Rice field soil emission reduction potential estimation method and system based on carbon-nitrogen coupling and medium

Abstract

The invention belongs to the technical field of data processing, and particularly relates to a rice field soil emission reduction potential estimation method and system based on carbon-nitrogen coupling and a medium, and the method comprises the steps: collecting rice field nitrogen application information in a target area through literature statistics, dividing rice fields with similar nitrogen application into the same type, and selecting a plurality of typical areas from each type; carrying out soil sampling at fixed intervals in the typical rice field area, analyzing the nitrogen concentration level of the soil, and obtaining representative samples; performing soil column experimental simulation by using the representative sample, and estimating the average emission of specific gas; and determining significant influence factors, setting different carbon-nitrogen coupling situations to carry out an orthogonal simulation experiment, quantifying factor importance, carrying out weighted accumulation to obtain a comprehensive emission estimation value, and evaluating the carbon-nitrogen coupling-based biochar rice field emission reduction capability. The method can effectively estimate the rice field soil emission reduction potential.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method, system and medium for estimating the emission reduction potential of paddy field soil based on carbon-nitrogen coupling. Background Art

[0002] Studying the effect of biochar on reducing CH4 and N2O emissions in rice fields is of great significance for addressing climate change. Rice fields are an important source of global greenhouse gas emissions, and the application of nitrogen fertilizers and biochar can affect the carbon and nitrogen cycle in the soil, thereby affecting the emissions of these gases. In China, due to the large differences in soil fertility, the use of nitrogen fertilizers is also different. Therefore, studying the interaction between nitrogen fertilizers, biochar and soil nitrogen is crucial to improving the emission reduction effect of biochar.

[0003] A similar prior art Chinese patent application with publication number CN118628278A provides a method for predicting weekly methane emission flux from rice fields based on real-time sensor monitoring data. This method integrates a series of real-time soil sensing devices, collects basic data on rice fields, real-time climate data, real-time root (interspheric) zone soil physical and chemical characteristics data, and real-time rice growth, and combines machine learning algorithms to develop a detection system that can realize small-scale real-time methane emissions from rice fields.

[0004] A similar prior art includes a Chinese patent application with publication number CN118798433A, which discloses a method and system for predicting multi-source nitrogen oxide emissions. The method includes obtaining multi-source data of a region to be predicted, including remote sensing column concentration data, concentration reanalysis data, and meteorological reanalysis data; preprocessing the multi-source data by unifying and matching the spatial resolution; inputting the preprocessed multi-source data into a trained multi-source prediction model to predict nitrogen oxide emissions from different emission sources; wherein the different emission sources include total nitrogen oxide emissions, anthropogenic nitrogen oxide emissions, nitrogen oxide biomass combustion emissions, nitrogen oxide lightning emissions, and nitrogen oxide soil emissions.

[0005] However, both of the above documents focus on the qualitative impact of nitrogen fertilizer types and application rates on emission reduction effects, but lack understanding of the microscopic processes and quantitative impacts of biochar coupling with different nitrogen elements. Therefore, the present invention provides a method, system and medium for estimating rice field soil emission reduction potential based on carbon-nitrogen coupling. Summary of the invention

[0006] The present invention quantifies the influence of carbon-nitrogen coupling on the emission reduction effect of biochar rice fields through literature statistical classification of rice fields, soil sampling analysis, soil column experimental simulation and orthogonal simulation experiments, so as to evaluate and optimize the emission reduction potential of biochar rice fields based on carbon-nitrogen coupling.

[0007] In order to achieve the above-mentioned object of the invention, the present invention provides the following method for estimating the emission reduction potential of paddy field soil based on carbon-nitrogen coupling, which is implemented by executing the following steps: Step S1, using a literature statistical method to collect nitrogen application information of rice fields in a target area, classifying rice field planting areas with similar nitrogen application information into the same type, and selecting a number of rice field planting areas from each type of rice field planting area as typical rice field planting areas; Step S2, sampling soil in each of the typical rice field planting areas at preset fixed intervals, maintaining a consistent sampling depth at all sampling points, performing experimental analysis on the sampled soil samples, obtaining the soil nitrogen concentration level of each typical rice field planting area, and obtaining representative soil samples based on the soil nitrogen concentration level; Step S3, using a representative soil sample to perform a soil injection experiment simulation to obtain a soil injection experiment simulation result, estimating the average emission of a specific gas in the soil of the typical rice field planting area based on the soil injection experiment simulation result, and analyzing the soil injection experiment simulation result to obtain an analysis result; Step S4, determining significant influencing factors based on the analysis results, setting different carbon-nitrogen coupling scenarios based on the significant influencing factors, conducting orthogonal simulation experiments, and quantifying the importance of each of the significant influencing factors using statistical methods based on the orthogonal simulation experiment results. The importance of the factors is used as weights, and the interpolation estimation results under each of the significant influencing factors are weightedly accumulated to obtain a comprehensive emission estimate of a specific gas, and then the emission reduction capacity of the biochar rice field based on carbon-nitrogen coupling is obtained based on the comprehensive emission estimate.

