Numerical prediction method, platform and storage medium for carbon flux in coastal ecosystems

Abstract

The present invention relates to the field of data processing technology, and more specifically to a numerical prediction method, platform and storage medium for carbon flux of coastal ecosystems. The method includes obtaining historical carbon flux monitoring data of a coastal area from a first database, obtaining land use plans and emission reduction plans related to the coastal area, and constructing future carbon flux monitoring data; creating a first prediction model and a second prediction model based on a neural network, training the first prediction model based on the first carbon flux monitoring data, and training the second prediction model based on the second carbon flux monitoring data; creating a third prediction model based on a neural network, combining the training data of the first prediction model and the training data of the second prediction model to form a data matrix, training the third prediction model, and the third prediction model outputting a prediction result corresponding to the prediction time as the final prediction result of the carbon flux value of the coastal ecosystem. The present invention can achieve accurate prediction of the carbon flux of the coastal ecosystem.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically to a numerical prediction method, platform and storage medium for carbon flux in coastal ecosystems. Background Art

[0002] Coastal ecosystems play a crucial role in the global carbon cycle, and accurate prediction of their carbon flux is crucial for assessing global climate change and developing carbon reduction strategies. However, existing carbon flux prediction methods rely on complex model calculations and field monitoring data, resulting in high costs, complex operations, and limited prediction accuracy.

[0003] Similar prior art includes a Chinese patent application with publication number CN118917559A, which relates to a method for assessing carbon sink enhancement potential in near-coastal areas, comprising the following steps: S1: collecting multi-dimensional marine environmental data to form a preliminary data set;

[0004] S2: Conduct an in-depth analysis of the carbon sink growth characteristics and long-term trends to identify factors influencing the nearshore carbon sink pattern; S3: Construct a static relationship model between the carbon sink pattern and these factors; S4: Construct a dynamic relationship model for the carbon sink pattern; S5: Combine the static and dynamic relationship models to conduct a comprehensive assessment to determine the spatial carrying capacity of carbon sinks in the nearshore area; S6: Based on the spatial carrying capacity assessment results obtained in S5, determine the carbon sink growth potential of each region. By integrating static and dynamic models to assess the carbon sink capacity of nearshore areas, this invention can provide more accurate carbon sink management and prediction.

[0005] Similar prior art includes a Chinese patent application with publication number CN118890604A, which discloses a multimodal environment fusion perception method and system, including: based on the multimodal environment fusion perception needs in a wide-area scenario, setting nodes with multiple functional attributes, and dividing strong nodes and weak nodes according to the perception performance of the nodes; creating a layered perception network through the deployment combination of strong nodes and weak nodes, collecting environmental data, and performing data preprocessing, analysis, prediction and storage in sequence based on the node computing power; establishing communication channels between nodes, and optimizing the perception performance and perception results of the layered perception network based on the data transmission and compensation mechanism between strong nodes and weak nodes; making response decisions based on the perception results, and generating and executing control commands. This invention speeds up decision-making by compensating and optimizing data by strong nodes.

[0006] However, neither of the above two patent applications took into account the prediction of carbon flux in coastal ecosystems, let alone the accuracy of carbon flux prediction. Summary of the Invention

[0007] The purpose of the present invention is to provide a numerical prediction method based on the carbon flux of coastal ecosystems. This method can utilize historical monitoring data and future land use plans to construct a prediction model through a neural network to achieve accurate prediction of the carbon flux of coastal ecosystems.

[0008] In order to achieve the above-mentioned object of the invention, the present invention provides a numerical prediction method of carbon flux of coastal ecosystems, which is implemented by performing the following steps:

[0009] Step S1: Acquire historical carbon flux monitoring data of a coastal zone from a first database as first carbon flux monitoring data, acquire a land use plan and an emission reduction plan related to the coastal zone, and construct future carbon flux monitoring data based on the land use plan and the emission reduction plan as second carbon flux monitoring data, and store the data in a second database;

[0010] Step S2: creating a first prediction model and a second prediction model based on a neural network, normalizing the first carbon flux monitoring data and the second carbon flux monitoring data respectively, training the first prediction model based on the normalized first carbon flux monitoring data, and obtaining the trained first prediction model, training the second prediction model based on the normalized second carbon flux monitoring data, and obtaining the trained second prediction model;

[0011] Step S3: Create a third prediction model based on a neural network, combine the training data of the first prediction model and the training data of the second prediction model to form a data matrix, and use the data in the data matrix as training data to train the third prediction model, calculate the first type of ecological data and the second type of ecological data corresponding to the prediction time and input them into the third prediction model as input data, the third prediction model outputs the prediction result corresponding to the prediction time, and uses the prediction result as the final prediction result of the carbon flux value of the coastal ecosystem.

[0012] As a preferred technical solution of the present invention, the step S3 further includes:

[0013] Step S4: Obtain the emission reduction target and compare it with the prediction result. When the prediction result is less than the emission reduction target, issue a warning message, and formulate a new land use plan in combination with the land use plan corresponding to the prediction result. Calculate new first-type ecological data and second-type ecological data based on the new land use plan, and input them into the third prediction model again to obtain a new prediction result. Determine whether the new prediction result is greater than the emission reduction target. If it is less than the emission reduction target, continue to adjust the new land use plan until the prediction result of the third prediction model is less than or equal to the emission reduction target.

