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

Abstract

The application 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. The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring performs spatio-temporal estimation on CCER forestry carbon sink project data through a deep learning model, accurately predicts the dynamic change of carbon sink capacity, extracts spatial features through a convolutional neural network, combines a long short-term memory network to analyze time series data, and constructs a carbon sink capacity prediction model. Through statistical analysis and a spatial interpolation algorithm, the carbon sink capacity change is monitored in real time, abnormal trends are 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. Through the intelligent carbon sink management scheme, sustainable ecological development and green economy popularization are promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon sink monitoring and optimization, and particularly to a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring. BACKGROUND

[0002] Global climate warming is increasingly severe, and greenhouse gas emissions such as carbon dioxide remain high. In response to climate change, countries have formulated carbon neutralization targets and reduced carbon emissions through various means. In this context, forestry carbon sink projects are considered one of the important tools for achieving carbon neutralization because they use forest vegetation to fix carbon dioxide in the atmosphere. China's CCER (Certified Emission Reduction) mechanism provides an effective way for enterprises and individuals to participate in carbon emission reduction trading through forestry carbon sinks and other means. However, the monitoring, verification and certification of forestry carbon sink projects still face technical challenges, and there is an urgent need to use 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 patrols, satellite remote sensing and fixed sensors, which have multiple shortcomings. Manual patrols are time-consuming, costly and difficult to obtain comprehensive forest carbon sink data; satellite remote sensing is affected by weather, clouds and other factors, making it difficult to provide continuous and accurate monitoring data; and fixed sensor monitoring has limited range and is difficult to dynamically and comprehensively reflect changes in forest carbon sinks. The above shortcomings result in the existing methods being unable to meet the needs of CCER forestry carbon sink projects in terms of real-time, coverage and data accuracy, limiting their widespread application in carbon emission reduction certification and trading. SUMMARY

[0004] To address the shortcomings of the prior art, the present application provides a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, which solves the problems of the above background technology.

[0005] To achieve the above object, the present application is realized by the following technical solutions: the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring comprises the following modules: a data acquisition module, a fine-grained carbon sink amount dynamic estimation module, a carbon sink dynamic monitoring module, and a carbon sink optimization decision module; the data acquisition module is used to acquire CCER forestry carbon sink project data and perform preprocessing; the fine-grained carbon sink amount dynamic estimation module is used to perform spatio-temporal estimation of the carbon sink amount according to the preprocessed CCER forestry carbon sink project data, construct a carbon sink amount dynamic estimation model, and generate carbon sink amount prediction data by using a deep learning model; the carbon sink dynamic monitoring module is used to monitor the carbon sink amount prediction data by statistical analysis, analyze the distribution of the carbon sink amount prediction data, and identify the carbon sink abnormal change trend; and the carbon sink optimization decision module is used to establish a carbon sink optimization decision model according to the carbon sink abnormal change trend, optimize and adjust the forest land resources and vegetation configuration, and generate a carbon sink optimization scheme by using a mixed integer linear programming algorithm.

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

[0007] Further, the specific process of spatio-temporal estimation of the carbon sink amount by using a deep learning model is as follows: spatial features in remote sensing images and geographic information system data are extracted by using a convolutional neural network, time series changes of meteorological and soil property data are analyzed by using a long short-term memory network, and a carbon sink amount dynamic estimation model is comprehensively constructed.

[0008] Further, the specific process of extracting spatial features in remote sensing images and geographic information system data by using a convolutional neural network is as follows: topographic features, vegetation coverage, and their distribution patterns in remote sensing images are extracted by using a convolutional neural network; geographic location, soil type, slope, and altitude spatial information in geographic information system data are combined for multi-dimensional fusion of spatial features, the spatial distribution rule of carbon sinks in the region is captured, and carbon sink spatial feature information is acquired.

[0009] Further, the specific process of analyzing the time series changes of meteorological and soil property data by the long short-term memory network is as follows: modeling the time series of meteorological data and soil property data by the long short-term memory network, the meteorological data including temperature, precipitation and relative humidity, and the soil property data including soil moisture, soil temperature and soil pH value; capturing the long-term dependence of the meteorological data and soil property data over time, analyzing the influence of the nonlinear time-varying relationship between the meteorological and soil property data on the carbon sink amount, and obtaining the time series prediction data output by the LSTM model.