[0008] As a preferred technical solution of the present invention, the rice field planting areas with similar nitrogen application information are divided into the same type, comprising the following steps: Step S11, performing data preprocessing on the collected nitrogen application information, removing abnormal values, filling missing values, and normalizing various data parameters in the nitrogen application information to preset a first quantity; Step S12, using a clustering algorithm to classify the nitrogen application information into the first number of types, obtaining characteristic nitrogen application information of the corresponding type by calculation for each type of the nitrogen application information, calculating a first deviation score of each nitrogen application information of the same type and the corresponding characteristic nitrogen application information, and adding the first deviation scores corresponding to all types as a first evaluation value corresponding to the current classification; Step S13, add one to the first number, repeat step S12, classify the nitrogen application information again, calculate the corresponding second evaluation value, and judge whether the classification result meets the preset conditions based on the first evaluation value and the second evaluation value. If yes, end this step; otherwise, repeat this step until the classification result meets the preset conditions.

[0009] As a preferred technical solution of the present invention, judging whether the classification result meets the preset condition based on the first evaluation value and the second evaluation value includes the following steps: Determine whether the second evaluation value is less than or equal to the first evaluation value. If so, calculate the first difference between the first evaluation value and the second evaluation value, and determine whether the first difference is greater than or equal to a preset second threshold. If so, determine that the preset condition is not met; otherwise, determine that the preset condition is met.

[0010] As a preferred technical solution of the present invention, estimating the average emission of specific gases in the soil of the typical rice field planting area based on the soil injection experiment simulation results includes the following steps: Use multiple soil injections, number each soil injection, fill the soil injections with collected soil samples, control the environmental factors of the soil injections to simulate the field conditions of each typical rice field planting area, obtain multiple influencing factors, preset multiple first processing conditions for each of the influencing factors, obtain a first number of the first processing conditions, obtain a second number of all soil injections, divide the second number by the first number to obtain a first result value, divide the multiple soil injections into first result value groups, set the same first processing conditions for each group of the soil injections, periodically collect gas samples from the soil injections, use analysis equipment to measure the concentration of specific gases, record the corresponding soil injection number, sampling time, sampling date and concentration data and save them in a first list, for each of the first processing conditions, add the specific gas concentrations of all measurement times corresponding to each group of soil injections to obtain the total emission of the specific gas during the test, and calculate the average emission corresponding to each of the first processing conditions based on the total emission.

[0011] As a preferred technical solution of the present invention, analyzing the soil injection test simulation results to obtain analysis results includes the following steps: For each of the first processing conditions, the group emission mean corresponding to each group of soil injections under the first processing condition is calculated, and the total emission mean of all groups is calculated. The inter-group variance under the first processing condition is calculated based on the group emission mean and the total emission mean. The intra-group variance of each group of soil injections is also calculated, and the inter-group mean square is calculated based on the inter-group variance, and the intra-group mean square is calculated based on the intra-group variance. The value obtained by dividing the inter-group mean square by the intra-group mean square is taken as the first numerical value. Based on the first numerical value, the number of soil injections contained in each group and the number of soil injection groups, the second numerical value is determined using statistical software.

[0012] As a preferred technical solution of the present invention, determining significant influencing factors based on the analysis results includes the following steps: The second value corresponding to the first processing condition is obtained. If the second value is less than a preset first threshold, the influencing factor corresponding to the first processing condition is used as the significant influencing factor.

[0013] As a preferred technical solution of the present invention, different carbon-nitrogen coupling scenarios are set based on the significant influencing factors, including the following steps: Different levels are set for each of the significant influencing factors, each level of each of the significant influencing factors is combined at least once with each level of other significant influencing factors to generate multiple influencing factor combinations, and different carbon-nitrogen coupling scenarios are set based on the multiple influencing factor combinations.

[0014] As a preferred technical solution of the present invention, based on the orthogonal simulation experiment results, a statistical method is used to quantify the importance of each of the significant influencing factors, including the following steps: The emission of each specific gas is taken as an observed variable, and the significant influencing factor is taken as a latent variable. A structural equation model is constructed based on the relationship between the observed variable and the latent variable. Relevant data is collected based on the experimental results of the orthogonal simulation experiment. The relevant data includes different levels of the significant influencing factors and the corresponding specific gas emissions. The structural equation model is fitted and estimated using statistical software. The goodness of fit of the structural equation model is evaluated using a goodness of fit test method. When the goodness of fit is less than a preset fitting threshold, the structural equation model is modified until the goodness of fit is greater than or equal to the fitting threshold. The first and second influencing parameters of each significant influencing factor on the specific gas emission are calculated according to the model parameters. The comprehensive influencing parameter is calculated based on the first influencing parameter and the second influencing parameter. The comprehensive influencing parameter is the factor importance of each significant influencing factor. The present invention also provides a paddy field soil emission reduction potential estimation system based on carbon-nitrogen coupling, comprising the following modules: A classification module collects nitrogen application information of paddy fields in a target area by using a literature statistical method, classifies paddy field planting areas with similar nitrogen application information into the same category, and selects several paddy field planting areas from each type of paddy field planting area as typical paddy field planting areas; A sampling module is used to perform soil sampling in each of the typical rice field planting areas at preset fixed intervals, maintain a consistent sampling depth at all sampling points, perform experimental analysis on the sampled soil samples, obtain the soil nitrogen concentration level of each typical rice field planting area, and obtain representative soil samples based on the soil nitrogen concentration level; The first analysis module uses a representative soil sample to perform a soil injection experiment simulation to obtain a soil injection experiment simulation result, estimates the average emission of a specific gas in the soil of the typical rice field planting area based on the soil injection experiment simulation result, and analyzes the soil injection experiment simulation result to obtain an analysis result; The second analysis module determines the significant influencing factors based on the analysis results, sets different carbon-nitrogen coupling scenarios based on the significant influencing factors, conducts orthogonal simulation experiments, and uses statistical methods to quantify the importance of each significant influencing factor based on the orthogonal simulation experiment results. The importance of the factor is used as a weight, and the interpolation estimation results under each of the significant influencing factors are weightedly accumulated to obtain a comprehensive emission estimate of a specific gas, and then the emission reduction capacity of the biochar rice field based on carbon-nitrogen coupling is obtained based on the comprehensive emission estimate.

