Method for monitoring carbon sink in non-carbonate rock area by using carbonate rock powder

Abstract

The application discloses a method for monitoring carbon sink in non-carbonate rock area by using carbonate rock powder, and relates to the technical field of carbon sink monitoring. The method for monitoring carbon sink in non-carbonate rock area by using carbonate rock powder realizes real-time monitoring and storage of carbon sink data in non-carbonate rock area, and ensures the continuity and integrity of the data. Based on multivariate regression analysis, the soil and vegetation carbon sink characteristics of the non-carbonate rock area are evaluated, the soil carbon sink index and the vegetation carbon sink index are obtained, and the overall carbon sink index is evaluated. By using the carbon sink index data, a probability distribution model is fitted, the critical value of the natural fluctuation range of the carbon sink index is identified, and the threshold range of the carbon sink index is determined. The linear relationship between the mesh number of the carbonate rock powder and the soil carbon sequestration rate is analyzed by using a linear regression model, and a carbon sink linear regression model is constructed. When the carbon sink index is lower than the threshold range, the application amount and frequency of the carbonate rock powder are dynamically adjusted according to the carbon sink linear regression model, and the carbon sink management is optimized.

Description

Technical Field

[0001] This invention relates to the field of carbon sink monitoring technology, specifically a method for monitoring carbon sinks in non-carbonate rock areas by utilizing carbonate rock powder. Background Technology

[0002] Global climate change has become a major challenge facing humanity. Increased emissions of greenhouse gases (mainly carbon dioxide) are causing global temperatures to rise, extreme weather events to become more frequent, and sea levels to rise, among other increasingly serious problems.

[0003] Non-carbonate rock regions typically lack natural carbonate rocks, limiting their ability to fix atmospheric carbon dioxide. Applying carbonate rock powder can artificially enhance the carbon sequestration capacity of these regions, effectively reducing atmospheric carbon dioxide concentration and mitigating global warming. Many non-carbonate rock regions also have acidic soils, negatively impacting plant growth and agricultural production. Carbonate rock powder can neutralize soil acidity, increase soil pH, improve soil structure and fertility, promote healthy plant growth, and increase crop yields.

[0004] In non-carbonate rock areas, carbon sequestration capacity is relatively low due to soil properties and a lack of available minerals. Carbonate rocks can react with carbon dioxide under natural conditions to form calcium carbonate, thereby sequestering carbon dioxide. Applying carbonate rock powder to non-carbonate rock areas can enhance soil carbon sequestration capacity and increase the carbon sink capacity of the region. However, the effect of carbonate rock powder of different mesh sizes on soil carbon sequestration rates is uncertain and requires optimization through precise monitoring and analysis. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder, thus solving the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder, comprising the following steps: S1. Real-time monitoring of carbon sequestration data in non-carbonate rock areas, and storing the carbon sequestration data in a time series to a database; S2. Based on the carbon sequestration data in non-carbonate rock areas, multivariate regression analysis is performed on the soil carbon sequestration characteristics and vegetation carbon sequestration characteristics of non-carbonate rock areas to obtain the soil carbon sequestration index and vegetation carbon sequestration index of non-carbonate rock areas, and to evaluate the carbon sequestration characteristics of non-carbonate rock areas. S3. Based on the carbon sink index of non-carbonate rock areas, fit a probability distribution model of the carbon sink index, identify the critical value of the natural fluctuation range of the carbon sink index, and obtain the threshold range of the carbon sink index of non-carbonate rock areas; S4. Analyze the linear relationship between the mesh size of carbonate rock powder and the soil carbon sequestration rate through a linear regression model, and establish a carbon sink linear regression model; S5. When the carbon sink index of non-carbonate rock areas is lower than the threshold range of the carbon sink index of non-carbonate rock areas, monitor the application amount and frequency of different amounts of carbonate rock powder through the carbon sink linear regression model.

