Forest landscape afforestation full life cycle carbon emission accounting system and method

Abstract

The invention relates to the technical field of forestry carbon neutralization, in particular to a forest landscape afforestation full life cycle carbon emission accounting system and method.Energy consumption, material use and operation data in the whole afforestation process are collected in real time through Internet of Things equipment, the data are stored through the block chain technology, and dynamically updated regionalized emission factors are matched; and analyzing a carbon emission trend and optimizing an operation scheme in combination with a machine learning model, and finally displaying a carbon balance analysis result through a visual platform. The real-time accurate accounting of carbon emission and the dynamic evaluation of the carbon sink capability are realized, the technical defects of data acquisition lag, fixed emission factors, unlinked carbon sink and the like in the prior art are solved, and a scientific basis is provided for the carbon emission reduction decision and carbon transaction of forest landscape afforestation.

Description

Technical Field

[0001] This invention relates to the field of forestry carbon neutrality technology, and in particular to a system and method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation. Background Technology

[0002] Forest landscaping afforestation is an important means of enhancing carbon sequestration capacity and improving the ecological environment. Its entire life cycle involves multiple stages, including land preparation, planting, tending and management, and infrastructure construction, each of which generates carbon emissions to varying degrees. Currently, there is a relative lack of carbon emission accounting methods for forestry systems, especially for the accurate accounting of forest landscaping afforestation, a crucial component.

[0003] The existing publication number CN119443482A discloses a method for carbon emission accounting throughout the entire life cycle of forest landscape afforestation. This method subdivides forest landscape afforestation activities into two main categories: new afforestation and landscape modification, and further classifies them into specific types such as national reserve forests, forest parks, forest health and wellness bases, low-efficiency plantations, and low-efficiency ecological public welfare forests. It clarifies the system boundaries and functional units of each sub-stage of the life cycle, constructs a carbon emission accounting model covering inputs such as fuel, electricity, chemicals, and fertilizers, and conducts carbon footprint analysis and sensitivity analysis. This method comprehensively considers the diversity and complexity of forest landscape afforestation, providing a scientific and comprehensive technical solution for forestry carbon emission accounting.

[0004] While the aforementioned methods consider carbon emissions from multiple input sources such as fuel, electricity, and chemicals, their data collection relies primarily on manual recording, uses fixed emission factors, and fails to incorporate carbon sequestration capacity into the accounting system. This static accounting approach cannot reflect the true carbon emissions of afforestation activities, nor can it assess the carbon absorption benefits after afforestation, resulting in significant deficiencies in carbon balance analysis. Therefore, there is an urgent need to develop a life-cycle carbon emission accounting method for forest landscape afforestation that can calculate carbon emissions in real time and dynamically assess carbon sequestration capacity. Summary of the Invention

[0005] The purpose of this invention is to provide a carbon emission accounting system and method for the entire life cycle of forest landscape afforestation, which solves the problem that the existing static accounting methods are unable to reflect the true carbon emission of afforestation activities and cannot assess the carbon absorption benefits after afforestation, resulting in major defects in carbon balance analysis.

[0006] To achieve the above objectives, this invention provides a method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation, comprising the following steps:

[0007] Real-time data collection of energy consumption, material usage, and operational data at each stage of the entire life cycle of forest landscape afforestation is achieved through IoT devices.

[0008] The collected data is stored in a blockchain network, and the optimal emission factor is matched based on a dynamic emission factor library. The dynamic emission factor library integrates regional emission data and is updated in real time.

[0009] Carbon emissions are calculated in real time based on energy consumption data, material usage data, and matching emission factors.

[0010] Machine learning models are used to analyze carbon emission trends, optimize operational plans, and generate emission reduction recommendations.

[0011] Carbon emission data, carbon sink analysis results, and optimization strategies are displayed through a visual interactive platform.

[0012] Among these, energy consumption data, material usage data, and operational data for each stage of the entire life cycle of forest landscape afforestation are collected in real time through IoT devices.

[0013] IoT devices include fuel sensors, GPS positioning modules, drone remote sensing equipment, and material RFID tags.

[0014] Among these, energy consumption data, material usage data, and operational data for each stage of the entire life cycle of forest landscape afforestation are collected in real time through IoT devices.

