A calibration method and device for a forward-diverging thin-sheet beam sampling droplet spectrometer

Abstract

The present invention provides a calibration method and device for a forward-diverging thin-sheet beam sampling disdrometer. The method includes: measuring the light intensity signal values corresponding to precipitation particles with different particle diameters by using the disdrometer, and establishing a particle size comparison table, where the particle size comparison table includes a set of particle diameters and a set of measured light intensity signal values, and records the one-to-one correspondence between the particle diameter and the measured light intensity signal value; using the set of particle diameters and the set of light intensity signal values to perform iterative calculations on a pre-established mapping relation function to determine the calculation parameters of the mapping relation function that meet the preset convergence conditions, where the mapping relation function is a relation function established by using the calculation parameters with the light intensity signal value as the independent variable and the particle diameter as the dependent variable. Using this calibration method for the forward-diverging thin-sheet beam sampling disdrometer does not require other cumbersome complex logics, not only reduces the calculation amount, but also improves the accuracy of the mapping relation function between the particle diameter and the light intensity signal value.

Description

Technical Field

[0001] The present invention relates to the technical field of disdrometer detection, and particularly to a calibration method and device for a forward-divergent thin-sheet beam sampling disdrometer. Background Art

[0002] In the field of meteorological observation applications, meteorological phenomena such as drizzle, heavy rain, hail, and snowflakes can all be monitored using a disdrometer. A disdrometer is an important observation and measurement instrument for the microphysical characteristics of precipitation. A disdrometer can be detected by acoustic, impact, optical, and other means. In the prior art, for the optical detection method, methods based on a camera to capture the diameter of precipitation particles by shooting and analyzing the light intensity time series based on beam sampling are often used. Capturing the diameter of precipitation particles by shooting with a camera can directly extract the size of precipitation particles from the image, but the processing process is relatively complex. It is necessary to extract the target particles from the background and then extract the particle contours. Not only is it necessary to select a uniform light source, but also the amount of image information is too large, and the processing requirements for image information are too high.

[0003] For example: One-dimensional linear array imaging analysis is a method that is currently used more frequently. Since the one-dimensional linear array imaging analysis method can directly obtain the direct information of the precipitation particle size, many users tend to prefer this method. In fact, when the one-dimensional linear array imaging analysis method samples precipitation particles with a relatively small diameter, its accuracy has relatively large limitations.

[0004] Secondly, the method of analyzing the diameter of precipitation particles based on the light intensity time series of beam sampling is also a method that is currently used more frequently. For example: In the Chinese patent "A Measurement Method for Laser Precipitation Weather Phenomena and a Laser Precipitation Weather Phenomena Instrument" (201210251284.2), a detailed description of the relevant principles of a laser disdrometer is disclosed. Specifically, the laser emission part will generate two thin-sheet beams, which can be called light bands. The detector receives on the opposite side. When precipitation particles fall through the thin-sheet beam, corresponding changes in the pulse intensity of the light intensity signal will be formed. By analyzing the characteristics of the light intensity signal, the relationship between the light intensity signal and the diameter of precipitation particles can be established. However, in the prior art, the establishment of the relationship between the light intensity signal and the diameter of precipitation particles all has problems of relatively low accuracy, large computational amount, and high power consumption. Summary of the Invention

[0005] Therefore, the present invention aims to solve the technical problems in the prior art that the establishment of the relationship between the light intensity signal and the diameter of precipitation particles all has relatively low accuracy and large computational amount, and thus provides a calibration method and device for a forward-divergent thin-sheet beam sampling disdrometer.

[0006] According to a first aspect, an embodiment of the present invention provides a calibration method for a forward-divergent thin-sheet beam sampling disdrometer, characterized by including the following steps:

[0007] Measure the light intensity signal values corresponding to precipitation particles with different particle diameters using a disdrometer, and establish a particle size comparison table. Among them, the particle size comparison table includes a set of particle diameters and a set of measured light intensity signal values, and records the one-to-one correspondence between the particle diameter and the measured light intensity signal value;

[0008] Use the set of particle diameters and the set of light intensity signal values to perform iterative calculations on a pre-established mapping relationship function to determine the calculation parameters of the mapping relationship function that meet the preset convergence conditions. Among them, the mapping relationship function is a relationship function established using the calculation parameters with the light intensity signal value as the independent variable and the particle diameter as the dependent variable.

[0009] Optionally, the using the set of particle diameters and the set of light intensity signal values to perform iterative calculations on a pre-established mapping relationship function to determine the calculation parameters of the mapping relationship function that meet the preset convergence conditions includes:

[0010] Step a, establish an initial mapping relationship function, and determine the parameter type of the calculation parameters of the initial mapping relationship function;

[0011] Step b, select multiple sets of training data from the particle size comparison table, substitute them into the initial mapping relationship function, and obtain multiple relationships about the calculation parameters;

[0012] Step c, use the multiple relationships about the calculation parameters to solve for the calculation parameters and obtain an intermediate mapping relationship function;

[0013] Step d, select multiple sets of light intensity data from the particle size comparison table, substitute them into the intermediate mapping relationship function for calculation, and obtain the calculated values of the particle diameters corresponding to the multiple sets of light intensity data. Among them, the multiple sets of light intensity data are light intensity data different from the multiple sets of training data;

[0014] Step e, calculate the mean square error between the actual particle diameters corresponding to the multiple sets of light intensity data and the calculated values of the particle diameters;

[0015] Repeat the above steps a to e until the calculated mean square error is less than the preset threshold, and use the corresponding intermediate mapping relationship function as the final mapping relationship function.

[0016] Optionally, the calculating the mean square error between the actual particle diameters corresponding to the multiple sets of light intensity data and the calculated values of the particle diameters includes:

[0017] Calculate the error between the actual particle diameter and the calculated value of the particle diameter corresponding to each set of light intensity data respectively, and obtain multiple error terms;

[0018] Square the error terms respectively and then calculate the average value to obtain the mean square error.