[0007] Furthermore, the specific process of obtaining the soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas through multivariate regression analysis is as follows: Based on the carbon sink data of non-carbonate rock areas, the correlation between the carbon sink data and soil carbon sink and vegetation carbon sink is evaluated by Pearson correlation coefficient. Feature data that conforms to the characteristics of soil carbon sink and vegetation carbon sink are extracted, and the dimensionality of the features is reduced by principal component analysis. The soil carbon sink index and vegetation carbon sink index are used as target variables, and the extracted feature data are used as input variables to establish a multivariate regression model. According to the trained multivariate regression model, real-time monitoring data is input to obtain the soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas.

[0008] Furthermore, the specific process for assessing the carbon sink index in non-carbonate rock areas is as follows: Based on the soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas, a comprehensive calculation is performed with the set soil carbon sink index threshold and vegetation carbon sink index threshold respectively to obtain the carbon sink index in non-carbonate rock areas.

[0009] Furthermore, the specific process of fitting the probability distribution model of the carbon sink index and identifying the critical value of the natural fluctuation range of the carbon sink index is as follows: Based on the carbon sink index of non-carbonate rock areas, the normality of the carbon sink index data is initially checked by histogram and QQ plot; the mean and standard deviation of the carbon sink index of non-carbonate rock areas are calculated by normal distribution model and the data is fitted; based on the properties of normal distribution, the critical value of the natural fluctuation range of the carbon sink index is identified.

[0010] Furthermore, the specific process for obtaining the threshold range of the carbon sink index in non-carbonate rock areas is as follows: Based on the mean and standard deviation of the carbon sink index in non-carbonate rock areas, at a set confidence level, the low and high thresholds of the carbon sink index in non-carbonate rock areas are calculated using a normal distribution model, and the low and high thresholds of the carbon sink index in non-carbonate rock areas are set as the threshold range of the carbon sink index in non-carbonate rock areas.

[0011] Furthermore, the specific process of analyzing the linear relationship between carbonate rock powder mesh size and soil carbon sequestration rate using a linear regression model is as follows: Obtain data on the application amount of carbonate rock powder with different mesh sizes and the corresponding soil carbon sequestration rate from the database; determine whether the linear relationship between carbonate rock powder mesh size and soil carbon sequestration rate is significant through a significance test, and identify positive or negative correlations; calculate the Pearson correlation coefficient between carbonate rock powder mesh size and soil carbon sequestration rate to quantify the strength of the relationship.

[0012] Furthermore, the specific process of establishing a linear regression model for carbon sinks is as follows: using the mesh size of carbonate rock powder as the independent variable and the soil carbon sequestration rate as the dependent variable, a preliminary linear regression model is established; the regression coefficients are estimated using the least squares method, and a regression equation is constructed; the model is subjected to significance testing and the strength of the relationship is quantified to evaluate the predictive performance and accuracy of the model; the regression model is optimized through cross-validation to establish a linear regression model for carbon sinks.

[0013] Furthermore, the application amount and frequency of different amounts of carbonate rock powder were monitored using a carbon sink linear regression model as follows: when the carbon sink index in non-carbonate rock areas is lower than the first carbon sink threshold, the application amount of fine carbonate rock powder is increased; when the carbon sink index in non-carbonate rock areas is lower than the second carbon sink threshold, the application amount of coarse carbonate rock powder is increased.

[0014] The present invention has the following beneficial effects:

[0015] (1) This monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder can acquire and update dynamic data on carbon sequestration in non-carbonate rock areas in a timely manner through real-time monitoring, ensuring the timeliness and accuracy of the data. Time series storage allows carbon sequestration data to be systematically recorded and managed, providing a continuous data foundation for subsequent analysis. Multivariate regression analysis can systematically reveal the key factors affecting soil and vegetation carbon sequestration and quantify the impact of these factors on carbon sequestration. This process ensures the accuracy of carbon sequestration index calculation, enhances the comprehensive understanding of carbon sequestration characteristics, and thus provides a scientific basis for formulating effective carbon sequestration management strategies.

