CN117609706A - Method for interpolating data of carbon water flux - Google Patents

Abstract

The invention relates to a method for interpolating data of water flux, which comprises the following steps: s1, acquiring flux data and meteorological data in a specified time period; wherein the flux data in the designated time period comprises a plurality of pieces of water flux data which are sequentially arranged according to the sequence of the corresponding time stamps; each piece of carbohydrate flux data corresponds to a timestamp for collecting the carbohydrate flux data; s2, carrying out abnormal value elimination processing on the flux data to obtain flux data subjected to abnormal value elimination processing corresponding to a specified time period; s3, correcting the meteorological data to obtain corrected meteorological data; s4, extracting missing features of any section of missing data in the flux data after the abnormal value elimination processing corresponding to the designated time period; s5, interpolating the missing data of any segment based on the missing features of the missing data and the meteorological data after correction processing to obtain an interpolation result.

Description

A method for interpolating carbohydrate flux data

Technical field

The invention relates to the technical field of data processing, and in particular to a method of interpolating carbohydrate flux data.

Background technique

In existing techniques for carbohydrate flux data imputation, linear interpolation is often used to fill in missing data points. However, linear interpolation assumes that the data changes continuously within the missing region, which may not be consistent with the actual situation. Nonlinear characteristics of the data can cause interpolation results to be inaccurate or produce unreliable predictions. In addition, existing interpolation methods often cannot directly handle cases of poor data quality, outliers, or noise. These issues can lead to distorted and misleading interpolation results.

Contents of the invention

In view of the above shortcomings and deficiencies of the prior art, the present invention provides a method for interpolating carbohydrate flux data, which solves the technical problem of inaccurate interpolation results or distortion of the interpolation results in the prior art of carbohydrate flux data interpolation.

In order to achieve the above objectives, the main technical solutions adopted by the present invention include:

Embodiments of the present invention provide a method for interpolating carbohydrate flux data, including:

S1. Obtain flux data and meteorological data within a specified time period;

The flux data within the specified time period includes multiple carbohydrate flux data arranged in sequence according to the corresponding timestamps; each carbohydrate flux data corresponds to the timestamp at which the carbohydrate flux data was collected;

The meteorological data includes: the saturated water vapor pressure difference VPD value and radiation data for each minute within the specified time period; the radiation data includes: total radiation TR, incident shortwave radiation SRIN, and photosynthetically active radiation PAR;

S2. Perform outlier elimination processing on the flux data to obtain flux data after outlier elimination processing corresponding to the specified time period;

S3. Perform correction processing on the meteorological data to obtain corrected meteorological data;

S4. Extract the missing features of any period of missing data in the flux data after outlier removal processing corresponding to the specified time period;

S5. Based on the missing characteristics of the missing data in any section and the corrected meteorological data, interpolate the missing data in this section to obtain an interpolation result.

Preferably, the S2 specifically includes:

S21. Eliminate the carbohydrate flux data that meets any outlier condition from the flux data within the specified time period to obtain the initial flux data;

The outlier conditions include:

The carbon dioxide flux value in the carbon water flux data is greater than or equal to 100 μmol/m ² /s;

The carbon dioxide flux value in the carbon water flux data is less than or equal to -50 μmol/m ² /s;

S22. Determine whether the quality level in the initial flux data is greater than 2. If so, remove all carbohydrate flux data with a quality level of 7, 8, or 9 in the initial flux data to obtain outlier removal. Processed flux data;

If it does not exist, all carbohydrate flux data with quality level 2 in the initial flux data will be eliminated to obtain the flux data after outlier elimination processing.

Preferably, correction processing is performed on the meteorological data in S3, which specifically includes:

S31. Determine whether the saturated water vapor pressure difference VPD value of each minute in the meteorological data within the specified time period meets the preset range. If not, use Formula 1 to obtain the new saturated water vapor pressure difference VPD corresponding to that minute. value;

The formula one is:

in,

Among them, P is the air pressure value in the meteorological data for this minute;

T ₀ is the temperature value in the meteorological data for this minute;

RH is the relative humidity corresponding to the meteorological data for this minute;

S32. Calculate the sunrise and sunset times based on the site location information, thereby extracting the nighttime radiation data Rg from the radiation data, and determining whether the nighttime radiation data Rg meets the preset conditions. If the nighttime radiation data Rg does not meet the preset conditions, then The nighttime radiation data Rg is updated according to a preset update method to obtain the updated nighttime radiation data Rg;

The radiation data Rg at night includes: total radiation TR at night, incident shortwave radiation SRIN at night, and photosynthetically active radiation PAR at night;

The preset condition is that in the nighttime radiation data Rg, the total radiation TR at night, the incident shortwave radiation SRIN at night, and the photosynthetically active radiation PAR at night are all greater than or equal to 0.