[0014] As a preferred technical solution of the present invention, the carbon flux monitoring data in step S1 includes:

[0015] Carbon flux monitoring data refers to first-type ecological data, second-type ecological data, and carbon flux, and the carbon flux monitoring data includes the first carbon flux monitoring data and the second carbon flux monitoring data, wherein the first-type ecological data refers to mangroves, salt marshes, seagrass beds, and large seaweeds that absorb carbon elements and their corresponding coverage areas, and the second-type ecological data refers to the number of animals that release carbon elements and the area covered by human activities;

[0016] Multiple remote sensing images of the coastal zone are periodically acquired within a preset time period, and ecological data and corresponding areas or quantities of different types of coastal zones on each remote sensing image are identified and stored in a first database in chronological order.

[0017] As a preferred technical solution of the present invention, in step S2, after normalizing the first carbon flux monitoring data and before training the first prediction model, the following steps are further included:

[0018] Based on a preset time length of the first carbon flux monitoring data, a prediction time is determined, and sequence data of historical carbon flux monitoring data based on the preset time length is created for the prediction time, and the sequence data is paired with the prediction time. The sequence data serves as input data of the first prediction model, the prediction time serves as the time corresponding to the prediction target, and the carbon flux corresponding to the prediction time serves as the output result of the first prediction model. The sequence data and the carbon flux corresponding to the prediction time constitute training data for the first prediction model, wherein the sequence data is data arranged in chronological order, and the prediction target refers to the carbon flux corresponding to the prediction time.

[0019] As a preferred technical solution of the present invention, in step S1, constructing future carbon flux monitoring data includes:

[0020] Based on big data or land use planning documents issued by relevant departments, a land use plan related to the coastal area is obtained. Based on the land use plan, the future first type of ecological data and future second type of ecological data and the corresponding future carbon flux corresponding to each preset time period of the coastal area are calculated, so as to construct future carbon flux monitoring data and store them in the second database.

[0021] As a preferred technical solution of the present invention, in step S3, the training data of the first prediction model and the training data of the second prediction model are combined to form a data matrix, including:

[0022] Obtain the training data of the first prediction model, create a data matrix with time, the first type of ecological data, the second type of ecological data and the carbon flux as titles, and the corresponding data as values, obtain the training data of the second prediction model, and align the training data of the second prediction model with the training data of the first prediction model according to the time, add them to the data matrix, and form a comprehensive data matrix containing the first carbon flux monitoring data and the second carbon flux monitoring data, use the first type of ecological data, the second type of ecological data, the future first type of ecological data and the future second type of ecological data in the data matrix as input data of the third prediction model, and use the first prediction result and the second prediction result in the data matrix as output data of the third prediction model to train the third prediction model.

[0023] As a preferred technical solution of the present invention, in step S2, training the first prediction model based on the normalized first carbon flux monitoring data includes:

[0024] obtaining normalized first carbon flux monitoring data and using the data as training data for the first prediction model, determining whether the data in the training data is uniform; if the data in the training data is not uniform, dividing the training data into first learning data and second learning data; and generating more new first learning data using a data enhancement technique based on data features of the first learning data and the second learning data;

[0025] Inputting the new first learning data into a judgment model, the judgment model outputting a judgment result, and when the value of the judgment result is greater than or equal to a first threshold and less than or equal to a second threshold, using the new first learning data as third learning data, calculating the similarity between the third learning data, and clustering the data with the similarity greater than or equal to the third threshold together to generate a clustering result for the third learning data;

[0026] The weights and parameters of the first prediction model are adjusted based on the clustering result of the third learning data, and the first prediction model is trained using the third learning data and the second learning data.

[0027] As a preferred technical solution of the present invention, determining whether the data in the training data is uniform, and when the data in the training data is not uniform, dividing the training data into first learning data and second learning data, includes:

[0028] Calculating the number of the training data that contains the first type of ecological data features and the number of the training data that does not contain the first type of ecological data features, and determining that the data in the training data is uneven when the number of the training data that does not contain the first type of ecological data features is less than N times the number of the training data that contains the first type of ecological data features, where N represents a positive integer greater than or equal to 2;

[0029] When the data in the training data is uneven, the training data is divided into first learning data and second learning data, wherein the first learning data refers to data that does not contain the first type of ecological data characteristics, and the second learning data refers to data that contains the first type of ecological data characteristics.

[0030] The present invention also provides a numerical prediction platform for coastal ecosystem carbon flux as described above, comprising the following modules:

[0031] an acquisition unit, configured to acquire historical carbon flux monitoring data of a coastal zone from a first database as first carbon flux monitoring data, acquire a land use plan and an emission reduction plan related to the coastal zone, and construct future carbon flux monitoring data based on the land use plan and the emission reduction plan as second carbon flux monitoring data, and store the data in a second database;

[0032] a training unit, configured to create a first prediction model and a second prediction model, normalize the first carbon flux monitoring data and the second carbon flux monitoring data, respectively, train the first prediction model based on the normalized first carbon flux monitoring data, and obtain the trained first prediction model, train the second prediction model based on the normalized second carbon flux monitoring data, and obtain the trained second prediction model;

[0033] An integration unit is used to create a third prediction model, combine the training data of the first prediction model and the training data of the second prediction model to form a data matrix, and use the data in the data matrix as training data to train the third prediction model, calculate the first type of ecological data and the second type of ecological data corresponding to the prediction time and input them into the third prediction model as input data, the third prediction model outputs the prediction result corresponding to the prediction time, and uses the prediction result as the final prediction result of the carbon flux value of the coastal ecosystem.

