CCER forestry carbon sink project development system based on artificial intelligence technology monitoring

Abstract

The invention discloses a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, and relates to the technical field of carbon sink monitoring and optimization. According to the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, space-time estimation is carried out on CCER forestry carbon sink project data through the deep learning model, and the dynamic change of the carbon sink amount is accurately predicted. Spatial features are extracted through a convolutional neural network, time sequence data are analyzed in combination with a long-short-term memory network, and a carbon sink prediction model is constructed. And through statistical analysis and a spatial interpolation algorithm, carbon sink quantity change is monitored in real time, an abnormal trend is identified, and early warning is provided. Based on a mixed integer linear programming algorithm, forest land resource allocation and vegetation types are optimized, a carbon sink optimization scheme is generated, and carbon sink benefit maximization is promoted. And through an intelligent carbon sink management scheme, sustainable ecological development and green and economic popularization are assisted.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon sink monitoring and optimization, and in particular to a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring. Background Art

[0002] Global warming is becoming increasingly severe, and greenhouse gas emissions such as carbon dioxide remain high. In response to climate change, countries have set carbon neutrality goals and reduced carbon emissions through various means. In this context, forestry carbon sink projects are regarded as one of the important tools for achieving carbon neutrality because they use forest vegetation to fix carbon dioxide in the atmosphere. The CCER (certified voluntary emission reduction) mechanism launched by China provides an effective way for enterprises and individuals to participate in carbon emission reduction transactions through forestry carbon sinks and other channels. However, the monitoring, verification and certification of forestry carbon sink projects still face technical challenges, and there is an urgent need to adopt innovative technologies such as artificial intelligence to improve the efficiency and accuracy of carbon sink management.

[0003] Existing forestry carbon sink project monitoring methods mainly rely on manual inspections, satellite remote sensing and fixed sensors, which have multiple shortcomings. Manual inspections have long cycles, high costs and difficulty in obtaining comprehensive forest carbon sink data; satellite remote sensing is affected by factors such as weather and clouds, making it difficult to provide continuous and accurate monitoring data; and fixed sensors have limited monitoring ranges and are difficult to dynamically and comprehensively reflect changes in forest carbon sinks. The above shortcomings have resulted in the inability of existing methods to meet the needs of CCER forestry carbon sink projects in terms of real-time performance, coverage and data accuracy, limiting their widespread application in carbon emission reduction certification and trading. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, which solves the problems of the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, including the following modules: a data acquisition module, a fine-grained carbon sink dynamic estimation module, a carbon sink dynamic monitoring module, and a carbon sink optimization decision module; the data acquisition module is used to obtain and preprocess CCER forestry carbon sink project data; the fine-grained carbon sink dynamic estimation module is used to perform spatiotemporal estimation of carbon sinks through a deep learning model based on the preprocessed CCER forestry carbon sink project data, construct a carbon sink dynamic estimation model, and generate carbon sink prediction data; the carbon sink dynamic monitoring module is used to monitor the carbon sink prediction data through statistical analysis, analyze the distribution of carbon sink prediction data, and identify abnormal changes in carbon sinks; the carbon sink optimization decision module is used to establish a carbon sink optimization decision model through a mixed integer linear programming algorithm based on the abnormal changes in carbon sinks, optimize and adjust forest resources and vegetation configuration, and generate a carbon sink optimization plan.

[0006] Furthermore, the specific process of obtaining and preprocessing CCER forestry carbon sink project data is as follows: CCER forestry carbon sink project data includes remote sensing images, meteorological data, geographic information system data, and soil property data to form a multi-source data set; the multi-source data set is cleaned to remove noise data and redundant information to ensure data integrity and accuracy, and the cleaned data is formatted and standardized to form a CCER forestry carbon sink project data set.

[0007] Furthermore, the specific process of spatiotemporal estimation of carbon sinks through deep learning models is as follows: convolutional neural networks are used to extract spatial features from remote sensing images and geographic information system data, and long-term and short-term memory networks are used to analyze the time series changes of meteorological and soil property data to comprehensively construct a dynamic estimation model for carbon sinks.

[0008] Furthermore, the specific process of extracting spatial features from remote sensing images and geographic information system data through convolutional neural networks is as follows: spatial features of terrain features, vegetation coverage and their distribution patterns in remote sensing images are extracted through convolutional neural networks; multi-dimensional fusion of spatial features is performed in combination with the geographical location, soil type, slope and altitude spatial information in the geographic information system data to capture the spatial distribution law of carbon sinks in the region and obtain the spatial characteristic information of carbon sinks.

[0009] Furthermore, the specific process of using the long short-term memory network to analyze the time series changes of meteorological and soil characteristic data is as follows: the time series of meteorological data and soil characteristic data are modeled through the long short-term memory network. The meteorological data include temperature, precipitation and relative humidity, and the soil characteristic data include soil moisture, soil temperature and soil pH value; the long-term dependence of meteorological data and soil characteristic data over time is captured, the impact of the nonlinear time-varying relationship between meteorological and soil characteristic data on carbon sequestration is analyzed, and the time series prediction data output by the LSTM model is obtained.

