Carbon sink data analysis method and system for coal mining subsidence land

Abstract

The invention relates to a carbon sink data analysis method and system for a coal mining subsidence land, and relates to the field of carbon sink data processing, and the method comprises the steps: carrying out the carbon sink influence trend analysis according to collapse feature information, and obtaining the carbon sink influence coefficients of soil, vegetation and water; carbon sink main body characteristic information is collected, auxiliary carbon sink trend influence analysis is carried out on the main carbon sink mode, an auxiliary carbon sink influence coefficient is obtained, influence correction is carried out on basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes in combination with soil, vegetation and water body carbon sink influence coefficients, and soil, vegetation and water body carbon sink parameters are obtained. According to the method, the technical problem that the accuracy and comprehensiveness of carbon sink data analysis are insufficient due to the fact that a traditional method does not fully consider the mutual influence between the special geographical environment and the carbon sink mode of the coal mining subsidence land can be solved; the scientificity, accuracy and comprehensiveness of carbon sink data analysis can be improved, and the effect of accurately evaluating the carbon sink capacity of the coal mining subsidence land under different geographical conditions is achieved.

Description

Technical Field

[0001] The present invention relates to the field of carbon sink data processing, and in particular to a carbon sink data analysis method and system for coal mining subsidence areas. Background Art

[0002] Carbon sink refers to a system in nature that can absorb and store carbon dioxide, including soil, vegetation, water bodies, etc. Coal mining subsidence, as a typical type of land degradation, is usually highly destructive and difficult to restore ecologically. The surface subsidence, water accumulation, soil degradation and other phenomena during coal mining have seriously affected the ecological functions of the land, especially the carbon sink function.

[0003] Traditional carbon sink assessment methods are mainly based on standardized climate and soil conditions, and fail to fully consider the impact of the special environment of coal mining subsidence. The geographical environment of coal mining subsidence is complex, and factors such as the depth, slope, and degree of water accumulation of the surface subsidence directly affect the carbon sink capacity of soil, vegetation, and water bodies. In addition, carbon sinks do not exist in isolation, but are intertwined and influence each other. For example, changes in soil composition will directly affect the growth of vegetation, and the growth of vegetation will in turn affect the carbon storage capacity of the soil. The organic carbon concentration in the water body and the carbon storage in the soil are also interrelated. Traditional carbon sink data analysis methods often ignore these interactions and dynamic changes, resulting in insufficient accuracy and comprehensiveness in the analysis of carbon sink data in coal mining subsidence areas. Summary of the invention

[0004] The present invention aims to solve the technical problem that traditional carbon sink data analysis methods fail to fully consider the special geographical environment of coal mining subsidence areas and the mutual influence between carbon sink modes, resulting in insufficient accuracy and comprehensiveness of carbon sink data analysis in coal mining subsidence areas. A carbon sink data analysis method and system for coal mining subsidence areas are provided to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a method for analyzing carbon sink data in coal mining subsidence areas, including: collecting collapse characteristic information of coal mining subsidence areas, classifying and obtaining soil carbon sink contribution coefficients, vegetation carbon sink contribution coefficients and water body carbon sink contribution coefficients, and determining a primary carbon sink mode and two other secondary carbon sink modes; collecting soil carbon sink data, vegetation carbon sink data and water body carbon sink data in the coal mining subsidence areas to obtain basic soil carbon sink parameters, basic vegetation carbon sink parameters and basic water body carbon sink parameters; and performing carbon sink impact trend analysis based on the collapse characteristic information. , obtain the soil carbon sink influence coefficient, the vegetation carbon sink influence coefficient and the water body carbon sink influence coefficient; collect the main carbon sink characteristic information of the two secondary carbon sink modes and the main carbon sink mode, perform auxiliary carbon sink trend impact analysis on the main carbon sink mode, and obtain the auxiliary carbon sink influence coefficient, and combine the soil carbon sink influence coefficient, the vegetation carbon sink influence coefficient and the water body carbon sink influence coefficient to perform impact correction on the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes, and obtain soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters as the carbon sink data analysis results.

[0006] Optionally, the carbon sink data analysis method for coal mining subsidence areas also includes: collecting the collapse slope and collapse depth of the coal mining subsidence areas as collapse characteristic information; constructing a carbon sink contribution classifier based on sample carbon sink data of sample coal mining subsidence areas; inputting the collapse characteristic information into the carbon sink contribution classifier, and outputting the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient of the soil carbon sink, vegetation carbon sink and water body carbon sink; selecting the carbon sink mode corresponding to the largest contribution coefficient as the main carbon sink mode, and the other two carbon sink modes as secondary carbon sink modes.

[0007] Optionally, the carbon sink data analysis method for coal mining subsidence areas also includes: collecting collapse characteristic information of multiple sample coal mining subsidence areas to obtain a collapse characteristic information set; collecting the carbon sink proportions of soil carbon sinks, vegetation carbon sinks, and water body carbon sinks in each sample coal mining subsidence area, and marking to obtain a sample soil carbon sink contribution coefficient set, a sample vegetation carbon sink contribution coefficient set, and a sample water body carbon sink contribution coefficient set; using the collapse characteristic information set as input features, using the sample soil carbon sink contribution coefficient set, the sample vegetation carbon sink contribution coefficient set, and the sample water body carbon sink contribution coefficient set as output features, and based on machine learning, constructing and training a carbon sink contribution classifier until convergence.

[0008] Optionally, the carbon sink data analysis method for coal mining subsidence areas also includes: testing the soil organic carbon content in the coal mining subsidence area to obtain soil carbon sink data; collecting the number of different vegetation in the coal mining subsidence area to calculate and obtain vegetation carbon sink data; testing the dissolved organic carbon concentration of water in the coal mining subsidence area to obtain water carbon sink data.

