CN113625369A - Miniaturized intelligent measuring system and method for atmospheric visibility - Google Patents
Miniaturized intelligent measuring system and method for atmospheric visibility Download PDFInfo
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Abstract
The invention discloses a miniaturized intelligent measuring system and method for atmospheric visibility, which comprises the following steps: collecting the series of atmospheric parameters and the atmospheric visibility observation data through a data collection module; calculating to obtain first atmospheric visibility calculation data by a first operation module by taking the series of atmospheric parameters as input; establishing a visibility inversion model by taking the first atmospheric visibility observation data, the second atmospheric visibility observation data and the first atmospheric visibility calculation data as input; and taking the series of atmospheric parameters as input, and substituting the visibility inversion model to calculate and predict second atmospheric visibility calculation data. According to the invention, the small sensor is used as a component to collect atmospheric visibility data, so that limitation caused by overlarge volume in the measurement process is avoided; an artificial intelligence algorithm is introduced to establish a visibility inversion model, so that the atmospheric visibility in different weather environments can be accurately calculated and predicted, and the problem of high cost of the conventional equipment is solved.
Description
Technical Field
The invention relates to the technical field of atmospheric visibility measuring systems and methods, in particular to a miniaturized atmospheric visibility intelligent measuring system and method.
Background
Atmospheric visibility standards are defined as the maximum horizontal distance a sighted person can see and recognize objects from the background of the sky under the prevailing weather conditions. The quantitative criterion is the luminous flux of a parallel beam of incandescent lamps with a color temperature of 2700K, measured by the path length through which the beam decays to the initial 5% during atmospheric transmission. The atmospheric visibility influences public transportation such as land transportation, shipping, sea transportation and the like and the health of people, so the intelligent measurement of the atmospheric visibility has important significance.
The components such as aerosol and water vapor in the atmosphere are key physical quantities for determining the visibility value, the atmospheric horizontal mixing is generally assumed to be uniform in a certain area range, and the atmospheric horizontal visibility information can be calculated by measuring the atmospheric extinction coefficient.
At present, a measuring method of atmospheric horizontal visibility is based on an optical principle and comprises a transmission type method, a forward scattering type method and an image recognition method, wherein the transmission type method is high in measuring accuracy, but equipment needs a transmitting end and a receiving end, and is large in size. For the detection of the vertical visibility and the oblique visibility of the atmosphere, the detection equipment at the present stage is mainly a laser radar and the like, and the realization cost is too high.
In conclusion, the conventional atmospheric visibility measuring method has the defects, and the development of the miniaturized visibility detecting equipment which can be carried on the sounding balloon and the unmanned aerial vehicle platform has important application value and market demand.
Disclosure of Invention
The invention provides a miniaturized intelligent measuring system and method for atmospheric visibility, which are used for overcoming the defects of overlarge volume, high measuring cost and low data accuracy of measuring equipment in the prior art and realizing the miniaturization and the intellectualization of atmospheric visibility measurement.
The invention provides a miniaturized atmospheric visibility intelligent measurement system, which comprises:
the data acquisition module is used for acquiring related data of atmospheric visibility, and the related data of atmospheric visibility comprises: the system comprises a series of atmospheric parameters and atmospheric visibility observation data, wherein the atmospheric visibility observation data comprise first atmospheric visibility observation data and second atmospheric visibility observation data;
the data operation module comprises a first operation module and a second operation module, wherein the first operation module takes the series of atmospheric parameters as input and performs operation to obtain first atmospheric visibility calculation data; and the second operation module takes the first atmospheric visibility observation data, the second atmospheric visibility observation data and the first atmospheric visibility calculation data as input to obtain second atmospheric visibility calculation data.
The miniaturized intelligent measuring system for atmospheric visibility further comprises a data transmission module, wherein the data transmission module is used for storing relevant data acquired by the data acquisition module and transmitting the relevant data to the operation module.
According to the miniaturized intelligent measuring system for atmospheric visibility, provided by the invention, the data acquisition module further comprises an intelligent sensor module, and the intelligent sensor module comprises a particle concentration sensor, a temperature sensor, a humidity sensor, a pressure sensor and a GPS sensor. Preferably, the particle concentration sensor may use an aerosol particle concentration sensor. Further preferably, the aerosol particle concentration sensor may use a laser dust sensor.