[0034] The present invention also provides a storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the numerical prediction method, platform and storage medium for coastal ecosystem carbon flux described above are implemented.

[0035] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0036] 1. The technical solution of the present invention obtains historical carbon flux monitoring data of the coastal area from a first database as the first carbon flux monitoring data to train a first prediction model; obtains land use plans and emission reduction plans related to the coastal area, and based on the land use plans and emission reduction plans, constructs future carbon flux monitoring data as the second carbon flux monitoring data to train a second prediction model. A first prediction model and a second prediction model are created based on a neural network, and the first carbon flux monitoring data and the second carbon flux monitoring data are normalized respectively. The first prediction model is trained based on the normalized first carbon flux monitoring data, and the trained first prediction model is obtained to identify the temporal trend of the carbon flux. A second prediction model is trained based on the normalized second carbon flux monitoring data, and the trained second prediction model is obtained to provide an expected perspective to predict the trend of future carbon flux changes.

[0037] 2. The technical solution of the present invention creates a third prediction model, combines the training data of the first prediction model and the training data of the second prediction model to form a data matrix, and captures the trend of carbon energy changes from different perspectives. The data in the data matrix is ​​used as training data to train the third prediction model to integrate the first prediction model and the second prediction model, consider the combined impact of historical trends and future plans, consider the combined impact of historical trends and future plans, combine the advantages of the two models, and use the different perspectives they provide to improve the accuracy of the prediction. The first type of ecological data and the second type of ecological data corresponding to the prediction time are calculated and input into the third prediction model as input data. The third prediction model outputs the prediction result corresponding to the prediction time, and the prediction result is used as the final prediction result of the carbon flux value of the coastal ecosystem. Since the third prediction model combines the historical carbon flux monitoring data and the carbon flux monitoring data of future plans, the output result of the third prediction model makes the numerical prediction of the carbon flux of the coastal ecosystem more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0039] Figure 1 This is a flow chart of the steps of the numerical prediction method for coastal ecosystem carbon flux in the present invention;

[0040] Figure 2 This is a structural diagram of the numerical prediction platform for coastal ecosystem carbon flux in the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] It is understood that the terms "first," "second," etc., used herein may be used to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script without departing from the scope of this application.

[0043] Coastal ecosystems play a crucial role in the global carbon cycle, and accurate prediction of their carbon flux is crucial for assessing global climate change and developing carbon reduction strategies. However, existing carbon flux prediction methods often rely on complex model calculations and field monitoring data, resulting in complex operations and limited prediction accuracy.

[0044] In view of the above technical problems, the present invention proposes Figure 1 The numerical prediction method of coastal ecosystem carbon flux is shown in the figure and is implemented by performing the following steps:

[0045] Step S1: Obtain historical carbon flux monitoring data of the coastal area from the first database as first carbon flux monitoring data, obtain land use plans and emission reduction plans related to the coastal area, and based on the land use plans and emission reduction plans, construct future carbon flux monitoring data as second carbon flux monitoring data, and store them in the second database.

[0046] Specifically, coastal ecosystem carbon flux encompasses various carbon exchange processes within the ecosystem, such as photosynthesis and respiration by plants, as well as respiration by soil microorganisms. These processes result in the exchange of carbon between the ecosystem and the atmosphere. Predicting coastal ecosystem carbon flux can help understand the ecosystem's carbon uptake and emissions, and thus assess climate change. Carbon flux monitoring data from the past ten or twenty years for the coastal region is collected and stored in a first database as training data for the first prediction model. This data primarily comes from field measurements, satellite remote sensing, or historical climate records. Furthermore, changes in coastal ecosystem carbon flux are influenced by a variety of factors, including climate, topography, vegetation type, soil conditions, and human activities. Changes in these factors can alter carbon exchange processes within the ecosystem, thereby affecting carbon flux. These factors are also influenced by land use plans and emission reduction targets established by local governments or environmental management agencies. Therefore, relevant land use plans and emission reduction targets for the coastal region, including urban planning, agricultural expansion, and the establishment of protected areas, are obtained from local governments or environmental management agencies to calculate future carbon flux monitoring data and store them in a second database, which serves as training data for the second prediction model. Among them, future carbon flux can be estimated in combination with emission reduction plans formulated by relevant policies.

[0047] Step S2: Create a first prediction model and a second prediction model based on a neural network, normalize the first carbon flux monitoring data and the second carbon flux monitoring data respectively, train the first prediction model based on the normalized first carbon flux monitoring data, and obtain the trained first prediction model, train the second prediction model based on the normalized second carbon flux monitoring data, and obtain the trained second prediction model.

