Cycle quality data evaluation method, system and equipment for carbon cycle

Abstract

The invention relates to a circulation quality data evaluation method, system and equipment for carbon circulation, and relates to the field of carbon circulation quality evaluation, and the method comprises the steps: carrying out the carbon emission prediction of a coal mining subsidence land, and obtaining the predicted carbon emission; scale change prediction of soil, vegetation and water is carried out according to the collapse characteristic information; soil physicochemical property parameters, vegetation type parameters and water body component parameters are collected, and the predicted carbon sequestration amount is obtained through calculation in combination with the change scale parameters of soil, vegetation and a water body; and calculating according to the predicted carbon sequestration amount and the predicted carbon emission amount to obtain a carbon cycle quality evaluation result. According to the method, the technical problem that the carbon cycle quality cannot be effectively and quantitatively evaluated due to the fact that the carbon emission and the carbon sequestration capacity cannot be accurately predicted because the special geographical environment characteristics and the change rule of the coal mining subsidence land are not fully considered in a traditional evaluation method can be solved; the scientificity, accuracy and reliability of carbon emission and carbon sequestration capability prediction can be remarkably improved, and accurate quantitative evaluation of carbon cycle quality is realized.

Description

Technical Field

[0001] The present invention relates to the field of carbon cycle quality assessment, and particularly to a method, system and device for evaluating cyclic quality data of carbon cycle. Background Art

[0002] Coal mining subsidence areas are special landforms formed due to ground subsidence during the coal mining process. They usually have complex topographical features and environmental conditions. Due to the extensive damage to the natural ecosystem during coal mining, coal mining subsidence areas often exhibit problems such as soil degradation, insufficient vegetation cover, and soil erosion. This makes the carbon cycle process in this area very complex and affects the prediction and assessment of carbon emissions and carbon sequestration capacity.

[0003] Currently, traditional carbon cycle assessment methods mostly rely on estimations based on experience or simplified models. These methods usually ignore the specific geographical condition environmental differences and mining subsidence characteristics in these areas. Therefore, the prediction accuracy of carbon emissions and carbon sequestration capacity is relatively low, and the actual carbon cycle status cannot be fully reflected. Summary of the Invention

[0004] In view of the technical problem that the traditional carbon cycle assessment method fails to fully consider the special geographical environment characteristics of coal mining subsidence areas, resulting in inaccurate prediction of carbon emissions and carbon sequestration capacity, and thus unable to effectively quantify and evaluate the carbon cycle quality, the present invention provides a method, system and device for evaluating cyclic quality data of carbon cycle to solve this problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a method for evaluating cyclic quality data of carbon cycle, including: collecting carbon emission characteristic information and subsidence characteristic information in a coal mining subsidence area, and predicting the carbon emissions of the coal mining subsidence area according to the carbon emission characteristic information to obtain predicted carbon emissions; collecting soil scale parameters, vegetation scale parameters and water body scale parameters in the coal mining subsidence area, and predicting the scale changes of soil, vegetation and water body according to the subsidence characteristic information to obtain changed soil scale parameters, changed vegetation scale parameters and changed water body scale parameters; collecting soil component parameters, vegetation type parameters and water body component parameters in the coal mining subsidence area, and calculating the predicted carbon sequestration amount in combination with the changed soil scale parameters, changed vegetation scale parameters and changed water body scale parameters; calculating a carbon cycle quality evaluation result according to the predicted carbon sequestration amount and the predicted carbon emissions.

[0007] Optionally, the method for evaluating cyclic quality data for carbon cycling further includes: collecting the scale information of the leakage carbon emission sources in the coal mining subsidence area as carbon emission characteristic information; collecting the subsidence area information, subsidence slope information, and subsidence depth information in the coal mining subsidence area as subsidence characteristic information.

[0008] Optionally, the method for evaluating cyclic quality data for carbon cycling further includes: collecting a set of sample carbon emission characteristic information according to the carbon emission monitoring data of the sample coal mining subsidence area, and collecting the carbon emissions under different sample carbon emission characteristic information to obtain a set of sample carbon emissions; using the set of sample carbon emission characteristic information as input features and the set of sample carbon emissions as output features to train a carbon emission predictor; inputting the carbon emission characteristic information into the carbon emission predictor to output and obtain the predicted carbon emissions.

[0009] Optionally, the method for evaluating cyclic quality data for carbon cycling further includes: collecting the soil scale parameters, vegetation scale parameters, and water body scale parameters in the coal mining subsidence area; training a change predictor including an integrated change prediction branch; calculating the ratio of the predicted carbon emissions to the maximum value in the set of sample carbon emissions, multiplying by the number of branches of the integrated change prediction branch and rounding to obtain the configured number of branches; randomly selecting the configured number of change prediction branches in the change predictor, inputting the soil scale parameters, vegetation scale parameters, and water body scale parameters, and predicting and outputting to obtain a set of predicted soil change coefficients, a set of predicted vegetation change coefficients, and a set of predicted water body change coefficients after a preset time length, and calculating the mean value to obtain the soil change coefficient, vegetation change coefficient, and water body change coefficient; using the soil change coefficient, vegetation change coefficient, and water body change coefficient to perform change calculations on the soil scale parameters, vegetation scale parameters, and water body scale parameters to obtain changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters.