[0010] Furthermore, the specific process of comprehensively constructing a dynamic estimation model for carbon sink and generating carbon sink prediction data is as follows: by fusing the spatial feature information extracted by convolutional neural network and the time series data analyzed by long short-term memory network, and combining the spatial features and time series data, a dynamic estimation model for carbon sink is constructed to generate carbon sink prediction data, including time series carbon sink prediction data and spatially distributed carbon sink data.

[0011] Furthermore, the specific process of analyzing the distribution of carbon sink prediction data and identifying abnormal changes in carbon sink trends is as follows: smoothing the time series carbon sink data, identifying long-term trends through sliding average analysis, calculating seasonal changes and periodic fluctuations, and analyzing the normal change pattern of carbon sinks over time; completing the spatially distributed carbon sink data through a spatial interpolation algorithm, identifying data-missing areas, analyzing the distribution patterns of carbon sinks in different geographical areas based on a geographic information system, and drawing a carbon sink distribution map; identifying abnormal points in time series and spatial distribution data based on the standard deviation threshold method, analyzing the abnormal points to determine whether they exceed the normal fluctuation range, and marking and recording the spatiotemporal characteristics of the abnormal data.

[0012] Furthermore, according to the abnormal change trend of carbon sinks, the specific process of establishing a carbon sink optimization decision-making model through a mixed integer linear programming algorithm is as follows: the identified abnormal change trend of carbon sinks and the predicted data of carbon sink volume are used as input data to determine the carbon optimization target, including optimizing the carbon sink volume and maximizing the carbon sink benefit, and setting constraints, including resource limitation constraints and environmental indicator constraints; the optimization decision-making model is constructed through a mixed integer linear programming algorithm, the carbon optimization target and constraints are converted into linear equations, and the constructed mixed integer linear programming model is solved through the branch and bound method.

[0013] Furthermore, the specific process of optimizing and adjusting forest resources and vegetation configuration to generate a carbon sink optimization plan is as follows: According to the carbon sink optimization objectives and constraints, the forest resource status of each region is analyzed, and the optimal resource allocation plan for each region is determined through a mixed integer linear programming algorithm to ensure the maximization of carbon sinks; According to the relationship between carbon sink prediction data and vegetation types, combined with environmental indicator constraints, the vegetation configuration plan for each region is optimized, and the most suitable vegetation type and coverage area are selected.

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

[0015] (1) The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring can efficiently acquire and preprocess forestry carbon sink project data through the data acquisition module, solving the high cost and low efficiency of traditional manual data collection. The fine-grained carbon sink dynamic estimation module uses a deep learning model to accurately estimate the carbon sink in time and space, which not only improves the accuracy of the predicted data, but also can update the carbon sink prediction in real time, avoiding the limitations of satellite remote sensing and traditional monitoring methods in dynamic monitoring. This module can accurately capture the changing laws of forest carbon sinks and provide high-quality data support for subsequent monitoring and decision-making.

[0016] (2) The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, the carbon sink dynamic monitoring module can identify abnormal carbon sink change trends through statistical analysis of carbon sink forecast data, realize timely early warning of forest carbon sink changes, and improve the response speed and real-time performance of carbon sink monitoring. The carbon sink optimization decision module combines the mixed integer linear programming algorithm to optimize the forest resources and vegetation configuration to generate a scientific carbon sink optimization plan. This intelligent optimization decision can ensure the maximization of carbon sinks, while taking into account resource limitations and environmental constraints, improving the carbon emission reduction benefits of forestry carbon sink projects and promoting the sustainable development of CCER forestry carbon sink projects.

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

[0018] Figure 1 This is a flow chart of the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring in the present invention. DETAILED DESCRIPTION

[0019] The embodiment of the present application solves the shortcomings of traditional monitoring methods in terms of real-time, accuracy and coverage through the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, and overcomes the problems of long manual inspection cycles, high costs, satellite remote sensing of weather and cloud effects, discontinuous data, and limited monitoring range of fixed sensors. Through the application of deep learning models and mixed integer linear programming algorithms, accurate estimation, dynamic monitoring and optimized decision-making of carbon sinks can be achieved, effectively improving the monitoring efficiency, accuracy and intelligent level of carbon sink management of forestry carbon sink projects.

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

[0021] The data acquisition module is used to obtain CCER forestry carbon sink project data and perform preprocessing.

[0022] Based on the preprocessed CCER forestry carbon sink project data, the carbon sink is estimated in time and space through a deep learning model, a dynamic estimation model for carbon sink is constructed, and carbon sink prediction data is generated.

[0023] Through statistical analysis, the carbon sink forecast data is monitored, the distribution of the carbon sink forecast data is analyzed, and abnormal changes in carbon sink trends are identified.

[0024] According to the abnormal change trend of carbon sink, a carbon sink optimization decision-making model is established through the mixed integer linear programming algorithm, and the forest resources and vegetation configuration are optimized to generate a carbon sink optimization plan.