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

[0011] Further, the specific process of analyzing the distribution of carbon sink prediction data and identifying the abnormal change trend of carbon sink is as follows: smoothing the time series carbon sink data, identifying the long-term trend by moving average analysis, calculating the seasonal changes and periodic fluctuations, and analyzing the normal change pattern of carbon sink over time; completing the spatial distribution carbon sink data by a spatial interpolation algorithm, identifying the data missing area, analyzing the distribution rule of carbon sink in different geographical areas based on geographic information system, and drawing a carbon sink distribution map; identifying abnormal points in the time series and spatial distribution data based on the standard deviation threshold method, analyzing the abnormal points, judging whether they are out of the normal fluctuation range, and marking and recording the spatiotemporal characteristics of abnormal data.

[0012] Further, the specific process of establishing a carbon sink optimization decision model according to the abnormal change trend of carbon sink is as follows: taking the identified abnormal change trend of carbon sink and carbon sink prediction data as input data, determining the carbon optimization target, including optimizing the carbon sink amount and maximizing the carbon sink benefit, setting the constraint conditions, including resource limitation constraint and environmental index constraint; constructing an optimization decision model by the mixed integer linear programming algorithm, converting the carbon optimization target and constraint conditions into linear equations, and solving the constructed mixed integer linear programming model by the branch and bound method.

[0013] Further, the specific process of optimizing and adjusting the forest land resources and vegetation configuration to generate a carbon sink optimization scheme is as follows: analyzing the forest land resource conditions of each region according to the carbon sink optimization target and constraint conditions, determining the best resource configuration scheme of each region by the mixed integer linear programming algorithm, and ensuring the maximization of carbon sink amount; optimizing the vegetation configuration scheme of each region according to the relationship between carbon sink prediction data and vegetation type, combining the environmental index constraint conditions, and selecting the most suitable vegetation type and coverage area.

[0014] The present application has the following advantages:

[0015] (1) The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring efficiently acquires and pre-processes forestry carbon sink project data through the data acquisition module, solving the high cost and low efficiency problems of traditional manual data collection. The fine-grained carbon sink amount dynamic estimation module uses a deep learning model to accurately estimate the carbon sink amount in time and space, not only improving the accuracy of the predicted data, but also updating the carbon sink amount prediction in real time, avoiding the limitations of satellite remote sensing and traditional monitoring methods in dynamic monitoring. This module can accurately capture the change law of forest carbon sink amount, providing 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 can identify abnormal trends in carbon sink changes through statistical analysis of carbon sink prediction data, achieving timely warning of changes in forest carbon sink amount and improving the response speed and real-time performance of carbon sink monitoring. The carbon sink optimization decision module optimizes and adjusts forest resources and vegetation configuration using a mixed integer linear programming algorithm to generate a scientific carbon sink optimization scheme. This intelligent optimization decision ensures the maximization of carbon sink amount while considering 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, implementing any product of the present application does not necessarily require all the advantages described above to be achieved simultaneously. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A flowchart of the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring of the present application. DETAILED DESCRIPTION

[0019] The CCER forestry carbon sink project development system based on artificial intelligence technology monitoring of the present application solves the deficiencies of traditional monitoring methods in real-time performance, accuracy, and coverage, overcomes the problems of long manual patrol cycles, high costs, weather and cloud layer influences on satellite remote sensing, data discontinuity, 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 optimization decision of carbon sink amount can be achieved, effectively improving the monitoring efficiency, accuracy, and intelligent level of carbon sink management of forestry carbon sink projects.

[0020] The general idea of the problems in the present application is as follows:

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

[0022] According to the pre-processed CCER forestry carbon sink project data, the carbon sink amount is estimated in space and time through a deep learning model, a dynamic carbon sink amount estimation model is constructed, and carbon sink prediction data is generated.

[0023] The carbon sink prediction data is monitored through statistical analysis, the distribution of the carbon sink prediction data is analyzed, and the abnormal change trend of the carbon sink is identified.

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

[0025] Please refer to Figure 1 The embodiment of the present application provides a technical scheme: a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, comprising the following modules: a data acquisition module, a fine-grained carbon sink amount dynamic estimation module, a carbon sink dynamic monitoring module, and a carbon sink optimization decision module; the data acquisition module is used for acquiring CCER forestry carbon sink project data and preprocessing; the fine-grained carbon sink amount dynamic estimation module is used for estimating the carbon sink amount in space and time according to the pre-processed CCER forestry carbon sink project data, constructing a dynamic carbon sink amount estimation model through a deep learning model, and generating carbon sink prediction data; the carbon sink dynamic monitoring module is used for monitoring the carbon sink prediction data through statistical analysis, analyzing the distribution of the carbon sink prediction data, and identifying the abnormal change trend of the carbon sink; the carbon sink optimization decision module is used for establishing a carbon sink optimization decision model through a mixed integer linear programming algorithm according to the abnormal change trend of the carbon sink, optimizing and adjusting the forest land resources and vegetation configuration, and generating a carbon sink optimization scheme.