[0010] Optionally, the method for evaluating cyclic quality data for carbon cycling further includes: collecting a set of sample subsidence characteristic information according to the change monitoring data of the soil, vegetation, and water body in the sample coal mining subsidence area, and collecting the scale change coefficients of the soil, vegetation, and water body after a preset time length, labeled as a set of sample soil change coefficients, a set of sample vegetation change coefficients, and a set of sample water body change coefficients; integrating the set of sample subsidence characteristic information, the set of sample soil change coefficients, the set of sample vegetation change coefficients, and the set of sample water body change coefficients, and dividing to obtain multiple pieces of change prediction data; based on ensemble learning, using the multiple pieces of change prediction data to train multiple change prediction branches to obtain a change predictor including an integrated change prediction branch.

[0011] Optionally, the method for evaluating cyclic quality data for carbon cycle further includes: collecting soil composition parameters, vegetation type parameters, and water body composition parameters within the coal mining subsidence area, and testing and obtaining soil carbon sequestration parameters, vegetation carbon sequestration parameters, and water body carbon sequestration parameters of the soil composition parameters, vegetation type parameters, and water body composition parameters; calculating predicted soil carbon sequestration amount, predicted vegetation carbon sequestration amount, and predicted water body carbon sequestration amount based on the soil carbon sequestration parameters, vegetation carbon sequestration parameters, and water body carbon sequestration parameters, in combination with the changing soil scale parameters, changing vegetation scale parameters, and changing water body scale parameters; calculating the sum of the predicted soil carbon sequestration amount, predicted vegetation carbon sequestration amount, and predicted water body carbon sequestration amount to obtain the predicted carbon sequestration amount.

[0012] Optionally, the method for evaluating cyclic quality data for carbon cycle further includes: calculating the ratio of the predicted carbon sequestration amount to the predicted carbon emission amount; using the ratio as the evaluation result of the carbon cycle quality.

[0013] In a second aspect, the present invention provides a system for evaluating cyclic quality data for carbon cycle, including: a carbon emission prediction module, configured to collect carbon emission characteristic information and subsidence characteristic information within the coal mining subsidence area, and perform carbon emission prediction for the coal mining subsidence area based on the carbon emission characteristic information to obtain a predicted carbon emission amount; a scale change prediction module, configured to collect soil scale parameters, vegetation scale parameters, and water body scale parameters within the coal mining subsidence area, and perform scale change prediction of soil, vegetation, and water body based on the subsidence characteristic information to obtain changing soil scale parameters, changing vegetation scale parameters, and changing water body scale parameters; a predicted carbon sequestration amount calculation module, configured to collect soil composition parameters, vegetation type parameters, and water body composition parameters within the coal mining subsidence area, and calculate the predicted carbon sequestration amount in combination with the changing soil scale parameters, changing vegetation scale parameters, and changing water body scale parameters; a carbon cycle quality evaluation result calculation module, configured to calculate the carbon cycle quality evaluation result based on the predicted carbon sequestration amount and the predicted carbon emission amount.

[0014] In a third aspect, the present invention further provides an electronic device, including:

[0015] at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the method for evaluating cyclic quality data for carbon cycle according to any one of the first aspects above.

[0016] The beneficial effects of the present invention are as follows: By collecting carbon emission characteristic information and subsidence characteristic information in the coal mining subsidence area, and predicting the carbon emissions of the coal mining subsidence area according to the carbon emission characteristic information, the predicted carbon emission amount is obtained; on the other hand, collecting the soil scale parameters, vegetation scale parameters and water body scale parameters in the coal mining subsidence area, and predicting the scale changes of soil, vegetation and water body according to the subsidence characteristic information, the changed soil scale parameters, changed vegetation scale parameters and changed water body scale parameters are obtained; further collecting the soil composition parameters, vegetation type parameters and water body composition parameters in the coal mining subsidence area, combining the changed soil scale parameters, changed vegetation scale parameters and changed water body scale parameters, and calculating the predicted carbon sequestration amount; finally, calculating the ratio of the predicted carbon sequestration amount to the predicted carbon emission amount, and taking the ratio as the carbon cycle quality evaluation result; that is to say, by combining a machine learning model, comprehensively analyzing the carbon sequestration amount and carbon emission amount according to the soil composition, vegetation type and water body environment of the coal mining subsidence area, the scientificity, accuracy and reliability of the prediction of carbon emission amount and carbon sequestration capacity can be significantly improved, so as to realize the accurate quantitative evaluation of the carbon cycle quality and provide a scientific basis for carbon emission reduction and ecological restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of a method for evaluating cyclic quality data for carbon cycle provided by the present invention;

[0018] Figure 2 It is a schematic structural diagram of a system for evaluating cyclic quality data for carbon cycle provided by the present invention;

[0019] Figure 3 It is a schematic structural diagram of an electronic device provided by the present invention.

[0020] In the drawings, the components represented by each reference numeral are described as follows:

[0021] Carbon emission prediction module 11, scale change prediction module 12, predicted carbon sequestration amount calculation module 13, carbon cycle quality evaluation result calculation module 14, electronic device 500, memory 510, processor 520, first computer program 511. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0024] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not described 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 to be accorded the widest scope consistent with the principles and features disclosed herein.

[0025] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a method for evaluating the cyclic mass data for carbon cycling, including:

[0026] S100: Collect the carbon emission characteristic information and subsidence characteristic information in the coal mining subsidence area, and according to the carbon emission characteristic information, conduct carbon emission prediction for the coal mining subsidence area to obtain the predicted carbon emission amount.

