Sunscope temperature drift correction method based on non-intrusive temperature measurement

Abstract

The invention discloses a heliometer temperature drift correction method based on non-intrusive temperature measurement, and belongs to the technical field of heliometer measurement. In order to optimize the accuracy of temperature measurement and the reliability of solar radiation data, a data collector is connected with a reference resistor at the signal output end of a heliometer to form a half-bridge circuit, and the internal temperature of the heliometer is collected; a data collector is connected with a thermocouple, and the thermocouple is installed on the lower portion of a dome of the heliometer and collects the external temperature of the heliometer. Based on the collected internal temperature of the heliometer and the collected external temperature of the heliometer, performing temperature drift correction by using a long-wave radiometer to obtain a correction value of zero drift; based on a correction value of zero drift, an observation field generally has an environment temperature, and a heliometer temperature drift correction model based on non-intrusive temperature measurement is constructed. According to the invention, the interference to the normal function of the heliometer is avoided, and the long-term observation reliability of data is enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of pyranometer measurement, and in particular relates to a pyranometer temperature drift correction method based on non-invasive temperature measurement. Background Art

[0002] The pyranometers currently on the market are mainly used to measure solar radiation. Their core technical features usually include the measurement of direct sunlight, the capture of diffuse radiation, and the use of photoelectric sensors. These devices generally use thermopile sensors to detect radiation intensity. Since the thermopile generates stray currents for all thermal radiation, it will cause zero drift due to environmental factors (such as temperature changes, humidity, etc.). Most traditional pyranometers are not designed with temperature correction in mind, so they have certain limitations in terms of long-term observation and data accuracy.

[0003] In order to study the impact of temperature drift on the observed values ​​of the pyranometer, some temperature drift correction experimental methods have been proposed one after another. They are divided into two types: invasive and non-invasive, depending on whether the experiment will change the internal structure of the pyranometer. The invasive method includes installing a thermistor or a barometer in the pyranometer, and the non-invasive method includes comparing the difference between the pyranometer and a reference pyranometer with negligible thermal offset and performing a capping experiment. First, the experimental test method: the invasive correction method requires the installation of additional sensors (such as thermistors or barometers) in the pyranometer, which not only changes the internal structure of the pyranometer, but may also affect its normal observation function. Such methods can usually only be used for short-term experiments and are not suitable for long-term applications; non-invasive methods, such as using a reference pyranometer for comparison or capping experiments, do not change the structure of the equipment, but these methods usually require additional equipment and complex experimental settings, which are difficult to implement in practical applications for a long time. In addition, these methods may not be able to maintain consistent correction effects under all environmental conditions. Secondly, there are experimental condition limitations: many existing correction methods can only be used under specific experimental conditions and are difficult to adapt to various field environments. This limits the general applicability of these technologies in different application scenarios.

[0004] Therefore, the above methods can only be used for short-term measurements in experimental conditions or on-site due to changes in the internal structure of the pyranometer and the impact on normal observations, and cannot meet the needs of long-term experiments. A simple experimental method that does not affect radiation observations should be used to study the temperature offset of the pyranometer. Summary of the invention

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

[0006] To achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement comprises the following steps:

[0008] S1. Use a data logger to connect a reference resistor to the signal output end of the pyranometer to form a half-bridge circuit and collect the internal temperature of the pyranometer;

[0009] S2. A data logger is connected to a thermocouple installed at the bottom of the pyranometer dome to collect the external temperature of the pyranometer;

[0010] S3. Based on the internal temperature of the pyranometer collected in step S1 and the external temperature of the pyranometer collected in step S2, a temperature drift correction is performed using a longwave pyranometer to obtain a correction value for the zero drift;

[0011] S4. Based on the correction value of the zero drift obtained in step S3 and taking into account the ambient temperature of the observation field, a temperature drift correction model of the pyranometer based on non-invasive temperature measurement is constructed.

[0012] Furthermore, in step S1, the reference resistor is connected to the thermistor inside the pyranometer, and the data logger collects the values ​​of R s The voltage V x , the circuit voltage V1 composed of the reference resistor and the thermistor inside the pyranometer, solve the voltage proportional coefficient X, the calculation formula is as follows:

[0013]

[0014] Among them, R f is the reference resistor, R s It is the thermistor inside the pyranometer;

[0015] Then we get:

[0016]

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

[0018] T ref =(α+[β·(ln(R s ))+γ·(ln(R s )) 3 ]) -1 -273.15

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

[0020] Furthermore, the reference resistor in step S1 is 10 kΩ, and the thermistor inside the pyranometer is Pt-100 or a 10 kΩ thermistor.

