Method for correcting pyranometer temperature drift based on non-invasive temperature measurement

Abstract

The application discloses a pyranometer temperature drift correction method based on non-invasive temperature measurement and belongs to the technical field of pyranometer measurement. In order to solve the problems of optimizing the accuracy of temperature measurement and the reliability of solar radiation data, a reference resistor is connected with a signal output end of a pyranometer by using a data collector to form a half-bridge circuit, so as to collect the internal temperature of the pyranometer; a thermocouple is connected with the data collector, and the thermocouple is installed at the lower part of a dome of the pyranometer, so as to collect the external temperature of the pyranometer; based on the collected internal temperature of the pyranometer and the collected external temperature of the pyranometer, a long-wave radiation table is used to perform temperature drift correction, so as to obtain a correction value of zero-point drift; based on the correction value of the zero-point drift, the fact that an observation field generally has an environmental temperature is considered, and a pyranometer temperature drift correction model based on non-invasive temperature measurement is constructed. The application avoids the interference on the normal function of the pyranometer, and meanwhile, the long-term observation reliability of data is enhanced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of pyrheliometer measurement, and particularly relates to a pyrheliometer temperature drift correction method based on non-invasive temperature measurement. BACKGROUND

[0002] The pyrheliometers on the current market are mainly used for measuring solar radiation, and the core technical features thereof generally include direct sunlight measurement, diffuse radiation capture and photoelectric sensor use. These devices generally use thermoelectric pile sensors to detect radiation intensity, and since the thermoelectric pile generates a stray current for all thermal radiation, zero point drift is caused by environmental factors such as temperature change and humidity. The design of most traditional pyrheliometers does not consider temperature correction, and therefore has certain limitations in long-time observation and data accuracy.

[0003] In order to study the influence of temperature drift on pyrheliometer observation values, some temperature drift correction experimental methods have been proposed, which are divided into invasive and non-invasive methods according to whether the internal structure of the pyrheliometer is changed. The invasive method includes installing a thermistor or a barometer in the pyrheliometer, and the non-invasive method includes comparing the difference between the pyrheliometer and a reference pyrheliometer with negligible thermal drift and performing a capping experiment. First, the experimental test method: the invasive correction method requires installing additional sensors (such as thermistors or barometers) in the pyrheliometer, which not only changes the internal structure of the pyrheliometer, but also may affect its normal observation function. This method can only be used for short-term experiments and is not suitable for long-term application; the non-invasive method, such as using a reference pyrheliometer for comparison or performing a capping experiment, does not change the structure of the device, but these methods usually require additional equipment and complex experimental settings, which are difficult to implement in long-term practical applications. In addition, these methods may not maintain consistent correction effects under all environmental conditions. Second, the experimental condition limitation: many existing correction methods can only be used under specific experimental conditions and are difficult to adapt to various field environments. This limits the universal applicability of these technologies in different application scenarios.

[0004] Therefore, the above methods usually can only be used in experimental conditions or short-term measurements in the field due to the change of the internal structure of the pyrheliometer and the influence on normal observation, and cannot meet the needs of long-term experiments. An experimental method that is simple and does not affect radiation observation should be used to study the temperature drift of the radiation meter. SUMMARY

[0005] The problem to be solved by the present application is to optimize the accuracy of temperature measurement and the reliability of solar radiation data, and to propose a pyrheliometer temperature drift correction method based on non-invasive temperature measurement.

[0006] To achieve the above purpose, the present application realizes the following technical solutions:

[0007] A pyrheliometer temperature drift correction method based on non-invasive temperature measurement, comprising the following steps:

[0008] S1. A data collector is connected to a reference resistance at the output end of the pyrheliometer signal to form a half-bridge circuit, and the internal temperature of the pyrheliometer is collected;

[0009] S2. A data collector is connected to a thermocouple, and the thermocouple is installed at the lower part of the dome of the pyrheliometer to collect the external temperature of the pyrheliometer;

[0010] S3. Based on the internal temperature of the pyrheliometer collected in step S1 and the external temperature of the pyrheliometer collected in step S2, a long-wave radiation meter is used for temperature drift correction to obtain a correction value of zero-point drift;

[0011] S4. Based on the correction value of zero-point drift obtained in step S3, the environmental temperature of the observation field is considered, and a pyrheliometer temperature drift correction model based on non-invasive temperature measurement is constructed.

