A method and system for calculating carbon budget before and after national land space planning

Abstract

The present invention proposes a method and system for carbon balance accounting before and after national land space planning; it belongs to the technical field of national land planning. The method comprises: obtaining relevant data before and after the implementation of national land space planning, and storing the relevant data in an accounting platform, preprocessing the relevant data through the accounting platform; storing the preprocessed relevant data in a cloud space, the cloud space processes the stored preprocessed relevant data, setting up a structured database in the cloud space, and storing the processed results in the database respectively. Through spatial analysis algorithms and distributed computing frameworks, accurate simulation and analysis of the spatiotemporal evolution of carbon sources and carbon sinks are achieved, and the impact of land use changes on carbon balances can be intuitively displayed. According to the accounting of carbon sources and carbon sinks after national land space planning, the preparation and implementation of national land space planning schemes are guided to achieve the goal of low-carbon development.

Description

Technical Field

[0001] The present invention proposes a carbon budget accounting method and system before and after national land space planning, belonging to the technical field of national land planning. Background Art

[0002] Traditional carbon budget accounting methods often rely on manual data collection and collation, which is not only inefficient, but also difficult to ensure the comprehensiveness and accuracy of the data. At the same time, since national land space planning involves multiple fields and complex systems, its carbon budget changes have a high degree of temporal and spatial heterogeneity, and traditional accounting methods are difficult to effectively capture these changing characteristics. Therefore, it is particularly important to develop a method that can efficiently, accurately and comprehensively calculate the changes in carbon budget before and after the implementation of national land space planning. Summary of the invention

[0003] The present invention provides a method and system for calculating carbon budget before and after national land space planning, which is used to solve the problems mentioned in the above background technology:

[0004] The present invention proposes a method for calculating carbon budget before and after national land space planning, the method comprising:

[0005] Obtain relevant data before and after the implementation of the national land space planning, store the relevant data in the accounting platform, and pre-process the relevant data through the accounting platform;

[0006] The pre-processed related data is stored in a cloud space, the cloud space processes the stored pre-processed related data, a structured database is set up in the cloud space, and the processed results are stored in the database respectively;

[0007] Through the processed data, based on the carbon source accounting model and the carbon sink accounting model, and through the spatial analysis algorithm, the temporal and spatial evolution analysis of the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the implementation of the national land space planning is carried out;

[0008] The accounting results are obtained through spatiotemporal evolution analysis and output in a visual manner.

[0009] Furthermore, the acquisition of relevant data before and after the implementation of the national land space planning and the storage of the relevant data in the accounting platform, and the pre-processing of the relevant data through the accounting platform include:

[0010] Obtain relevant data before and after national land space planning, and store the relevant data into the accounting platform according to data type;

[0011] The accounting platform stores the received related data of different types into different storage spaces respectively, and allocates computing resources to each storage space;

[0012] Each storage space pre-processes related data based on the allocated computing resources through a parallel processing algorithm, and adjusts the resource load of each storage space through an elastic scaling algorithm;

[0013] After the processing is completed, the preprocessing related data obtained in each storage space is copied, and the copied related data are compressed respectively, and the compressed preprocessing related data is transmitted to the cloud platform through a multi-channel transmission protocol.

[0014] Furthermore, the pre-processed related data is stored in a cloud space, the cloud space processes the stored pre-processed related data, a structured database is set up in the cloud space, and the processed results are stored in the database respectively; including:

[0015] The cloud platform receives the pre-processed relevant data, decompresses it, and processes the relevant data;

[0016] Analyze the processed relevant data and determine the database design requirements based on the analysis results

[0017] Based on the results of demand analysis, select the database and design the corresponding data table according to the classification and relevance of the data;

[0018] Define fields for each data table, set a primary key for each data table, set foreign keys based on the relationships between data tables, and create indexes;

[0019] Create a database instance in the cloud space and configure it accordingly based on the selected database;

[0020] Import the processed relevant data into the database; perform performance testing on the database, and optimize and adjust the database based on the test results.

[0021] Furthermore, the processing of the relevant data includes:

[0022] The preprocessed related data is decomposed into multiple computing units according to the data type. The data in each computing unit is divided into multiple small subtasks. The task scheduler is responsible for assigning the subtasks to available computing nodes.

[0023] Each computing node executes the assigned subtasks through a parallel computing framework;

[0024] During the execution process, the processing tasks are executed asynchronously. After the execution is completed, the results of all subtasks are merged and aggregated to generate the final processing result.

[0025] Furthermore, the processed data, based on the carbon source accounting model and the carbon sink accounting model, uses a spatial analysis algorithm to perform spatiotemporal evolution analysis on the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the implementation of the national land space planning; including:

[0026] Extracting processed relevant data from the cloud space structured database, organizing the relevant data in time series, and forming a continuous data stream before and after the implementation of the plan;

[0027] Apply spatial analysis algorithms to analyze the spatial pattern and dynamic changes of carbon source distribution in different regions before and after planning; conduct spatial overlay analysis on land use change data to identify land use transition areas;

[0028] The extracted data is input into the carbon source accounting model and the carbon sink accounting model, and the model operation is performed through the distributed computing framework to obtain the total carbon source and carbon sink at each time node; the operation includes agricultural carbon emission calculation, urban and other construction land carbon emission calculation, transportation carbon emission calculation and industrial carbon emission calculation;

[0029] Use spatiotemporal data analysis techniques to analyze the evolution of carbon budget over time and compare the carbon balance status before and after the implementation of the plan;

[0030] The rationality of the preliminary accounting results is verified, and the accounting results are revised and calibrated by comparing historical statistical data and field monitoring data.

[0031] The present invention proposes a carbon budget accounting system before and after national land space planning, the system comprising:

[0032] Data acquisition module: obtain relevant data before and after the implementation of national land space planning, store the relevant data in the accounting platform, and pre-process the relevant data through the accounting platform;

[0033] Data storage module: storing the pre-processed relevant data into the cloud space, the cloud space processes the stored pre-processed relevant data, sets up a structured database in the cloud space, and stores the processed results into the database respectively;

[0034] Analysis and calculation module: Based on the carbon source accounting model and the carbon sink accounting model, the spatial analysis algorithm is used to analyze the spatiotemporal evolution of the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the implementation of the national land space planning.

[0035] Result output module: obtain the calculation results through spatiotemporal evolution analysis, and output the calculation results in a visual way.

[0036] Furthermore, the data acquisition module includes:

[0037] Type classification module: obtain relevant data before and after national land space planning, and store the relevant data into the accounting platform according to the data type;

[0038] Resource allocation module: The accounting platform stores different types of related data received into different storage spaces, and allocates computing resources to each storage space;

[0039] Load adjustment module: Each storage space pre-processes related data based on the allocated computing resources through a parallel processing algorithm, and adjusts the resource load of each storage space through an elastic scaling algorithm;

[0040] Transmission module: After the processing is completed, the pre-processing related data obtained from each storage space is copied, and the copied related data are compressed respectively, and the compressed pre-processing related data is transmitted to the cloud platform through a multi-channel transmission protocol.

[0041] Furthermore, the data storage module includes:

[0042] Decompression module: The cloud platform receives the pre-processed relevant data, decompresses it, and processes the relevant data;

[0043] Requirements design module: analyzes the processed relevant data and determines the design requirements of the database based on the analysis results;

[0044] Database selection module: select database based on the results of demand analysis; design corresponding data tables according to data classification and relevance;

[0045] Index creation module: define fields for each data table, set primary keys for each data table, set foreign keys based on the relationships between data tables, and create indexes;

[0046] Corresponding configuration module: Create a database instance in the cloud space and perform corresponding configuration according to the selected database;

[0047] Optimization and adjustment module: import the processed relevant data into the database; perform performance testing on the database, and optimize and adjust the database based on the test results.