[0009] Optionally, the carbon sink data analysis method for coal mining subsidence areas also includes: collecting collapse characteristic information of multiple sample coal mining subsidence areas to obtain a collapse characteristic information set; collecting the change ratio of soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters under different sample coal mining subsidence areas within a preset time length to obtain a sample soil carbon sink influence coefficient set, a sample vegetation carbon sink influence coefficient set and a sample water body carbon sink influence coefficient set; using the collapse characteristic information set as input features, using the sample soil carbon sink influence coefficient set, the sample vegetation carbon sink influence coefficient set and the sample water body carbon sink influence coefficient set as output features, based on machine learning, constructing and training a carbon sink impact trend analyzer until convergence; inputting the collapse characteristic information into the carbon sink impact trend analyzer, and outputting the obtained soil carbon sink influence coefficient, vegetation carbon sink influence coefficient and water body carbon sink influence coefficient.

[0010] Optionally, the carbon sink data analysis method for coal mining subsidence areas also includes: collecting the main carbon sink characteristic information of the two secondary carbon sink modes and the main carbon sink mode, wherein the main carbon sink characteristic information of soil carbon sink, vegetation carbon sink and water carbon sink includes soil composition information, vegetation type information and water composition information; collecting a set of sample carbon sink main characteristic information, and collecting the change ratio of carbon sink parameters of other carbon sink modes within a preset time length under each sample carbon sink main characteristic information, and marking it as a set of sample auxiliary carbon sink influence coefficients; using the sample carbon sink main characteristic information set as input features, and using the sample auxiliary carbon sink influence coefficient set as output features, constructing an auxiliary carbon sink impact analyzer and training it to convergence; respectively inputting the carbon sink main characteristic information of the two secondary carbon sink modes into the auxiliary carbon sink impact analyzer, and outputting two auxiliary carbon sink influence coefficients.

[0011] Optionally, the carbon sink data analysis method for coal mining subsidence areas also includes: calculating three total carbon sink influence coefficients based on the soil carbon sink influence coefficient, the vegetation carbon sink influence coefficient and the water body carbon sink influence coefficient, combined with the two auxiliary carbon sink influence coefficients; using the three total carbon sink influence coefficients to perform impact correction on the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes, and obtaining soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters as carbon sink data analysis results.

[0012] In a second aspect, the present invention provides a carbon sink data analysis system for coal mining subsidence areas, including: a carbon sink contribution coefficient acquisition module, which is used to collect the collapse characteristic information of the coal mining subsidence area, classify and obtain the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient, and determine the main carbon sink mode and the other two secondary carbon sink modes; a basic carbon sink parameter acquisition module, which is used to collect the soil carbon sink data, vegetation carbon sink data and water body carbon sink data in the coal mining subsidence area, and obtain basic soil carbon sink parameters, basic vegetation carbon sink parameters and basic water body carbon sink parameters; a carbon sink impact trend analysis module, which is used to analyze the influence trend of the carbon sink according to the collapse characteristics. The information is used to perform carbon sink impact trend analysis to obtain soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient; a basic carbon sink parameter correction module is used to collect the main carbon sink characteristic information of the two secondary carbon sink modes and the main carbon sink mode, perform auxiliary carbon sink trend impact analysis on the main carbon sink mode, obtain the auxiliary carbon sink impact coefficient, and combine the soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient to perform impact correction on the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes, and obtain soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters as the carbon sink data analysis result.

[0013] The beneficial effects of the present invention are as follows: by collecting the collapse characteristic information of the coal mining subsidence area, the soil carbon sink contribution coefficient, the vegetation carbon sink contribution coefficient and the water body carbon sink contribution coefficient are classified and obtained, and the main carbon sink mode and the other two secondary carbon sink modes are determined; then the soil carbon sink data, the vegetation carbon sink data and the water body carbon sink data in the coal mining subsidence area are collected to obtain basic soil carbon sink parameters, basic vegetation carbon sink parameters and basic water body carbon sink parameters; then, according to the collapse characteristic information, a carbon sink impact trend analysis is performed to obtain the soil carbon sink impact coefficient, the vegetation carbon sink impact coefficient and the water body carbon sink impact coefficient; on the other hand, the carbon sink main characteristic information of the two secondary carbon sink modes and the main carbon sink mode is collected, and the main carbon sink mode is analyzed. The auxiliary carbon sink trend impact analysis is carried out on the main carbon sink mode to obtain the auxiliary carbon sink impact coefficient; finally, the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes are corrected by combining the soil carbon sink impact coefficient, the vegetation carbon sink impact coefficient and the water carbon sink impact coefficient, and the soil carbon sink parameters, the vegetation carbon sink parameters and the water carbon sink parameters are obtained as the carbon sink data analysis results; that is to say, by using machine learning algorithms to accurately analyze the collapse characteristics of coal mining subsidence areas and the mutual influence of different carbon sink modes, the scientificity, accuracy and comprehensiveness of the carbon sink data analysis of coal mining subsidence areas can be improved, thereby achieving the technical effect of accurately evaluating the carbon sink capacity of coal mining subsidence areas under different geographical conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of a process flow of a carbon sink data analysis method for coal mining subsidence areas provided by the present invention; Figure 2 A structural schematic diagram of a carbon sink data analysis system for coal mining subsidence areas provided by the present invention.

[0015] In the accompanying drawings, the components represented by the reference numerals are described as follows: A carbon sink contribution coefficient obtaining module 11, a basic carbon sink parameter obtaining module 12, a carbon sink impact trend analysis module 13, and a basic carbon sink parameter correction module 14. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0019] Embodiment 1, as Figure 1 As shown, an embodiment of the present invention provides a carbon sink data analysis method for coal mining subsidence, comprising: S100: Collect the collapse characteristic information of the coal mining subsidence area, classify and obtain the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient, and determine the main carbon sink mode and the other two secondary carbon sink modes.

[0020] Furthermore, step S100 of the present invention further includes: S110: Collect the collapse slope and collapse depth of the coal mining collapse area as collapse feature information.

[0021] Specifically, characteristic parameters such as the groundwater level, collapse slope and collapse depth of the coal mining subsidence area are collected as collapse characteristic information. Preferably, the collapse slope and collapse depth of the coal mining subsidence area are collected as collapse characteristic information.