[0016] (2) This monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder can accurately identify the natural fluctuation range of the carbon sequestration index and determine its critical value for normal fluctuation by fitting a probability distribution model. Setting a threshold range for the carbon sequestration index helps to monitor carbon sequestration changes in real time and provide timely warnings of abnormal fluctuations, thereby ensuring the robustness of carbon sequestration management. The establishment of a linear regression model quantifies the impact of carbonate rock powder mesh size on soil carbon sequestration rate, providing a quantitative scientific basis for carbon sequestration optimization. When the carbon sequestration index is lower than the set threshold range, the application amount and frequency of carbonate rock powder can be dynamically adjusted through the existing regression model to quickly correct the downward trend of carbon sequestration and improve the carbon sequestration effect. This process ensures the sustainability of carbon sequestration and avoids a significant reduction in carbon sequestration capacity.

[0017] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0018] Figure 1 This is a flowchart of the monitoring method for increasing carbon sinks in non-carbonate rock areas using carbonate rock powder, as described in this invention. Detailed Implementation

[0019] This application provides a monitoring method for increasing carbon sequestration in non-carbonate rock areas by utilizing carbonate rock powder. This method addresses the issues of low carbon sequestration capacity in non-carbonate rock areas, unclear effects of carbonate rock powder application, and lack of systematic monitoring and optimization methods.

[0020] The problem addressed in this application's embodiments can be summarized as follows:

[0021] Real-time monitoring of carbon sequestration data in non-carbonate rock areas, and storage of the carbon sequestration data in non-carbonate rock areas into a database in time series.

[0022] Based on carbon sink data from non-carbonate rock areas, multivariate regression analysis was conducted to analyze the soil carbon sink characteristics and vegetation carbon sink characteristics of non-carbonate rock areas, obtain the soil carbon sink index and vegetation carbon sink index of non-carbonate rock areas, and evaluate the carbon sink index of non-carbonate rock areas.

[0023] Based on the carbon sink index in non-carbonate rock areas, a probability distribution model of the carbon sink index is fitted to identify the critical value of the natural fluctuation range of the carbon sink index and obtain the threshold range of the carbon sink index in non-carbonate rock areas.

[0024] A linear regression model was established to analyze the linear relationship between the mesh size of carbonate rock powder and the soil carbon sequestration rate, thereby establishing a carbon sink linear regression model.

[0025] When the carbon sink index in non-carbonate rock areas is below the threshold range for carbon sink index in non-carbonate rock areas, the application amount and frequency of different amounts of carbonate rock powder are monitored using a carbon sink linear regression model.

[0026] Please see Figure 1 This invention provides a technical solution: a monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder, comprising the following steps: S1. Real-time monitoring of carbon sequestration data in non-carbonate rock areas, and storing the data in a time series to a database; S2. Based on the carbon sequestration data, multivariate regression analysis of soil and vegetation carbon sequestration characteristics in non-carbonate rock areas to obtain soil carbon sequestration index and vegetation carbon sequestration index, and to evaluate the carbon sequestration index in non-carbonate rock areas; S3. Based on the carbon sequestration index in non-carbonate rock areas, fitting a probability distribution model of the carbon sequestration index, identifying the critical value of the natural fluctuation range of the carbon sequestration index, and obtaining the threshold range of the carbon sequestration index in non-carbonate rock areas; S4. Analyzing the linear relationship between the mesh size of carbonate rock powder and the soil carbon sequestration rate using a linear regression model, and establishing a carbon sequestration linear regression model; S5. When the carbon sequestration index in non-carbonate rock areas is lower than the threshold range, monitoring the application amount and frequency of different amounts of carbonate rock powder using the carbon sequestration linear regression model.