[0026] In this embodiment, the data collection 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 to make it suitable for subsequent analysis and modeling process. Fine-grained carbon sink dynamic estimation module: This module uses deep learning models to estimate the spatio-temporal changes of the processed project data, and then estimates the dynamic changes of carbon sink. By constructing a dynamic estimation model of carbon sink, the change trend of carbon sink can be more accurately predicted. This module can process fine-grained data and accurately predict carbon sink based on spatio-temporal features, providing accurate data support for subsequent analysis. Carbon sink dynamic monitoring module: This module monitors the changes in carbon sink by statistically analyzing the predicted carbon sink data and identifying its distribution characteristics. Using the analysis results, the system can effectively identify the abnormal change trend of carbon sink. By discovering these abnormal changes in a timely manner, measures can be taken early to optimize the implementation strategy of carbon sink projects. Carbon sink optimization decision module: When the abnormal change trend of carbon sink is identified, the system uses a mixed integer linear programming algorithm to establish a carbon sink optimization decision model. This module can optimize the allocation of forest resources and vegetation configuration based on the carbon sink optimization goal and regional resource and environmental constraints. Through model solving, the final carbon sink optimization scheme is generated to maximize carbon sink and improve carbon sink efficiency. CCER (Certified Emission Reduction): CCER is a carbon emission reduction certification mechanism in China, which allows enterprises or individuals to obtain carbon emission reduction by reducing greenhouse gas emissions or increasing carbon sinks (such as absorbing carbon dioxide through afforestation, etc.), and trading in the market. This mechanism provides a compliant and tradable carbon emission reduction for participants in carbon emission reduction. CCER can be used to support China's carbon neutralization goal, especially in forestry carbon sink projects, through vegetation planting and forest management activities to fix carbon dioxide. Deep learning model: Deep learning is a method in machine learning that uses multi-layer neural network models to learn and extract complex features from data. In this invention, a deep learning model is used to estimate the spatio-temporal changes of carbon sink. By learning historical data and various environmental variables, the deep learning model can predict the future trend of forest carbon sink, achieving accurate dynamic estimation. 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 this invention, MILP is used to establish a carbon sink optimization decision model to help determine the optimal forest resource allocation and vegetation configuration scheme. By setting goals (such as maximizing carbon sink) and constraints (such as resource limitations, environmental protection, etc.), the algorithm can find the optimal solution to help achieve optimal management of carbon sink. Carbon sink dynamic estimation model: This is a mathematical model used to predict the changes in carbon sink by historical data and real-time collected environmental data. The model combines spatio-temporal estimation methods to dynamically calculate and predict carbon sink based on the forest resource conditions of different regions and time periods.This model can help identify carbon sink trends in a timely manner and provide data support for optimal decision-making. Carbon sink optimization decision model: This model is based on a mixed integer linear programming algorithm, combined with carbon sink optimization objectives (such as maximizing carbon sink volume and benefits) and specific constraints (such as resource limitations, environmental protection, etc.) to make optimal decisions. By solving this model, the system can automatically generate the best carbon sink optimization scheme, effectively adjust forest resources and vegetation configuration, and improve carbon sink benefits and the scientificity of project implementation. Carbon sink abnormal change trend: refers to the abnormal change of carbon sink volume over a period of time, such as sudden decrease or increase in carbon sink volume. Through statistical analysis and monitoring, the system can identify these abnormal change trends, providing a reference for subsequent optimization adjustment and decision-making.

[0027] Specifically, 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, forming a multi-source data set; clean the multi-source data set to remove noise data and redundant information, ensure data integrity and accuracy, and format uniformity and standardization processing of the cleaned data to form a CCER forestry carbon sink project data set.

