Ecological system carbon sink accounting method and system based on territorial space planning target

Abstract

The invention provides an ecological system carbon sink accounting method and system based on a territorial space planning target. The method belongs to the technical field of carbon income and expenditure, and comprises the steps: obtaining and integrating basic data of ecological system carbon sink accounting and territorial space planning, and constructing a high-precision territorial space database based on the basic data; dividing the research area into different types of land utilization units according to a territorial space planning target, and determining carbon sink key parameters of each type of land utilization unit; the method comprises the following steps: collecting and integrating basic data related to ecological environment and territorial space planning through multiple ways, and constructing a high-precision territorial space database containing various information such as remote sensing images, topography and landform, climate, land utilization status, vegetation coverage and the like; through parallel computing and resource optimization management, efficient data preprocessing and spatial overlay analysis are realized, and a solid data basis is provided for subsequent carbon sink accounting.

Description

Technical Field

[0001] The present invention proposes an ecosystem carbon sink accounting method and system based on national land space planning objectives, belonging to the field of carbon budget technology. Background Art

[0002] Traditional ecosystem carbon sink accounting is mostly based on methods such as sample site surveys, remote sensing inversion, or model simulation, focusing on macro-scale total estimation and often lacking in-depth coupling with specific spatial locations and land use patterns. However, in the practice of national land space planning, land use type, layout, and its dynamic changes directly determine the structure and function of the ecosystem, which in turn significantly affects the regional carbon sequestration capacity. Currently, there are still problems such as data fragmentation, model disconnection, and insufficient coordination between carbon sink accounting and national land space planning. As a result, carbon sink assessment results are difficult to effectively integrate into specific planning links such as ecological restoration layout and urban development boundary determination, which restricts the implementation of nature-based climate solutions in spatial planning.

[0003] With the development of high-resolution remote sensing, geographic information systems (GIS), and big data technologies, it has become possible to construct high-precision, fine-grained national spatial databases, providing technical support for making carbon sink accounting spatially explicit and dynamic. Therefore, there is an urgent need for a technical approach that can deeply integrate carbon sink accounting with national spatial planning, incorporating carbon sinks as a core ecological indicator into planning and decision-making systems.

[0004] While some existing research has attempted to integrate carbon sink models with GIS platforms for spatial mapping or to consider ecological functions in planning, most remain at the static assessment level, lacking analysis of responses to land-use change scenarios guided by planning objectives and failing to establish a dynamic feedback mechanism between carbon sink accounting and planning implementation. Furthermore, insufficient local calibration of key carbon sink parameters for different land-use units (such as forestland, cultivated land, and construction land) limits the accuracy of estimation results, making it difficult to support refined management and differentiated policy implementation. Summary of the Invention

[0005] The present invention provides an ecosystem carbon sink accounting method and system based on national land space planning objectives to solve the problems mentioned in the above background technology: The present invention proposes an ecosystem carbon sink accounting method based on national land space planning objectives, the method comprising: S1. Acquire and integrate basic data for ecosystem carbon sink accounting and national land space planning, and build a high-precision national land space database based on the basic data; S2. Divide the study area into different types of land use units according to the national land space planning objectives and determine the key carbon sequestration parameters of each type of land use unit; S3. Calibrate and verify the parameters of existing carbon sink models for different land use types using historical data. Use a geographic information system to embed the carbon sink estimation model into the national land space database, calculate the carbon sink amount and spatial distribution characteristics of each land use unit, and generate a carbon sink distribution map. S4. Evaluate the carbon sequestration potential of various land use units based on national land space planning objectives; and combine multiple factors to develop carbon sequestration optimization strategies; S5. Establish a dynamic coupling mechanism between carbon sink accounting and national land space planning, monitor changes in carbon sinks, and adjust optimization strategies in a timely manner based on monitoring results.

[0006] The present invention proposes an ecosystem carbon sink accounting system based on national land space planning objectives, the system comprising: Data acquisition module: acquires and integrates basic data for ecosystem carbon sink accounting and national land space planning, and builds a high-precision national land space database based on the basic data; Regional division module: Based on the national land space planning objectives, the study area is divided into different types of land use units, and the key carbon sequestration parameters of each type of land use unit are determined; Carbon sink distribution module: Calibrate and verify the parameters of existing carbon sink models for different land use types using historical data; use geographic information systems to embed carbon sink estimation models into the national land space database, calculate the carbon sink amount and spatial distribution characteristics of each land use unit, and generate carbon sink distribution maps; Strategy Acquisition Module: Evaluate the carbon sequestration potential of various land use units based on national land space planning objectives; and combine multiple factors to obtain carbon sequestration optimization strategies; Strategy optimization module: Establish a dynamic coupling mechanism between carbon sink accounting and national land space planning, monitor changes in carbon sinks, and adjust optimization strategies in a timely manner based on monitoring results.

[0007] The beneficial effects of the present invention are as follows: by collecting and integrating basic data related to the ecological environment and national land space planning through multiple channels, a high-precision national land space database containing a variety of information such as remote sensing images, topography, climate, land use status and vegetation cover is constructed; through parallel computing and resource optimization management, efficient data preprocessing and spatial overlay analysis are achieved, providing a solid data foundation for subsequent carbon sink accounting; scientifically dividing land use units according to actual planning goals and specifically determining the key carbon sink parameters of each unit helps to accurately quantify the contribution of different land types to carbon sinks, and ensures that these parameters are calibrated and verified based on measured data, thereby improving the accuracy and reliability of carbon sink estimation; using historical data and machine learning algorithms to calibrate and optimize the carbon sink model can improve the model's prediction ability, and integrating the calibrated model into the GIS system can achieve accurate calculation and visualization of the carbon sink total amount and its spatial distribution characteristics of each land use unit, which is conducive to intuitive understanding of the spatial pattern and dynamic changes of carbon sinks; establishing a dynamic coupling mechanism between carbon sink accounting and national land space planning can regularly monitor carbon sink changes and timely adjust and optimize land use strategies based on monitoring results to maximize the utilization of carbon sink potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 Schematic diagram of the method of the present invention; Figure 2 Schematic diagram of the system of the present invention; Figure 3 This is a schematic diagram of carbon sink change monitoring according to the present invention; Figure 4 This is a schematic diagram of the carbon sink content and its changes described in the present invention. DETAILED DESCRIPTION

[0009] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein may be combined with each other.