[0015] Energy consumption data includes fuel consumption and electricity usage; material usage data includes the use of fertilizers, chemicals and consumables; and operational data includes machinery operating hours, transportation distance and afforestation area.

[0016] The process involves storing the collected data on a blockchain network and matching the optimal emission factor based on a dynamic emission factor database. This dynamic emission factor database integrates regional emission data and is updated in real time. Specific steps include:

[0017] The collected energy consumption data, material usage data, and operational data are uploaded to the distributed ledger via blockchain nodes;

[0018] Based on the data collection location information, the emission factor data of the corresponding region is retrieved from the dynamic emission factor database. The dynamic emission factor database includes emission factors for power production, fuel production, and materials production, which are divided by geographical region.

[0019] Based on supply chain information recorded by material RFID tags, specific emission factors in the material production process are matched.

[0020] The timeliness of factor data is ensured by automatically updating regionalized emission data in the emission factor database through smart contracts.

[0021] The optimal emission factor after matching is associated with and stored with the collected operational data.

[0022] Among them, carbon emissions are calculated in real time based on energy consumption data, material usage data, and matching emission factors.

[0023] The formula for calculating carbon emissions is:

[0024] E 总 =E 燃料 +E 电力 +E 材料 +E 运输 -S 碳汇

[0025] Among them, E 总 E represents the net carbon emissions over the entire life cycle of forest landscaping afforestation, i.e., the difference between total emissions and carbon sink absorption; 燃料 E represents the carbon emissions generated by the consumption of fuel in machinery during afforestation; 电力 E represents carbon emissions generated from electricity use. 材料 This indicates carbon emissions from the production and use of fertilizers, chemicals, consumables, etc.; E 运输 Indicates carbon emissions generated from the transportation of goods; S 碳汇 This represents the real-time carbon sink absorption estimated based on remote sensing data and growth models.

[0026] The process involves using machine learning models to analyze carbon emission trends, optimize operational plans, and generate emission reduction recommendations. Specific steps include:

[0027] Construct a training dataset that includes historical energy consumption data, material usage data, operational data, and corresponding carbon emissions;

[0028] A carbon emission prediction model is established using the random forest algorithm, and the current operation parameters are input to predict future carbon emission trends.

[0029] A carbon sink growth prediction model was constructed based on LSTM neural network, and the carbon sink potential of different afforestation schemes was predicted by combining remote sensing monitoring data.

[0030] Establish a multi-objective optimization model with the goals of minimizing carbon emissions and maximizing carbon sinks, and optimize machinery scheduling, material selection, and operation sequence parameters;

[0031] By dynamically adjusting optimization strategies through reinforcement learning algorithms, emission reduction recommendations are generated, including low-carbon material recommendations, machinery usage optimization, and operation timing adjustments.

[0032] The process of displaying carbon emission data, carbon sink analysis results, and optimization strategies through a visual interactive platform includes the following steps:

[0033] Build a web-based 3D visualization platform that integrates a map engine to display the spatial distribution and progress of afforestation areas in real time;

[0034] Use a dynamic dashboard to display key indicators;

[0035] Heat maps are used to display the carbon emission intensity distribution, spatial differences in carbon sink capacity, and predictions of the effectiveness of optimization strategies in each work area.

[0036] Provides interactive query functionality;

[0037] Generate exportable emission reduction decision reports.

[0038] A forest landscape afforestation full life cycle carbon emission accounting system includes a data acquisition module, a storage module, a calculation module, an analysis module, and a visualization module. The storage module is connected to the data acquisition module, the calculation module is connected to the storage module, the analysis module is connected to the calculation module, and the visualization module is connected to the analysis module.

[0039] The data acquisition module is used to collect energy consumption data, material usage data, and operation data at each stage of the entire life cycle of forest landscape afforestation in real time through IoT devices;

[0040] The storage module is used to store the collected data to the blockchain network and match the optimal emission factor based on the dynamic emission factor library. The dynamic emission factor library integrates regional emission data and updates it in real time.

[0041] The calculation module is used to calculate carbon emissions in real time based on energy consumption data, material usage data, and matching emission factors;

[0042] The analysis module is used to analyze carbon emission trends using machine learning models, optimize operational plans, and generate emission reduction recommendations.