According to the miniaturized intelligent measuring system for atmospheric visibility provided by the invention, the data acquisition module further comprises a high-precision visibility meter and a miniaturized visibility meter, wherein the high-precision visibility meter is used for acquiring observation data of first atmospheric visibility; the miniaturized visibility meter is used for collecting second atmospheric visibility observation data. Preferably, the high-precision visibility meter may use a laser particle concentration sensor, and more preferably, the high-precision visibility meter may use a laser radar.
According to the miniaturized intelligent measuring system for atmospheric visibility provided by the invention, the second atmospheric visibility observation data is an outdoor environment image acquired by the miniaturized visibility meter. Preferably, the outdoor environment image is acquired by an infrared and visible camera.
The invention also provides a method for calculating and predicting the atmospheric visibility based on the miniaturized intelligent atmospheric visibility measuring system, which comprises the following steps:
s1, collecting the series of atmospheric parameters and the atmospheric visibility observation data through a data collection module;
s2, taking the series of atmospheric parameters as input, and obtaining first atmospheric visibility calculation data through the calculation of a first operation module;
s3, establishing a visibility inversion model by taking the first atmospheric visibility observation data, the second atmospheric visibility observation data and the first atmospheric visibility calculation data as input;
and S4, taking the series of atmospheric parameters as input, substituting the atmospheric parameters into the visibility inversion model to calculate and predict second atmospheric visibility calculation data.
According to the method for calculating and predicting the atmospheric visibility, provided by the invention, the series of atmospheric parameters comprise particle concentration, temperature, humidity, pressure value and GPS data.
According to the method for calculating and predicting atmospheric visibility provided by the invention, in the step S2, the first module operation is a basic operation, wherein the basic operation includes a linear operation and a nonlinear operation.
According to the method for calculating and predicting atmospheric visibility provided by the invention, in the step S3, the visibility inversion model can be established by using a depth neural network, and preferably, the visibility inversion model can be established by using a recurrent neural network. Further preferably, the visibility inversion model may use multivariate non-linear regression.
According to the system and the method for intelligently measuring the small-sized atmospheric visibility, the small-sized sensor is used as a component to collect atmospheric visibility data, so that the size of measuring equipment is greatly reduced, and the limitation caused by overlarge size in the measuring process is avoided; on the other hand, the visibility inversion model is established by introducing an artificial intelligence algorithm, so that the atmospheric visibility in different weather environments can be accurately calculated and predicted, and the construction and maintenance cost of the scheme is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a miniaturized intelligent measurement method for atmospheric visibility provided by the present invention;
FIG. 2 is a diagram of a particle concentration detector in a miniaturized intelligent measurement method for atmospheric visibility according to the present invention
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The four son king flags of inner Mongolia in this embodiment are as an example, provide a miniaturized atmospheric visibility intelligence measurement system, include:
and a data acquisition module.
In an embodiment of the present invention, the data acquisition module is configured to acquire atmospheric visibility related data, where the atmospheric visibility related data includes: the system comprises a series of atmospheric parameters and atmospheric visibility observation data, wherein the atmospheric visibility observation data comprise first atmospheric visibility observation data and second atmospheric visibility observation data.
In one embodiment of the invention, the series of atmospheric parameters are acquired by a particle concentration sensor, a temperature sensor, a humidity sensor, a pressure sensor, a GPS sensor. The GPS sensor can acquire longitude and latitude and altitude values. Preferably, the particle concentration sensor may use an aerosol particle concentration sensor. Further preferably, the aerosol particle concentration sensor may use a laser dust sensor. The volume of the intelligent sensor is 10-30 square centimeters, and limitation caused by overlarge volume in the measurement process is avoided. In other examples of the invention, a laser radar or the like device can be used to obtain the series of atmospheric parameters.
In one embodiment of the present invention, the high-precision visibility meter may use a laser particle concentration sensor, and further preferably, the high-precision visibility meter may use a laser radar.
