Forestry carbon sink dynamic accounting method, device, equipment, storage medium and program product

Abstract

The invention discloses a forestry carbon sink dynamic accounting method, a forestry carbon sink dynamic accounting device, forestry carbon sink dynamic accounting equipment, a storage medium and a program product, and relates to the technical field of forestry carbon sink accounting. And further determining actual contents of key accounting factors of the forestry carbon sink accounting project based on the project characteristics, and finally performing forestry carbon sink accounting according to an accounting scheme obtained by adopting the actual contents of the key accounting factors. In this way, compared with the situation that it is difficult to accurately and comprehensively determine the accounting core factors of different accounting periods through manual maintenance, the project features of the forestry carbon sink accounting project of the current accounting period are obtained through recognition, and the actual content of the comprehensive and accurate key accounting factors is determined; therefore, accurate forestry carbon sink accounting is carried out according to the accounting scheme obtained by adopting the actual content of the key accounting factors.

Description

Technical Field

[0001] The present application relates to the technical field of forestry carbon sink accounting, and in particular to a dynamic accounting method for forestry carbon sinks based on machine learning, a dynamic accounting device for forestry carbon sinks based on machine learning, a dynamic accounting equipment for forestry carbon sinks based on machine learning, a storage medium, and a computer program product. Background Art

[0002] When conducting long-term forestry carbon sink accounting, the core accounting factors vary across different accounting cycles. Accurate dynamic forestry carbon sink accounting requires systematic consideration of numerous key factors, all of which are interrelated and collectively impact the accuracy and reliability of the final accounting results. Currently, manually maintaining core accounting factors for different accounting cycles often results in inaccuracies and incompleteness, making it difficult to guarantee the accuracy of the final actual accounting plan.

[0003] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a dynamic accounting method for forestry carbon sinks based on machine learning, a dynamic accounting device for forestry carbon sinks based on machine learning, a dynamic accounting equipment for forestry carbon sinks based on machine learning, a storage medium and a computer program product, aiming to solve the technical problem of inaccurate accounting during dynamic accounting of forestry carbon sinks.

[0005] To achieve the above objectives, this application proposes a dynamic accounting method for forestry carbon sinks based on machine learning, which includes: Determine the forestry carbon sink accounting projects to be implemented in the current accounting cycle, and collect project information of the forestry carbon sink accounting projects from the project application materials of the forestry carbon sink accounting projects; Identifying project characteristics of the forestry carbon sink accounting project from the project information based on machine learning; wherein the project characteristics include the project type, project scale, topography of the project area, and environmental characteristics of the project area; According to the project characteristics, the actual content of the key accounting factors of the forestry carbon sink accounting project is determined, and the forestry carbon sink accounting is carried out according to the accounting plan obtained by adopting the actual content of the key accounting factors.

[0006] In one embodiment, the step of determining the actual content of the key accounting factors of the forestry carbon sink accounting project based on the project characteristics includes: Determine the first actual content of the carbon pool, baseline and disturbance type among the key accounting factors of the forestry carbon sink accounting project based on the project type of the forestry carbon sink accounting project; Determine the second actual content of the monitoring target in the key accounting factors of the forestry carbon sink accounting project based on the project scale and the topography of the project area; According to the environmental characteristics of the project area where the forestry carbon sink accounting project is located, the third actual content of the accounting model and parameters in the key accounting factors of the forestry carbon sink accounting project is determined.

[0007] In one embodiment, the step of determining the actual content of the key accounting factors of the forestry carbon sink accounting project based on the project characteristics includes: Determine the carbon pool scope and priority of the forestry carbon sink accounting project based on the first actual content; Determine the monitoring method, accuracy, and frequency of the forestry carbon sink accounting project based on the second practical content; Determine the biomass model and accounting parameters of the forestry carbon sink accounting project based on the third actual content; Based on the carbon pool scope and priority, monitoring methods and accuracy, frequency, biomass model and accounting parameters of the forestry carbon sink accounting project, a baseline scenario plan is formulated and the baseline scenario plan after removing the uncertainty sources is used as the accounting plan.

[0008] In one embodiment, the step of determining the actual content of the key accounting factors of the forestry carbon sink accounting project based on the project characteristics further includes: The project features are used as input features, and the output labels corresponding to the input features are inferred based on the pre-trained selection model, wherein the output labels are label values corresponding to the actual content of the key accounting factors of the forestry carbon sink accounting project.

[0009] In one embodiment, the step of using the project features as input features and obtaining output labels corresponding to the input features by inference based on a pre-trained selection model includes: Obtain historical project information and historical accounting plans for historical forestry carbon sink accounting projects, and determine the historical actual content of key accounting factors of the forestry carbon sink accounting projects from the historical accounting plans; A training set is constructed through the historical project information and historical actual content of the historical forestry carbon sink accounting project, and a selection model is obtained by training based on the training set.

[0010] In one embodiment, the method further comprises: When conducting forestry carbon sink accounting, determine the target forest area to be accounted for and the forest growth stage of the target forest area; Acquiring multimodal carbon sink-related data of the target forest area through a three-dimensional monitoring network of the target forest area; The carbon sequestration amount of the target forest area is estimated based on the forest growth stage of the target forest area and the multimodal carbon sequestration-related data of the target forest area.