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

[0022] Furthermore, the thermocouple described in step S2 is installed at the lower 1 / 3 of the dome of the pyranometer, and monitoring is performed continuously for 8 hours from 21:00 after sunset to 5:00 a.m. the next day;

[0023] The differential voltage signal at the thermocouple compensation end is received by the data logger and is expressed as:

[0024] T dome =kE+T log

[0025] Where E is the voltage difference at the compensation end received by the data logger, T dome is the pyranometer case temperature, T log is the compensated collector temperature at the data collector end, measured inside the data collector, and k is the Seebeck coefficient of the thermocouple;

[0026] The two reference voltages at the compensation end of the data logger are V a and V b , calculate the pyranometer housing temperature through the compensation terminal voltage and the compensated collector temperature:

[0027] T dome = k(V a -V b )+T log .

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

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

[0030] The long-wave radiation meter is used to observe the long-wave, and the calculation formula is:

[0031]

[0032] Where NetIR is the long-wave irradiance, S is the sensitivity coefficient, and U efm is the voltage measured by the longwave radiation meter;

[0033] The total radiant power in the thermopile inside the pyrheometer is related to the temperature T inside the pyrheometer. LW 4 The relationship between them is:

[0034] P=σT LW4

[0035] Where P is the total radiation energy per unit area and σ is the Stefan Boltzmann constant, which is 5.67×10 -8 W / m 2 K, T LW 4 is the temperature inside the long wave table in Kelvin;

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

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

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

[0039] Furthermore, in step S3, the model set in the multiple linear regression analysis is represented by vectors and matrices as follows:

[0040] Y=Xβ+∈

[0041] Where Y is the fitting result of the n×1 dimensional regression method, X is the n×(p+1) design matrix, where the first column is all 1s, followed by the independent variable columns, β is the estimated vector of parameters, and ∈ is the n×1 error vector;

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

[0043] S=(Y-Xβ) T (Y-Xβ)

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

[0045] Taking the derivative of β and setting the derivative to zero, we get the least squares solution, expressed as:

[0046] β=(X T X) -1 X T Y.

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

[0050] R corrected =R measured -OS

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

[0052] Beneficial effects of the present invention:

[0053] The present invention discloses a method for correcting the temperature drift of a pyranometer based on non-invasive temperature measurement, which improves the accuracy and stability of data. The accuracy and stability of solar radiation data are significantly improved through non-invasive temperature measurement and zero drift correction mechanisms. Compared with the traditional correction method that requires modifying the internal structure of the device (such as installing a thermistor), this solution maintains the integrity of the device, avoids interference with the normal function of the pyranometer, and enhances the long-term observation reliability of the data.

[0054] The temperature drift correction method of a pyranometer based on non-invasive temperature measurement described in the present invention realizes the enhancement of application flexibility and extensiveness. Unlike the existing methods that can only be used under specific experimental conditions, the technology of the present invention is applicable to a variety of field environments. The temperature drift is studied by a simple method that does not affect the radiation observation, and the adaptability to various field conditions is achieved by combining a variety of sensors and signal processing algorithms, so that it has a wide range of application potentials in agriculture, meteorology, construction, energy and other fields.

[0055] The temperature drift correction method of a pyranometer based on non-invasive temperature measurement described in the present invention is convenient for equipment integration and operation. It adopts non-invasive measurement and intelligent signal correction technology, does not require additional complex equipment and experimental settings, and can be seamlessly integrated into the existing sensor network. This not only simplifies the operation process, but its real-time data correction capability also greatly improves the user's operational convenience and efficiency, and is suitable for long-term and continuous environmental observation applications. These advantages directly demonstrate the superiority and efficiency of the invention in actual operation, and it is highly competitive in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of a method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to the present invention;

[0057] Figure 2 A schematic diagram of hardware connection relationship of a method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to the present invention;