[0048] Specifically, a prediction model is constructed based on a neural network, and a long short-term memory network (LSTM) is selected as the first prediction model. The LSTM model can capture the long-term dependencies between data, and historical carbon flux monitoring data usually has time series characteristics. Therefore, the LSTM model can effectively process such data and identify the temporal trend of carbon flux. However, when relying solely on past carbon flux monitoring data, it may not be possible to fully capture future changes in carbon flux, because past carbon flux monitoring data may not fully reflect future external influences, such as land changes, resulting in a reduction in mangrove area, etc. The reduction in mangrove area directly affects carbon flux. Therefore, considering future land use plans and emission reduction targets is an important factor in predicting future carbon fluxes. The second prediction model provides an expectation-based perspective by considering future land use plans and expected emission reduction targets in the coastal zone to predict future trends in carbon flux changes. Therefore, a multi-layer perceptron (MLP) is selected as the second prediction model. The MLP model is good at capturing nonlinear relationships between input data. Future planned carbon flux monitoring data will contain multiple variables, and the relationship between these variables and future carbon fluxes may mostly be nonlinear. Unlike LSTM, the MLP model does not have inherent time series processing capabilities, so it is more suitable for processing one-time future planned carbon flux monitoring data that is independent of time. Future land use plans, policy changes and other factors may affect changes in carbon flux. By incorporating these expected changes, the second prediction model makes the prediction results more adaptable to possible future situations. At the same time, choosing the MLP model as the second prediction model usually predicts results faster than the recurrent neural network.

[0049] Step S3: Create a third prediction model based on the neural network, combine the training data of the first prediction model and the training data of the second prediction model to form a data matrix, and use the data in the data matrix as training data to train the third prediction model, calculate the first type of ecological data and the second type of ecological data corresponding to the prediction time and input them into the third prediction model as input data, the third prediction model outputs the prediction result corresponding to the prediction time, and uses the prediction result as the final prediction result of the carbon flux value of the coastal ecosystem.

[0050] Specifically, the first prediction model is based on historical carbon flux monitoring data, and the second prediction model is based on future planned carbon flux monitoring data. They capture the trend of carbon energy changes from different perspectives. At this time, it is necessary to create a new multi-layer perceptron model, namely the third prediction model, to integrate the first and second prediction models, consider the combined impact of historical trends and future plans, combine the advantages of these two models, and use the different perspectives they provide to improve the accuracy of the prediction. Specifically, the training data of the two models are combined into a data matrix, including historical carbon flux monitoring data and future planned carbon flux monitoring data. The data matrix is ​​used to train the third prediction model, and when the carbon flux monitoring data corresponding to the prediction time is input, the third model outputs the carbon flux prediction result corresponding to the prediction time. Because the third prediction model combines historical carbon flux monitoring data and future planned carbon flux monitoring data, the output results of the third prediction model make the numerical prediction of coastal ecosystem carbon flux more accurate.

[0051] Therefore, by coordinating the above steps and combining historical carbon flux monitoring data with future planned carbon flux monitoring data, the accuracy of numerical predictions of carbon flux in coastal ecosystems can be effectively improved.

[0052] Furthermore, after step S3, the following steps are further included:

[0053] Step S4: Obtain the emission reduction target and compare it with the prediction result. When the prediction result is less than the emission reduction target, issue a warning message, and formulate a new land use plan in combination with the land use plan corresponding to the prediction result. Calculate new first-type ecological data and second-type ecological data based on the new land use plan, and input them into the third prediction model again to obtain a new prediction result. Determine whether the new prediction result is greater than the emission reduction target. If it is less than the emission reduction target, continue to adjust the new land use plan until the prediction result of the third prediction model is less than or equal to the emission reduction target.

[0054] Specifically, the prediction results can also serve as a guide for future land planning, enabling preemptive ecological and environmental planning. Specific emission reduction targets are obtained from government environmental policies or sustainable development goals. The final prediction of the coastal ecosystem carbon flux value obtained in step S3 is compared with the emission reduction target. If the prediction exceeds the emission reduction target, the system automatically issues an early warning message, prompting managers to take measures to reduce carbon emissions. Furthermore, the early warning information can be combined with the current land use plan to formulate a new land use plan or land planning, including increasing vegetation planting area, improving wetland protection measures, or reducing human activities to reduce carbon emissions. Based on the new land use plan, the first type of ecological data and the second type of ecological data are recalculated. The new ecological data is input into the third prediction model to obtain a new prediction result. If the new prediction result still exceeds the emission reduction target, the land use plan is adjusted and the prediction process is repeated until the prediction result meets or falls below the emission reduction target. This method ensures that the carbon flux prediction results of the coastal ecosystem meet or fall below the established emission reduction target, contributing to the achievement of regional climate change mitigation goals. Furthermore, by continuously adjusting the land use plan to meet the emission reduction target, the resilience and carbon sequestration function of the coastal ecosystem are enhanced.

[0055] Furthermore, the carbon flux monitoring data in step S1 above includes:

[0056] Carbon flux monitoring data refers to first-type ecological data, second-type ecological data and carbon flux. Carbon flux monitoring data includes first-type carbon flux monitoring data and second-type carbon flux monitoring data. Among them, first-type ecological data refers to mangroves, salt marshes, seagrass beds and large seaweeds that absorb carbon elements and the corresponding coverage area; second-type ecological data refers to the number of animals that release carbon elements and the area covered by human activities.

[0057] Multiple remote sensing images of the coastal zone are periodically acquired within a preset time period, and ecological data and corresponding areas or quantities of different types of coastal zones on each remote sensing image are identified and stored in a first database in chronological order.