[0048] Furthermore, the processing of the relevant data includes:

[0049] Data decomposition module: decomposes the preprocessed related data into multiple computing units according to the data type. The data in each computing unit is divided into multiple small subtasks. The task scheduler is responsible for assigning the subtasks to available computing nodes.

[0050] Execution module: Each computing node executes the assigned subtasks through a parallel computing framework;

[0051] Result generation module: During the execution process, the processing tasks are executed asynchronously. After the execution is completed, the results of all subtasks are merged and aggregated to generate the final processing results.

[0052] Furthermore, the analysis and calculation module includes:

[0053] Data extraction module: extracts the processed relevant data from the cloud space structured database, organizes the relevant data in time series, and forms a continuous data stream before and after the implementation of the plan;

[0054] Regional identification module: Apply spatial analysis algorithms to analyze the spatial pattern and dynamic changes of carbon source distribution in different regions before and after planning; perform spatial overlay analysis on land use change data to identify land use transition areas;

[0055] Calculation module: input the extracted data into the carbon source accounting model and the carbon sink accounting model, execute the model calculation through the distributed computing framework, and obtain the total carbon source and carbon sink at each time node;

[0056] Comparison module: Use spatiotemporal data analysis technology to analyze the evolution of carbon balance over time and compare the carbon balance status before and after the implementation of the plan;

[0057] Correction and calibration module: Verify the rationality of the preliminary accounting results, and correct and calibrate the accounting results by comparing historical statistical data and field monitoring data.

[0058] Beneficial effects of the present invention: The present invention makes full use of the accounting platform and cloud space to achieve efficient integration and preprocessing of a large amount of relevant data before and after the implementation of national land space planning. Through parallel processing algorithms and elastic scaling resource management, the data processing efficiency and system stability are significantly improved, ensuring the rapid loading and accurate preprocessing of large-scale data; the cloud space not only provides a safe and reliable large-scale data storage environment, but also realizes the construction of a structured database through intelligent analysis and design. This method helps to clearly classify data, quickly query, and optimize the database according to data characteristics, thereby supporting complex spatial analysis and carbon balance accounting; in the data processing stage, task decomposition and parallel computing frameworks are used to improve computing speed and resource utilization. Asynchronous execution and result aggregation ensure the efficiency and integrity of data processing, and meet the needs of spatiotemporal evolution analysis for big data processing; through spatial analysis algorithms and distributed computing frameworks, accurate simulation and analysis of the spatiotemporal evolution of carbon sources and carbon sinks are achieved, which can intuitively show the impact of land use changes on carbon balance. This method can deeply explore the temporal dynamics and spatial pattern of carbon revenue and expenditure before and after the implementation of the plan, and provide decision makers with intuitive and detailed evidence of the temporal and spatial evolution of carbon revenue and expenditure; the accounting results are output in a visual form, which enhances the readability and communication efficiency of the data and facilitates the understanding and communication of different stakeholders. In addition, through comparative verification with historical statistics and field monitoring data, the accuracy and reliability of the accounting results are ensured, providing strong data support for the scientific formulation and adjustment of national land space planning. According to the accounting of carbon sources and carbon sinks after national land space planning, the preparation and implementation of national land space planning schemes can be guided to achieve the goal of low-carbon development. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of the method of the present invention;

[0060] Figure 2 Schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0061] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] 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 the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0064] One embodiment of the present invention, as Figure 1 As shown, a method for calculating carbon budget before and after national land space planning includes:

[0065] S1: Obtain relevant data before and after the implementation of the national land space planning, and store the relevant data in the accounting platform, and pre-process the relevant data through the accounting platform, wherein the relevant data includes energy consumption data;

[0066] S2 stores the pre-processed related data in a cloud space, the cloud space processes the stored pre-processed related data, sets up a structured database in the cloud space, and stores the processed results in the database respectively;

[0067] S3 uses the processed data, based on the carbon source accounting model and the carbon sink accounting model, and uses spatial analysis algorithms to analyze the spatiotemporal evolution of the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the implementation of the national land space planning;

[0068] S4 obtains accounting results through spatiotemporal evolution analysis and outputs the accounting results in a visual manner. It also guides the preparation and implementation of national land space planning schemes based on the accounting results to achieve the goal of low-carbon development.

[0069] The working principle of the above technical solution is as follows: First, it is necessary to obtain relevant data before the implementation of the national land space planning, including energy consumption data. These data may come from various sources, such as research institutions or third-party data providers. Then, according to the guidance goals of the national land space, the existing machine learning model is used for simulation calculation to obtain the planned data; the planned data also includes the planned energy consumption data; after obtaining the data before and after the implementation of the plan, it is stored in the accounting platform and pre-processed to ensure the accuracy and consistency of the data; the pre-processed relevant data is stored in the cloud space, a structured database is set up in the cloud space, and the processed results are stored in the database respectively. Doing so helps to effectively manage and quickly retrieve a large amount of data, and provides convenient conditions for subsequent analysis and calculation; based on the processed data, a carbon source accounting model and a carbon sink accounting model are established. Carbon source refers to the source of carbon emissions, such as industrial production, transportation, etc.; carbon sinks are places that absorb carbon, such as forests and wetlands. These models can evaluate carbon emissions and carbon absorption before and after the implementation of national land space planning based on different data indicators and algorithms; use spatial analysis algorithms to perform spatiotemporal evolution analysis on the output data of the carbon source accounting model and the output data of the carbon sink accounting model. This means that changes in spatial and temporal dimensions will be considered to more comprehensively evaluate the impact of national land space planning on carbon balance. Spatial analysis methods that may be used include geographic information system (GIS) technology, remote sensing technology, etc.; finally, the results of carbon balance accounting are obtained through spatiotemporal evolution analysis. These results can be output in a visual way, such as maps, charts, etc., so that decision makers or other stakeholders can more intuitively understand the carbon impact of the implementation of national land space planning, thereby guiding future planning. And guide the preparation and implementation of national land space planning schemes based on the accounting results to achieve the goal of low-carbon development.

[0070] The effect of the above technical solution is: by acquiring and processing a variety of relevant data, such as energy consumption data, it can provide comprehensive and accurate information, making it possible to comprehensively evaluate the carbon balance before and after the implementation of the national land space planning. It can help decision makers better understand the sources and influencing factors of carbon emissions, and improve the efficiency of data management by storing data in the cloud space and establishing a structured database. The use of cloud space makes data storage more flexible, and the establishment of a structured database facilitates the rapid retrieval and processing of data. It can improve the efficiency of the entire carbon balance accounting process, thereby saving time and resource costs; through the spatial analysis algorithm, the spatiotemporal evolution analysis can deeply understand the impact of national land space planning on carbon balance. This in-depth insight can help decision makers better understand the spatiotemporal distribution of carbon emissions and carbon absorption, thereby providing a scientific basis and decision-making support for future planning; by outputting the accounting results in a visual way, such as maps, charts, etc., it can intuitively show the impact of national land space planning on carbon balance. Such visual output not only enhances the communication effect between decision makers and other stakeholders, but also promotes the sharing and dissemination of information. This technical solution can not only conduct a one-time assessment of carbon revenue and expenditure before and after the implementation of national land space planning, but also establish a long-term monitoring and evaluation mechanism to achieve tracking, evaluation and continuous improvement of carbon reduction policies. Such continuous evaluation can help decision makers adjust policy measures in a timely manner to ensure the achievement of carbon reduction targets.

[0071] In one embodiment of the present invention, the step of obtaining relevant data before and after the implementation of the national land space planning and storing the relevant data in a calculation platform, and preprocessing the relevant data through the calculation platform includes:

[0072] Obtain relevant data before and after the national land space planning, and store the relevant data in the accounting platform according to the data type;

[0073] The accounting platform stores the received related data of different types into different storage spaces respectively, and allocates computing resources to each storage space;

[0074] Each storage space pre-processes related data based on the allocated computing resources through a parallel processing algorithm, and adjusts the resource load of each storage space through an elastic scaling algorithm;

[0075] After the processing is completed, the preprocessing related data obtained in each storage space is copied, and the copied related data are compressed respectively, and the compressed preprocessing related data is transmitted to the cloud platform through a multi-channel transmission protocol.