[0025] See also Figure 1 The embodiment of the present invention provides a technical solution: a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, including the following modules: a data acquisition module, a fine-grained carbon sink dynamic estimation module, a carbon sink dynamic monitoring module, and a carbon sink optimization decision module; the data acquisition module is used to obtain and preprocess the CCER forestry carbon sink project data; the fine-grained carbon sink dynamic estimation module is used to perform spatiotemporal estimation of carbon sinks through a deep learning model based on the preprocessed CCER forestry carbon sink project data, construct a carbon sink dynamic estimation model, and generate carbon sink prediction data; the carbon sink dynamic monitoring module is used to monitor the carbon sink prediction data through statistical analysis, analyze the distribution of carbon sink prediction data, and identify abnormal changes in carbon sinks; the carbon sink optimization decision module is used to establish a carbon sink optimization decision model through a mixed integer linear programming algorithm based on the abnormal change trend of carbon sinks, optimize and adjust forest resources and vegetation configuration, and generate a carbon sink optimization plan.

[0026] In this implementation plan, data acquisition module: This module is responsible for obtaining various data from the CCER forestry carbon sink project and preprocessing these data. The purpose of data preprocessing is to clean and standardize the data so that it is suitable for subsequent analysis and modeling processes. Fine-grained carbon sink dynamic estimation module: This module estimates the dynamic changes of carbon sinks by using a deep learning model to perform spatiotemporal estimation of the processed project data. By constructing a dynamic estimation model for carbon sinks, the changing trend of carbon sinks can be predicted more accurately. This module is capable of processing fine-grained data and accurately predicting carbon sinks based on spatiotemporal characteristics, thereby providing accurate data support for subsequent analysis. Carbon sink dynamic monitoring module: This module monitors the changes in carbon sinks and identifies their distribution characteristics by performing statistical analysis on carbon sink prediction data. Using the analysis results, the system can effectively identify abnormal changes in carbon sinks. By timely discovering these abnormal changes, measures can be taken early to optimize the implementation strategy of carbon sink projects. Carbon sink optimization decision module: After identifying the abnormal trend of carbon sinks, the system uses a mixed integer linear programming algorithm to establish a carbon sink optimization decision model. This module can optimize forest resource allocation and vegetation configuration based on the carbon sink optimization target, combined with regional resources and environmental constraints. Through model solving, a carbon sink optimization plan is finally generated to maximize the carbon sink volume and improve the carbon sink benefits. CCER (Certified Voluntary Emission Reduction): CCER is a carbon emission reduction certification mechanism in China that allows companies or individuals to obtain carbon emission reductions by reducing greenhouse gas emissions or increasing carbon sinks (such as absorbing carbon dioxide through afforestation and other means) and trade them on the market. This mechanism provides a compliant and tradable carbon emission reduction for entities involved in carbon emission reduction. CCER can be used to support China's carbon neutrality goals, especially in forestry carbon sink projects, by fixing carbon dioxide through activities such as vegetation planting and forest management. Deep learning model: Deep learning is a method in machine learning that learns and extracts complex features in data through a multi-layer neural network model. In the present invention, a deep learning model is used to perform spatiotemporal estimation of carbon sinks. By learning historical data and a variety of environmental variables, the deep learning model can predict future trends in forest carbon sinks and achieve accurate dynamic estimates. Mixed integer linear programming algorithm: Mixed integer linear programming (MILP) is an optimization algorithm used to solve linear optimization problems involving integer variables and continuous variables. In the present invention, MILP is used to establish a carbon sink optimization decision model to help determine the best forest resource allocation and vegetation configuration plan. By setting goals (such as maximizing carbon sinks) and constraints (such as resource limitations, environmental protection, etc.), the algorithm can find the optimal solution, thereby helping to achieve optimal management of carbon sinks. Carbon sink dynamic estimation model: This is a mathematical model used to predict changes in carbon sinks based on historical data and real-time collected environmental data. Combined with spatiotemporal estimation methods, this model can dynamically calculate and predict carbon sinks based on the status of forest resources in different regions and time periods.This model can help to identify carbon sink trends in a timely manner and provide data support for optimizing decisions. Carbon sink optimization decision model: This model is based on a mixed integer linear programming algorithm, combining carbon sink optimization goals (such as maximizing carbon sink volume and benefits) and specific constraints (such as resource limitations, environmental protection, etc.) to make optimization decisions. By solving this model, the system can automatically generate the best carbon sink optimization plan and effectively adjust forest resources and vegetation configuration, thereby improving carbon sink benefits and the scientific nature of project implementation. Abnormal carbon sink change trend: refers to the unconventional changes in carbon sink volume over a period of time, such as abnormal situations where carbon sink volume suddenly decreases or increases. Through statistical analysis and monitoring, the system can identify these abnormal change trends, thereby providing a reference for subsequent optimization adjustments and decision-making.

[0027] Specifically, the specific process of obtaining CCER forestry carbon sink project data and preprocessing it is as follows: CCER forestry carbon sink project data includes remote sensing images, meteorological data, geographic information system data, and soil property data to form a multi-source data set; the multi-source data set is cleaned to remove noise data and redundant information to ensure data integrity and accuracy, and the cleaned data is formatted and standardized to form a CCER forestry carbon sink project data set.