[0058] Specifically, satellites or drones are used to periodically acquire remote sensing images of coastal areas over preset time periods, such as one, two, or five years. This periodic acquisition of remote sensing images allows for efficient monitoring of changes in coastal ecosystems, eliminating the need for extensive field surveys and reducing data collection costs and time. These remote sensing images can cover visible, near-infrared, and other spectral bands, facilitating the identification of different ecosystem types and monitoring their changes. Image processing software and machine learning algorithms are used to analyze remote sensing images to identify first-type ecological data, such as mangroves, salt marshes, seagrass beds, and macroalgae, as well as second-type ecological data, such as areas of animal activity and areas affected by human activity. This improves the accuracy of ecological data identification and ensures the quality of carbon flux monitoring data. Image recognition is a well-known technique and will not be further elaborated here. Using image segmentation, object recognition, and classification techniques, the coverage area or number of each ecological type is determined. Specific ecological data, including the coverage area of ​​mangroves, salt marshes, seagrass beds, and macroalgae, as well as the number of animals and areas affected by human activity, is extracted from the processed remote sensing images. The extracted ecological data and their corresponding timestamps are stored in chronological order in a first database, providing basic data for subsequent carbon flux prediction. Carbon flux is closely related to the first and second types of ecological data. The relationship between them is learned through a prediction model. By calculating the first and second types of ecological data for future periods, the prediction model is used to predict carbon flux in the future.

[0059] Furthermore, in the above step S2, after normalizing the first carbon flux monitoring data and before training the first prediction model, the following steps are further included:

[0060] Based on the preset time length of the first carbon flux monitoring data, the prediction time is determined, and sequence data of the historical carbon flux monitoring data based on the preset time length is created for the prediction time, and the sequence data is paired with the prediction time. The sequence data is used as the input data of the first prediction model, the prediction time is used as the time corresponding to the prediction target, and the carbon flux corresponding to the prediction time is used as the output result of the first prediction model. The sequence data and the carbon flux corresponding to the prediction time constitute the training data of the first prediction model, wherein the sequence data is data arranged in chronological order, and the prediction target refers to the carbon flux corresponding to the prediction time.

[0061] Specifically, a preset time length, such as three years, is selected as the time window for the model input data, and a prediction time point, such as year XX, is determined as the future time point we want to predict. Historical carbon flux monitoring data for the three years prior to year XX, i.e., data from years XX-3 to XX-1, is extracted from the first database. This data includes the coverage area of ​​mangroves, salt marshes, seagrass beds, and large seaweeds (first type of ecological data), as well as animal populations and the area covered by human activities (second type of ecological data). The extracted three-year sequence data is paired with the prediction time of year XX, and year XX is determined as the time corresponding to the prediction target, while the carbon flux corresponding to year XX is used as the output of the first prediction model. The sequence data (carbon flux monitoring data from years XX-3 to XX-1) and the carbon flux corresponding to the prediction time (year XX) constitute the training dataset for the first prediction model. The sequence data are arranged in chronological order, and the prediction target refers to the carbon flux corresponding to the prediction time (year XX). Before training the model, the sequence data in the training dataset are normalized to eliminate the influence of different dimensions and magnitudes so that the data can be compared on the same scale. The first prediction model was trained using the normalized training dataset, and the LSTM model parameters, such as the number of hidden layers and neurons, were adjusted to optimize model performance. By using historical carbon flux monitoring data, the LSTM model was able to capture long-term dependencies in time series data, improving the accuracy of future carbon flux forecasts.

[0062] Furthermore, in the above step S1, constructing future carbon flux monitoring data includes:

[0063] Based on big data or land use planning documents issued by relevant departments, the land use plan related to the coastal area is obtained. Based on the land use plan, the future first-type ecological data and future second-type ecological data and the corresponding future carbon flux corresponding to each preset time period in the coastal area are calculated, so as to construct future carbon flux monitoring data and store them in the second database.

[0064] Specifically, by analyzing the big data platform or land use planning documents obtained from relevant departments, land use plans related to specific coastal areas are collected, including information such as urban expansion, agricultural development, industrial construction, ecological protection area demarcation, and vegetation planting plans. A preset time period is determined, the future time period is divided, and the corresponding ecological data is predicted. For each preset time period, the new area corresponding to the future first type of ecological data and the future second type of ecological data is calculated. Based on the first type of ecological data and the future second type of ecological data, the corresponding future carbon flux is calculated, including the carbon absorption potential of vegetation and carbon emissions due to animal and human activities. The calculated future first type of ecological data, future second type of ecological data, and the corresponding future carbon flux are integrated into future carbon flux monitoring data and stored in the second database to provide data support for subsequent model training and prediction.

[0065] Furthermore, in the above step S3, the training data of the first prediction model and the training data of the second prediction model are combined to form a data matrix, including:

[0066] Obtain the training data of the first prediction model, create a data matrix with time, first type ecological data, second type ecological data and carbon flux as titles, and the corresponding data as values, obtain the training data of the second prediction model, and align the training data of the second prediction model with the training data of the first prediction model by time, add them to the data matrix, and form a comprehensive data matrix containing the first carbon flux monitoring data and the second carbon flux monitoring data, use the first type ecological data, second type ecological data, future first type ecological data and future second type ecological data in the data matrix as input data of the third prediction model, and use the first prediction results and second prediction results in the data matrix as output data of the third prediction model to train the third prediction model.