[0010] The following description sets forth numerous specific details to facilitate a thorough understanding of the present invention. The embodiments described are merely a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0012] One embodiment of the present invention, as Figure 1 As shown, a method for calculating ecosystem carbon sinks based on national land space planning objectives includes: S1: Acquire and integrate basic data for ecosystem carbon sink accounting and national land space planning, including remote sensing images, topography, climate data, land use types, and vegetation coverage, and build a high-precision national land space database based on this basic data; S2: Based on the national land space planning objectives, the study area is divided into different types of land use units, such as forest, grassland, farmland, wetland, urban green space, etc.; and the key carbon sink parameters of each land use unit are determined (such as biomass, soil organic carbon content, carbon turnover rate, etc.); S3: Calibrate and validate the parameters of existing carbon sink models for different land use types using historical data; embed the carbon sink estimation model into the national land space database using a geographic information system, calculate the carbon sink amount and spatial distribution characteristics of each land use unit, and generate a carbon sink distribution map; S4: Evaluate the carbon sequestration potential of various land use units based on national land space planning objectives; and combine multiple factors to develop carbon sequestration optimization strategies; S5: Establish a dynamic coupling mechanism between carbon sink accounting and national land space planning, update data regularly, monitor changes in carbon sinks, and adjust optimization strategies in a timely manner based on monitoring results to form a closed-loop feedback system for carbon sink management.

[0013] The working principle of the above technical solution is as follows: First, high-quality basic data needs to be obtained, including topography, climate data, land use types and vegetation coverage. The data can be obtained through remote sensing technology and integrated and processed through geographic information systems to build a high-precision national land space database; according to the national land space planning goals, the study area is divided into different types of land use units. This division is usually based on land use types and ecosystem characteristics, such as forests, grasslands, farmlands, wetlands and urban green spaces; for each land use unit, key carbon sink parameters need to be determined, such as biomass, soil organic carbon content and carbon turnover rate. These parameters are crucial for the accurate calculation of carbon sinks; the carbon sink models of different land use types are calibrated and verified through historical data to ensure the accuracy and reliability of the model. This includes comparison with field survey data and fine-tuning of the model; using geographic information systems, the carbon sink estimation model is embedded in the national land space database to realize the calculation and visualization of the carbon sink amount and spatial distribution characteristics of each land use unit, such as Figure 4As shown in the following example: Based on the national land space planning objectives, the carbon sequestration potential of various land use units will be assessed. A dynamic coupling mechanism between carbon sink accounting and national land space planning will be established. By regularly updating data and monitoring carbon sink changes, timely adjustments and optimization strategies will be made to form a closed-loop feedback system for carbon sink management.

[0014] The effect of the above technical solution is: by integrating remote sensing images, topography, climate data, land use types, vegetation coverage and other basic data, a high-precision national land space database is constructed. Such a comprehensive data foundation ensures the accuracy and comprehensiveness of ecosystem carbon sink accounting; through the geographic information system (GIS), the carbon sink estimation model is embedded in the national land space database, which can realize the spatial analysis and visualization of the carbon sink distribution of each land use unit. This can not only help planners intuitively understand the spatial distribution characteristics of carbon sinks, but also provide a scientific basis for national land space planning; according to the national land space planning goals, the study area is divided into different types of land use units, and the key carbon sink parameters of each land use unit are determined. The accuracy of the model can be ensured by calibrating and verifying the parameters of the carbon sink model of different land use types through historical data. Based on these parameters, the carbon sink enhancement potential of various land use units can be evaluated to provide a scientific basis for the formulation of carbon sink optimization strategies; a dynamic coupling mechanism for carbon sink accounting and national land space planning is established, data is updated regularly, and changes in carbon sinks are monitored, such as Figure 3 As shown in the figure, once carbon sink trends are detected, strategies can be promptly adjusted and optimized based on monitoring results, forming a closed-loop feedback system for carbon sink management. This dynamic adjustment and management can effectively address the challenges posed by carbon sink changes, ensure ecosystem stability, and thus protect and enhance ecosystem carbon sink reserves.

[0015] In one embodiment of the present invention, the step S1 includes: S11. Collect basic data for ecosystem carbon sink accounting and national land space planning through multiple means. The basic data include remote sensing image data, topographic data (including elevation, slope, and aspect), historical climate data (including rainfall, temperature, and wind speed), current land use data (including different types of land use and their area distribution), and vegetation cover data; S12. Different types of basic data are stored in a computing platform. The computing platform allocates the basic data to different computing units according to the basic data type. Each computing unit preprocesses the basic data through a parallel processor. The preprocessing includes performing radiation calibration, geometric correction, and atmospheric correction on remote sensing images; interpolating and normalizing climate data to eliminate spatial and temporal discontinuities; and classifying and recoding land use data to unify data formats and classification standards.

[0016] S13. Monitor the computing resource usage of each computing unit in real time through the resource manager, and schedule the computing resources of each computing unit in real time based on the queue scheduling algorithm; S14. Use the geographic information system to spatially overlay and associate attributes of the pre-processed data; build a national land space database containing multi-source data, and optimize the structure and index design of the database.

[0017] The working principle of the above technical solution is as follows: A variety of basic data are collected, including remote sensing imagery, topographic data, historical climate data, current land use data, and vegetation coverage data. This data can be obtained from various government departments, scientific research institutions, remote sensing satellites, and other channels. Different types of basic data are then preprocessed accordingly. For example, remote sensing images undergo radiometric calibration, geometric correction, and atmospheric correction; climate data undergoes interpolation and normalization; and land use data undergoes classification and recoding to ensure data quality and consistency. Parallel processors on the computing platform are used to perform parallel computations on the preprocessed data to improve computational efficiency. A resource manager monitors the computing resource usage of each computing unit in real time and schedules computing resources for each unit based on a queue scheduling algorithm to ensure efficient resource allocation and utilization. Using a geographic information system (GIS), the preprocessed data is spatially overlaid and attribute-linked to integrate data from different sources. A national land spatial database containing multi-source data is constructed, and the database structure and index design are optimized to improve data query and retrieval efficiency. This can be achieved using existing GIS software or a self-developed database management system.