[0028] In this implementation scheme, remote sensing images refer to image data collected by remote sensing equipment such as satellites or drones, which are usually used to obtain information such as forest coverage, vegetation type, and terrain. Through remote sensing images, the spatial distribution of forest areas and the health of vegetation can be intuitively analyzed, providing preliminary information about carbon sinks. Meteorological data include temperature, humidity, precipitation, wind speed, and meteorological conditions that are closely related to carbon sink changes. Meteorological data is a key factor in studying the dynamic changes of carbon sinks, because climate conditions have a direct impact on the growth and carbon absorption capacity of forests. Geographic Information System (GIS) data provides geospatial information about forest areas, such as forest boundaries, terrain slopes, soil types, and altitudes. This information can help with regional division and carbon sink analysis, and understand the carbon sink situation in different geographical environments. Soil property data include soil type, depth, nutrient content, and moisture data. The characteristics of soil affect the carbon storage capacity of forests, because soil is an important storage reservoir for carbon, and soil quality determines the conditions for plant growth. Data cleaning: In practical applications, the original data may contain some erroneous information or incomplete data, which needs to be cleaned. The data cleaning process includes: Removing noise data: removing outliers caused by equipment failure, transmission errors or other factors (such as extreme meteorological data or unreasonable remote sensing image data). Removing redundant information: deleting duplicate data to ensure that there are no duplicate items in the data set to improve computing efficiency and data accuracy. Ensuring data integrity and accuracy: by processing missing values ​​and correcting data errors, ensuring that each data item conforms to the actual situation and is not omitted, ensuring the reliability of the data in subsequent analysis. Data format unification and standardization processing: different data sources may use different formats and units. In order to make the data can be processed uniformly, the format needs to be unified and standardized. This includes: converting different types of data into the same standard format (for example, unifying all meteorological data to the same timestamp). Converting data in different units into a unified unit of measurement (such as temperature unified to degrees Celsius, precipitation unified to millimeters, etc.) to ensure the consistency of subsequent analysis. Forming a CCER forestry carbon sink project data set. This data set contains all the cleaned and formatted data, ready for subsequent deep learning models, statistical analysis, and carbon sink estimation operations.

[0029] Specifically, the specific process of spatiotemporal estimation of carbon sinks through deep learning models is as follows: convolutional neural networks are used to extract spatial features from remote sensing images and geographic information system data, and long-term and short-term memory networks are used to analyze the time series changes of meteorological and soil property data to comprehensively construct a dynamic estimation model for carbon sinks.

[0030] In this implementation scheme, convolutional neural network: Convolutional neural network is used to extract spatial features from remote sensing images and geographic information system data. CNN is good at processing image data. Through multi-layer convolution operations, it can identify spatial patterns in images (vegetation distribution, terrain changes), thereby providing information for the spatial distribution of carbon sinks. Long short-term memory network (LSTM): Combined with long short-term memory network to analyze the time series changes of meteorological and soil property data. LSTM is suitable for processing time series data, which can capture the laws of changes in factors such as temperature, precipitation, soil moisture over time, and help estimate the dynamic changes of carbon sinks over time.

[0031] Carbon sink dynamic estimation model: By integrating the spatial features extracted by CNN and the time series features analyzed by LSTM, a carbon sink dynamic estimation model is comprehensively constructed. This model can take into account both spatial distribution and temporal changes and provide accurate carbon sink predictions.

[0032] Specifically, the specific process of extracting spatial features from remote sensing images and geographic information system data through convolutional neural networks is as follows: spatial features of terrain features, vegetation coverage and their distribution patterns in remote sensing images are extracted through convolutional neural networks; geographical location, soil type, slope and altitude spatial information in geographic information system data are combined to perform multi-dimensional fusion of spatial features, capture the spatial distribution law of carbon sinks in the region, and obtain spatial characteristic information of carbon sinks.

[0033] In this embodiment, the terrain features and vegetation coverage in the remote sensing image are extracted: the convolutional neural network is used to analyze the terrain features and vegetation coverage and its distribution pattern in the remote sensing image. CNN gradually extracts local features (e.g., trees, vegetation types, etc.) in the image through multiple convolutional layers, and captures more complex spatial patterns through deep convolution. This process helps to identify the vegetation type, distribution and terrain structure in the region, thereby obtaining the spatial distribution information of carbon sinks. Spatial information fusion in geographic information system data: CNN also uses spatial data (such as geographic location, soil type, slope, altitude, etc.) in the geographic information system (GIS) to further enhance feature extraction. GIS data provides more accurate geographic environment data, which is crucial for carbon sink estimation. For example, different soil types affect the carbon absorption capacity of plants, while geographical factors such as slope and altitude affect the vegetation growth environment and carbon storage. Multidimensional fusion of spatial features: The spatial features in the remote sensing image and the spatial information in the GIS data are combined to form a multidimensional feature fusion. By integrating these information from different data sources, CNN can more comprehensively and accurately identify the distribution patterns of carbon sinks in the region. This fusion process enables the spatial characteristics of carbon sinks to be fully presented, providing important spatial basic data for the subsequent dynamic estimation of carbon sink amounts.