[0011] In addition, to achieve the above-mentioned purpose, the present application also proposes a dynamic accounting device for forestry carbon sinks based on machine learning, which includes: A preparation module, configured to determine a forestry carbon sink accounting project to be implemented in the current accounting cycle, and to collect project information of the forestry carbon sink accounting project from project application materials of the forestry carbon sink accounting project; an identification module, configured to identify project characteristics of the forestry carbon sink accounting project from the project information based on machine learning; wherein the project characteristics include the project type, project scale, and topography and environmental characteristics of the project area; A generation module is used to determine the actual content of the key accounting factors of the forestry carbon sink accounting project based on the project characteristics, and perform forestry carbon sink accounting based on the accounting plan obtained by adopting the actual content of the key accounting factors.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a dynamic accounting device for forestry carbon sinks based on machine learning, which includes: a memory, a processor, and a computer program stored on the memory and runnable on the processor, and the computer program is configured to implement the steps of the dynamic accounting method for forestry carbon sinks based on machine learning as described above.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the dynamic accounting method of forestry carbon sinks based on machine learning as described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the dynamic accounting method of forestry carbon sinks based on machine learning as described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: In this application, by collecting project information of forestry carbon sink accounting projects to be implemented in the current accounting cycle from project application materials, and identifying the project characteristics of the forestry carbon sink accounting projects, the actual content of the key accounting factors of the forestry carbon sink accounting projects is further determined based on the project characteristics, and finally the forestry carbon sink accounting is performed according to the accounting scheme obtained by adopting the actual content of the key accounting factors. In this way, compared with manual maintenance, which is difficult to accurately and comprehensively determine the core accounting factors of different accounting cycles, by identifying the project characteristics of the forestry carbon sink accounting projects in the current accounting cycle, the actual content of the key accounting factors is determined to be comprehensive and accurate, so that accurate forestry carbon sink accounting is performed according to the accounting scheme obtained by adopting the actual content of the key accounting factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flowchart illustrating the first embodiment of the method for dynamic forestry carbon sink accounting based on machine learning provided in this application; Figure 2 An application diagram provided for the first embodiment of the method for dynamic accounting of forestry carbon sinks based on machine learning in this application; Figure 3 A flow chart illustrating the second embodiment of the method for dynamic forestry carbon sink accounting based on machine learning provided in this application; Figure 4 This is a schematic diagram of the module structure of the forestry carbon sink dynamic accounting device based on machine learning in an embodiment of the present application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the dynamic accounting method of forestry carbon sinks based on machine learning in the embodiment of this application.

[0019] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0021] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0022] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of performing the above functions, a machine learning-based forestry carbon sink dynamic accounting device, etc. The following uses the machine learning-based forestry carbon sink dynamic accounting device as an example to illustrate this embodiment and the following embodiments.

[0023] Based on this, the embodiment of the present application provides a dynamic accounting method for forestry carbon sinks based on machine learning, referring to Figure 1 and Figure 2 , Figure 1 This is a flow chart of the first embodiment of the dynamic accounting method for forestry carbon sinks based on machine learning in this application. Figure 2 This is an application diagram provided for the first embodiment of the dynamic accounting method for forestry carbon sinks based on machine learning in this application.

[0024] In this embodiment, the forestry carbon sink dynamic accounting method based on machine learning includes steps S10 to S30: Step S10: determining the forestry carbon sink accounting projects to be implemented in the current accounting cycle, and collecting project information of the forestry carbon sink accounting projects from the project application materials of the forestry carbon sink accounting projects; Forestry carbon sequestration refers to the process by which forests absorb CO2 through photosynthesis and store it long-term in the form of biomass (trees, soil, etc.), thereby reducing greenhouse gas concentrations. The accounting objective is to determine the incremental carbon sequestration achieved by a specific region or project over a specified period of time (e.g., tons of CO2 absorbed annually).

[0025] Because the dynamic nature of carbon sinks requires continuous monitoring and assessment, forestry carbon sink accounting projects typically involve multiple accounting cycles to ensure the project's carbon sink effectiveness and accuracy. These cycles include: 1. Annual accounting: Suitable for projects requiring frequent monitoring, such as those in high-risk areas or high-interference areas. 2. Interval accounting, such as every 3-5 years: Suitable for most forest projects, balancing monitoring costs and data accuracy. 3. Long-term accounting: Certain projects, such as long-term forest management or carbon sequestration projects, may require longer accounting cycles.

[0026] Project information for a forestry carbon sink accounting project refers to a collection of data used to describe key dimensions such as the project's essential attributes, implementation conditions, ecological environment, and management strategies. This information can be collected from the project application materials for the forestry carbon sink accounting project. Project application materials generally include project design documents, feasibility study reports, geospatial data, field survey data, historical and real-time monitoring data, etc. Project information for a forestry carbon sink accounting project can be obtained from the project application materials. Project application materials may also include monitoring reports, land use maps, satellite remote sensing images, meteorological data, soil distribution maps, business plans, and capital budget tables. In this embodiment, the method for obtaining project information for a forestry carbon sink accounting project is not limited.

[0027] Step S20: identifying project characteristics of the forestry carbon sink accounting project from the project information based on machine learning; wherein the project characteristics include the project type, project scale, and topography and environmental characteristics of the project area. When using machine learning to identify project characteristics for forestry carbon sink accounting projects from project information, text classification methods such as pre-trained language models (such as BERT and RoBERTa) or traditional machine learning models (such as SVM and random forest) can be used to classify project descriptions, identifying text keywords such as new afforestation, forest management, and REDD+, as well as project objectives such as carbon trading and ecological protection. Regression analysis can be performed using linear regression, random forest regression, or neural network models to predict project areas, including keywords in project descriptions such as large-scale and small-area, forest cover in remote sensing imagery, and regional area in geographic data. Spatial analysis can be performed using GIS tools to extract terrain features such as elevation, slope, aspect, and terrain relief calculated from DEM data. This ultimately outputs terrain characteristic indicators such as flat land, mountainous terrain, and complex terrain. Environmental factor extraction can be performed using environmental data such as climate and soil data to extract features such as climate zones, annual precipitation, average annual temperature, and soil types, ultimately outputting environmental characteristic indicators such as humid climate, arid climate, and fertile soil. In this embodiment, the method of identifying the project characteristics of the forestry carbon sink accounting project from the project information based on machine learning is not limited.

[0028] Step S30: determining the actual content of the key accounting factors of the forestry carbon sink accounting project according to the project characteristics, and performing forestry carbon sink accounting according to the accounting scheme obtained by using the actual content of the key accounting factors.

[0029] Because project characteristics contain key information about forestry carbon sink accounting projects, the actual content of the key accounting factors for forestry carbon sink accounting projects can be determined based on project characteristics. That is, the actual content of the key accounting factors for a project can be clarified. These factors cover all aspects of forestry carbon sink accounting and can comprehensively and accurately reflect the carbon sequestration capacity of the project. For example, the project type and applicable methodology within the key accounting factors of a forestry carbon sink accounting project can be determined based on project characteristics. The monitoring methods and sample plot design within the key accounting factors of a forestry carbon sink accounting project can be determined based on the project's geographical characteristics and area. The buffer mechanism and uncertainty management within the key accounting factors of a forestry carbon sink accounting project can be designed and determined based on the historical disturbance conditions and risk factors of the project area.