[0067] Specifically, the training data of the first prediction model is extracted from the first database, including time, first-type ecological data (coverage area of ​​mangroves, salt marshes, seagrass beds and large seaweeds), second-type ecological data (animal population and human activity coverage area) and carbon flux. A data matrix is ​​created with time as the row index, first-type ecological data, second-type ecological data and carbon flux as column headers, and the corresponding numerical values ​​are filled in. The training data of the second prediction model is extracted from the second database. These data are based on the future first-type ecological data and second-type ecological data estimated based on future land use plans. The training data of the second prediction model are aligned with the training data of the first prediction model in time and added to the data matrix to form a comprehensive data matrix containing historical and future carbon flux monitoring data. In the comprehensive data matrix, the first-type ecological data, the second-type ecological data, the future first-type ecological data and the future second-type ecological data are defined as the input data of the third prediction model, and the first prediction results and the second prediction results are defined as the output data of the third prediction model. The third prediction model is trained using the comprehensive data matrix. By combining historical and future carbon flux monitoring data, the third prediction model can provide a more comprehensive understanding of carbon flux changes in coastal ecosystems and improve the accuracy of predictions.

[0068] Furthermore, in the above step S2, training the first prediction model based on the normalized first carbon flux monitoring data includes:

[0069] obtaining normalized first carbon flux monitoring data and using it as training data for a first prediction model, determining whether the data in the training data is uniform; if the data in the training data is not uniform, dividing the training data into first learning data and second learning data; and generating more new first learning data using data enhancement technology based on data features of the first learning data and the second learning data;

[0070] Input the new first learning data into the judgment model, and the judgment model outputs a judgment result. When the value of the judgment result is greater than or equal to the first threshold and less than or equal to the second threshold, the new first learning data is used as the third learning data, the similarity between the third learning data is calculated, and the data with similarity greater than or equal to the third threshold are clustered together to generate a clustering result for the third learning data;

[0071] The weights and parameters of the first prediction model are adjusted based on the clustering result of the third learning data, and the first prediction model is trained using the third learning data and the second learning data.

[0072] Specifically, the first carbon flux monitoring data is normalized so that it has a unified data scale so that it can be used to train the first prediction model (LSTM). The normalized training data is analyzed to check whether the training data is uniform. The definition of whether the training data is uniform will be explained below. Uneven data may affect the training effect and prediction accuracy of the model. When the training data is uneven, the training data is divided into first learning data (the part with a smaller amount of feature category data) and second learning data (the part with a larger amount of feature category data). Based on the data features of the first learning data, more new first learning data are generated through data enhancement technology to increase the data volume of the first learning data. The newly generated first learning data is input into the judgment model. The judgment model can evaluate the authenticity of the newly generated data to determine how close the newly generated learning data is to the original first learning data. The closer the judgment result output by the judgment model is to the midpoint between the first threshold and the second threshold (the first threshold can be set to 0.45 and the second threshold can be set to 0.55), the more likely it is that the judgment model cannot determine whether the newly generated first learning data is actual data or generated virtual data. For example, if the judgment model outputs a judgment result of 0.499, it indicates that the judgment model cannot determine whether the newly generated first learning data is actual data or generated virtual data. This is used to select valid new data as the third learning data. Similarity is calculated for the selected new data (third learning data). Data with similarity greater than or equal to the third threshold are clustered together to generate clustering results. The similarity between data points is evaluated to identify and reduce redundancy and lack of diversity in the third learning data, so that it represents the real-world data distribution and enhances the prediction model's recognition of different categories and subcategories in the learning data. Based on the clustering results of the third learning data, the weights and parameters of the first prediction model are adjusted. Clustering results can help identify patterns and structures in the data, accelerate the model training process, and improve the model's performance on new data, especially when the new data is similar to the old data. Using the adjusted parameters and the third learning data (clustered data) and the second learning data to train the first prediction model not only improves the efficiency of prediction model training, but also improves the accuracy of the prediction model's predictions.

[0073] Further, determining whether the data in the training data is uniform, and when the data in the training data is not uniform, dividing the training data into first learning data and second learning data, including:

[0074] Calculate the number of training data that contain the first type of ecological data features and the number of training data that do not contain the first type of ecological data features. When the number of training data that does not contain the first type of ecological data features is less than N times the number of training data that contain the first type of ecological data features, determine that the data in the training data is uneven, where N represents a positive integer greater than or equal to 2.

[0075] When the data in the training data is uneven, the training data is divided into first learning data and second learning data, wherein the first learning data refers to data that does not contain the first type of ecological data features, and the second learning data refers to data that contains the first type of ecological data features.

[0076] Specifically, assuming that there are a total of 2,000 training data, the number of training data containing the first type of ecological data features is 100, and the number of training data that does not contain the first type of ecological data features is 1,900. The number of data containing the first type of ecological data features is much smaller than the number of data not containing the first type of ecological data features, and there is an imbalance in the two feature categories. When there is an imbalance in the two feature categories in the training data, the output results of the prediction model may be biased towards data with a large number of feature categories, because data with a large number of feature categories have more samples, and the prediction model is more likely to learn features from these samples, thereby ignoring data with a small number of feature categories. Therefore, when the data in the training data is uneven, the training data is divided into first learning data and second learning data. The purpose of dividing the training data is to make the prediction model training process also focus on data with a small number of feature categories, so as to more effectively deal with the problem of uneven feature categories in the training data and improve the accuracy of the prediction model. This method is also used to process the training data of the second prediction model.