[0027] Furthermore, step S100 of the present invention further includes:

[0028] S110: Collect the scale information of the leaked carbon emission sources in the coal mining subsidence area as the carbon emission characteristic information; S120: Collect the subsidence area information, subsidence slope information, and subsidence depth information in the coal mining subsidence area as the subsidence characteristic information.

[0029] Specifically, during the coal mining process, the development of the water-conducting fracture zone caused by the underground coal mining and subsidence reaches the ground surface, which will cause a part of the coal to be exposed to the air. The exposed coal will undergo an oxidation reaction under the action of oxygen and moisture, releasing greenhouse gases such as carbon dioxide. First, collect the scale information of the leaked carbon emission sources in the coal mining subsidence area, including the exposed area of the coal, the reserves, and the carbon leakage rate, etc., to obtain the carbon emission characteristic information. By obtaining the carbon emission characteristic information, the carbon emission sources in the coal mining subsidence area can be understood, and data support can be provided for carbon emission prediction.

[0030] On the other hand, collect the subsidence area information, subsidence slope information, and subsidence depth information within the coal mining subsidence land as subsidence characteristic information. For example, if the water-conducting fracture zone developed by a large subsidence reaches the ground surface, the subsidence area is larger, which means more coal or other organic matters are exposed, possibly leading to a larger range of carbon emissions; in areas with a larger slope, soil erosion may be accelerated, further affecting the loss rate of soil organic carbon, thus increasing carbon emissions; deeper subsidence land may affect the oxidation process of coal, and the time and manner of exposure of deep coal may change the carbon emission rate.

[0031] Furthermore, step S100 of the present invention further includes:

[0032] S130: According to the carbon emission monitoring data of the sample coal mining subsidence land, collect the sample carbon emission characteristic information set, and collect the carbon emissions under different sample carbon emission characteristic information to obtain the sample carbon emission set; S140: Use the sample carbon emission characteristic information set as the input feature and the sample carbon emission set as the output feature to train the carbon emission predictor; S150: Input the carbon emission characteristic information into the carbon emission predictor and output the predicted carbon emissions.

[0033] Specifically, collect the monitoring data related to carbon emissions of different coal mining subsidence lands to obtain the sample carbon emission characteristic information (such as the exposed area, reserves of coal, and the rate of carbon leakage, etc.) set. Then, collect the carbon emissions under different sample carbon emission characteristic information to obtain the sample carbon emission set, where the sample carbon emissions and the sample carbon emission characteristic information are in one-to-one correspondence.

[0034] Next, a carbon emission predictor is constructed based on the BP neural network, that is, a machine learning model is used to process the collected carbon emission feature information and predict future carbon emissions. The BP neural network is a common feedforward neural network that learns through the backpropagation algorithm. Through supervised training, the BP neural network can identify the complex relationships between carbon emissions and various features, and then predict carbon emissions. Among them, the carbon emission predictor includes an input layer, multiple hidden layers, and an output layer. The input data of its input layer is carbon emission feature information, and the output data of the output layer is carbon emissions. Then, using the sample carbon emission feature information as the input and the sample carbon emissions as the output, and using the sample carbon emission feature information set and the sample carbon emissions set as training data, the carbon emission predictor is supervised and trained. The training process is as follows: First, the input layer receives carbon emission feature information (such as coal exposure area, soil type, etc.). After being processed by multiple hidden layers, the final output layer will give a predicted value of carbon emissions. Then, using the mean square error loss function, by comparing the predicted output of the network with the actual carbon emissions, the loss (error) is calculated. Then, through the backpropagation algorithm, the contribution of each weight and bias to the loss is calculated, that is, the gradient of each weight is calculated (the gradient is the partial derivative of the loss function with respect to the weight). Further, according to the calculated gradient, an optimization algorithm (such as the gradient descent method) is used to update the weights and biases of the network, so that the loss function gradually decreases. The above process will be repeated multiple times on the training data. Each time, the prediction result is calculated through forward propagation, and the weights and biases are adjusted through backpropagation. After each adjustment, the loss of the model will become smaller, and the prediction result will become more and more accurate. The training process is completed through multiple training cycles until the loss function converges to the minimum value, for example, the loss is less than 0.05, and a trained carbon emission predictor is obtained. Finally, the carbon emission feature information is input into the trained carbon emission predictor, and the predicted carbon emissions are output.

[0035] By constructing a carbon emission predictor based on the BP neural network, the intelligence and scientific nature of carbon emission prediction can be improved. At the same time, the accuracy and efficiency of carbon emission prediction can also be improved, thereby improving the scientific nature, accuracy, and efficiency of carbon cycle quality assessment.

[0036] S200: Collect the soil scale parameters, vegetation scale parameters, and water body scale parameters in the coal mining subsidence area. According to the subsidence feature information, predict the scale changes of the soil, vegetation, and water body to obtain the changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters.

[0037] Furthermore, step S200 of the present invention further includes:

[0038] S210: Collect the soil scale parameters, vegetation scale parameters, and water body scale parameters in the coal mining subsidence area.

