Laser wind measurement observation data quality evaluation method

Abstract

The invention relates to the technical field of meteorological observation data quality evaluation, and discloses a laser wind measurement observation data quality evaluation method, which analyzes the reliability of standard observation data and predicts the reasonable threshold range of the standard observation data in a mode of constructing an error analysis model. Taking the frequency of the standard observation data exceeding a reasonable threshold range as a reliability analysis result, then deriving a distribution range of the laser wind measurement observation data in a normal environment by utilizing the reliability analysis result and combining an Arrhenius equation, and performing reliability evaluation on the real-time observation data collected in a laser wind measurement mode according to the distribution range; therefore, the accuracy and reliability of observation data are improved, and a solid foundation is provided for research in the fields of meteorology, atmospheric physics, wind energy and the like.

Description

Technical Field

[0001] The invention relates to the technical field of meteorological observation data quality assessment, in particular to a laser wind measurement observation data quality assessment method. Background Art

[0002] As an advanced meteorological observation method, laser wind measurement can measure wind speed and direction in real time and with high precision, providing important data support for research in fields such as meteorology, atmospheric physics and wind energy. In order to ensure the quality of observation data, technicians need to evaluate the quality of data observed by laser wind measurement instruments, thereby providing effective support for research in fields such as meteorology, atmospheric physics and wind energy.

[0003] In order to promote the continuous improvement of meteorological observation data quality and accurately, comprehensively and objectively evaluate the quality of meteorological observation data, a person skilled in the art has disclosed a patent for a meteorological observation data quality assessment method, the publication number of which is: CN115018284A; the patent determines the type of meteorological observation to determine the assessment items for assessing the quality of meteorological observation data, obtains the assessment parameters corresponding to the assessment items, and calculates the assessment parameters to determine the assessment data corresponding to the observation type. This enables accurate and effective judgment of the meteorological assessment quality and ensures the reliability of the meteorological assessment results.

[0004] However, the above evaluation method is not ideal for the evaluation of observation data. The above evaluation method mainly determines the reliability of observation data by calculating the set evaluation parameters. This method is overly dependent on the setting of evaluation parameters, and the setting of evaluation parameters is too idealized and cannot be compatible with the various influencing factors that may exist in the actual observation process, which leads to obvious errors in the data. Therefore, those skilled in the art need a real-time observation data quality evaluation method that can fully consider the various influencing factors in the actual observation process. Summary of the invention

[0005] The purpose of the present invention is to solve the above problems and to design a method for evaluating the quality of laser wind observation data.

[0006] The technical solution of the present invention to achieve the above-mentioned purpose is a method for evaluating the quality of laser wind observation data, which comprises: Collect standard observation data, and collect standard observation data of the wind field by laser wind measurement without external interference. It should be noted that the conditions without external interference include but are not limited to: air density, temperature and humidity; Construct error analysis model: According to standard observation data and based on Wiener process, establish error analysis model, and use maximum likelihood estimation method to obtain unknown parameters of error analysis model; Analyze the reliability of standard observation data: use the error analysis model to predict the reasonable threshold range of standard observation data, and use the frequency of standard observation data exceeding the reasonable threshold range as the reliability analysis result; Reliability assessment of observation data: The reliability analysis results are used in combination with the Arrhenius equation to derive the distribution range of laser wind measurement observation data under normal conditions. Based on this distribution range, the reliability of the real-time observation data collected by laser wind measurement is assessed. The longer the real-time observation data exceeds the distribution range, the lower the reliability of the real-time observation data.

[0007] The normal environment refers to the environment in which the laser wind measuring device is deployed in actual observations, including but not limited to: cities, mountains, jungles, hills and deserts.

[0008] The construction process of the error analysis model is as follows: The univariate linear Wiener model is constructed using standard observation data as the feature quantity: (1); Where: t is the acquisition time of the standard observation data; λ is the drift parameter; σ is the diffusion parameter; W(t) is the standard Brownian motion; X(t) is the change of the standard observation data at time t, which has an expectation of λt and a variance of σ 2 Normal distribution of t, that is, X(t)-N(λt,σ 2 t); Let x ef is the change in the standard observation data of the e-th standard observation data at the f-th moment, where: e=1,2,…,u; j=1,2,…,v; x ef Subject to the expectation λΔt and variance σ 2 The normal distribution of Δt, that is, x ef -N(λΔt,σ 2 Δt); use the maximum likelihood estimation method to make point estimates of the drift parameter λ and the diffusion parameter σ, and the corresponding maximum likelihood estimation function is: (2); Taking the logarithm of formula (2) yields: (3); The maximum likelihood estimates of the parameters λ and α in equation (3) are obtained by taking partial derivatives: (4); The reliability analysis result is defined as: (5); Where V is a reasonable threshold for standard observation data; From equations (4) and (5), we can get the probability density function of the standard observation data: (6); Reliability is defined as the probability that the standard observation data exceeds the reasonable threshold range within the specified time and under the specified conditions, that is, (7); Substituting formula (6) into formula (7), the reliability function is obtained as follows: (8); From the above formulas, the expectation and variance of the reliability analysis result T can be obtained as follows: (9).

