CN-122022170-A - IPCC-based highway greening carbon sink dynamic evaluation system and method

CN122022170ACN 122022170 ACN122022170 ACN 122022170ACN-122022170-A

Abstract

The invention discloses an IPCC-based highway greening carbon sink dynamic evaluation system and method, wherein the system comprises a data acquisition layer, a data analysis layer and a data analysis layer, wherein the data acquisition layer is used for acquiring multi-source heterogeneous data of a highway road domain through an air-space integrated monitoring network; the system comprises a data storage and processing layer, a model calculation layer, an application service layer and a model calculation layer, wherein the data storage and processing layer is connected with the data acquisition layer and used for preprocessing, space-time registration and standardization of multi-source heterogeneous data and storing the multi-source heterogeneous data into a forest database and a management decision database, the model calculation layer is connected with the data storage and processing layer and used for executing carbon sink dynamic assessment and prediction based on the preprocessed data, and the application service layer is connected with the model calculation layer and used for providing carbon sink visualization, carbon asset management and decision support services for users. The invention realizes accurate and dynamic assessment of vegetation carbon sink quantity of different road segments along the road.

Inventors

HE XINPING
ZHU JIAXUAN
XIAO YIQIAN
CUI PENGFEI
YANG XIUYUN
YAN XIANGYANG
You Tianzhou
ZHOU JIAN
LIU JIN
ZHANG YUE

Assignees

中国公路工程咨询集团有限公司
中咨华科交通建设技术有限公司
中咨数据有限公司
山西农业大学

Dates

Publication Date: 20260512
Application Date: 20260131

Claims (10)

1. An IPCC-based highway greening carbon sink dynamic assessment system, comprising: the data acquisition layer is used for acquiring multi-source heterogeneous data of the highway road domain through an air-space-ground integrated monitoring network; the data storage and processing layer is connected with the data acquisition layer and is used for preprocessing, space-time registration and standardization of the multi-source heterogeneous data and storing the multi-source heterogeneous data into a forest database and a management decision database; The model calculation layer is connected with the data storage and processing layer and is used for executing carbon sink dynamic assessment and prediction based on the preprocessed data; And the application service layer is connected with the model calculation layer and is used for providing carbon sink visualization, carbon asset management and decision support services for users.
2. The IPCC-based highway greening carbon sink dynamic assessment system according to claim 1, wherein the data acquisition layer comprises: The satellite remote sensing module is used for automatically interpreting tree classification, crown area and vegetation index of the vegetation of the large-range road area through high-resolution multispectral and radar satellite images and combining a deep learning algorithm; the unmanned aerial vehicle remote sensing module is used for carrying out fine scanning on a heavy road section through an unmanned aerial vehicle carrying a hyperspectral sensor and a laser radar to obtain high-precision tree height and canopy structure information; the ground sensor network is arranged along the highway and used for monitoring the organic carbon content of the soil, the soil humidity, the temperature and the atmospheric CO2 concentration in real time; and the manual investigation terminal is used for assisting in collecting and verifying data.
3. The IPCC-based highway greening carbon sink dynamic assessment system according to claim 1, wherein the model calculation layer comprises: The IPCC carbon sink accounting module is used for calculating carbon reserves and annual carbon sequestration of vegetation and soil based on an IPCC basic method; the multi-mode data fusion module is used for fusing mode information from different data sources to generate a unified feature vector; And the dynamic evaluation and prediction module is constructed based on a machine learning model and is used for predicting the future carbon sink change trend and the carbon sink benefits under different management strategies by utilizing the historical data.
4. The IPCC-based highway greening carbon sink dynamic assessment system according to claim 3, wherein the multi-modal data fusion module generates a unified feature vector by adopting a deep learning model based on an attention mechanism, specifically: F_fused = Σ (α_i F_i); where f_i is the feature vector of the ith modality, α_i is the weight learned by the model, and f_fused is the feature vector.
5. The IPCC-based highway greening carbon sink dynamic assessment system according to claim 3, wherein the machine learning model adopted by the dynamic assessment and prediction module is a multi-layer perceptron, and the construction of the dynamic assessment and prediction module comprises: the input layer receives characteristic vectors including tree species, tree height, breast diameter, soil parameters and meteorological data; transforming through at least one hidden layer containing a nonlinear activation function; the output layer outputs a predicted value for the future carbon sink benefit.
6. The IPCC-based highway greening carbon sink dynamic assessment system according to claim 1, wherein the application service layer comprises a blockchain certification module for non-tamperable certification of key monitoring data, model calculation process and assessment results by intelligent contracts and generating digital certificates comprising geofences and time stamps.
7. An IPCC-based highway greening carbon sink dynamic assessment method applied to the system according to any one of claims 1-6, comprising: Collecting multisource data of highway road area vegetation and wetland through an air-space-ground integrated monitoring network, and preprocessing and standardizing; Calculating vegetation carbon reserves, soil carbon reserves and annual carbon sequestration of all areas along the highway at the current time point based on the IPCC method which refers to the preprocessed data; Inputting the multi-mode data into a trained machine learning prediction model, dynamically predicting the future carbon sink change trend, and simulating carbon sink gains under different management strategies; Performing blockchain verification on input data, model parameters, a calculation process and an evaluation result to generate a carbon sink evaluation report or a digital certificate; and dynamically optimizing parameters of the machine learning prediction model according to the continuously monitored new data to form a closed-loop optimization system.
8. The method of claim 7, wherein calculating the vegetation carbon reserves is: C_vegetation=A×B×CF; Wherein A is the wood accumulation amount, the unit is m3, B is the wood density, the unit is t/m3, CF is the carbon-containing coefficient, and C_ vegetation is the vegetation carbon accumulation amount.
9. The method of claim 8, wherein the training data of the machine-learned predictive model includes historical forest growth data, historical environmental monitoring data, and historical management decision data, and wherein the management strategy includes changing tree species, adjusting irrigation solutions, or enhancing wetland maintenance.
10. The method of claim 8, wherein the digital certificate is a non-homogenous certificate embedded with geofence information, a time stamp, and a confidence level corresponding to the evaluation result.