[0030] In a feasible implementation, step S30 may include steps S30A10 to S30A30: Step S30A10: determining first actual content of carbon pool, baseline, and interference type among key accounting factors of the forestry carbon sink accounting project according to the project type of the forestry carbon sink accounting project; Step S30A20: Determine the second actual content of the monitoring target in the key accounting factors of the forestry carbon sink accounting project based on the project scale and the terrain of the project area; Step S30A30: Determine the third actual content of the accounting model and parameters in the key accounting factors of the forestry carbon sink accounting project based on the environmental characteristics of the project area where the forestry carbon sink accounting project is located.

[0031] When conducting dynamic accounting for forestry carbon sinks, key accounting factors include: 1. The scope and completeness of carbon pools. This refers to which carbon pools are included in the accounting (aboveground biomass, belowground biomass, litter, dead wood, and soil organic carbon). Ignoring important carbon pools (such as soil organic carbon or dead wood) can underestimate or overestimate the net carbon sink of the project / activity (if certain pools are net emissions sources). For example, soil carbon losses after logging or fire may be overlooked; accounting only for the aboveground parts of trees can overlook carbon accumulated in roots and litter. It is crucial to select an accounting scope that is consistent with the project objectives, ecosystem type, and carbon pools affected by the activity. 2. Project boundaries. This refers to clear, precise, fixed, and verifiable geographic boundaries (geographic coordinates, area). Changes in boundaries over time (such as natural expansion, the addition of newly planted areas, and area reduction due to land use change) can lead to errors in the calculated area. Unclear boundaries or inaccurate measurements (such as low-resolution remote sensing imagery and inaccurate field surveys) can lead to errors in the calculated area. Failure to promptly track and record boundary changes can lead to the erroneous inclusion of non-project areas or omission of areas that should be included, directly impacting the attribution and total amount of carbon stock estimates. 3. Baseline scenario. This describes the trajectory of carbon stock changes over time without project activities. The baseline serves as a reference point for calculating the additional emission reductions from the project. Setting an overly optimistic (high baseline) or overly pessimistic (low baseline) baseline will distort the true incremental carbon sequestration benefits. Underestimating the baseline means overestimating the project's emission reductions; overestimating the baseline underestimates the project's value. A reasonable baseline should be based on historical evidence (land use history, management practices), regional averages, model predictions, and clear and reasonable assumptions. Baseline uncertainty is often the primary source of uncertainty in the overall accounting. 4. Monitoring methods, frequency, and accuracy. Methods include ground-based measurements (sample plots), remote sensing (e.g., optical, radar), model simulation, or a combination of these. Monitoring frequency refers to the interval (e.g., one, three, or five years) at which carbon stock changes are regularly measured. Measurement accuracy includes the accuracy of field instruments, operator skill, sample analysis accuracy (laboratory), and the spatial, spectral, and temporal resolution and interpretation accuracy of remote sensing. For monitoring methods used for data collection, low-cost remote sensing or sparse sample coverage may lead to serious bias (failure to capture small-scale heterogeneity). Too low a measurement frequency will miss disturbance events (fire, windfall) or changes in rapid growth stages, resulting in a distorted carbon stock change curve. Therefore, high-precision, appropriately frequent, and methodologically sound monitoring is key to obtaining reliable carbon stock change data. 5. Biomass model and expansion / conversion factors. This includes parameters such as the biomass equation, root-to-crown ratio, carbon content, and basic density used to convert measured values (such as diameter at breast height, tree height) into biomass and carbon storage. Model / parameter mismatch (such as using a temperate model to estimate tropical tree species), parameter aging (failure to update in a timely manner), and poor parameter regionalization (using universal parameters instead of localized parameters) will lead to systematic errors. For example, an overestimated growth model will make the accounting results biased, and it is necessary to reasonably use localized models for specific regions, specific tree species, or forest ages.6. Land use, land-use change, and disturbance. Natural or human disturbances occurring within and outside the project area (wildfires, pests and diseases, windstorms, droughts, logging, encroachment, etc.) and their impacts on carbon stocks (leakage or project-wide losses) can lead to overestimation of actual carbon stocks if disturbances are not monitored or their losses are underestimated, seriously compromising the accuracy of the accounting results. Clear and conservative disturbance monitoring and reporting mechanisms and leakage estimation methods are required, and corresponding losses should be deducted from the total carbon sink or deposited into a buffer pool. 7. Quantifying uncertainty. Identify the sources of uncertainty in each link (sampling error, measurement error, model error, baseline uncertainty) and use appropriate methods (such as Monte Carlo simulation and error propagation analysis) to quantify the total uncertainty. Ignoring uncertainty will produce erroneous and distorted precision. Quantifying uncertainty is a key indicator of the reliability of the results. Accurately estimating uncertainty facilitates decision-making (such as setting the buffer pool size) and guides how to optimize subsequent monitoring to reduce key sources of uncertainty. 8. Temporal dynamics and baseline consistency. Carbon stock changes due to project activities (tree planting, fertilization, reduced logging) and natural processes (growth and aging) are non-linear. Furthermore, baseline scenarios need to simulate dynamic changes over time (e.g., the succession of secondary forests). Using static models or ignoring the dynamic effects of project activities (e.g., the short-term accelerating effects of fertilization) can lead to unrealistic estimates for a specific time period. Baseline scenarios need to reflect both natural trends over time and anticipated human activities (e.g., potential conversion of forestland to other uses under land market pressure). Inconsistent baselines can introduce additional systematic biases. 9. Persistence Risk and Accounting Period Selection. Forestry carbon sinks carry the potential risk of reversal (the release of sequestered carbon back into the atmosphere due to disturbance). The length of the accounting period (e.g., 20, 40, 100 years) influences the assessment of carbon sink effectiveness. Ignoring reversal risks or failing to consider maintenance costs / risks after the end of the accounting period may overestimate the long-term climate benefits of carbon sinks. Dynamic accounting should continuously assess risks (through monitoring and buffer pool mechanisms) and clearly state the time constraints when reporting net carbon sinks. An accounting period that is too short may fail to capture the potential and risks of the project throughout its life cycle.