[0009] The analysis process of the reliability of the standard observation data includes: From formula (4), we can get the parameters λ and σ. Substituting parameters such as λ and σ into formula (6) and formula (8), we can get the reasonable threshold range of the standard observation data. The reliability is determined by the frequency of the standard observation data exceeding the reasonable threshold range.

[0010] The reliability assessment process includes: Assume that the expression of the Arrhenius model is: (10); Where: M is the distribution range of laser wind measurement observation data under normal environment; k is the Boltzmann constant; T is the reliability analysis result; A0 is a constant; t is the observation time; ΔE is the factor coefficient that affects the distribution of standard observation data, which is determined by the type and correlation of the influencing factors, including but not limited to: the parameters of the laser wind measurement instrument, the response speed and the error of the instrument itself; ∂M / ∂t represents the rate of change of the distribution range of laser wind measurement observation data under normal environment; Integrating both sides of equation (10), we get: (11); Let ΔM=M2-M1, and define L as the length of time that the real-time observation data exceeds the distribution range of the laser wind observation data from t1 to t2, and let L=t2-t1, then: Taking the logarithm of both sides of equation (11) we get: (12); According to formula (12), the duration that the real-time observation data exceeds the distribution range of the laser wind measurement observation data under normal conditions is obtained, and the size of this duration is used to evaluate the reliability of the real-time observation data.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention predicts the reasonable threshold range of standard observation data by constructing an error analysis model, and then uses the Arrhenius equation to deduce the distribution range of laser wind measurement observation data under normal conditions, and performs reliability evaluation on real-time observation data collected by laser wind measurement based on the distribution range, thereby improving the accuracy and reliability of the observation data, effectively eliminating the errors in the laser wind radar observation data, and thus better meeting the needs of meteorological observation work; 2. The method adopted in the present invention evaluates the reliability of real-time observation data by establishing a corresponding relationship between standard observation data and actual measurements, thereby minimizing the impact of external factors on real-time observation data and improving the accuracy of reliability analysis of real-time observation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of a method for evaluating the quality of laser wind observation data according to the present invention; Figure 2 is a distribution diagram of laser wind measurement observation data under normal environment described in the present invention; Figure 3 It is a reliability curve diagram of the real-time observation data of the present invention. DETAILED DESCRIPTION

[0013] The present invention will be described in detail below in conjunction with the accompanying drawings. Figure 1-3 As shown; The innovation of the present application is that the reliability of standard observation data is analyzed and a reasonable threshold range of the standard observation data is predicted through a constructed error analysis model, the frequency of the standard observation data exceeding the reasonable threshold range is taken as the reliability analysis result, and then the reliability analysis result is used in combination with the Arrhenius equation to derive the distribution range of laser wind measurement observation data under normal conditions, and the reliability of real-time observation data collected by laser wind measurement is evaluated based on the distribution range.

[0014] In the technical solution of this application, the construction process of the error analysis model is: The univariate linear Wiener model is constructed using standard observation data as the feature quantity: (1); Where: t is the acquisition time of the standard observation data; λ is the drift parameter; σ is the diffusion parameter; W(t) is the standard Brownian motion; X(t) is the change of the standard observation data at time t, which has an expectation of λt and a variance of σ 2 Normal distribution of t, that is, X(t)-N(λt,σ 2 t); Let xef is the change in the standard observation data of the e-th standard observation data at the f-th moment, where: e=1,2,…,u; j=1,2,…,v; x ef Subject to the expectation λΔt and variance σ 2 The normal distribution of Δt, that is, x ef -N(λΔt,σ 2 Δt); use the maximum likelihood estimation method to make point estimates of the drift parameter λ and the diffusion parameter σ, and the corresponding maximum likelihood estimation function is: (2); Taking the logarithm of formula (2) yields: (3); The maximum likelihood estimates of the parameters λ and α in equation (3) are obtained by taking partial derivatives: (4); The reliability analysis result is defined as: (5); Where V is a reasonable threshold for standard observation data; From equations (4) and (5), we can get the probability density function of the standard observation data: (6); Reliability is defined as the probability that the standard observation data exceeds the reasonable threshold range within the specified time and under the specified conditions, that is, (7); Substituting formula (6) into formula (7), the reliability function is obtained as follows: (8); From the above formulas, the expectation and variance of the reliability analysis result T can be obtained as follows: (9).

[0015] It should be noted that the analysis process of the reliability of standard observation data includes: From formula (4), we can get the parameters λ and σ. Substituting parameters such as λ and σ into formula (6) and formula (8), we can get the reasonable threshold range of the standard observation data. The reliability is determined by the frequency of the standard observation data exceeding the reasonable threshold range.