[0028] In this embodiment, remote sensing images refer to image data collected by satellite or unmanned aerial vehicle and other remote sensing devices, which are usually used to obtain information such as forest coverage, vegetation type, and terrain. Through remote sensing images, the spatial distribution and vegetation health of forest areas can be intuitively analyzed, providing preliminary information about carbon sinks. Meteorological data: including temperature, humidity, precipitation, wind speed and other meteorological conditions 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 geographic spatial information about forest areas, such as forest boundaries, terrain slope, soil type, and elevation. These information can help with regional division and carbon sink analysis, and understand carbon sink conditions in different geographical environments. Soil property data: including soil type, depth, nutrient content, and moisture data. The characteristics of the soil affect the carbon storage capacity of the forest, because the soil is an important carbon reservoir, and the quality of the soil determines the conditions for plant growth. Data cleaning: in practical applications, the original data may contain some incorrect information or incomplete data, which needs to be cleaned. The process of data cleaning includes: removing noise data: removing abnormal values caused by equipment failure, transmission errors or other factors (such as extreme weather data or unreasonable remote sensing image data). Remove redundant information: delete duplicate data to ensure that there are no duplicates in the data set to improve computing efficiency and data accuracy. Ensure data integrity and accuracy: by handling missing values, correcting data errors, ensure that each data item is consistent with the actual situation and there is no omission, to ensure 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 uniformly processed, need to carry on the format unification and standardization. This includes: converting different types of data into the same standard format (for example, all weather data is unified to the same timestamp). Convert data in different units to a unified unit of measurement (such as temperature unified to Celsius, precipitation unified to millimeters, etc.), to ensure the consistency of subsequent analysis. Form a CCER forestry carbon sink project data set. This data set contains all the cleaned and formatted data, ready for use in subsequent deep learning models, statistical analysis, and carbon sink estimation operations.

[0029] Specifically, the specific process of spatio-temporal estimation of carbon sink amount by deep learning model is as follows: spatial features in remote sensing images and geographic information system data are extracted by convolutional neural network, and time series changes of meteorological and soil property data are analyzed by long short-term memory network, to comprehensively construct a dynamic estimation model of carbon sink amount.

[0030] In this embodiment, the convolutional neural network: extracts spatial features from remote sensing images and geographic information system data using a convolutional neural network. CNN is good at processing image data, through multiple convolution operations, it can identify the spatial patterns in the image (vegetation distribution, terrain changes), thus providing information for the spatial distribution of carbon sink. Long short-term memory network (LSTM): combines long short-term memory network to analyze the time series changes of meteorological and soil characteristics data. LSTM is suitable for processing time series data, which can capture the rules of factors such as temperature, precipitation, soil moisture changing over time, and help estimate the dynamic changes of carbon sink 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 constructed. This model can consider both spatial distribution and temporal change, providing accurate carbon sink prediction.

[0032] Specifically, the specific process of extracting spatial features from remote sensing images and geographic information system data by convolutional neural network is as follows: through convolutional neural network, the terrain features, vegetation coverage and its distribution pattern in remote sensing images are extracted; combined with the geographic location, soil type, slope and elevation spatial information in geographic information system data, the multi-dimensional fusion of spatial features is carried out, the spatial distribution rule of carbon sink in the region is captured, and the carbon sink spatial feature information is obtained.

[0033] In this embodiment, the terrain features and vegetation coverage in remote sensing images are extracted: convolutional neural network is used to analyze the terrain features and vegetation coverage and its distribution pattern in remote sensing images. CNN extracts local features (such as trees, vegetation types, etc.) in images step by step through multiple convolution layers, and captures more complex spatial patterns through deep convolution. This process helps to identify the vegetation types, distribution and terrain structure in the region, thus obtaining the spatial distribution information of carbon sink. Spatial information fusion in geographic information system data: CNN also uses spatial data in geographic information system (GIS) (such as geographic location, soil type, slope, elevation, etc.) 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 will affect the carbon absorption capacity of plants, and geographical factors such as slope and elevation will affect the vegetation growth environment and carbon storage. Multi-dimensional fusion of spatial features: combine the spatial features in remote sensing images and the spatial information in GIS data to form multi-dimensional feature fusion. CNN integrates information from different data sources to more comprehensively and accurately identify the carbon sink distribution rule in the region. This fusion process makes the spatial features of carbon sink complete, providing important spatial basic data for subsequent dynamic estimation of carbon sink.

[0034] Specifically, the specific process of analyzing the time series changes of meteorological and soil property data by the long short-term memory network is as follows: modeling the time series of meteorological data and soil property data by the long short-term memory network, the meteorological data including temperature, precipitation and relative humidity, and the soil property data including soil moisture, soil temperature and soil pH value; capturing the long-term dependence of the meteorological data and soil property data over time, analyzing the influence of the nonlinear time-varying relationship between the meteorological and soil property data on the carbon sink, and obtaining the time series prediction data output by the LSTM model.