[0022] Among them, the collapse slope refers to the inclination angle of the collapsed surface, which reflects the severity of ground subsidence. A larger slope (such as a collapse slope of 35°-40°) usually means that the surface subsidence is more serious, and soil erosion is prone to occur on the soil surface, making it difficult for vegetation to grow. A large slope may also lead to an aggravation of the problem of water accumulation, which in turn affects the carbon sink capacity of the water body. The collapse depth refers to the depth of surface settlement caused by coal mining activities, which reflects the degree and scope of land settlement. In areas with large subsidence depths (such as collapse depths of 8m-12m), the physical properties of the soil may change, affecting the soil carbon storage capacity; deep subsidence areas may experience water accumulation, resulting in an increase in the impact of water carbon sinks, but the durability and stability of water carbon sinks still need to be considered; at the same time, vegetation restoration in deep subsidence areas is greatly challenged, and the vegetation carbon sink capacity is limited.

[0023] By collecting characteristic information on collapse slope and depth, it will help to accurately analyze the carbon sink characteristics of different coal mining subsidence areas and understand the mutual influence between various carbon sink modes (such as soil carbon sink, vegetation carbon sink, and water carbon sink), thereby providing key data support for the assessment of carbon sink capacity of coal mining subsidence areas.

[0024] S120: Construct a carbon sink contribution classifier based on the sample carbon sink data of the sample coal mining subsidence area.

[0025] Furthermore, step S120 of the present invention further includes: S121: Collect collapse characteristic information of multiple sample coal mining subsidence areas to obtain a collapse characteristic information set; S122: Collect the carbon sink proportions of soil carbon sink, vegetation carbon sink and water body carbon sink in each sample coal mining subsidence area, and mark to obtain a sample soil carbon sink contribution coefficient set, a sample vegetation carbon sink contribution coefficient set and a sample water body carbon sink contribution coefficient set; S123: Use the collapse characteristic information set as input features, use the sample soil carbon sink contribution coefficient set, the sample vegetation carbon sink contribution coefficient set and the sample water body carbon sink contribution coefficient set as output features, and build and train a carbon sink contribution classifier based on machine learning until convergence.

[0026] Specifically, first, the collapse characteristic information (ground water level, collapse slope and collapse depth) of multiple sample coal mining subsidence areas is collected to obtain a collapse characteristic information set; then, the carbon sink ratio of soil carbon sink, vegetation carbon sink and water carbon sink in each sample coal mining subsidence area is collected, that is, the ratio of the carbon sink of a single carbon sink mode to the total carbon sink, and the carbon sink ratio is marked as the carbon sink contribution coefficient. For example, in a certain coal mining subsidence area, the soil carbon sink accounts for 60% of the total carbon sink, the vegetation carbon sink accounts for 30%, and the water carbon sink accounts for 10%. The soil carbon sink contribution coefficient is 0.6, the vegetation carbon sink contribution coefficient is 0.3, and the water carbon sink contribution coefficient is 0.1; the sample soil carbon sink contribution coefficient set, the sample vegetation carbon sink contribution coefficient set and the sample water carbon sink contribution coefficient set are obtained, wherein the collapse characteristic information and the sample soil carbon sink contribution coefficient, the sample vegetation carbon sink contribution coefficient and the sample water carbon sink contribution coefficient have a corresponding relationship.

[0027] A carbon sink contribution classifier is constructed based on a machine learning algorithm. In the process of constructing the carbon sink contribution classifier, the task of machine learning is to extract patterns from the relationship between the collected collapse feature information and the corresponding carbon sink contribution coefficient, and use it to predict the carbon sink contribution coefficient of new data. For example, a carbon sink contribution classifier is constructed based on a BP neural network. The carbon sink contribution classifier is a BP neural network model that can be iteratively optimized in machine learning, including an input layer (input data is collapse feature information), multiple hidden layers and an output layer (output data is soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient); further, the collapse feature information set is used as an input feature, and the sample soil carbon sink contribution coefficient set, sample vegetation carbon sink contribution coefficient set and sample water body carbon sink contribution coefficient set are used as output features to perform supervised training on the carbon sink contribution classifier. First, the input data (collapse feature information) is passed to the network layer by layer. After nonlinear transformation of the activation function, the calculation result of the output layer is the predicted value of the input feature, and the predicted soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient are obtained; wherein the activation function adopts the ReLU function, as shown in the following formula: f(x)=max(0,x), and the nonlinear output capability of the carbon sink contribution classifier is enhanced by the activation function.

[0028] Next, the error between the predicted output and the actual output of the network is calculated by the mean square error loss function. The error represents the gap between the carbon sink contribution coefficient predicted by the model and the actual value. The mean square error loss function is as follows: ; Among them, LOSS is the error, m is the number of sample data, The carbon sink contribution classifier predicts the output of the i-th group of soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient. is the true soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water carbon sink contribution coefficient of the i-th group of samples. The difference between the three is calculated respectively, and then the square of the sum of the differences is calculated to obtain the error. Then, for each output node, the gradient of the output error to each weight and bias is calculated. During the back propagation process, the output error is propagated back to the network, and the gradient of each layer can be calculated by the chain rule, as shown in the following formula: ; Among them, LOSS is the error, is the gradient of the error with respect to the output layer weights, is the weight matrix from the hidden layer to the output layer, is the activation value of the output group i of soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient in the activation function; and the gradient descent method is used to update the weight and bias of each connection, and the model gradually approaches the optimal solution by continuously adjusting the learning rate and gradient descent method; the above process is repeated until the model converges, that is, the error converges to a smaller value after multiple training iterations, for example, the error is less than 0.001, and the training is completed. Among them, each training will adjust the weights and biases in the network so that the prediction results are closer and closer to the true value, and a trained carbon sink contribution classifier is obtained.