[0018] The above technical solution achieves the following: Data collection through multiple means ensures that data obtained from different sources covers as many aspects as possible, including remote sensing imagery, topography, historical climate, and current land use. This data not only covers multiple spatial dimensions but also traces back through time, enabling a comprehensive understanding of the current status and changing trends of ecosystems and national land space. The parallel computing capabilities of the computing platform enable rapid processing of large amounts of data. Preprocessing of basic data using parallel processors, such as radiometric calibration, geometric correction, and atmospheric correction of remote sensing imagery, interpolation and normalization of climate data, and classification and recoding of land use data, improves the efficiency and speed of data processing. This preprocessing step not only improves data processing efficiency but also enhances data accuracy and reliability. Correction and normalization eliminate potential errors and inconsistencies in the data, ensuring data quality and reliability. Constructing a national land space database is a key step in the entire process. The pre-processed data are spatially superimposed and attribute-associated through the geographic information system, and the database structure is optimized and indexed, which improves the efficiency of data query and retrieval, enabling users to obtain the required information more quickly; the constructed high-precision national land space database provides reliable data support for ecosystem carbon sink accounting and national land space planning.

[0019] In one embodiment of the present invention, the step S13 includes: Use the resource manager to collect key metrics such as utilization, idle rate, and response time of each computing unit (including CPU, GPU, memory, and disk I / O) in real time. Using big data analysis technology, the collected data is quickly processed to identify performance bottlenecks in the current system, such as excessive use or idleness of certain resources; It also analyzes historical task execution records to extract features such as task type, execution duration, and resource consumption patterns.

[0020] Build prediction models based on time series analysis or machine learning to predict resource demand trends for various tasks over a period of time; Dynamically evaluate the priority of each task based on factors such as task urgency, data type, and computational complexity; Based on the prediction results and current resource status, the task queue is dynamically adjusted to prioritize resources for high-priority tasks. When a computing unit is overloaded, some tasks are automatically migrated to other idle or less loaded computing units; Maintain an elastic and scalable computing resource pool and dynamically increase or decrease computing resources according to task requirements.

[0021] Based on prediction results and real-time demand, computing resources are automatically allocated or reclaimed from the resource pool to ensure maximum resource utilization.

[0022] Regularly perform health checks on computing units to identify potential failure points; Once a fault is detected, the affected computing unit is immediately isolated and backup resources are started to take over. Collect and analyze fault data, optimize system configuration and scheduling strategies, and prevent similar faults from happening again.

[0023] The working principle of the above technical solution is as follows: The resource manager continuously monitors the real-time status of each computing unit (CPU, GPU, memory, disk I / O, etc.), including key indicators such as utilization, idle rate, and response time. This data is collected in real time and transmitted to the data processing center, providing basic data for subsequent analysis and scheduling. Leveraging big data analytics technologies, the collected data is rapidly processed, and statistical analysis and data mining methods are used to identify performance bottlenecks in the current system. For example, if the CPU utilization of a computing unit is consistently high, while the memory and disk I / O are relatively idle, this may indicate that the computing capacity of that unit has become a bottleneck. Historical task execution records are reviewed and analyzed to extract features such as task type, execution duration, and resource consumption patterns to understand the resource demand patterns of different tasks. Based on these features, a predictive model based on time series analysis or machine learning is constructed to predict resource demand trends for various tasks over a period of time. Task priorities are dynamically assessed based on factors such as task urgency, data type, and computational complexity. Based on the predicted results and current resource status, the task queue is dynamically adjusted to prioritize resources for high-priority tasks, ensuring that critical tasks receive priority processing. When a computing unit becomes overloaded, the system automatically triggers a load balancing mechanism, migrating some tasks to idle or less-loaded computing units. This process is achieved through an intelligent task allocation algorithm, ensuring balanced distribution of the overall system load and improving resource utilization. A flexible and scalable computing resource pool is maintained, dynamically increasing or decreasing computing resources based on task demand. When future resource demand is predicted to increase, the system automatically allocates more resources from the resource pool; when demand decreases, excess resources are recycled to reduce costs. Computing units are regularly checked for health, identifying potential failure points through heartbeat monitoring, log analysis, and other means. Once a failure is detected, the affected computing unit is immediately isolated to prevent the failure from spreading. Simultaneously, backup resources are initiated to take over and ensure that data processing is not affected. Failure data is collected and analyzed to optimize system configuration and scheduling strategies to prevent similar failures from recurring.

[0024] The effects of the above technical solution are as follows: by collecting key indicator data of computing units in real time, the system can accurately understand the current resource usage and perform intelligent scheduling based on this data. This avoids excessive use or idleness of resources, thereby improving overall resource utilization; maintains an elastic and scalable computing resource pool, dynamically increases or decreases resources according to task requirements, ensures that resources can be allocated on demand, and avoids resource waste; dynamically evaluates priority based on factors such as the urgency of the task, data type, and computational complexity, ensuring that critical tasks are processed first, thereby shortening the waiting time and execution cycle of the task; when a computing unit is overloaded, it automatically migrates some tasks to other idle or less loaded computing units, achieving balanced utilization of computing resources and avoiding performance bottlenecks caused by single-point overload; regularly performs health checks on computing units to promptly identify and isolate potential fault points. At the same time, by starting the backup resource takeover task, the data processing process is ensured to be unaffected, thereby improving the stability and reliability of the system; building a prediction model based on time series analysis or machine learning to predict future resource demand trends, and optimizing the system configuration and scheduling strategy based on the prediction results to prevent similar failures from happening again, further enhancing the robustness of the system; automatically allocating or recycling computing resources based on the prediction results and real-time needs, avoiding unnecessary waste of resources, thereby reducing operating costs; through intelligent scheduling and load balancing mechanisms, computing resources are used more reasonably, reducing additional expenses caused by insufficient or excessive resources; this technical solution has good scalability and can dynamically adjust the size of the computing resource pool according to task requirements, supporting large-scale data processing scenarios; by optimizing resource allocation and scheduling strategies, the speed and efficiency of data processing are improved, meeting the high requirements of large-scale data processing for performance and efficiency.

[0025] In one embodiment of the present invention, the step S2 includes: S21. Divide the study area into several land use units according to the objectives and requirements of national land space planning. The land use units include cultivated land, forest land, grassland, water area and unused land. S22. Determine the key carbon sequestration parameters for different types of land use units and calibrate and verify the key parameters based on known experimental data.

