfall particles in real time according to claim 3, wherein analyzing the raindrop image and identifying the type of rainfall comprises:
collecting raindrop images of different types of rainfall by using a linear array camera, and marking the rainfall type of each raindrop image to form a sample data set;
dividing the sample dataset into a training dataset and a test dataset;
training the convolutional neural network model based on the training data set, and obtaining an optimized precipitation type recognition model by adjusting model parameters;
evaluating the performance of the precipitation type identification model using the test dataset;
and inputting the raindrop image to be identified into a tested precipitation type identification model to obtain the precipitation type of the raindrops to be identified.
5. A method for monitoring rainfall particles in real time according to claim 3 wherein the calculating the size of the raindrops comprises:
extracting the width of raindrops from the raindrop images acquired by the linear array camera;
continuously shooting raindrop images by using two linear array cameras which are orthogonal and have a vertical distance which is a set distance value, so as to obtain a large amount of raindrop width data and raindrop height data;
training a machine learning model based on the raindrop width data and the raindrop height data, the machine learning model establishing a correspondence between raindrop width and raindrop height;
and inputting the width of the raindrops extracted from the raindrop images acquired by the linear array camera into the machine learning model to acquire the height of the raindrops.
6. The method for monitoring rainfall particles in real time according to claim 5, wherein calculating the dropping speed of the raindrops comprises:
when a complete raindrop image is obtained, a line a is scanned by a camera, the scanning time interval of the camera is t=1/f seconds, f is the scanning frequency of a linear array camera, and the raindrop falling time is calculated;
according to the raindrop height h, a calculation formula for calculating the raindrop dropping speed v is as follows:
。
7. the method of claim 6, wherein calculating the rainfall particles comprises:
the whole raindrop is regarded as being composed of innumerable small cylinders, and the bottom edge of the ith small cylinder is the measured raindrop width w i Meter with height being the product of the camera scanning interval and the raindrop dropping speedT is the scanning time interval of the camera, V is the dropping speed of the raindrops, and the volume V of each raindrop n The calculation formula of (2) is as follows:
；
wherein n represents an nth raindrop;
in the primary rainfall process, a total of m raindrops are monitored to obtain a total rainfall volume V in the primary rainfall process total The method comprises the following steps:
；
the total rainfall P millimeters for the primary rainfall process is:
；
from t 0 From hour, rainfall was observed, t 1 And stopping rainfall at the time of hours, wherein the rainfall rate R of the rainfall is as follows:
；
wherein R is in mm/hr.
8. The method of claim 7, wherein calculating a raindrop size distribution comprises:
according to the volume V of each raindrop in the rainfall of the field n Calculate each rainEquivalent diameter D of drop n The calculation formula is as follows:
；
using the calculated equivalent diameter D of each raindrop n And constructing a raindrop diameter histogram of the rainfall field, and displaying the raindrop quantity in each diameter interval through the raindrop diameter histogram.
9. The method for monitoring rainfall particles in real time according to claim 8, further comprising describing the size distribution of the rainfall drops by using a Marshall-Palmer distribution model, wherein the formula is:
；
wherein N (D) is the number of raindrops per cubic meter per millimeter, i.e. the number of raindrops with a raindrop diameter between D and D+dD in units of 1/m mm in space per cubic meter; n (N) 0 Is a constant, D is the diameter of the raindrop, and the unit is millimeter; the [ mu ] is a shape parameter for describing the shape of the raindrop size distribution; lambda is a proportional parameter describing the width of the raindrop size distribution;
solving a parameter N according to the constructed raindrop diameter histogram 0 And [ mu ] and [ lambda ].
10. The method according to claim 9, wherein the parameter N is solved according to the constructed raindrop diameter histogram 0 Mu and lambda, comprising:
is N 0 Selecting a group of initial values as parameter values by [ mu ] and [ lambda ];
calculating the number of the predicted raindrops corresponding to the diameter of each raindrop by using the current parameter value and a Marshall-Palmer distribution model;
comparing the predicted raindrop quantity with the actual observed raindrop quantity, and calculating residual errors;
updating model parameter values using a gradient descent method such that the sum of squares of residuals is minimized;
repeating the above steps until the parameter value converges or reaches the preset iteration times to obtain the parameter value N of the rainfall 0 And [ mu ] and [ lambda ].