[0043] The visualization module is used to display carbon emission data, carbon sink analysis results, and optimization strategies through a visual interactive platform.

[0044] This invention discloses a carbon emission accounting system and method for the entire life cycle of forest landscape afforestation. It collects energy consumption, material usage, and operational data throughout the afforestation process in real time using IoT devices, stores the data using blockchain technology and matches it with dynamically updated regional emission factors, analyzes carbon emission trends using machine learning models, and optimizes operational plans. Finally, it displays the carbon balance analysis results through a visualization platform. This system achieves real-time and accurate carbon emission accounting and dynamic assessment of carbon sink capacity, overcoming technical shortcomings in existing technologies such as lagging data collection, fixed emission factors, and lack of linkage with carbon sinks. It provides a scientific basis for carbon emission reduction decisions and carbon trading in forest landscape afforestation. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0046] Figure 1 This is a flowchart of the steps in the forest landscape afforestation full life cycle carbon emission accounting method according to the first embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of the principle of the forest landscape afforestation full life cycle carbon emission accounting system according to the second embodiment of the present invention.

[0048] In the diagram: 201 - Acquisition module, 202 - Storage module, 203 - Calculation module, 204 - Analysis module, 205 - Visualization module. Detailed Implementation

[0049] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0050] The first embodiment of this application is as follows:

[0051] Please see Figure 1 ,in, Figure 1 This is a flowchart of the steps in the forest landscape afforestation full life cycle carbon emission accounting method according to the first embodiment of the present invention.

[0052] This invention provides a method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation, comprising the following steps:

[0053] S101: Real-time collection of energy consumption data, material usage data, and operational data at each stage of the entire life cycle of forest landscape afforestation through IoT devices;

[0054] Specifically, firstly, high-precision fuel flow sensors and GPS positioning terminals are deployed on afforestation machinery and equipment, such as excavators, tractors, and transport vehicles. The fuel sensors use Coriolis mass flow meters with a measurement accuracy of ±0.5%, monitoring the diesel and gasoline consumption of various types of machinery in real time. In addition to providing location information, the GPS module records the equipment's operating trajectory and working time, transmitting the data to a cloud server in real time via 4G / 5G networks. This configuration can accurately obtain the spatiotemporal distribution characteristics of fuel consumption for each piece of equipment during land preparation, seedling transportation, and other stages. Secondly, a smart meter monitoring network is deployed at key nodes in the afforestation area. For fixed facilities, such as irrigation pumping stations and seedling greenhouses, industrial-grade smart meters are installed; for mobile devices, portable energy monitoring instruments are used, achieving centralized collection of electricity consumption data through LoRa wireless networking. All meters comply with the DL / T645-2007 communication protocol and can record parameters such as voltage, current, and power factor at 15-minute intervals, calculating accurate energy consumption data. In terms of material management, this invention establishes an RFID-based material tracking system. All incoming fertilizers, chemicals, and other materials are packaged with UHF RFID tags to record key information such as product batch, composition, and manufacturer. During requisition and use, handheld RFID readers scan and record the material flow, combined with electronic scales for precise measurement of usage. This system not only achieves digital management of material consumption but also provides a data foundation for subsequent carbon emission traceability. To supplement ground-based monitoring, this invention also constructs an integrated "air-space-ground" remote sensing monitoring system: 1) A DJI M300RTK drone equipped with a multispectral sensor conducts monthly aerial photography of the afforestation area, acquiring orthophotos with a resolution of 0.05 meters; 2) Sentinel-2 satellite data is used to extract vegetation indices such as NDVI; 3) IoT weather stations and soil moisture meters are deployed on the ground to monitor microenvironmental parameters. This data is preprocessed through an edge computing gateway and then uploaded to the cloud platform, providing multi-dimensional environmental baseline data for carbon sink calculations. All data acquisition devices are managed through a unified IoT management platform, which features: standardized communication between devices using the MQTT protocol; built-in data verification algorithms to automatically identify and remove outliers; support for resuming interrupted transmissions to ensure data integrity during network instability; and device health monitoring to promptly detect sensor malfunctions. Through this technical solution, the present invention achieves the following throughout the entire lifecycle of forest afforestation: 1) real-time monitoring of mechanical fuel consumption; 2) itemized metering of electricity usage; 3) full-process tracking of material flow; 4) spatial representation of work progress; and 5) continuous observation of environmental parameters. This multi-dimensional, high-precision data acquisition system fundamentally solves the problems of poor timeliness, large errors, and incomplete coverage inherent in traditional manual recording methods, laying a data foundation for subsequent accurate carbon accounting.It is particularly noteworthy that this system can accurately match specific work activities with corresponding resource consumption through the spatiotemporal correlation between devices.

