Monitoring method for increasing carbon sink in non-carbonate rock area by using carbonate rock powder

Abstract

The invention discloses a monitoring method for increasing carbon sequestration in a non-carbonate rock area by using carbonate rock powder, and relates to the technical field of carbon sequestration monitoring. According to the monitoring method for increasing the carbon sequestration in the non-carbonate rock area by using the carbonate rock powder, the carbon sequestration data of the non-carbonate rock area are monitored and stored in real time, and the continuity and integrity of the data are ensured. Based on multivariate regression analysis, soil and vegetation carbon sink characteristics of the non-carbonate rock region are evaluated, a soil carbon sink index and a vegetation carbon sink index are obtained, and therefore the overall carbon sink index is evaluated. And fitting a probability distribution model by using the carbon sink index data, identifying a natural fluctuation range critical value of the carbon sink index, and determining a threshold range of the carbon sink index. And analyzing a linear relationship between the carbonate rock powder mesh number and the soil carbon sequestration rate through a linear regression model, and constructing a carbon sequestration linear regression model. And when the carbon sink index is lower than a threshold range, dynamically adjusting the application amount and frequency of the carbonate rock powder according to a carbon sink linear regression model, and optimizing carbon sink management.

Description

Technical Field

[0001] The invention relates to the technical field of carbon sink monitoring, and in particular to a monitoring method for increasing carbon sinks in non-carbonate rock areas by using carbonate rock powder. Background Art

[0002] Global climate change has become a major challenge facing humanity. The increase in greenhouse gas (mainly carbon dioxide) emissions has led to rising global temperatures, frequent extreme weather events, and increasingly serious problems such as rising sea levels.

[0003] Non-carbonate rock areas usually lack natural carbonate rocks, and these areas have limited ability to fix atmospheric carbon dioxide. By applying carbonate rock powder, the carbon sink capacity of these areas can be artificially enhanced, effectively reducing the concentration of carbon dioxide in the atmosphere and alleviating the problem of global warming. The soil in many non-carbonate rock areas is often acidic, which affects 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, the carbon sequestration capacity is relatively low due to the nature of the soil and the lack of effective minerals. Carbonate rocks can react with carbon dioxide under natural conditions to produce calcium carbonate, thereby fixing carbon dioxide. Applying carbonate rock powder to non-carbonate rock areas can enhance the carbon sequestration capacity of the soil and increase the carbon sink capacity of the area. However, there is uncertainty about the effect of carbonate rock powder of different mesh sizes on the soil carbon sequestration rate, which needs to be optimized through careful monitoring and analysis. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a monitoring method for increasing carbon sinks in non-carbonate rock areas by using carbonate rock powder, which solves the problems of the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a monitoring method for increasing carbon sinks in non-carbonate rock areas using carbonate rock powder, comprising the following steps: S1. real-time monitoring of carbon sink data in non-carbonate rock areas, and storing the carbon sink data in non-carbonate rock areas in a database in time series; S2. based on the carbon sink data in non-carbonate rock areas, analyzing the soil carbon sink characteristics and vegetation carbon sink characteristics in non-carbonate rock areas through multivariate regression, obtaining the soil carbon sink index and vegetation carbon sink index in non-carbonate rock areas, and evaluating the carbon sink characteristics of non-carbonate rock areas. Regional carbon sink index; S3. Based on the carbon sink index of non-carbonate rock areas, fit the 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 in non-carbonate rock areas; S4. Analyze the linear relationship between the mesh size of carbonate rock powder and the soil carbon fixation 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 a carbon sink linear regression model.

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

[0008] Furthermore, the specific process of evaluating the carbon sink index in non-carbonate rock areas is as follows: based on the soil carbon sink index in non-carbonate rock areas and the vegetation carbon sink index in non-carbonate rock areas, comprehensive calculations are performed with the set soil carbon sink index threshold and the 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 in non-carbonate rock areas, the normality of the carbon sink index data is preliminarily checked through histograms and QQ plots; the mean and standard deviation of the carbon sink index in non-carbonate rock areas are calculated through the normal distribution model, and the data are fitted; according to the properties of the normal distribution, the critical value of the natural fluctuation range of the carbon sink index is identified.