[0019] Optionally, the mean square error is calculated by the following formula:

[0020]

[0021] where D eq-im (i, j) is the actual particle diameter of the j-th particle in the i-th group, and D eq-fit (i, j) is the particle diameter of the j-th particle in the i-th group calculated according to the intermediate mapping relationship function, and ω j is the weight coefficient, M is the total number of groups of particle diameters, N is the total number of particles in each group, and D MS is all particle diameters in M groups, E S is the cumulative error term, and i and j are constants.

[0022] Optionally, establishing the initial mapping relationship function and determining the parameter types of the calculation parameters of the initial mapping relationship function include:

[0023] Establish a power polynomial between the light intensity signal value and the particle diameter;

[0024] Determine the highest order and the coefficients of each order in the power polynomial as the calculation parameters.

[0025] Optionally, the initial mapping relationship function is:

[0026]

[0027] where y is the calculated value of the particle diameter, x is the light intensity signal value, and a k is the power coefficient corresponding to the mapping relationship function, and n is the highest order in the power polynomial.

[0028] Optionally, the actual particle diameter of the precipitation particles is determined by the following steps:

[0029] Use a high-speed imaging camera to take images of particles passing through the disdrometer;

[0030] Adopt the equivalent spherical diameter calculation method to obtain the particle diameter.

[0031] According to the second aspect, an embodiment of the present invention provides a disdrometer calibration device, including:

[0032] A recording module, configured to measure the light intensity signal values corresponding to precipitation particles with different particle diameters by using a disdrometer, and establish a particle size comparison table, where the particle size comparison table includes a particle diameter set and a measured light intensity signal value set, and records the one-to-one correspondence between the particle diameter and the measured light intensity signal value;

[0033] A calculation module, configured to perform iterative calculation on a pre-established mapping relation function by using the particle diameter set and the light intensity signal value set, so as to determine calculation parameters of the mapping relation function that meet a preset convergence condition, where the mapping relation function is a relation function established by using the calculation parameters, with the light intensity signal value as the independent variable and the particle diameter as the dependent variable.

[0034] According to a third aspect, an embodiment of the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other, wherein computer instructions are stored in the memory, and the processor executes the computer instructions to execute the above-mentioned forward-scattering thin-sheet beam sampling droplet spectrometer calibration method.

[0035] According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores computer instructions for causing a computer to execute the above-mentioned forward-scattering thin-sheet beam sampling droplet spectrometer calibration method.

[0036] The technical solution of the present invention has the following advantages:

[0037] 1. In this embodiment, not only the method of establishing a particle size comparison table is adopted to record the one-to-one correspondence between the particle diameter measured by the droplet spectrometer and the measured light intensity signal value, but also the method of pre-establishing a mapping relation function is adopted; iterative calculation is performed by using the particle size comparison table to finally determine the calculation parameters of the mapping relation function that meet the preset convergence condition; by adopting these two methods, the accuracy of data, the convenience of data retrieval, and the efficiency of calculating the mapping relation function are ensured when calculating the mapping relation function.

[0038] 2. By establishing an initial mapping relation function, the highest order and the coefficients of each order in the power series polynomial are pre-determined as calculation parameters; multiple groups of training data in the particle size comparison table are selected and substituted into the initial mapping relation function to solve for the calculation parameters and obtain an intermediate mapping relation function; multiple groups of light intensity data are selected from the particle size comparison table and substituted into the intermediate mapping relation function for calculation to obtain the calculated values of the particle diameters corresponding to the multiple groups of light intensity data; calculate the mean square error between the actual particle diameters corresponding to the multiple groups of light intensity data and the calculated values of the particle diameters; repeat the above steps until the calculated mean square error is less than a preset threshold, and finally use the corresponding intermediate mapping relation function as the final mapping relation function. According to the final mapping relation function, accurate particle diameters can be calculated by obtaining light intensity signal values. This iterative calculation process does not require other cumbersome and complex logics, which not only reduces the calculation amount but also improves the accuracy of the mapping relation function between the particle diameter and the light intensity signal. Description of the Drawings

[0039] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1 It is a flowchart of a specific example of a calibration method for a forward-scattering thin-sheet beam sampling disdrometer in Embodiment 1 of the present invention;

[0041] Figure 2 It is a schematic diagram of the optical principle of a specific example of forward-scattering thin-sheet beam sampling in Embodiment 1 of the present invention;

[0042] Figure 3 It is a block diagram of a specific example of tracing the diameter of precipitation particles to the length reference in Embodiment 1 of the present invention;

[0043] Figure 4 It is an effect diagram of a specific example of converting the profiles of precipitation particles with different diameters into equivalent spherical diameters in Embodiment 1 of the present invention;

[0044] Figure 5 It is a fitting effect diagram of a specific example of the fitting curve of precipitation particles calculated in Embodiment 1 of the present invention;

[0045] Figure 6 It is a method block diagram of a specific example of single-particle ratio measurement calibration in Embodiment 1 of the present invention;

[0046] Figure 7 It is a principle block diagram of a specific example of a calibration device for a forward-scattering thin-sheet beam sampling disdrometer in Embodiment 2 of the present invention;

[0047] Figure 8 It is a schematic structural diagram of a specific example of a computer device in Embodiment 3 of the present invention. Specific Embodiments

[0048] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the drawings. Obviously, the described embodiments are some embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0049] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation on the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0050] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and can also be the communication inside two elements. It can be a wireless connection or a wired connection. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0051] In addition, the technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0052] Embodiment 1

[0053] This embodiment provides a calibration method for a forward-diffusing thin-sheet beam sampling disdrometer. This calibration method can be used by a forward-diffusing thin-sheet beam sampling disdrometer to collect the particle diameter and light intensity signal values corresponding to precipitation particles, and a server or other devices can establish an initial mapping function relationship between the particle diameter and the light intensity signal value, and substitute the data collected by the disdrometer for corresponding iterative calculations, determination of the minimum error, and data output, so as to realize the calibration of the mapping relationship between the light intensity signal of the disdrometer and the precipitation particle diameter, as Figure 1 shown, including the following steps:

[0054] Step S101, use the disdrometer to measure the light intensity signal values corresponding to precipitation particles with different particle diameters, and establish a particle size comparison table. Among them, the particle size comparison table includes a set of particle diameters and a set of measured light intensity signal values, and records the one-to-one correspondence between the particle diameter and the measured light intensity signal value.