[0026] The working principle of the above technical solution is as follows: First, the land use types of the study area are divided according to the goals and requirements of the national land space planning. The study area is usually divided into different types of land use units such as cultivated land, forest land, grassland, water area and unused land. Each type of land use unit has specific ecological and environmental characteristics and carbon storage; for each type of land use unit, its key carbon sink parameters are determined. These parameters usually include soil organic carbon content, aboveground vegetation carbon content, underground root carbon content, etc. These parameters can be obtained through field surveys and field sampling, and can also be estimated and extrapolated using remote sensing technology and ground monitoring data; based on known experimental data, the determined key carbon sink parameters are calibrated and verified. This includes comparative analysis of field sampling data and remote sensing monitoring data to verify the accuracy of the remote sensing estimation results; The benefits of this technical solution are: by dividing the study area into different types of land use units, land resources can be planned and managed scientifically and rationally. This helps optimize land use structure, improve land resource utilization efficiency, and achieve the goals and requirements of national land space planning. Determining key carbon sink parameters for each type of land use unit helps assess the carbon storage and sequestration potential of different land types. Calibration and verification of key parameters based on known experimental data can improve the accuracy and reliability of the model. This helps ensure the scientific and practical nature of the land carbon sink model and provides reliable technical support for national land space planning and ecological and environmental protection.

[0027] In one embodiment of the present invention, the step S3 includes: S31. Collect historical carbon sink data, including vegetation biomass and soil organic carbon content; calibrate and optimize existing carbon sink models using machine learning algorithms; and evaluate the performance and accuracy of the calibrated carbon sink models through cross-validation and independent dataset testing. S32. Embed the calibrated carbon sink model into the national land space database and calculate the carbon sink amount of each land use unit; The calculation formula for the carbon sink is: in, The total amount of carbon sink, is the area of ​​the i-th land use type, is the carbon sink coefficient of the i-th land use type, is the carbon sink conversion coefficient of the i-th land use type considering the three dimensions of land use bottom line control, structural layout optimization, and management improvement; The carbon sink coefficient is shown in Table 1: Table 1 Land use type and carbon sink coefficient

[0028]

[0029] described The values ​​of are shown in Table 2: Table 2, Value Table

[0030] S33. Analyze the spatial distribution characteristics of carbon sinks using a spatial analysis algorithm of a geographic information system, wherein the spatial distribution characteristics include carbon sink density and carbon sink hotspots; S34, and generating a carbon sink distribution map based on the analysis results.

[0031] The technical solution works as follows: First, historical carbon sink data, including information on vegetation biomass and soil organic carbon content, are collected. Machine learning algorithms are then used to calibrate and optimize the parameters of the existing carbon sink model to improve its accuracy and performance. The calibrated carbon sink model is embedded in a national spatial database for computation and analysis using a geographic information system (GIS). This database contains spatial information and related data for various land use units. A calculation formula is used to calculate the total carbon sink for each land use unit, taking into account the unit area and the corresponding carbon sink coefficient. The carbon sink coefficient is determined based on different land use types and subtypes, reflecting the contribution of each land use type to carbon sinks. Using GIS spatial analysis algorithms, the spatial distribution characteristics of carbon sinks are analyzed, including carbon sink density, carbon sink hotspots, and confidence levels for these spatial characteristics (hotspots and coldspots). These characteristics reflect the carbon sink status of different regions and land use types. Based on the spatial analysis results, a carbon sink distribution map is generated, visually displaying the spatial distribution of carbon sinks in the study area, providing an important reference for national spatial planning and ecological and environmental protection.

[0032] The effects of the above technical solution are: by collecting historical data and applying machine learning algorithms, the parameters of the existing carbon sink model are calibrated and optimized, thereby improving the accuracy and reliability of the model; the solution takes into account the contribution of different land use types to carbon sinks, including cultivated land, forest land, grassland, water area and unused land, making the estimation of carbon sinks more comprehensive; using the spatial analysis algorithm of the geographic information system, the spatial distribution characteristics of carbon sinks are analyzed, including carbon sink density and carbon sink hotspots, providing an important reference for regional carbon sink management and ecological environmental protection; the generated carbon sink distribution map intuitively shows the spatial distribution of carbon sinks in the study area.

[0033] In one embodiment of the present invention, the step S4 includes: S41. Based on national land space planning objectives, such as ecological protection red lines and urban green space construction, a quantitative assessment of carbon sequestration potential should be conducted for various land use units using carbon sequestration models in combination with historical data and current status. During the assessment process, not only the carbon sequestration capacity of the land use unit itself should be considered, but also its interactions and impacts with other units to ensure the accuracy and comprehensiveness of the assessment results.

[0034] S42. Conduct a comprehensive multi-factor analysis based on factors such as ecological and environmental carrying capacity. Consider the impact of development needs, population distribution, and industrial structure on land use and carbon sequestration potential. Also, assess ecological and environmental carrying capacity to ensure that the implementation of carbon sequestration optimization strategies will not negatively impact the environment. Based on this analysis, formulate a carbon sequestration optimization strategy. During strategy formulation, consider regional characteristics and development needs to propose targeted optimization measures. For example, in ecological protection redline areas, implement strict ecological protection measures to enhance the carbon sequestration capacity of ecosystems such as forests and grasslands. In urban green space development, optimize green space layout, increase green space area, and enhance the carbon sequestration benefits of urban green spaces.

[0035] The working principle of the above technical solution is as follows: First, a carbon sequestration model is used to assess the carbon sequestration potential of various land use units. This requires considering national land space planning objectives, such as ecological protection red lines and urban green space development, to quantitatively assess the carbon sequestration capacity of different land use units. During the assessment process, not only the carbon sequestration capacity of the land use unit itself must be considered, but also its interactions and impacts with other units. This includes considering the impact of factors such as population distribution and industrial structure on land use and carbon sequestration potential. It also requires assessing the ecological carrying capacity to ensure that the implementation of carbon sequestration optimization strategies will not have negative impacts on the environment. Based on this comprehensive analysis, a carbon sequestration optimization strategy is formulated. This requires considering regional characteristics and development needs to propose targeted optimization measures. For example, in ecological protection red line areas, strict ecological protection measures can be implemented to enhance the carbon sequestration capacity of ecosystems such as forests and grasslands. In urban green space development, green space layout can be optimized, green space area can be increased, and the carbon sequestration benefits of urban green spaces can be enhanced. Finally, the formulated carbon sequestration optimization strategy is implemented, and its effectiveness is regularly monitored and evaluated. Based on the monitoring results, the optimization strategy can be adjusted in a timely manner.