[0055] S102: The collected data is stored in the blockchain network, and the optimal emission factor is matched based on the dynamic emission factor library. The dynamic emission factor library integrates regional emission data and is updated in real time.

[0056] Specifically, the collected energy consumption data (fuel, electricity), material usage data (fertilizer, chemicals), and operational data (machine working hours, transportation distance, etc.) are transmitted to the blockchain network via an IoT gateway. The blockchain adopts a consortium blockchain architecture, with nodes including forestry management departments, carbon emission monitoring agencies, and third-party certification bodies to ensure data credibility. Data is encrypted before being uploaded to the blockchain and formatted using smart contracts to ensure compliance with international carbon emission data standards. Each data record includes metadata such as timestamps, geographic coordinates, and data collection device IDs, forming a complete audit and traceability chain. The emission factor database adopts a layered design: Basic Factor Layer: Integrates default emission factors from authoritative databases such as IPCC and Ecoinvent, categorized and stored by fuel type (diesel, gasoline), power source (thermal power, hydropower, wind power, etc.), and material type (nitrogen fertilizer, phosphate fertilizer, pesticides, etc.). Regionalization Correction Layer: Based on a Geographic Information System (GIS), regional grids are divided, such as 1km×1km, and factor values ​​are dynamically adjusted based on local energy structures, such as the proportion of clean energy in the regional power grid and differences in production processes, such as the technical routes of fertilizer plants. For example, the emission factor of the same type of fertilizer in Province A is 15% lower than that in Province B due to the use of low-carbon processes. Real-time update mechanism: Smart contracts monitor the following data sources: quarterly updates of power grid carbon emission factors published by the National Energy Administration; life cycle assessment (LCA) reports provided by material suppliers; and regional land use changes, such as carbon emission changes caused by deforestation, obtained through remote sensing. When new data is detected, the factor database is automatically updated and written to the blockchain after verification through a consensus mechanism. The matching process consists of three steps: Spatial matching: Based on the GPS coordinates of the operational data, the GIS engine is used to locate the corresponding regional grid and extract factors such as electricity and fuel for that region. For example, if an afforestation project is located in Yunnan Province, the real-time carbon emission factor of the Southern Power Grid (0.45 kg CO2e / kWh) is automatically matched. Supply chain matching: The supply chain blockchain is linked through material RFID tags to trace manufacturers and processes. For example, if a batch of urea fertilizer is labeled as originating from a low-carbon fertilizer plant, the specific emission factor of that plant (1.2 kg CO2e / kg, lower than the industry average of 1.5 kg CO2e / kg) is used. Time-series matching: Combining data timestamps, the system selects factor versions for corresponding time periods to avoid deviations caused by policy adjustments (such as the implementation of carbon taxes). The matched emission factors are stored in association with the original operational data via blockchain hash values; any modifications require consensus from a majority of nodes. The system periodically performs the following checks: Cross-validation: Comparing the consistency between the raw IoT data and on-chain records; Anomaly detection: Identifying abnormal fluctuations in factors using statistical models (such as the 3σ principle); Third-party auditing: Authorized certification bodies can conduct random checks on data authenticity at any time. This system is suitable for large-scale cross-regional afforestation projects, effectively addressing the computational complexity caused by geographical differences and policy changes, and providing reliable data support for carbon neutrality goals.