[0055] In this embodiment, the precipitation particle sampling method of the forward-diffusing thin-sheet beam is mainly adopted. The disdrometer used for sampling mainly includes optical element 11, convex lens 12, Powell prism 13, cylindrical lens 14, photodetector 15, etc., as Figure 2 shown.

[0056] In the schematic diagram of the optical principle, precipitation particles can be assumed to be spherical particles. A thin-sheet beam is generated using optical elements. There are many methods for generating a thin-sheet beam, mainly including cylindrical lenses (wavy lenses), Powell prisms, and diffractive optical elements (DOEs). Since the thin-sheet beam generated by the Powell prism has high uniformity, a uniformity of 95% can be achieved under general processing quality requirements, and a uniformity of 99% can be achieved in the best case. Therefore, in the embodiments of the present invention, a Powell prism is mainly used to generate the thin-sheet beam.

[0057] In the design of a specific forward-scattering thin-sheet beam sampling droplet spectrometer, the thickness and width of the thin-sheet beam can be optimized and set within a conventional range according to factors such as the detection limit, range, and signal and processing characteristics. The parameters of the thin-sheet beam can be determined by the parameter design of the front-end optical elements. The thickness of the thin-sheet beam is determined by the diameter of the output beam. The thickness of the thin-sheet beam is related to the design of precipitation velocity measurement and sampling time window, and the thickness of the thin-sheet beam can usually be set within the range of 0.1 - 0.5 mm. The width of the thin-sheet beam is related to the sampling area of precipitation particles, and the width of the thin-sheet beam can usually be set within the range of 20 - 100 mm.

[0058] The optical element for generating the light source in this embodiment can be selected as a laser diode. The light source output by the laser diode has the characteristics of a Gaussian beam. Therefore, a convex lens is also used before the Powell prism for collimation to improve the beam coupling efficiency. After the Powell prism, a cylindrical lens is used to compress the width of the thin-sheet beam and improve the coupling efficiency. A convex lens is placed in front of the photodetector to improve the coupling efficiency, and the light beam within the entire beam width can be converged and collected on the detector.

[0059] The light beam emitted by the light source passes through the convex lens, Powell prism, and cylindrical lens to generate a thin-sheet beam. The precipitation particles vertically fall through the thin-sheet beam. According to the principles of light refraction and reflection, the light beam passing through the precipitation particles will change in direction and energy distribution. A photodetector is placed at a certain angular position of the thin-sheet beam, and the light power intensity that changes the beam direction to the detector due to the optical effect of the precipitation particles during the process of the precipitation particles passing through the thin-sheet beam can be obtained. In this embodiment, the light power intensity is called the light intensity signal value.

[0060] In this embodiment, a forward-scattering thin-sheet beam sampling disdrometer can be used to measure precipitation particles of different diameters, and the light intensity signal value corresponding to each precipitation particle can be obtained. The particle diameter of each precipitation particle and the light intensity signal value corresponding to the particle diameter of each precipitation particle are both recorded in a particle size comparison table. That is to say, the particle size comparison table not only includes the set of particle diameters measured by the disdrometer and the set of measured light intensity signal values, but also has a one-to-one correspondence between the particle diameter and the light intensity signal value. In this embodiment, the forward-scattering thin-sheet beam sampling disdrometer uses the light intensity time series analysis method to analyze the mapping relationship between the precipitation particle diameter and the light intensity signal value. The light intensity time series analysis has high time resolution. Through the accurate mapping relationship between the precipitation particle diameter and the light intensity signal value obtained in the embodiment of the present invention, more complete and accurate sampling of the precipitation particle diameter can be achieved.

[0061] Step S102: Use the set of particle diameters and the set of light intensity signal values to perform iterative calculations on a pre-established mapping relationship function to determine the calculation parameters of the mapping relationship function that meet the preset convergence conditions, where the mapping relationship function is a relationship function established using the calculation parameters with the light intensity signal value as the independent variable and the particle diameter as the dependent variable.

[0062] During the precipitation process, there is a monotonic proportional relationship between the light intensity signal value received by the photodetector and the precipitation particle diameter, mainly proportional to the contact area between the thin-sheet beam and the precipitation particle. The contact area is approximately an arc-shaped annulus on the spherical surface. When the thickness of the thin-sheet beam is very thin, the contact area can be approximated as an arc on the spherical surface, that is, proportional to the sphere diameter. Through the above principle elaboration, a basic fact can be obtained, that is, under ideal conditions, there is a certain proportional relationship between the light intensity signal value received by the photodetector and the precipitation particle diameter during the precipitation process; however, in actual application, there are certain offsets and errors in the relationship between the light intensity signal value and the precipitation particle diameter, that is, the linear proportional relationship under ideal conditions turns into a curve relationship.

[0063] In this embodiment, a power series polynomial form can be used as the mapping relationship function. Using the one-to-one correspondence between the particle diameter and the light intensity signal value in the set of particle diameters, an intermediate mapping relationship function is calculated and iterative calculations are performed to finally determine the calculation parameters of the mapping relationship function that meet the preset convergence conditions. Specifically, it will be introduced below.