[0036] The effects of the above technical scheme are: through the assessment based on national land space planning objectives and carbon sink models, the carbon sink potential of various land use units can be scientifically and accurately assessed, providing a reliable basis for the formulation of carbon sink optimization strategies; the scheme combines multi-factor comprehensive analysis, not only considering the carbon sink capacity of the land use unit itself, but also considering factors such as the ecological environmental carrying capacity, ensuring the comprehensiveness and accuracy of the assessment results; by formulating targeted carbon sink optimization strategies for different land use units, the carbon sink potential of each region can be maximized and the optimization of carbon sink management can be achieved; the scheme focuses on assessing the ecological environmental carrying capacity to ensure that the implementation of carbon sink optimization strategies will not have a negative impact on the environment.

[0037] In one embodiment of the present invention, the S5 includes: S51. Regularly update and maintain basic data, including remote sensing images, climate data, and land use change data; and establish a data update mechanism and data quality monitoring system; S52. Use geographic information systems and remote sensing to conduct real-time monitoring and dynamic analysis of carbon sink changes; compare carbon sink distribution maps and total data from different periods to identify trends and characteristics of carbon sink changes; S53. Evaluate and adjust existing optimization strategies based on the monitoring results of carbon sink changes.

[0038] S54. The assessment and adjustment include formulating corresponding intervention measures and improvement plans for areas where carbon sinks are decreasing or growing slowly.

[0039] The working principle of the above technical solution is as follows: Basic data (such as remote sensing imagery, climate data, and land use change data) are regularly updated and maintained to ensure timeliness and accuracy. A data update mechanism and quality monitoring system are also established to ensure data reliability and integrity. Real-time monitoring and dynamic analysis of carbon sink changes are conducted using geographic information systems and remote sensing technology. By comparing carbon sink distribution maps and total data from different time periods, trends and characteristics of carbon sink changes, including increases, decreases, or slow growth, are identified. Based on the monitoring results of carbon sink changes, existing optimization strategies are evaluated and adjusted. For areas experiencing decreased or slow growth in carbon sinks, appropriate intervention measures and improvement plans are formulated to promote carbon sink growth and protect the ecological environment. A dynamic coupling mechanism between carbon sink accounting and national land space planning is established, integrating carbon sink monitoring results with national land space planning to enable monitoring of carbon sink changes and dynamic adjustments to national land space planning. This coupling mechanism can make national land space planning more scientific and rational, and is conducive to promoting carbon sink growth and protecting the ecological environment.

[0040] The effects of the above technical solutions are: by regularly updating and maintaining basic data, as well as real-time monitoring and dynamic analysis of carbon sink changes, areas where carbon sinks are decreasing or growing slowly can be discovered in a timely manner, so that optimization strategies can be adjusted in a timely manner to effectively respond to carbon sink changes; monitoring and analysis of carbon sink changes based on geographic information systems and remote sensing technologies can provide scientific data support for decision makers, promote the growth of carbon sinks and the protection of the ecological environment; by comparing carbon sink distribution maps and total data in different periods, trends and characteristics of carbon sink changes can be identified, which is conducive to the rational planning and utilization of resources and maximizing the storage and absorption capacity of carbon sinks; by formulating intervention measures and improvement plans for areas where carbon sinks are decreasing or growing slowly, the ecological environment can be effectively protected and the recovery and healthy development of the ecosystem can be promoted; by establishing a dynamic coupling mechanism between carbon sink accounting and national land space planning, the carbon sink monitoring results are combined with national land space planning, which is conducive to the coordinated development of society and the environment.

[0041] One embodiment of the present invention, as Figure 2 As shown, an ecosystem carbon sink accounting system based on national land space planning objectives includes: Data acquisition module: Acquires and integrates basic data for ecosystem carbon sink accounting and national land space planning, including remote sensing images, topography, climate data, land use types, and vegetation coverage, and builds a high-precision national land space database based on this basic data; Regional division module: Based on the national land space planning objectives, the study area is divided into different types of land use units, such as forest, grassland, farmland, wetland, urban green space, etc.; and the key carbon sink parameters of each land use unit (such as biomass, soil organic carbon content, carbon turnover rate, etc.) are determined; Carbon sink distribution module: Calibrate and verify the parameters of existing carbon sink models for different land use types using historical data; use geographic information systems to embed carbon sink estimation models into the national land space database, calculate the carbon sink amount and spatial distribution characteristics of each land use unit, and generate carbon sink distribution maps; Strategy acquisition module: Based on the national land space planning objectives, the carbon sequestration enhancement potential of various land use units is evaluated; and multiple factors, including the ecological environment carrying capacity, are combined to obtain carbon sequestration optimization strategies; such as adjusting the land use structure and implementing carbon sequestration enhancement projects.

[0042] Strategy optimization module: Establish a dynamic coupling mechanism between carbon sink accounting and national land space planning, update data regularly, monitor changes in carbon sinks, adjust optimization strategies in a timely manner based on monitoring results, and form a closed-loop feedback system for carbon sink management.

[0043] The working principle of the above technical solution is as follows: First, high-quality basic data, including topography, climate, land use types, and vegetation cover, must be obtained. This data can be acquired through remote sensing technology and integrated and processed using a geographic information system (GIS) to construct a high-precision national spatial database. Based on national spatial planning objectives, the study area is divided into different land use units. This division is typically based on land use type and ecosystem characteristics, such as forest, grassland, farmland, wetland, and urban green space. For each land use unit, key carbon sink parameters, such as biomass, soil organic carbon content, and carbon turnover rate, must be determined. These parameters are crucial for accurately calculating carbon sink amounts. Carbon sink models for different land use types are calibrated and validated using historical data to ensure model accuracy and reliability. This involves comparing with field survey data and fine-tuning the model. Using a GIS, the carbon sink estimation model is embedded in the national spatial database to calculate and visualize the carbon sink amount and spatial distribution characteristics of each land use unit. Based on national spatial planning objectives, the carbon sink enhancement potential of each land use unit is assessed. This involves comprehensively considering multiple factors such as the carrying capacity of the ecological environment, and formulating corresponding carbon sink optimization strategies; establishing a dynamic coupling mechanism between carbon sink accounting and national land space planning, and adjusting optimization strategies in a timely manner through regular data updates and monitoring of carbon sink changes to form a closed-loop feedback system for carbon sink management.