[0076] The working principle of the above technical solution is as follows: first, relevant data before and after the implementation of the national land space planning are obtained from various sources, and these data are stored in different storage spaces of the accounting platform according to their types; the accounting platform allocates computing resources according to different types of data, and allocates appropriate resources to each storage space. These resources can be the processing power, memory, etc. of the computer. Each storage space uses a parallel processing algorithm to pre-process the received relevant data to improve the processing efficiency; the elastic scaling algorithm is used to dynamically adjust the resource load of each storage space. If a storage space has more processing tasks, the system will automatically allocate more resources to the space to ensure high efficiency and stability of the processing; after the processing is completed, the processed relevant data is copied and backed up, and the copied and backed up relevant data is compressed to reduce the data volume. Then, the compressed pre-processed relevant data is transmitted to the cloud platform or other target location through a multi-channel transmission protocol. Multi-channel transmission can improve the security and stability of data transmission.

[0077] The effect of the above technical solution is: various relevant data before and after the national land space planning are integrated and stored in the accounting platform, which can be used for convenient and fast data management and access. Storing relevant data according to data type helps to improve the organization and manageability of data; the accounting platform allocates appropriate computing resources to each storage space according to different types of data requirements, making the data processing process more efficient and stable; through parallel processing algorithms, multiple data tasks can be processed at the same time, which improves the speed and efficiency of data processing and shortens the time cycle of data processing; elastic scaling algorithms can dynamically adjust resource allocation according to the load of each storage space, ensure the stability and efficiency of the system, and save waste of resources; copying and backing up the processed data, compressing it and transmitting it to the cloud platform through a multi-channel transmission protocol can greatly improve the security and stability of data transmission and reduce the risk of data loss during the process.

[0078] In one embodiment of the present invention, each storage space pre-processes related data through a parallel processing algorithm based on the allocated computing resources, and adjusts the resource load of each storage space through an elastic scaling algorithm, including:

[0079] The deep learning model is used to automatically identify and classify various types of data before and after planning. Based on the classification results, feature engineering methods are used to extract the key features of each type of data, such as time series analysis to identify land change trends and spatial statistical analysis to quantify regional differences.

[0080] Through the distributed parallel processing strategy and the MapReduce framework, the preprocessing task is decomposed into multiple subtasks that can be executed in parallel and distributed to each storage space (i.e. computing node) for execution;

[0081] Through the dynamic resource scheduling mechanism, based on the real-time load of each storage space (computing node) and the task priority, the computing resource allocation is dynamically adjusted through elastic scaling algorithms (such as the automatic expansion function of Kubernetes), ensuring that high-load nodes are replenished with resources in a timely manner and low-load nodes release resources for other tasks;

[0082] Through data localization optimization strategy, data storage and computing tasks are arranged on the same or adjacent physical nodes to reduce data transmission delay and bandwidth consumption;

[0083] Integrate multiple anomaly detection algorithms (such as statistical-based methods, cluster analysis, and isolation forests) to comprehensively scan the data before and after planning, identify and mark outliers or inconsistent data;

[0084] According to the anomaly detection results, data cleaning strategies (such as filling missing values, correcting erroneous values, deleting invalid records, etc.) are used to clean the data;

[0085] Implement data quality assessment and quantitatively evaluate the quality of pre-processed data by calculating indicators such as data completeness, accuracy, and consistency;

[0086] Establish a preprocessing result verification mechanism to verify the rationality and reliability of the preprocessing results through cross-validation and comparison of historical data;

[0087] If problems are found during the verification process, the reprocessing process will be automatically triggered, or an alert will be sent to the system administrator to collect performance indicators during the preprocessing process (such as processing time, resource consumption, error rate, etc.), forming a feedback loop to continuously optimize the preprocessing process and resource allocation strategy.

[0088] The working principle of the above technical solution is as follows: First, the automatic learning ability of deep learning models (such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), etc.) is used to intelligently identify and classify various types of data before and after planning. Through training, these models can accurately divide complex data sets into categories with clear business meanings, such as land use types, population density changes, etc.; based on data classification, feature engineering methods are used to extract key features from each type of data. These features may include the results of time series analysis (used to identify land change trends), the results of spatial statistical analysis (used to quantify regional differences), etc. The purpose of feature extraction is to convert raw data into a format that is easier to analyze and understand, laying the foundation for subsequent data processing and analysis; using distributed computing frameworks such as MapReduce, the preprocessing tasks are decomposed into multiple subtasks that can be executed in parallel. These subtasks are assigned to different computing nodes (i.e., storage spaces) for execution to make full use of the computing resources of the cluster. Through parallel processing, data processing time can be significantly shortened and processing efficiency can be improved; through dynamic resource scheduling mechanisms (such as the automatic expansion function of Kubernetes), the load status and task priority of each computing node can be monitored in real time. When a node is overloaded, the system will automatically allocate more computing resources (such as CPU, memory, etc.) to it; when the node load is low, some resources will be released to other tasks that require higher computing power. This elastic scaling capability ensures the effective use of resources and the rapid execution of tasks; in order to reduce data transmission delays and bandwidth consumption, a data localization optimization strategy is adopted. That is, data storage and computing tasks are arranged on the same or adjacent physical nodes as much as possible. In this way, when data processing is required, data can be read directly from local or adjacent storage media without transmitting through the network, thereby improving processing efficiency; multiple anomaly detection algorithms (such as statistical-based methods, cluster analysis, and isolation forests, etc.) are integrated to conduct a comprehensive scan of the data before and after planning. Through algorithm analysis, outliers or inconsistent data are identified and marked. Subsequently, according to the anomaly detection results, data cleaning strategies are used to process the data (such as filling missing values, correcting erroneous values, deleting invalid records, etc.) to ensure the accuracy and reliability of the data; by calculating indicators such as data integrity, accuracy, and consistency, the quality of the pre-processed data is quantitatively evaluated. These indicators can help users understand the overall quality level of the data and provide support for subsequent data analysis and decision-making; establish a preprocessing result verification mechanism to verify the rationality and reliability of the preprocessing results through cross-validation and comparison of historical data. If a problem is found, the reprocessing process is automatically triggered or an alert is sent to the system administrator. At the same time, performance indicators (such as processing time, resource consumption, error rate, etc.) during the preprocessing process are collected to form a feedback loop. Based on this feedback information, the preprocessing process and resource allocation strategy are continuously optimized to improve the efficiency and accuracy of data processing.

[0089] The effects of the above technical solutions are as follows: through the distributed parallel processing strategy, the preprocessing task is decomposed into multiple subtasks that can be executed in parallel, and assigned to multiple computing nodes for execution, which significantly improves the speed and efficiency of data processing; the dynamic resource scheduling and elastic scaling algorithm can automatically adjust the computing resource allocation according to the real-time load situation and task priority of the computing node, ensuring that high-load nodes are replenished with resources in a timely manner, avoiding resource bottlenecks, and further improving processing efficiency; the system can dynamically adjust the allocation of computing resources according to actual needs, avoiding over-allocation or under-allocation of resources, and maximizing the utilization of resources; by arranging data storage and computing tasks on the same or adjacent physical nodes, the delay and bandwidth consumption of data transmission are reduced, and the resource utilization efficiency is further improved; the deep learning model is used to automatically identify and classify the data before and after planning, and the feature engineering method is used to extract key features to ensure the accuracy and richness of the data; a variety of anomaly detection algorithms are integrated to comprehensively scan and identify The outliers and inconsistent data in the data were then processed through the data cleaning strategy, which significantly improved the accuracy and reliability of the data; the quality of the pre-processed data was quantitatively evaluated by calculating indicators such as data integrity, accuracy, and consistency, providing a clear measurement standard for data quality; a pre-processing result verification mechanism was established to verify the rationality and reliability of the pre-processing results through cross-validation and comparison of historical data, ensuring the accuracy and credibility of data processing; the entire pre-processing process is highly automated, reducing manual intervention and the risk of human error; the system can automatically trigger the re-processing process, or send an alert to the system administrator when a problem is found, forming a closed-loop feedback mechanism, which helps to continuously optimize the pre-processing process and resource allocation strategy; the technical solution supports the processing of multiple data types and can be customized and optimized according to different needs; as the amount of data increases and needs change, the system can easily expand computing resources and storage capabilities to meet future data processing needs.