[0012] Further, in step S1, the reference resistance is connected to the thermistor inside the pyrheliometer, and the data collector is used to collect the voltage V0 of the reference resistance, the voltage V1 of the circuit composed of the reference resistance and the thermistor inside the pyrheliometer, and the voltage V2 of the two-terminal voltage divider, respectively. s The voltage V1 of the circuit composed of the reference resistance and the thermistor inside the pyrheliometer is calculated. x The voltage V1 of the circuit composed of the reference resistance and the thermistor inside the pyrheliometer is calculated.

[0013]

[0014] Wherein, R f is the reference resistance, and R s is the thermistor inside the pyrheliometer.

[0015] Then we get:

[0016]

[0017] Through X and R f , R s is calculated, and then the internal temperature T ref of the pyrheliometer is converted according to the empirical formula, and the calculation formula is:

[0018] T ref = (α + [β·(ln(R s ))+γ·(ln(R s )) 3 ]) -1

[0019] Wherein, α, β, γ are the first parameter, the second parameter and the third parameter in the empirical formula, respectively.

[0020] Further, the reference resistance in step S1 is 10kΩ, and the thermistor inside the pyrheliometer is Pt-100 or 10kΩ thermistor.

[0021] Further, the thermistor inside the CMP 22 is measured in step S1.

[0022] Further, the thermocouple is installed at the lower 1 / 3 of the pyrheliometer dome in step S2, and the monitoring is performed for 8 hours from 21:00 after sunset to 5:00 of the next morning every day.

[0023] The differential voltage signal of the compensation end of the thermocouple is received by the data collector, and the expression is:

[0024]

[0025] Wherein, E is the voltage difference of the compensation end received by the data collector, T dome is the pyrheliometer shell temperature, T log is the compensated collector temperature at the data collector end, which is measured inside the data collector, and k is the Seebeck coefficient of the thermocouple.

[0026] The two reference voltages of the compensation end of the data collector are V a and V b , and the pyrheliometer shell temperature is calculated by the compensation end voltage and the compensated collector temperature:

[0027]

[0028] Further, the model of the data collector in step S2 is CR3000.

[0029] Further, the specific implementation method of step S3 is:

[0030] The long wave is observed by using the long wave radiometer, and the calculation formula is:

[0031]

[0032] Wherein, NetIR is the long wave irradiance, S is the sensitivity coefficient, U efm is the voltage measured by the long wave radiometer.

[0033] The relationship between the total radiation power in the thermocouple inside the radiometer and the temperature T LW 4 in the long wave table is:

[0034] P=σT LW 4

[0035] Wherein, P is the total radiant energy per unit area, σ is the Stefan-Boltzmann constant, is 5.67x10 -8 W / m 2 K 4 , T LW 4 is the long-wave table temperature, in Kelvin;

[0036] The empirical relationship expression obtained by multiple linear regression analysis is:

[0037] OS = b0 + b1NetIR + b2σ(T dome -T ref )

[0038] Wherein, OS is the correction value of zero drift, b0, b1, b2 are the 0th regression coefficient, the 1st regression coefficient, and the 2nd regression coefficient, respectively.

[0039] Further, the model used in the multiple linear regression analysis in step S3 is represented by a vector and a matrix as follows:

[0040] Y = Xβ + ∈

[0041] Wherein, Y is the fitting result of the regression method with n x 1 dimension, X is the design matrix with n x (p+1) dimension, wherein the first column is all 1, and the following columns are the independent variable columns, β is the estimated vector of parameters, and ∈ is the error vector with n x 1 dimension;

[0042] The objective of the least squares method is to minimize, which is expressed as:

[0043] S = (Y - Xβ) T (Y - Xβ)

[0044] Wherein, S is the variance of the corresponding points on the y-axis between the fitting result and the original result;

[0045] The derivative of β is taken and set to zero to obtain the solution of the least squares method, which is expressed as:

[0046] β = (X T X) -1 X T Y.