In order to further increase the accuracy of atmospheric visibility, in an embodiment of the present invention, a visible light camera and an infrared camera may be used for acquiring the second atmospheric visibility observation data so as to acquire a real-time outdoor environment image of a detection place, and the visible light camera and the infrared camera may take 800 ten thousand-pixel pictures. Preferably, the camera setting scheme is that the visible light camera is used daytime, and the infrared camera is used at night, so that the real-time environment image of the effective detection area can be obtained at different times.
In order to further accurately observe the second atmospheric visibility, the invention selects the environment representative picture by calculating the minimum value of RGB channels in the pixel region of the real-time outdoor environment image of the detection area. Further preferably, the pixel calculation window is 5 × 5 to 9 × 9, so that the selected real-time outdoor environment photo can be more accurate.
And a data transmission module.
In an embodiment of the present invention, the data transmission module is configured to store the relevant data acquired by the data acquisition module, and transmit the relevant data to the operation module. The transmission module comprises a communication module, and the communication module can use the internet and a sonde for data transmission.
And a data operation module.
In an embodiment of the present invention, the data operation module includes a first operation module and a second operation module, wherein the first operation module performs operation by using the series of atmospheric parameters as input to obtain first atmospheric visibility calculation data; and the second operation module takes the first atmospheric visibility observation data, the second atmospheric visibility observation data and the first atmospheric visibility calculation data as input to obtain second atmospheric visibility calculation data.
Preferably, the operation module can use DSP and FPGA processor. Further preferably, the operation module may use a raspberry pi to perform the data operation processing.
The embodiment is based on a method for calculating and predicting atmospheric visibility, which is realized by a miniaturized intelligent atmospheric visibility measuring system. And analyzing the non-sleet weather state and the sleet weather state respectively to obtain the atmospheric visibility in different weather environments.
Under the non-rainy and snowy weather condition, the horizontal atmospheric visibility mainly depends on the concentration of atmospheric particulate matters and the water vapor content, a series of atmospheric parameters can be collected through the data collection module, and the first atmospheric visibility calculation data can be obtained through calculation. The method for calculating the first atmospheric visibility calculation data comprises the following steps:
and step S1, collecting the series of atmospheric parameters and the atmospheric visibility observation data through a data collection module.
In one embodiment of the invention, the data acquisition module comprises a smart sensor module comprising a particle concentration sensor, a temperature sensor, a humidity sensor, a pressure sensor, a GPS sensor. The series of atmospheric parameters comprise particle concentration, temperature, humidity, pressure values and GPS data, wherein the particle concentration is obtained by the particle concentration sensor, the temperature is obtained by the temperature sensor, the humidity is obtained by the humidity sensor, the pressure values are obtained by the pressure sensor, and the GPS data is obtained by the GPS sensor.
The atmospheric visibility observation data comprises first atmospheric visibility observation data and second atmospheric visibility observation data.
And step S2, taking the series of atmospheric parameters as input, and obtaining first atmospheric visibility calculation data through the calculation of a first calculation module.
In an embodiment of the present invention, specifically, in a non-rainy or snowy weather condition, the calculating the first atmospheric visibility data includes the following steps:
step S21 is to establish an aerosol particle number concentration distribution spectrum f (r) based on the above series of atmospheric parameters.
Particle number concentration distribution spectrum selects 0.3 ~ 10um aerosol particle number to establish, and is preferred, and the particle size range of selecting is 0.3um, 0.5um, 1.0um, 2.5um, 5.0um and 10um, and further preferred, the particle size number counting channel of selecting is 6. Here, the number vector x measured according to the 6 particle size number counting channels is [ x1, x2, x3, x4, x5, x6], RMSE is calculated according to the 6 particle size number counting channels, the particle spectrum distribution pattern, namely, the unimodal, bimodal or trimodal distribution, is determined according to the RMSE size, f (r) fitted when the RMSE is the minimum is selected as the particle spectrum distribution result, and the total particle number is represented by the number of particles larger than 0.3 um.
Establishing an aerosol particle number concentration profile f (r) by said obtaining a particle size range, as follows:
f(r)＝a*exp(-b*x)+c
in the formula, a, b and c are all parameters to be fitted, and f (r) follows log normal distribution.