[0058] Figure 3 The nighttime zero drift correction curve diagram of the pyranometer of the present invention, (a) is the fitting curve of the measurement data and correction data of the first pyranometer on March 26, (b) is the fitting curve of the measurement data and correction data of the first pyranometer on March 27, (c) is the fitting curve of the measurement data and correction data of the first pyranometer on March 28, (d) is the fitting curve of the measurement data and correction data of the first pyranometer on March 29, (e) is the fitting curve of the measurement data and correction data of the second pyranometer on March 26, (f) is the fitting curve of the measurement data and correction data of the second pyranometer on March 27, (g) is the fitting curve of the measurement data and correction data of the second pyranometer on March 28, and (h) is the fitting curve of the measurement data and correction data of the second pyranometer on March 29. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0060] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected specific embodiments of the present invention. Based on the specific embodiments of the present invention, all other specific embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0061] In order to further understand the content, features and effects of the present invention, the following specific implementation methods are given as examples, and the attached Figure 1 -Attached Figure 3 The detailed instructions are as follows:

[0062] Embodiment 1:

[0063] A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement comprises the following steps:

[0064] S1. Use a data logger to connect a reference resistor to the signal output end of the pyranometer to form a half-bridge circuit and collect the internal temperature of the pyranometer;

[0065] Furthermore, in step S1, the reference resistor is connected to the thermistor inside the pyranometer, and the data logger collects the values ​​of R s The voltage Vx , the circuit voltage V1 composed of the reference resistor and the thermistor inside the pyranometer, solve the voltage proportional coefficient X, the calculation formula is as follows:

[0066]

[0067] Among them, R f is the reference resistor, R s It is the thermistor inside the pyranometer;

[0068] Then we get:

[0069]

[0070] By X and R f Calculate R s , and then converted to the internal temperature T of the pyranometer according to the empirical formula ref , the calculation formula is:

[0071] T ref =(α+[β·(ln(R s ))+γ·(ln(R s )) 3 ]) -1 -273.15

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

[0073] Furthermore, α = 1.03 × 10 -3 , β=2.38×10 -4 ,γ=1.59×10 -7 ;

[0074] Furthermore, the reference resistor in step S1 is 10 kΩ, and the thermistor inside the pyranometer is Pt-100 or a 10 kΩ thermistor.

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

[0076] S2. A data logger is connected to a thermocouple installed at the bottom of the pyranometer dome to collect the external temperature of the pyranometer;

[0077] Furthermore, the thermocouple described in step S2 is installed at the lower 1 / 3 of the dome of the pyranometer, and monitoring is performed continuously for 8 hours from 21:00 after sunset to 5:00 a.m. the next day;

[0078] The differential voltage signal at the thermocouple compensation end is received by the data logger and is expressed as:

[0079] T dome=kE+T log

[0080] Where E is the voltage difference at the compensation end received by the data logger, T dome is the pyranometer case temperature, T log is the compensated collector temperature at the data collector end, measured inside the data collector, and k is the Seebeck coefficient of the thermocouple;

[0081] The two reference voltages at the compensation end of the data logger are V a and V b , calculate the pyranometer housing temperature through the compensation terminal voltage and the compensated collector temperature:

[0082] T dome = k(V a -V b )+T log .

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

[0084] In order to verify the effect of temperature on zero drift, the internal temperature and quartz dome temperature of the pyranometer need to be monitored during the experiment. The temperature-sensitive resistor that comes with the device can be used to monitor the internal temperature changes. The external quartz dome temperature is measured by pasting thermocouples. Considering that the quartz dome is blocked and will affect the radiation observation during the day, the thermocouples are pasted at 21:00 every day after sunset and removed at 5:00 the next morning for 8 hours of continuous monitoring.

[0085] The temperature measurement of the quartz dome of the pyranometer needs to minimize the obstruction of the pyranometer and the impact on the local environment while being accurate. Thermocouple is a common temperature sensor based on the thermoelectric effect, which is widely used in contact temperature measurement. Its working principle relies on the voltage difference generated between two different conductors under temperature difference conditions, and this difference is proportional to the temperature change. Thermocouple consists of a thermocouple, a protective tube and an insulating sleeve, among which the thermocouple is the main temperature measurement component, which consists of two different metal wires, one end of which is called the working end and the other end is the compensation end. During the measurement process, the working end is in close contact with the object to be measured. By measuring the voltage difference between the compensation ends, the temperature difference between the working end and the compensation end can be obtained, and the reference temperature of the compensation end is added to obtain the actual temperature of the working end. It has the characteristics of wide temperature measurement range, short response time and simple and easy use, which is suitable for field measurement in complex environments.