Preferably, the preset range is 0-50.

Preferably, if the radiation data Rg at night is not satisfactory, the radiation data Rg at night is updated according to a preset update method, which specifically includes:

If the radiation data Rg at night is not satisfied, then for the total radiation TR at night and/or the incident shortwave radiation SRIN at night and/or the photosynthetically active radiation PAR at night that is less than 0 in the radiation data Rg at night, Set to 0 to obtain the updated nighttime radiation data Rg.

Preferably, the S4 specifically includes:

S41. Extract the timestamps corresponding to the first flux data, the last flux data and the first flux data in the first data set, and form a first timestamp sequence;

The first flux data is carbon water flux data in which the carbon dioxide flux value is a null value;

The first data set is the flux data after outlier removal processing corresponding to the specified time period;

S42. Based on the first timestamp sequence, obtain the difference between the latter timestamp and the previous timestamp of any two adjacent timestamps in the first timestamp sequence;

S43. Obtain the first time difference sequence ΔT _stp based on the difference between the latter timestamp and the previous timestamp of any two adjacent timestamps in the first timestamp sequence;

Among them, ΔT _{stp c-(c-1)} is the c-1th element in the first time difference sequence;

Among them, T _c is the c-th timestamp among the total n timestamps in the first timestamp sequence;

S44. Based on the first time difference sequence, obtain the missing features of any segment of missing data.

Preferably, the S44 specifically includes:

S441. Add the lengths of consecutive adjacent elements with a value equal to 1 in the first time difference sequence to obtain a total length of N pieces of missing data;

Among them, the length of each element in the first time difference sequence is 1;

S442. Add the lengths of consecutive adjacent elements with values not equal to 1 in the first time difference sequence to obtain a total of N+1 consecutive lengths;

S443. Use the continuous length of the i-th segment among the N+1 continuous lengths as the forward continuous length Ts _fi of the length of the i-th segment of missing data among the lengths of the N segments of missing data;

The backward continuous length Ts _bi of the length of the i-th piece of missing data among the lengths of N pieces of missing data is used as the i+1th piece of continuous length among the N+1 pieces of continuous length;

Among them, missing features include the length of missing data, forward continuous length, and backward continuous length.

Preferably, the S5 specifically includes:

If the ratio of Tm _i /T is greater than 0.06, then determine whether the first value is less than the F value. If the first value is greater than or equal to the F value, use a large-scale interpolation method based on the corrected meteorological data. The missing data in segment i is interpolated to obtain the large-scale interpolation result, and the large-scale interpolation result is used as the interpolation result;

Tm _i is the length of the i-th segment of missing data among the lengths of N segments of missing data;

T _st is the starting point of the specified time period, and T _ed is the end point of the specified time period;

Wherein, the first numerical value is W;

W=Tm _i /(Ts _fi +Ts _bi );

F=4.17×(Ts _fi +Ts _bi )/0.06T.

Preferably, the S5 also specifically includes:

S51. If the ratio of Tm _i /T is less than or equal to 0.06, based on the corrected meteorological data, use the small-scale interpolation method to interpolate the missing data in the i-th section to obtain the small-scale interpolation result; or, if Tm When the ratio of _i /T is greater than or equal to 0.06, and the first value is less than the F value, the meteorological data after correction will be corrected, and the small-scale interpolation method will be used to interpolate the missing data in the i-th section to obtain the small-scale interpolation result;

S52. Merge the small-scale interpolation results and the flux data after outlier removal processing corresponding to the specified time period to form a process interpolation flux data set, and extract any missing segment in the process interpolation flux data set. missing features of the data;

S53. Based on the missing characteristics of any segment of missing data in the process interpolation flux data set and the corrected meteorological data, interpolate the missing data of the segment to obtain an interpolation result.