[0027] In this implementation plan, non-carbonate rock areas refer to regions where carbonate minerals (such as limestone and dolomite) are not the primary components. These areas have lower carbonate content in their soils and rocks, resulting in lower natural carbon sequestration capacity. Carbon sequestration refers to the process by which ecosystems or anthropogenic technologies remove and store carbon dioxide from the atmosphere. Soil and vegetation are important carbon sequestrants, helping to mitigate climate change by absorbing carbon dioxide. The non-carbonate rock area carbon sequestration index is an indicator used to quantify and assess the carbon sequestration capacity of a region, typically based on the carbon storage of regional soils and vegetation and its changing trends. Multivariate regression analysis is a statistical method used to analyze the relationships between multiple variables. In this context, regression analysis is used to assess the impact of soil carbon sequestration characteristics and vegetation carbon sequestration characteristics on the carbon sequestration index. A probability distribution model is a mathematical model that describes the probability distribution of data across different value ranges. In carbon sequestration monitoring, the normal distribution model is often used to simulate the fluctuation range of the carbon sequestration index. The normal distribution is a classic probability distribution, exhibiting a bell-shaped curve, with data concentrated around the mean and gradually decreasing towards both sides. The normal distribution model is often used to identify the fluctuation range of the carbon sequestration index. In carbon sequestration monitoring, the critical value refers to the boundary of the natural fluctuation range of the carbon sequestration index, i.e., the upper and lower limits of the carbon sequestration index under normal circumstances, used to assess the stability of carbon sequestration. A linear regression model is a statistical method used to analyze the linear relationship between two or more variables. Here, it is used to quantify the impact of carbonate rock powder mesh size on soil carbon sequestration rate, helping to optimize carbon sequestration measures. Carbonate rock powder mesh size refers to the particle size of carbonate rock powder, usually expressed by mesh number (i.e., the number of openings per inch of sieve). A larger mesh number indicates finer particles, which may have different effects on soil carbon sequestration efficiency. Soil carbon sequestration rate refers to the rate at which soil absorbs and stores carbon through natural processes or anthropogenic measures, and is an important indicator for assessing soil carbon sequestration capacity.

[0028] Furthermore, the specific process of obtaining the soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas through multivariate regression analysis is as follows: Based on the carbon sink data of non-carbonate rock areas, the correlation between the carbon sink data and soil carbon sink and vegetation carbon sink is evaluated by Pearson correlation coefficient. Feature data that conforms to the characteristics of soil carbon sink and vegetation carbon sink are extracted, and the dimensionality of the features is reduced by principal component analysis. The soil carbon sink index and vegetation carbon sink index are used as target variables, and the extracted feature data are used as input variables to establish a multivariate regression model. According to the trained multivariate regression model, real-time monitoring data is input to obtain the soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas.

[0029] In this implementation plan, the Pearson correlation coefficient is used to assess the correlation between carbon sink data in non-carbonate rock areas and soil and vegetation carbon sink characteristics. This assessment helps identify which feature data are associated with carbon sinks, thereby selecting variables that best represent soil and vegetation carbon sink characteristics. After identifying the correlations, features related to soil and vegetation carbon sink characteristics are extracted from the carbon sink data. Since these features may have high dimensionality, principal component analysis (PCA) is used to reduce the dimensionality of the data. PCA preserves the main information of the data by transforming the original variables into a few uncorrelated principal components, while simplifying the complexity of the model. Using the extracted feature data as input variables, and soil and vegetation carbon sink indices as target variables, a multivariate regression model is established. This model is used to quantify the relationship between input features and target variables, and can predict soil and vegetation carbon sink indices with new data input. The formula for calculating the soil carbon sink index in non-carbonate rock areas is as follows: CSI s =β 0,s +β 1,s X 1,s +β 2,s X 2,s +…+β n,s X n,s +∈ s Among them, CSI s This represents the soil carbon sink index in non-carbonate rock areas. β 0,s β represents the intercept of the regression model, and β represents the soil carbon sink index when all input variables are zero. 1,s ,β 2,s ,…,β n,s : Regression coefficients of soil carbon sequestration characteristics, representing the strength of the influence of each input variable on the soil carbon sequestration index. X 1,s ,X 2,s ,…,X n,s These represent characteristic variables related to soil carbon sequestration, including soil organic carbon content, soil moisture, temperature, and soil texture. s This represents the error term, indicating the difference between the model's predicted and actual values. The formula for calculating the vegetation carbon sink index (CSI) in non-carbonate rock areas is as follows: CSI v =α 0,v +α 1,v X 1,v +α 2,v X 2,v +…+α n,v X n,v +∈ v In the formula, CSI v β represents the vegetation carbon sink index in non-carbonate rock areas. 0,v α represents the intercept of the regression model, and α represents the vegetation carbon sink index when all input variables are zero. 1,v ,α2,v ,…,α n,v X represents the regression coefficients indicating the characteristics of vegetation carbon sink, and represents the strength of the influence of each input variable on the vegetation carbon sink index. 1,v ,X 2,v ,…,X n,v These represent characteristic variables related to vegetation carbon sinks, including vegetation cover, biomass, and net primary productivity. v This represents the error term, indicating the difference between the model's predicted value and the actual value.