[0039] Specifically, soil scale parameters, vegetation scale parameters, and water body scale parameters within the coal mining subsidence area are collected. Among them, the soil scale parameters include soil type, soil volume, etc., the vegetation scale parameters include vegetation species, vegetation coverage area, etc.; the water body scale parameters include water body area, water depth, etc. By collecting and analyzing the soil, vegetation, and water body scale parameters of the coal mining subsidence area, and combining with subsidence characteristic information such as terrain and slope, future ecological changes can be predicted, and these changes have a profound impact on the carbon cycle (especially carbon sequestration and carbon emissions).

[0040] S220: Train a change predictor including an integrated change prediction branch.

[0041] Furthermore, step S220 of the present invention further includes:[[]]

[0042] S221: According to the change monitoring data of soil, vegetation, and water bodies within the sample coal mining subsidence area, collect a set of sample subsidence characteristic information, and collect the scale change coefficients of soil, vegetation, and water bodies after a preset time length, which are labeled as the sample soil change coefficient set, the sample vegetation change coefficient set, and the sample water body change coefficient set; S222: Integrate the set of sample subsidence characteristic information, the sample soil change coefficient set, the sample vegetation change coefficient set, and the sample water body change coefficient set, and divide to obtain multiple pieces of change prediction data; S223: Based on ensemble learning, use the multiple pieces of change prediction data to train multiple change prediction branches to obtain a change predictor including an integrated change prediction branch.

[0043] Specifically, according to the monitoring data of the changes in soil, vegetation, and water bodies in the sample coal mining subsidence area, sample subsidence characteristic information is collected, such as the phreatic level, slope, depth, area, subsidence speed, etc. These characteristic information will serve as the key factors affecting the changes in soil, vegetation, and water bodies, and a set of sample subsidence characteristic information is obtained; then, the scale change coefficients of the soil, vegetation, and water bodies after a preset time length (such as one month) are collected. The preset time length can be set according to the actual scenario. The scale change coefficient is the ratio of the difference between the parameter scale after the preset time length and the initial parameter scale to the initial parameter scale. For example, the soil change coefficient is (soil scale after preset time - initial soil scale) / initial soil scale. Among them, the scale change coefficients of soil, vegetation, and water bodies can be positive or negative, indicating the increase or decrease of these elements within a certain period of time. For example, a positive soil scale change coefficient indicates an increase in parameters such as organic carbon content, humidity, or other parameters in the soil, indicating that the soil in this area has better carbon sequestration ability, which may be due to factors such as vegetation restoration or increased precipitation; a negative soil scale change coefficient indicates a decrease in soil parameters such as organic carbon content, humidity, or pH, indicating the deterioration of soil quality, which may be due to erosion, soil erosion, or other degradation processes; a set of sample soil change coefficients, a set of sample vegetation change coefficients, and a set of sample water body change coefficients are obtained. Among them, there is a corresponding relationship between the sample subsidence characteristic information and the sample soil change coefficients, sample vegetation change coefficients, and sample water body change coefficients.

[0044] Next, the set of sample subsidence characteristic information, the set of sample soil change coefficients, the set of sample vegetation change coefficients, and the set of sample water body change coefficients are integrated to obtain a sample data set. Among them, each sample data contains the information in the above four sets, and there is a corresponding relationship between them. Each group of samples includes the subsidence characteristic information within the same time period and the change coefficients of the soil, vegetation, and water bodies within that time period. Then the sample data set is divided into Q parts, where Q is a positive integer greater than 1, and the value of Q can be set according to actual needs. For example, Q is set to 30; multiple pieces of change prediction data are obtained.

[0045] Furthermore, a change prediction branch is constructed based on the BP neural network. The change prediction branch is used to predict the change trends of soil, vegetation, and water bodies at a future time point based on the characteristic information of the coal mining subsidence area (such as slope, depth, area, etc.). The change prediction branch includes an input layer (the input data is the subsidence characteristic information), multiple hidden layers, and an output layer (the output data is the sample soil change coefficient, the sample vegetation change coefficient, and the sample water body change coefficient). Then, the multiple change prediction data are used to perform supervised training on the change prediction branch respectively until convergence, and multiple change prediction branches are obtained. Then, based on the principle of ensemble learning, the multiple change prediction branches are combined to obtain a change predictor including an integrated change prediction branch. Among them, the training method of a single change prediction branch is as follows. First, the input data is transmitted to the hidden layer through the input layer and calculated through weights and biases to obtain the predicted values of the output layer (i.e., the predicted soil, vegetation, and water body change coefficients). Then, during the training process, the difference between the predicted output of the network and the true label is measured by a loss function (such as the mean square error loss function). After the loss function is calculated, the backpropagation algorithm is used to calculate the gradient of the weights of each layer, and the weights are updated according to the gradient. The goal is to minimize the loss function so that the network can more accurately predict the change coefficients of soil, vegetation, and water bodies. Repeatedly perform multiple iterations until the loss function converges, for example, the loss is less than 0.05, and the prediction ability of the network is stable, and a trained change prediction branch is obtained.

[0046] S230: Calculate the ratio of the predicted carbon emission to the maximum value in the sample carbon emission set, multiply it by the number of branches of the integrated change prediction branch and round it to obtain the configured number of branches; S240: Randomly select the configured number of change prediction branches in the change predictor, input the soil scale parameter, vegetation scale parameter, and water body scale parameter, and predict the output to obtain the predicted soil change coefficient set, predicted vegetation change coefficient set, and predicted water body change coefficient set after a preset time length, and calculate the mean value to obtain the soil change coefficient, vegetation change coefficient, and water body change coefficient; S250: Use the soil change coefficient, vegetation change coefficient, and water body change coefficient to perform change calculations on the soil scale parameter, vegetation scale parameter, and water body scale parameter to obtain the changed soil scale parameter, changed vegetation scale parameter, and changed water body scale parameter.