[0057] S103: Calculate carbon emissions in real time based on energy consumption data, material usage data, and matching emission factors;

[0058] Specifically, through formula E 总 =E 燃料 +E 电力 +E 材料 +E 运输 -S 碳汇 Achieving accurate net carbon emission accounting throughout the entire lifecycle. The computing engine first standardizes and preprocesses the raw data collected by the Internet of Things. The pulse signals provided by the fuel sensor are converted into actual consumption using the formula V = K × N × ρ, where K is the instrument coefficient, N is the number of pulses, and ρ is the fuel density, ensuring that the diesel and gasoline consumption of all machinery is uniformly expressed in kilograms. The voltage and current signals collected by smart meters are used to calculate real-time power using P = U × I × cosφ, and integrated to obtain accurate kilowatt-hours of electricity consumption. In the data verification phase, the rationality of the machinery's working status is verified by combining GPS trajectory data. For example, when the fuel consumption rate of an excavator suddenly increases, its location information is retrieved to determine whether it is in special working conditions such as climbing. After data cleaning, the system matches the optimal emission factor for each data item based on the spatiotemporal tags stored in the blockchain: for fuel consumption E 燃料 =∑(Q i ×EF i ), where Q i Let EF be the consumption of the i-th type of fuel. i This provides a regionalized emission factor for a blockchain dynamic factor library, which adjusts seasonally, such as the emission factor correction when using oxygenated fuels in winter; electricity emissions E 电力 =G 电力 ×EF 电力 , of which E 电力 G represents the carbon emissions generated from electricity use. 电力 Indicates electricity consumption, EF 电力 This represents the electricity emission factor, specifically the grid emission factor EF in electricity. 电力 Updated every 15 minutes from the National Energy Data Center to ensure real-time reflection of power generation structure. Material carbon emissions E 材料 =∑(M i ×EF i ×(1-η)), where M i Let EF be the actual amount of material i used in the afforestation process. i Let η be the carbon emission intensity generated by the i-th material throughout its entire life cycle in the production unit, and let η be the recycling rate or biodegradable proportion of the i-th material after use, used to offset part of the carbon emissions. The calculation includes not only the basic production emission factor EF. iFurthermore, a recycling coefficient η is introduced to reflect the emission reduction benefits of recycling; for example, the η value of biodegradable seedling containers can reach 0.3. Transportation link E 运输 =Σ(W j ×D j ×EF j A multimodal computational model is adopted, in which W j Let D be the total weight of the goods (such as seedlings, fertilizer, equipment, etc.) transported in the j-th shipment. j Let EF be the actual distance traveled from the starting point to the destination of the j-th transport. j Carbon emission intensity per unit weight of goods transported per unit distance is directly related to the type of transport vehicle. For truck transport, an additional load factor correction factor α is considered (α = 1.2 when empty, α = 0.9 when fully loaded). This is included in the carbon sink calculation S. 碳汇 In the equation = A × GPP × ε, A represents the afforestation area, GPP is the total primary productivity derived from remote sensing, and ε is the species-specific conversion coefficient, which dynamically adjusts according to an S-shaped curve as the forest age increases. All sub-calculation results are aggregated in real time through a distributed computing framework, generating a new net carbon emission (E) value every 5 minutes. Cross-node verification is performed via blockchain smart contracts to ensure the immutability of the calculation results. Specifically, a dynamic balance feedback mechanism between carbon emissions and carbon sinks is established. When a sustained increase in carbon sink (S) is detected in a certain area, the afforestation operation plan for that area is automatically optimized to reduce the contribution of E fuel and E materials. This calculation method, based on physical formulas and data-driven approaches, improves the temporal resolution by more than 1000 times compared to traditional annual static accounting, achieving spatial accuracy at the level of individual operational plots. This makes the carbon footprint management of afforestation projects truly controllable and reliable. All calculation parameters and intermediate results are stored on the blockchain, forming a complete carbon accounting evidence chain that meets the stringent data traceability requirements of international carbon verification standards (such as VCS), providing an authoritative metrological basis for subsequent carbon trading.

[0059] S104: Use machine learning models to analyze carbon emission trends, optimize operational plans, and generate emission reduction recommendations;