[0064] In this embodiment, not only is the one-to-one correspondence between the particle diameters measured by the droplet spectrometer and the measured light intensity signal values recorded by establishing a particle size comparison table, but also a mapping relationship function is established in advance; iterative calculations are performed using the particle size comparison table to finally determine the calculation parameters of the mapping relationship function that meet the preset convergence conditions; both of these methods are used simultaneously to ensure the accuracy of data, the convenience of data retrieval, and the efficiency of calculating the mapping relationship function when calculating the mapping relationship function.

[0065] As an alternative embodiment, in the embodiment of the present invention, the iterative calculation of the pre-established mapping relationship function using the set of particle diameters and the set of light intensity signal values to determine the calculation parameters of the mapping relationship function that meet the preset convergence conditions includes:

[0066] Step a, establish an initial mapping relationship function and determine the parameter types of the calculation parameters of the initial mapping relationship function.

[0067] As described above, a power series polynomial form can be used as the mapping relationship function. The determination of the initial mapping relationship function can be based on the error situation of the droplet spectrometer. That is, if the relationship error between the particle diameter and the light intensity signal value of the droplet spectrometer is large, a higher order can be selected as the initial mapping relationship function. In this embodiment, the highest order and the coefficients of each order in the power series polynomial can be determined in advance as the calculation parameters, and an initial mapping relationship function is established according to the determined highest order and the coefficients of each order. For example: y = a2x 2 + a1x + a0, y = a3x 3 + a2x 2 + a1x + a0, etc.

[0068] Step b, select multiple sets of training data from the particle size comparison table, substitute them into the initial mapping relationship function, and obtain multiple relationships about the calculation parameters.

[0069] Step c, use the multiple relationships about the calculation parameters to solve for the calculation parameters and obtain an intermediate mapping relationship function.

[0070] In this embodiment, the training data can be the data of the one-to-one correspondence between the particle diameters measured by the droplet spectrometer and the measured light intensity signal values in the particle size comparison table as the training data.

[0071] In this embodiment, taking y = a2x 2 + a1x + a0 as an example, for the quadratic polynomial initial mapping relationship function, at least two sets need to be selected from the particle size comparison table as the training data and substituted into the initial mapping relationship function. For example: y1 = a2x1 2 + a1x1 + a0, y2 = a2x2 2+ a1x^2 + a0, and through simultaneous calculation, the specific values of the coefficients a2, a1, and a0 at each order are obtained, and finally a specific relational expression of the polynomial with respect to the calculation parameter is obtained, that is, the intermediate mapping relationship function.

[0072] Step d, select multiple groups of light intensity data from the particle size comparison table, substitute them into the intermediate mapping relationship function for calculation, and obtain the calculated values of the particle diameters corresponding to the multiple groups of light intensity data, where the multiple groups of light intensity data are different from the light intensity data in the multiple groups of training data.

[0073] As described above, the particle size comparison table includes not only the set of particle diameters measured by the droplet spectrometer but also the set of measured light intensity signal values. The light intensity data can be the corresponding set of light intensity signal values in the particle size comparison table. Selecting multiple groups of light intensity data from the particle size comparison table is equivalent to selecting multiple groups of light intensity signal values and substituting them into the intermediate mapping relationship function for calculation. The calculated values of the particle diameters corresponding to each light intensity signal value are calculated through the intermediate mapping relationship function. Specifically, taking y = a2x 2 + a1x + a0 as an example, where the coefficients a2, a1, and a0 are specific values. Substitute the multiple groups of light intensity data selected from the particle size comparison table, that is, multiple groups of light intensity signal values, into y = a2x 2 + a1x + a0 to calculate the calculated values of the particle diameters corresponding to each light intensity data.

[0074] Step e, calculate the mean square error between the actual particle diameters corresponding to the multiple groups of light intensity data and the calculated values of the particle diameters.

[0075] The actual particle diameters are different from the particle diameters and the calculated values of the particle diameters, and they are different parameters. The actual particle diameters are measured by the droplet spectrometer to obtain precipitation particles with different diameters in step 101, and while obtaining the light intensity signal value corresponding to each precipitation particle, use a high-speed imaging camera to take images of the particles passing through the thin beam of the droplet spectrometer, and adopt the equivalent spherical diameter calculation method to obtain the final actual particle diameters. That is to say, during the measurement by the droplet spectrometer, a high-speed imaging camera is used for shooting simultaneously, which can not only obtain the particle diameters measured by the droplet spectrometer and the light intensity signal values corresponding to each particle diameter, but also obtain the actual particle diameters corresponding to each particle diameter. That is, the same light intensity signal value corresponds to a particle diameter and an actual particle diameter. Perform variance calculation on the same light intensity signal value corresponding to a particle diameter and an actual particle diameter as an error term.

[0076] Furthermore, variance operations are performed on the actual particle diameters corresponding to the multiple sets of light intensity data in the particle diameter comparison table and the calculated values ​​of the particle diameters, respectively, to obtain a corresponding number of error terms, all the error terms are added and averaged, and finally the mean square error of the actual particle diameters corresponding to the multiple sets of light intensity data and the calculated values ​​of the particle diameters is obtained.

[0077] Repeat the above steps a to e until the calculated mean square error is less than a preset threshold, and use the corresponding intermediate mapping relationship function as the final mapping relationship function.

[0078] With y = a2x 2 +a1x+a0 as an example, in the above steps ae, only a set of coefficients a2, a1, a0 are used as the intermediate mapping function. In the final result of the mean square error, there is only one mean square error. According to the only mean square error, it is impossible to determine whether the current coefficients a2, a1, a0 are the best power series coefficients, that is, the corresponding relationship between the final particle diameter and the light intensity signal value determined by the power series function with coefficients a2, a1, a0 may not be accurate enough.

[0079] Therefore, repeat the above steps ae, that is, iteratively determine multiple groups of coefficients a2, a1, a0, that is, multiple groups of intermediate mapping relationship functions, and finally calculate multiple groups of mean square errors again. In the process of determining multiple groups of mean square errors, the a2, a1, a0 coefficients corresponding to the mean square error that is less than the preset threshold or the minimum value, that is, the intermediate mapping relationship function corresponding to the mean square error that is less than the preset threshold or the minimum value can be used as the final mapping relationship function. According to the final mapping relationship function, the light intensity signal value is obtained to calculate the accurate particle diameter. In this embodiment, the light intensity signal and the light intensity signal value refer to the same meaning.