[0015] The present invention also provides a medium storing program instructions, wherein when the program instructions are executed, the device where the medium is located is controlled to execute any one of the above-mentioned methods for estimating paddy field soil emission reduction potential based on carbon-nitrogen coupling.

[0016] Compared with the prior art, the beneficial effects of the present invention are at least as follows: In the present invention, firstly, the information on nitrogen application of rice fields in the target area is collected through literature statistics, and the rice fields with similar nitrogen application are divided into one type, and several typical areas are selected from each type, so as to facilitate the subsequent selection of representative soil samples based on typical rice field planting areas, and then facilitate the study of the emission reduction potential of different rice field soil types; soil sampling is carried out at fixed intervals in typical rice field areas, and the soil nitrogen concentration level is analyzed to obtain representative samples, thereby ensuring the representativeness of soil sample collection; soil column experiment simulation is carried out using representative samples, and the average emission of specific gases is estimated based on the experimental results, and the average emission helps researchers evaluate the impact of specific gas emissions under different control conditions under different control conditions; significant influencing factors are determined, and different carbon-nitrogen coupling scenarios are set for orthogonal simulation experiments, the importance of factors is quantified, and the comprehensive emission estimate is obtained by weighted accumulation, and the emission reduction capacity of biochar rice fields based on carbon-nitrogen coupling is evaluated, which helps us understand the potential changes in specific gas emissions of rice field soils under different combinations of factors and provides a scientific basis for agricultural management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flowchart of the steps of the method for estimating the emission reduction potential of paddy field soil based on carbon-nitrogen coupling; Figure 2 A CH4 emission data chart of the present invention; Figure 3 is an orthogonal table of multiple significant influencing factors of the present invention; Figure 4 This is a structural diagram of the composition of the paddy field soil emission reduction potential estimation system based on carbon-nitrogen coupling of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0020] The present invention provides Figure 1 The method for estimating the emission reduction potential of paddy field soil based on carbon-nitrogen coupling is shown in the figure, which is implemented by executing the following steps: Step S1, using a literature statistical method to collect nitrogen application information of rice fields in a target area, classifying rice field planting areas with similar nitrogen application information into the same type, and selecting several rice field planting areas from each type of rice field planting area as typical rice field planting areas.

[0021] Specifically, in order to estimate the emission reduction potential of paddy field soil, we first use literature statistical methods to collect nitrogen application information of paddy fields. The nitrogen application information includes nitrogen fertilizer type, nitrogen fertilizer concentration, fertilizer amount, fertilization method and fertilization frequency. Paddy fields with similar nitrogen application information usually have similar soil nitrogen levels. The nitrogen application information can be used to understand how paddy fields use nitrogen fertilizers, such as what type of fertilizers are used and how much is used. According to the similarity of nitrogen application information, the rice field planting areas are divided into different types. For example, rice fields with similar nitrogen fertilizer types and concentrations can be classified as the same type. From each type of rice fields, several rice fields are randomly or according to specific standards selected as typical rice field planting areas, which facilitates the subsequent selection of representative soil samples based on typical rice field planting areas, and then facilitates the study of the emission reduction potential of different rice field soil types.

[0022] Step S2: soil sampling is performed at preset fixed intervals in each typical rice field planting area, the sampling depth is kept consistent at all sampling points, the sampled soil samples are experimentally analyzed, the soil nitrogen concentration level of each typical rice field planting area is obtained, and representative soil samples are obtained based on the soil nitrogen concentration level analysis.

[0023] Specifically, in order to ensure the representativeness of soil sample collection, soil sampling is carried out at preset fixed intervals in typical rice field planting areas. In order to ensure that representative soil samples can be selected later, the fixed intervals are generally set to be small. Since the nitrogen concentration of the soil may change with depth, the sampling depth is maintained consistent at all sampling points. The collected soil samples are then analyzed to obtain the soil nitrogen concentration level of each typical rice field planting area. The soil nitrogen concentration level refers to the nitrogen content in the soil, such as the nitrogen content per kilogram of soil (mg / kg). The soil nitrogen concentration level helps to understand the impact of soil nitrogen on crop growth and the environment, and how to optimize nitrogen utilization through management measures. Representative soil samples are obtained through different soil nitrogen concentration levels. Subsequent experimental analysis can better understand the soil characteristics and specific gas emissions of the entire area. Specific gases refer to CH4 and N2O.