[0077] The present invention also provides a Figure 2 The numerical prediction platform for coastal ecosystem carbon flux shown in the figure includes the following modules:

[0078] an acquisition unit, configured to acquire historical carbon flux monitoring data of the coastal zone from the first database as first carbon flux monitoring data, acquire a land use plan and an emission reduction plan related to the coastal zone, and construct future carbon flux monitoring data based on the land use plan and the emission reduction plan as second carbon flux monitoring data, and store the data in the second database;

[0079] a training unit, configured to create a first prediction model and a second prediction model, normalize the first carbon flux monitoring data and the second carbon flux monitoring data, respectively, train the first prediction model based on the normalized first carbon flux monitoring data, and obtain the trained first prediction model, train the second prediction model based on the normalized second carbon flux monitoring data, and obtain the trained second prediction model;

[0080] The integration unit is used to create a third prediction model, combine the training data of the first prediction model and the training data of the second prediction model to form a data matrix, and use the data in the data matrix as training data to train the third prediction model, calculate the first type of ecological data and the second type of ecological data corresponding to the prediction time and input them into the third prediction model as input data, the third prediction model outputs the prediction result corresponding to the prediction time, and uses the prediction result as the final prediction result of the carbon flux value of the coastal ecosystem.

[0081] The present invention also provides a computer storage medium, which stores program instructions. When the program instructions are executed, the device where the computer storage medium is located is controlled to execute the above-mentioned numerical prediction method for coastal ecosystem carbon flux.

[0082] In summary, the technical solution of the present invention obtains historical carbon flux monitoring data of the coastal area from a first database as first carbon flux monitoring data to train a first prediction model; obtains land use plans and emission reduction plans related to the coastal area, and based on the land use plans and emission reduction plans, constructs future carbon flux monitoring data as second carbon flux monitoring data to train a second prediction model. A first prediction model and a second prediction model are created based on a neural network, and the first carbon flux monitoring data and the second carbon flux monitoring data are normalized respectively. The first prediction model is trained based on the normalized first carbon flux monitoring data, and the trained first prediction model is obtained to identify the temporal trend of the carbon flux. A second prediction model is trained based on the normalized second carbon flux monitoring data, and the trained second prediction model is obtained to provide an expected perspective to predict the trend of future carbon flux changes.

[0083] A third prediction model is created based on a neural network. The training data from the first and second prediction models are combined to form a data matrix, capturing carbon energy trends from different perspectives. The data in the data matrix is ​​then used to train the third prediction model, integrating the first and second prediction models. This model considers the combined impact of historical trends and future plans, combining the strengths of both models and leveraging the different perspectives they provide to improve prediction accuracy. The first and second types of ecological data corresponding to the prediction time are calculated and fed into the third prediction model as input. The third prediction model then outputs a prediction result corresponding to the prediction time, which serves as the final prediction of the coastal ecosystem carbon flux. Because the third prediction model combines historical and planned carbon flux monitoring data, its output results provide more accurate predictions of coastal ecosystem carbon flux.

[0084] By coordinating the above steps and combining historical carbon flux monitoring data with future planned carbon flux monitoring data, the accuracy of numerical predictions of carbon flux in coastal ecosystems can be effectively improved.

[0085] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0086] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0087] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] The above embodiments merely represent several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be based on the appended claims.

[0089] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A numerical prediction method for carbon flux in coastal ecosystems, characterized in that: The method comprises: Step S1: Acquire historical carbon flux monitoring data of a coastal zone from a first database as first carbon flux monitoring data, acquire a land use plan and an emission reduction plan related to the coastal zone, and construct future carbon flux monitoring data based on the land use plan and the emission reduction plan as second carbon flux monitoring data, and store the data in a second database; The carbon flux monitoring data in step S1 includes: carbon flux monitoring data refers to first-type ecological data, second-type ecological data and carbon flux, and the carbon flux monitoring data includes the first carbon flux monitoring data and the second carbon flux monitoring data, wherein the first-type ecological data refers to mangroves, salt marshes, seagrass beds and large seaweeds that absorb carbon elements and the corresponding coverage area, and the second-type ecological data refers to the number of animals that release carbon elements and the area covered by human activities; periodically acquiring multiple remote sensing images of the coastal zone within a preset time period, and identifying different types of ecological data of the coastal zone and the corresponding area or quantity on each of the remote sensing images, and storing them in a first database in chronological order; Step S2: creating a first prediction model and a second prediction model based on a neural network, normalizing the first carbon flux monitoring data and the second carbon flux monitoring data respectively, training the first prediction model based on the normalized first carbon flux monitoring data, and obtaining the trained first prediction model, training the second prediction model based on the normalized second carbon flux monitoring data, and obtaining the trained second prediction model; Step S3: Create a third prediction model based on a neural network, combine the training data of the first prediction model and the training data of the second prediction model to form a data matrix, and use the data in the data matrix as training data to train the third prediction model, calculate the first type of ecological data and the second type of ecological data corresponding to the prediction time and input them into the third prediction model as input data, the third prediction model outputs the prediction result corresponding to the prediction time, and uses the prediction result as the final prediction result of the carbon flux value of the coastal ecosystem.

2. The method according to claim 1, characterized in that After step S3, the following steps are also included: Step S4: Obtain the emission reduction target and compare it with the prediction result. When the prediction result is less than the emission reduction target, issue a warning message, and formulate a new land use plan in combination with the land use plan corresponding to the prediction result. Calculate new first-type ecological data and second-type ecological data based on the new land use plan, and input them into the third prediction model again to obtain a new prediction result. Determine whether the new prediction result is greater than the emission reduction target. If it is less than the emission reduction target, continue to adjust the new land use plan until the prediction result of the third prediction model is less than or equal to the emission reduction target.