[0080] In this embodiment, by establishing an initial mapping relationship function, the highest order and coefficients of each order in the power polynomial are predetermined as calculation parameters; multiple sets of training data in the particle size comparison table are selected, substituted into the initial mapping relationship function, the calculation parameters are obtained by solving, and an intermediate mapping relationship function is obtained; multiple sets of light intensity data are selected from the particle size comparison table, substituted into the intermediate mapping relationship function for calculation, and the particle diameter calculation values ​​corresponding to the multiple sets of light intensity data are obtained; the mean square error between the actual particle diameter corresponding to the multiple sets of light intensity data and the particle diameter calculation value is calculated; the above steps are repeated until the calculated mean square error is less than the preset threshold value, and finally the corresponding intermediate mapping relationship function is used as the final mapping relationship function. According to the final mapping relationship function, the light intensity signal value can be obtained to calculate the accurate particle diameter. This iterative calculation process does not require other cumbersome and complex logic, which not only reduces the amount of calculation, but also improves the accuracy of the mapping relationship function between the particle diameter and the light intensity signal.

[0081] As an alternative implementation, in the embodiments of the present invention, calculating the mean square error of the actual particle diameters corresponding to the multiple groups of light intensity data and the calculated particle diameter values includes:

[0082] Calculating the error between the actual particle diameter corresponding to each group of light intensity data and the calculated particle diameter value respectively to obtain a plurality of error terms.

[0083] Squaring each of the error terms and then taking the average to obtain the mean square error.

[0084] As described above, performing a variance operation on the same light intensity signal value, that is, the same light intensity data, corresponding to a particle diameter and an actual particle diameter as an error term; performing a variance operation on the actual particle diameter corresponding to each group of light intensity data and the calculated particle diameter value respectively, that is, obtaining a plurality of error terms. Adding all the error terms and taking the average, finally obtaining the mean square error of the actual particle diameters corresponding to the multiple groups of light intensity data and the calculated particle diameter value.

[0085] As an alternative implementation, in the embodiments of the present invention, the mean square error is calculated by the following formula:

[0086]

[0087] Where D eq-im (i, j) is the j-th actual particle diameter of the i-th group, D eq-fit (i, j) is the j-th particle diameter of the i-th group calculated according to the intermediate mapping relationship function, ω j is the weight coefficient, M is the total number of groups of particle diameters, N is the total number of particles in each group, D MS is all the particle diameters in M groups, E S is the cumulative error term, and i and j are constants.

[0088] In this embodiment, when measuring the light intensity signal values corresponding to precipitation particles with different particle diameters by using a disdrometer, the measured particles can be classified in groups. For example: the particles in the first group have the same particle diameter, which can be particles with a particle diameter of 0.5 mm in the first group, particles with a particle diameter of 2 mm in the second group, particles with a particle diameter of 5 mm in the third group, etc. Each particle in each group is numbered, that is, when corresponding to the particle size comparison table, it can be clearly known which particle in the first group and the corresponding particle diameter and light intensity signal value. E S is the cumulative error term, that is, the sum of squared errors, E S (D MS , M, N) represents the final mean square error, that is, the final error.

[0089] In this embodiment, a weight coefficient equal to the reciprocal of the actual particle diameter is added to each summation term, and the sum of squared absolute errors is converted into the sum of squared relative errors. Specifically, ω j The weight coefficient is

[0090] As an alternative embodiment, in the embodiment of the present invention, establishing the initial mapping relationship function and determining the parameter types of the calculation parameters of the initial mapping relationship function include:

[0091] Establishing a power-level polynomial between the light intensity signal value and the particle diameter.

[0092] Determining the highest order and the coefficients of each order in the power-level polynomial as the calculation parameters.

[0093] In an ideal situation, during the precipitation process, there is a certain proportional relationship between the light intensity signal value received by the photoelectric detector and the precipitation particle diameter; however, in the actual application process, there are certain offsets and errors in the relationship between the light intensity signal value and the precipitation particle diameter, that is, the linear proportional relationship in the ideal situation is converted into a curve relationship.

[0094] A power-level polynomial can be used as the mapping relationship function. Therefore, in this embodiment, the highest order and the coefficients of each order in the power-level polynomial can be determined in advance as the calculation parameters, and an initial mapping relationship function can be established according to the determined highest order and the coefficients of each order. For example: y = a2x 2 + a1x + a0, y = a3x 3 + a2x 2 + a1x + a0, etc. Among them, the coefficients a2, a1, a0 can be the calculation parameters in this embodiment.

[0095] As an alternative embodiment, in the embodiment of the present invention, the initial mapping relationship function is:

[0096]

[0097] where y is the calculated value of the particle diameter, x is the light intensity signal value, a k is the power-level coefficient corresponding to the mapping relationship function, and n is the highest order in the power-level polynomial. k can be the order of the power-level coefficient and can only be used to distinguish the power-level coefficients. For example: a1, a2, etc. Among them, a k is the power-level coefficient corresponding to the mapping relationship function of each term in the initial mapping relationship function.

[0098] The determination of the initial mapping relationship function can be made according to the error condition of the disdrometer. That is, if the error between the particle diameter and the light intensity signal value of the disdrometer is large, a higher order can be selected as the initial mapping relationship function.

[0099] As an alternative implementation, in the embodiments of the present invention, the actual particle diameter of the precipitation particles is determined through the following steps:

[0100] Use a high-speed imaging camera to capture images of the particles passing through the disdrometer;

[0101] Obtain the particle diameter using the equivalent spherical diameter calculation method.