[0029] S130: Input the collapse characteristic information into the carbon sink contribution classifier, and output the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient of the soil carbon sink, vegetation carbon sink and water body carbon sink; S140: Select the carbon sink mode corresponding to the largest contribution coefficient as the main carbon sink mode, and the other two carbon sink modes as secondary carbon sink modes.

[0030] Specifically, the collapse feature information is input into the trained carbon sink contribution classifier, and the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water carbon sink contribution coefficient of the soil carbon sink, vegetation carbon sink and water carbon sink are obtained by output. Finally, the carbon sink mode corresponding to the largest contribution coefficient is selected as the main carbon sink mode, and the other two carbon sink modes are used as secondary carbon sink modes. For example, assuming that the soil carbon sink contribution coefficient is 0.6, the vegetation carbon sink contribution coefficient is 0.3, and the water carbon sink contribution coefficient is 0.1, then the main carbon sink mode is the soil carbon sink, and the other two secondary carbon sink modes are the vegetation carbon sink and the water carbon sink.

[0031] By constructing a carbon sink contribution classifier based on machine learning, the intelligence and scientificity of the classification of carbon sink contribution coefficients can be improved, and then the scientificity, accuracy and efficiency of the classification of carbon sink contribution coefficients can be improved, providing more accurate and comprehensive data support for the carbon sink capacity assessment of coal mining subsidence areas.

[0032] S200: Collecting soil carbon sink data, vegetation carbon sink data and water body carbon sink data in the coal mining subsidence area to obtain basic soil carbon sink parameters, basic vegetation carbon sink parameters and basic water body carbon sink parameters.

[0033] Furthermore, step S200 of the present invention further includes: S210: testing the soil organic carbon content in the coal mining subsidence area to obtain soil carbon sink data; S220: collecting the number of different vegetation in the coal mining subsidence area, and calculating to obtain vegetation carbon sink data; S230: testing the dissolved organic carbon concentration of the water body in the coal mining subsidence area to obtain water body carbon sink data.

[0034] Specifically, soil sample analysis is used to test the soil organic carbon content in the coal mining subsidence area, such as dry oxidation method, elemental analysis method, etc., to determine the organic carbon content in the soil sample. Based on the collected soil organic carbon content data, the total carbon storage of the soil in the area is calculated and set as soil carbon sink data. In the coal mining subsidence area, vegetation information of different types and quantities is collected, such as using drones or satellite images to obtain vegetation coverage and distribution. Different types of vegetation have different carbon sink capacities (the amount of carbon contained in a unit biomass), which can be determined by consulting literature or conducting field experiments. Finally, the total vegetation carbon sink is calculated based on the unit vegetation carbon sink corresponding to the number of vegetation and the type of vegetation, and set as vegetation carbon sink data.

[0035] By collecting and analyzing water samples, the dissolved organic carbon (DOC) concentration in the water body of the coal mining subsidence area is tested, such as using high performance liquid chromatography (HPLC) or spectroscopy (such as ultraviolet visible spectroscopy) to determine the organic carbon concentration in the water body, and multiplying the dissolved organic carbon concentration by the water volume to calculate the carbon sink of the water body, which is set as the water body carbon sink data. By collecting data such as soil organic carbon content, vegetation quantity and type, and dissolved organic carbon concentration in water bodies, the carbon sink capacity of coal mining subsidence areas can be comprehensively and accurately evaluated.

[0036] S300: Performing a carbon sink impact trend analysis based on the collapse characteristic information to obtain a soil carbon sink impact coefficient, a vegetation carbon sink impact coefficient, and a water body carbon sink impact coefficient.

[0037] Furthermore, step S300 of the present invention further includes: S310: Collect collapse characteristic information of multiple sample coal mining subsidence areas to obtain a collapse characteristic information set; S320: Collect the change ratio of soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters in different sample coal mining subsidence areas within a preset time length to obtain a sample soil carbon sink influence coefficient set, a sample vegetation carbon sink influence coefficient set and a sample water body carbon sink influence coefficient set; S330: Use the collapse characteristic information set as input features, use the sample soil carbon sink influence coefficient set, the sample vegetation carbon sink influence coefficient set and the sample water body carbon sink influence coefficient set as output features, and build and train a carbon sink influence trend analyzer based on machine learning until convergence; S340: Input the collapse characteristic information into the carbon sink impact trend analyzer, and output the soil carbon sink influence coefficient, the vegetation carbon sink influence coefficient and the water body carbon sink influence coefficient.

[0038] Specifically, the collapse characteristic information (water table, collapse slope and collapse depth) of multiple sample coal mining subsidence areas is collected to obtain a collapse characteristic information set. Next, the change ratio of soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters of different sample coal mining subsidence areas within a preset time length is collected, where the preset time length can be set according to actual conditions, such as three months, half a year; collapse characteristics will affect different forms of carbon sinks, for example, a larger slope (such as a collapse slope of 35°-40°) may lead to soil organic carbon loss, vegetation cannot grow, resulting in a decrease in soil and vegetation carbon sinks, and an increase in water volume may lead to an increase in water body carbon sinks; obtain a set of sample soil carbon sink influence coefficients, a set of sample vegetation carbon sink influence coefficients and a set of sample water body carbon sink influence coefficients; where the carbon sink influence coefficient reflects the impact of collapse characteristics on carbon sink methods, carbon The sink impact coefficient is (current carbon sink amount - initial carbon sink amount) / initial carbon sink amount. This ratio can be positive (increase carbon sink amount) or negative (decrease carbon sink amount). The soil carbon sink impact coefficient represents the change ratio of soil carbon sink amount. A larger slope may lead to soil carbon loss, and a deeper depth (such as a collapse depth of 8m-12m) may increase carbon storage; the vegetation carbon sink impact coefficient represents the change ratio of vegetation carbon sink amount. For example, when vegetation growth is restricted in a collapsed area, the carbon sink capacity decreases; the water body carbon sink impact coefficient represents the impact of collapse characteristics on the water body carbon sink amount, especially the impact of changes in water accumulation and water body area on carbon sink capacity. For example, a larger slope may cause water to converge into low-lying areas, thereby increasing water body carbon storage.