[0086] S3. Based on the internal temperature of the pyranometer collected in step S1 and the external temperature of the pyranometer collected in step S2, a temperature drift correction is performed using a longwave pyranometer to obtain a correction value for the zero drift;

[0087] Furthermore, 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 the long-wave irradiance, S is the sensitivity coefficient, and U efm is the voltage measured by the longwave radiation meter;

[0091] The total radiant power in the thermopile inside the long-wave pyrheometer is related to the temperature T inside the long-wave pyrheometer. LW 4 The relationship between them is:

[0092] P=σT LW 4

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

[0094] Furthermore, considering that the correction of zero drift should be normalized into daily observations to correct the overall data, the radiation correction parameters should be obtained from the daily observation data. A long-wave meter that is consistent with the observation of the pyranometer and has the same waveform trend should be selected as the correction parameter to provide the trend component of the correction parameter. In addition, since the heating system has a drastic effect on the amplitude of the pyranometer, the measurement of internal and external temperatures should be added.

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

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

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

[0098] Furthermore, in step S3, the model set in the multiple linear regression analysis is represented by vectors and matrices as follows:

[0099] Y=Xβ+∈

[0100] Where Y is the fitting result of the n×1 dimensional regression method, X is the n×(p+1) design matrix, where the first column is all 1s, followed by the independent variable columns, β is the estimated vector of parameters, and ∈ is the n×1 error vector;

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

[0102] S=(Y-Xβ) T (Y-Xβ)

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

[0104] Taking the derivative of β and setting the derivative to zero, we get the least squares solution, expressed as:

[0105] β=(X T X) -1 X T Y.

[0106] S4. Based on the correction value of the zero drift obtained in step S3 and taking into account the ambient temperature of the observation field, a temperature drift correction model of the pyranometer based on non-invasive temperature measurement is constructed.

[0107] Furthermore, the specific implementation method of step S4 is to consider that the observation field is generally measured at an ambient temperature T, and convert the empirical formula into a pyranometer temperature drift correction model based on non-invasive temperature measurement:

[0108] OS=b0+b1NetIR+b2σT;

[0109] The 0th regression coefficient, the 1st regression coefficient, and the 2nd regression coefficient are obtained, and the irradiance is corrected. The correction formula is:

[0110] R corrected =R measured -OS

[0111] Among them, R corrected is the corrected irradiance measurement, R measured is the measured irradiance observation value.

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

[0113] Table 1 Surrounding environment conditions and HV system operating conditions during the observation period (105 m from the observation site)

[0114]

[0115] Figure 3 The figure shows the fit between the measured and corrected data of the two pyranometers from March 26 to 29. The chart is arranged in two groups, each containing four sub-graphs. The vertical axis represents the radiation value (in W / m2) and the horizontal axis represents the time (14:00 to 21:00). Each sub-graph compares the "Measured" (gray) and "Corrected" (blue) data, where "Measured" represents the measured data and "Corrected" represents the corrected data. The first row is the data of the first pyranometer (ad) and the second row is the data of the second pyranometer (eh). The figure shows that there are zero-point drift fluctuations in the radiation values ​​during these dates and time periods. The corrected data basically coincides with the measured data curve, indicating that the correction improves the accuracy of the data. Measured1 is the measured value of the first pyranometer, Corrected1 is the corrected value of the first pyranometer, and the second row is the measured and corrected values ​​of the second pyranometer. In the figure (a) and (e), the fitting parameters are average due to precipitation on March 26. The parameter fitting of the two pyranometers in the following three days is very good, which can fully fit the trend and volatility of the measured values. The corrected regression curve has a higher degree of fit with the observed data points, indicating that the corrected model successfully captures the intrinsic structure and trend of the data. The corrected regression model not only improves the accuracy of radiation observations, but also enhances the robustness of the model to outliers, showing more stable performance.

[0116] This implementation has broad application potential in a variety of fields. In meteorological monitoring, this technology can be applied to solar radiation measurement in meteorological stations to provide accurate and stable radiation data to support weather forecasting and climate research. Its non-invasive measurement method reduces interference with equipment, while the zero drift correction function improves the reliability of long-term data collection. In addition, in the agricultural field, the invention can monitor the amount of solar radiation in farmland, help optimize irrigation and fertilization plans, and thus improve crop yields and resource utilization efficiency.

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

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

[0119] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0120] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space 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 method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement, characterized in that: The steps include: S1. Use a data logger to connect a reference resistor to the signal output end of the pyranometer to form a half-bridge circuit and collect the internal temperature of the pyranometer; S2. A data logger is connected to a thermocouple installed at the bottom of the pyranometer dome to collect the external temperature of the pyranometer; S3. Based on the internal temperature of the pyranometer collected in step S1 and the external temperature of the pyranometer collected in step S2, a temperature drift correction is performed using a longwave pyranometer to obtain a correction value for the zero drift; S4. Based on the correction value of the zero drift obtained in step S3 and taking into account the ambient temperature of the observation field, a temperature drift correction model of the pyranometer based on non-invasive temperature measurement is constructed.