[0044] The above technical solution achieves the following: By integrating multiple basic data sets, including remote sensing imagery, topography, climate data, land use types, and vegetation coverage, a high-precision national spatial database is constructed. This comprehensive data foundation ensures the accuracy and comprehensiveness of ecosystem carbon sink accounting. By embedding carbon sink estimation models into the national spatial database through a geographic information system (GIS), spatial analysis and visualization of carbon sink distribution within each land use unit can be performed. This not only helps planners intuitively understand the spatial distribution characteristics of carbon sinks but also provides a scientific basis for national spatial planning. Based on national spatial planning objectives, the study area is divided into different land use units, and key carbon sink parameters for each land use unit are determined. Parameters of the carbon sink models for different land use types are calibrated and validated using historical data to ensure model accuracy. Based on these parameters, the carbon sink enhancement potential of each land use unit can be assessed, providing a scientific basis for formulating carbon sink optimization strategies. A dynamic coupling mechanism between carbon sink accounting and national spatial planning is established, with regular data updates and monitoring of carbon sink changes. Once a trend in carbon sink changes is detected, strategies can be promptly adjusted and optimized based on monitoring results, forming a closed-loop feedback system for carbon sink management. This dynamic adjustment and management can effectively address the challenges posed by carbon sink changes and safeguard ecosystem stability. Effective management of carbon sink resources can also help adjust land use structures and implement carbon sink enhancement projects.

[0045] In one embodiment of the present invention, an ecosystem carbon sink accounting system based on national land space planning objectives is provided, wherein the data acquisition module includes: Data collection module: collects basic data for ecosystem carbon sink accounting and national land space planning through multiple means, including remote sensing image data, topographic data (including elevation, slope, and aspect), historical climate data (including rainfall, temperature, and wind speed), current land use data (including different types of land use and their area distribution), and vegetation coverage data; Data calculation module: Different types of basic data are stored in the calculation platform. The calculation platform allocates the basic data to different calculation units according to the basic data type. Each calculation unit preprocesses the basic data through a parallel processor; the preprocessing includes radiometric calibration, geometric correction and atmospheric correction of remote sensing images; interpolation and normalization of climate data to eliminate spatial and temporal discontinuities; classification and recoding of land use data to unify data formats and classification standards.

[0046] Resource monitoring module: monitors the computing resource usage of each computing unit in real time through the resource manager, and schedules the computing resources of each computing unit in real time based on the queue scheduling algorithm; Index design module: spatially overlay and associate attributes of pre-processed data through geographic information system; build a national land space database containing multi-source data, and optimize the structure and index design of the database.

[0047] The working principle of the above technical solution is as follows: A variety of basic data are collected, including remote sensing imagery, topographic data, historical climate data, current land use data, and vegetation coverage data. This data can be obtained from various government departments, scientific research institutions, remote sensing satellites, and other channels. Different types of basic data are then preprocessed accordingly. For example, remote sensing images undergo radiometric calibration, geometric correction, and atmospheric correction; climate data undergoes interpolation and normalization; and land use data undergoes classification and recoding to ensure data quality and consistency. Parallel processors on the computing platform are used to perform parallel computations on the preprocessed data to improve computational efficiency. A resource manager monitors the computing resource usage of each computing unit in real time and schedules computing resources for each unit based on a queue scheduling algorithm to ensure efficient resource allocation and utilization. Using a geographic information system (GIS), the preprocessed data is spatially overlaid and attribute-linked to integrate data from different sources. A national land spatial database containing multi-source data is constructed, and the database structure and index design are optimized to improve data query and retrieval efficiency. This can be achieved using existing GIS software or a self-developed database management system.

[0048] The above technical solution achieves the following: Data collection through multiple means ensures that data obtained from different sources covers as many aspects as possible, including remote sensing imagery, topography, historical climate, and current land use. This data not only covers multiple spatial dimensions but also traces back through time, enabling a comprehensive understanding of the current status and changing trends of ecosystems and national land space. The parallel computing capabilities of the computing platform enable rapid processing of large amounts of data. Preprocessing of basic data using parallel processors, such as radiometric calibration, geometric correction, and atmospheric correction of remote sensing imagery, interpolation and normalization of climate data, and classification and recoding of land use data, improves the efficiency and speed of data processing. This preprocessing step not only improves data processing efficiency but also enhances data accuracy and reliability. Correction and normalization eliminate potential errors and inconsistencies in the data, ensuring data quality and reliability. Constructing a national land space database is a key step in the entire process. The pre-processed data are spatially superimposed and attribute-associated through the geographic information system, and the database structure is optimized and indexed, which improves the efficiency of data query and retrieval, enabling users to obtain the required information more quickly; the constructed high-precision national land space database provides reliable data support for ecosystem carbon sink accounting and national land space planning.

[0049] In one embodiment of the present invention, the region division module includes: Unit division module: According to the goals and requirements of national land space planning, the study area is divided into several land use units, including cultivated land, forest land, grassland, water area and unused land; Calibration and verification module: Determine the key carbon sequestration parameters of different types of land use units and calibrate and verify the key parameters based on known experimental data.

[0050] The working principle of the above technical solution is as follows: First, the land use types of the study area are divided according to the goals and requirements of the national land space planning. The study area is usually divided into different types of land use units such as cultivated land, forest land, grassland, water area and unused land. Each type of land use unit has specific ecological and environmental characteristics and carbon storage; for each type of land use unit, its key carbon sink parameters are determined. These parameters usually include soil organic carbon content, aboveground vegetation carbon content, underground root carbon content, etc. These parameters can be obtained through field surveys and field sampling, and can also be estimated and extrapolated using remote sensing technology and ground monitoring data; based on known experimental data, the determined key carbon sink parameters are calibrated and verified. This includes comparative analysis of field sampling data and remote sensing monitoring data to verify the accuracy of the remote sensing estimation results; The benefits of this technical solution are: by dividing the study area into different types of land use units, land resources can be planned and managed scientifically and rationally. This helps optimize land use structure, improve land resource utilization efficiency, and achieve the goals and requirements of national land space planning. Determining key carbon sink parameters for each type of land use unit helps assess the carbon storage and sequestration potential of different land types. Calibration and verification of key parameters based on known experimental data can improve the accuracy and reliability of the model.