[0039] A carbon sink impact trend analyzer is further constructed based on machine learning. For example, a carbon sink impact trend analyzer is constructed based on a BP neural network. The carbon sink impact trend analyzer includes an input layer (the input data is collapse feature information), multiple hidden layers and an input layer (the output data is soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient); then the collapse feature information set is used as input feature, and the sample soil carbon sink impact coefficient set, the sample vegetation carbon sink impact coefficient set and the sample water body carbon sink impact coefficient set are used as output feature to perform supervised training on the carbon sink impact trend analyzer. First, The input features are passed to each hidden layer through the input layer. In each layer, the activation value of each node is calculated by weighted summation, and then the activation value is passed to the next layer through the activation function until the output layer. The output result is the predicted soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient; then, the error between the predicted output and the actual output is calculated by the mean square error loss function; then, the error of each layer of nodes is calculated by the back propagation algorithm, and the error is passed from the output layer to the input layer layer by layer. By calculating the gradient of each layer weight, the weight in the network is updated using an optimization algorithm (such as the gradient descent method). Among them, the activation function, loss function and gradient calculation method of the carbon sink impact trend analyzer are the same as those of the carbon sink contribution classifier in the above content, but the specific training data are different. Therefore, after the training converges, a carbon sink impact trend analyzer with different network parameters such as weights can be obtained, and the output soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient are predicted and output by collapsing feature information.

[0040] Through multiple iterations, the process of forward propagation, loss calculation, back propagation and weight update is repeatedly performed. Each iteration adjusts the weights of the neural network so that the loss function gradually decreases, and the final model can accurately predict the carbon sink impact coefficient. When the loss function converges to a smaller value (such as 0.001) or reaches a predetermined number of training times (such as 100 cycles of iterative training), the training is stopped to obtain a trained carbon sink impact trend analyzer.

[0041] Finally, the subsidence characteristic information is input into the carbon sink impact trend analyzer for analysis, and the soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient are output. By constructing a carbon sink impact trend analyzer based on machine learning, the intelligence and scientificity of carbon sink impact trend analysis can be improved, and then the scientificity, accuracy and efficiency of carbon sink impact coefficient can be improved, providing more accurate and comprehensive data support for the carbon sink capacity assessment of coal mining subsidence areas.

[0042] S400: Collect the main characteristic information of carbon sinks of the two secondary carbon sink modes and the main carbon sink mode, perform auxiliary carbon sink trend impact analysis on the main carbon sink mode, obtain the auxiliary carbon sink impact coefficient, and combine the soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient to perform impact correction on the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes, and obtain soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters as the carbon sink data analysis results.

[0043] Furthermore, step S400 of the present invention further includes: S410: Collect the main carbon sink characteristic information of the two secondary carbon sink modes and the main carbon sink mode, wherein the main carbon sink characteristic information of soil carbon sink, vegetation carbon sink and water body carbon sink includes soil composition information, vegetation type information and water body composition information; S420: Collect a set of sample carbon sink main characteristic information, and collect the change ratio of carbon sink parameters of other carbon sink modes within a preset time length under each sample carbon sink main characteristic information, and mark them as a set of sample auxiliary carbon sink influence coefficients; S430: Use the sample carbon sink main characteristic information set as input features, and use the sample auxiliary carbon sink influence coefficient set as output features to construct an auxiliary carbon sink impact analyzer and train it to convergence; S440: Input the main carbon sink characteristic information of the two secondary carbon sink modes into the auxiliary carbon sink impact analyzer respectively, and output two auxiliary carbon sink influence coefficients.

[0044] Specifically, first, the carbon sink main characteristic information of the two secondary carbon sink modes and the main carbon sink mode is collected, wherein the carbon sink main characteristic information of soil carbon sink, vegetation carbon sink and water body carbon sink includes soil composition information (including soil organic carbon content, mineral composition, pH value, etc., key factors affecting soil carbon sink capacity), vegetation type information (such as grass, shrubs, trees, etc., different vegetation types have different carbon sink capacities) and water body composition information (dissolved organic carbon concentration, pH value, temperature and other information of water bodies, affecting water body carbon sink capacity). Next, the sample carbon sink main characteristic information set is collected, and the change ratio of carbon sink parameters of other carbon sink modes within a preset time length under each sample carbon sink main characteristic information is collected. The carbon sink ratio is (current carbon sink amount-initial carbon sink amount) / initial carbon sink amount. The ratio can be a positive value (increase carbon sink amount) or a negative value (decrease carbon sink amount), which is marked as the sample auxiliary carbon sink influence coefficient, and the sample auxiliary carbon sink influence coefficient set is obtained.

[0045] Then, an auxiliary carbon sink impact analyzer is constructed based on the BP neural network, and the auxiliary carbon sink impact analyzer includes an input layer (the input data is the main feature information of the carbon sink), multiple hidden layers and an output layer (the output data is the auxiliary carbon sink impact coefficient); further, the sample carbon sink main feature information set is used as the input feature, and the sample auxiliary carbon sink impact coefficient set is used as the output feature, and the auxiliary carbon sink impact analyzer is supervised and trained, firstly, the carbon sink main feature information is input into the neural network through the input layer, and a predicted value is obtained through the output layer, that is, the auxiliary carbon sink impact coefficient predicted for the current input carbon sink main feature information; then for each group of samples, the difference between the predicted value and the true value (that is, the auxiliary carbon sink impact coefficient in the sample) is calculated. The commonly used error calculation method is the mean square error; then, based on the calculated error, the back propagation algorithm is used to calculate the gradient of each layer, that is, the adjustment direction of the weights and biases. Through the chain rule, the error is back-propagated to the input layer layer by layer, and the contribution of each weight and bias to the total error is calculated. The gradient value indicates how to adjust each weight and bias to reduce the prediction error; then, based on the gradient calculated by back propagation, an optimization algorithm (such as gradient descent) is used to update the weights and biases in the network. The optimization algorithm reduces the error and makes the network gradually approach the true auxiliary carbon sink impact coefficient; repeat the steps of forward propagation, error calculation, back propagation and weight update until the network error converges (that is, reaches the preset error threshold or the maximum number of training rounds), and a trained auxiliary carbon sink impact analyzer is obtained.