[0034] Specifically, the specific process of analyzing the time series changes of meteorological and soil characteristic data by long short-term memory network is as follows: the time series of meteorological data and soil characteristic data are modeled through long short-term memory network. The meteorological data include temperature, precipitation and relative humidity, and the soil characteristic data include soil moisture, soil temperature and soil pH value; the long-term dependence of meteorological data and soil characteristic data over time is captured, the impact of the nonlinear time-varying relationship between meteorological and soil characteristic data on carbon sequestration is analyzed, and the time series prediction data output by the LSTM model is obtained.

[0035] In this implementation scheme, time series modeling: LSTM model is used to model the time series of meteorological data and soil property data. Meteorological data usually include temperature, precipitation and relative humidity, and these factors have a significant time series relationship on carbon sinks. Soil property data include soil moisture, soil temperature and soil pH, and changes in these data have a direct impact on vegetation growth and carbon storage. Capturing long-term dependencies: LSTM is a special recursive neural network that can capture long-term dependencies in time series data. Through LSTM, the system can identify the complex relationship between meteorological and soil property data at different time points. For example, some meteorological changes may have an impact on carbon sinks only after a long time. LSTM maintains and transmits information about these long-term dependencies through its internal memory units. Analyzing nonlinear time-varying relationships: LSTM can not only identify the linear trend of data changing over time, but also handle the nonlinear time-varying relationships therein. For example, changes in temperature may have a greater impact on carbon sinks in some periods and a smaller impact in other periods. Soil factors such as soil moisture, soil temperature, and pH value may also show complex time-varying effects. The LSTM model can effectively capture these nonlinear changes, thereby making more accurate predictions of changes in carbon sinks. Time series forecast data output: After the above analysis, the LSTM model can output forecast data on future trends in meteorological and soil property data. These forecast data not only provide trends in carbon sinks over a period of time in the future, but also help further optimize carbon sink management strategies and improve the accuracy of carbon sink monitoring and optimization decisions.

[0036] Specifically, the specific process of comprehensively constructing a dynamic estimation model for carbon sinks and generating carbon sink prediction data is as follows: by integrating the spatial feature information extracted by convolutional neural networks and the time series data analyzed by long and short-term memory networks, and combining spatial features and time series data, a dynamic estimation model for carbon sinks is constructed to generate carbon sink prediction data, including time series carbon sink prediction data and spatially distributed carbon sink data.

[0037] In this implementation scheme, the spatial feature information extracted by the convolutional neural network (CNN) and the time series data obtained by the long short-term memory network (LSTM) analysis are fused. The spatial feature information includes the terrain characteristics, vegetation coverage and its distribution pattern in the region, while the time series data includes meteorological data and soil property data. These information work together to affect the changes in carbon sinks. Construction of a dynamic estimation model for carbon sinks: Combining spatial feature information and time series data, a dynamic estimation model is constructed. The model can simultaneously consider the spatial distribution law and the time change trend, so as to estimate the carbon sink more accurately. A possible modeling method is to combine spatial feature information with time series data through a multi-layer perceptron (MLP) or an integrated model to predict carbon sinks. The spatial feature information and time series data are: S = {s1, s2,…, s n}, (spatial eigenvector) T = {t1, t2, …, t m}(time series data), where s i is the spatial feature extracted by the convolutional neural network, and t j The meteorological and soil data analyzed by the LSTM model are fused in the following way: Fusion model formula: Assume that the carbon sink estimation C is a function of the spatial feature S and the time series data T, expressed as: C = f(S, T) = f(CNN(S), LSTM(T)); where f represents the comprehensive estimation function, which comprehensively analyzes the spatial feature S extracted by the convolutional neural network (CNN) and the time series data T obtained by the LSTM, and outputs the carbon sink estimation result C. Time series carbon sink prediction data: This is the prediction result of the model based on time changes, indicating the trend of carbon sinks in the future. The time series prediction result is usually a continuous carbon sink change value. Spatially distributed carbon sink data: This is the carbon sink prediction result based on spatial feature information, showing the distribution of carbon sinks at different spatial locations. These data can provide a detailed view of the spatial distribution law of carbon sinks and help formulate more accurate carbon sink optimization measures.

[0038] Specifically, the specific process of analyzing the distribution of carbon sink prediction data and identifying abnormal changes in carbon sink trends is as follows: smoothing the time series carbon sink data, identifying long-term trends through sliding average analysis, calculating seasonal changes and periodic fluctuations, and analyzing the normal change pattern of carbon sinks over time; completing the spatially distributed carbon sink data through spatial interpolation algorithms, identifying data-missing areas, analyzing the distribution patterns of carbon sinks in different geographical areas based on geographic information systems, and drawing carbon sink distribution maps; identifying abnormal points in time series and spatial distribution data based on the standard deviation threshold method, analyzing the abnormal points to determine whether they exceed the normal fluctuation range, and marking and recording the spatiotemporal characteristics of the abnormal data.