[0035] In this embodiment, time series modeling: the LSTM model is used to model the time series of meteorological data and soil property data. Meteorological data usually includes temperature, precipitation and relative humidity, and these factors have significant time series relationship on carbon sink. Soil property data includes soil moisture, soil temperature and soil pH value, and the changes of these data have direct influence on the growth of vegetation and carbon storage. Capture long-term dependence: LSTM is a special recurrent 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 sink after a long time. LSTM retains and transmits the information of these long-term dependencies through its internal memory cells. Analyze nonlinear time-varying relationship: LSTM can not only identify the linear trend of data over time, but also handle the nonlinear time-varying relationship in it. For example, the change of temperature may have a greater impact on carbon sink in some periods, while the impact is smaller in other periods. Soil factors such as soil moisture, soil temperature and pH value may also show complex time-varying effects, and the LSTM model can effectively capture these nonlinear changes to make more accurate predictions of carbon sink changes. Time series prediction data output: after the above analysis, the LSTM model can output prediction data about the future trend of meteorological and soil property data. These prediction data not only provide the trend of carbon sink changes in the future period, but also help to further optimize carbon sink management strategies and improve the accuracy of carbon sink monitoring and optimization decisions.

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

[0037] In this embodiment, the spatial feature information extracted by the convolutional neural network (CNN) and the time series data analyzed by the long short-term memory network (LSTM) are fused. The spatial feature information includes the terrain features, vegetation coverage and its distribution pattern within the region, while the time series data includes meteorological data and soil property data. These information jointly affect the change of carbon sink. Construction of dynamic estimation model of carbon sink amount: combining spatial feature information and time series data, a dynamic estimation model is constructed, which can consider both spatial distribution rule and time variation trend, so as to more accurately estimate the carbon sink amount. A possible modeling method is to combine spatial feature information and time series data through multilayer perceptron (MLP) or ensemble model to predict the carbon sink amount. Spatial feature information and time series data are respectively: S = {s1, s2, …, s n}, (spatial feature vector) T = {t1, t2, …, t m} (time series data), where s i is the spatial feature extracted by the convolutional neural network, and t j is the meteorological and soil data analyzed by the LSTM model, which are fused by the following method: fusion model formula: assuming that the carbon sink estimation C is a function of spatial feature S and time series data T, it is expressed as: C = f(S, T) = f(CNN(S), LSTM(T)); wherein 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 LSTM, and outputs the estimation result C of the carbon sink amount. Time series carbon sink prediction data: this is the prediction result of the model based on time variation, which shows the trend of carbon sink amount in a future period of time. The time series prediction result is usually the continuous carbon sink change value. Spatial distribution carbon sink data: this is the carbon sink prediction result based on spatial feature information, which shows the carbon sink distribution at different spatial positions. These data can provide a detailed view of the spatial distribution rule of carbon sink, helping to develop more accurate carbon sink optimization measures.

[0038] Specifically, the specific process of analyzing the distribution of carbon sink prediction data and identifying the specific trend of abnormal change of carbon sink is as follows: smoothing the time series carbon sink data, identifying the long-term trend through moving average analysis, calculating the seasonal variation and periodic fluctuation, and analyzing the normal change pattern of carbon sink amount with time; complete the spatial distribution carbon sink data through spatial interpolation algorithm, identify the data missing area, analyze the distribution rule of carbon sink amount in different geographical regions based on geographic information system, and draw the carbon sink distribution map; identify the abnormal points in the time series and spatial distribution data based on the standard deviation threshold method, analyze the abnormal points, judge whether they are out of the normal fluctuation range, mark and record the spatiotemporal characteristics of abnormal data.

[0039] In this embodiment, smoothing of time series carbon sink data: smoothing of time series carbon sink data, common smoothing techniques include moving average method, which can help to eliminate short-term fluctuations, extract long-term trends. Through the sliding average analysis, the long-term trend of the data can be identified, and then the normal change pattern of carbon sink with time can be analyzed. This smoothing process can effectively reduce the influence of noise and highlight the long-term trend of carbon sink. By analyzing the seasonal variation and periodic fluctuation of time series, further understand the fluctuation of carbon sink in different seasons or periods, for example, carbon sink may change due to climate factors in some seasons. Spatial interpolation algorithm for data completion: for spatial distribution of carbon sink data, if there is data missing or missing area, spatial interpolation algorithm can be used for completion. Spatial interpolation technique can infer the carbon sink value of the missing area according to the information of the existing data points, provide more complete spatial distribution data. After completing the data, through the visualization tool, the distribution map of carbon sink can be generated, which helps to identify which area has high carbon sink and which area has low carbon sink, and provides intuitive information for subsequent decision-making. Identify outliers: 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 can be identified according to the threshold of standard deviation. If the value of some data points deviates from the mean value by more than a certain multiple, it can be judged as an outlier. These outliers may represent abnormal changes in carbon sink, which need to be further analyzed. In-depth analysis of outliers to determine whether they are beyond the normal fluctuation range. For example, abnormally high or low carbon sink may be caused by environmental disasters, measurement errors or data anomalies. Analysis of its spatio-temporal characteristics (i.e. time and location) can help trace the root cause of the abnormal phenomenon. Once the outliers are identified, the system will automatically mark and record the spatio-temporal characteristics of these data, including the specific time and space location of the abnormal data. This not only helps to track and handle abnormal situations in the later stage, but also provides basis for optimizing carbon sink management and improving data quality.

