CN-122019955-A - Urban carbon emission statistical system based on multisource data fusion

CN122019955ACN 122019955 ACN122019955 ACN 122019955ACN-122019955-A

Abstract

The invention discloses a multi-source data fusion-based urban carbon emission statistical system which comprises a data acquisition module, a data processing module, a carbon emission accounting model construction module, a high-resolution spatialization module and a visualization and management module. The data acquisition module is used for acquiring multi-source data, including statistics annual-image data, energy consumption data, night light data, point of interest (POI) data and land utilization/coverage data. The data processing module performs cleaning, alignment and dimension reduction processing on the data. The carbon emission accounting model construction module combines night light data and POI data to construct a carbon emission spatialization model of the branch industry. The system utilizes an improved clustering algorithm to accurately classify different urban areas and combines multi-source data weight distribution, so that the problems of statistics of one-sided, data loss, poor timeliness and low spatial resolution in the prior art are solved. The invention can realize high-precision, real-time and dynamic monitoring and partition statistics of urban carbon emission.

Inventors

SUN YAMIN
GUAN MIN
ZHAO MINGXIA

Assignees

冠县统计数据信息服务中心

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (7)

1. A multi-source data fusion-based urban carbon emission statistical system, comprising: The data acquisition module is used for acquiring multisource data related to urban carbon emission, wherein the multisource data comprises statistics annual-image data, energy consumption list data, night light data observed by satellites, POI (point of interest) data and urban land utilization data; the data preprocessing module is used for cleaning and normalizing the multi-source data and unifying the data of different sources on time and space scales; The urban functional area identification module is used for carrying out functional partition identification on the urban area by adopting a clustering algorithm based on the POI data and the land utilization data, and dividing the city into industrial, traffic, resident life, service industry and ecological functional areas; The carbon emission spatialization model construction module is used for constructing a carbon emission spatialization model, distributing carbon emission weights for each partition according to the characteristics of different functional partitions, and mapping the carbon emission data with the statistical scale into a grid with high spatial resolution by combining night light data and energy consumption data; The carbon emission dynamic accounting module is used for carrying out real-time accounting and statistics on carbon emission of different functional partitions in the city based on the carbon emission spatialization model by combining the real-time updated energy source flow data and the monitoring data of the Internet of things; And the visualization and management module is used for carrying out space visualization display on the calculated carbon emission statistical result and providing a data query, trend analysis and emission reduction policy evaluation interface.
2. The urban carbon emission statistical system based on multi-source data fusion according to claim 1, wherein the clustering algorithm adopted in the urban functional area identification module is a modified clustering algorithm, the algorithm comprising: Performing dimension reduction processing on the POI density data and the land utilization characteristics by utilizing the analysis of the nuclear main components; Clustering the dimension reduced data by adopting a clustering algorithm based on a density peak value or an optimized K-means algorithm, and determining the function type of each grid.
3. The urban carbon emission statistical system based on multi-source data fusion according to claim 1, wherein the carbon emission spatialization model construction module is specifically configured to: establishing a regression relation between the total carbon emission and the night lamplight intensity; Introducing POI density as an auxiliary variable to correct the carbon emission space distribution of different functional partitions of industry, traffic, service industry and the like; And calculating the carbon sequestration amount of the urban ecological system by using the ecological land information in the land utilization data and combining a vegetation carbon sequestration model, and deducting or singly listing the carbon sequestration amount in the total carbon emission statistics.
4. The municipal carbon emission statistical system based on multi-source data fusion according to claim 1, wherein the data preprocessing module comprises: The time alignment unit is used for performing time scale matching on the statistical data issued according to the years, the satellite remote sensing data of high frequency and the real-time data of the Internet of things; and the space alignment unit is used for mapping the statistical data of the administrative region scale into a space grid consistent with the satellite data through spatial interpolation or resampling.
5. The municipal carbon emission statistical system based on multi-source data fusion according to claim 1, wherein the visualization and management module comprises: the carbon emission thermodynamic diagram generating unit is used for displaying carbon emission intensities of different areas and different industries in different color depths on the urban map; and the time trend analysis unit is used for displaying the change curve of the urban carbon emission with time and marking the influence of the serious event or policy implementation node on the carbon emission.
6. The urban carbon emission statistical system based on multi-source data fusion according to claim 1, wherein the system is further provided with a data verification module for comparing the carbon emission result estimated based on the model with the actual measurement data of the ground monitoring station, and adjusting the parameters of the carbon emission spatialization model according to the error feedback.
7. The system for calculating urban carbon emission based on multi-source data fusion according to claim 1, wherein when using night light data and POI data for carbon emission spatialization, the following logic is adopted: for industrial areas and commercial service areas, higher weight is given to POIs to reflect high energy consumption activities; Giving higher weight to night lamplight data to the residential living area so as to reflect living energy consumption; and for the traffic function area, joint weight distribution is carried out by combining the road network density and the traffic facility data in the POI.