[0047] Specifically, first, calculate the ratio of the predicted carbon emission to the maximum value in the sample carbon emission set, and multiply the ratio by the number of branches of the integrated change prediction branch and round it to obtain the configured number of branches. For example, assume that the predicted carbon emission is 1000 tons / CO₂, the maximum value in the sample carbon emission set is 4000 tons / CO₂, and the number of branches of the integrated change prediction branch is 30. Then the configured number of branches is the integer obtained by rounding 1000 / 4000 multiplied by 30, that is, the configured number of branches is 8.

[0048] By dynamically adjusting the number of branches according to the current predicted carbon emissions, that is, the larger the carbon emissions, the higher the complexity and uncertainty the model may face. Therefore, using more prediction branches helps improve the prediction accuracy; the smaller the carbon emissions, the fewer branch numbers are selected to save computing power resources and improve analysis efficiency. Thus, it can flexibly adapt to prediction tasks of different scales, ensure accurate evaluation in complex situations, and reduce resource consumption when the carbon emissions are small.

[0049] Next, randomly select a change prediction branch for configuring the number of branches within the change predictor, and input the soil scale parameter, vegetation scale parameter, and water body scale parameter to predict and output a set of predicted soil change coefficients, a set of predicted vegetation change coefficients, and a set of predicted water body change coefficients after a preset time length (such as one month); then calculate the mean values of the set of predicted soil change coefficients, the set of predicted vegetation change coefficients, and the set of predicted water body change coefficients respectively, that is, average the prediction results of all prediction branches to obtain a more stable and reliable result. By this method, the deviation of individual prediction branches can be reduced, and the overall prediction accuracy can be improved, and the soil change coefficient, vegetation change coefficient, and water body change coefficient are output.

[0050] Furthermore, the soil change coefficient, vegetation change coefficient, and water body change coefficient are further used to perform change calculations on the soil scale parameter, vegetation scale parameter, and water body scale parameter respectively, that is, multiply the sum of the soil change coefficient and 1 by the soil scale parameter, and take the product of the two as the changed soil scale parameter; multiply the sum of the vegetation change coefficient and 1 by the vegetation scale parameter to obtain the changed vegetation scale parameter; multiply the sum of the water body change coefficient and 1 by the water body scale parameter to obtain the changed water body scale parameter.

[0051] S300: Collect the soil composition parameter, vegetation type parameter, and water body composition parameter in the coal mining subsidence area, and calculate the predicted carbon sequestration amount in combination with the changed soil scale parameter, changed vegetation scale parameter, and changed water body scale parameter.

[0052] Furthermore, step S300 of the present invention further includes:

[0053] S310: Collect the soil composition parameters, vegetation type parameters, and water body composition parameters within the coal mining subsidence area, and test to obtain the soil carbon sequestration parameters, vegetation carbon sequestration parameters, and water body carbon sequestration parameters for the soil composition parameters, vegetation type parameters, and water body composition parameters; S320: Based on the soil carbon sequestration parameters, vegetation carbon sequestration parameters, and water body carbon sequestration parameters, combined with the changing soil scale parameters, changing vegetation scale parameters, and changing water body scale parameters, calculate the predicted soil carbon sequestration amount, predicted vegetation carbon sequestration amount, and predicted water body carbon sequestration amount; S330: Calculate the sum of the predicted soil carbon sequestration amount, predicted vegetation carbon sequestration amount, and predicted water body carbon sequestration amount to obtain the predicted carbon sequestration amount.

[0054] Specifically, first, collect the soil composition parameters (such as the mineral content, pH value, humidity, etc. of the soil), vegetation type parameters (such as the type, density, coverage, root depth, etc. of the vegetation), and water body composition parameters (such as the pH value, dissolved oxygen, temperature, dissolved organic matter, and inorganic salts contained in the water body) within the coal mining subsidence area; then test (obtain the carbon sequestration parameters through experimental tests or existing literature data) to obtain the soil carbon sequestration parameters, vegetation carbon sequestration parameters, and water body carbon sequestration parameters for the soil composition parameters, vegetation type parameters, and water body composition parameters. Among them, the soil carbon sequestration parameter refers to the total carbon sequestration amount of the soil under specific conditions, calculated by the amount of carbon that can be sequestered per unit mass (for example, the carbon storage amount per unit area); the vegetation carbon sequestration parameter refers to the total carbon sequestration amount of the plant, calculated by the carbon sequestration amount per unit biomass; the water body carbon sequestration parameter refers to the total carbon sequestration amount of the water body, calculated by the carbon sequestration ability of algae, microorganisms, etc. in the water body (such as the carbon sequestration amount per unit water body area).

[0055] Next, multiply the soil carbon sequestration parameter by the changing soil scale parameter to obtain the predicted soil carbon sequestration amount, multiply the vegetation carbon sequestration parameter by the changing vegetation scale parameter to obtain the predicted vegetation carbon sequestration amount, multiply the water body carbon sequestration parameter by the changing water body scale parameter to obtain the predicted water body carbon sequestration amount. Finally, add up the predicted soil carbon sequestration amount, predicted vegetation carbon sequestration amount, and predicted water body carbon sequestration amount to obtain the predicted carbon sequestration amount.