[0040] Specifically, according to the abnormal change trend of carbon sink, the specific process of establishing carbon sink optimization decision model by mixed integer linear programming algorithm is as follows: taking the identified carbon sink abnormal change trend and carbon sink prediction data as input data, determining the carbon optimization target, including optimizing carbon sink and maximizing carbon sink benefit, setting constraint conditions, including resource limitation constraint and environmental index constraint; constructing optimization decision model by mixed integer linear programming algorithm, converting carbon optimization target and constraint condition into linear equation, solving the constructed mixed integer linear programming model by branch and bound method.

[0041] In this embodiment, the objective function includes optimizing carbon sink and carbon sink benefit. The objective function is set as: objective function: Z: optimization objective, represents the sum of carbon sink benefits, C r,t : carbon sink amount of region r and time period t (can be calculated by carbon sink amount prediction data).E r,t : carbon trading market price of region r and time period t (can be calculated by carbon sink amount and related economic benefit model).α r : benefit weight coefficient of region r, represents the relative importance of carbon sink benefits in different regions (this coefficient can be calculated according to local economic conditions). Constraint conditions: constraint conditions involve resource limitations, environmental standards, etc. Resource limitation constraints: R r,t : resource demand of region r and time period t (specific values can be obtained by database query), x r,t : decision variable, represents whether to take optimization measures in region r and time period t, 1 represents deciding to implement carbon sink optimization measures in region r in time period t, 0 represents deciding not to implement carbon sink optimization measures in region r in time period t, B: total resource available amount (can be calculated according to actual resource limitations). Environmental standard constraints: E max : maximum allowed carbon sink benefit, E r,t : carbon sink benefit of region r and time period t. Carbon sink amount non-negative constraints: C r,t : carbon sink amount of region r and time period t, ensures that the carbon sink amount is non-negative. Resource usage constraints: x r,t : binary decision variable, represents whether to take carbon sink optimization measures in region r and time period t. If x r,t = 1, optimization measures are taken; if x r,t = 0, no optimization measures are taken. Build carbon sink amount prediction model: in order to calculate C r,t and E r,t , deep learning models such as CNN and LSTM are used to train historical data to obtain carbon sink amount prediction data. Carbon sink amount prediction (C r , t): C r,t = f(C past , features r , time t ); C past : historical carbon sink amount data, features r : spatial features related to region r (vegetation type, soil quality), time t : time features (season, year). Carbon sink benefit prediction (E r , t): E r,t = g(C r,t , price); E r,tCarbon sink benefits of region r and time period t. C r,t : Carbon sink amount. price: Price of carbon trading market. Mixed integer linear programming (MILP) solution: Based on the above objective function and constraint conditions, a mixed integer linear programming model is constructed, and the Branch and Bound method is used to solve the optimization process. Solution steps: Model construction: The objective function, constraint conditions, decision variables, etc. are included in the mathematical model. Branch and Bound solution: The Branch and Bound method is used to traverse the decision variables and find the optimal carbon sink allocation scheme. Optimal solution output: The optimal solution obtained includes the carbon sink amount C r,t and carbon sink benefits E r,t , and the value of the optimization measures x r,t . Through the MILP model solution, the output results include: optimal carbon sink amount C r,t and optimal carbon sink benefits E r,t . The value of the decision variable x r,t indicates which regions and times to take carbon sink optimization measures. The optimal solution, i.e. according to the resource constraints, environmental standards and carbon sink benefit maximization constraints, makes specific carbon sink optimization decisions.

[0042] Specifically, the specific process of generating the carbon sink optimization scheme is as follows: according to the carbon sink optimization target and constraint conditions, the forest resource conditions of each region are analyzed, the best resource allocation scheme of each region is determined through the mixed integer linear programming algorithm, and the maximization of carbon sink amount is ensured; according to the relationship between carbon sink amount prediction data and vegetation type, combined with environmental index constraint conditions, the vegetation configuration scheme of each region is optimized, and the most suitable vegetation type and coverage area are selected.