[0032] In this embodiment, the carbon pool, baseline, and disturbance type among the key accounting factors of the forestry carbon sink accounting project are determined based on the project type of the forestry carbon sink accounting project and used as the first actual content. For example, when the project type is new afforestation / reforestation, the carbon pool among the key accounting factors is full carbon pool accounting, the baseline is unforested land / degraded forest land, and the disturbance type is to focus on the impact of afforestation activities; when the project type is forest management / improved management, the carbon pool among the key accounting factors is aboveground / belowground biomass and soil carbon, the baseline is the existing forest stand natural growth baseline, and the disturbance type is conventional management measures and natural disturbance; when the project type is to reduce deforestation and degradation, the carbon pool among the key accounting factors is the key aboveground biomass, the baseline is the historical deforestation rate baseline, and the disturbance type is human encroachment and fire risk.

[0033] In this embodiment, the monitoring objectives of the key accounting factors for the forestry carbon sink accounting project are determined based on the project scale and the terrain of the project area, and are used as the second actual content. For example, when the project scale and the terrain of the project area are small and simple, the monitoring objectives of the key accounting factors are mainly based on high-precision ground sampling. When the project scale and the terrain of the project area are large and complex, the monitoring objectives of the key accounting factors are based on a combination of remote sensing and ground sampling, with the boundaries accurately surveyed by GIS.

[0034] In this embodiment, the accounting model and parameters for the key accounting factors of the forestry carbon sink accounting project are determined based on the environmental characteristics of the project's location, and serve as the third actual content. For example, if the project's location is a high-interference risk zone, the key accounting factor's accounting model is a high-frequency monitoring and high-buffer pool model, with anti-interference parameters. If the project's location is a stable climate zone, the key accounting factor's accounting model is a general growth model, with default parameters.

[0035] The purpose of dynamic accounting is to reflect as closely as possible the changes in carbon stocks in the project / activity area relative to the baseline scenario due to changes in project implementation or management. On this basis, the above-mentioned key accounting factors are not isolated, they interact with each other and cumulatively affect the final accounting results. For example, errors in the biomass model will be introduced into the remote sensing estimation based on the model, which will ultimately affect the carbon stock calculation. Therefore, it is necessary to fully cover the carbon pool; accurately define and monitor; adopt a transparent, verifiable, conservative and reasonable baseline setting method; adopt the best feasible monitoring technology combination, use locally verified models and parameters that match the tree species / age class; strictly monitor and quantify internal and external interference and leakage; conduct uncertainty assessments for all key steps, and report the total uncertainty range.

[0036] In another feasible implementation, step S30 may include: Based on the first practical content, determine the carbon pool scope and priority of forestry carbon sink accounting projects; According to the second practical content, determine the monitoring method, accuracy and frequency of the forestry carbon sink accounting project; According to the third practical content, determine the biomass model and accounting parameters of the forestry carbon sink accounting project; Based on the carbon pool scope and priority, monitoring methods and accuracy, frequency, biomass model and accounting parameters of the forestry carbon sink accounting project, a baseline scenario plan is formulated and the baseline scenario plan after removing the uncertainty sources is used as the accounting plan.

[0037] After determining the first, second, and third practical aspects above, the carbon pool scope and priority of forestry carbon sink accounting projects can be further determined based on the first practical aspect. Specifically, the objectives of afforestation / reforestation projects typically include forest restoration and carbon sink enhancement. The project area is typically non-forested or degraded forest land. The carbon pool scope includes aboveground biomass, belowground biomass, litter, dead wood, and soil organic carbon, with the priority being soil organic carbon > aboveground biomass > belowground biomass > litter > dead wood. Forest management / improved management projects may aim to optimize forest resource management and enhance carbon sink potential. The project area is typically existing forest areas. The carbon pool scope includes aboveground biomass, belowground biomass, and soil organic carbon, with the priority being aboveground biomass > belowground biomass > soil organic carbon. Reduced Deforestation and Degradation (REDD+) projects may aim to optimize forest resource management and enhance carbon sink potential. The project area is typically existing forest areas. The carbon pool scope includes aboveground biomass, litter, and dead wood, with the priority being aboveground biomass > litter > dead wood.

[0038] After determining the first, second, and third practical aspects, the monitoring method, accuracy, and frequency for the forestry carbon sink accounting project can be further determined based on the second practical aspect. Specifically, the optimal monitoring technology and frequency selection strategy can be determined based on the project scale (small / large) and terrain complexity (simple / complex). Specifically, the scale category and terrain complexity category are determined based on the project scale and the terrain of the project area, i.e., the project scale and terrain complexity. In one embodiment, if the project is small and the terrain is simple, the monitoring technology will primarily be ground sampling (e.g., setting up fixed sampling plots and using high-precision measurement equipment). Depending on the project type and tree species growth characteristics, a medium-frequency monitoring frequency (e.g., every 2-3 years) can generally be set. The ground sampling method involves setting up fixed sampling plots and conducting field measurements using tools such as dendrometers and growth cones. If the project is small and the terrain is complex, the ground sampling method will be used (but the impact of terrain needs to be considered, which may require increasing the number of sampling plots or using auxiliary low-altitude drone photography). A medium-frequency monitoring frequency (every 2-3 years) can be set. For medium-sized projects, the monitoring technology is a combination of remote sensing (such as high-resolution satellite imagery or drone imagery) and ground-based sample verification (stratified sampling), with a moderate monitoring frequency (every 2-3 years). For large-scale projects with simple terrain (considered a type of E2), the monitoring technology is primarily remote sensing (satellite remote sensing, such as Sentinel-2 and Landsat), supplemented by a limited number of ground-based sample verification. Due to the high cost of ground surveys over large areas, the monitoring frequency is lower (such as every 3-5 years). For large-scale projects with complex terrain, the monitoring technology is high-precision remote sensing (such as LiDAR combined with multispectral and high-resolution imagery) combined with ground-based sample verification (using GIS technology to accurately lay out sample plots and ensure coverage of diverse terrain and vegetation types). GIS technology is used for boundary verification, including high-precision GPS or BeiDou for boundary verification. The monitoring frequency is determined based on the availability and cost of remote sensing data, as well as project requirements: typically every 2-4 years. If the project is located in an area with high interference risk, the frequency may be increased appropriately.