Preferably, the S53 specifically includes:

S531. Determine whether the ratio of the length of any missing data segment in the process interpolation flux data set to T is greater than 0.06. If the length of the missing data segment in the process interpolation flux data set is greater than 0.06, determine whether the second value is less than Q value, if the second value is greater than or equal to the Q value, use the large-scale interpolation method to interpolate the missing data in this section of the process interpolation flux data set based on the corrected meteorological data to obtain the large-scale interpolation result. , and use the large-scale interpolation results as the interpolation results;

Wherein, the second numerical value is E;

E=The length of any segment of missing data in the interpolation flux data set/(The forward continuous length of the missing data in the interpolation flux data set + The backward continuous length of the missing data in the interpolation flux data set );

Q=4.17×(forward continuous length of the missing data in the interpolation flux data set + backward continuous length of the missing data in the interpolation flux data set)/0.06T;

S532. If the ratio of the length of any segment of missing data in the process interpolation flux data set to T is less than or equal to 0.06, use the small-scale interpolation method based on the corrected meteorological data to calculate the length of the missing data in the process interpolation flux data set. Interpolate the missing data in a segment to obtain a small-scale interpolation result; or, when the ratio of the length of any segment of missing data in the process interpolation flux data set to T is greater than or equal to 0.06, and the second value is less than the Q value, then Based on the corrected meteorological data, the small-scale interpolation method is used to interpolate the missing data in this section of the process interpolation flux data set to obtain the small-scale interpolation results;

S533. Merge the small-scale interpolation results and the process interpolation flux data set to form a new process interpolation flux data set;

S534. Repeat steps S531-S533 until the ratio of the length of any segment of missing data in the new process interpolation flux data set to T is greater than 0.06, and the second value is greater than or equal to the Q value, and based on the corrected meteorological data, use The large-scale interpolation method interpolates the missing data in this section of the new process interpolation flux data set to obtain the large-scale interpolation result, and uses the large-scale interpolation result as the interpolation result.

The beneficial effects of the present invention are: the method of interpolating carbohydrate flux data of the present invention extracts the missing characteristics of any missing data in any section of the flux data after the outlier elimination process corresponding to the specified time period, and based on According to the missing characteristics of any section of missing data and the corrected meteorological data, the missing data of this section is interpolated to obtain the interpolation result. Therefore, the carbon water flux data interpolation method of the present invention is used to improve Accuracy of interpolation results.

Description of drawings

Figure 1 is a flow chart of a method for interpolating carbohydrate flux data according to the present invention;

Figure 2 is a schematic structural diagram of a carbohydrate flux data interpolation device in an embodiment of the present invention.

Detailed ways

In order to better explain the present invention and facilitate understanding, the present invention will be described in detail below through specific embodiments in conjunction with the accompanying drawings.

In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a clearer and thorough understanding of the invention, and to fully convey the scope of the invention to those skilled in the art.

Referring to Figure 1, this embodiment provides a method for interpolating carbohydrate flux data, including:

S1. Obtain flux data and meteorological data within a specified time period.

Wherein, the flux data within the specified time period includes a plurality of carbohydrate flux data arranged in sequence according to the corresponding timestamp; each carbohydrate flux data corresponds to the timestamp at which the carbohydrate flux data was collected; said Meteorological data includes: saturated water vapor pressure difference VPD value and radiation data for each minute within the specified time period; the radiation data includes: total radiation TR, incident shortwave radiation SRIN, and photosynthetically active radiation PAR.

S2. Perform outlier elimination processing on the flux data to obtain flux data after outlier elimination processing corresponding to the specified time period.

In this embodiment, the S2 specifically includes:

S21. Eliminate the carbon water flux data that meets any abnormal value condition from the flux data within the specified time period to obtain initial flux data; the abnormal value conditions include: the carbon dioxide flux value in the carbon water flux data is greater than or equal to 100 μmol/m ² /s; the carbon dioxide flux value in the carbohydrate flux data is less than or equal to -50 μmol/m ² /s.

S22. Determine whether the quality level in the initial flux data is greater than 2. If so, remove all carbohydrate flux data with a quality level of 7, 8, or 9 in the initial flux data to obtain outlier removal. Processed flux data.