[0043] In this embodiment, the forest resource conditions of each region are analyzed: a comprehensive analysis of the forest resource conditions of each region is conducted, mainly including factors such as the available area of land, soil quality, existing vegetation coverage, climate conditions, and water resources. These factors will determine whether a region is suitable for a carbon sink project and the carbon sink potential that can be achieved. Specifically, the assessment of forest resource conditions considers the use restrictions of the land, the carbon absorption capacity of the existing vegetation, the carbon fixation potential of the soil, etc. Through the comprehensive analysis of these factors, the potential of each region in the carbon sink project can be determined, thereby providing decision-making basis for optimizing the scheme. The optimal resource allocation scheme is determined by using a mixed integer linear programming (MILP) algorithm: the forest resources are optimally allocated using a mixed integer linear programming (MILP) algorithm, with the goal of maximizing the carbon sink amount. In this process, the algorithm will allocate resources among multiple regions to ensure that limited resources (such as funds, labor, land, etc.) are used efficiently. The algorithm will consider the resource conditions and carbon sink potential of each region to determine the amount and manner of resources that should be invested in each region. The optimized resource allocation scheme can ensure maximum carbon sink benefits while complying with resource limitations and environmental protection requirements. The vegetation configuration scheme is optimized based on the relationship between carbon sink amount prediction data and vegetation types: there is a close relationship between carbon sink amount and vegetation types. Different types of vegetation (such as forests, grasslands, shrubs, etc.) have different carbon fixation capabilities, so the appropriate vegetation type needs to be selected based on the relationship between carbon sink amount prediction data and vegetation types. In the optimization process, environmental factors such as climate, soil type, existing vegetation conditions, and water resources are considered to select the most suitable vegetation type. For example, some regions may be more suitable for planting drought-tolerant plants, while other regions may be more suitable for planting plants that can tolerate wet conditions, in order to achieve the best carbon sink effect. At the same time, the coverage area of the vegetation needs to be considered to ensure reasonable allocation, so that the vegetation configuration of each region can maximize the carbon sink amount under the given resources and environmental conditions. Selecting appropriate vegetation types and coverage areas: based on carbon sink amount prediction data and the carbon absorption capacity of different vegetation types, environmental indicators (such as soil quality, water resources, climate conditions, etc.) are considered to make decisions, select appropriate vegetation types, and determine their coverage areas. Through this optimized allocation, the carbon sink capacity of each region can be improved, ensuring the maximum efficiency of carbon fixation. In addition, the selection of vegetation configuration also needs to consider ecological environmental protection and the sustainability of land use, in order to avoid negative impacts on the existing ecological system. Selecting appropriate vegetation types and coverage areas helps to maximize carbon sink amount without damaging the ecological balance.

[0044] In summary, the present 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 spatio-temporal carbon sink through a deep learning model, generate high-precision carbon sink prediction data, and provide scientific decision-making basis for carbon sink project management. By using the carbon sink dynamic monitoring module, the change trend of carbon sink can be monitored in real time, and carbon sink abnormal changes can be identified through statistical analysis to ensure 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 land resources and vegetation configuration are optimized and adjusted to maximize the carbon sink. This optimization method not only improves the efficiency of carbon sink, but also helps to reasonably allocate ecological resources and improve the overall sustainability of carbon sink projects. The system preprocesses and integrates various data sources (such as remote sensing images, meteorological data, geographic information data, etc.) through the data acquisition module, overcoming the limitations of data cleaning, standardization and processing in traditional methods, ensuring the accuracy and consistency of the data, and supporting more efficient carbon sink estimation and decision-making. Combined with the dynamic monitoring and optimization decision-making of artificial intelligence technology, the system can automatically identify abnormal changes in carbon sink projects and optimize management strategies based on real-time data, realizing the intelligent and efficient operation of carbon sink projects. The system provides a scientific and flexible management means for carbon sink projects, supports the maximization of carbon sink and the improvement of carbon benefits, and provides strong technical support for achieving carbon neutrality, promoting environmental protection and sustainable development.

[0046] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems, or computer program products. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) having computer usable program code embodied thereon.

[0047] The present application is described with reference to flowcharts and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The means for implementing each flow or multiple flows and / or blocks Figure 1 The means for implementing each flow or multiple flows and / or blocks

[0048] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0049] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0050] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those skilled in the art once they learn of the basic inventive concepts. Therefore, the appended claims are intended to encompass within their scope all such variations and modifications as are included within the scope of the application.