Description

IPCC-based highway greening carbon sink dynamic evaluation system and method Technical Field The invention relates to the technical field of climate change slowing, in particular to a road greening carbon sink dynamic assessment system and method based on IPCC. Background Global climate change has become a major challenge for humans, and the transportation industry is one of the major sources of greenhouse gas emissions. According to statistics, the carbon emission of the transportation industry is inferior to that of the energy industry in the global scope, and is also at a higher level in China. To address this challenge, road greening is becoming increasingly important as an important carbon sink. However, the existing road greening carbon sink assessment technology has a plurality of defects: The data acquisition precision is low, the traditional method mainly relies on manual investigation and periodic measurement, which not only wastes time and labor, but also is difficult to acquire continuous and comprehensive data. For example, existing highway carbon sink assessment is often based on limited sample points and static data, and cannot reflect the dynamic process and spatial heterogeneity of vegetation growth. Although telemetry has been applied, it is often limited to a single data source (e.g., optical telemetry), is susceptible to weather conditions, and lacks ground verification data, resulting in greater uncertainty in the assessment results. The model has poor adaptability, and the existing carbon sink evaluation model mostly adopts an empirical coefficient method or a static biomass equation, and cannot fully consider the influences of growth characteristics, tree age structures and environmental factors (such as soil moisture, nutrient conditions and meteorological conditions) of different tree species. For example, some models simply establish a linear relationship between carbon reserves and tree breast diameter, tree height, ignoring the nonlinear processes of vegetation growth and the dynamic effects of climate change. IPCC (inter-government climate change specialized committee), while providing a basic framework and default for carbon sink calculation, is intended for national or regional scale applications, and direct carbon sink assessment for such linear engineering of highways can introduce significant errors. The dynamic monitoring capability is insufficient, and the existing system often lacks the capability of real-time monitoring and dynamic updating. Carbon sink assessment results generally represent static conditions at a certain point in time and cannot reflect the dynamic effects of seasonal changes, extreme climatic events (e.g., drought, heavy rain), and human activities (e.g., pruning, reseeding) on carbon sink capacity. In addition, the prior art has difficulty in predicting future carbon sink potential, limiting its application in long-term carbon asset management. The problems of easy data tampering, opaque process, difficult verification of results and the like of the traditional carbon sink assessment method are solved, and the high standard requirements of the carbon transaction market on the reliability and transparency of the data are difficult to meet. This makes it difficult for many highway greening carbon sink projects to obtain international carbon credit certification (e.g., VCS, GS standards), preventing them from entering the carbon trade market. The system integration level is low, the current carbon sink evaluation is limited to a single link or a single element, and comprehensive consideration of the whole life cycle (early construction period, operation period, maintenance period and dismantling period) of the highway is lacked. For example, there are few carbon sink assessment systems designed for highway features that can simultaneously take into account factors such as road vegetation, wetland soil, and carbon emission cancellation for vehicles traveling on the highway. In the field of road greening carbon sink evaluation, an IPCC standard method, a process model simulation method and a remote sensing inversion method are three main technical means, but have obvious limitations in adapting to the dynamics and the accuracy of road scenes: the IPCC standard method is a carbon sink estimation method developed based on the national greenhouse gas inventory guidelines issued by the inter-government climate change committee (IPCC). The core logic is that a national or regional default static carbon absorption factor (such as annual carbon fixation amount of forest and carbon conversion coefficient of herbaceous plant biomass in unit area) is adopted, basic data such as vegetation area, stand age and the like obtained by checking artificial sample land are combined, and the total carbon sink amount is calculated through a multiplication formula of carbon absorption factor multiplied by vegetation area. The limitation of the method is mainly reflected in the