[0024] Step S3: Use representative soil samples to perform soil injection experiment simulation to obtain soil injection experiment simulation results, estimate the average emission of specific gases in the soil of a typical rice field planting area based on the soil injection experiment simulation results, and analyze the soil injection experiment simulation results to obtain analysis results.

[0025] Specifically, the collected soil samples are subjected to soil column experiment simulation. By controlling different environmental factors, such as temperature, humidity, pH value and other environmental factors in the soil injection, the field conditions of a typical rice field planting area are simulated. Gas samples are regularly collected from the top or side of the soil injection. The concentration of specific gases is measured using a meteorological chromatograph or other suitable analysis equipment. The time, date and concentration data of each gas sampling are recorded, and the total emission of specific gases during the experiment is calculated. The average emission of specific gases in the soil of a typical rice field planting area is calculated based on the total emission. The average emission can help researchers evaluate the impact of specific gas emissions under different control conditions, as well as the long-term impact of these control conditions on the soil carbon and nitrogen cycle. The specific gases refer to CH4 and N2O.

[0026] Step S4, determine the significant influencing factors based on the analysis results, set different carbon-nitrogen coupling scenarios based on the significant influencing factors, conduct orthogonal simulation experiments, use statistical methods to quantify the importance of each significant influencing factor based on the orthogonal simulation experiment results, use the factor importance as a weight, and perform weighted accumulation on the interpolation estimation results under each significant influencing factor to obtain a comprehensive emission estimate of a specific gas, and then obtain the emission reduction capacity of the biocarbon rice field based on carbon-nitrogen coupling based on the comprehensive emission estimate.

[0027] Specifically, in order to more accurately evaluate and optimize the carbon sequestration and emission reduction capacity of farmland soil, thereby achieving the effect of improving crop yields and soil fertility, different carbon-nitrogen coupling scenarios are set in combination with nitrogen application information. By setting different carbon-nitrogen coupling scenarios such as biochar temperature, nitrogen fertilizer type, and nitrogen fertilizer concentration for orthogonal simulation experiments, the effects of these influencing factors on the specific gas emission reduction potential of paddy field soil can be systematically evaluated, which helps to identify the most critical factors and optimize emission reduction strategies. Statistical methods such as structural equation models or regression analysis methods are used to quantify the impact of influencing factors on emission reduction effects, which can determine which significant influencing factors contribute most to emission reduction, which is helpful for farmland management. Provide specific guidance, such as selecting the optimal biochar use temperature and nitrogen fertilizer use strategy, and then use the interpolation method to estimate the specific gas emissions at different levels for each level of each influencing factor, and then multiply the interpolation estimate of each influencing factor by the corresponding influencing factor importance, and then add up these weighted results to obtain a comprehensive emission estimate. The weighted accumulation takes into account the differences in the contribution of different influencing factors to the emission reduction effect, making the final emission estimate more accurate and comprehensive. The above method helps us understand the potential changes in specific gas emissions from paddy field soils under different combinations of factors, and provides a scientific basis for agricultural management.

[0028] Furthermore, the rice field planting areas with similar nitrogen application information are classified into the same category, including the following steps: Step S11, performing data preprocessing on the collected nitrogen application information, removing abnormal values, filling missing values, and normalizing various data parameters in the nitrogen application information to preset a first quantity; Step S12, using a clustering algorithm to classify the nitrogen application information into a first number of types, obtaining characteristic nitrogen application information of a corresponding type by calculation for each type of nitrogen application information, calculating a first deviation score of each nitrogen application information of the same type and the corresponding characteristic nitrogen application information, and adding the first deviation scores corresponding to all types as a first evaluation value corresponding to the current classification; Step S13, add one to the first number, repeat step S12, classify the nitrogen application information again, calculate the corresponding second evaluation value, and determine whether the classification result meets the preset conditions based on the first evaluation value and the second evaluation value. If so, end this step; otherwise, repeat this step until the classification result meets the preset conditions.

[0029] Specifically, in order to classify the rice field planting area scientifically and reasonably, a first number is preset, and the existing clustering algorithm such as K-means is used to classify the nitrogen application information for the first time. For example, if the first number is 2, it is divided into 2 categories. In order to determine whether the classified types are reasonable, the corresponding characteristic nitrogen application information is first calculated for each type of nitrogen application information. For example, the average value of each data parameter in the same type of nitrogen application information is calculated, and the combination of the average values ​​is used as the characteristic nitrogen application information. Then, the first deviation score of each type of characteristic nitrogen application information and the nitrogen application information of the same type is calculated, and the first deviation scores of all types of the current classification are added as the corresponding class. The first evaluation value of the type is calculated, and the first evaluation value can represent the degree of aggregation of the current classification. The larger the first evaluation value, the smaller the clustering density of the current classification. Then, the first number is increased by 1. At this time, the value of the first number is 3. The clustering algorithm is used again to classify the nitrogen application information. At this time, the nitrogen application information is divided into 3 categories. Then, step S12 is repeated to calculate the second evaluation values ​​corresponding to the 3 categories. Based on the first evaluation value and the second evaluation value, it is determined whether the classification result meets the preset conditions. If so, this step is terminated. Otherwise, this step is repeated until the classification result meets the preset conditions. The specific method for determining whether the preset conditions are met will be explained in detail later.