[0030] Furthermore, the specific process for assessing the carbon sink index in non-carbonate rock areas is as follows: Based on the soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas, a comprehensive calculation is performed with the set soil carbon sink index threshold and vegetation carbon sink index threshold respectively to obtain the carbon sink index in non-carbonate rock areas.

[0031] In this implementation plan, the formula for calculating the carbon sink index in non-carbonate rock areas is as follows: In the formula, δFR represents the carbon sink index in non-carbonate rock areas, GB represents the soil carbon sink index threshold, and FB represents the vegetation carbon sink index threshold.

[0032] Furthermore, the specific process of fitting the probability distribution model of the carbon sink index and identifying the critical value of the natural fluctuation range of the carbon sink index is as follows: Based on the carbon sink index of non-carbonate rock areas, the normality of the carbon sink index data is initially checked by histogram and QQ plot; the mean and standard deviation of the carbon sink index of non-carbonate rock areas are calculated by normal distribution model and the data is fitted; based on the properties of normal distribution, the critical value of the natural fluctuation range of the carbon sink index is identified.

[0033] In this implementation scheme, a histogram is a graph that displays the data distribution by dividing the data into several intervals and plotting the frequency of data points within each interval. The histogram provides a visual check of whether the data approximates a normal distribution. If the data distribution resembles a bell curve, it indicates that the data may conform to a normal distribution. In a QQ plot, if the data points are roughly distributed along the diagonal, it indicates that the sample data approximates a normal distribution. If the data points deviate significantly from the diagonal, it indicates that the data distribution does not conform to a normal distribution. The mean (μ) and standard deviation (σ) are calculated from the carbon sink index data. These statistical parameters are used to fit a normal distribution model to obtain the probability density function of the carbon sink index, as shown in the following formula: Here, f(x) represents the relative probability or density of the carbon sink index x. A higher value indicates a greater likelihood of that value occurring. μ (mean) represents the average value of the carbon sink index, and σ (standard deviation) measures the dispersion of the data distribution. The standard deviation σ describes the width of the data points around the mean μ. A larger standard deviation indicates a wider data distribution; a smaller standard deviation indicates a more concentrated data distribution. represents a constant used to normalize the normal distribution so that its total area is 1. This describes the relationship between the squared difference and the standard deviation of the carbon sink index x from the mean μ. This part determines how the probability density of the carbon sink index changes with x. The farther away from the mean, the lower the probability density.

[0034] Furthermore, the specific process for obtaining the threshold range of the carbon sink index in non-carbonate rock areas is as follows: Based on the mean and standard deviation of the carbon sink index in non-carbonate rock areas, at a set confidence level, the low and high thresholds of the carbon sink index in non-carbonate rock areas are calculated using a normal distribution model, and the low and high thresholds of the carbon sink index in non-carbonate rock areas are set as the threshold range of the carbon sink index in non-carbonate rock areas.