[0010] Furthermore, the specific process of 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, under the set confidence level, the low threshold and high threshold of the carbon sink index in non-carbonate rock areas are calculated by the normal distribution model, and the low threshold and high threshold 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 fixation rate through linear regression model is as follows: obtain the application amount of carbonate rock powder with different mesh sizes and the corresponding soil carbon fixation rate data in the database; through significance test, determine whether the linear relationship between carbonate rock powder mesh size and soil carbon fixation rate is significant, and identify positive or negative correlation; calculate the Pearson correlation coefficient between carbonate rock powder mesh size and soil carbon fixation rate to quantify the strength of the relationship.

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

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

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

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

[0016] (2) This method of monitoring carbon sinks in non-carbonate rock areas using carbonate rock powder can accurately identify the natural fluctuation range of the carbon sink index and determine the critical value of its normal fluctuation by fitting a probability distribution model. The setting of the threshold range of the carbon sink index helps to monitor carbon sink changes in real time and timely warn of abnormal fluctuations in the carbon sink, thereby ensuring the robustness of carbon sink management. The establishment of a linear regression model quantifies the effect of carbonate rock powder mesh size on soil carbon fixation rate, providing a quantitative scientific basis for the optimization of carbon sinks. When the carbon sink 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 the carbon sink and improve the carbon sink effect. This process ensures the sustainability of the carbon sink and avoids a significant reduction in carbon sink capacity.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of a monitoring method for increasing carbon sinks in non-carbonate rock areas by using carbonate rock powder. DETAILED DESCRIPTION

[0019] The embodiment of the present application solves the problems of low carbon sink capacity in non-carbonate rock areas, unclear effects of carbonate rock powder application, and lack of systematic monitoring and optimization methods by using carbonate rock powder to increase the monitoring method of carbon sink in non-carbonate rock areas.

[0020] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0021] Monitor carbon sink data in non-carbonate rock areas in real time, and store the carbon sink data in non-carbonate rock areas in the database in time series.

[0022] Based on the carbon sink data of non-carbonate rock areas, the soil carbon sink characteristics and vegetation carbon sink characteristics of non-carbonate rock areas were analyzed through multivariate regression to obtain the soil carbon sink index of non-carbonate rock areas and the vegetation carbon sink index of non-carbonate rock areas, and to evaluate the carbon sink index of non-carbonate rock areas.

[0023] Based on the carbon sink index in non-carbonate rock areas, the probability distribution model of the carbon sink index 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 in non-carbonate rock areas is obtained.

[0024] The linear relationship between carbonate rock powder mesh size and soil carbon fixation rate was analyzed through a linear regression model, and a carbon sink linear regression model was established.

[0025] When the carbon sink index in the non-carbonate rock area is lower than the threshold range of the carbon sink index in the non-carbonate rock area, the application amount and frequency of different amounts of carbonate rock powder are monitored through the carbon sink linear regression model.

[0026] See also Figure 1 The embodiment of the present invention provides a technical solution: a monitoring method for increasing carbon sinks in non-carbonate rock areas by using carbonate rock powder, comprising the following steps: S1. real-time monitoring of carbon sink data in non-carbonate rock areas, and storing the carbon sink data in non-carbonate rock areas in a database in time series; S2. based on the carbon sink data in non-carbonate rock areas, analyzing the soil carbon sink characteristics and vegetation carbon sink characteristics in non-carbonate rock areas through multivariate regression, obtaining the soil carbon sink index in non-carbonate rock areas and the vegetation carbon sink index in non-carbonate rock areas, and evaluating the carbon sink index in non-carbonate rock areas; S3. based on the carbon sink index in non-carbonate rock areas, fitting a probability distribution model of the carbon sink index, identifying the critical value of the natural fluctuation range of the carbon sink index, and obtaining the threshold range of the carbon sink index in non-carbonate rock areas; S4. analyzing the linear relationship between the mesh size of carbonate rock powder and the soil carbon fixation rate through a linear regression model, and establishing a carbon sink linear regression model; S5. when the carbon sink index in non-carbonate rock areas is lower than the threshold range of the carbon sink index in non-carbonate rock areas, monitoring the application amount and frequency of different amounts of carbonate rock powder through a carbon sink linear regression model.