[0030] The variance between the characteristic nitrogen application information and the nitrogen application information of the same type may be used as the first deviation score.

[0031] Further, judging whether the classification result meets the preset condition based on the first evaluation value and the second evaluation value includes the following steps: Determine whether the second evaluation value is less than or equal to the first evaluation value. If so, calculate the first difference between the first evaluation value and the second evaluation value, and determine whether the first difference is greater than or equal to a preset second threshold. If so, determine that it does not meet the preset conditions; otherwise, determine that it meets the preset conditions.

[0032] Specifically, since the first evaluation value and the second evaluation value represent the clustering tightness of the current classification result, the larger the evaluation value, the smaller the clustering tightness of the current classification result. Therefore, by comparing the evaluation values ​​of two adjacent classification results, if the second evaluation value is less than or equal to the first evaluation value, it means that the clustering tightness of the classification result after re-classification is better than the previous classification. Therefore, the classification result after re-classification is relatively scientific. However, in order to determine whether there is still room for optimization in classification, the first difference between the two evaluation values ​​is compared with the preset second threshold. If the first difference is greater than the second threshold, it means that the clustering tightness of the two classifications is quite different, indicating that there is still room for optimization in classification. Therefore, it is judged that the preset conditions are not met, and the first number is continued to be increased by one for re-classification. As the number of classifications increases, the clustering tightness of the classification results will become better and better, and the first difference between the evaluation values ​​of the corresponding two adjacent classifications will become smaller and smaller, and the optimization space of the corresponding classification will also become smaller and smaller. Therefore, when the first difference is less than the second threshold, it is judged that the preset conditions are met and classification is stopped.

[0033] Furthermore, the average emission of specific gases in the soil of a typical rice field planting area is estimated based on the simulation results of the soil injection experiment, including the following steps: Use multiple soil injections, number each soil injection, fill the collected soil samples into the soil injections, control the environmental factors of the soil injections to simulate the field conditions of each typical rice field planting area, obtain multiple influencing factors, preset multiple first processing conditions for each influencing factor, obtain a first number of first processing conditions, obtain a second number of all soil injections, divide the second number by the first number to obtain a first result value, divide the multiple soil injections into first result value groups, set the same first processing condition for each group of soil injections, periodically collect gas samples from the soil injections, use analysis equipment to measure the concentration of specific gases, record the corresponding soil injection number, sampling time, sampling date and concentration data and save them in a first list, for each first processing condition, add the specific gas concentrations of all measurement times corresponding to each group of soil injections to obtain the total emission of the specific gas during the test, and calculate the average emission corresponding to each first processing condition based on the total emission.

[0034] Specifically, the influencing factors include nitrogen fertilizer type, nitrogen fertilizer addition amount and biochar addition amount. The first treatment condition refers to the use of different nitrogen fertilizers, different nitrogen fertilizer addition amounts, and different types and amounts of biochar in soil injection. Soil injection experiments usually focus on a single environmental factor, such as the impact of a single nitrogen fertilizer amount on the results. In order to clearly explain the calculation method of the average emission, the following examples only use a small number of simple examples. In fact, a large number of first treatment conditions can be set. Assume that the first treatment condition is different nitrogen fertilizer application levels (0kg N / ha, 60kg N / ha, 120kg N / ha). Taking CH4 as an example, in order to study the effects of different nitrogen fertilizer application levels on CH4 emissions from paddy soil, 3 soil injections were configured for each nitrogen fertilizer application level. The experimental period was 30 days, and CH4 emissions were measured every 5 days. The CH4 emissions of each soil injection were recorded at each measurement time point (5th day, 10th day, 15th day, 20th day, 25th day, and 30th day), such as Figure 2 The following is a graph of the CH4 emissions data we obtained, based on Figure 2 , the CH4 emissions of all measured time points of each soil injection group were added together to obtain the total emission of the soil injection group during the experiment. For each nitrogen fertilizer application level, the average total emission of all soil injections was calculated. The average emission under the 0kg N / ha treatment was (0.1+0.2+0.15+0.2+0.18+0.22)+(0.12+0.22+0.13+0.21+0.19+0.23)+(0.11+0.21+0.14+0.2+0.17+0.2) / (3*6), which was approximately equal to 0.21gCH4 / m². The average emission under the 60kg N / ha treatment was calculated by the same method to be 0.34gCH4 / m², and the 120kg The average emission under the N / ha treatment condition was 0.39 g CH4 / m². The average CH4 emission and the average N2O emission of paddy field soil under each first treatment condition could be calculated by the above method, and then the effects of different first treatment conditions on CH4 and N2O emissions could be evaluated.

[0035] Furthermore, the soil injection test simulation results are analyzed to obtain analysis results, including the following steps: For each first treatment condition, the group emission mean corresponding to each group of soil injections under the first treatment condition is calculated, and the total emission mean of all groups is also calculated. The inter-group variance under the first treatment condition is calculated based on the group emission mean and the total emission mean. The intra-group variance of each group of soil injections is also calculated, the inter-group mean square is calculated based on the inter-group variance, and the intra-group mean square is calculated based on the intra-group variance. The value obtained by dividing the inter-group mean square by the intra-group mean square is taken as the first value. Based on the first value, the number of soil injections contained in each group and the number of soil injection groups, the second value is determined using statistical software.