[0051] In one embodiment of the present invention, the carbon sink distribution module includes: Calibration and Optimization Module: This module collects historical carbon sink data, including vegetation biomass and soil organic carbon content; uses machine learning algorithms to calibrate and optimize parameters of existing carbon sink models; and evaluates the performance and accuracy of the calibrated carbon sink models through cross-validation and independent dataset testing. Carbon sink calculation module: embeds the calibrated carbon sink model into the national land space database to calculate the carbon sink amount of each land use unit; The calculation formula for the carbon sink is:

[0052] in, The total amount of carbon sink, is the area of ​​the i-th land use type, is the carbon sink coefficient of the i-th land use type, is the carbon sink conversion coefficient of the i-th land use type considering the three dimensions of land use bottom line control, structural layout optimization, and management improvement; The carbon sink coefficient is shown in Table 1: Table 1 Land use type and carbon sink coefficient

[0053]

[0054] described The values ​​of are shown in Table 2: Table 2, Value Table

[0055] Analyze the spatial distribution characteristics of carbon sinks using a spatial analysis algorithm of a geographic information system, wherein the spatial distribution characteristics include carbon sink density and carbon sink hotspots; And generate a carbon sink distribution map based on the analysis results.

[0056] The technical solution works as follows: First, historical carbon sink data, including information on vegetation biomass and soil organic carbon content, are collected. Machine learning algorithms are then used to calibrate and optimize the parameters of the existing carbon sink model to improve its accuracy and performance. The calibrated carbon sink model is embedded in a national spatial database and analyzed using a geographic information system (GIS). This database contains spatial information and related data for various land use units. A calculation formula is used to calculate the total carbon sink for each land use unit, taking into account the unit area and the corresponding carbon sink coefficient. The carbon sink coefficient is determined based on different land use types and subtypes, reflecting the contribution of each land use type to carbon sinks. Using GIS spatial analysis algorithms, the spatial distribution characteristics of carbon sinks are analyzed, including carbon sink density and carbon sink hotspots. These characteristics reflect the carbon sink status of different regions and land use types. Based on the spatial analysis results, a carbon sink distribution map is generated, visually displaying the spatial distribution of carbon sinks in the study area, providing an important reference for national spatial planning and ecological and environmental protection.

[0057] The effects of the above technical solution are: by collecting historical data and applying machine learning algorithms, the parameters of the existing carbon sink model are calibrated and optimized, thereby improving the accuracy and reliability of the model; the solution takes into account the contribution of different land use types to carbon sinks, including cultivated land, forest land, grassland, water area and unused land, making the estimation of carbon sinks more comprehensive; using the spatial analysis algorithm of the geographic information system, the spatial distribution characteristics of carbon sinks are analyzed, including carbon sink density and carbon sink hotspots, etc., providing an important reference for regional carbon sink management and ecological environmental protection; the generated carbon sink distribution map intuitively shows the spatial distribution of carbon sinks in the study area, providing a scientific basis and decision-making support for national land space planning and ecological environmental management.

[0058] In one embodiment of the present invention, the strategy optimization module includes: Update and maintenance module: regularly update and maintain basic data, including remote sensing images, climate data, land use changes, etc.; and establish a data update mechanism and data quality monitoring system; Trend Identification Module: Utilizes geographic information systems and remote sensing to conduct real-time monitoring and dynamic analysis of carbon sink changes; compares carbon sink distribution maps and total data over different periods to identify trends and characteristics of carbon sink changes; Evaluation and adjustment module: Evaluate and adjust existing optimization strategies based on the monitoring results of carbon sink changes.

[0059] The assessment and adjustment include formulating corresponding intervention measures and improvement plans for areas where carbon sinks are decreasing or growing slowly.

[0060] The working principle of the above technical solution is as follows: Basic data (such as remote sensing imagery, climate data, and land use change data) are regularly updated and maintained to ensure timeliness and accuracy. A data update mechanism and quality monitoring system are also established to ensure data reliability and integrity. Real-time monitoring and dynamic analysis of carbon sink changes are conducted using geographic information systems and remote sensing technology. By comparing carbon sink distribution maps and total data from different time periods, trends and characteristics of carbon sink changes, including increases, decreases, or slow growth, are identified. Based on the monitoring results of carbon sink changes, existing optimization strategies are evaluated and adjusted. For areas experiencing decreased or slow growth in carbon sinks, appropriate intervention measures and improvement plans are formulated to promote carbon sink growth and protect the ecological environment. A dynamic coupling mechanism between carbon sink accounting and national land space planning is established, integrating carbon sink monitoring results with national land space planning to enable monitoring of carbon sink changes and dynamic adjustments to national land space planning. This coupling mechanism can make national land space planning more scientific and rational, and is conducive to promoting carbon sink growth and protecting the ecological environment.

[0061] The effects of the above technical solutions are: by regularly updating and maintaining basic data, as well as real-time monitoring and dynamic analysis of carbon sink changes, areas where carbon sinks are decreasing or growing slowly can be discovered in a timely manner, so that optimization strategies can be adjusted in a timely manner to effectively respond to carbon sink changes; monitoring and analysis of carbon sink changes based on geographic information systems and remote sensing technologies can provide scientific data support for decision makers, promote carbon sink growth and ecological environment protection; by comparing carbon sink distribution maps and total data in different periods, trends and characteristics of carbon sink changes can be identified, which is conducive to rational planning and utilization of resources and maximizing the storage and absorption capacity of carbon sinks; by formulating intervention measures and improvement plans for areas where carbon sinks are decreasing or growing slowly, the ecological environment can be effectively protected and the recovery and healthy development of the ecosystem can be promoted; by establishing a dynamic coupling mechanism between carbon sink accounting and national land space planning, the carbon sink monitoring results are combined with national land space planning to achieve coordinated environmental development.

[0062] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for calculating ecosystem carbon sinks based on national land space planning objectives, characterized by: The method comprises: S1. Acquire and integrate basic data for ecosystem carbon sink accounting and national land space planning, and build a high-precision national land space database based on the basic data; S2. Divide the study area into different types of land use units according to the national land space planning objectives and determine the key carbon sequestration parameters of each type of land use unit; S3. Calibrate and verify the parameters of existing carbon sink models for different land use types using historical data. Use a geographic information system to embed the carbon sink estimation model into the national land space database, calculate the carbon sink amount and spatial distribution characteristics of each land use unit, and generate a carbon sink distribution map. S4. Evaluate the carbon sequestration potential of various land use units based on national land space planning objectives; and combine multiple factors to develop carbon sequestration optimization strategies; S5. Establish a dynamic coupling mechanism between carbon sink accounting and national land space planning, monitor changes in carbon sinks, and adjust optimization strategies in a timely manner based on monitoring results.