[0039] In this implementation scheme, smoothing of time series carbon sink data: smoothing of time series carbon sink data, common smoothing techniques include sliding average method, which can help eliminate short-term fluctuations and extract long-term trends. Through sliding average analysis, long-term trends in the data can be identified, and then the normal change pattern of carbon sink over time can be analyzed. This smoothing process can effectively reduce the impact of noise and highlight the long-term change trend of carbon sink. By analyzing the seasonal changes and periodic fluctuations of the time series, we can further understand the fluctuations of carbon sinks in different seasons or cycles. For example, carbon sinks may change in certain seasons due to changes in climate factors. Spatial interpolation algorithm for data completion: For spatially distributed carbon sink data, if there are missing data or vacant areas, spatial interpolation algorithms can be used for completion. Spatial interpolation technology can infer the carbon sink values ​​of vacant areas based on the information of existing data points, and provide more complete spatial distribution data. After completing the data, a distribution map of carbon sinks can be generated through visualization tools to help identify which areas have higher carbon sinks and which areas have lower carbon sinks, providing intuitive information for subsequent decision-making. Identify outliers: The standard deviation threshold method is a common method for identifying outliers. By calculating the mean and standard deviation of time series data and spatial distribution data, outliers are identified according to the threshold of the standard deviation. If the value of some data points deviates from the mean by more than a certain multiple, they can be judged as outliers. These outliers may represent abnormal changes in carbon sinks, and their causes need to be further analyzed. In-depth analysis of outliers is carried out to determine whether they exceed the normal fluctuation range. For example, abnormally high or abnormally low carbon sinks may be caused by environmental disasters, measurement errors, or data anomalies. Analyzing its spatiotemporal characteristics (i.e., the time and place of occurrence) can help track the root cause of the abnormal phenomenon. Once the outliers are identified, the system will automatically mark and record the spatiotemporal characteristics of these data, including the specific time and spatial location of the abnormal data. Doing so will not only help track and handle abnormal situations in the later stage, but also provide a basis for optimizing carbon sink management and improving data quality.

[0040] Specifically, according to the abnormal change trend of carbon sinks, the specific process of establishing a carbon sink optimization decision-making model through a mixed integer linear programming algorithm is as follows: the identified abnormal change trend of carbon sinks and the predicted data of carbon sink volume are used as input data, and the carbon optimization target is determined, including optimizing the carbon sink volume and maximizing the carbon sink benefit, and constraints are set, including resource limitation constraints and environmental indicator constraints; the optimization decision-making model is constructed through a mixed integer linear programming algorithm, the carbon optimization target and constraints are converted into linear equations, and the constructed mixed integer linear programming model is solved through the branch and bound method.

[0041] In this implementation, the objective function includes optimizing carbon sink volume and carbon sink benefits. The objective function is set as: Z: optimization target, representing the sum of carbon sequestration benefits, C r,t : Carbon sink in region r and time period t (can be calculated using carbon sink prediction data). r,t : The carbon trading market price in region r and time period t (can be calculated through carbon sink volume and related economic benefit models). r : The benefit weight coefficient of region r indicates the relative importance of carbon sequestration benefits in different regions (this coefficient can be calculated based on local economic conditions). Constraints: Constraints involve factors such as resource limitations and environmental standards. Resource limitation constraints: R r,t : Resource requirements for region r and time period t (specific values ​​can be obtained through database query), x r,t : Decision variable, indicating whether to take optimization measures in region r and time period t. 1 indicates that it is decided to implement carbon sink optimization measures in region r in time period t, and 0 indicates that it is decided not to implement carbon sink optimization measures in region r in time period t. B: Total available resources (can be calculated based on actual resource constraints). Environmental standard constraints: E max : Maximum allowable carbon sink benefit, E r,t : Carbon sink benefits in region r and time period t. Carbon sink non-negative constraint: C r,t : The carbon sink in region r and time period t, ensuring that the carbon sink is non-negative. Resource usage constraints: x r,t : Binary decision variable, indicating whether to take carbon sink optimization measures in region r and time period t. r,t =1, indicating that optimization measures are taken; if x r,t =0, indicating no action. Constructing a carbon sink prediction model: In order to calculate C r,t and E r,t , it is necessary to use deep learning models (such as CNN and LSTM) to train historical data and obtain carbon sink prediction data. r ,t):C r,t =f(C past ,features r ,time t );C past :Historical carbon sink data, features r : Spatial characteristics associated with region r, (vegetation type, soil quality), time t : Time characteristics (season, year). Carbon sink benefit prediction (E r ,t):E r,t =g(C r,t ,price); E r,t: Carbon sequestration benefit in region r and time period t. r,t :Carbon sink amount. price: The price in the carbon trading market. Mixed integer linear programming (MILP) solution: Based on the above objective function and constraints, a mixed integer linear programming model is constructed, and the branch and bound method is used to optimize the decision-making process. Solution steps: Model construction: Incorporate the objective function, constraints, decision variables, etc. into the mathematical model. Branch and bound method solution: Traverse the decision variables through the branch and bound method to find the optimal carbon sink configuration plan. Optimal solution output: The optimal solution obtained includes the carbon sink amount C r,t and carbon sequestration benefit E r,t , and the optimization measures taken r,t The output of the MILP model includes: the optimal carbon sink C r,t and the optimal carbon sink benefit E r,t The decision variable x r,t The value of indicates in which regions and time carbon sink optimization measures are taken. The optimization plan is to make specific carbon sink optimization decisions based on resource limitations, environmental standards and the constraints of maximizing carbon sink benefits.