Description

Urban carbon emission statistical system based on multisource data fusion Technical Field The invention relates to the technical field of environmental monitoring and big data processing, in particular to an urban carbon emission statistical system based on multi-source data fusion. Background Carbon emissions generally refer to greenhouse gas emissions. Greenhouse gas is discharged, which causes a greenhouse effect and increases the global air temperature. With the acceleration of industrialization and urbanization, cities have become one of the main sources of greenhouse gas emissions. At present, the urban rate of China is about 65%, people and economic activities still gather towards cities for a period of time, and the carbon emission ratio of the cities of China can be predicted to be further improved. The activities of energy consumption, industrial production, transportation, resident life and the like in cities generate a large amount of greenhouse gases such as carbon dioxide and the like, and have remarkable influence on global climate change. Scientific accounting of municipal carbon emissions is the core and foundation of municipal carbon emission management. The existing urban carbon emission accounting mainly adopts a manual statistical method, carbon emission data are reported from bottom to top by various industries, and finally the integral carbon emission is obtained through statistics. This IPCC (inter-government climate change committee) guideline and provincial list based approach, while highly accurate, has significant limitations: (1) The efficiency is low, the related industries are more, the fuel type and the process type are complex, and special personnel are required to carry out inventory compiling and calculation, so that the time lag is high; (2) Statistics of one-sided and data loss is easily affected by datagram integrity and is difficult to cover all emission sources; (3) The existing statistical method usually takes administrative division as a unit, lacks spatial information in the city, and is difficult to reflect the detailed carbon emission conditions of different areas and different industries in the city, so that certain difficulty is brought to estimating the carbon dioxide emission of the functional division with high spatial resolution scale; (4) Static estimation lacks real-time performance, wherein the traditional method depends on macroscopic statistical data, cannot reflect the change trend of urban carbon emission and oxygen consumption in real time, and is difficult to adapt to the requirements of urban rapid development and intelligent urban management on real-time monitoring; (5) Neglecting the effect of the ecological system, most estimation methods mainly focus on carbon emission of human activities, and consider the carbon fixation and oxygen release effects of urban ecological systems (such as forests, grasslands, wetlands and the like) insufficiently, so that the estimation result is not comprehensive. In addition, since different urban functional partitions (industry, traffic, resident life, service industry, etc.) are located in different areas, there is a large spatial difference in carbon emissions inside cities. Although research has been conducted into the spatialization of carbon dioxide emissions using satellite-observed night light intensity (NTL) data, currently there are relatively few studies to integrate satellite-observed data and point-of-interest density data to build high-definition carbon emission data for different departments. Meanwhile, in the aspect of urban feature classification, the traditional clustering method (such as K-means) has the defects that local optimization is easy to fall into when nonlinear distribution and mixed feature parameters are processed, and the urban functional area clustering is possibly deviated, so that the spatial precision of carbon emission is affected. In view of the foregoing, there is a need for an urban carbon emission statistical system that can fuse multiple source data, ensure accuracy, and achieve high spatial resolution and real-time dynamic monitoring. For this reason, we propose a new urban carbon emission statistical system based on multi-source data fusion. Disclosure of Invention The invention aims to provide a city carbon emission statistical system based on multi-source data fusion, which realizes high-precision spacial, real-time accounting and partition statistics of city carbon emission by fusing multi-source information such as statistical data, night lamplight data, POI data and the like and provides scientific support for city low-carbon development planning. In order to achieve the purpose, the invention provides the following technical scheme that the urban carbon emission statistical system based on multi-source data fusion comprises: The data acquisition module is used for acquiring multisource data related to urban carbon emission, wherein the multisource data comprises