If there is no case greater than 2, all carbohydrate flux data with a quality level of 2 in the initial flux data will be eliminated, and the flux data after outlier elimination processing will be obtained.

In practical applications, if the carbohydrate flux data that meets any outlier conditions and the carbohydrate flux data that does not meet the quality level are involved in the interpolation, it is likely to cause the interpolation results to fluctuate abnormally and the error will become larger, and the specified time period will be eliminated. The carbohydrate flux data and quality grade in the flux data that meet any outlier conditions do not meet the conditions (there are cases greater than 2, and all carbohydrate flux data in the initial flux data with a quality score of 7 or 8 or 9 ; If 2 does not exist, the carbohydrate flux data of all carbohydrate flux data with quality level 2 in the initial flux data can improve the accuracy and stability of the interpolation results.

S3. Perform correction processing on the meteorological data to obtain corrected meteorological data.

The S3 performs correction processing on the meteorological data, specifically including:

S31. Determine whether the saturated water vapor pressure difference VPD value of each minute in the meteorological data within the specified time period meets the preset range. If not, use Formula 1 to obtain the new saturated water vapor pressure difference VPD corresponding to that minute. value. In this embodiment, the preset range is 0-50.

The formula one is:

in,

Among them, P is the air pressure value in the minute weather data; T ₀ is the temperature value in the minute weather data; RH is the relative humidity corresponding to the minute weather data.

S32. Calculate the sunrise and sunset times based on the site location information, thereby extracting the nighttime radiation data Rg from the radiation data, and determining whether the nighttime radiation data Rg meets the preset conditions. If the nighttime radiation data Rg does not meet the preset conditions, then The nighttime radiation data Rg is updated according to a preset update method to obtain updated nighttime radiation data Rg.

The radiation data Rg at night includes: total radiation TR at night, incident shortwave radiation SRIN at night, and photosynthetically active radiation PAR at night.

The preset condition is that in the nighttime radiation data Rg, the total radiation TR at night, the incident shortwave radiation SRIN at night, and the photosynthetically active radiation PAR at night are all greater than or equal to 0.

If the radiation data Rg at night is not satisfactory, the radiation data Rg at night will be updated according to a preset update method, which specifically includes:

If the radiation data Rg at night is not satisfied, then for the total radiation TR at night and/or the incident shortwave radiation SRIN at night and/or the photosynthetically active radiation PAR at night that is less than 0 in the radiation data Rg at night, Set to 0 to obtain the updated nighttime radiation data Rg.

In practical applications, if there are abnormalities in the meteorological data themselves, great uncertainty will be introduced into the interpolation results. Correcting the meteorological data can greatly improve the accuracy of the interpolation results, reduce the difficulty of interpolation, and improve calculations. efficiency.

S4. Extract the missing features of any period of missing data in the flux data after outlier removal processing corresponding to the specified time period.

Specifically, the S4 specifically includes:

S41. Extract the timestamps corresponding to the first flux data, the last flux data and the first flux data in the first data set, and form a first timestamp sequence.

The first flux data is carbon water flux data in which the carbon dioxide flux value is a null value.

The first data set is the flux data after outlier removal processing corresponding to the specified time period.

S42. Based on the first timestamp sequence, obtain the difference between the latter timestamp and the previous timestamp of any two adjacent timestamps in the first timestamp sequence.

S43. Obtain the first time difference sequence ΔT _stp based on the difference between the latter timestamp and the previous timestamp of any two adjacent timestamps in the first timestamp sequence;

Among them, ΔT _{stp c-(c-1)} is the c-1th element in the first time difference sequence;

Among them, T _c is the c-th timestamp among the total n timestamps in the first timestamp sequence;

S44. Based on the first time difference sequence, obtain the missing characteristics of any segment of missing data, where the S44 specifically includes:

S441. Add the lengths of consecutive adjacent elements with a value equal to 1 in the first time difference sequence to obtain a total length of N pieces of missing data; where the length of each element in the first time difference sequence is 1.

S442. Add the lengths of consecutive adjacent elements in the first time difference sequence whose values are not equal to 1 to obtain a total of N+1 consecutive lengths.