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117875147A (en) * | 2024-03-11 | 2024-04-12 | 杭州经纬信息技术股份有限公司 | Method and system for simulating rain and fog phenomena in real time and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011047743A (en) * | 2009-08-26 | 2011-03-10 | Toshiba Corp | Weather radar apparatus and rainfall rate method, and program |
| CN103033857A (en) * | 2012-12-25 | 2013-04-10 | 中国人民解放军理工大学 | Rainfall and snowfall automatic observation method based on parallel light large visual field |
| CN103439756A (en) * | 2013-07-31 | 2013-12-11 | 中国人民解放军理工大学 | Natural precipitation particle micro physical characteristic measuring method based on particle forming speed measurement |
| CN108489547A (en) * | 2018-04-09 | 2018-09-04 | 湖南农业大学 | A kind of raindrop parameter test device |
| CN110543893A (en) * | 2019-08-07 | 2019-12-06 | 河海大学 | Microwave attenuation precipitation particle type identification method based on BP neural network |
| CN110610190A (en) * | 2019-07-31 | 2019-12-24 | 浙江大学 | Convolutional neural network rainfall intensity classification method for rainy pictures |
| CN113552656A (en) * | 2021-07-26 | 2021-10-26 | 福建农林大学 | Rainfall intensity monitoring method and system based on outdoor image multi-space-time fusion |
| CN114509826A (en) * | 2022-03-28 | 2022-05-17 | 周进 | Direct-measuring type precipitation measurer |
| KR20220138698A (en) * | 2021-04-06 | 2022-10-13 | 서울대학교산학협력단 | Method and apparatus for rainfall computation |
| CN115542327A (en) * | 2022-09-26 | 2022-12-30 | 成都东日星河科技有限公司 | Millimeter wave radar rain measurement method and system |
-
2023
- 2023-10-26 CN CN202311399464.XA patent/CN117129390B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011047743A (en) * | 2009-08-26 | 2011-03-10 | Toshiba Corp | Weather radar apparatus and rainfall rate method, and program |
| CN103033857A (en) * | 2012-12-25 | 2013-04-10 | 中国人民解放军理工大学 | Rainfall and snowfall automatic observation method based on parallel light large visual field |
| CN103439756A (en) * | 2013-07-31 | 2013-12-11 | 中国人民解放军理工大学 | Natural precipitation particle micro physical characteristic measuring method based on particle forming speed measurement |
| CN108489547A (en) * | 2018-04-09 | 2018-09-04 | 湖南农业大学 | A kind of raindrop parameter test device |
| CN110610190A (en) * | 2019-07-31 | 2019-12-24 | 浙江大学 | Convolutional neural network rainfall intensity classification method for rainy pictures |
| CN110543893A (en) * | 2019-08-07 | 2019-12-06 | 河海大学 | Microwave attenuation precipitation particle type identification method based on BP neural network |
| KR20220138698A (en) * | 2021-04-06 | 2022-10-13 | 서울대학교산학협력단 | Method and apparatus for rainfall computation |
| CN113552656A (en) * | 2021-07-26 | 2021-10-26 | 福建农林大学 | Rainfall intensity monitoring method and system based on outdoor image multi-space-time fusion |
| CN114509826A (en) * | 2022-03-28 | 2022-05-17 | 周进 | Direct-measuring type precipitation measurer |
| CN115542327A (en) * | 2022-09-26 | 2022-12-30 | 成都东日星河科技有限公司 | Millimeter wave radar rain measurement method and system |
Non-Patent Citations (2)
| Title |
|---|
| 冯婉悦 等: "非球形降水粒子测量模型的初步研究", 《成都信息工程大学学报》, vol. 31, no. 02, pages 136 - 141 * |
| 岑家生 等: "降水粒子的成像探测技术及仪器初探", 《大气与环境光学学报》, vol. 6, no. 06, pages 415 - 422 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117875147A (en) * | 2024-03-11 | 2024-04-12 | 杭州经纬信息技术股份有限公司 | Method and system for simulating rain and fog phenomena in real time and storage medium |
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