[0102] The precipitation particle diameter and the detected light intensity signal value can be achieved through the following several comparison test methods. The measurement of the final precipitation particle diameter should be traced back to the geometric measurement instrument of the high-speed imaging method, and the geometric measurement instrument of the high-speed imaging method is traced back to the national length dimension standard of the metrology institute through the geometric calibration method of the standard target board. The block diagram of the complete traceability chain is as Figure 3 shown.

[0103] The traceability reference method for the precipitation particle diameter is the high-speed imaging measurement method. The precipitation particle diameter is directly measured according to the image. The calibration of the image measurement system in the high-speed imaging device is traced back to the national length standard of the metrology institute through the standard geometric target and the high-precision micrometer. Among them, for the comparative calibration of the high-speed imaging device, it is also necessary to consider the size measurement under the condition of no perspective error and no distortion in high-speed imaging. If not, it is necessary to calibrate the front diffusive thin film sampling sensor of the reference instrument. After calibration to the condition of no perspective error and no distortion, the precipitation particles are photographed again. Further, the camera is calibrated through the standard geometric target, and calculations are performed through image processing and particle diameter measurement, and finally traced back to the national length standard of the metrology institute. This method can ensure the unity of the measured value in the metrology link of determining the relationship function between the precipitation particle diameter and the received light intensity signal value for the same front diffusive thin film sampling disdrometer in different production batches and different manufacturers.

[0104] The following gives the process of how to calculate the precipitation particle diameter according to the image in the high-speed imaging measurement method.

[0105] The precipitation particle diameter is expressed by the equivalent spherical diameter (ESD, Equivalent spherical diameters), and its specific meaning is defined as: for an object with an irregular shape, the diameter of a sphere with the same volume. As Figure 4 shown, the effect of converting the profiles of precipitation particles with different diameters into equivalent spherical diameters is given.

[0106] Specifically, precipitation particles with a diameter of less than 1.5 mm can be approximately considered spherical, and the spherical diameter fitting calculation can be directly carried out. For precipitation particles with a diameter of 1.5 mm - 6 mm, the equivalent spherical diameter needs to be obtained according to the fitting equation. The following is the fitting calculation equation:

[0107]

[0108] where x and y are Cartesian coordinates, and c1, c2, c3, and c4 are fitting parameters.

[0109] The fitting parameters c1, c2, c3, and c4 are used to obtain the average dependence on the equivalent spherical diameter D eq (in millimeters). The following is the equation for the fitting parameters and the equivalent spherical diameter:

[0110]

[0111]

[0112]

[0113]

[0114] c4 = 0 when 1.5 mm ≤ D eq ≤ 4 mm

[0115] As Figure 5 shown, there are the probability profiles and fitting curves of two different diameter particles. Among them, the fitting curve is the curve after fitting the precipitation particles calculated according to the above corresponding formulas. The gray contour area is the probability profile of the precipitation particles, and the black line is the fitting curve calculated according to the formula.

[0116] The spatial probability density contour of the precipitation particle diameter, or the probability profile, is the data directly obtained by the high-speed imaging device. Due to the oscillation when the precipitation particle falls, the probability profile can more effectively reflect the width of the vibration change of the precipitation particle profile.

[0117] For the comparison measurement between the high-speed imaging measurement and the forward-scattering thin-sheet beam sampling disdrometer, the precipitation particle generation conditions are also required. It can be divided into two methods: precipitation simulation and natural precipitation. Natural precipitation is the actual precipitation measurement condition outdoors. Since natural precipitation does not have controllable characteristics, it is generally only used for verification tests. The determination and calibration of the key response characteristic curve of the disdrometer (the function relationship between the particle diameter and the light intensity signal value of the forward-scattering thin-sheet beam sampling disdrometer) need to be realized under the indoor precipitation simulation condition. Three typical precipitation simulation systems are briefly described below.

[0118] The three typical precipitation simulation systems are: the standard droplet system, the surrogate particle system, and the precisely controlled spraying system. The standard droplet system is also known as the standard precipitation particle system. Since it can generate relatively more precise precipitation particle diameters, it is the preferred system for determining the relationship function between the precipitation particle diameter and the light intensity signal value in the sampling of the forward-scattered thin-sheet beam droplet spectrometer, and it is also the precipitation simulation system selected in this embodiment.

[0119] Specifically, for the standard droplet system, pure water and tap water, or other equivalent liquid sources are used to precisely control the diameter of the simulated precipitation particles through physical means. A relatively large precipitation coverage area can be achieved through a multi-channel generation method.

[0120] The main method for controlling the precipitation particle diameter is through aperture control, that is, the precipitation particles are formed when water naturally falls through a fixed pinhole. If the pinhole is a controllable variable-diameter pinhole, the precipitation particle diameter can be relatively adjusted, or electrolytes can be added to the water to assist in the electric field force control of the pinhole. The standard droplet system is suitable for single-particle measurement and can obtain relatively precise control of the droplet particle size.

[0121] For single-particle ratio measurement and calibration, for the same precipitation particle, while sampling with a thin-sheet beam, the contour of the precipitation particle is captured by high-speed imaging in a bypass, and then the equivalent spherical diameter of the precipitation particle is extracted through image processing and recorded in the particle size comparison table. The method block diagram of single-particle ratio measurement and calibration is as Figure 6 shown.