[0047] Further, the specific implementation method of step S4 is to consider that the observation field generally has an environmental temperature T for measurement, and to convert the empirical formula into a pyranometer temperature drift correction model based on non-intrusive temperature measurement:

[0048] OS = b0 + b1NetIR + b2σT;

[0049] The 0th regression coefficient, the 1st regression coefficient, and the 2nd regression coefficient are obtained, and the irradiance is corrected, and the correction formula is:

[0050] R corrected = R measured -OS

[0051] wherein, R corrected is the corrected irradiance measurement value, R measured is the measured irradiance observation value.

[0052] The beneficial effects of the present application are:

[0053] The solar radiation meter temperature drift correction method based on non-invasive temperature measurement of the present application realizes the improvement of data accuracy and stability. Through non-invasive temperature measurement and zero drift correction mechanism, the accuracy and stability of solar radiation data are significantly improved. Compared with the traditional correction method which needs to modify the internal structure of the equipment (such as installing a thermistor), this scheme maintains the integrity of the equipment, avoids the interference to the normal function of the solar radiation meter, and at the same time enhances the long-term observation reliability of the data.

[0054] The solar radiation meter temperature drift correction method based on non-invasive temperature measurement of the present application realizes the enhancement of application flexibility and universality. Unlike the existing method which can only be used under specific experimental conditions, the present application technology is suitable for various field environments. Through a simple method without affecting radiation observation, temperature drift is studied, and combined with various sensors and signal processing algorithms, the adaptability to various field conditions is realized, which has wide application potential in many fields such as agriculture, meteorology, building, energy and so on.

[0055] The solar radiation meter temperature drift correction method based on non-invasive temperature measurement of the present application realizes the integration of equipment and the convenience of operation. Non-invasive measurement and intelligent signal correction technology are adopted, without the need for additional complex equipment and experimental settings, and can be seamlessly integrated into existing sensor networks. This not only simplifies the operation process, but also greatly improves the operation convenience and efficiency of users due to the real-time data correction capability, which is suitable for long-term and continuous environmental observation applications. These advantages directly show the superiority and efficiency of the present application in actual operation, which has great market competitiveness. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The flowchart of the solar radiation meter temperature drift correction method based on non-invasive temperature measurement of the present application is shown in the figure;

[0057] Figure 2 The hardware connection relationship diagram of the solar radiation meter temperature drift correction method based on non-invasive temperature measurement of the present application is shown in the figure;

[0058] Figure 3The night zero drift correction curve diagram of the solar radiation meter of the present application, (a) is the fitting curve of the measured data and the corrected data of the first solar radiation meter on March 26, (b) is the fitting curve of the measured data and the corrected data of the first solar radiation meter on March 27, (c) is the fitting curve of the measured data and the corrected data of the first solar radiation meter on March 28, (d) is the fitting curve of the measured data and the corrected data of the first solar radiation meter on March 29, (e) is the fitting curve of the measured data and the corrected data of the second solar radiation meter on March 26, (f) is the fitting curve of the measured data and the corrected data of the second solar radiation meter on March 27, (g) is the fitting curve of the measured data and the corrected data of the second solar radiation meter on March 28, and (h) is the fitting curve of the measured data and the corrected data of the second solar radiation meter on March 29. DETAILED DESCRIPTION

[0059] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application, that is, the described specific embodiments are only a part of the embodiments of the present application, but not all the specific embodiments. The components of the specific embodiments of the present application generally described and shown in the drawings herein can be arranged and designed in various different configurations, and the present application can also have other embodiments.

[0060] Therefore, the following detailed description of the specific embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected specific embodiments of the present application. All other specific embodiments obtained by those skilled in the art based on the specific embodiments of the present application without making creative efforts are within the scope of protection of the present application.