[0027] In this embodiment, non-carbonate rock areas refer to areas where carbonate minerals (such as limestone and dolomite) are not the main components. The carbonate content in the soil and rocks in these areas is low, so the natural carbon sink capacity is low. Carbon sink refers to the process by which ecosystems or human technology remove and store carbon dioxide from the atmosphere. Soil and vegetation are important carbon sinks that help mitigate climate change by absorbing carbon dioxide. Carbon sink index in non-carbonate rock areas is an indicator used to quantify and evaluate the carbon sink capacity of a region, usually based on the carbon storage and changing trends of regional soil and vegetation. Multivariate regression analysis is a statistical method for analyzing the relationship between multiple variables. In this context, regression analysis is used to evaluate the impact of soil carbon sink characteristics and vegetation carbon sink characteristics on the carbon sink index. A probability distribution model is a mathematical model that describes the probability distribution of data within different value ranges. In carbon sink monitoring, the normal distribution model is often used to simulate the fluctuation range of the carbon sink index. The normal distribution is a classic probability distribution, which is a bell-shaped curve, with data concentrated near the mean and gradually decreasing on both sides. The normal distribution model is often used to identify the fluctuation range of the carbon sink index. In carbon sink monitoring, the critical value refers to the boundary of the natural fluctuation range of the carbon sink index, that is, the upper and lower limits of the carbon sink index under normal circumstances, which is used to evaluate the stability of the carbon sink. The 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 effect of the mesh size of carbonate rock powder on the soil carbon sequestration rate to help optimize carbon sink measures. The mesh size of carbonate rock powder refers to the particle size of carbonate rock powder, usually expressed by the mesh number (that is, the number of holes per inch of the sieve). The larger the mesh number, the finer the particles, which may have different effects on the soil carbon sequestration effect. The soil carbon sequestration rate refers to the rate at which the soil absorbs and stores carbon through natural processes or artificial measures, and is an important indicator for evaluating the soil carbon sink capacity.

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

[0029] In this embodiment, the Pearson correlation coefficient is used to evaluate the correlation between carbon sink data in non-carbonate rock areas and soil carbon sink characteristics and vegetation carbon sink characteristics. This evaluation helps to determine which characteristic data are associated with carbon sinks, thereby screening out variables that best represent soil and vegetation carbon sink characteristics. After identifying the correlation, features related to soil and vegetation carbon sink characteristics are extracted from the carbon sink data. Since these features may have higher dimensions, the dimensions of the data are reduced by principal component analysis (PCA). PCA retains the main information of the data by converting the original variables into a few unrelated principal components while simplifying the complexity of the model. Using the extracted characteristic data as input variables, the soil carbon sink index and the vegetation carbon sink index are used as target variables, respectively, to establish a multivariate regression model. This model is used to quantify the relationship between the input features and the target variables, and can predict the soil carbon sink index and the vegetation carbon sink index when new data is input. The calculation formula for 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 Represents the soil carbon sink index in non-carbonate rock areas. 0,s Represents the intercept of the regression model, which represents the soil carbon sink index when all input variables are zero. 1,s ,β 2,s ,…,β n,s : The regression coefficient of soil carbon sink characteristics, indicating the influence intensity of each input variable on the soil carbon sink index. 1,s ,X 2,s ,…,X n,s Represents characteristic variables related to soil carbon sink, including soil organic carbon content, soil moisture, temperature, and soil texture. ∈ s Represents the error term, which represents the difference between the model prediction value and the actual value. The calculation formula of vegetation carbon sink index in non-carbonate rock area 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 ; Where, CSI v represents the vegetation carbon sink index in non-carbonate rock areas, β 0,v Represents the intercept of the regression model, which represents the vegetation carbon sink index when all input variables are zero. 1,v ,α2,v ,…,α n,v Represents the regression coefficient of vegetation carbon sink characteristics, indicating the impact intensity of each input variable on the vegetation carbon sink index. 1,v ,X 2,v ,…,X n,v Represents characteristic variables related to vegetation carbon sink, including vegetation coverage, biomass, and net primary productivity. ∈ v Represents the error term, which represents the difference between the model's predicted value and the actual value.