[0056] S400: Based on the predicted carbon sequestration amount and the predicted carbon emission amount, calculate the carbon cycle quality evaluation result.

[0057] Furthermore, step S400 of the present invention further includes:

[0058] S410: Calculate the ratio of the predicted carbon sequestration amount to the predicted carbon emission amount; S420: Use the ratio as the carbon cycle quality evaluation result.

[0059] Specifically, set the ratio of the predicted carbon sequestration amount to the predicted carbon emission amount as the carbon cycle quality evaluation result to obtain the carbon cycle quality evaluation result. Among them, if the ratio is greater than 1, it indicates that the carbon sequestration capacity of the coal mining subsidence area exceeds its carbon emissions, and the carbon cycle quality is good, indicating that this area can achieve a carbon sink effect and contribute to reducing the concentration of greenhouse gases in the atmosphere; when the ratio is equal to 1, it indicates that the carbon emissions and carbon sequestration amount in this area are roughly balanced, and the carbon cycle quality is in a stable state; when the ratio is less than 1, it indicates that the carbon emissions are greater than the carbon sequestration amount, indicating that the carbon cycle quality of this area is poor, which may lead to excessive carbon emissions and affect the ecological balance and climate regulation. By using the ratio of the predicted carbon sequestration amount to the predicted carbon emission amount as the carbon cycle quality evaluation result, the health status of the carbon cycle in the coal mining subsidence area can be effectively quantified and evaluated, providing a scientific basis for subsequent carbon emission reduction, ecological restoration, and carbon management decisions.

[0060] The method for evaluating the cycle quality data for carbon cycle provided by the embodiment of the present invention has at least the following technical effects:

[0061] By collecting the carbon emission characteristic information and subsidence characteristic information in the coal mining subsidence area, and predicting the carbon emissions of the coal mining subsidence area according to the carbon emission characteristic information to obtain the predicted carbon emission amount; on the other hand, collecting the soil scale parameter, vegetation scale parameter, and water body scale parameter in the coal mining subsidence area, and predicting the scale changes of the soil, vegetation, and water body according to the subsidence characteristic information to obtain the changed soil scale parameter, changed vegetation scale parameter, and changed water body scale parameter; further collecting the soil composition parameter, vegetation type parameter, and water body composition parameter in the coal mining subsidence area, and combining the changed soil scale parameter, changed vegetation scale parameter, and changed water body scale parameter to calculate and obtain the predicted carbon sequestration amount; finally, calculating the ratio of the predicted carbon sequestration amount to the predicted carbon emission amount, and using the ratio as the carbon cycle quality evaluation result; that is to say, by combining a machine learning model and comprehensively analyzing the carbon sequestration amount and carbon emissions according to the soil composition, vegetation type, and water body environment of the coal mining subsidence area, the scientificity, accuracy, and reliability of the prediction of carbon emissions and carbon sequestration capacity can be significantly improved, so as to achieve the precise quantitative evaluation of the carbon cycle quality and provide a scientific basis for carbon emission reduction and ecological restoration.

[0062] Example two, as Figure 2As shown, based on the same inventive concept as the method for evaluating cyclic quality data for carbon cycling provided in Embodiment 1, an embodiment of the present invention further provides a system for evaluating cyclic quality data for carbon cycling, including: a carbon emission prediction module 11, configured to collect carbon emission characteristic information and subsidence characteristic information within a coal mining subsidence area, and perform carbon emission prediction for the coal mining subsidence area according to the carbon emission characteristic information to obtain a predicted carbon emission amount; a scale change prediction module 12, configured to collect soil scale parameters, vegetation scale parameters, and water body scale parameters within the coal mining subsidence area, and perform scale change prediction for soil, vegetation, and water body according to the subsidence characteristic information to obtain changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters; a predicted carbon sequestration amount calculation module 13, configured to collect soil composition parameters, vegetation type parameters, and water body composition parameters within the coal mining subsidence area, and calculate a predicted carbon sequestration amount in combination with the changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters; a carbon cycling quality evaluation result calculation module 14, configured to calculate a carbon cycling quality evaluation result according to the predicted carbon sequestration amount and the predicted carbon emission amount.

[0063] Further, the system for evaluating cyclic quality data for carbon cycling is further configured to: collect the scale information of leaked carbon emission sources within the coal mining subsidence area as the carbon emission characteristic information; collect the subsidence area information, subsidence slope information, and subsidence depth information within the coal mining subsidence area as the subsidence characteristic information.

[0064] Further, the system for evaluating cyclic quality data for carbon cycling is further configured to: collect a set of sample carbon emission characteristic information according to the carbon emission monitoring data of sample coal mining subsidence areas, and collect the carbon emission amounts under different sample carbon emission characteristic information to obtain a set of sample carbon emission amounts; use the set of sample carbon emission characteristic information as input features, use the set of sample carbon emission amounts as output features, and train a carbon emission predictor; input the carbon emission characteristic information into the carbon emission predictor, and output to obtain the predicted carbon emission amount.