[0061] In order to further understand the inventive content, characteristics and effects of the present application, the following specific embodiments are exemplified, and the drawings are attached Figure 1 -ATTACHMENT Figure 3 The detailed description is as follows:

[0062] Example 1:

[0063] A solar radiation meter temperature drift correction method based on non-invasive temperature measurement, comprising the following steps:

[0064] S1. A data collector is used to connect a reference resistance at the output end of the solar radiation meter to form a half-bridge circuit, and the internal temperature of the solar radiation meter is collected.

[0065] Further, the reference resistance in step S1 is connected to the thermistor inside the solar radiation meter, and the voltage drop V s The voltage drop V x, the reference resistance and the thermistor inside the pyrheliometer, to obtain a voltage V1, and solve a voltage proportion coefficient X, and the calculation formula is as follows:

[0066]

[0067] wherein R f is the reference resistance, R s is the thermistor inside the pyrheliometer;

[0068] Then, the following is obtained:

[0069]

[0070] Through X and R f , R s is calculated, and then the internal temperature T ref of the pyrheliometer is converted according to an empirical formula, and the calculation formula is as follows:

[0071] T ref = (α + [β·(ln(R s ))+γ·(ln(R s )) 3 ] ) -1

[0072] wherein α, β and γ are respectively a first parameter, a second parameter and a third parameter in the empirical formula.

[0073] Further, α = 1.03×10 -3 , β = 2.38×10 -4 , and γ = 1.59×10 -7 .

[0074] Further, the reference resistance in step S1 is 10kΩ, and the thermistor inside the pyrheliometer is a Pt-100 or a 10kΩ thermistor.

[0075] Further, the thermistor inside the CMP 22 is measured in step S1.

[0076] S2. A data collector is used to connect a thermocouple installed at the lower part of the dome of the pyrheliometer to collect the external temperature of the pyrheliometer.

[0077] Further, the thermocouple is installed at the lower part of the dome of the pyrheliometer in step S2, and the thermocouple is installed at 1 / 3 of the lower part of the dome, and the monitoring is performed continuously for 8 hours from 21:00 after sunset to 5:00 of the next morning every day.

[0078] The differential voltage signal of the compensation end of the thermocouple is received by the data collector, and the expression is as follows:

[0079]

[0080] wherein E is the voltage difference of the compensation end received by the data collector, T dome is the pyrheliometer housing temperature, T log is the compensated collector temperature at the data collector end, measured internally by the data collector, and k is the Seebeck coefficient of the thermocouple;

[0081] The two reference voltages at the compensation end of the data collector are V a and V b The pyrheliometer housing temperature is calculated by the compensation end voltage and the compensated collector temperature:

[0082]

[0083] Further, the model of the data collector in step S2 is CR3000.

[0084] In order to verify the influence of temperature on zero drift, the internal temperature of the pyrheliometer and the temperature of the quartz dome need to be monitored during the experiment. The temperature-sensitive resistance provided by the manufacturer of the equipment can be used to monitor the internal temperature changes. The external quartz dome temperature is measured by pasting a thermocouple. Considering that the quartz dome will block the radiation observation during the day, the thermocouple is pasted at 21 o'clock after sunset every day and removed at 5 o'clock in the morning of the next day, and the monitoring is continued for 8 hours.

[0085] The temperature measurement of the pyrheliometer quartz dome needs to be accurate and minimize the impact on the pyrheliometer and the local environment. Thermocouples are a common temperature sensor based on the thermoelectric effect, widely used in contact temperature measurement. Its working principle relies on the voltage difference generated between two different conductors under temperature difference, which is proportional to temperature change. The thermocouple is composed of a hot electrode, a protective tube and an insulating sleeve, among which the hot electrode is the main temperature measurement component, composed of two different metal wires, connected at one end called the working end, and the other end is the compensation end. In the measurement process, the working end is in close contact with the measured object, and the temperature difference between the working end and the compensation end can be obtained by measuring the voltage difference between the compensation ends, and the actual temperature of the working end is the reference temperature of the compensation end. Wide measurement range, short response time and simple use, suitable for complex field measurement.