[0042] Specifically, the specific process of optimizing and adjusting forest resources and vegetation configuration to generate a carbon sink optimization plan is as follows: According to the carbon sink optimization goals and constraints, the forest resource status of each region is analyzed, and the optimal resource allocation plan for each region is determined through a mixed integer linear programming algorithm to ensure the maximization of carbon sinks; According to the relationship between carbon sink prediction data and vegetation type, combined with environmental indicator constraints, the vegetation configuration plan for each region is optimized, and the most suitable vegetation type and coverage area are selected.

[0043] In this implementation plan, the forest resource status of each region is analyzed: a comprehensive analysis of the forest resource status of each region is conducted, mainly including factors such as the available area of ​​land, soil quality, existing vegetation cover, climatic conditions and water resources. These factors will determine whether a region is suitable for carbon sink projects and the carbon sink potential that can be achieved. Specifically, the assessment of forest resource status takes into account land use restrictions, carbon absorption capacity of existing vegetation, carbon fixation potential of soil, etc. Through a comprehensive analysis of these factors, the potential of each region in the carbon sink project can be determined, thereby providing a decision-making basis for the optimization plan. Determine the optimal resource allocation plan through a mixed integer linear programming algorithm: Use a mixed integer linear programming (MILP) algorithm to optimize the allocation of forest resources, with the goal of maximizing carbon sinks. In this process, the algorithm will allocate resources among multiple regions to ensure that limited resources (such as funds, labor, land, etc.) can be used efficiently. The algorithm will comprehensively consider the resource conditions and carbon sink potential of each region to determine the amount and method of resources that should be invested in each region. The optimized resource allocation plan can ensure the maximization of carbon sink benefits while complying with resource limitations and environmental protection requirements. Optimize vegetation configuration scheme according to the relationship between carbon sink prediction data and vegetation type: There is a close relationship between carbon sink and vegetation type. Different types of vegetation (such as forests, grasslands, shrubs, etc.) have different carbon fixation capabilities. Therefore, it is necessary to select the appropriate vegetation type based on the relationship between carbon sink prediction data and vegetation type. In the optimization process, the most suitable vegetation type is selected considering environmental factors such as climate, soil type, existing vegetation conditions, and water resources in each region. For example, some areas may be more suitable for planting drought-resistant plants, while other areas are suitable for planting moisture-resistant plants in order to achieve the best carbon sink effect. At the same time, it is necessary to consider the coverage area of ​​vegetation to ensure reasonable configuration so that the vegetation configuration in each area can maximize carbon sink under given resource and environmental conditions. Select appropriate vegetation type and coverage area: Based on the carbon sink prediction data and the carbon absorption capacity of different vegetation types, combined with environmental indicators (such as soil quality, water resources, climate conditions, etc.), make decisions, select appropriate vegetation types and determine their coverage area. Through this optimized configuration, the carbon sink capacity of each region can be improved to ensure the maximum efficiency of carbon fixation. In addition, the choice of vegetation configuration must also take into account ecological environmental protection and the sustainability of land use to avoid negative impacts on existing ecosystems. Selecting appropriate vegetation types and coverage areas can help maximize carbon sinks without disrupting ecological balance.

[0044] In summary, this application has at least the following effects:

[0045] The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring can accurately estimate the carbon sink volume in time and space through the deep learning model, generate high-precision carbon sink forecast data, and provide a scientific decision-making basis for carbon sink project management. The dynamic monitoring module of carbon sink can monitor the changing trend of carbon sink volume in real time, and identify abnormal changes in carbon sink through statistical analysis, so as to ensure the dynamic management and timely adjustment of carbon sink projects, thereby improving the operation efficiency of the project. Through the mixed integer linear programming algorithm, the forest resources and vegetation configuration are optimized and adjusted to ensure the maximization of carbon sink volume. This optimization method not only improves the benefits of carbon sink, but also helps to reasonably allocate ecological resources and improve the overall sustainability of carbon sink projects. The system pre-processes and integrates multiple data sources (such as remote sensing images, meteorological data, geographic information data, etc.) through the data acquisition module, overcomes the limitations of data cleaning, standardization and processing in traditional methods, ensures the accuracy and consistency of data, and supports more efficient carbon sink estimation and decision-making. Combined with the dynamic monitoring and optimization decision-making of artificial intelligence technology, it can automatically identify abnormal changes in carbon sink projects, optimize management strategies based on real-time data, and realize the intelligent and efficient operation of carbon sink projects. This system provides scientific and flexible management methods for carbon sink projects, supports the maximization of carbon sink volume and the improvement of carbon benefits, and provides strong technical support for achieving carbon neutrality goals and promoting environmental protection and sustainable development.