[0051] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A CCER forestry carbon sequestration project development system based on artificial intelligence technology monitoring, characterized in that, It includes the following modules: data acquisition module, fine-grained dynamic estimation module for carbon sequestration, dynamic monitoring module for carbon sequestration, and carbon sequestration optimization decision-making module; The data acquisition module is used to acquire and preprocess data from CCER forestry carbon sink projects. The fine-grained dynamic carbon sink estimation module is used to estimate the carbon sink in time and space based on the preprocessed CCER forestry carbon sink project data through a deep learning model, construct a dynamic carbon sink estimation model, and generate carbon sink prediction data. The carbon sequestration dynamic monitoring module is used to monitor carbon sequestration prediction data through statistical analysis, analyze the distribution of carbon sequestration prediction data, and identify abnormal carbon sequestration change trends. The carbon sink optimization decision module is used to establish a carbon sink optimization decision model based on the abnormal trend of carbon sink changes, and to optimize and adjust forest resources and vegetation configuration to generate a carbon sink optimization scheme. The specific process for analyzing the distribution of carbon sink forecast data and identifying abnormal trends in carbon sink changes is as follows: The time series carbon sink data is smoothed, and long-term trends are identified through moving average analysis. Seasonal changes and periodic fluctuations are calculated, and the normal change pattern of carbon sink over time is analyzed. Spatial interpolation algorithms are used to complete spatially distributed carbon sink data, identify missing data areas, and analyze the distribution patterns of carbon sink in different geographical regions based on geographic information systems to draw carbon sink distribution maps. Outliers in time series and spatial distribution data are identified based on the standard deviation threshold method. The outliers are analyzed to determine whether they exceed the normal fluctuation range, and the spatiotemporal characteristics of the outlier data are marked and recorded. Based on the abnormal trends in carbon sink changes, the specific process of establishing a carbon sink optimization decision model using a mixed-integer linear programming algorithm is as follows: The identified abnormal trends in carbon sinks and the predicted carbon sink volume are used as input data. Define carbon optimization objectives, including optimizing carbon sink volume and maximizing carbon sink benefits, and set constraints, including resource constraints and environmental indicator constraints; An optimization decision model is constructed using a mixed-integer linear programming algorithm. The carbon optimization objective and constraints are transformed into linear equations, and the constructed mixed-integer linear programming model is solved using the branch and bound method. The specific process of optimizing and adjusting forest land resources and vegetation configuration to generate a carbon sink optimization plan is as follows: Based on the carbon sink optimization objectives and constraints, the forest land resource status of each region is analyzed, and the optimal resource allocation scheme for each region is determined by a mixed integer linear programming algorithm to ensure the maximization of carbon sink. Based on the relationship between carbon sink prediction data and vegetation type, and combined with environmental indicator constraints, the vegetation configuration scheme of each region is optimized to select the most suitable vegetation type and coverage area.

2. The CCER forestry carbon sequestration project development system based on artificial intelligence monitoring according to claim 1, characterized in that: The specific process for obtaining and preprocessing CCER forestry carbon sequestration project data is as follows: The CCER forestry carbon sequestration project data includes remote sensing imagery, meteorological data, geographic information system data, and soil property data, forming a multi-source dataset; The multi-source datasets are cleaned to remove noisy data and redundant information, ensuring data integrity and accuracy. The cleaned data is then formatted and standardized to form the CCER forestry carbon sink project dataset.

3. The CCER forestry carbon sequestration project development system based on artificial intelligence monitoring according to claim 2, characterized in that: The specific process of using a deep learning model to estimate carbon sequestration in time and space is as follows: Spatial features are extracted from remote sensing images and geographic information system data using convolutional neural networks. By combining this with long short-term memory networks to analyze the time series changes in meteorological and soil property data, a dynamic carbon sink estimation model is constructed.

4. The CCER forestry carbon sequestration project development system based on artificial intelligence monitoring according to claim 3, characterized in that: The specific process of extracting spatial features from remote sensing images and geographic information system data using convolutional neural networks is as follows: Spatial features of terrain features, vegetation coverage and distribution patterns in remote sensing images are extracted using convolutional neural networks. By combining spatial information such as geographic location, soil type, slope and altitude from geographic information system data, multidimensional fusion of spatial characteristics is carried out to capture the spatial distribution pattern of carbon sinks in the region and obtain spatial characteristic information of carbon sinks.

5. The CCER forestry carbon sequestration project development system based on artificial intelligence monitoring according to claim 4, characterized in that: The specific process of analyzing the time series changes of meteorological and soil property data using long short-term memory networks is as follows: Time series models of meteorological and soil property data were created using long short-term memory networks. Meteorological data includes temperature, precipitation, and relative humidity; soil property data includes soil moisture, soil temperature, and soil pH. This study captures the long-term dependence of meteorological and soil property data over time, analyzes the impact of nonlinear time-varying relationships between meteorological and soil property data on carbon sink, and obtains time-series prediction data output by the LSTM model.

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

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