[0035] In this implementation plan, statistical methods are used to determine the reasonable range of the carbon sink index. In non-carbonate rock areas, the distribution of the carbon sink index is assumed to be normally distributed. By calculating the mean and standard deviation of the sample, the central tendency and dispersion of the data can be described. After setting a 95% confidence level, a normal distribution model is used to determine the low and high thresholds, which define the range of the carbon sink index. Within this range, we can have 95% confidence that the actual carbon sink index value will fall within this interval. This method ensures that our assessment of the carbon sink index is both scientific and reliable in a statistical sense.

[0036] Furthermore, the specific process of analyzing the linear relationship between carbonate rock powder mesh size and soil carbon sequestration rate using a linear regression model is as follows: Obtain data on the application amount of carbonate rock powder with different mesh sizes and the corresponding soil carbon sequestration rate from the database; determine whether the linear relationship between carbonate rock powder mesh size and soil carbon sequestration rate is significant through a significance test, and identify positive or negative correlations; calculate the Pearson correlation coefficient between carbonate rock powder mesh size and soil carbon sequestration rate to quantify the strength of the relationship.

[0037] In this implementation plan, the significance test is conducted through a linear relationship test: a statistical significance test is used to determine whether a significant linear relationship exists between the mesh size of carbonate rock powder and the soil carbon sequestration rate. Null hypothesis: The effect of carbonate rock powder mesh size on the soil carbon sequestration rate is not significant, i.e., the regression coefficient is zero. Alternative hypothesis: The mesh size of carbonate rock powder has a significant effect on the soil carbon sequestration rate, i.e., the regression coefficient is not zero. Result interpretation: If the p-value of the significance test is less than the set significance level (usually 0.05), the null hypothesis is rejected, and a linear relationship is considered to exist between the two. Calculate the Pearson correlation coefficient: Where, x i and y i These are the observed values ​​for the mesh size of carbonate rock powder and the soil carbon sequestration rate, respectively. and This represents the mean of carbonate rock powder mesh size and soil carbon sequestration rate. r represents the Pearson correlation coefficient, which measures the strength and direction of the linear relationship between carbonate rock powder mesh size and soil carbon sequestration rate. Range: r values ​​range from -1 to +1. r close to +1 indicates a strong positive correlation, close to -1 indicates a strong negative correlation, and close to 0 indicates no significant linear relationship.

[0038] Furthermore, the specific process of establishing a linear regression model for carbon sinks is as follows: using the mesh size of carbonate rock powder as the independent variable and the soil carbon sequestration rate as the dependent variable, a preliminary linear regression model is established; the regression coefficients are estimated using the least squares method, and a regression equation is constructed; the model is subjected to significance testing and the strength of the relationship is quantified to evaluate the predictive performance and accuracy of the model; the regression model is optimized through cross-validation to establish a linear regression model for carbon sinks.

[0039] In this implementation scheme, the process of estimating regression coefficients using the least squares method is as follows: setting the partial derivative of the residual sum of squares (RSS) to zero refers to the process of finding the optimal parameters of the regression model in the least squares estimation. By setting the partial derivative of the RSS to zero, the following estimation formula for the regression coefficients is obtained: in, All observations x i and y i The sum of the products. and This represents the sum of the independent and dependent variables. Represents the sum of squares of the independent variables, regression coefficients intercept The linear regression model is established using the following formula: V = β0 + β1C + ω; where V represents the soil carbon sequestration rate, C represents the mesh size of the carbonate rock powder, β0 represents the intercept (i.e., the intersection of the regression line with the Y-axis), β1 represents the regression coefficient (i.e., the degree of influence of the mesh size of the carbonate rock powder on the soil carbon sequestration rate), and ω represents the error term (i.e., random fluctuations that the model cannot explain).

[0040] Furthermore, the application amount and frequency of different amounts of carbonate rock powder were monitored using a carbon sink linear regression model as follows: when the carbon sink index in non-carbonate rock areas is lower than the first carbon sink threshold, the application amount of fine carbonate rock powder is increased; when the carbon sink index in non-carbonate rock areas is lower than the second carbon sink threshold, the application amount of coarse carbonate rock powder is increased.