[0046] Finally, the carbon sink main characteristic information of the two secondary carbon sink modes is respectively input into the auxiliary carbon sink impact analyzer, and two auxiliary carbon sink impact coefficients of the two secondary carbon sink modes are output.

[0047] Furthermore, step S400 of the present invention further includes: S450: According to the soil carbon sink influence coefficient, the vegetation carbon sink influence coefficient and the water body carbon sink influence coefficient, combined with the two auxiliary carbon sink influence coefficients, three total carbon sink influence coefficients are calculated; S460: Using the three total carbon sink influence coefficients, the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes are corrected to obtain soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters as the carbon sink data analysis results.

[0048] Specifically, first, the total carbon sink impact coefficient is calculated according to the soil carbon sink impact coefficient, the vegetation carbon sink impact coefficient and the water body carbon sink impact coefficient in combination with the two auxiliary carbon sink impact coefficients, wherein the total carbon sink impact coefficient of the main carbon sink mode is 1 plus the sum of the main carbon sink impact coefficient corresponding to the main carbon sink mode and the other two auxiliary carbon sink impact coefficients; the total carbon sink impact coefficient of the other two secondary carbon sink modes is 1 plus the sum of the main carbon sink impact coefficient corresponding to the main carbon sink mode. For example, assuming that the soil carbon sink impact coefficient, the vegetation carbon sink impact coefficient and the water body carbon sink impact coefficient are 0.3, 0.2 and 0.1 respectively, the main carbon sink mode is soil carbon sink, and the two auxiliary carbon sink impact coefficients are 0.05 and 0.06 respectively, then the total carbon sink impact coefficient corresponding to the main carbon sink mode is 1 plus 0.3 plus 0.05 and 0.06, which is equal to 1.41; the total carbon sink impact coefficient of the two secondary carbon sink modes is 1 plus 0.3, which is equal to 1.3.

[0049] Finally, the three total carbon sink impact coefficients are used to correct the impact of the basic carbon sink parameters of the main carbon sink method and the two secondary carbon sink methods, that is, the total carbon sink impact coefficient of the main carbon sink method is multiplied by the basic carbon sink parameters of the main carbon sink method, and the total carbon sink impact coefficient of the secondary carbon sink method is multiplied by the basic carbon sink parameters of the two secondary carbon sink methods to obtain soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters as carbon sink data analysis results. By combining the total carbon sink impact coefficients of soil, vegetation and water bodies, and correcting the impact of the basic carbon sink parameters of each carbon sink method, the real carbon sink capacity of each carbon sink method in coal mining subsidence can be more accurately reflected. This process takes into account the interaction between carbon sink methods, thereby providing a more scientific and comprehensive basis for the assessment of carbon sink capacity in coal mining subsidence areas.

[0050] The carbon sink data analysis method for coal mining subsidence provided by the embodiment of the present invention has at least the following technical effects: By collecting the collapse characteristic information of the coal mining subsidence area, the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient are classified, and the main carbon sink mode and the other two secondary carbon sink modes are determined; then the soil carbon sink data, vegetation carbon sink data and water body carbon sink data in the coal mining subsidence area are collected to obtain basic soil carbon sink parameters, basic vegetation carbon sink parameters and basic water body carbon sink parameters; then, a carbon sink impact trend analysis is performed based on the collapse characteristic information to obtain the soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient; on the other hand, the carbon sink main characteristic information of the two secondary carbon sink modes and the main carbon sink mode is collected, and the main carbon sink mode is analyzed. An auxiliary carbon sink trend impact analysis is performed to obtain the auxiliary carbon sink impact coefficient; finally, the soil carbon sink impact coefficient, the vegetation carbon sink impact coefficient and the water carbon sink impact coefficient are combined to perform impact correction on the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes, and soil carbon sink parameters, vegetation carbon sink parameters and water carbon sink parameters are obtained as the carbon sink data analysis results; that is to say, by using machine learning algorithms to accurately analyze the collapse characteristics of coal mining subsidence areas and the mutual influence of different carbon sink modes, the scientificity, accuracy and comprehensiveness of the carbon sink data analysis of coal mining subsidence areas can be improved, thereby achieving the technical effect of accurately evaluating the carbon sink capacity of coal mining subsidence areas under different geographical conditions.

[0051] Embodiment 2, as Figure 2 As shown, based on the same inventive concept as the method for analyzing carbon sink data in coal mining subsidence areas provided in Example 1, an embodiment of the present invention further provides a carbon sink data analysis system for coal mining subsidence areas, including: a carbon sink contribution coefficient acquisition module 11, used to collect subsidence characteristic information of coal mining subsidence areas, classify and obtain soil carbon sink contribution coefficients, vegetation carbon sink contribution coefficients and water body carbon sink contribution coefficients, and determine the main carbon sink mode and the other two secondary carbon sink modes; a basic carbon sink parameter acquisition module 12, used to collect soil carbon sink data, vegetation carbon sink data and water body carbon sink data in the coal mining subsidence areas, and obtain basic soil carbon sink parameters, basic vegetation carbon sink parameters and basic water body carbon sink parameters; carbon sink effect The impact trend analysis module 13 is used to perform carbon sink impact trend analysis based on the collapse characteristic information, and obtain the soil carbon sink impact coefficient, the vegetation carbon sink impact coefficient and the water body carbon sink impact coefficient; the basic carbon sink parameter correction module 14 is used to collect the main carbon sink characteristic information of the two secondary carbon sink modes and the main carbon sink mode, perform auxiliary carbon sink trend impact analysis on the main carbon sink mode, obtain the auxiliary carbon sink impact coefficient, and combine the soil carbon sink impact coefficient, the vegetation carbon sink impact coefficient and the water body carbon sink impact coefficient to perform impact correction on the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes, and obtain soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters as the carbon sink data analysis results.