S443. Use the i-th continuous length among the N+1 continuous lengths as the forward continuous length Ts _fi of the i-th missing data length among the N missing data lengths.

The i+1th continuous length among the N+1 continuous lengths is taken as the backward continuous length TS _bi of the length of the i-th missing data among the lengths of the N missing data.

Among them, missing features include the length of missing data, forward continuous length, and backward continuous length.

S5. Based on the missing characteristics of the missing data in any section and the corrected meteorological data, interpolate the missing data in this section to obtain an interpolation result. The S5 specifically includes:

If the ratio of Tm _i /T is greater than 0.06, then determine whether the first value is less than the F value. If the first value is greater than or equal to the F value, use a large-scale interpolation method based on the corrected meteorological data. The missing data in segment i is interpolated to obtain the large-scale interpolation result, and the large-scale interpolation result is used as the interpolation result.

Tm _i is the length of the i-th segment of missing data among the lengths of N segments of missing data;

T _st is the starting point of the specified time period, and T _ed is the end point of the specified time period;

Wherein, the first numerical value is W.

W=Tm _i /(Ts _fi +Ts _bi );

F=4.17×(Ts _fi +Ts _bi )/0.06T.

In the actual application of this embodiment, the S5 also specifically includes:

S51. If the ratio of Tm _i /T is less than or equal to 0.06, based on the corrected meteorological data, use the small-scale interpolation method to interpolate the missing data in the i-th section to obtain the small-scale interpolation result; or, if Tm When the ratio of _i /T is greater than or equal to 0.06, and the first value is less than the F value, the processed meteorological data will be corrected and the small-scale interpolation method will be used to interpolate the missing data in the i-th section to obtain the small-scale interpolation result.

S52. Merge the small-scale interpolation results and the flux data after outlier removal processing corresponding to the specified time period to form a process interpolation flux data set, and extract any missing segment in the process interpolation flux data set. missing features of the data.

S53. Based on the missing characteristics of any segment of missing data in the process interpolation flux data set and the corrected meteorological data, interpolate the missing data of the segment to obtain an interpolation result.

In practical applications, the S53 specifically includes:

S531. Determine whether the ratio of the length of any missing data segment in the process interpolation flux data set to T is greater than 0.06. If the length of the missing data segment in the process interpolation flux data set is greater than 0.06, determine whether the second value is less than Q value, if the second value is greater than or equal to the Q value, use the large-scale interpolation method to interpolate the missing data in this section of the process interpolation flux data set based on the corrected meteorological data to obtain the large-scale interpolation result. , and use the large-scale interpolation results as the interpolation results.

Wherein, the second numerical value is E;

E=The length of any segment of missing data in the interpolation flux data set/(The forward continuous length of the missing data in the interpolation flux data set + The backward continuous length of the missing data in the interpolation flux data set ).

Q=4.17×(forward continuous length of the missing data in the interpolation flux data set + backward continuous length of the missing data in the interpolation flux data set)/0.06T.

The methods of interpolating carbon and water flux data can be divided into large-scale interpolation methods and small-scale interpolation methods. Specifically, the small-scale interpolation method is mainly an interpolation method for short-term missing data. Due to its proportion in the total length Small, the interpolation results are more credible, and the small-scale interpolation results will participate in the subsequent iterations of small-scale interpolation and the final large-scale interpolation. The large-scale interpolation method is mainly aimed at the interpolation of missing data for a long time. Its imputation reliability is lower than that of small-scale interpolation, so it cannot be used as the basis for other data interpolation. By selectively using large-scale interpolation methods and small-scale interpolation methods, the overall accuracy and credibility of the interpolation results can be improved.

S532. If the ratio of the length of any segment of missing data in the process interpolation flux data set to T is less than or equal to 0.06, use the small-scale interpolation method based on the corrected meteorological data to calculate the length of the missing data in the process interpolation flux data set. Interpolate the missing data in a segment to obtain a small-scale interpolation result; or, when the ratio of the length of any segment of missing data in the process interpolation flux data set to T is greater than or equal to 0.06, and the second value is less than the Q value, then Based on the corrected meteorological data, the small-scale interpolation method is used to interpolate the missing data in this section of the process interpolation flux data set, and the small-scale interpolation results are obtained.

S533. Merge the small-scale interpolation results with the process interpolation flux data set to form a new process interpolation flux data set.