[0039] After determining the first, second, and third practical aspects, the biomass model and accounting parameters for the forestry carbon sink accounting project can be further determined based on the third practical aspect. Specifically, for high-disturbance risk areas determined by the environmental characteristics of the project area, a dynamic biomass model and corresponding parameters should be selected, taking into account the impact of disturbance events on tree growth. A model and corresponding parameters that can simulate rapid tree growth, mortality, and the impact of disturbance events should be used. For stable climate zones determined by the environmental characteristics of the project area, a static or semi-static model and its corresponding parameters suitable for a stable environment should be selected. The model assumes fewer disturbance events and makes predictions based on stable climate conditions.

[0040] Furthermore, a baseline scenario can be developed based on the carbon pool scope and priority, monitoring methods, accuracy, and frequency, as well as biomass models and accounting parameters of the forestry carbon sink accounting project. This baseline scenario, after removing sources of uncertainty, can be used as the accounting solution. Specifically, the carbon pool scope clarifies the carbon pools to be accounted for (aboveground / belowground / soil, etc.), which in turn determines the carbon pools required for constructing the baseline scenario. Monitoring capabilities, such as remote sensing accuracy, ground sampling density, and monitoring frequency, determine the data source selection for the baseline scenario, such as historical remote sensing or field monitoring. The accuracy level and regional adaptability of the biomass model influence the reliability of growth predictions in the baseline scenario. After developing the baseline scenario, its sources of uncertainty, including measurement uncertainty, model parameter uncertainty, and baseline scenario uncertainty, can be determined. This baseline scenario, after removing these sources of uncertainty, can be used as the accounting solution.

[0041] In another feasible implementation, step S30 may include step S30B: Step S30B, taking the project features as input features, and inferring the output labels corresponding to the input features based on the pre-trained selection model, wherein the output labels are label values corresponding to the actual content of the key accounting factors of the forestry carbon sink accounting project.

[0042] In this embodiment, another method for determining the actual content of key accounting factors for forestry carbon sink accounting projects based on project characteristics is provided. Key core factors are defined as output labels for a selection model, such as the scope and completeness of the carbon pool, project boundaries, baseline scenario settings, and monitoring methods and frequency. Project characteristics are collected, including both numerical and categorical data, such as categorical project type (new afforestation / forest management / REDD+ / agroforestry, etc.) and numerical project scale. Next, data preprocessing is performed, including addressing missing values: imputing numerical features with the mean / median and filling missing values for categorical features with new categories; encoding categorical features using one-hot encoding or embedding; and normalizing numerical features using methods such as Z-score normalization or Min-Max normalization. Furthermore, the preprocessed project features are used as input features, and the pretrained selection model is used to infer the corresponding output labels, which are the label values corresponding to the actual content of the key accounting factors for the forestry carbon sink accounting project.

[0043] In another feasible implementation manner, step S30B may include: Obtain historical project information and historical accounting plans for historical forestry carbon sink accounting projects, and determine the historical actual content of key accounting factors of forestry carbon sink accounting projects from the historical accounting plans; A training set is constructed through historical project information and historical actual content of historical forestry carbon sink accounting projects, and a selection model is obtained based on the training set.

[0044] When selecting a machine learning model, you can use a commonly used model for multi-label classification. Convert multiple labels into multiple binary classifications or combinations of labels. Adapt a multi-label algorithm, such as ML-kNN (Multi-label k-Nearest Neighbors) or MLP (Multi-label Decision Tree), and construct a neural network. The output layer uses multiple sigmoid activation functions, allowing each label to be independently classified into two categories. In one embodiment, a deep neural network (DNN) or tree model (such as a multi-label version of a random forest or XGBoost combined with a problem transformation method) is used to capture complex nonlinear relationships and feature interactions. The input layer should correspond to the number of project features. In the hidden layer, several fully connected layers (e.g., 256, 128, or 64 neurons) are configured, each followed by an activation function (e.g., ReLU) and a dropout layer to prevent overfitting. The output layer should include neurons corresponding to the number of key core factors, each using a sigmoid activation function and independently performing binary classification. A loss function and optimizer should be defined. Since each label is an independent binary classification, a binary cross entropy loss function can be used. The loss for each label can be calculated and then averaged (or weighted averaged). The Adam optimizer was used as the optimizer. During training, the model was trained on the training set, with weights updated with each batch. During validation, the validation set was used to monitor model performance, with early stopping and hyperparameter adjustments performed based on validation loss or evaluation metrics. For example, the validation set was used to adjust hyperparameters such as the number of network layers and neurons, learning rate, dropout ratio, and batch size. Finally, the trained model was deployed, project features were input, and the core factor selection results were output.

[0045] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction and will not be described in detail later. Figure 3 , the method further includes steps T10 to T30: Step T10: When performing forestry carbon sink accounting, determine the target forest area to be accounted for and the forest growth stage of the target forest area; The growth stage of a forest affects the amount of carbon sequestration, and different forest growth stages have different abilities to absorb and release carbon dioxide. Therefore, the growth stage can be determined through field surveys, satellite image analysis, and historical data. Specifically, the forest growth stage of the target forest area may be determined using indicators such as tree age, height, and density. In this embodiment, the method for determining the target forest area to be calculated and the forest growth stage of the target forest area is not limited. For example, an age structure analysis can be performed: the growth stage of the forest area can be determined based on the age distribution of trees. Generally, the forest growth stage can be divided into young forests, middle-aged forests, near-mature forests, mature forests, and over-mature forests. Field surveys can also be conducted to measure indicators such as the diameter at breast height, height, and crown width of trees to assess the growth stage of the forest area. Remote sensing image analysis can also be performed: high-resolution images obtained by satellites or drones can be used to analyze forest coverage, canopy density, etc. to assist in determining the growth stage.