[0090] In one embodiment of the present invention, the pre-processed related data is stored in a cloud space, the cloud space processes the stored pre-processed related data, a structured database is set up in the cloud space, and the processed results are stored in the database respectively; including:

[0091] The cloud platform receives the pre-processed relevant data, decompresses it, and processes the relevant data;

[0092] Analyze the processed relevant data to determine the data type, structure, size, and growth trend. And based on the analysis results, determine the database design requirements, including storage capacity, query performance, and data security;

[0093] Based on the results of the demand analysis, select a database. For example, if the data volume is large and needs to support complex query operations, you can choose a relational database such as MySQL, Oracle, etc.; if the data structure is changeable or requires a more flexible storage method, you can consider using a NoSQL database such as MongoDB, Cassandra, etc.; according to the classification and relevance of the data, design the corresponding data table; for example, the energy consumption data table, etc.

[0094] Define fields for each data table, including field name, data type, length, whether to allow nulls, etc. Set a primary key for each data table, set foreign keys based on the relationship between data tables, and create indexes;

[0095] Create a database instance in the cloud space and perform corresponding configurations based on the selected database, such as setting storage size, allocating computing resources, etc.

[0096] Import the processed relevant data into the database; perform performance tests on the database, including query speed, concurrent processing capabilities, etc. Optimize and adjust the database based on the test results, such as adjusting indexes, optimizing query statements, etc.

[0097] Set up a regular backup strategy for the database to prevent data loss or damage. At the same time, formulate a data recovery plan to ensure that the database can be quickly restored to normal operation in the event of an unexpected situation.

[0098] The working principle of the above technical solution is as follows: after receiving the relevant data after preprocessing, the cloud platform first performs a decompression operation to restore the data to its original format in preparation for subsequent processing; the decompressed data is processed, including data cleaning, format conversion, calculation, and removal of redundant data operations to ensure the accuracy and integrity of the data; the processed data is analyzed to determine the type, structure, size, growth trend, etc. of the data, and then the design requirements of the database are determined. According to the requirements, the appropriate database type is selected and the database structure is designed, including table classification, field definition, primary key, foreign key and index setting, etc.; a database instance is created in the cloud space and configured accordingly according to the selected database type, including storage size, computing resource allocation, etc.; the processed data is imported into the database and performance tested, including query speed, concurrent processing capability and other indicators. According to the test results, the database is optimized and adjusted to improve the performance and stability of the database; a regular backup strategy is set to ensure the security and reliability of the data. At the same time, a data recovery plan is formulated to deal with data loss or damage in unexpected situations and ensure the normal operation of the database.

[0099] The effects of the above technical solutions are as follows: by receiving, decompressing and processing data on the cloud platform, the efficiency and speed of data processing can be improved, saving manpower and time costs; based on the results of processed data analysis and demand analysis, the appropriate database type is selected, and the corresponding database structure is designed to better meet the needs of data storage, query and management; by performance testing and optimizing the database, the query speed and concurrent processing capabilities of the database can be improved, and the stability and reliability of the system can be improved; designing regular backup strategies and data recovery plans can effectively prevent data loss or damage and ensure data security and integrity; according to different data types and needs, the appropriate database type is selected, and a flexible data table structure is designed to meet the diverse storage and query needs of data, and has good scalability to adapt to changes in future business development needs.

[0100] In one embodiment of the present invention, the processing of the relevant data includes:

[0101] The preprocessed related data is decomposed into multiple computing units according to the data type. The data in each computing unit is divided into multiple small subtasks. The task scheduler is responsible for assigning the subtasks to available computing nodes.

[0102] Each computing node executes the assigned subtasks through a parallel computing framework (such as Spark, Hadoop, etc.);

[0103] During the execution process, the processing tasks are executed asynchronously. After the execution is completed, the results of all subtasks are merged and aggregated to generate the final processing result.

[0104] The cloud platform also has elastic expansion capabilities. Through an automated resource manager, computing resources can be adjusted in a timely manner to ensure that tasks can be completed on time while minimizing costs.

[0105] The working principle of the above technical solution is as follows: the relevant data after preprocessing is first decomposed into multiple computing units, each of which contains multiple small subtasks. The task scheduler is responsible for assigning these subtasks to available computing nodes; after each computing node receives the assigned subtask, it uses a parallel computing framework (such as Spark, Hadoop, etc.) to execute the task. These frameworks provide distributed computing capabilities and can effectively process large-scale data and complex computing tasks; during the execution process, tasks can be performed asynchronously, that is, each computing node can execute multiple subtasks at the same time. After the execution is completed, the results of all subtasks are merged and aggregated to generate the final processing result; the cloud platform has elastic expansion capabilities, and through the automated resource manager, computing resources can be dynamically adjusted according to the needs of the task. This ensures that the task can be completed on time, saves costs as much as possible, and avoids waste of resources.

[0106] The effects of the above technical solution are: decomposing data into multiple computing units by type, and each computing unit is further divided into multiple small subtasks. The tasks are reasonably allocated to available computing nodes through the task scheduler, thereby achieving efficient execution of tasks and rational use of resources; using parallel computing frameworks (such as Spark, Hadoop, etc.) to execute subtasks can fully utilize the advantages of distributed computing, accelerate the speed of data processing, and improve the throughput of the system; asynchronous execution of processing tasks can fully utilize computing resources, while avoiding waiting time between tasks, and improving the efficiency and performance of the system; after execution, the results of all subtasks are merged and aggregated to generate the final processing result, which can ensure the integrity and accuracy of data processing; the cloud platform has elastic expansion capabilities, and can adjust computing resources in a timely manner according to task requirements to ensure that tasks can be completed on time while maximizing cost savings.

[0107] In one embodiment of the present invention, the processed data is based on the carbon source accounting model and the carbon sink accounting model, and a spatial analysis algorithm is used to perform spatiotemporal evolution analysis on the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the implementation of the national land space planning; including:

[0108] Extracting processed relevant data from the cloud space structured database, organizing the relevant data in time series, and forming a continuous data stream before and after the implementation of the plan;

[0109] Spatial analysis algorithms are used to analyze the spatial pattern and dynamic changes of carbon source distribution in different regions before and after planning; spatial overlay analysis of land use change data is performed to identify land use transition areas; and their impact on carbon emissions is quantified.

[0110] By using energy consumption data and combining it with information on changes in energy structure, we estimate the temporal evolution characteristics of carbon emissions in various fields.