[0122] Single-particle ratio measurement calibration. Precipitation particles are generated using a standard droplet system, and high-speed imaging is used in the bypass to measure the diameter of precipitation particles. Specifically, the generation of precipitation particles is based on the following: the diameter range of liquid precipitation particles is approximately below 5 mm, and the droplet diameter of drizzle is usually less than 0.5 mm. For the synchronous comparison observation calibration of individual precipitation particles, a high-speed camera with a telecentric lens is assembled in the bypass of the forward-scattering thin-sheet beam sampling disdrometer to synchronously capture the falling process of particles. The diameter value of an individual precipitation particle is obtained by image calculation, and the measurement accuracy of the diameter size by the high-speed camera can be traced back to the length dimension through a length standard. Further, the measurement method can adopt two-point calibration or three-point calibration. When the requirement for measurement accuracy is relatively low, assuming that the relationship between the precipitation particle diameter and the detected light intensity signal value is a stable linear relationship, simple two-point calibration can be adopted, that is, the upper and lower limits are determined, and precipitation particles with two diameters, including D = 0.5 mm and D = 5 mm, are sufficient. When the requirement for measurement accuracy is relatively high, assuming that the relationship between the precipitation particle diameter and the detected light intensity signal value has a certain degree of non-linearity, in addition to the necessary upper and lower limit values in the two-point calibration, several necessary intermediate calibration points are selected at the common numerical ends in the conventional measurement. For example, precipitation particles with various diameters such as 1 mm, 2 mm, and 5 mm. When precipitation particles are generated in the standard droplet system, assuming there are M groups of particle diameters, and the particle diameters in each group are different (M >= 2, and the cases of D = 0.5 and D = 5 must be included), and each group includes N particles (N >= 20), the particle size comparison table data is actually composed of M × N data.

[0123] Example 2

[0124] This embodiment provides a calibration device for a forward-scattering thin-sheet beam sampling disdrometer. This device can be used to execute the calibration method of the forward-scattering thin-sheet beam sampling disdrometer in the above-mentioned Example 1. This device can be set inside a server or other equipment, and the modules cooperate with each other to realize the calibration of the mapping relationship between the light intensity signal value of the disdrometer and the precipitation particle diameter, as Figure 7 shown, this device includes:

[0125] A recording module 201, which is used to measure the light intensity signal values corresponding to precipitation particles with different particle diameters by using the disdrometer and establish a particle size comparison table. Among them, the particle size comparison table includes a set of particle diameters and a set of measured light intensity signal values, and records the one-to-one correspondence between the particle diameter and the measured light intensity signal value;

[0126] A calculation module 202 is configured to perform iterative calculations on a pre-established mapping relationship function by using the set of particle diameters and the set of light intensity signal values, so as to determine calculation parameters of the mapping relationship function that meet a preset convergence condition. The mapping relationship function is a relationship function established by using the calculation parameters, with the light intensity signal value as the independent variable and the particle diameter as the dependent variable.

[0127] In this embodiment, a particle size comparison table is established to record the one-to-one correspondence between the particle diameters measured by the droplet spectrometer and the measured light intensity signal values. A pre-established mapping relationship function is used to perform iterative calculations by using the particle size comparison table, and finally the calculation parameters of the mapping relationship function that meet the preset convergence condition are determined. The use of these two methods simultaneously ensures the accuracy of the data, the convenience of data retrieval, and the efficiency of calculating the mapping relationship function when calculating the mapping relationship function.

[0128] For the specific description of the above device part, reference can be made to the above method embodiment, which will not be elaborated here.

[0129] Embodiment 3

[0130] This embodiment provides a computer device, as Figure 8 shown. The computer device includes a processor 301 and a memory 302. The processor 301 and the memory 302 can be connected through a bus or other means. Figure 8 Taking the connection through the bus as an example.

[0131] The processor 301 can be a central processing unit (CPU). The processor 301 can also be other general-purpose processors, digital signal processors (DSPs), graphics processing units (GPUs), embedded neural network processors (NPUs), or other dedicated deep learning coprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or a combination of the above types of chips.

[0132] The memory 302, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the calibration method of the forward-scattering thin-sheet beam sampling droplet spectrometer in the embodiments of the present invention. The corresponding program instructions / modules. The processor 301 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 302, that is, to implement the calibration method of the forward-scattering thin-sheet beam sampling droplet spectrometer in the above method embodiments.

[0133] The memory 302 may further include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created by the processor 301 and the like. In addition, the memory 302 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 302 may optionally include a memory remotely disposed relative to the processor 301, and these remote memories can be connected to the processor 301 through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0134] One or more modules are stored in the memory 302, and when executed by the processor 301, they execute the calibration method of the forward-scattering thin-sheet beam sampling droplet spectrometer in the embodiments as Figure 1 shown.

[0135] For specific details of the above computer device, reference can be made to Figure 1 the corresponding related descriptions and effects in the shown embodiments for understanding, and details are not described herein again.

[0136] The embodiments of the present invention further provide a computer-readable storage medium, and the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions can execute the calibration method of the forward-scattering thin-sheet beam sampling droplet spectrometer in any of the above embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviation: HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0137] Obviously, the above embodiments are merely examples for clear illustration and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or variations derived therefrom are still within the protection scope of the present invention.