[0041] In this implementation plan, a linear regression model for carbon sequestration is established by analyzing the relationship between the application rate, mesh size (particle size) of carbonate rock powder, and the soil carbon sequestration rate. This model can predict the soil carbon sequestration rate and its impact on the overall carbon sequestration index at specific application rates and mesh sizes. Therefore, this model can be used to monitor and adjust the application strategy of carbonate rock powder to maintain or improve the carbon sequestration index. When the carbon sequestration index in non-carbonate rock areas falls below a set first threshold, it means that the soil's carbon sequestration capacity is weakening, but has not yet reached a very dangerous level. In this case, the monitoring logic instructs an increase in the application rate of fine carbonate rock powder. Due to its larger specific surface area, fine carbonate rock powder can react with the soil more quickly, rapidly increasing the soil's carbon sequestration rate and thus rapidly improving the carbon sequestration index. The second threshold for carbon sequestration represents the lower limit of the carbon sequestration index. When the carbon sequestration index falls below this value, the soil's carbon sequestration capacity has significantly decreased, requiring more forceful intervention measures. In this case, the monitoring logic instructs an increase in the application rate of coarse carbonate rock powder. Coarse carbonate rock powder, due to its larger particle size, has a slower reaction rate but a longer duration of presence in the soil, providing a more sustained carbon sequestration effect and helping the soil restore and stabilize its carbon sequestration capacity. By monitoring changes in the carbon sequestration index in non-carbonate rock areas and adjusting according to set thresholds, the application rate and particle size of carbonate rock powder can be dynamically regulated to ensure the carbon sequestration index remains within an ideal range. This adjustment mechanism effectively addresses fluctuations in soil carbon sequestration capacity, maximizes the carbon sequestration effect, and avoids the negative effects of excessive or insufficient application of carbonate rock powder.

[0042] In summary, this application has at least the following effects:

[0043] This study utilizes carbonate rock powder to enhance carbon sequestration monitoring in non-carbonate rock areas. Real-time monitoring allows for timely acquisition and updating of dynamic carbon sequestration data in these areas, ensuring data timeliness and accuracy. Time-series storage enables systematic recording and management of carbon sequestration data, providing a continuous data foundation for subsequent analysis. Multivariate regression analysis systematically reveals key factors influencing soil and vegetation carbon sequestration and quantifies their impact. This process ensures accurate calculation of the carbon sequestration index, enhances a comprehensive understanding of carbon sequestration characteristics, and provides a scientific basis for developing effective carbon sequestration management strategies. By fitting a probability distribution model, the natural fluctuation range of the carbon sequestration index can be accurately identified, and its critical value for normal fluctuations can be determined. Setting a threshold range for the carbon sequestration index helps in real-time monitoring of carbon sequestration changes and timely warning of abnormal fluctuations, thus ensuring the robustness of carbon sequestration management. The establishment of a linear regression model quantifies the impact of carbonate rock powder mesh size on soil carbon sequestration rates, providing a quantitative scientific basis for carbon sequestration optimization. When the carbon sequestration index falls below a set threshold, the application rate and frequency of carbonate rock powder can be dynamically adjusted using existing regression models to quickly correct the downward trend in carbon sequestration and improve its effectiveness. This process ensures the sustainability of carbon sequestration and avoids a significant reduction in carbon sequestration capacity.