[0060] Specifically, the process begins by integrating historical carbon emission data, real-time monitoring data, and environmental parameters to construct a multi-dimensional time-series database. This data, after cleaning and standardization, is then input into a machine learning model for in-depth analysis. In terms of model architecture, a hybrid modeling strategy is employed. The random forest algorithm is used to process structured data, such as conventional monitoring indicators like machinery fuel consumption and electricity usage. This algorithm can automatically identify carbon emission correlations between different operational stages and assess the importance ranking of various influencing factors. Simultaneously, an LSTM neural network is used to process time-series data, such as monthly changes in carbon sink capacity and seasonal fluctuations in fuel use. This network structure is particularly suitable for capturing long-term dependencies and non-linear characteristics of carbon emissions. To improve prediction accuracy, environmental variables such as meteorological data and soil moisture are incorporated as auxiliary features. For example, when consecutive droughts are predicted, the model automatically adjusts the expected carbon emissions from irrigation operations. During model training, cross-validation and early stopping mechanisms are used to prevent overfitting, and the model is periodically incrementally trained with the latest data to ensure that predictive capabilities remain up-to-date. Based on the outputs of these models, a multi-objective optimization engine is constructed. This engine comprehensively considers three objectives: minimizing carbon emissions, maximizing carbon sequestration, and controlling operational costs. It uses a genetic algorithm to search for the optimal combination of operational parameters. The optimization results generate specific emission reduction recommendations, including machinery scheduling plans (such as scheduling energy-intensive operations during peak carbon sequestration periods to balance net emissions), material substitution suggestions (recommending low-carbon fertilizer varieties suitable for specific regions), and operational timing adjustments (optimizing the time window for tending operations based on weather forecasts). Each recommendation is accompanied by an assessment of the expected emission reduction effect and an implementation plan, such as "Switching to electric tending equipment can reduce the current plot's emissions by 15%." 电力 "Emissions require the construction of supporting photovoltaic charging facilities." A reinforcement learning mechanism is also designed to automatically adjust model parameters and optimization strategies by continuously tracking the deviation between actual emission reduction effects and predicted values, forming a continuous improvement cycle of "prediction-decision-feedback." All analysis results and optimization suggestions are structured and visualized using knowledge graph technology, supporting forestry managers in interactive exploration and scheme comparison. This not only breaks through the limitations of traditional experience-based decision-making but, more importantly, achieves predictability and accuracy in carbon emission management, enabling afforestation projects to predict carbon footprints during the planning stage, dynamically adjust strategies during implementation, and ultimately achieve optimal control of carbon emission reduction throughout the entire life cycle. Special consideration has been given to the specific needs of different regions and tree species; all suggested solutions have undergone localized calibration to ensure operability in actual operations.

[0061] S105: Display carbon emission data, carbon sink analysis results, and optimization strategies through a visual interactive platform.

[0062] Specifically, the visualization platform adopts a three-layer architecture. The bottom data service layer receives real-time accounting results stored on the blockchain and optimization schemes generated by machine learning. After processing by a distributed computing engine, the middle business logic layer performs data association and scenario-based organization, and finally presents the data to users in a rich visualization format at the presentation layer. The platform's core functional modules include three main components: a dynamic carbon map, a multi-dimensional analysis dashboard, and an intelligent report generator. The dynamic carbon map uses WebGL technology to achieve 3D terrain rendering, overlaying high-resolution satellite imagery and drone aerial data. It intuitively displays the carbon emission intensity distribution of different afforestation areas through color gradients and dynamic particle effects. Users can slide the timeline to view historical trends or click on specific plots to drill down for detailed data. The multi-dimensional analysis dashboard adopts a configurable component-based design, allowing users to freely combine various charts, such as pie charts to display energy consumption structure, heat maps to display machinery usage efficiency, and line charts to compare planned and actual emissions. All charts support real-time data updates and interactive filtering. The intelligent report generator has built-in templates and can automatically extract key indicators to generate carbon accounting reports that meet international standards, and supports one-click export to PDF or Excel format. The platform features a scenario simulation function, allowing users to adjust operational parameters via drag-and-drop. The system predicts carbon emission changes in real time and provides visual feedback; for example, when a user increases the proportion of electric machinery used, the corresponding area on the map immediately transitions to a green hue. To enhance the mobile user experience, the platform has developed a companion AR application. On-site staff can scan site markers with their phones to overlay real-time carbon emission data and optimization suggestions for that area. All visualization elements adhere to internationally accepted carbon emission color coding standards, using red to represent high emissions and green to represent areas with carbon sink advantages. Abnormal data points are automatically highlighted through intelligent labeling. The platform's backend has a robust data update mechanism, synchronizing the latest calculation results every 15 minutes to ensure users always see the most up-to-date carbon situation. In terms of interaction design, the platform supports multi-level permission management, providing customized views for different roles, such as displaying strategic carbon balance trends to managers and providing specific operational guidelines to workers. The system also integrates collaborative annotation functionality, supporting online team discussions of emission reduction plans and recording the decision-making process. This visualization platform not only addresses the pain points of traditional carbon emission data being "incomprehensible and unusable," but more importantly, it transforms complex carbon data into actionable business insights through an immersive interactive experience. This truly achieves closed-loop management from data collection to decision-making and execution, providing strong digital support for the low-carbon operation of forest landscape afforestation. The platform's design fully considers the unique characteristics of forestry operations; for example, it has developed offline data caching and synchronization mechanisms to address unstable network conditions in the field, ensuring stable service in various environments.