[0046] Step T20, obtaining multimodal carbon sink-related data of the target forest area through the three-dimensional monitoring network of the target forest area; The data sources of the multimodal carbon sink-related data of the target forest area monitored by the three-dimensional monitoring network include satellite remote sensing, ground sensors, drones, etc., covering coverage area, tree height, biomass, soil conditions, and meteorological data. Furthermore, the non-uniform formats and time resolution differences of the carbon sink-related data of each modality are processed. In one embodiment, the established three-dimensional monitoring network includes satellite remote sensing: using multispectral, high-resolution satellite images to obtain information such as the coverage area, tree height, biomass, etc. of the forest area. Commonly used satellite data include Landsat, Sentinel-2, etc. Ground sensors: sensors are arranged inside the forest area to monitor environmental parameters such as soil moisture, temperature, precipitation, and the growth status of trees. Drone monitoring: using multispectral or laser radar (LiDAR) equipment carried by drones to obtain high-precision terrain and tree structure data. Meteorological data: collecting meteorological data of the target forest area, including temperature, precipitation, wind speed, etc., to assess the impact of climate on carbon sinks. Furthermore, multimodal carbon sink data is cleaned to remove noise and outliers. Data fusion is then performed to integrate data from different sources to ensure temporal and spatial consistency. For example, satellite imagery is combined with ground-based sensor data to improve data accuracy and resolution. Finally, the processed data is stored in a database to facilitate subsequent analysis and modeling.

[0047] Step T30, estimating the carbon sequestration amount of the target forest area based on the forest growth stage of the target forest area and the multimodal carbon sequestration-related data of the target forest area.

[0048] When selecting a carbon sequestration model, you can choose a biomass model, which estimates carbon sequestration based on the relationship between tree biomass and carbon storage. These include forest biomass models (such as the CASA model) and tree growth models (such as the Cohort model). Alternatively, you can choose a carbon cycle model, which considers the input and output of carbon in forest ecosystems, including processes such as photosynthesis, respiration, and litter decomposition. These models include the Carnegie-Ames-Stanford Approach (CASA) model or the CENTURY model. Input growth stage data and multimodal data: adjust the model parameters based on the growth stage determined in step T10. For example, trees at different growth stages respond differently to photosynthesis and respiration. Input the multimodal data obtained in step T20, including biomass, environmental parameters, and meteorological data. Finally, run the carbon sequestration model to simulate carbon sequestration at different time points. Dynamic estimation means that the model can adjust its carbon sequestration estimates based on real-time or periodic data updates. The accuracy of the model estimates should be verified using field measurements or other independent data sources. For example, the model's estimated carbon storage can be compared with actual tree biomass measurements. Furthermore, the results can be visualized: carbon sequestration estimates can be visualized, such as by generating maps or charts, to facilitate management and decision-making. Furthermore, based on the model's output, forest management strategies can be dynamically adjusted, such as optimizing afforestation, tending, or harvesting plans to enhance carbon sequestration capacity.

[0049] In one embodiment, step T30 includes: Step T301: Obtain historical forest growth stages and historical multimodal carbon sink-related data for the accounted forest area to construct a training set of historical forest growth stages; Collect historical forest growth stage data. Obtain historical data from accounted forest areas, including forest growth stages in different years (such as young forests, middle-aged forests, mature forests, etc.). Record the growth stages of forest areas at different time points, usually based on indicators such as tree age, biomass, and cover. Collect historical multimodal carbon sink-related data. This includes satellite remote sensing data (such as Landsat, Sentinel-2), ground sensor data (such as soil moisture, temperature, and precipitation), and drone data (such as LiDAR and multispectral imagery). Record carbon sink-related indicators of historical forest areas, such as tree biomass, carbon storage, net primary productivity (NPP), and environmental factors (such as temperature, precipitation, and radiation). Data preprocessing. This includes 1. Data cleaning: removing outliers and filling missing values. 2. Data standardization: standardizing data from different sources and formats to ensure data consistency. 3. Feature extraction: extracting features related to carbon sinks, such as tree height, crown width, biomass density, and environmental factors.

[0050] Step T302: Based on the training set of historical forest growth stages, a prediction model for dynamically estimating forest area carbon sinks based on multimodal carbon sink-related data at the historical forest growth stages is trained; Define the training set structure. Input features: historical multimodal carbon sink-related data (such as biomass, environmental factors, tree height, etc.). Output labels: historical forest growth stages (such as young forest, middle-aged forest, mature forest, etc.). Data annotation: Based on the calculated historical data, label the corresponding forest growth stage for each forest area at each time point. Data partitioning: Divide the historical data into training and validation sets, usually with a ratio of 70% training and 30% validation. Data storage: Store the processed data in a database or file to facilitate subsequent model training and verification.

[0051] Select an appropriate model based on the size and complexity of your data. For example, LSTM or RNN are more suitable for time series data; for nonlinear relationships, random forest or gradient boosting models are more effective. Regression models include linear regression, random forest regression, and gradient boosting regression (such as XGBoost and LightGBM). Machine learning models include support vector machines (SVM) and artificial neural networks (ANN). Deep learning models include recurrent neural networks (RNN), long short-term memory networks (LSTM), and convolutional neural networks (CNN). Train the model using the training set data, with multimodal carbon sink-related data as input and the target forest growth stage as output. Optimize model parameters through cross-validation (such as grid search and random search) to improve model performance. Evaluate model performance using validation data, calculating metrics such as accuracy, mean squared error (MSE), and coefficient of determination (R²). If model performance is unsatisfactory, adjust the model structure or reselect the algorithm.

[0052] Step T303: Dynamically estimate the carbon sink amount of the target forest area through the target prediction model and the multimodal carbon sink related data of the target forest area.

[0053] First, obtain real-time or multimodal data of the target forest area. Data source: Real-time data of the target forest area is obtained through a three-dimensional monitoring network (such as satellites, drones, and ground sensors). Data content: includes tree biomass, environmental factors, terrain information, etc. Data preprocessing: The real-time data of the target forest area is preprocessed, including cleaning, standardization, and feature extraction to make it consistent with the training set data format. Then, model inference is performed, and the preprocessed target forest area data is input into the trained prediction model to dynamically estimate the carbon sequestration of the target forest area. Finally, the results are output and visualized. The estimation results are output in the form of charts or maps for easy analysis and presentation. In addition, the estimation results of carbon sequestration are dynamically adjusted according to the update frequency of real-time data (such as daily, weekly, and monthly).