[0111] The extracted data is input into the carbon source accounting model and the carbon sink accounting model, and the model operation is performed through the distributed computing framework to obtain the total carbon source and carbon sink at each time node; the operation includes agricultural carbon emission calculation, urban and other construction land carbon emission calculation, transportation carbon emission calculation and industrial carbon emission calculation;

[0112] The agricultural carbon emission calculation formula is as follows:

[0113]

[0114] Among them, C C represents agricultural carbon emissions, C A represents carbon emissions from agricultural production; C Lrepresents livestock metabolic carbon emissions; F is the amount of fertilizer applied, M is the total mechanical power, P and C a are the annual pig and cattle feeding numbers; S i is the planned irrigation area; G is the proportion of high-standard farmland in the plan; a is the emission reduction coefficient of high-standard farmland, J represents the parameter of livestock metabolic carbon emissions, b is a constant, representing other factors affecting livestock metabolic carbon emissions, and K1-K5 represent the influence coefficients;

[0115] The calculation formula for carbon emissions from towns and other construction land is as follows:

[0116]

[0117] in, and Represents the carbon emissions of urban and rural residents respectively; It is the carbon emission from new urban construction land; It is the industrial carbon emissions carried by other urban construction land; is the proportion of GDP in the construction industry; It is the proportion of GDP of wholesale, retail, accommodation and catering industries; is the energy consumption of the catalog (standard coal); is the carbon emission coefficient of various energy sources; is each energy type; It is the total type of industrial energy consumption; is the natural gas consumption of urban residents (standard coal), is the consumption of liquefied petroleum gas (standard coal) by urban residents. and are the carbon emission factors of natural gas and liquefied petroleum gas, respectively; is the number of permanent residents. In this plan, the planned number of permanent residents is used; is the per capita carbon emission coefficient; is the urbanization rate, is the proportion of green industries; △S is the newly added land area in the plan; a is the conversion coefficient, which is 0.9 when the urban development boundary area is between 100-200, and 1.1 when the urban development boundary area is greater than 200; Evenly distribute carbon emissions for urban construction.

[0118] The calculation formula for traffic carbon emissions is as follows:

[0119]

[0120] in, It’s carbon emissions from transportation; It is green travel carbon emission; Is it non-green travel carbon emissions? It is the transport carbon reduction measures carried out; It is Carbon reduction coefficient of each carbon reduction measure; is the total traffic mileage; is the number of permanent residents; is the average mileage per person; is the green travel ratio; is the carbon emission coefficient of green travel; It is the carbon emission coefficient of non-green travel.

[0121] The industrial carbon emission calculation formula is as follows:

[0122]

[0123] in, It is industrial carbon emissions; is the total energy consumption; is the amount of clean energy input (hydropower, nuclear clean energy); It is Percentage of fossil fuel consumption; It is The carbon emission coefficient of each fossil fuel is shown in the table below. is the number of permanent residents; is the per capita energy consumption.

[0124] Among them, the carbon emission factor coefficients of the above-mentioned various energy sources are shown in the following table:

[0125]

[0126]

[0127] Using spatiotemporal data analysis technology, we analyze the evolution of carbon balance over time and compare the carbon balance status before and after the implementation of the plan; revealing the impact of spatial planning on regional carbon balance.

[0128] The rationality of the preliminary accounting results is verified, and the accounting results are revised and calibrated by comparing historical statistical data and field monitoring data.

[0129] The working principle of the above technical solution is: extract the processed relevant data from the cloud space structured database, organize it into a continuous data stream before and after the implementation of the plan according to the time series, including land use data; use the spatial analysis algorithm to conduct spatiotemporal evolution analysis on the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the planning. Through spatial analysis algorithms, the spatial pattern and dynamic changes of carbon source distribution in different regions before and after planning are analyzed, land use transition areas are identified, and their impact on carbon emissions is quantified; energy consumption data and energy structure change information are used to estimate the temporal evolution characteristics of carbon emissions in various fields, revealing the relationship between carbon emissions and energy consumption; the extracted data are input into the carbon source accounting model and the carbon sink accounting model, and the model operations are executed through the distributed computing framework to obtain the total carbon source and carbon sink totals at each time node, including carbon emission calculations in agriculture, transportation, industry and other fields; spatiotemporal data analysis technology is used to analyze the evolution of carbon revenue and expenditure over time, compare the carbon balance status before and after the implementation of the plan, and reveal the impact of spatial planning on regional carbon revenue and expenditure; the rationality of the preliminary accounting results is verified, and the accounting results are corrected and calibrated by comparing historical statistical data and field monitoring data to ensure the accuracy and credibility of the results.

[0130] The effects of the above technical solutions are: by conducting a detailed analysis of the changes in carbon emissions and carbon sinks before and after the implementation of the national land space planning, scientific data support can be obtained, providing a solid basis for the formulation of future carbon emission reduction strategies; using spatial analysis algorithms and land use change data, key areas of carbon emissions and land use transition areas can be accurately identified, and carbon emission reduction measures can be formulated in a targeted manner; through model calculations and spatiotemporal data analysis, the trend of carbon emissions over time can be comprehensively evaluated, and the evolution of carbon revenue and expenditure can be revealed, providing an important reference for the formulation of future carbon emission reduction targets; by verifying and correcting the rationality of the preliminary accounting results, the accuracy and credibility of the data can be ensured, thereby improving the implementation effect and effectiveness of the carbon emission reduction strategy. The establishment of the above calculation formula provides scientific methods and tools for carbon source accounting and carbon sink accounting, which helps to promote the development of carbon emission management and emission reduction work, thereby contributing to the response to climate change. Among them, the formula covers multiple fields such as agriculture, urban construction, transportation and industry, and can comprehensively consider the sources of carbon emissions; the formula uses specific parameters and coefficients, which can be adjusted and updated according to actual conditions to make the model more operational; the formula takes into account various influencing factors, and uses specific data and parameters to calculate carbon emissions more accurately; these formulas can be applied to actual carbon emission accounting work, providing powerful tools for enterprises and research institutions to help them understand and manage carbon emissions and formulate corresponding emission reduction policies and measures.

[0131] One embodiment of the present invention, as Figure 2As shown, a carbon budget accounting system before and after national land space planning, the system includes:

[0132] Data acquisition module: obtain relevant data before and after the implementation of national land space planning, and store the relevant data in the accounting platform, and pre-process the relevant data through the accounting platform.

[0133] Data storage module: storing the pre-processed related data into the cloud space, the cloud space processes the stored pre-processed related data, sets up a structured database in the cloud space, and stores the processed results into the database respectively;

[0134] Analysis and calculation module: Based on the carbon source accounting model and the carbon sink accounting model, the spatial analysis algorithm is used to analyze the spatiotemporal evolution of the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the implementation of the national land space planning.

[0135] Result output module: obtain accounting results through spatiotemporal evolution analysis, and output the accounting results in a visual way, and guide the preparation and implementation of national land space planning schemes based on the accounting results to achieve the goal of low-carbon development.

[0136] The working principle of the above technical solution is as follows: First, it is necessary to obtain relevant data before the implementation of the national land space planning, which may come from various sources, such as third-party data providers. Then, according to the guiding goals of the national land space, the existing machine learning model is used for simulation calculation to obtain the planned data, which also includes the planned energy consumption data; after obtaining the data before and after the implementation of the plan, the data is stored in the accounting platform and pre-processed to ensure the accuracy and consistency of the data; the pre-processed relevant data is stored in the cloud space, a structured database is set up in the cloud space, and the processed results are stored in the database respectively. This helps to effectively manage and quickly retrieve a large amount of data, and provides convenient conditions for subsequent analysis and calculation; based on the processed data, a carbon source accounting model and a carbon sink accounting model are established. Carbon source refers to the source of carbon emissions, such as industrial production, transportation, etc.; carbon sinks are places that absorb carbon, such as forests and wetlands. These models can evaluate carbon emissions and carbon absorption before and after the implementation of national land space planning based on different data indicators and algorithms; use spatial analysis algorithms to perform spatiotemporal evolution analysis on the output data of the carbon source accounting model and the output data of the carbon sink accounting model. This means that changes in spatial and temporal dimensions will be considered to more comprehensively evaluate the impact of national land space planning on carbon balance. Spatial analysis methods that may be used include geographic information system (GIS) technology, remote sensing technology, etc.; finally, the results of carbon balance accounting are obtained through spatiotemporal evolution analysis. These results can be output in a visual way, such as maps, charts, etc., so that decision makers or other stakeholders can more intuitively understand the carbon impact of the implementation of national land space planning, thereby guiding future planning and policy making.