[0030] Furthermore, the specific process of evaluating the carbon sink index in non-carbonate rock areas is as follows: based on the soil carbon sink index in non-carbonate rock areas and the vegetation carbon sink index in non-carbonate rock areas, comprehensive calculations are performed with the set soil carbon sink index threshold and the vegetation carbon sink index threshold respectively to obtain the carbon sink index in non-carbonate rock areas.

[0031] In this implementation plan, the calculation formula for the carbon sink index in non-carbonate rock areas is as follows: Where 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 in non-carbonate rock areas, the normality of the carbon sink index data is preliminarily checked through histograms and QQ plots; the mean and standard deviation of the carbon sink index in non-carbonate rock areas are calculated through the normal distribution model, and the data are fitted; according to the properties of the normal distribution, the critical value of the natural fluctuation range of the carbon sink index is identified.

[0033] In this embodiment, the histogram is a chart that shows the distribution of data by dividing the data into several intervals and plotting the frequency of data points in each interval. Through the histogram, you can visually check whether the data is close to a normal distribution. If the data distribution is a bell-shaped curve, it means that the data may conform to a normal distribution. In the QQ graph, if the data points are distributed roughly along the diagonal, it means that the sample data is close to a normal distribution. If the data points deviate significantly from the diagonal, it means that the data distribution does not conform to a normal distribution. Calculate the mean (μ) and standard deviation (σ) from the carbon sink index data, use these statistical parameters to fit the normal distribution model, and obtain the probability density function of the carbon sink index, the formula is as follows: Among them, f(x) represents the relative probability or density of the carbon sink index x. The higher the value, the greater the probability of the value. μ (mean) represents the average value of the carbon sink index, and σ (standard deviation) is used to measure the dispersion of data distribution. The standard deviation σ describes the distribution width of data points around the mean μ. The larger the standard deviation, the wider the distribution of the data; the smaller the standard deviation, the more concentrated the distribution of the data. Represents the constant used to normalize the normal distribution so that its total area is 1. It is used to describe the relationship between the squared deviation of the carbon sink index x from the mean μ and the standard deviation. This part determines how the probability density of the carbon sink index changes with the change of x. The farther from the mean, the lower the probability density.

[0034] Furthermore, the specific process of 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, under the set confidence level, the low threshold and high threshold of the carbon sink index in non-carbonate rock areas are calculated by the normal distribution model, and the low threshold and high threshold 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 a normal distribution. By calculating the mean and standard deviation of the sample, the central tendency and dispersion of the data can be described. After setting the confidence level of 95%, the normal distribution model is used to determine the low threshold and the high threshold, which define the range of the carbon sink index. Within this range, we can have 95% confidence that the true 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 fixation rate through linear regression model is as follows: obtain the application amount of carbonate rock powder with different mesh sizes and the corresponding soil carbon fixation rate data in the database; through significance test, determine whether the linear relationship between carbonate rock powder mesh size and soil carbon fixation rate is significant, and identify positive or negative correlation; calculate the Pearson correlation coefficient between carbonate rock powder mesh size and soil carbon fixation rate to quantify the strength of the relationship.

[0037] In this embodiment, the significance test is carried out through the linear relationship test: a statistical significance test is used to determine whether there is a significant linear relationship between the mesh size of carbonate rock powder and the soil carbon fixation rate. Null hypothesis: the effect of the mesh size of carbonate rock powder on the soil carbon fixation rate is not significant, that is, the regression coefficient is zero, and the alternative hypothesis: the mesh size of carbonate rock powder has a significant effect on the soil carbon fixation rate, that is, 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 it is believed that there is a linear relationship between the two. Significance test, through the linear relationship test: a statistical significance test is used to determine whether there is a significant linear relationship between the mesh size of carbonate rock powder and the soil carbon fixation rate. Null hypothesis: the effect of the mesh size of carbonate rock powder on the soil carbon fixation rate is not significant, that is, the regression coefficient is zero. Alternative hypothesis: the mesh size of carbonate rock powder has a significant effect on the soil carbon fixation rate, that is, 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 it is believed that there is a linear relationship between the two. Calculate the Pearson correlation coefficient: Among them, x i and i are the observed values ​​of carbonate rock powder mesh size and soil carbon fixation rate, respectively. and is the mean of carbonate rock powder size and soil carbon fixation rate. r represents the Pearson correlation coefficient, which measures the strength and direction of the linear relationship between carbonate rock powder size and soil carbon fixation 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 the carbon sink linear regression model is as follows: taking the mesh size of carbonate rock powder as the independent variable and the soil carbon fixation rate as the dependent variable, a preliminary linear regression model is established; the regression coefficient is estimated using the least squares method and a regression equation is constructed; the model is tested for significance and the strength of the relationship is quantified to evaluate the model's predictive performance and accuracy, the regression model is optimized through cross-validation, and a carbon sink linear regression model is established.