[0054] It should be noted that the above examples are only used to understand this application and do not constitute a limitation on the dynamic accounting method of forestry carbon sinks based on machine learning in this application. More forms of simple transformations based on this technical concept are all within the scope of protection of this application.

[0055] This application also provides a forestry carbon sink dynamic accounting device based on machine learning, please refer to Figure 4 The forestry carbon sink dynamic accounting device based on machine learning includes: Preparation module 10, for determining the forestry carbon sink accounting projects to be implemented in the current accounting cycle, and collecting project information of the forestry carbon sink accounting projects from the project application materials of the forestry carbon sink accounting projects; Identification module 20, for identifying project characteristics of a forestry carbon sink accounting project from project information based on machine learning; wherein the project characteristics include the project type, project scale, and topography and environmental characteristics of the project area; The generation module 30 is used to determine the actual content of the key accounting factors of the forestry carbon sink accounting project according to the project characteristics, and perform forestry carbon sink accounting according to the accounting plan obtained by adopting the actual content of the key accounting factors.

[0056] In one embodiment, the generating module 30 is further configured to: According to the project type of forestry carbon sink accounting project, determine the first practical content of carbon pool, baseline and disturbance type in the key accounting factors of forestry carbon sink accounting project; Determine the second practical content of the monitoring target in the key accounting factors of the forestry carbon sink accounting project based on the project scale and the terrain of the project area; According to the environmental characteristics of the project area where the forestry carbon sink accounting project is located, the third actual content of the accounting model and parameters in the key accounting factors of the forestry carbon sink accounting project is determined.

[0057] In one embodiment, the generating module 30 is further configured to: Based on the first practical content, determine the carbon pool scope and priority of forestry carbon sink accounting projects; According to the second practical content, determine the monitoring method, accuracy and frequency of the forestry carbon sink accounting project; According to the third practical content, determine the biomass model and accounting parameters of the forestry carbon sink accounting project; Based on the carbon pool scope and priority, monitoring methods and accuracy, frequency, biomass model and accounting parameters of the forestry carbon sink accounting project, a baseline scenario plan is formulated and the baseline scenario plan after removing the uncertainty sources is used as the accounting plan.

[0058] In one embodiment, the generating module 30 is further configured to: Taking the project features as input features, the output labels corresponding to the input features are inferred based on the pre-trained selection model, where the output labels are the label values corresponding to the actual content of the key accounting factors of the forestry carbon sink accounting projects.

[0059] In one embodiment, the generating module 30 is further configured to: Obtain historical project information and historical accounting plans for historical forestry carbon sink accounting projects, and determine the historical actual content of key accounting factors of forestry carbon sink accounting projects from the historical accounting plans; A training set is constructed through historical project information and historical actual content of historical forestry carbon sink accounting projects, and a selection model is obtained based on the training set.

[0060] In one embodiment, the apparatus further includes an application module configured to: When conducting forestry carbon sink accounting, determine the target forest area to be accounted for and the forest growth stage of the target forest area; Obtain multimodal carbon sequestration-related data in the target forest area through a three-dimensional monitoring network in the target forest area; The carbon sequestration amount of the target forest area is estimated based on the forest growth stage of the target forest area and the multimodal carbon sequestration-related data of the target forest area.

[0061] The machine learning-based dynamic accounting device for forestry carbon sinks provided in this application, which employs the machine learning-based dynamic accounting method for forestry carbon sinks in the above-mentioned embodiments, can resolve the technical problem of inaccurate dynamic accounting of forestry carbon sinks. Compared with the prior art, the beneficial effects of the machine learning-based dynamic accounting device for forestry carbon sinks provided in this application are the same as those of the machine learning-based dynamic accounting method for forestry carbon sinks provided in the above-mentioned embodiments. Other technical features of the machine learning-based dynamic accounting device for forestry carbon sinks are the same as those disclosed in the above-mentioned embodiments and are not further elaborated here.

[0062] The present application provides a dynamic accounting device for forestry carbon sinks based on machine learning. The dynamic accounting device for forestry carbon sinks based on machine learning includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the dynamic accounting method for forestry carbon sinks based on machine learning in the above-mentioned embodiment one.

[0063] Reference below Figure 5, which shows a schematic diagram of the structure of a machine learning-based dynamic accounting device for forestry carbon sinks suitable for implementing the embodiments of the present application. The machine learning-based dynamic accounting device for forestry carbon sinks in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The forestry carbon sink dynamic accounting device based on machine learning shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of this application.

[0064] like Figure 5 As shown, the machine learning-based forestry carbon sink dynamic accounting device may include a processing device 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the machine learning-based forestry carbon sink dynamic accounting device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape or hard disk; and a communication device 1009. The communication device 1009 can allow the machine learning-based forestry carbon sink dynamic accounting device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a machine learning-based forestry carbon sink dynamic accounting device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0065] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0066] The machine learning-based dynamic accounting device for forestry carbon sinks provided in this application adopts the machine learning-based dynamic accounting method for forestry carbon sinks in the above-mentioned embodiment, which can solve the technical problem of inaccurate accounting during dynamic accounting of forestry carbon sinks. Compared with the existing technology, the beneficial effects of the machine learning-based dynamic accounting device for forestry carbon sinks provided in this application are the same as the beneficial effects of the machine learning-based dynamic accounting method for forestry carbon sinks provided in the above-mentioned embodiment, and the other technical features of the machine learning-based dynamic accounting device for forestry carbon sinks are the same as the features disclosed in the method of the previous embodiment, and are not further described here.

[0067] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0068] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0069] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the dynamic accounting method of forestry carbon sinks based on machine learning in the above-mentioned embodiment.

[0070] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0071] The above-mentioned computer-readable storage medium may be included in the forestry carbon sink dynamic accounting device based on machine learning; or it may exist independently without being assembled into the forestry carbon sink dynamic accounting device based on machine learning.