[0137] The effect of the above technical solution is: by acquiring and processing a variety of relevant data, such as energy consumption data, it can provide comprehensive and accurate information, making it possible to conduct a comprehensive assessment of the carbon balance before and after the implementation of the national land space planning. It can help decision makers better understand the sources and influencing factors of carbon emissions; by storing data in the cloud space and establishing a structured database, the efficiency of data management is improved. The use of cloud space makes data storage more flexible, and the establishment of a structured database facilitates the rapid retrieval and processing of data. It can improve the efficiency of the entire carbon balance accounting process, thereby saving time and resource costs; through the spatial analysis algorithm, the spatiotemporal evolution analysis can deeply understand the impact of national land space planning on carbon balance. This in-depth insight can help decision makers better understand the spatiotemporal distribution of carbon emissions and carbon absorption, thereby providing a scientific basis and decision-making support for future planning; by outputting the accounting results in a visual way, such as maps, charts, etc., it can intuitively show the impact of national land space planning on carbon balance. Such visual output not only enhances the communication effect between decision makers and other stakeholders, but also promotes the sharing and dissemination of information, and improves the overall awareness and participation in carbon reduction and sustainable development; this technical solution can not only conduct a one-time assessment of carbon revenue and expenditure before and after the implementation of national land space planning, but also establish a long-term monitoring and evaluation mechanism to achieve tracking, evaluation and continuous improvement of carbon reduction policies. Such continuous evaluation can help decision makers adjust policy measures in a timely manner to ensure the achievement of carbon reduction targets.

[0138] In one embodiment of the present invention, the data acquisition module includes:

[0139] Type classification module: obtain relevant data before and after national land space planning, and store the relevant data into the accounting platform according to the data type;

[0140] Resource allocation module: The accounting platform stores different types of related data received into different storage spaces, and allocates computing resources to each storage space;

[0141] Load adjustment module: Each storage space pre-processes related data based on the allocated computing resources through a parallel processing algorithm, and adjusts the resource load of each storage space through an elastic scaling algorithm;

[0142] Transmission module: After the processing is completed, the pre-processing related data obtained from each storage space is copied, and the copied related data are compressed respectively, and the compressed pre-processing related data is transmitted to the cloud platform through a multi-channel transmission protocol.

[0143] The working principle of the above technical solution is as follows: first, relevant data before and after the implementation of the national land space planning are obtained from various sources, and these data are stored in different storage spaces of the accounting platform according to their types; the accounting platform allocates computing resources according to different types of data, and allocates appropriate resources to each storage space. These resources can be the processing power, memory, etc. of the computer. Each storage space uses a parallel processing algorithm to pre-process the received relevant data to improve the processing efficiency; the elastic scaling algorithm is used to dynamically adjust the resource load of each storage space. If a storage space has more processing tasks, the system will automatically allocate more resources to the space to ensure high efficiency and stability of the processing; after the processing is completed, the processed relevant data is copied and backed up, and the copied and backed up relevant data is compressed to reduce the data volume. Then, the compressed pre-processed relevant data is transmitted to the cloud platform or other target location through a multi-channel transmission protocol. Multi-channel transmission can improve the speed and stability of data transmission.

[0144] The effect of the above technical solution is: various relevant data before and after the national land space planning are integrated and stored in the accounting platform, which can be used for convenient and fast data management and access. Storing relevant data according to data type helps to improve the organization and manageability of data; the accounting platform allocates appropriate computing resources to each storage space according to different types of data requirements, making the data processing process more efficient and stable; through parallel processing algorithms, multiple data tasks can be processed at the same time, which improves the speed and efficiency of data processing and shortens the time cycle of data processing; elastic scaling algorithms can dynamically adjust resource allocation according to the load of each storage space, ensure the stability and efficiency of the system, and save waste of resources; copying and backing up the processed data, compressing it and transmitting it to the cloud platform through a multi-channel transmission protocol can greatly improve the efficiency and stability of data transmission and reduce the risk of data loss during the process.

[0145] In one embodiment of the present invention, the data storage module includes:

[0146] Decompression module: The cloud platform receives the pre-processed relevant data, decompresses it, and processes the relevant data;

[0147] Demand design module: Analyze the processed relevant data to determine the type, structure, size, and growth trend of the data. Based on the analysis results, determine the design requirements of the database, including storage capacity, query performance, and data security;

[0148] Database selection module: select a database based on the results of demand analysis; for example, if the data volume is large and needs to support complex query operations, you can choose a relational database such as MySQL, Oracle, etc.; if the data structure is changeable or a more flexible storage method is required, you can consider using a NoSQL database such as MongoDB, Cassandra, etc.; design the corresponding data table according to the classification and relevance of the data;

[0149] Index creation module: define fields for each data table, including field name, data type, length, whether to allow nulls, etc. Set a primary key for each data table, set foreign keys based on the relationship between data tables, and create indexes;

[0150] Corresponding configuration module: Create a database instance in the cloud space and perform corresponding configuration based on the selected database; such as setting storage size, allocating computing resources, etc.

[0151] Optimization and adjustment module: import the processed relevant data into the database; perform performance tests on the database, including query speed, concurrent processing capabilities, etc. According to the test results, optimize the database, such as adjusting indexes, optimizing query statements, etc.

[0152] Set up a regular backup strategy for the database to prevent data loss or damage. At the same time, formulate a data recovery plan to ensure that the database can be quickly restored to normal operation in the event of an unexpected situation.

[0153] The working principle of the above technical solution is as follows: after receiving the relevant data after preprocessing, the cloud platform first performs a decompression operation to restore the data to its original format in preparation for subsequent processing; the decompressed data is processed, which may include data cleaning, format conversion, calculation, and removal of redundant data operations to ensure the accuracy and integrity of the data; the processed data is analyzed to determine the type, structure, size, growth trend, etc. of the data, and then the design requirements of the database are determined. According to the requirements, the appropriate database type is selected and the database structure is designed, including table classification, field definition, primary key, foreign key and index setting, etc.; a database instance is created in the cloud space and configured accordingly according to the selected database type, including storage size, computing resource allocation, etc.; the processed data is imported into the database and performance tested, including query speed, concurrent processing capability and other indicators. The database is optimized and adjusted according to the test results to improve the performance and stability of the database; a regular backup strategy is set to ensure the security and reliability of the data. At the same time, a data recovery plan is formulated to deal with data loss or damage in unexpected situations and ensure the normal operation of the database.

[0154] The effects of the above technical solutions are as follows: by receiving, decompressing and processing data on the cloud platform, the efficiency and speed of data processing can be improved, saving manpower and time costs; based on the results of processed data analysis and demand analysis, the appropriate database type is selected, and the corresponding database structure is designed to better meet the needs of data storage, query and management; by performance testing and optimizing the database, the query speed and concurrent processing capabilities of the database can be improved, and the stability and reliability of the system can be improved; designing regular backup strategies and data recovery plans can effectively prevent data loss or damage and ensure data security and integrity; according to different data types and needs, the appropriate database type is selected, and a flexible data table structure is designed to meet the diverse storage and query needs of data, and has good scalability to adapt to changes in future business development needs.

[0155] In one embodiment of the present invention, the processing of the relevant data includes:

[0156] Data decomposition module: decomposes the preprocessed related data into multiple computing units according to the data type. The data in each computing unit is divided into multiple small subtasks. The task scheduler is responsible for assigning the subtasks to available computing nodes.