[0086] S3. Based on the internal temperature of the pyrheliometer collected in step S1 and the external temperature of the pyrheliometer collected in step S2, the long-wave radiation meter is used for temperature drift correction to obtain the correction value of zero drift;

[0087] Further, the specific implementation method of step S3 is:

[0088] The long-wave radiation meter is used to observe the long-wave, and the calculation formula is:

[0089]

[0090] Where NetIR is long wave irradiance, S is the sensitivity coefficient, U efm is the voltage measured by the long wave pyranometer;

[0091] The relationship between the total radiant power in the thermopile inside the pyranometer and the temperature T LW 4 in the long wave pyranometer is:

[0092] P = σT LW 4

[0093] Where P is the total radiant energy per unit area, σ is the Stefan-Boltzmann constant, which is 5.67 x 10 -8 W / m 2 K 4 , T LW 4 is the temperature in the long wave pyranometer, in Kelvin;

[0094] Further, the correction of the zero drift should be normalized to the daily observations, so that the overall data is corrected, and the radiation correction parameters should be obtained from the daily observation data. The long wave pyranometer that is consistent with the pyranometer observation and has the same waveform trend should be selected as the correction parameter to provide the trend component of the correction parameter. In addition, due to the strong influence of the heating system on the pyrheliometer, the measurement of the internal and external temperatures should be added.

[0095] The empirical relationship expression obtained by multiple linear regression analysis is:

[0096] OS = b0 + b1NetIR + b2σ(T dome -T ref )

[0097] Where OS is the correction value of the zero drift, b0, b1, and b2 are the 0th, 1st, and 2nd regression coefficients, respectively.

[0098] Further, the model used in the multiple linear regression analysis in step S3 is represented by a vector and a matrix as follows:

[0099] Y = Xβ + ∈

[0100] Where Y is the fitting result of the regression method with n x 1 dimensions, X is the design matrix with n x (p + 1) dimensions, the first column is all 1, and the following columns are the independent variable columns, β is the estimated vector of the parameters, and ∈ is the error vector with n x 1 dimensions;

[0101] The objective of the least squares method is to minimize, which is expressed as:

[0102] S = (Y - Xβ)T (Y-Xβ)

[0103] Wherein, S is the variance of the corresponding points of the fitting result and the original result on the y-axis;

[0104] Derivation of β and setting the derivative to zero, the solution of the least squares method is obtained, the expression is:

[0105] β=(X T X) -1 X T Y.

[0106] S4. Based on the correction value of the zero point drift obtained in step S3, considering the environmental temperature of the observation field, a pyranometer temperature drift correction model based on non-intrusive temperature measurement is constructed.

[0107] Further, the specific implementation method of step S4 is to consider that the observation field is generally measured with environmental temperature T, and the empirical formula is converted into a pyranometer temperature drift correction model based on non-intrusive temperature measurement:

[0108] OS=b0+b1NetIR+b2σT;

[0109] Get the 0th regression coefficient, the 1st regression coefficient, and the 2nd regression coefficient, correct the irradiance, and the correction formula is:

[0110] R corrected =R measured -OS

[0111] Wherein, R corrected is the corrected irradiance measurement value, and R measured is the measured irradiance observation value.

[0112] In order to reflect the correction effect of this method, a four-day comparative experiment was carried out in Qiqihar Fuyu County Meteorological Bureau from March 26 to 29, 2024. The measurement data of two pyranometers with consistent working conditions were corrected according to the above method, and the obtained correction coefficients are shown in Table 1, and the effect is shown in Figure 3

[0113] Table 1 Surrounding environmental conditions and HV system operating conditions during observation (105m away from the observation field)

[0114]