[0036] Specifically, the between-group variance is divided by the number of soil injection groups minus one to obtain the between-group mean square, and the within-group variance is divided by the number of soil injections within the group minus the number of soil injection groups to obtain the within-group mean square. The first value can be used to indicate the difference between the between-group differences relative to the within-group differences. If the first value is larger, it means that the difference is larger, which means that the mean of at least one group is significantly different from that of other groups. The second value is used to compare with the preset third threshold. The third threshold refers to the preset significance level. If the second value is less than the third threshold, it is considered that there are significant differences between at least two groups under the first treatment condition, indicating that the first treatment condition has a significant impact on the emission of specific gases. The influencing factors corresponding to the first treatment conditions are taken as significant influencing factors. The statistical software can use AMOS software.

[0037] Further, determining significant influencing factors based on the analysis results includes the following steps: A second value corresponding to the first processing condition is obtained. If the second value is less than a preset third threshold, the influencing factor corresponding to the first processing condition is taken as a significant influencing factor.

[0038] Furthermore, different carbon-nitrogen coupling scenarios are set based on significant influencing factors, including the following steps: Different levels are set for each significant influencing factor, each level of each significant influencing factor is combined with each level of other significant influencing factors at least once to generate multiple influencing factor combinations, and different carbon-nitrogen coupling scenarios are set based on the multiple influencing factor combinations.

[0039] Specifically, for example, there are three significant influencing factors, namely nitrogen fertilizer type A: no nitrogen fertilizer (1), urea (2), ammonia water (3), nitrogen fertilizer concentration (B): low (1), medium (2), high (3), biochar addition (C): 0% (1), 1% (2), 2% (3), then the generated multiple influencing factor combinations are as follows: Figure 3 As shown, Figure 3 It is also an orthogonal table of multiple significant influencing factors. Based on the orthogonal table, different carbon-nitrogen coupling scenarios are set up for orthogonal simulation experiments. The design of the orthogonal table reduces the number of required tests, making the experiment more efficient, while still providing sufficient information to evaluate multiple influencing factors.

[0040] Furthermore, based on the orthogonal simulation experiment results, a statistical method is used to quantify the importance of each of the influencing factors, including the following steps: Taking each specific gas emission as the observed variable and the significant influencing factor as the latent variable, a structural equation model is constructed based on the assumed relationship between the observed variable and the latent variable. Relevant data are collected based on the experimental results of the orthogonal simulation experiment. The relevant data include different levels of the significant influencing factors and the corresponding specific gas emissions. Statistical software is used to fit the structural equation model to estimate the model parameters. The goodness of fit test method is used to evaluate the goodness of fit of the structural equation model. When the goodness of fit is less than the preset fitting threshold, the structural equation model is modified until the goodness of fit is greater than or equal to the fitting threshold. The first and second influencing parameters of each significant influencing factor on the specific gas emission are calculated according to the model parameters. The comprehensive influencing parameter is calculated based on the first and second influencing parameters. The comprehensive influencing parameter is the factor importance of each significant influencing factor.

[0041] Specifically, in order to quantify the importance of significant influencing factors on specific gas emissions, the structural equation model is used for analysis. The structural equation model is a multivariate statistical analysis technique used to analyze the complex relationship between variables. First, the significant influencing factors (such as nitrogen fertilizer application) are taken as latent variables, and other specific emissions are taken as observed variables. The relationship between the observed variables and the latent variables is assumed, and then the experimental results of the orthogonal simulation experiment are collected as relevant data. Based on the relevant data, statistical software such as AMOS is used to estimate the model parameters, and the fit test methods such as chi-square value are used to evaluate the fit of the structural equation model. When the fit is less than the preset fit threshold, the structural equation model is modified, that is, the model parameters are modified until The degree of fit is greater than or equal to the fitting threshold, and then the first influencing parameter and the second influencing parameter are calculated based on the model parameters. The first influencing parameter refers to the direct impact of the significant influencing factor on the specific gas emissions, and the second influencing parameter refers to the indirect impact of the significant influencing factor on the specific gas emissions through at least one intermediate variable. The comprehensive influencing parameter refers to the overall impact of the significant influencing factor on the specific gas emissions. Assuming that the first influencing parameter is 0.5 and the second influencing parameter is 0.15, the comprehensive influencing parameter is 0.5 plus 0.15 = 0.65. Through the above method, we can fully understand the impact of different significant influencing factors on specific gas emissions, scientifically quantify the relationship between the two, and provide a scientific basis for subsequent decision-making.