[0157] Execution module: Each computing node executes the assigned subtasks through a parallel computing framework (such as Spark, Hadoop, etc.);

[0158] Result generation module: During the execution process, the processing tasks are executed asynchronously. After the execution is completed, the results of all subtasks are merged and aggregated to generate the final processing results.

[0159] The cloud platform also has elastic expansion capabilities. Through an automated resource manager, computing resources can be adjusted in a timely manner to ensure that tasks can be completed on time while minimizing costs.

[0160] The working principle of the above technical solution is as follows: the relevant data after preprocessing is first decomposed into multiple computing units, each of which contains multiple small subtasks. The task scheduler is responsible for assigning these subtasks to available computing nodes; after each computing node receives the assigned subtask, it uses a parallel computing framework (such as Spark, Hadoop, etc.) to execute the task. These frameworks provide distributed computing capabilities and can effectively process large-scale data and complex computing tasks; during the execution process, tasks can be performed asynchronously, that is, each computing node can execute multiple subtasks at the same time. After the execution is completed, the results of all subtasks are merged and aggregated to generate the final processing result; the cloud platform has elastic expansion capabilities, and through the automated resource manager, computing resources can be dynamically adjusted according to the needs of the task. This ensures that the task can be completed on time, saves costs as much as possible, and avoids waste of resources.

[0161] The effects of the above technical solution are: decomposing data into multiple computing units by type, and each computing unit is further divided into multiple small subtasks. The tasks are reasonably allocated to available computing nodes through the task scheduler, thereby achieving efficient execution of tasks and rational use of resources; using parallel computing frameworks (such as Spark, Hadoop, etc.) to execute subtasks can fully utilize the advantages of distributed computing, accelerate the speed of data processing, and improve the throughput of the system; asynchronous execution of processing tasks can fully utilize computing resources, while avoiding waiting time between tasks, and improving the efficiency and performance of the system; after execution, the results of all subtasks are merged and aggregated to generate the final processing result, which can ensure the integrity and accuracy of data processing; the cloud platform has elastic expansion capabilities, and can adjust computing resources in a timely manner according to task requirements to ensure that tasks can be completed on time while maximizing cost savings.

[0162] In one embodiment of the present invention, the analysis and calculation module includes:

[0163] Data extraction module: extracts the processed relevant data from the cloud space structured database, organizes the relevant data in time series, and forms a continuous data stream before and after the implementation of the plan;

[0164] Regional identification module: Apply spatial analysis algorithms to analyze the spatial pattern and dynamic changes of carbon source distribution in different regions before and after planning; conduct spatial overlay analysis of land use change data to identify land use transition areas; and quantify their impact on carbon emissions.

[0165] By using energy consumption data and combining it with information on changes in energy structure, we estimate the temporal evolution characteristics of carbon emissions in various fields.

[0166] Calculation module: input the extracted data into the carbon source accounting model and the carbon sink accounting model, perform model calculations through the distributed computing framework, and obtain the total carbon source and carbon sink at each time node; the calculations include agricultural carbon emission calculations, urban and other construction land carbon emission calculations, transportation carbon emission calculations, and industrial carbon emission calculations;

[0167] The agricultural carbon emission calculation formula is as follows:

[0168]

[0169] Among them, C C represents agricultural carbon emissions, C A represents carbon emissions from agricultural production; C L represents livestock metabolic carbon emissions; F is the amount of fertilizer applied, M is the total mechanical power, P and C a are the annual pig and cattle feeding numbers; S i is the planned irrigation area; G is the proportion of high-standard farmland in the plan; a is the emission reduction coefficient of high-standard farmland, J represents the parameter of livestock metabolic carbon emissions, b is a constant, representing other factors affecting livestock metabolic carbon emissions, and K1-K5 represent the influence coefficients;

[0170] The calculation formula for carbon emissions from towns and other construction land is as follows:

[0171]

[0172] in, and Represents the carbon emissions of urban and rural residents respectively; It is the carbon emission from new urban construction land; It is the industrial carbon emissions carried by other urban construction land; is the proportion of GDP in the construction industry; It is the proportion of GDP of wholesale, retail, accommodation and catering industries; is the energy consumption of the catalog (standard coal); is the carbon emission coefficient of various energy sources; is each energy type; It is the total type of industrial energy consumption; is the natural gas consumption of urban residents (standard coal), is the consumption of liquefied petroleum gas (standard coal) by urban residents. and are the carbon emission factors of natural gas and liquefied petroleum gas, respectively; is the number of permanent residents. In this plan, the planned number of permanent residents is used; is the per capita carbon emission coefficient; is the urbanization rate, is the proportion of green industries; △S is the newly added land area in the plan; a is the conversion coefficient, which is 0.9 when the urban development boundary area is between 100-200, and 1.1 when the urban development boundary area is greater than 200; Evenly distribute carbon emissions for urban construction.

[0173] The calculation formula for traffic carbon emissions is as follows:

[0174]

[0175] in, It’s carbon emissions from transportation; It is green travel carbon emission; Is it non-green travel carbon emissions? It is the transport carbon reduction measures carried out; It is Carbon reduction coefficient of each carbon reduction measure; is the total traffic mileage; is the number of permanent residents; is the average mileage per person; is the green travel ratio; is the carbon emission coefficient of green travel; It is the carbon emission coefficient of non-green travel.

[0176] The industrial carbon emission calculation formula is as follows:

[0177]

[0178] in, It is industrial carbon emissions; is the total energy consumption; is the amount of clean energy input (hydropower, nuclear clean energy); It is Percentage of fossil fuel consumption; It is The carbon emission coefficient of each fossil fuel is shown in the table below. is the number of permanent residents; is the per capita energy consumption.

[0179] Among them, the carbon emission factor coefficients of the above-mentioned various energy sources are shown in the following table:

[0180]

[0181] Comparison module: Use spatiotemporal data analysis technology to analyze the evolution of carbon balance over time, and compare the carbon balance status before and after the implementation of the plan; reveal the impact of spatial planning on regional carbon balance.

[0182] Correction and calibration module: Verify the rationality of the preliminary accounting results, and correct and calibrate the accounting results by comparing historical statistical data and field monitoring data.

[0183] The working principle of the above technical solution is: extract the processed relevant data from the cloud space structured database, organize it into a continuous data stream before and after the implementation of the plan according to the time series, including land use data; use the spatial analysis algorithm to conduct spatiotemporal evolution analysis on the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the planning. Through spatial analysis algorithms, the spatial pattern and dynamic changes of carbon source distribution in different regions before and after planning are analyzed, land use transition areas are identified, and their impact on carbon emissions is quantified; energy consumption data and energy structure change information are used to estimate the temporal evolution characteristics of carbon emissions in various fields, revealing the relationship between carbon emissions and energy consumption; the extracted data are input into the carbon source accounting model and the carbon sink accounting model, and the model operations are executed through the distributed computing framework to obtain the total carbon source and carbon sink totals at each time node, including carbon emission calculations in agriculture, urban construction, transportation, and industry; spatiotemporal data analysis technology is used to analyze the evolution of carbon revenue and expenditure over time, compare the carbon balance status before and after the implementation of the plan, and reveal the impact of spatial planning on regional carbon revenue and expenditure; the rationality of the preliminary accounting results is verified, and the accounting results are corrected and calibrated by comparing historical statistical data and field monitoring data to ensure the accuracy and credibility of the results.