Step S22 is a process of calculating an atmospheric extinction coefficient in a non-rainy and snowy weather state according to the particle number in the particle size range, the aerosol particle number concentration distribution spectrum, and by combining with the Mie scattering theory, as follows:
α＝-1/2(R1-R2)·In[p(R001)R1 2/p(R2)R2 2]
in the formula, α is an atmospheric extinction coefficient in a non-rainy or snowy weather state, and R1 and R2 are particle radii of different wavelengths.
The process of calculating the atmospheric extinction coefficient in the weather condition without rain or snow is as follows:
α＝τ/H
in the formula, alpha is an atmospheric extinction coefficient under the non-rainy and snowy weather condition, tau is the total integral of the extinction coefficient under the non-rainy and snowy weather condition in the vertical direction, and H is height.
Further preferably, in order to make the atmospheric extinction coefficient under the non-rainy and snowy weather condition more accurate, a meter scattering theory is combined, and the process of calculating the atmospheric extinction coefficient under the non-rainy and snowy weather condition is as follows:
wherein N is the total particle number concentration of 0.3um or more, Q is the extinction efficiency factor, m is the atmospheric birefringence index, λ is the wavelength of the sensor emitting light source, and f (r) is the particle number concentration distribution spectrum.
In the formula, the particle size distribution range is 0.3-10 um.
Step S23 is to calculate first atmospheric visibility calculation data according to the atmospheric extinction coefficient in the non-rainy/snowy weather condition, the process being as follows:
V＝a/σ·(λ/b)-q
wherein V is atmospheric visibility; a is a visibility coefficient, preferably, the value range of a is 0-5, and further preferably, 3.912 is taken as a; sigma is an atmospheric extinction coefficient; b is a second atmospheric visibility coefficient, preferably, the value range of b is 0-1, and further preferably, b is 0.55; and q is a third atmospheric visibility coefficient, preferably, the value range of q is 0-10, and further preferably, q is 1.3.
And calculating first atmospheric visibility calculation data according to the atmospheric extinction coefficient under the non-rainy and snowy weather condition, wherein the process is as follows:
V＝aH/τ
wherein V is atmospheric visibility; a is a first atmospheric visibility coefficient, preferably, the value range of a is 0-5, and further preferably, 3.912 is taken as a; h is the height; τ is the total integral of the extinction coefficient in the vertical direction.
Further preferably, the first atmospheric visibility calculation data is calculated according to the atmospheric extinction coefficient in the non-rainy-snowy weather state, and the process is as follows:
V＝a/σ
wherein V is atmospheric visibility; sigma is the atmospheric extinction coefficient, a is an atmospheric visibility constant, preferably, the value range of a is 0-5, and further preferably, a can be 3.912.
In an embodiment of the invention, the first atmospheric visibility calculation data is atmospheric visibility in a non-rainy and snowy weather state.
Under the rainy and snowy weather conditions, the atmospheric visibility is mainly influenced by the absorption and scattering of larger particles to light, and the visibility information can be acquired through the visible light and the infrared camera. Inputting the first atmospheric visibility observation data, the second atmospheric visibility observation data and the first atmospheric visibility calculation data, further establishing a visibility inversion model, and calculating the second atmospheric visibility calculation data, namely the atmospheric visibility in a rainy and snowy weather state according to the visibility inversion model, wherein the steps are as follows:
step S3 is to establish a visibility inversion model by using the first atmospheric visibility observation data, the second atmospheric visibility observation data, and the first atmospheric visibility calculation data as inputs.
In an embodiment of the present invention, specifically, the establishing of the visibility inversion model includes the following steps:
step S31 is to form a training sample from the first atmospheric visibility observation data and the second atmospheric visibility observation data.
Step S32 dynamically adjusts parameters in the inverse model according to different observation locations in the training sample.
And step S33, establishing a visibility inversion model according to the training samples and the inversion model parameters.