[0115] Figure 3 The fitting degree of the measurement data and the corrected data of the two pyranometers during March 26 to 29 is shown in Figure 3. The chart is arranged in two groups from top to bottom, and each group contains four subgraphs. The vertical coordinate represents the radiation value (unit: W / m 2 ​), with the horizontal axis representing time (14:00 to 21:00). Each subplot compares the "Measured" (gray) and "Corrected" (blue) data, where "Measured" represents the measured data and "Corrected" represents the corrected data. The first row shows data from the first pyranometer (a-d), and the second row shows data from the second pyranometer (e-h). The plots show that there are zero-point drift fluctuations in the radiation values during these dates and time periods. The corrected data closely matches the measured data curves, indicating that the correction improves the accuracy of the data. Measured1 is the measured value of the first pyranometer, and Corrected1 is the corrected value of the first pyranometer. The second row shows the measured and corrected values of the second pyranometer. In the plots (a) and (e), the fitting parameters are generally good due to precipitation on March 26, and the fitting parameters of the two pyranometers for the following three days are very good, completely fitting the trend and volatility of the measured values. The regression curve after correction has a higher degree of fitting with the observed data points, indicating that the correction model successfully captures the internal structure and trend of the data. The corrected regression model not only improves the accuracy of radiation observation but also enhances the robustness of the model to outliers, demonstrating more stable performance.

[0116] This embodiment has broad application potential in various fields. In meteorological monitoring, this technology can be applied to solar radiation measurement at weather stations, providing accurate and stable radiation data to support weather forecasting and climate research. Its non-invasive measurement method reduces interference with equipment, and the zero-point drift correction function improves the reliability of long-term data collection. In addition, in the field of agriculture, this invention can monitor the amount of solar radiation in farmland, helping to optimize irrigation and fertilization plans, thereby improving crop yield and resource utilization efficiency.

[0117] In the renewable energy industry, this technology helps to evaluate the efficiency of photovoltaic panels in solar power stations by providing accurate radiation data to optimize solar power generation processes. In the field of architecture and urban planning, it also contributes to the analysis of building sunlight, helping to design energy-saving buildings to reduce energy consumption. For environmental science research, it provides high-precision and stable data support for scientific experiments on atmospheric composition and climate change. In the field of aerospace, the non-invasive design of this pyranometer is particularly suitable for lightweight and high-precision applications, such as radiation measurement in satellite and unmanned aerial vehicle earth observation.

[0118] In industrial process control, the application can effectively monitor solar radiation in industrial production processes, helping to optimize process parameters to improve production efficiency and product quality, while reducing energy consumption. Specific application methods include integrating the technology into existing weather sensor networks to enhance data collection capabilities, and combining with Internet of Things technology to achieve remote monitoring and data analysis, providing real-time feedback and early warning for users. In addition, the development of supporting data correction software can automatically correct zero drift, improving the accuracy of measurement data. The design of portable measurement equipment makes it suitable for on-site rapid measurement and emergency applications, expanding its practical application scenarios. These diverse applications show that the application not only promotes technological progress in multiple industries, but also has important significance for achieving sustainable development.