Claims

1. A calibration method for a forward-diverging thin-sheet beam sampling droplet spectrometer, characterized in that, Including the following steps: Measuring the light intensity signal values corresponding to precipitation particles with different particle diameters by using a disdrometer, and establishing a particle size comparison table. The particle size comparison table includes a set of particle diameters and a set of measured light intensity signal values, and records the one-to-one correspondence between the particle diameter and the measured light intensity signal value; Iteratively calculating the pre-established mapping relationship function by using the set of particle diameters and the set of light intensity signal values to determine the calculation parameters of the mapping relationship function that meet the preset convergence conditions. The mapping relationship function is a relationship function established by using the calculation parameters with the light intensity signal value as the independent variable and the particle diameter as the dependent variable; The step of iteratively calculating the pre-established mapping relationship function by using the set of particle diameters and the set of light intensity signal values to determine the calculation parameters of the mapping relationship function that meet the preset convergence conditions includes: Step a, establishing an initial mapping relationship function and determining the parameter types of the calculation parameters of the initial mapping relationship function; Step b, selecting multiple groups of training data from the particle size comparison table and substituting them into the initial mapping relationship function to obtain multiple relational expressions about the calculation parameters; Step c, using the multiple relational expressions about the calculation parameters to solve for the calculation parameters to obtain an intermediate mapping relationship function; Step d, selecting multiple groups of light intensity data from the particle size comparison table and substituting them into the intermediate mapping relationship function for calculation to obtain the calculated particle diameter values corresponding to the multiple groups of light intensity data. The multiple groups of light intensity data are light intensity data different from the multiple groups of training data; Step e, calculating the mean square error between the actual particle diameters corresponding to the multiple groups of light intensity data and the calculated particle diameter values; Repeating the above steps a to e until the calculated mean square error is less than the preset threshold, and taking the corresponding intermediate mapping relationship function as the final mapping relationship function; The actual particle diameter of the precipitation particles is determined through the following steps: Using a high-speed imaging camera to take images of particles passing through the disdrometer; Adopting an equivalent spherical diameter calculation method to obtain the particle diameter; Among them, precipitation particles below 1.5 mm are approximately spherical, and direct spherical diameter fitting calculation is carried out. The equivalent spherical diameter of 1.5 mm - 6 mm needs to be calculated according to the fitting equation. The following is the fitting calculation equation: where x and y are Cartesian coordinates, and c1, c2, c3, and c4 are fitting parameters. The fitting parameters c1, c2, c3, and c4 are used to obtain the average dependence on the equivalent spherical diameter D eq The following are the equations for the fitting parameters and the equivalent spherical diameter:

2. The method for calibrating a forward-dispersed thin-sheet beam sampling droplet spectrometer according to claim 1, wherein The calculation of the mean square error between the actual particle diameters corresponding to the multiple groups of light intensity data and the calculated particle diameter values includes: Respectively calculating the errors between the actual particle diameters corresponding to each group of light intensity data and the calculated particle diameter values to obtain multiple error terms; Respectively squaring the error terms and then taking the average value to obtain the mean square error.

3. The calibration method of the forward-dispersed thin-sheet beam sampling droplet spectrometer according to claim 1, wherein The mean square error is calculated through the following formula: Among them, D eq-im (i, j) is the j-th actual particle diameter in the i-th group, D eq-fit (i, j) is the j-th particle diameter in the i-th group calculated according to the intermediate mapping relation function, ω j is the weight coefficient, M is the total number of groups of particle diameters, N is the total number of particles in each group, D MS is all the particle diameters in M groups, E S is the cumulative error term, and i and j are constants.

4. The calibration method of the forward-diverging thin-sheet beam sampling droplet spectrometer according to claim 1, wherein The step of establishing an initial mapping relationship function and determining the parameter types of the calculation parameters of the initial mapping relationship function includes: Establishing a power series polynomial between the light intensity signal value and the particle diameter; Determining the highest order and the coefficients of each order in the power series polynomial as the calculation parameters.

5. The method for calibrating a forward-dispersed thin-sheet beam sampling droplet spectrometer according to claim 4, wherein The initial mapping relationship function is: Among them, y is the calculated value of the particle diameter, x is the light intensity signal value, and a k is the power series coefficient corresponding to the mapping relation function, and n is the highest order in the power series polynomial.

6. A calibration device for a forward-diverging thin-sheet beam sampling droplet spectrometer, characterized in that, Including: A recording module, configured to measure light intensity signal values corresponding to precipitation particles with different particle diameters by using a disdrometer, and establish a particle size comparison table, wherein the particle size comparison table includes a set of particle diameters and a set of measured light intensity signal values, and records the one-to-one correspondence between the particle diameters and the measured light intensity signal values; A calculation module, configured to perform iterative calculation on a pre-established mapping relation function by using the set of particle diameters and the set of light intensity signal values to determine calculation parameters of the mapping relation function that meet a preset convergence condition, wherein the mapping relation function is a relation function established by using the calculation parameters with the light intensity signal value as the independent variable and the particle diameter as the dependent variable; wherein, the performing iterative calculation on the pre-established mapping relation function by using the set of particle diameters and the set of light intensity signal values to determine calculation parameters of the mapping relation function that meet a preset convergence condition includes: Step a, establish an initial mapping relation function, and determine the parameter types of the calculation parameters of the initial mapping relation function; Step b, select multiple groups of training data from the particle size comparison table, substitute them into the initial mapping relation function, and obtain multiple relational expressions about the calculation parameters; Step c, use the multiple relational expressions about the calculation parameters to solve for the calculation parameters and obtain an intermediate mapping relation function; Step d, select multiple groups of light intensity data from the particle size comparison table, substitute them into the intermediate mapping relation function for calculation, and obtain calculated particle diameter values corresponding to the multiple groups of light intensity data, wherein the multiple groups of light intensity data are light intensity data different from the multiple groups of training data; Step e, calculate the mean square error between the actual particle diameters corresponding to the multiple groups of light intensity data and the calculated particle diameter values; Repeat the above steps a to e until the calculated mean square error is less than a preset threshold, and use the corresponding intermediate mapping relation function as the final mapping relation function; The actual particle diameter of the precipitation particles is determined through the following steps: Use a high-speed imaging camera to take images of particles passing through the disdrometer; Adopt an equivalent spherical diameter calculation method to obtain the particle diameter; Wherein, precipitation particles with a diameter of less than 1.5 mm are approximated as spherical, and direct spherical diameter fitting calculation is performed. The equivalent spherical diameter of 1.5 mm - 6 mm needs to be calculated according to a fitting equation. The following is the fitting calculation equation: where x and y are Cartesian coordinates, and c1, c2, c3, and c4 are fitting parameters, and the fitting parameters c1, c2, c3, and c4 are used to obtain the average dependence on the equivalent spherical diameter D eq The following are the equations for the fitting parameters and the equivalent spherical diameter:

7. A computer device, characterized in that, Including: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the calibration method for the forward-scattering thin-sheet beam sampling disdrometer according to any one of claims 1 - 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the calibration method for the forward-scattering thin-sheet beam sampling disdrometer according to any one of claims 1 - 5.

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