[0052] Furthermore, the carbon sink data analysis system for coal mining subsidence areas also includes: collecting the collapse slope and collapse depth of the coal mining subsidence areas as collapse characteristic information; constructing a carbon sink contribution classifier based on sample carbon sink data of sample coal mining subsidence areas; inputting the collapse characteristic information into the carbon sink contribution classifier, and outputting the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient of the soil carbon sink, vegetation carbon sink and water body carbon sink; selecting the carbon sink mode corresponding to the largest contribution coefficient as the main carbon sink mode, and the other two carbon sink modes as secondary carbon sink modes.

[0053] Furthermore, the carbon sink data analysis system for coal mining subsidence areas also includes: collecting collapse characteristic information of multiple sample coal mining subsidence areas to obtain a collapse characteristic information set; collecting the carbon sink proportions of soil carbon sinks, vegetation carbon sinks, and water body carbon sinks in each sample coal mining subsidence area, and marking to obtain a sample soil carbon sink contribution coefficient set, a sample vegetation carbon sink contribution coefficient set, and a sample water body carbon sink contribution coefficient set; using the collapse characteristic information set as an input feature, and using the sample soil carbon sink contribution coefficient set, the sample vegetation carbon sink contribution coefficient set, and the sample water body carbon sink contribution coefficient set as output features, and based on machine learning, constructing and training a carbon sink contribution classifier until convergence.

[0054] Furthermore, the carbon sink data analysis system for coal mining subsidence areas also includes: testing the soil organic carbon content in the coal mining subsidence area to obtain soil carbon sink data; collecting the number of different vegetation in the coal mining subsidence area to calculate and obtain vegetation carbon sink data; testing the dissolved organic carbon concentration of water in the coal mining subsidence area to obtain water carbon sink data.

[0055] Furthermore, the carbon sink data analysis system for coal mining subsidence areas also includes: collecting collapse characteristic information of multiple sample coal mining subsidence areas to obtain a collapse characteristic information set; collecting the change ratio of soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters in different sample coal mining subsidence areas within a preset time length to obtain a sample soil carbon sink influence coefficient set, a sample vegetation carbon sink influence coefficient set and a sample water body carbon sink influence coefficient set; using the collapse characteristic information set as input features, using the sample soil carbon sink influence coefficient set, the sample vegetation carbon sink influence coefficient set and the sample water body carbon sink influence coefficient set as output features, based on machine learning, constructing and training a carbon sink impact trend analyzer until convergence; inputting the collapse characteristic information into the carbon sink impact trend analyzer, and outputting the soil carbon sink influence coefficient, the vegetation carbon sink influence coefficient and the water body carbon sink influence coefficient.

[0056] Furthermore, the carbon sink data analysis system for coal mining subsidence areas also includes: collecting the main carbon sink characteristic information of the two secondary carbon sink modes and the main carbon sink mode, wherein the main carbon sink characteristic information of soil carbon sink, vegetation carbon sink and water body carbon sink includes soil composition information, vegetation type information and water body composition information; collecting a set of sample carbon sink main characteristic information, and collecting the change ratio of carbon sink parameters of other carbon sink modes within a preset time length under each sample carbon sink main characteristic information, marked as a set of sample auxiliary carbon sink influence coefficients; using the sample carbon sink main characteristic information set as input features, using the sample auxiliary carbon sink influence coefficient set as output features, constructing an auxiliary carbon sink impact analyzer and training it to convergence; respectively inputting the carbon sink main characteristic information of the two secondary carbon sink modes into the auxiliary carbon sink impact analyzer, and outputting two auxiliary carbon sink influence coefficients.

[0057] Furthermore, the carbon sink data analysis system for coal mining subsidence areas also includes: calculating three total carbon sink influence coefficients based on the soil carbon sink influence coefficient, the vegetation carbon sink influence coefficient and the water body carbon sink influence coefficient, combined with the two auxiliary carbon sink influence coefficients; using the three total carbon sink influence coefficients, performing impact correction on the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes, and obtaining soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters as carbon sink data analysis results.

[0058] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0059] 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 belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for analyzing carbon sink data in coal mining subsidence areas, characterized in that: Methods include: Collect the subsidence characteristic information of coal mining subsidence areas, classify and obtain the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water carbon sink contribution coefficient, and determine the main carbon sink mode and the other two secondary carbon sink modes; Collecting soil carbon sink data, vegetation carbon sink data and water body carbon sink data in the coal mining subsidence area to obtain basic soil carbon sink parameters, basic vegetation carbon sink parameters and basic water body carbon sink parameters; According to the collapse characteristic information, a carbon sink impact trend analysis is performed to obtain a soil carbon sink impact coefficient, a vegetation carbon sink impact coefficient, and a water body carbon sink impact coefficient; The main characteristic information of carbon sinks of the two secondary carbon sink modes and the main carbon sink mode is collected, and an auxiliary carbon sink trend impact analysis is performed on the main carbon sink mode to obtain the auxiliary carbon sink impact coefficient. Combined with the soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient, the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes are corrected to obtain soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters as the carbon sink data analysis results.

2. The carbon sink data analysis method for coal mining subsidence according to claim 1 is characterized in that: Collect the subsidence characteristic information of coal mining subsidence areas, classify and obtain the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water carbon sink contribution coefficient, and determine the main carbon sink mode and the other two secondary carbon sink modes, including: Collect the collapse slope and collapse depth of the coal mining collapse area as collapse characteristic information; Based on the sample carbon sink data of sample coal mining subsidence areas, a carbon sink contribution classifier is constructed; Input the collapse characteristic information into the carbon sink contribution classifier, and output the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient of the soil carbon sink, vegetation carbon sink and water body carbon sink; The carbon sink method corresponding to the largest contribution coefficient is selected as the main carbon sink method, and the other two carbon sink methods are selected as secondary carbon sink methods.