[0184] The effects of the above technical solutions are: by conducting a detailed analysis of the changes in carbon emissions and carbon sinks before and after the implementation of the national land space planning, a solid basis is provided for the formulation of future carbon emission reduction strategies; by using spatial analysis algorithms and land use change data, key areas of carbon emissions and land use transition areas can be accurately identified, and carbon emission reduction measures can be formulated in a targeted manner; a reference is provided for the formulation of a comprehensive carbon emission reduction strategy; through model calculations and spatiotemporal data analysis, the trend of carbon emissions over time can be comprehensively evaluated, the evolution of carbon revenue and expenditure can be revealed, and an important reference can be provided for the formulation of future carbon emission reduction targets; by verifying and correcting the rationality of the preliminary accounting results, the accuracy and credibility of the data can be ensured, thereby improving the implementation effect and effectiveness of carbon emission reduction policies. The establishment of the above calculation formula provides scientific methods and tools for carbon source accounting and carbon sink accounting, which helps to promote the development of carbon emission management and emission reduction work, thereby contributing to the response to climate change. Among them, the formula covers multiple fields such as agriculture, urban construction, transportation and industry, and can comprehensively consider the sources of carbon emissions; the formula uses specific parameters and coefficients, which can be adjusted and updated according to actual conditions to make the model more operational; the formula takes into account various influencing factors, and uses specific data and parameters to calculate carbon emissions more accurately; these formulas can be applied to actual carbon emission accounting work, providing powerful tools for enterprises and research institutions to help them understand and manage carbon emissions and formulate corresponding emission reduction policies and measures.

[0185] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for calculating carbon budget before and after national land space planning, characterized in that: The method comprises: Obtain relevant data before and after the implementation of the national land space planning, store the relevant data in the accounting platform, and pre-process the relevant data through the accounting platform; The obtaining of relevant data before and after the implementation of the national land space planning, storing the relevant data in the accounting platform, and preprocessing the relevant data through the accounting platform include: Obtain relevant data before and after national land space planning, and store the relevant data into the accounting platform according to data type; The accounting platform stores the received related data of different types into different storage spaces respectively, and allocates computing resources to each storage space; Each storage space pre-processes related data based on the allocated computing resources through a parallel processing algorithm, and adjusts the resource load of each storage space through an elastic scaling algorithm; After the processing is completed, the pre-processing related data obtained in each storage space is copied, and the copied related data are compressed respectively, and the compressed pre-processing related data is transmitted to the cloud platform through a multi-channel transmission protocol; The pre-processed related data is stored in a cloud space, the cloud space processes the stored pre-processed related data, a structured database is set up in the cloud space, and the processed results are stored in the database respectively; The method comprises storing the pre-processed related data in a cloud space, processing the stored pre-processed related data in the cloud space, setting a structured database in the cloud space, and storing the processed results in the database respectively; comprising: The cloud platform receives the pre-processed relevant data, decompresses it, and processes the relevant data; Analyze the processed relevant data and determine the database design requirements based on the analysis results; Based on the results of demand analysis, select the database; design the corresponding data table according to the classification and relevance of the data; Define fields for each data table, set a primary key for each data table, set foreign keys based on the relationships between data tables, and create indexes; Create a database instance in the cloud space and configure it accordingly based on the selected database; Import the processed relevant data into the database; perform performance testing on the database and optimize and adjust the database based on the test results; The processing of the relevant data includes: The preprocessed related data is decomposed into multiple computing units according to the data type. The data in each computing unit is divided into multiple small subtasks. The task scheduler is responsible for assigning the subtasks to available computing nodes. Each computing node executes the assigned subtasks through a parallel computing framework; During the execution process, the processing tasks are executed asynchronously. After the execution is completed, the results of all subtasks are merged and aggregated to generate the final processing result; Through the processed data, based on the carbon source accounting model and the carbon sink accounting model, and through the spatial analysis algorithm, the temporal and spatial evolution analysis of the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the implementation of the national land space planning is carried out; The processed data, based on the carbon source accounting model and the carbon sink accounting model, uses a spatial analysis algorithm to perform spatiotemporal evolution analysis on the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the implementation of the national land space planning; including: Extracting processed relevant data from the cloud space structured database, organizing the relevant data in time series, and forming a continuous data stream before and after the implementation of the plan; Apply spatial analysis algorithms to analyze the spatial pattern and dynamic changes of carbon source distribution in different regions before and after planning; conduct spatial overlay analysis on land use change data to identify land use transition areas; The extracted data is input into the carbon source accounting model and the carbon sink accounting model, and the model operation is performed through the distributed computing framework to obtain the total carbon source and carbon sink amount at each time node; Use spatiotemporal data analysis techniques to analyze the evolution of carbon budget over time and compare the carbon balance status before and after the implementation of the plan; Verify the rationality of the preliminary accounting results, and revise and calibrate the accounting results by comparing historical statistical data with field monitoring data; The accounting results are obtained through spatiotemporal evolution analysis and output in a visual manner.

2. A carbon budget accounting system before and after national land space planning, characterized in that: The system comprises: Data acquisition module: obtain relevant data before and after the implementation of national land space planning, store the relevant data in the accounting platform, and pre-process the relevant data through the accounting platform; Wherein, the data acquisition module includes: Type classification module: obtain relevant data before and after national land space planning, and store the relevant data into the accounting platform according to the data type; Resource allocation module: The accounting platform stores different types of related data received into different storage spaces, and allocates computing resources to each storage space; Load adjustment module: Each storage space pre-processes related data based on the allocated computing resources through a parallel processing algorithm, and adjusts the resource load of each storage space through an elastic scaling algorithm; Transmission module: After the processing is completed, the pre-processing related data obtained in each storage space is copied, and the copied related data is compressed respectively, and the compressed pre-processing related data is transmitted to the cloud platform through a multi-channel transmission protocol; Data storage module: storing the pre-processed relevant data into the cloud space, the cloud space processes the stored pre-processed relevant data, sets up a structured database in the cloud space, and stores the processed results into the database respectively; Wherein, the data storage module includes: Decompression module: The cloud platform receives the pre-processed relevant data, decompresses it, and processes the relevant data; Requirements design module: analyzes the processed relevant data and determines the design requirements of the database based on the analysis results; Database selection module: select database based on the results of demand analysis; design corresponding data tables according to data classification and relevance; Index creation module: define fields for each data table, set primary keys for each data table, set foreign keys based on the relationships between data tables, and create indexes; Corresponding configuration module: Create a database instance in the cloud space and perform corresponding configuration according to the selected database; Optimization and adjustment module: import the processed relevant data into the database; perform performance testing on the database, and optimize and adjust the database based on the test results; The processing of the relevant data includes: Data decomposition module: decomposes the preprocessed related data into multiple computing units according to the data type. The data in each computing unit is divided into multiple small subtasks. The task scheduler is responsible for assigning the subtasks to available computing nodes. Execution module: Each computing node executes the assigned subtasks through a parallel computing framework; Result generation module: During the execution process, the processing tasks are executed asynchronously. After the execution is completed, the results of all subtasks are merged and aggregated to generate the final processing results; Analysis and calculation module: Based on the carbon source accounting model and the carbon sink accounting model, the spatial analysis algorithm is used to analyze the spatiotemporal evolution of the output data of the carbon source accounting model and the output data of the carbon sink accounting model before and after the implementation of the national land space planning. Wherein, the analysis and calculation module includes: Data extraction module: extracts the processed relevant data from the cloud space structured database, organizes the relevant data in time series, and forms a continuous data stream before and after the implementation of the plan; Regional identification module: Apply spatial analysis algorithms to analyze the spatial pattern and dynamic changes of carbon source distribution in different regions before and after planning; perform spatial overlay analysis on land use change data to identify land use transition areas; Calculation module: input the extracted data into the carbon source accounting model and the carbon sink accounting model, execute the model calculation through the distributed computing framework, and obtain the total carbon source and carbon sink at each time node; Comparison module: Use spatiotemporal data analysis technology to analyze the evolution of carbon balance over time and compare the carbon balance status before and after the implementation of the plan; Correction and calibration module: verify the rationality of the preliminary accounting results, and correct and calibrate the accounting results by comparing historical statistical data with field monitoring data; Result output module: obtain the calculation results through spatiotemporal evolution analysis, and output the calculation results in a visual way.

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