2. A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to claim 1, characterized in that: In step S1, the reference resistor is connected to the thermistor inside the pyranometer, and the data collected by the data collector are respectively s The voltage V x The circuit voltage V1 composed of the reference resistor and thermistor is calculated by solving the voltage proportional coefficient X. The calculation formula is as follows: Among them, R f is the reference resistor, R s It is the thermistor inside the pyranometer; Then we get: By X and R f Calculate R s , and then converted to the internal temperature T of the pyranometer according to the empirical formula ref , the calculation formula is: T ref =(α+[β·(ln(R s ))+γ·(ln(R s )) 3 ]) -1 -273.15 Among them, α, β, and γ are the first parameter, the second parameter, and the third parameter in the empirical formula respectively.

3. A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to claim 2, characterized in that: The reference resistor in step S1 is 10 kΩ, and the thermistor inside the pyranometer is a Pt-100 or a 10 kΩ thermistor.

4. A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to claim 3, characterized in that: In step S1 , the thermistor inside CMP 22 is measured.

5. A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to claim 4, characterized in that: The thermocouple described in step S2 is installed at the lower 1 / 3 of the dome of the pyranometer, and monitoring is performed continuously for 8 hours from 21:00 after sunset to 5:00 a.m. the next day; The differential voltage signal at the thermocouple compensation end is received by the data logger and is expressed as: T dome =kE+T log Where E is the voltage difference at the compensation end received by the data logger, T dome is the pyranometer case temperature, T log is the compensated collector temperature at the data collector end, measured inside the data collector, and k is the Seebeck coefficient of the thermocouple; The two reference voltages at the compensation end of the data logger are V a and V b , calculate the pyranometer housing temperature through the compensation terminal voltage and the compensated collector temperature: T dome =k(V a -V b )+T log 。 6. A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to claim 5, characterized in that: The model of the data logger in step S2 is CR3000.

7. A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to claim 6, characterized in that: The specific implementation method of step S3 is: The long-wave radiation meter is used to observe the long-wave, and the calculation formula is: Where NetIR is the long-wave irradiance, S is the sensitivity coefficient, and U efm is the voltage measured by the longwave radiation meter; The total radiant power in the thermopile inside the long-wave pyrheometer is related to the temperature T inside the long-wave pyrheometer. LW 4 The relationship between them is: P=σT LW 4 Where P is the total radiation energy per unit area and σ is the Stefan Boltzmann constant, which is 5.67×10 -8 W / m 2 K, T LW 4 is the temperature inside the long wave table in Kelvin; The empirical relationship expression obtained through multiple linear regression analysis is: OS=b0+b1NetIR+b2σ(T dome -T ref ) Among them, OS is the correction value of zero drift, b0, b1, and b2 are the 0th regression coefficient, the 1st regression coefficient, and the 2nd regression coefficient, respectively.

8. A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to claim 7, characterized in that: The model set in the multivariate linear regression analysis in step S3 is represented by vectors and matrices as follows: Y=Xβ+∈ Where Y is the fitting result of the n×1 dimensional regression method, X is the n×(p+1) design matrix, where the first column is all 1s, followed by the independent variable columns, β is the estimated vector of parameters, and ∈ is the n×1 error vector; The goal of the least squares method is to minimize, which is expressed as: S=(Y-Xβ) T (Y-Xβ) Among them, S is the variance of the corresponding points of the fitting result and the original result on the y-axis; Taking the derivative of β and setting the derivative to zero, we get the least squares solution, expressed as: β=(X T X) -1 X T Y。 9. A method for correcting temperature drift of a pyranometer based on non-invasive temperature measurement according to claim 8, characterized in that: The specific implementation method of step S4 is to consider that the observation field generally has an ambient temperature T for measurement, and convert the empirical formula into a pyranometer temperature drift correction model based on non-invasive temperature measurement: OS=b0+b1NetIR+b2σT; The 0th regression coefficient, the 1st regression coefficient, and the 2nd regression coefficient are obtained, and the irradiance is corrected. The correction formula is: R corrected =R measured -US Among them, R corrected is the corrected irradiance measurement, R measured is the measured irradiance observation value.

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