[0063] It enables real-time and accurate accounting of carbon emissions and dynamic assessment of carbon sink capacity, solving technical deficiencies in existing technologies such as data collection lag, fixed emission factors, and lack of linkage with carbon sinks, and providing a scientific basis for carbon emission reduction decisions and carbon trading in forest landscape afforestation.

[0064] The second embodiment of this application is as follows:

[0065] Based on the first embodiment, please refer to Figure 2 ,in, Figure 2 This is a schematic diagram of the principle of the forest landscape afforestation full life cycle carbon emission accounting system according to the second embodiment of the present invention.

[0066] This embodiment of a forest landscape afforestation full life cycle carbon emission accounting system includes a data acquisition module, a storage module, a calculation module, an analysis module, and a visualization module.

[0067] In this specific embodiment, the storage module is connected to the acquisition module, the computing module is connected to the storage module, the analysis module is connected to the computing module, and the visualization module is connected to the analysis module;

[0068] The data acquisition module is used to collect energy consumption data, material usage data, and operation data at each stage of the entire life cycle of forest landscape afforestation in real time through IoT devices;

[0069] The storage module is used to store the collected data to the blockchain network and match the optimal emission factor based on the dynamic emission factor library. The dynamic emission factor library integrates regional emission data and updates it in real time.

[0070] The calculation module is used to calculate carbon emissions in real time based on energy consumption data, material usage data, and matching emission factors;

[0071] The analysis module is used to analyze carbon emission trends using machine learning models, optimize operational plans, and generate emission reduction recommendations.

[0072] The visualization module is used to display carbon emission data, carbon sink analysis results, and optimization strategies through a visual interactive platform.

[0073] This embodiment of the forest landscape afforestation full life cycle carbon emission accounting system acquires energy consumption and operational data for each stage of afforestation in real time through IoT devices in the acquisition module. The storage module uses blockchain technology to ensure reliable data storage and matches dynamically updated regional emission factors. The calculation module accurately calculates real-time carbon emissions, the analysis module uses machine learning to predict trends and generate optimization schemes, and finally, the visualization module realizes multi-dimensional interactive display of carbon data. This system achieves end-to-end digital management from data acquisition to decision support, providing reliable technical support for forestry carbon neutrality.

[0074] The above-disclosed embodiments are merely one or more preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments and equivalent changes made in accordance with the claims of this application still fall within the scope of this application.

Claims

1. A method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation, characterized in that, Includes the following steps: Real-time data collection of energy consumption, material usage, and operational data at each stage of the entire life cycle of forest landscape afforestation is achieved through IoT devices. The collected data is stored in a blockchain network, and the optimal emission factor is matched based on a dynamic emission factor library. The dynamic emission factor library integrates regional emission data and is updated in real time. Carbon emissions are calculated in real time based on energy consumption data, material usage data, and matching emission factors. Machine learning models are used to analyze carbon emission trends, optimize operational plans, and generate emission reduction recommendations. Carbon emission data, carbon sink analysis results, and optimization strategies are displayed through a visual interactive platform.

2. The method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation as described in claim 1, characterized in that, Real-time data on energy consumption, material usage, and operational data at each stage of the entire lifecycle of forest afforestation is collected using IoT devices. IoT devices include fuel sensors, GPS positioning modules, drone remote sensing equipment, and material RFID tags.

3. The method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation as described in claim 2, characterized in that, Real-time data on energy consumption, material usage, and operational data at each stage of the entire lifecycle of forest afforestation is collected using IoT devices. Energy consumption data includes fuel consumption and electricity usage; material usage data includes the use of fertilizers, chemicals and consumables; and operational data includes machinery operating hours, transportation distance and afforestation area.