[0042] According to another aspect of the embodiment of the present invention, Figure 4 As shown, a paddy field soil emission reduction potential estimation system based on carbon-nitrogen coupling is also provided, including an integration module, a transmission module, a storage module and a backup module, which are used to implement the paddy field soil emission reduction potential estimation method based on carbon-nitrogen coupling as described above. The specific functions of each module are as follows: The classification module uses literature statistics to collect nitrogen application information of rice fields in the target area, classifies rice fields with similar nitrogen application information into the same type, and selects several rice fields from each type of rice field as typical rice fields; The sampling module is used to take soil samples at preset fixed intervals in each typical rice field planting area, maintain a consistent sampling depth at all sampling points, conduct experimental analysis on the sampled soil samples, obtain the soil nitrogen concentration level in each typical rice field planting area, and obtain representative soil samples based on the soil nitrogen concentration level analysis; The first analysis module uses representative soil samples to simulate soil injection experiments to obtain soil injection experiment simulation results, estimates the average emission of specific gases in the soil of a typical rice field planting area based on the soil injection experiment simulation results, and analyzes the soil injection experiment simulation results to obtain analysis results; In the second analysis module, significant influencing factors are determined based on the analysis results, different carbon-nitrogen coupling scenarios are set based on the significant influencing factors, and orthogonal simulation experiments are conducted. Based on the results of the orthogonal simulation experiments, statistical methods are used to quantify the importance of each significant influencing factor. The importance of the factor is used as the weight, and the interpolation estimation results under each significant influencing factor are weightedly accumulated to obtain the comprehensive emission estimate of the specific gas. Based on the comprehensive emission estimate, the emission reduction capacity of the biocarbon rice field based on carbon-nitrogen coupling is obtained.

[0043] According to another aspect of an embodiment of the present invention, a medium is also provided, wherein the medium stores program instructions, wherein when the program instructions are executed, the device where the medium is located is controlled to execute any one of the above-mentioned methods for estimating rice field soil emission reduction potential based on carbon-nitrogen coupling.

[0044] In summary, the present invention provides a method, system and medium for estimating the emission reduction potential of paddy field soil based on carbon-nitrogen coupling, the method comprising collecting information on nitrogen application of paddy fields in the target area through literature statistics, classifying paddy fields with similar nitrogen application into the same type, and selecting several typical areas from each type; sampling soil at fixed intervals in typical paddy field areas, analyzing soil nitrogen concentration levels, and obtaining representative samples; using representative samples to simulate soil column experiments, estimating the average emission of specific gases; determining significant influencing factors, setting different carbon-nitrogen coupling scenarios for orthogonal simulation experiments, quantifying the importance of factors, and weighted accumulation to obtain a comprehensive emission estimation value, and evaluating the emission reduction capacity of biochar paddy fields based on carbon-nitrogen coupling. The present invention can effectively estimate the emission reduction potential of paddy field soil.

[0045] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0046] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0047] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0049] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating the emission reduction potential of paddy field soil based on carbon-nitrogen coupling, characterized in that: The steps include: Step S1, using a literature statistical method to collect nitrogen application information of rice fields in a target area, classifying rice field planting areas with similar nitrogen application information into the same type, and selecting a number of rice field planting areas from each type of rice field planting area as typical rice field planting areas; Step S2, sampling soil in each of the typical rice field planting areas at preset fixed intervals, maintaining a consistent sampling depth at all sampling points, performing experimental analysis on the sampled soil samples, obtaining the soil nitrogen concentration level of each typical rice field planting area, and obtaining representative soil samples based on the soil nitrogen concentration level; Step S3, using a representative soil sample to perform a soil injection experiment simulation to obtain a soil injection experiment simulation result, estimating the average emission of a specific gas in the soil of the typical rice field planting area based on the soil injection experiment simulation result, and analyzing the soil injection experiment simulation result to obtain an analysis result; Step S4, determining significant influencing factors based on the analysis results, setting different carbon-nitrogen coupling scenarios based on the significant influencing factors, conducting orthogonal simulation experiments, and quantifying the importance of each of the significant influencing factors using statistical methods based on the orthogonal simulation experiment results. The importance of the factors is used as weights, and the interpolation estimation results under each of the significant influencing factors are weightedly accumulated to obtain a comprehensive emission estimate of a specific gas, and then the emission reduction capacity of the biochar rice field based on carbon-nitrogen coupling is obtained based on the comprehensive emission estimate.

2. The method according to claim 1, characterized in that: Classifying the rice field planting areas with similar nitrogen application information into the same type comprises the following steps: Step S11, performing data preprocessing on the collected nitrogen application information, removing abnormal values, filling missing values, and normalizing various data parameters in the nitrogen application information to preset a first quantity; Step S12, using a clustering algorithm to classify the nitrogen application information into the first number of types, obtaining characteristic nitrogen application information of the corresponding type by calculation for each type of the nitrogen application information, calculating a first deviation score of each nitrogen application information of the same type and the corresponding characteristic nitrogen application information, and adding the first deviation scores corresponding to all types as a first evaluation value corresponding to the current classification; Step S13, add one to the first number, repeat step S12, classify the nitrogen application information again, calculate the corresponding second evaluation value, and judge whether the classification result meets the preset conditions based on the first evaluation value and the second evaluation value. If yes, end this step; otherwise, repeat this step until the classification result meets the preset conditions.

3. The method according to claim 2, characterized in that Judging whether the classification result meets the preset condition based on the first evaluation value and the second evaluation value includes the following steps: Determine whether the second evaluation value is less than or equal to the first evaluation value. If so, calculate the first difference between the first evaluation value and the second evaluation value, and determine whether the first difference is greater than or equal to a preset second threshold. If so, determine that the preset condition is not met; otherwise, determine that the preset condition is met.