2. The ecosystem carbon sink accounting method based on national land space planning objectives according to claim 1 is characterized in that: Said S1 comprises: S11. Collect basic data for ecosystem carbon sink accounting and national land space planning through multiple means, including remote sensing image data, topographic data, historical climate data, current land use data, and vegetation coverage data; S12. Storing different types of basic data into a computing platform, the computing platform assigns the basic data to different computing units according to the basic data type, and each computing unit preprocesses the basic data using a parallel processor; S13. Monitor the computing resource usage of each computing unit in real time through the resource manager, and schedule the computing resources of each computing unit in real time based on the queue scheduling algorithm; S14. Use the geographic information system to spatially overlay and associate attributes of the pre-processed data; build a national land space database containing multi-source data, and optimize the structure and index design of the database.

3. The ecosystem carbon sink accounting method based on national land space planning objectives according to claim 1 is characterized in that: Said S2 comprises: S21. Divide the study area into several land use units according to the objectives and requirements of national land space planning. The land use units include cultivated land, forest land, grassland, water area and unused land. S22. Determine the key carbon sequestration parameters for different types of land use units and calibrate and verify the key parameters based on known experimental data.

4. The ecosystem carbon sink accounting method based on national land space planning objectives according to claim 1 is characterized in that: The S3 includes: S31. Collect historical carbon sink data, including vegetation biomass and soil organic carbon content; calibrate and optimize existing carbon sink models using machine learning algorithms; and evaluate the performance and accuracy of the calibrated carbon sink models through cross-validation and independent dataset testing. S32. Embed the calibrated carbon sink model into the national land space database and calculate the carbon sink amount of each land use unit; The calculation formula for the carbon sink is: in, The total amount of carbon sink, is the area of ​​the i-th land use type, is the carbon sink coefficient of the i-th land use type, is the carbon sink conversion coefficient of the i-th land use type considering the three dimensions of land use bottom line control, structural layout optimization, and management improvement; S33. Analyze the spatial distribution characteristics of carbon sinks using a spatial analysis algorithm of a geographic information system, wherein the spatial distribution characteristics include carbon sink density and carbon sink hotspots; S34, and generating a carbon sink distribution map based on the analysis results.

5. The ecosystem carbon sink accounting method based on national land space planning objectives according to claim 1 is characterized in that: Said S5 comprises: S51. Regularly update and maintain basic data, and establish a data update mechanism and data quality monitoring system; S52. Use geographic information systems and remote sensing to conduct real-time monitoring and dynamic analysis of carbon sink changes; compare carbon sink distribution maps and total data from different periods to identify trends and characteristics of carbon sink changes; S53. Evaluate and adjust existing optimization strategies based on the monitoring results of carbon sink changes.

6. An ecosystem carbon sink accounting system based on national land space planning objectives, characterized by: The system comprises: Data acquisition module: acquires and integrates basic data for ecosystem carbon sink accounting and national land space planning, and builds a high-precision national land space database based on the basic data; Regional division module: Based on the national land space planning objectives, the study area is divided into different types of land use units, and the key carbon sequestration parameters of each type of land use unit are determined; Carbon sink distribution module: Calibrate and verify the parameters of existing carbon sink models for different land use types using historical data; use geographic information systems to embed carbon sink estimation models into the national land space database, calculate the carbon sink amount and spatial distribution characteristics of each land use unit, and generate carbon sink distribution maps; Strategy Acquisition Module: Evaluate the carbon sequestration potential of various land use units based on national land space planning objectives; and combine multiple factors to obtain carbon sequestration optimization strategies; Strategy optimization module: Establish a dynamic coupling mechanism between carbon sink accounting and national land space planning, monitor changes in carbon sinks, and adjust optimization strategies in a timely manner based on monitoring results.

7. The ecosystem carbon sink accounting system based on national land space planning objectives according to claim 6 is characterized in that: The data acquisition module includes: Data collection module: collects basic data for ecosystem carbon sink accounting and national land space planning through multiple means, including remote sensing image data, topographic data, historical climate data, current land use data (including different types of land use and their area distribution), and vegetation coverage data; Data calculation module: stores different types of basic data into the calculation platform, and the calculation platform distributes the basic data to different calculation units according to the basic data type. Each calculation unit pre-processes the basic data through a parallel processor; Resource monitoring module: monitors the computing resource usage of each computing unit in real time through the resource manager, and schedules the computing resources of each computing unit in real time based on the queue scheduling algorithm; Index design module: spatially overlay and associate attributes of pre-processed data through geographic information system; build a national land space database containing multi-source data, and optimize the structure and index design of the database.

8. The ecosystem carbon sink accounting system based on national land space planning objectives according to claim 6 is characterized in that: The area division module includes: Unit division module: According to the goals and requirements of national land space planning, the study area is divided into several land use units, including cultivated land, forest land, grassland, water area and unused land; Calibration and verification module: Determine the key carbon sequestration parameters of different types of land use units and calibrate and verify the key parameters based on known experimental data.

9. The ecosystem carbon sink accounting system based on national land space planning objectives according to claim 6 is characterized in that: The carbon sink distribution module includes: Calibration and Optimization Module: This module collects historical carbon sink data, including vegetation biomass and soil organic carbon content; uses machine learning algorithms to calibrate and optimize parameters of existing carbon sink models; and evaluates the performance and accuracy of the calibrated carbon sink models through cross-validation and independent dataset testing. Carbon sink calculation module: embeds the calibrated carbon sink model into the national land space database to calculate the carbon sink amount of each land use unit; The calculation formula for the carbon sink is: in, The total amount of carbon sink, is the area of ​​the i-th land use type, is the carbon sink coefficient of the i-th land use type, is the carbon sink conversion coefficient of the i-th land use type considering the three dimensions of land use bottom line control, structural layout optimization, and management improvement; Analyze the spatial distribution characteristics of carbon sinks using a spatial analysis algorithm of a geographic information system, wherein the spatial distribution characteristics include carbon sink density and carbon sink hotspots; And generate a carbon sink distribution map based on the analysis results.

10. The ecosystem carbon sink accounting system based on national land space planning objectives according to claim 6, characterized in that: The strategy optimization module includes: Update and maintenance module: regularly update and maintain basic data, and establish a data update mechanism and data quality monitoring system; Trend Identification Module: Utilizes geographic information systems and remote sensing to conduct real-time monitoring and dynamic analysis of carbon sink changes; compares carbon sink distribution maps and total data over different periods to identify trends and characteristics of carbon sink changes; Evaluation and adjustment module: Evaluate and adjust existing optimization strategies based on the monitoring results of carbon sink changes.

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