4. The method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation as described in claim 3, characterized in that, The collected data is stored in a blockchain network, and the optimal emission factor is matched based on a dynamic emission factor library. The dynamic emission factor library integrates regional emission data and is updated in real time. The specific steps include: The collected energy consumption data, material usage data, and operational data are uploaded to the distributed ledger via blockchain nodes; Based on the data collection location information, the emission factor data of the corresponding region is retrieved from the dynamic emission factor database. The dynamic emission factor database includes emission factors for power production, fuel production, and materials production, which are divided by geographical region. Based on supply chain information recorded by material RFID tags, specific emission factors in the material production process are matched. The timeliness of factor data is ensured by automatically updating regionalized emission data in the emission factor database through smart contracts. The optimal emission factor after matching is associated with and stored with the collected operational data.

5. The method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation as described in claim 4, characterized in that, Carbon emissions are calculated in real time based on energy consumption data, material usage data, and matching emission factors. The formula for calculating carbon emissions is: AND 总 =And 燃料 +E 电力 +E 材料 +E 运输 -S 碳汇 Among them, E 总 E represents the net carbon emissions over the entire life cycle of forest landscaping afforestation, i.e., the difference between total emissions and carbon sink absorption; 燃料 E represents the carbon emissions generated by the consumption of fuel in machinery during afforestation; 电力 E represents carbon emissions generated from electricity use. 材料 This indicates carbon emissions from the production and use of fertilizers, chemicals, consumables, etc.; E 运输 Indicates carbon emissions generated from the transportation of goods; S 碳汇 This represents the real-time carbon sink absorption estimated based on remote sensing data and growth models.

6. The method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation as described in claim 5, characterized in that, The machine learning model is used to analyze carbon emission trends, optimize operational plans, and generate emission reduction recommendations. Specific steps include: Construct a training dataset that includes historical energy consumption data, material usage data, operational data, and corresponding carbon emissions; A carbon emission prediction model is established using the random forest algorithm, and the current operation parameters are input to predict future carbon emission trends. A carbon sink growth prediction model was constructed based on LSTM neural network, and the carbon sink potential of different afforestation schemes was predicted by combining remote sensing monitoring data. Establish a multi-objective optimization model with the goals of minimizing carbon emissions and maximizing carbon sinks, and optimize machinery scheduling, material selection, and operation sequence parameters; By dynamically adjusting optimization strategies through reinforcement learning algorithms, emission reduction recommendations are generated, including low-carbon material recommendations, machinery usage optimization, and operation timing adjustments.

7. The method for calculating carbon emissions throughout the entire life cycle of forest landscape afforestation as described in claim 6, characterized in that, The process of displaying carbon emission data, carbon sink analysis results, and optimization strategies through a visual interactive platform includes the following steps: Build a web-based 3D visualization platform that integrates a map engine to display the spatial distribution and progress of afforestation areas in real time; Use a dynamic dashboard to display key indicators; Heat maps are used to display the carbon emission intensity distribution, spatial differences in carbon sink capacity, and predictions of the effectiveness of optimization strategies in each work area. Provides interactive query functionality; Generate exportable emission reduction decision reports.

8. A forest landscape afforestation life-cycle carbon emission accounting system, applicable to the forest landscape afforestation life-cycle carbon emission accounting method as described in claim 1, characterized in that, It includes a data acquisition module, a storage module, a computing module, an analysis module, and a visualization module. The storage module is connected to the data acquisition module, the computing module is connected to the storage module, the analysis module is connected to the computing module, and the visualization module is connected to the analysis module. The data acquisition module is used to collect energy consumption data, material usage data, and operation data at each stage of the entire life cycle of forest landscape afforestation in real time through IoT devices; The storage module is used to store the collected data to the blockchain network and match the optimal emission factor based on the dynamic emission factor library. The dynamic emission factor library integrates regional emission data and updates it in real time. The calculation module is used to calculate carbon emissions in real time based on energy consumption data, material usage data, and matching emission factors; The analysis module is used to analyze carbon emission trends using machine learning models, optimize operational plans, and generate emission reduction recommendations. The visualization module is used to display carbon emission data, carbon sink analysis results, and optimization strategies through a visual interactive platform.

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