4. The method according to claim 1, characterized in that: The average emission of specific gases in the soil of the typical rice field planting area is estimated based on the experimental simulation results, including the following steps: Use multiple soil injections, number each soil injection, fill the soil injections with collected soil samples, control the environmental factors of the soil injections to simulate the field conditions of each typical rice field planting area, obtain multiple influencing factors, preset multiple first processing conditions for each of the influencing factors, obtain a first number of the first processing conditions, obtain a second number of all soil injections, divide the second number by the first number to obtain a first result value, divide the multiple soil injections into first result value groups, set the same first processing conditions for each group of the soil injections, periodically collect gas samples from the soil injections, use analysis equipment to measure the concentration of specific gases, record the corresponding soil injection number, sampling time, sampling date and concentration data and save them in a first list, for each of the first processing conditions, add the specific gas concentrations of all measurement times corresponding to each group of soil injections to obtain the total emission of the specific gas during the test, and calculate the average emission corresponding to each of the first processing conditions based on the total emission.

5. The method according to claim 4, characterized in that Analyzing the soil injection test results to obtain analysis results includes the following steps: For each of the first processing conditions, the group emission mean corresponding to each group of soil injections under the first processing condition is calculated, and the total emission mean of all groups is calculated. The inter-group variance under the first processing condition is calculated based on the group emission mean and the total emission mean. The intra-group variance of each group of soil injections is also calculated, and the inter-group mean square is calculated based on the inter-group variance, and the intra-group mean square is calculated based on the intra-group variance. The value obtained by dividing the inter-group mean square by the intra-group mean square is taken as the first numerical value. Based on the first numerical value, the number of soil injections contained in each group and the number of soil injection groups, the second numerical value is determined using statistical software.

6. The method according to claim 4, characterized in that Determining significant influencing factors based on the analysis results includes the following steps: The second value corresponding to the first processing condition is obtained. If the second value is less than a preset first threshold, the influencing factor corresponding to the first processing condition is used as the significant influencing factor.

7. The method according to claim 1, characterized in that Different carbon-nitrogen coupling scenarios are set based on the significant influencing factors, including the following steps: Different levels are set for each of the significant influencing factors, each level of each of the significant influencing factors is combined at least once with each level of other significant influencing factors to generate multiple influencing factor combinations, and different carbon-nitrogen coupling scenarios are set based on the multiple influencing factor combinations.

8. The method according to claim 1, characterized in that: Based on the orthogonal simulation experiment results, the importance of each of the significant influencing factors is quantified using a statistical method, including the following steps: The emission of each specific gas is taken as an observed variable, and the significant influencing factors are taken as potential variables. A structural equation model is constructed based on the relationship between the observed variables and the potential variables. Relevant data is collected based on the experimental results of the orthogonal simulation experiment, and the relevant data includes different levels of significant influencing factors and corresponding specific gas emissions. The structural equation model is fitted and estimated using statistical software. The goodness of fit of the structural equation model is evaluated using a goodness of fit test method. When the goodness of fit is less than a preset fitting threshold, the structural equation model is modified until the goodness of fit is greater than or equal to the fitting threshold. The first influencing parameter and the second influencing parameter of each significant influencing factor on the specific gas emission are calculated according to the model parameters. The comprehensive influencing parameter is calculated based on the first influencing parameter and the second influencing parameter. The comprehensive influencing parameter is the factor importance of each significant influencing factor.

9. A paddy field soil emission reduction potential estimation system based on carbon-nitrogen coupling, used to implement the paddy field soil emission reduction potential estimation method based on carbon-nitrogen coupling as claimed in any one of claims 1 to 8, characterized in that: Includes the following modules: A classification module collects nitrogen application information of paddy fields in a target area by using a literature statistical method, classifies paddy field planting areas with similar nitrogen application information into the same type, and selects several paddy field planting areas from each type of paddy field planting area as typical paddy field planting areas; A sampling module is used to perform soil sampling in each of the typical rice field planting areas at preset fixed intervals, maintain a consistent sampling depth at all sampling points, perform experimental analysis on the sampled soil samples, obtain the soil nitrogen concentration level of each of the typical rice field planting areas, and obtain representative soil samples based on the soil nitrogen concentration level; The first analysis module uses a representative soil sample to perform a soil injection experiment simulation to obtain a soil injection experiment simulation result, estimates the average emission of a specific gas in the soil of the typical rice field planting area based on the soil injection experiment simulation result, and analyzes the soil injection experiment simulation result to obtain an analysis result; The second analysis module determines the significant influencing factors based on the analysis results, sets different carbon-nitrogen coupling scenarios based on the significant influencing factors, conducts orthogonal simulation experiments, and uses statistical methods to quantify the importance of each of the significant influencing factors based on the orthogonal simulation experiment results. The importance of the factors is used as weights to perform weighted accumulation on the interpolation estimation results under each of the significant influencing factors to obtain a comprehensive emission estimate of a specific gas, and then obtains the emission reduction capacity of the biochar rice field based on carbon-nitrogen coupling based on the comprehensive emission estimate.

10. A medium, characterized in that The medium stores program instructions, wherein when the program instructions are executed, the device where the medium is located is controlled to execute the paddy field soil emission reduction potential estimation method based on carbon-nitrogen coupling as described in any one of claims 1-8.

Images