3. The method according to claim 1, characterized in that In step S2, after normalizing the first carbon flux monitoring data and before training the first prediction model, the method further includes: Based on a preset time length of the first carbon flux monitoring data, a prediction time is determined, and sequence data of historical carbon flux monitoring data based on the preset time length is created for the prediction time, and the sequence data is paired with the prediction time. The sequence data serves as input data of the first prediction model, the prediction time serves as the time corresponding to the prediction target, and the carbon flux corresponding to the prediction time serves as the output result of the first prediction model. The sequence data and the carbon flux corresponding to the prediction time constitute training data for the first prediction model, wherein the sequence data is data arranged in chronological order, and the prediction target refers to the carbon flux corresponding to the prediction time.

4. The method according to claim 1, wherein In step S1, constructing future carbon flux monitoring data includes: Based on big data or land use planning documents issued by relevant departments, a land use plan related to the coastal area is obtained. Based on the land use plan, the future first type of ecological data and future second type of ecological data and the corresponding future carbon flux corresponding to each preset time period of the coastal area are calculated, so as to construct future carbon flux monitoring data and store them in the second database.

5. The method according to claim 4, characterized in that In step S3, the training data of the first prediction model and the training data of the second prediction model are combined to form a data matrix, including: Obtain the training data of the first prediction model, create a data matrix with time, the first type of ecological data, the second type of ecological data and the carbon flux as titles, and the corresponding data as values, obtain the training data of the second prediction model, and align the training data of the second prediction model with the training data of the first prediction model according to the time, add them to the data matrix, and form a comprehensive data matrix containing the first carbon flux monitoring data and the second carbon flux monitoring data, use the first type of ecological data, the second type of ecological data, the future first type of ecological data and the future second type of ecological data in the data matrix as input data of the third prediction model, and use the first prediction result and the second prediction result in the data matrix as output data of the third prediction model to train the third prediction model.

6. The method according to claim 1, characterized in that In step S2, training the first prediction model based on the normalized first carbon flux monitoring data includes: obtaining normalized first carbon flux monitoring data and using the data as training data for the first prediction model, determining whether the data in the training data is uniform; if the data in the training data is not uniform, dividing the training data into first learning data and second learning data; and generating more new first learning data using a data enhancement technique based on data features of the first learning data and the second learning data; Inputting the new first learning data into a judgment model, the judgment model outputting a judgment result, and when the value of the judgment result is greater than or equal to a first threshold and less than or equal to a second threshold, using the new first learning data as third learning data, calculating the similarity between the third learning data, and clustering the data with the similarity greater than or equal to the third threshold together to generate a clustering result for the third learning data; The weights and parameters of the first prediction model are adjusted based on the clustering result of the third learning data, and the first prediction model is trained using the third learning data and the second learning data.

7. The method according to claim 6, characterized in that Determining whether the data in the training data is uniform, and when the data in the training data is not uniform, dividing the training data into first learning data and second learning data, including: Calculating the number of the training data that contains the first type of ecological data features and the number of the training data that does not contain the first type of ecological data features, and determining that the data in the training data is uneven when the number of the training data that does not contain the first type of ecological data features is less than N times the number of the training data that contains the first type of ecological data features, where N represents a positive integer greater than or equal to 2; When the data in the training data is uneven, the training data is divided into first learning data and second learning data, wherein the first learning data refers to data that does not contain the first type of ecological data characteristics, and the second learning data refers to data that contains the first type of ecological data characteristics.

8. A numerical prediction platform for carbon flux of coastal ecosystems, used to implement the method according to any one of claims 1 to 7, characterized in that: The platform includes the following modules: The acquisition unit is configured to acquire historical carbon flux monitoring data of a coastal zone from a first database as first carbon flux monitoring data, acquire a land use plan and an emission reduction plan related to the coastal zone, and construct future carbon flux monitoring data based on the land use plan and the emission reduction plan as second carbon flux monitoring data, and store the data in a second database; The carbon flux monitoring data includes: carbon flux monitoring data refers to first-type ecological data, second-type ecological data and carbon flux, and the carbon flux monitoring data includes the first carbon flux monitoring data and the second carbon flux monitoring data, wherein the first-type ecological data refers to mangroves, salt marshes, seagrass beds and large seaweeds that absorb carbon elements and the corresponding coverage area, and the second-type ecological data refers to the number of animals that release carbon elements and the area covered by human activities; periodically acquiring multiple remote sensing images of the coastal zone within a preset time period, and identifying different types of ecological data of the coastal zone and the corresponding area or quantity on each of the remote sensing images, and storing them in a first database in chronological order; a training unit, configured to create a first prediction model and a second prediction model, normalize the first carbon flux monitoring data and the second carbon flux monitoring data, respectively, train the first prediction model based on the normalized first carbon flux monitoring data, and obtain the trained first prediction model, train the second prediction model based on the normalized second carbon flux monitoring data, and obtain the trained second prediction model; An integration unit is used to create a third prediction model, combine the training data of the first prediction model and the training data of the second prediction model to form a data matrix, and use the data in the data matrix as training data to train the third prediction model, calculate the first type of ecological data and the second type of ecological data corresponding to the prediction time and input them into the third prediction model as input data, the third prediction model outputs the prediction result corresponding to the prediction time, and uses the prediction result as the final prediction result of the carbon flux value of the coastal ecosystem.

9. A computer storage medium, characterized in that The storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute the numerical prediction method for coastal ecosystem carbon flux according to any one of claims 1 to 7.

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