[0046] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0047] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0048] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0049] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0050] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0051] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring is characterized by: It includes the following modules: data acquisition module, fine-grained carbon sink dynamic estimation module, carbon sink dynamic monitoring module, and carbon sink optimization decision module; The data acquisition module is used to obtain CCER forestry carbon sink project data and perform preprocessing; The fine-grained carbon sink dynamic estimation module is used to perform spatiotemporal estimation of carbon sinks based on the pre-processed CCER forestry carbon sink project data through a deep learning model, build a carbon sink dynamic estimation model, and generate carbon sink prediction data; The carbon sink dynamic monitoring module is used to monitor the carbon sink prediction data through statistical analysis, analyze the distribution of the carbon sink prediction data, and identify abnormal changes in carbon sink trends; The carbon sink optimization decision module is used to establish a carbon sink optimization decision model through a mixed integer linear programming algorithm according to the abnormal change trend of the carbon sink, optimize and adjust the forest resources and vegetation configuration, and generate a carbon sink optimization plan.

2. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring according to claim 1 is characterized by: The specific process of obtaining CCER forestry carbon sink project data and preprocessing it is as follows: The CCER forestry carbon sequestration project data includes remote sensing images, meteorological data, geographic information system data, and soil property data, forming a multi-source data set; Clean the multi-source data sets, remove noise data and redundant information, ensure data integrity and accuracy, unify the format and standardize the cleaned data to form the CCER forestry carbon sink project data set.

3. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring according to claim 2 is characterized by: The specific process of temporal and spatial estimation of carbon sinks through deep learning models is as follows: The spatial features in remote sensing images and geographic information system data are extracted through convolutional neural networks, and the time series changes of meteorological and soil property data are analyzed in combination with long and short-term memory networks to comprehensively construct a dynamic estimation model for carbon sinks.

4. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring according to claim 3 is characterized by: The specific process of extracting spatial features from remote sensing images and geographic information system data through convolutional neural networks is as follows: The spatial features of terrain features, vegetation coverage and distribution patterns in remote sensing images are extracted through convolutional neural networks; Combining the geographical location, soil type, slope and altitude spatial information in the geographic information system data, multi-dimensional fusion of spatial characteristics is performed to capture the spatial distribution pattern of carbon sinks in the region and obtain the spatial characteristic information of carbon sinks.

5. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring according to claim 4 is characterized by: The specific process of analyzing the time series changes of meteorological and soil characteristic data using the long short-term memory network is as follows: The time series of meteorological data and soil characteristic data are modeled through long short-term memory networks. The meteorological data include temperature, precipitation and relative humidity, and the soil characteristic data include soil moisture, soil temperature and soil pH value. Capture the long-term dependence of meteorological data and soil characteristic data over time, analyze the impact of the nonlinear time-varying relationship between meteorological and soil characteristic data on carbon sequestration, and obtain the time series prediction data output by the LSTM model.

6. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring according to claim 5 is characterized by: The specific process of comprehensively constructing a dynamic estimation model for carbon sinks and generating carbon sink prediction data is as follows: By integrating the spatial feature information extracted by convolutional neural networks and the time series data analyzed by long short-term memory networks, a dynamic carbon sink estimation model is constructed by combining spatial features and time series data, and carbon sink prediction data is generated, including time series carbon sink prediction data and spatially distributed carbon sink data.

7. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring according to claim 6 is characterized by: The specific process of analyzing the distribution of carbon sink forecast data and identifying abnormal changes in carbon sinks is as follows: Smoothing the time series carbon sink data, identifying long-term trends through sliding average analysis, calculating seasonal changes and cyclical fluctuations, and analyzing the normal change pattern of carbon sinks over time; The spatially distributed carbon sink data are supplemented through spatial interpolation algorithms, data-missing areas are identified, the distribution patterns of carbon sinks in different geographical areas are analyzed based on the geographic information system, and a carbon sink distribution map is drawn; Based on the standard deviation threshold method, outliers in time series and spatial distribution data are identified, analyzed to determine whether they exceed the normal fluctuation range, and the temporal and spatial characteristics of the abnormal data are marked and recorded.

8. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring according to claim 7 is characterized by: According to the abnormal change trend of carbon sink, the specific process of establishing the carbon sink optimization decision model through the mixed integer linear programming algorithm is as follows: The identified abnormal change trend of carbon sink and the predicted carbon sink amount data are used as input data. Determine carbon optimization goals, including optimizing carbon sequestration and maximizing carbon sequestration benefits, and set constraints, including resource constraints and environmental indicator constraints; An optimization decision-making model is constructed through a mixed integer linear programming algorithm, the carbon optimization objectives and constraints are converted into linear equations, and the constructed mixed integer linear programming model is solved by the branch and bound method.

9. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring according to claim 8 is characterized by: The specific process of optimizing and adjusting forest resources and vegetation configuration to generate a carbon sink optimization plan is as follows: According to the carbon sink optimization objectives and constraints, the forest resource status of each region is analyzed, and the optimal resource allocation plan for each region is determined through a mixed integer linear programming algorithm to ensure the maximization of carbon sinks; Based on the relationship between carbon sink prediction data and vegetation type, combined with environmental indicator constraints, the vegetation configuration plan for each region is optimized and the most suitable vegetation type and coverage area are selected.

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