S534. Repeat steps S531-S533 until the ratio of the length of any segment of missing data in the new process interpolation flux data set to T is greater than 0.06, and the second value is greater than or equal to the Q value, and based on the corrected meteorological data, use The large-scale interpolation method interpolates the missing data in this section of the new process interpolation flux data set to obtain the large-scale interpolation result, and uses the large-scale interpolation result as the interpolation result.

The interpolation results obtained in this example are used to evaluate the carbon budget status of ecosystems or regions, specifically including:

Use the interpolation results to calculate the carbon budget of an ecosystem or region. The carbon flux balance method is usually used to determine the carbon budget status of an ecosystem or region by calculating the difference between carbon sequestration and carbon emissions per unit area. Carbon sequestration and carbon emissions per unit area can be calculated based on measured flux data, or predicted using models and estimation methods.

By comparing the amount of carbon sequestered and emitted per unit area and considering the time scale, the intensity of the carbon sink source of an ecosystem or region can be judged. If the amount of carbon sequestered per unit area is greater than the amount of carbon emitted, it means that the ecosystem or region is a carbon sink and has the ability to absorb and fix carbon, indicating a negative carbon sink. If the amount of carbon emitted per unit area is greater than the amount of carbon sequestered, it means that the ecosystem or region is a carbon source and has the ability to emit carbon, indicating a positive carbon source. The value of carbon sink source intensity can be used to measure the carbon absorption or emission capacity of an ecosystem or region.

A carbohydrate flux data interpolation method in this embodiment extracts outliers corresponding to the specified time period and removes the missing characteristics of any missing data in any section of the processed flux data, and based on any section of missing data The missing features and the corrected meteorological data are used to interpolate the missing data of this section to obtain the interpolation result. Therefore, a carbon water flux data interpolation method in this embodiment is used to improve the interpolation result. accuracy.

Referring to Figure 2, in this embodiment, a device for carbohydrate flux data interpolation is also provided, including: at least one processor; and at least one memory communicatively connected to the processor, wherein the memory stores information that can be The program instructions executed by the processor can execute a carbohydrate flux data interpolation method as in the above embodiment by calling the program instructions.

In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, features defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the present invention, "plurality" means two or more than two, unless otherwise explicitly and specifically limited.

In the present invention, unless otherwise clearly stated and limited, the terms "installation", "connection", "connection", "fixing" and other terms should be understood in a broad sense. For example, it can be a fixed connection or a detachable connection. , or integrated; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be an internal connection between two elements or an interaction between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

In the present invention, unless otherwise expressly stated and limited, a first feature is "on" or "below" a second feature, which may mean that the first and second features are in direct contact, or the first and second features are in direct contact through an intermediary. indirect contact. Furthermore, the terms "above", "above" and "above" the second feature may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is higher in level than the second feature. . The first feature being "below", "below" and "under" the second feature may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is lower in level than the second feature.

In the description of this specification, the terms "one embodiment", "some embodiments", "embodiments", "examples", "specific examples" or "some examples", etc., refer to the description in conjunction with the embodiment or example. A specific feature, structure, material, or characteristic described is included in at least one embodiment or example of the invention. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.

Although the embodiments of the present invention have been shown and described above, it can be understood that the above-mentioned embodiments are illustrative and should not be construed as limitations of the present invention. Those of ordinary skill in the art can make modifications to the above-mentioned embodiments within the scope of the present invention. The embodiments are subject to alterations, modifications, substitutions and variations.

Claims (10)

1. A method of interpolating a carbon flux data, comprising:

s1, acquiring flux data and meteorological data in a specified time period;

wherein the flux data in the designated time period comprises a plurality of pieces of water flux data which are sequentially arranged according to the sequence of the corresponding time stamps; each piece of carbohydrate flux data corresponds to a timestamp for collecting the carbohydrate flux data;

the meteorological data includes: saturated vapor pressure difference VPD value and radiation data of each minute in a specified time period; the radiation data includes: total radiation TR, incident short wave radiation SRIN, photosynthetically active radiation PAR;

s2, carrying out abnormal value elimination processing on the flux data to obtain flux data subjected to abnormal value elimination processing corresponding to a specified time period;

s3, correcting the meteorological data to obtain corrected meteorological data;

s4, extracting missing features of any section of missing data in the flux data after the abnormal value elimination processing corresponding to the designated time period;

s5, interpolating the missing data of any segment based on the missing features of the missing data and the meteorological data after correction processing to obtain an interpolation result.