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

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

[0041] In this embodiment, the carbon sink linear regression model establishes a mathematical model by analyzing the relationship between the application amount, mesh size (particle size) and soil carbon fixation rate of carbonate rock powder. This model can predict the soil carbon fixation rate and its impact on the overall carbon sink index at a specific application amount and mesh size. Therefore, the model can be used to monitor and adjust the application strategy of carbonate rock powder to maintain or improve the carbon sink index. When the carbon sink index in non-carbonate rock areas is lower than the set first threshold, it means that the carbon fixation capacity of the soil is weakening, but has not reached a very dangerous level. In this case, the monitoring logic indicates to increase the amount of fine carbonate rock powder used. Due to its large specific surface area, fine carbonate rock powder can react with the soil more quickly, increase the soil carbon fixation rate more quickly, and thus quickly improve the carbon sink index. The second carbon sink threshold represents the lower critical point of the carbon sink index. When the carbon sink index is lower than this value, the soil's carbon fixation capacity has significantly decreased, and more powerful intervention measures are needed. In this case, the monitoring logic indicates to increase the amount of coarse carbonate rock powder used. Coarse carbonate powders have a larger particle size and a slower reaction rate, but they last longer in the soil and can provide a more lasting carbon sink effect, thereby helping the soil to restore and stabilize its carbon sink capacity. By monitoring changes in the carbon sink index in non-carbonate areas and adjusting according to set thresholds, the application amount and particle size of carbonate powder can be dynamically adjusted to ensure that the carbon sink index remains within the ideal range. This adjustment mechanism can effectively respond to fluctuations in soil carbon sequestration capacity, maximize carbon sink effects, and avoid the negative effects of excessive or insufficient application of carbonate powder.

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

[0043] The monitoring method of carbon sink in non-carbonate rock area using carbonate rock powder can timely obtain and update the dynamic data of carbon sink in non-carbonate rock area through real-time monitoring, ensuring the real-time and accuracy of data. Time series storage enables carbon sink data to be systematically recorded and managed, providing a continuous data basis for subsequent analysis. Multivariate regression analysis can systematically reveal the key factors affecting soil and vegetation carbon sinks and quantify the influence of these factors on carbon sinks. This process ensures the accuracy of carbon sink index calculation and improves the comprehensive understanding of carbon sink characteristics, thus providing a scientific basis for formulating effective carbon sink management strategies. By fitting the probability distribution model, the natural fluctuation range of carbon sink index can be accurately identified and the critical value of its normal fluctuation can be determined. The setting of the threshold range of carbon sink index helps to monitor carbon sink changes in real time and timely warn of abnormal fluctuations of carbon sink, thereby ensuring the robustness of carbon sink management. The establishment of linear regression model quantifies the effect of carbonate rock powder mesh size on soil carbon fixation rate, providing a quantitative scientific basis for the optimization of carbon sink. When the carbon sink 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 sink and improve the carbon sink effect. This process ensures the sustainability of carbon sink and avoids a significant reduction in carbon sink capacity.