[0065] Furthermore, the cyclic mass data evaluation system for carbon cycle is also used for: collecting the soil scale parameters, vegetation scale parameters and water body scale parameters in the coal mining subsidence area; training a change predictor including an integrated change prediction branch; calculating the ratio of the predicted carbon emission to the maximum value in the sample carbon emission set, multiplying by the number of branches of the integrated change prediction branch and rounding to obtain the configured number of branches; randomly selecting the change prediction branches with the configured number of branches in the change predictor, inputting the soil scale parameters, vegetation scale parameters and water body scale parameters, and predicting and outputting to obtain a predicted soil change coefficient set, a predicted vegetation change coefficient set and a predicted water body change coefficient set after a preset time length, and calculating the mean value to obtain the soil change coefficient, the vegetation change coefficient and the water body change coefficient; using the soil change coefficient, the vegetation change coefficient and the water body change coefficient to perform change calculations on the soil scale parameters, vegetation scale parameters and water body scale parameters to obtain the changed soil scale parameters, changed vegetation scale parameters and changed water body scale parameters.

[0066] Furthermore, the cyclic mass data evaluation system for carbon cycle is also used for: collecting a set of sample subsidence feature information according to the change monitoring data of the soil, vegetation and water body in the sample coal mining subsidence area, and collecting the scale change coefficients of the soil, vegetation and water body after a preset time length, and labeling them as a sample soil change coefficient set, a sample vegetation change coefficient set and a sample water body change coefficient set; integrating the set of sample subsidence feature information, the sample soil change coefficient set, the sample vegetation change coefficient set and the sample water body change coefficient set, and dividing them to obtain multiple pieces of change prediction data; based on ensemble learning, using the multiple pieces of change prediction data to train multiple change prediction branches to obtain a change predictor including an integrated change prediction branch.

[0067] Furthermore, the cyclic mass data evaluation system for carbon cycle is also used for: collecting the soil composition parameters, vegetation type parameters and water body composition parameters in the coal mining subsidence area, and testing and obtaining the soil carbon sequestration parameters, vegetation carbon sequestration parameters and water body carbon sequestration parameters of the soil composition parameters, vegetation type parameters and water body composition parameters; according to the soil carbon sequestration parameters, vegetation carbon sequestration parameters and water body carbon sequestration parameters, combining with the changed soil scale parameters, changed vegetation scale parameters and changed water body scale parameters, calculating to obtain the predicted soil carbon sequestration amount, predicted vegetation carbon sequestration amount and predicted water body carbon sequestration amount; calculating the sum of the predicted soil carbon sequestration amount, predicted vegetation carbon sequestration amount and predicted water body carbon sequestration amount to obtain the predicted carbon sequestration amount.

[0068] Furthermore, the cyclic mass data evaluation system for carbon cycle is also used for: calculating the ratio of the predicted carbon sequestration amount to the predicted carbon emission; using the ratio as the carbon cycle quality evaluation result.

[0069] Example three, please refer to Figure 3, Figure 3 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. As Figure 3 shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored on the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: collecting carbon emission characteristic information and subsidence characteristic information in the coal mining subsidence area, and performing carbon emission prediction of the coal mining subsidence area according to the carbon emission characteristic information to obtain a predicted carbon emission amount; collecting soil scale parameters, vegetation scale parameters, and water body scale parameters in the coal mining subsidence area, and performing scale change prediction of soil, vegetation, and water body according to the subsidence characteristic information to obtain changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters; collecting soil composition parameters, vegetation type parameters, and water body composition parameters in the coal mining subsidence area, and calculating a predicted carbon sequestration amount in combination with the changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters; and calculating a carbon cycle quality evaluation result according to the predicted carbon sequestration amount and the predicted carbon emission amount.

[0070] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0071] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0072] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0073] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the flowchart(s) Figure 1 step(s) or multiple step(s) and / or block(s) Figure 1 block(s) or multiple block(s).

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in the flowchart(s) Figure 1 step(s) or multiple step(s) and / or block(s) Figure 1 block(s) or multiple block(s).

[0075] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept.

[0076] 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 present invention and its equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A method for evaluating cyclic mass data for the carbon cycle, characterized in that The method includes: Collecting carbon emission characteristic information and subsidence characteristic information in the coal mining subsidence area, and based on the carbon emission characteristic information, predicting the carbon emissions of the coal mining subsidence area to obtain the predicted carbon emission amount; Collecting the soil scale parameter, vegetation scale parameter and water body scale parameter in the coal mining subsidence area, and based on the subsidence characteristic information, predicting the scale changes of the soil, vegetation and water body to obtain the changed soil scale parameter, changed vegetation scale parameter and changed water body scale parameter; Collecting the soil composition parameter, vegetation type parameter and water body composition parameter in the coal mining subsidence area, and combining the changed soil scale parameter, changed vegetation scale parameter and changed water body scale parameter to calculate and obtain the predicted carbon sequestration amount; Calculating and obtaining the carbon cycle quality evaluation result based on the predicted carbon sequestration amount and the predicted carbon emission amount.

2. The method for evaluating cyclic mass data for carbon cycling according to claim 1, wherein, Collecting the carbon emission characteristic information and subsidence characteristic information in the coal mining subsidence area includes: Collecting the scale information of the leaked carbon emission source in the coal mining subsidence area as the carbon emission characteristic information; Collecting the subsidence area information, subsidence slope information and subsidence depth information in the coal mining subsidence area as the subsidence characteristic information.