Establishing the visibility inversion model according to the first atmospheric visibility observation data, the second atmospheric visibility observation data and the first atmospheric visibility calculation data, wherein the process comprises the following steps:
and correcting the following formula by comparing the first atmospheric visibility observation data with the second atmospheric visibility observation data:
A＝a*B+b*C+c*T+d*R+e
in the formula, a is the calculated data of the first atmospheric visibility, B is the observed data of the first atmospheric visibility, C is the observed data of the second atmospheric visibility, T is temperature, R is relative humidity, and a, B, C, d and e are correction coefficients.
Establishing an inversion model, wherein the process is as follows:
V＝(a/b)*c+e
wherein V is atmospheric visibility; a is the first atmospheric visibility calculation data; b is an image parameter, preferably, the value range of b is 0-3; and e is the inversion parameter.
And step S4, taking the series of atmospheric parameters as input, and substituting the visibility inversion model to calculate and predict second atmospheric visibility calculation data.
In an embodiment of the invention, according to the visibility inversion model, the series of atmospheric parameters are taken as input and are brought into the visibility inversion model, and atmospheric visibility information in a rainy or snowy weather state within a period of time can be predicted through calculation.
According to the system and the method for the miniaturized intelligent measurement of the atmospheric visibility, disclosed by the invention, data are acquired and calculated by taking open source hardware as a main control, so that the volume of measurement equipment is greatly reduced, and the problems of large volume and high cost of the measurement equipment in the known method are solved. On the other hand, an artificial intelligence algorithm is introduced and applied to visibility inversion, so that the accuracy is improved, and meanwhile, the construction and maintenance cost of the scheme is reduced.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A miniaturized atmospheric visibility intelligence measurement system characterized in that includes:
the data acquisition module is used for acquiring related data of atmospheric visibility, and the related data of atmospheric visibility comprises: the system comprises a series of atmospheric parameters and atmospheric visibility observation data, wherein the atmospheric visibility observation data comprise first atmospheric visibility observation data and second atmospheric visibility observation data;
the data operation module comprises a first operation module and a second operation module, wherein the first operation module takes the series of atmospheric parameters as input and performs operation to obtain first atmospheric visibility calculation data; and the second operation module takes the first atmospheric visibility observation data, the second atmospheric visibility observation data and the first atmospheric visibility calculation data as input to obtain second atmospheric visibility calculation data.
2. The system of claim 1, wherein the data collection module comprises an intelligent sensor, and the set of atmospheric parameters is collected by the intelligent sensor.
3. The system as claimed in claim 1, wherein the data acquisition module further comprises a high-precision visibility meter and a miniaturized visibility meter, the high-precision visibility meter is used for acquiring observation data of first atmospheric visibility; the miniaturized visibility meter is used for collecting second atmospheric visibility observation data.
4. The system as claimed in claim 1, further comprising a data transmission module for storing the related data collected by said data collection module and transmitting the data to said operation module.
5. The system according to claim 1, wherein the second atmospheric visibility observation data is an outdoor environment image acquired by the miniaturized visibility meter, wherein the outdoor environment image is acquired by an infrared and visible light camera.
6. A method for calculating and predicting atmospheric visibility implemented by the miniaturized intelligent atmospheric visibility measuring system according to claim 1, comprising the steps of:
s1, collecting the series of atmospheric parameters and the atmospheric visibility observation data through a data collection module;
s2, taking the series of atmospheric parameters as input, and obtaining first atmospheric visibility calculation data through the calculation of a first operation module;
s3, establishing a visibility inversion model by taking the first atmospheric visibility observation data, the second atmospheric visibility observation data and the first atmospheric visibility calculation data as input;
and S4, taking the series of atmospheric parameters as input, substituting the atmospheric parameters into the visibility inversion model to calculate and predict second atmospheric visibility calculation data.
7. The method of claim 6, wherein in the step S1, the series of atmospheric parameters include particle concentration, temperature, humidity, pressure values, GPS data.
8. The method according to claim 6, wherein in step S2, the first module operation is a basic operation, wherein the basic operation includes a linear operation and a non-linear operation.
9. The method as claimed in claim 6, wherein in the step S3, the visibility inversion model is established by using multivariate nonlinear regression, deep neural network, and recurrent neural network.
10. The method of claim 8, wherein the linear operation comprises a matrix addition operation, a matrix multiplication operation, a vector addition operation, or a vector multiplication operation.
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