2. The method of interpolating a carbohydrate flux data according to claim 1, wherein S2 specifically comprises:

s21, eliminating the carbon water flux data meeting any abnormal value condition in flux data in a specified time period to obtain initial flux data;

the outlier condition includes:

carbon dioxide flux values in the carbon water flux data are greater than or equal to 100 μmol/m ² /s；

Carbon dioxide flux values in the carbon water flux data are less than or equal to-50 μmol/m ² /s；

S22, judging whether the quality grade in the initial flux data is greater than 2, if so, eliminating all the water flux data with the quality grade of 7, 8 or 9 in the initial flux data to obtain flux data subjected to abnormal value elimination processing;

if the abnormal value is not found, eliminating all the water flux data with the quality score of 2 in the initial flux data to obtain flux data after abnormal value elimination treatment.

3. The method of interpolating carbohydrate flux data according to claim 2, wherein the correcting process for the meteorological data in S3 specifically includes:

s31, judging whether the saturated vapor pressure difference VPD value of each minute in the meteorological data in the specified time period meets a preset range, and if not, acquiring a new saturated vapor pressure difference VPD value corresponding to the minute by adopting a formula I;

the first formula is:

wherein,

wherein P is the air pressure value in the minute meteorological data;

T ₀ air temperature values in the minute weather data;

RH is the relative humidity corresponding to the minute meteorological data;

s32, calculating sunrise and sunset time according to site position information, so as to extract night radiation data Rg in the radiation data, judging whether the night radiation data Rg meet preset conditions, and if the night radiation data Rg do not meet the preset conditions, updating the night radiation data Rg according to a preset updating mode to obtain updated night radiation data Rg;

the night radiation data Rg includes: total radiation TR at night, incident short wave radiation SRIN at night, photosynthetically active radiation PAR at night;

the preset condition is that the total night radiation TR, the incident short wave radiation SRIN at night and the photosynthetically active radiation PAR at night in the night radiation data Rg are all more than or equal to 0.

4. A method of interpolating a carbon water flux data as claimed in claim 3, wherein the predetermined range is 0-50.

5. The method for interpolating carbohydrate flux data according to claim 4, wherein if said nocturnal radiation data Rg is not satisfied, updating said nocturnal radiation data Rg according to a predetermined updating manner, comprising:

if the night radiation data Rg is not satisfied, setting the value of the total night radiation TR and/or the incident short wave radiation SRIN and/or the photosynthetic active radiation PAR to be 0, and obtaining updated night radiation data Rg.

6. The method of interpolating a carbohydrate flux data according to claim 5, wherein S4 specifically comprises:

s41, extracting first flux data, last flux data and time stamps corresponding to the first flux data in the first data set, and forming a first time stamp sequence;

the first flux data is the carbon water flux data with the carbon dioxide flux value being null;

the first data set is flux data after abnormal value elimination processing corresponding to a specified time period;

s42, based on the first time stamp sequence, obtaining the difference value of subtracting the previous time stamp from the next time stamp in any two adjacent time stamps in the first time stamp sequence;

s43, obtaining a first time difference sequence delta T based on the difference value of subtracting the previous time stamp from the next time stamp in any two adjacent time stamps in the first time stamp sequence _stp ；

ΔT _stp ＝{ΔT _{stp 2-1} ，ΔT _{stp 3-2} ，…，ΔT _{stp c-(c-1)} ，...，ΔT _{stp n-(n-1)} }；

Wherein DeltaT _{stp c-(c-1)} C-1 th element in the first time difference sequence;

wherein T is _c C-th timestamp of n total timestamps in the first sequence of timestamps;

s44, acquiring missing features of any section of missing data based on the first time difference sequence.