3. The carbon sink data analysis method for coal mining subsidence according to claim 2 is characterized in that: According to the sample carbon sink data of sample coal mining subsidence areas, a carbon sink contribution classifier is constructed, including: Collecting collapse characteristic information of multiple sample coal mining subsidence areas to obtain a collapse characteristic information set; Collect the carbon sink proportions of soil carbon sink, vegetation carbon sink, and water body carbon sink in each sample coal mining subsidence area, and annotate to obtain the sample soil carbon sink contribution coefficient set, sample vegetation carbon sink contribution coefficient set, and sample water body carbon sink contribution coefficient set; The collapse feature information set is used as input features, and the sample soil carbon sink contribution coefficient set, the sample vegetation carbon sink contribution coefficient set and the sample water body carbon sink contribution coefficient set are used as output features. Based on machine learning, a carbon sink contribution classifier is constructed and trained until convergence.

4. The carbon sink data analysis method for coal mining subsidence according to claim 1, characterized in that: The soil carbon sink data, vegetation carbon sink data and water body carbon sink data in the coal mining subsidence area are collected to obtain basic soil carbon sink parameters, basic vegetation carbon sink parameters and basic water body carbon sink parameters, including: Testing the soil organic carbon content in the coal mining subsidence area to obtain soil carbon sink data; Collecting the number of different vegetation in the coal mining subsidence area and calculating and obtaining vegetation carbon sink data; The dissolved organic carbon concentration of the water in the coal mining subsidence area is tested to obtain water carbon sink data.

5. The carbon sink data analysis method for coal mining subsidence according to claim 1, characterized in that: According to the collapse characteristic information, a carbon sink impact trend analysis is performed to obtain the soil carbon sink impact coefficient, the vegetation carbon sink impact coefficient and the water body carbon sink impact coefficient, including: Collecting collapse characteristic information of multiple sample coal mining subsidence areas to obtain a collapse characteristic information set; Collect the change ratios of soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters of different samples of coal mining subsidence underground within a preset time length, and obtain a set of sample soil carbon sink influence coefficients, a set of sample vegetation carbon sink influence coefficients and a set of sample water body carbon sink influence coefficients; Using the collapse feature information set as input features, using the sample soil carbon sink influence coefficient set, the sample vegetation carbon sink influence coefficient set and the sample water body carbon sink influence coefficient set as output features, based on machine learning, constructing and training a carbon sink impact trend analyzer until convergence; The collapse characteristic information is input into the carbon sink impact trend analyzer, and the soil carbon sink impact coefficient, the vegetation carbon sink impact coefficient and the water body carbon sink impact coefficient are obtained as output.

6. The carbon sink data analysis method for coal mining subsidence according to claim 1, characterized in that: Collecting the carbon sink main characteristic information of the two secondary carbon sink modes and the main carbon sink mode, performing auxiliary carbon sink trend impact analysis on the main carbon sink mode, and obtaining the auxiliary carbon sink impact coefficient, including: Collecting the carbon sink main characteristic information of the two secondary carbon sink modes and the main carbon sink mode, wherein the carbon sink main characteristic information of soil carbon sink, vegetation carbon sink and water body carbon sink includes soil composition information, vegetation type information and water body composition information; Collect the main characteristic information set of sample carbon sinks, and under each main characteristic information of sample carbon sinks, the change ratio of carbon sink parameters of other carbon sink methods within a preset time length, marked as the sample auxiliary carbon sink influence coefficient set; Using the sample carbon sink main feature information set as input features, using the sample auxiliary carbon sink influence coefficient set as output features, constructing an auxiliary carbon sink influence analyzer and training it until convergence; The carbon sink main characteristic information of the two secondary carbon sink modes is respectively input into the auxiliary carbon sink impact analyzer, and two auxiliary carbon sink impact coefficients are obtained as output.

7. The carbon sink data analysis method for coal mining subsidence according to claim 6, characterized in that: Combined with the soil carbon sink influence coefficient, the vegetation carbon sink influence coefficient and the water body carbon sink influence coefficient, the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes are corrected for impact, and soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters are obtained as carbon sink data analysis results, including: According to the soil carbon sink influence coefficient, the vegetation carbon sink influence coefficient and the water body carbon sink influence coefficient, combined with the two auxiliary carbon sink influence coefficients, three total carbon sink influence coefficients are calculated; The three total carbon sink impact coefficients are used to perform impact correction on the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes, and soil carbon sink parameters, vegetation carbon sink parameters and water body carbon sink parameters are obtained as carbon sink data analysis results.

8. A carbon sink data analysis system for coal mining subsidence, characterized in that: The steps for implementing the carbon sink data analysis method for coal mining subsidence land according to any one of claims 1 to 7 include: The carbon sink contribution coefficient acquisition module is used to collect the collapse characteristic information of coal mining subsidence areas, classify and obtain the soil carbon sink contribution coefficient, vegetation carbon sink contribution coefficient and water body carbon sink contribution coefficient, and determine the main carbon sink mode and the other two secondary carbon sink modes; A basic carbon sink parameter acquisition module is used to collect soil carbon sink data, vegetation carbon sink data and water body carbon sink data in the coal mining subsidence area to obtain basic soil carbon sink parameters, basic vegetation carbon sink parameters and basic water body carbon sink parameters; A carbon sink impact trend analysis module is used to perform carbon sink impact trend analysis based on the collapse characteristic information to obtain soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water body carbon sink impact coefficient; The basic carbon sink parameter correction module is used to collect the main carbon sink characteristic information of the two secondary carbon sink modes and the main carbon sink mode, perform auxiliary carbon sink trend impact analysis on the main carbon sink mode, obtain the auxiliary carbon sink impact coefficient, and combine the soil carbon sink impact coefficient, vegetation carbon sink impact coefficient and water carbon sink impact coefficient to perform impact correction on the basic carbon sink parameters of the main carbon sink mode and the two secondary carbon sink modes, and obtain soil carbon sink parameters, vegetation carbon sink parameters and water carbon sink parameters as the carbon sink data analysis results.

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