[0119] It should be noted that the relational terms herein, such as first and second, and the like, are used solely to distinguish one from another entity or action, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0120] Although the present application has been described above with reference to specific embodiments, various modifications and changes can be made thereto without departing from the scope of the application. In particular, features of the specific embodiments disclosed herein can be combined with each other, unless there are structural conflicts, and the combinations are not exhaustively described in the specification, which is merely for the purpose of omitting and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A pyrheliometer temperature drift correction method based on non-invasive temperature measurement, characterized by, Comprise the following steps: S1. Using a data collector to connect a reference resistance at the output end of the pyrheliometer signal to form a half-bridge circuit, collect the internal temperature of the pyrheliometer; S2. Using a data collector to connect a thermocouple, the thermocouple is installed at the lower part of the pyrheliometer dome, and the external temperature of the pyrheliometer is collected; S3. Based on the internal temperature of the pyrheliometer collected in step S1, the external temperature of the pyrheliometer collected in step S2, and the temperature drift correction of the long-wave radiation table, the correction value of the zero drift is obtained; The specific implementation method of step S3 is: Use the long-wave radiation table to observe the long-wave, and the calculation formula is: where NetIR is the long-wave irradiance, S is the sensitivity factor, U efm is the voltage measured by the long-wave radiometer. The relationship between the total radiant power in the thermopile of the radiometer and the temperature T in the long-wave meter is: LW 4 ​ P = σT LW 4 Wherein, P is the total radiant energy per unit area, σ is the Stefan-Boltzmann constant, which is 5.67x10 -8 W / m 2 K 4 , T LW 4 is the long-wave table temperature in Kelvin. Through multiple linear regression analysis, the empirical relationship expression is obtained: OS = b0 + b1 NetIR + b2 σ(T dome - T ref ) Wherein, OS is the correction value of zero point drift, b0, b1, b2 are respectively the 0th regression coefficient, the 1st regression coefficient, the 2nd regression coefficient; T ref is the empirical formula converted into the internal temperature of the pyrheliometer; T dome is the shell temperature of the pyrheliometer; The model setting in the multiple linear regression analysis in step S3 is represented by a vector and a matrix: Y=Xβ+∈ Where Y is an n×1 dimensional regression method fitting result, X is an n×(p+1) design matrix, the first column is all 1, and the following is the independent variable column, β is the parameter estimation vector, and ∈ is an n×1 error vector; The goal of the least squares method is to minimize, and the expression is: S = (Y - Xβ) T (Y - Xβ) Where S is the variance of the corresponding point on the y-axis of the fitting result and the original result; Take the derivative of β and set the derivative to zero to get the solution of the least squares method, and the expression is: β = (X T X) -1 X T Y; S4. Based on the correction value of the zero drift obtained in step S3, consider the environmental temperature of the observation field, and construct a pyrheliometer temperature drift correction model based on non-intrusive temperature measurement; The specific implementation method of step S4 is to consider that the observation field generally has an environmental temperature T for measurement, and convert the empirical formula into a pyrheliometer temperature drift correction model based on non-intrusive temperature measurement: OS=b0+b1NetIR+b2σT; Get the 0th regression coefficient, the 1st regression coefficient, and the 2nd regression coefficient, and correct the irradiance, and the correction formula is: R corrected = R measured -OS wherein R corrected is the corrected irradiance measurement, R measured is the measured irradiance observation.

2. The method for pyrheliometer temperature drift correction based on non-invasive temperature measurement according to claim 1, characterized in that, The reference resistance connects the thermistor inside the pyrheliometer in step S1, and the thermistor R s The two-end voltage V x The circuit voltage V1 composed of the reference resistance and the thermistor is calculated, and the voltage proportionality coefficient X is solved, and the calculation formula is as follows: wherein R f is a reference resistance, R s is a thermistor inside the pyrheliometer; Then get: By X and R f R is calculated s The internal temperature T of the pyrheliometer is converted from the above-mentioned temperature T according to an empirical formula ref The calculation formula is: T ref = (a + [b • (ln(R s ))+ g • (ln(R s ) )) 3 ]) -1 Where α, β, and γ are the first parameter, the second parameter, and the third parameter in the empirical formula, respectively.

3. The method for pyranometer temperature drift correction based on non-invasive temperature measurement according to claim 2, characterized in that, The reference resistance in step S1 is 10kΩ, and the thermistor inside the pyrheliometer is Pt-100 or 10kΩ thermistor.

4. The method for pyranometer temperature drift correction based on non-invasive temperature measurement according to claim 3, characterized in that, The thermistor inside the CMP22 is measured in step S1.

5. The pyrheliometer temperature drift correction method based on non-invasive temperature measurement according to claim 4, characterized in that, The thermocouple in step S2 is installed at the lower 1 / 3 of the pyrheliometer dome, and the thermocouple is installed at the lower 1 / 3 of the pyrheliometer dome. Monitor for 8 hours continuously from 21:00 after sunset to 5:00 the next morning every day; The differential voltage signal of the thermocouple compensation end is received by the data collector, and the expression is: Wherein, E is the voltage difference of the compensation end received by the data collector, T dome is the shell temperature of the pyrheliometer, T log is the compensated collector temperature at the data collector end, measured internally by the data collector, and k is the Seebeck coefficient of the thermocouple; The two reference voltages at the compensation terminals of the data acquisition unit are V a and V b The pyranometer housing temperature is calculated by the compensation terminal voltage and the compensated acquisition unit temperature:

6. The method for pyranometer temperature drift correction based on non-intrusive temperature measurement according to claim 5, characterized in that, The model of the data collector in step S2 is CR3000.

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