7. The method of interpolating a carbohydrate flux data according to claim 6, wherein S44 specifically includes:

s441, adding lengths of elements which are continuously adjacent in the first time difference sequence and have the value equal to 1 to obtain the length of N segments of missing data;

wherein the length of each element in the first time difference sequence is 1;

s442, adding lengths of elements which are continuously adjacent in the first time difference sequence and have the numerical value not equal to 1 to obtain total continuous lengths of N+1 sections;

s443, regarding the i-th continuous length of the N+1 continuous lengths as the forward continuous length Ts of the length of the i-th missing data of the length of the N missing data _fi ；

A backward continuous length Ts of the length of the ith section missing data in the length of the N section missing data, which is the (i+1) th section continuous length in the N+1 section continuous lengths _bi ；

Wherein the missing features include a length of missing data, a forward continuous length, a backward continuous length.

8. The method of interpolating a carbohydrate flux data according to claim 7, wherein S5 specifically comprises:

if Tm _i When the ratio of the ratio to the T is greater than 0.06, judging whether the first value is smaller than the F value, if the first value is greater than or equal to the F valueAccording to the modified meteorological data, interpolation is carried out on the i-th missing data in a large-scale interpolation mode to obtain a large-scale interpolation result, and the large-scale interpolation result is used as an interpolation result;

Tm _i the length of the data missing in the ith section in the length of the data missing in the N sections;

T _st to specify the start of the time period, T _ed For the end of a specified time period;

wherein the first value is W;

W＝Tm _i /(Ts _fi +Ts _bi )；

F＝4.17×(Ts _fi +Ts _bi )/0.06T。

9. the method of interpolating a carbohydrate flux data according to claim 8, wherein S5 further specifically comprises:

s51, if Tm _i When the ratio of/T is less than or equal to 0.06, interpolating the i-th missing data by adopting a small-scale interpolation mode based on the meteorological data after correction processing to obtain a small-scale interpolation result; or, if Tm _i When the ratio of/T is greater than or equal to 0.06 and the first value is smaller than the F value, correcting the processed meteorological data, and performing interpolation on the i-th missing data by adopting a small-scale interpolation mode to obtain a small-scale interpolation result;

s52, combining the small-scale interpolation result and the flux data subjected to outlier rejection processing corresponding to the specified time period to form a process interpolation flux data set, and extracting missing features of any section of missing data in the process interpolation flux data set;

s53, interpolating the missing data of any segment in the flux data set based on the missing feature of the missing data of the segment and the modified meteorological data to obtain an interpolation result.

10. The method of interpolating a carbohydrate flux data according to claim 8, wherein S53 specifically includes:

s531, judging whether the ratio of the length of any section of missing data in the process interpolation flux data set to T is greater than 0.06, if the length of the section of missing data in the process interpolation flux data set is greater than 0.06, judging whether the second value is smaller than the Q value, if the second value is greater than or equal to the Q value, interpolating the section of missing data in the process interpolation flux data set in a large-scale interpolation mode according to the modified meteorological data to obtain a large-scale interpolation result, and taking the large-scale interpolation result as the interpolation result;

wherein the second value is E;

e = length of any segment of missing data in the interpolated flux data/(forward continuous length of the segment of missing data in the interpolated flux data + backward continuous length of the segment of missing data in the interpolated flux data);

q=4.17× (forward continuous length of the segment of missing data in the interpolation flux data set+backward continuous length of the segment of missing data in the interpolation flux data set)/0.06T;

s532, if the ratio of the length of any section of missing data in the process interpolation flux data set to T is smaller than or equal to 0.06, based on the modified meteorological data, performing interpolation on the section of missing data in the process interpolation flux data set in a small-scale interpolation mode to obtain a small-scale interpolation result; or when the ratio of the length of any section of missing data in the process interpolation flux data set to T is more than or equal to 0.06 and the second value is less than the Q value, interpolating the section of missing data in the process interpolation flux data set by adopting a small-scale interpolation mode based on the modified meteorological data to obtain a small-scale interpolation result;

s533, combining the small-scale interpolation result with the process interpolation flux data set to form a new process interpolation flux data set;

s534, repeating the steps S531-S533 until the ratio of the length of any section of missing data in the new process interpolation flux data set to T is greater than 0.06, and the second value is greater than or equal to the Q value, and interpolating the section of missing data in the new process interpolation flux data set by adopting a large-scale interpolation mode according to the modified meteorological data to obtain a large-scale interpolation result, wherein the large-scale interpolation result is used as the interpolation result.