3. The method for evaluating cyclic mass data for carbon cycling according to claim 1, characterized in that, Based on the carbon emission characteristic information, predicting the carbon emissions of the coal mining subsidence area to obtain the predicted carbon emission amount, including: According to the carbon emission monitoring data of the sample coal mining subsidence area, collecting the sample carbon emission characteristic information set and collecting the carbon emission amounts under different sample carbon emission characteristic information to obtain the sample carbon emission amount set; Using the sample carbon emission characteristic information set as the input feature and the sample carbon emission amount set as the output feature to train the carbon emission predictor; Inputting the carbon emission characteristic information into the carbon emission predictor and outputting to obtain the predicted carbon emission amount.

4. The cyclic mass data evaluation method for carbon cycle according to claim 3, wherein, Collecting the soil scale parameter, vegetation scale parameter and water body scale parameter in the coal mining subsidence area, and based on the subsidence characteristic information, predicting the scale changes of the soil, vegetation and water body, including: Collecting the soil scale parameter, vegetation scale parameter and water body scale parameter in the coal mining subsidence area; Training a change predictor including an integrated change prediction branch; Calculating the ratio of the predicted carbon emission amount to the maximum value in the sample carbon emission amount set, multiplying by the number of branches of the integrated change prediction branch and rounding to obtain the configured number of branches; Randomly selecting the configured number of change prediction branches in the change predictor, inputting the soil scale parameter, vegetation scale parameter and water body scale parameter, predicting and outputting to obtain the predicted soil change coefficient set, predicted vegetation change coefficient set and predicted water body change coefficient set after a preset time length, and calculating the mean value to obtain the soil change coefficient, vegetation change coefficient and water body change coefficient; Using the soil change coefficient, vegetation change coefficient and water body change coefficient to perform change calculations on the soil scale parameter, vegetation scale parameter and water body scale parameter to obtain the changed soil scale parameter, changed vegetation scale parameter and changed water body scale parameter.

5. The method for evaluating the cyclic mass data for carbon cycling according to claim 4, wherein Training a change predictor including an integrated change prediction branch, including: According to the monitoring data of the changes in soil, vegetation, and water bodies in the sample coal mining subsidence area, collect the sample subsidence characteristic information set, and collect the scale change coefficients of soil, vegetation, and water bodies after a preset time length, which are labeled as the sample soil change coefficient set, the sample vegetation change coefficient set, and the sample water body change coefficient set; Integrate the sample subsidence characteristic information set, the sample soil change coefficient set, the sample vegetation change coefficient set, and the sample water body change coefficient set, and divide to obtain multiple change prediction data; Based on ensemble learning, use the multiple change prediction data to train multiple change prediction branches to obtain a change predictor including an integrated change prediction branch.

6. The cyclic mass data evaluation method for carbon cycle according to claim 1, wherein Collect the soil component parameters, vegetation type parameters, and water body component parameters in the coal mining subsidence area, and combine the changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters to calculate the predicted carbon sequestration amount, including: Collect the soil component parameters, vegetation type parameters, and water body component parameters in the coal mining subsidence area, and test to obtain the soil carbon sequestration parameters, vegetation carbon sequestration parameters, and water body carbon sequestration parameters of the soil component parameters, vegetation type parameters, and water body component parameters; According to the soil carbon sequestration parameters, vegetation carbon sequestration parameters, and water body carbon sequestration parameters, combine the changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters to calculate the predicted soil carbon sequestration amount, predicted vegetation carbon sequestration amount, and predicted water body carbon sequestration amount; Calculate the sum of the predicted soil carbon sequestration amount, predicted vegetation carbon sequestration amount, and predicted water body carbon sequestration amount to obtain the predicted carbon sequestration amount.

7. The method for evaluating cyclic mass data for carbon cycling according to claim 1, characterized in that, According to the predicted carbon sequestration amount and the predicted carbon emissions, calculate the carbon cycle quality evaluation result, including: Calculate the ratio of the predicted carbon sequestration amount to the predicted carbon emissions; Take the ratio as the carbon cycle quality evaluation result.

8. A cyclic mass data evaluation system for the carbon cycle, characterized in that, The steps for implementing a cycle quality data evaluation method for carbon cycle according to any one of claims 1 to 7 include: A carbon emission prediction module, configured to collect carbon emission characteristic information and subsidence characteristic information in the coal mining subsidence area, and perform carbon emission prediction on the coal mining subsidence area according to the carbon emission characteristic information to obtain the predicted carbon emissions; A scale change prediction module, configured to collect the soil scale parameters, vegetation scale parameters, and water body scale parameters in the coal mining subsidence area, and perform scale change prediction on soil, vegetation, and water bodies according to the subsidence characteristic information to obtain the changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters; A predicted carbon sequestration amount calculation module, configured to collect the soil component parameters, vegetation type parameters, and water body component parameters in the coal mining subsidence area, and combine the changed soil scale parameters, changed vegetation scale parameters, and changed water body scale parameters to calculate the predicted carbon sequestration amount; A carbon cycle quality evaluation result calculation module, configured to calculate the carbon cycle quality evaluation result according to the predicted carbon sequestration amount and the predicted carbon emissions.

9. An electronic device, characterized in that, Including: A memory, configured to store computer software programs; A processor, configured to read and execute the computer software programs, and further implement the steps of a cycle quality data evaluation method for carbon cycle according to any one of claims 1 to 7.

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