[0072] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the forestry carbon sink dynamic accounting device based on machine learning, the forestry carbon sink dynamic accounting device based on machine learning is enabled to: determine the forestry carbon sink accounting projects to be implemented in the current accounting cycle, and collect project information of the forestry carbon sink accounting projects from the project application materials of the forestry carbon sink accounting projects; identify the project characteristics of the forestry carbon sink accounting projects from the project information based on machine learning; wherein the project characteristics include the project type, project scale and terrain of the project area, and environmental characteristics of the project area; determine the actual content of the key accounting factors of the forestry carbon sink accounting project based on the project characteristics, and perform forestry carbon sink accounting according to the accounting plan obtained by adopting the actual content of the key accounting factors.

[0073] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0074] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0075] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0076] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned machine learning-based dynamic accounting method for forestry carbon sinks. This computer-readable storage medium can address the technical issue of inaccurate dynamic accounting for forestry carbon sinks. Compared to the prior art, the computer-readable storage medium provided in this application offers the same beneficial effects as the machine learning-based dynamic accounting method for forestry carbon sinks provided in the aforementioned embodiments, and will not be further elaborated upon here.

[0077] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for dynamic accounting of forestry carbon sinks based on machine learning.

[0078] The computer program product provided in this application can address the technical issue of inaccurate dynamic accounting of forestry carbon sinks. Compared to existing technologies, the beneficial effects of the computer program product provided in this application are similar to those of the machine learning-based dynamic accounting method for forestry carbon sinks provided in the aforementioned embodiments, and are not further elaborated here.

[0079] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for dynamic accounting of forestry carbon sinks based on machine learning, characterized in that: The forestry carbon sink dynamic accounting method based on machine learning includes: Determine the forestry carbon sink accounting projects to be implemented in the current accounting cycle, and collect project information of the forestry carbon sink accounting projects from the project application materials of the forestry carbon sink accounting projects; Identifying project characteristics of the forestry carbon sink accounting project from the project information based on machine learning; wherein the project characteristics include the project type, project scale, topography of the project area, and environmental characteristics of the project area; According to the project characteristics, the actual content of the key accounting factors of the forestry carbon sink accounting project is determined, and the forestry carbon sink accounting is carried out according to the accounting plan obtained by adopting the actual content of the key accounting factors.

2. The method for dynamic forestry carbon sink accounting based on machine learning according to claim 1, characterized in that: The step of determining the actual content of the key accounting factors of the forestry carbon sink accounting project based on the project characteristics includes: Determine the first actual content of the carbon pool, baseline and disturbance type among the key accounting factors of the forestry carbon sink accounting project based on the project type of the forestry carbon sink accounting project; Determine the second actual content of the monitoring target in the key accounting factors of the forestry carbon sink accounting project based on the project scale and the topography of the project area; According to the environmental characteristics of the project area where the forestry carbon sink accounting project is located, the third actual content of the accounting model and parameters in the key accounting factors of the forestry carbon sink accounting project is determined.

3. The method for dynamic forestry carbon sink accounting based on machine learning according to claim 2, characterized in that: After the step of determining the actual content of the key accounting factors of the forestry carbon sink accounting project based on the project characteristics, the following steps are included: Determine the carbon pool scope and priority of the forestry carbon sink accounting project based on the first actual content; Determine the monitoring method, accuracy, and frequency of the forestry carbon sink accounting project based on the second practical content; Determine the biomass model and accounting parameters of the forestry carbon sink accounting project based on the third actual content; Based on the carbon pool scope and priority, monitoring methods and accuracy, frequency, biomass model and accounting parameters of the forestry carbon sink accounting project, a baseline scenario plan is formulated and the baseline scenario plan after removing the uncertainty sources is used as the accounting plan.

4. The method for dynamic forestry carbon sink accounting based on machine learning according to claim 1, characterized in that: The step of determining the actual content of the key accounting factors of the forestry carbon sink accounting project based on the project characteristics further includes: The project features are used as input features, and the output labels corresponding to the input features are inferred based on the pre-trained selection model, wherein the output labels are label values corresponding to the actual content of the key accounting factors of the forestry carbon sink accounting project.

5. The method for dynamic forestry carbon sink accounting based on machine learning according to claim 4, characterized in that: The step of using the project features as input features and obtaining output labels corresponding to the input features by inference based on the pre-trained selection model includes: Obtain historical project information and historical accounting plans for historical forestry carbon sink accounting projects, and determine the historical actual content of key accounting factors of the forestry carbon sink accounting projects from the historical accounting plans; A training set is constructed through the historical project information and historical actual content of the historical forestry carbon sink accounting project, and a selection model is obtained by training based on the training set.

6. The method for dynamic forestry carbon sink accounting based on machine learning according to claim 1, characterized in that: The method further comprises: When conducting forestry carbon sink accounting, determine the target forest area to be accounted for and the forest growth stage of the target forest area; Acquiring multimodal carbon sink-related data of the target forest area through a three-dimensional monitoring network of the target forest area; The carbon sequestration amount of the target forest area is estimated based on the forest growth stage of the target forest area and the multimodal carbon sequestration-related data of the target forest area.

7. A dynamic accounting device for forestry carbon sinks based on machine learning, characterized in that: The forestry carbon sink dynamic accounting device based on machine learning includes: A preparation module, configured to determine a forestry carbon sink accounting project to be implemented in the current accounting cycle, and to collect project information of the forestry carbon sink accounting project from project application materials of the forestry carbon sink accounting project; an identification module, configured to identify project characteristics of the forestry carbon sink accounting project from the project information based on machine learning; wherein the project characteristics include the project type, project scale, and topography and environmental characteristics of the project area; A generation module is used to determine the actual content of the key accounting factors of the forestry carbon sink accounting project based on the project characteristics, and perform forestry carbon sink accounting based on the accounting plan obtained by adopting the actual content of the key accounting factors.

8. A forestry carbon sink dynamic accounting device based on machine learning, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the dynamic accounting method for forestry carbon sinks based on machine learning as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the dynamic accounting method of forestry carbon sinks based on machine learning as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, which, when executed by a processor, implements the steps of the dynamic accounting method for forestry carbon sinks based on machine learning as described in any one of claims 1 to 6.

Images