[0016] The reliability assessment process includes: Assume that the expression of the Arrhenius model is: (10); Where: M is the distribution range of laser wind measurement observation data under normal environment; k is the Boltzmann constant; T is the reliability analysis result; A0 is a constant; t is the observation time; ΔE is the factor coefficient that affects the distribution of standard observation data, which is determined by the type and correlation of the influencing factors, including but not limited to: the parameters of the laser wind measurement instrument, the response speed and the error of the instrument itself; ∂M / ∂t represents the rate of change of the distribution range of laser wind measurement observation data under normal environment; Integrating both sides of equation (10), we get: (11); Let ΔM=M2-M1, and define L as the length of time that the real-time observation data exceeds the distribution range of the laser wind observation data from t1 to t2, and let L=t2-t1, then: Taking the logarithm of both sides of equation (11) we get: (12); According to formula (12), the duration that the real-time observation data exceeds the distribution range of the laser wind measurement observation data under normal conditions is obtained, and the size of this duration is used to evaluate the reliability of the real-time observation data.

[0017] In the specific implementation process, ln(ΔM / A0) and ΔE / k are both measurement constants, and the value of ln(ΔM / A0) can be negative. Then the reliability analysis result of the time length L is determined by T. Substituting the measurement constant into formula (12) yields: ; From the above formula, we can see that the duration represented by L is determined by the change of T.

[0018] The above technical solutions only reflect the preferred technical solutions of the technical solutions of the present invention. Some changes that may be made to certain parts thereof by technicians in this technical field all reflect the principles of the present invention and fall within the protection scope of the present invention.

Claims

1. A method for evaluating the quality of laser wind observation data, characterized in that: The method includes: Collect standard observation data, collect standard observation data of wind field by laser wind measurement without external interference; Construct an error analysis model, establish the error analysis model based on standard observation data and Wiener process, and obtain the unknown parameters of the error analysis model; Analyze the reliability of standard observation data, use the error analysis model to predict the reasonable threshold range of standard observation data, and use the frequency of standard observation data exceeding the reasonable threshold range as the reliability analysis result; Reliability assessment of observation data: The reliability analysis results are used in combination with the Arrhenius equation to derive the distribution range of laser wind measurement observation data under normal conditions, and the reliability of real-time observation data collected by laser wind measurement is assessed based on this distribution range.

2. A laser wind measurement observation data quality assessment method according to claim 1, characterized in that: The construction process of the error analysis model is as follows: The univariate linear Wiener model is constructed using standard observation data as the feature quantity: （1）； Where: t is the acquisition time of the standard observation data; λ is the drift parameter; σ is the diffusion parameter; W(t) is the standard Brownian motion; X(t) is the change of the standard observation data at time t, which has an expectation of λt and a variance of σ 2 Normal distribution of t, that is, X(t)-N(λt,σ 2 t); Let x ef is the change in the standard observation data of the e-th standard observation data at the f-th moment, where: e=1,2,…,u; j=1,2,…,v; x ef Subject to the expectation of λΔt and the variance of σ 2 The normal distribution of Δt, that is, x ef -N(λΔt,σ 2 Δt); use the maximum likelihood estimation method to make point estimates of the drift parameter λ and the diffusion parameter σ, and the corresponding maximum likelihood estimation function is: （2）； Taking the logarithm of formula (2) yields: （3）； The maximum likelihood estimates of the parameters λ and α in equation (3) are obtained by taking partial derivatives: （4）； The reliability analysis result is defined as: （5）； Where V is a reasonable threshold for standard observation data; From equations (4) and (5), we can get the probability density function of the standard observation data: （6）； Reliability is defined as the probability that the standard observation data exceeds the reasonable threshold range within the specified time and under the specified conditions, that is, （7）； Substituting formula (6) into formula (7), the reliability function is obtained as follows: （8）； From the above formulas, the expectation and variance of the reliability analysis result T can be obtained as follows: （9）。 3. A laser wind measurement observation data quality assessment method according to claim 2, characterized in that: The analysis process of the reliability of the standard observation data includes: From formula (4), we can get the parameters λ and σ. Substituting parameters such as λ and σ into formula (6) and formula (8), we can get the reasonable threshold range of the standard observation data. The reliability is determined by the frequency of the standard observation data exceeding the reasonable threshold range.

4. A laser wind measurement observation data quality assessment method according to claim 3, characterized in that: The reliability assessment process includes: Assume that the expression of the Arrhenius equation is: （10）； Where: M is the distribution range of laser wind observation data under normal environment; k is the Boltzmann constant; T is the reliability analysis result; A0 is a constant; t is the observation time; ΔE is the coefficient of factors affecting the distribution of standard observation data; ∂M / ∂t represents the rate of change of the distribution range of laser wind observation data under normal environment; Integrating both sides of equation (10), we get: （11）； Let ΔM = M2-M1, and define L as the length of time that the real-time observation data exceeds the distribution range of the laser wind measurement observation data during the time period from t1 to t2, and let L = t2-t1, then: Taking the logarithm of both sides of equation (11) we get: （12）； According to formula (12), the duration that the real-time observation data exceeds the distribution range of the laser wind measurement observation data under normal conditions is obtained, and the size of this duration is used to evaluate the reliability of the real-time observation data.

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