[0044] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0045] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0046] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0047] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0048] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0049] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder, characterized in that, Includes the following steps: S1. Monitor carbon sink data in non-carbonate rock areas in real time and store the carbon sink data in non-carbonate rock areas into the database in time series order; S2. Based on carbon sink data in non-carbonate rock areas, multivariate regression analysis was conducted on soil carbon sink characteristics and vegetation carbon sink characteristics in non-carbonate rock areas to obtain soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas and to evaluate carbon sink index in non-carbonate rock areas. S3. Based on the carbon sink index in non-carbonate rock areas, a probability distribution model of the carbon sink index is fitted to identify the critical value of the natural fluctuation range of the carbon sink index, and the threshold range of the carbon sink index in non-carbonate rock areas is obtained; the specific process of fitting the probability distribution model of the carbon sink index and identifying the critical value of the natural fluctuation range of the carbon sink index is as follows: Based on the carbon sink index of non-carbonate rock areas, the normality of the carbon sink index data was preliminarily examined using histograms and QQ plots. The mean and standard deviation of the carbon sink index in non-carbonate rock areas were calculated using a normal distribution model, and the data were fitted. Based on the properties of the normal distribution, identify the critical value of the natural fluctuation range of the carbon sink index; The specific process for obtaining the threshold range of the carbon sink index in non-carbonate rock areas is as follows: Based on the mean and standard deviation of the carbon sink index in non-carbonate rock areas, at a set confidence level, the low and high thresholds of the carbon sink index in non-carbonate rock areas are calculated using a normal distribution model, and the low and high thresholds of the carbon sink index in non-carbonate rock areas are set as the threshold range of the carbon sink index in non-carbonate rock areas. S4. Analyze the linear relationship between the mesh size of carbonate rock powder and the soil carbon sequestration rate using a linear regression model, and establish a carbon sink linear regression model; S5. The application rate of carbonate rock powder of different mesh sizes is monitored by carbon sink linear regression model; when the carbon sink index of non-carbonate rock area is lower than the high threshold of carbon sink index of non-carbonate rock area, the application rate of fine carbonate rock powder is increased. When the carbon sink index of non-carbonate rock areas is lower than the low threshold of the carbon sink index of non-carbonate rock areas, the amount of coarse carbonate rock powder used should be increased; among them, the mesh size of fine carbonate rock powder should be greater than that of coarse carbonate rock powder.

2. The monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder according to claim 1, characterized in that: The specific process of obtaining the soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas through multivariate regression analysis of soil and vegetation carbon sink characteristics in non-carbonate rock areas is as follows: Based on carbon sink data from non-carbonate rock areas, the correlation between carbon sink data and soil carbon sink and vegetation carbon sink in non-carbonate rock areas was assessed by Pearson correlation coefficient. Feature data that conform to the characteristics of soil carbon sink and vegetation carbon sink were extracted, and the dimensionality of the features was reduced by principal component analysis. Soil carbon sink index and vegetation carbon sink index were used as target variables, and the extracted feature data were used as input variables to establish a multivariate regression model. Based on the trained multivariate regression model, real-time monitoring data is input to obtain the soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas.

3. The monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder according to claim 2, characterized in that: The specific process for assessing the carbon sink index in non-carbonate rock areas is as follows: Based on the soil carbon sink index and vegetation carbon sink index of non-carbonate rock areas, the carbon sink index of non-carbonate rock areas is obtained by comprehensively calculating and processing them with the set soil carbon sink index threshold and vegetation carbon sink index threshold respectively.

4. The monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder according to claim 1, characterized in that: The specific process of analyzing the linear relationship between the mesh size of carbonate rock powder and the soil carbon sequestration rate using a linear regression model is as follows: Obtain data from the database on the application rate of carbonate rock powder with different mesh sizes and the corresponding soil carbon sequestration rate; The significance test was used to determine whether the linear relationship between the mesh size of carbonate rock powder and the soil carbon fixation rate was significant, and to identify whether the relationship was positive or negative. Calculate the Pearson correlation coefficient between the mesh size of carbonate rock powder and the soil carbon sequestration rate to quantify the strength of the relationship.

5. The monitoring method for increasing carbon sequestration in non-carbonate rock areas using carbonate rock powder according to claim 4, characterized in that: The specific process of establishing a linear regression model for carbon sinks is as follows: A preliminary linear regression model was established by taking the mesh size of carbonate rock powder as the independent variable and the soil carbon sequestration rate as the dependent variable. The regression coefficients are estimated using the least squares method, and the regression equation is constructed. The model is subjected to significance testing and the strength of the relationship is quantified to evaluate its predictive performance and accuracy. The regression model is optimized through cross-validation to establish a linear regression model for carbon sinks.

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