[0044] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0047] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0048] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0049] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for monitoring carbon sinks in non-carbonate rock areas using carbonate rock powder, characterized in that: The following steps are involved: S1. Real-time monitoring of carbon sink data in non-carbonate rock areas, and storage of carbon sink data in non-carbonate rock areas in a database in time series; S2. Based on the carbon sink data of non-carbonate rock areas, the soil carbon sink characteristics and vegetation carbon sink characteristics of non-carbonate rock areas were analyzed through multivariate regression to obtain the soil carbon sink index and vegetation carbon sink index of non-carbonate rock areas, and to evaluate the carbon sink index of non-carbonate rock areas; S3. Based on the carbon sink index of non-carbonate rock regions, fit the 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 in non-carbonate rock regions; S4. Analyze the linear relationship between carbonate rock powder mesh size and soil carbon fixation rate through linear regression model, and establish a carbon sink linear regression model; S5. When the carbon sink index of the non-carbonate rock area is lower than the threshold range of the carbon sink index of the non-carbonate rock area, the application amount and frequency of different amounts of carbonate rock powder are monitored by a carbon sink linear regression model.

2. The method for monitoring carbon sink in non-carbonate rock areas by 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 by multivariate regression analysis of soil carbon sink characteristics and vegetation carbon sink characteristics in non-carbonate rock areas is as follows: Based on the carbon sink data of non-carbonate rock areas, the correlation between the carbon sink data of non-carbonate rock areas and soil carbon sink and vegetation carbon sink was evaluated by Pearson correlation coefficient, and the characteristic data that conforms to the characteristics of soil carbon sink and vegetation carbon sink were extracted, and the dimension of the characteristics was reduced by principal component analysis. The soil carbon sink index and vegetation carbon sink index were used as target variables, and the extracted characteristic data were used as input variables to establish a multivariate regression model. According to the trained multivariate regression model, the real-time monitoring data is input to obtain the soil carbon sink index in non-carbonate rock areas and the vegetation carbon sink index in non-carbonate rock areas.

3. The method for monitoring carbon sink in non-carbonate rock areas by using carbonate rock powder according to claim 2, characterized in that: The specific process of evaluating the carbon sink index in non-carbonate rock areas is as follows: Based on the soil carbon sink index in non-carbonate rock areas and the vegetation carbon sink index in non-carbonate rock areas, comprehensive calculations are 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.

4. The method for monitoring carbon sink in non-carbonate rock areas by using carbonate rock powder according to claim 3, characterized in that: 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 in non-carbonate rock areas, the normality of the carbon sink index data was preliminarily checked through histograms and QQ plots; The mean and standard deviation of the carbon sink index in non-carbonate rock areas were calculated using the normal distribution model, and data fitting was performed; According to the properties of normal distribution, the critical value of the natural fluctuation range of the carbon sink index is identified.

5. The method for monitoring carbon sink in non-carbonate rock areas by using carbonate rock powder according to claim 4, characterized in that: The specific process of obtaining the carbon sink index threshold range 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, under the set confidence level, the low threshold and high threshold of the carbon sink index in non-carbonate rock areas are calculated by the normal distribution model, and the low threshold and high threshold 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.

6. The method for monitoring carbon sinks in non-carbonate rock areas using carbonate rock powder according to claim 5, characterized in that: The specific process of analyzing the linear relationship between carbonate rock powder mesh size and soil carbon fixation rate through 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 fixation rate in the database; Through significance test, determine whether the linear relationship between carbonate rock powder mesh size and soil carbon fixation rate is significant, and identify positive or negative correlation; The Pearson correlation coefficient between carbonate rock powder mesh size and soil carbon sequestration rate was calculated to quantify the strength of the relationship.

7. The method for monitoring carbon sinks in non-carbonate rock areas using carbonate rock powder according to claim 6, characterized in that: The specific process of establishing the carbon sink linear regression model is as follows: A preliminary linear regression model was established with the mesh size of carbonate rock powder as the independent variable and soil carbon fixation rate as the dependent variable. Use the least squares method to estimate the regression coefficients and construct the regression equation; The model was tested for significance and quantitative relationship strength, the predictive performance and accuracy of the model were evaluated, the regression model was optimized through cross-validation, and a carbon sink linear regression model was established.

8. The method for monitoring carbon sinks in non-carbonate rock areas using carbonate rock powder according to claim 7, characterized in that: The logic of monitoring the application amount and frequency of different amounts of carbonate rock powder through the carbon sink linear regression model is as follows: When the carbon sink index in non-carbonate rock areas is lower than the first carbon sink threshold, increase the amount of fine carbonate rock powder used; When the carbon sink index in the non-carbonate rock area is lower than the second carbon sink threshold, increase the use of coarse carbonate rock powder.

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