Cold region city carbon emission dynamic monitoring control method based on big data

Abstract

The invention relates to the technical field of carbon emission monitoring, in particular to a cold region city carbon emission dynamic monitoring control method based on big data, and the method comprises the steps: obtaining regional point source carbon emission data, city space distribution data, meteorological data and ecological resource data of different monitoring regions of a cold region city; evaluating the spatial aggregation of the carbon emission intensity of the monitoring area; evaluating a carbon emission intensity high-value accumulation area; evaluating the carbon emission diffusivity of the carbon emission intensity high-value accumulation area; the carbon absorption capacity of the carbon emission intensity high-value accumulation area is evaluated; and according to the carbon emission substance diffusion capability and the carbon absorption capability, evaluating the regional carbon budget and expenditure balance capability of the carbon emission intensity high-value accumulation region and optimizing the regional carbon budget and expenditure. According to the method, the influence of urban spatial distribution and ecological resource distribution on carbon emission substance diffusion and carbon absorption capability is quantified, so that accurate evaluation and effective control on the carbon emission of the urban in the cold region are realized.

Description

Technical Field

[0001] This application relates to the technical field of carbon emission monitoring, and particularly to a dynamic monitoring and control method for carbon emissions in cold-region cities based on big data. Background Art

[0002] Carbon emission monitoring can accurately grasp the spatio-temporal distribution and dynamic changes of carbon emissions, providing data support for scientifically formulating emission reduction and carbon sink enhancement strategies.

[0003] Due to their unique climate conditions and energy consumption characteristics, cold-region cities face more complex carbon emission management challenges. The winter heating demand in cold-region cities leads to a significant increase in urban carbon emissions. At the same time, the low-temperature environment inhibits the growth and carbon absorption capacity of natural carbon sinks such as vegetation, further exacerbating the imbalance of the carbon budget.

[0004] Traditional carbon emission monitoring methods rely on point-source data from fixed monitoring stations, making it difficult to comprehensively reflect the carbon emission situation in different regions of the city. Moreover, existing technologies ignore the influence of urban spatial and ecological resource distributions on the diffusion of carbon emission substances and carbon absorption capacity. The urban spatial distribution determines the diffusion path of carbon emission substances, and a dense urban structure will lead to local aggregation of carbon emission substances. As an important urban carbon sink, ecological resources can absorb carbon dioxide through photosynthesis, helping to relieve the urban carbon emission pressure. Therefore, ignoring the influence of urban spatial and ecological resource distributions on the diffusion of carbon emission substances and carbon absorption capacity will result in a reduction in the accuracy of carbon emission assessment and make it difficult to formulate scientific and effective emission reduction and carbon sink enhancement strategies. Summary of the Invention

[0005] In order to overcome the defects and deficiencies of the existing technology, this application provides a dynamic monitoring and control method for carbon emissions in cold-region cities based on big data, which realizes the accurate assessment and effective control of carbon emissions in cold-region cities by quantifying the influence of urban spatial distribution and ecological resource distribution on the diffusion of carbon emission substances and carbon absorption capacity.

[0006] To achieve the above object, this application adopts the following technical solutions: In the first aspect, this application provides a dynamic monitoring and control method for carbon emissions in cold-region cities based on big data, including the following steps: Obtain the regional point-source carbon emission data, urban spatial distribution data, meteorological data, and ecological resource data of different monitoring regions in the cold-region city; Evaluate the spatial aggregation of carbon emission intensity in the monitoring region through the regional point-source carbon emission data; Evaluate the high-value aggregation area of carbon emission intensity according to the spatial aggregation of carbon emission intensity; Evaluate the diffusion capacity of carbon emission substances in the high-value aggregation area of carbon emission intensity by combining the urban spatial distribution data with the meteorological data; Evaluate the carbon absorption capacity of high-value aggregation areas of carbon emission intensity through ecological resource data; Evaluate the regional carbon budget balance capacity of high-value aggregation areas of carbon emission intensity according to the carbon emission substance diffusion capacity combined with the carbon absorption capacity, and optimize the regional carbon budget.

[0007] Preferably, the evaluation of the spatial aggregation of the carbon emission intensity in the monitoring area includes: Obtain the regional point source carbon emission data of different monitoring areas, where the regional point source carbon emission data includes the regional location and the corresponding regional carbon emissions; Construct a spatial weight matrix according to the regional location, with the weight value corresponding to adjacent monitoring areas being 1 and the weight value corresponding to non-adjacent monitoring areas being 0; Conduct a local spatial autocorrelation analysis based on the regional carbon emissions combined with the spatial weight matrix to obtain the local spatial autocorrelation index, and evaluate the spatial aggregation of the carbon emission intensity in the monitoring area through the local spatial autocorrelation index.

[0008] Preferably, the evaluation of the high-value aggregation areas of carbon emission intensity includes: Obtain the local spatial autocorrelation index of the monitoring area; Take the monitoring areas where the local spatial autocorrelation index is positive and the regional carbon emissions are greater than the average of the regional carbon emissions of all monitoring areas as the high-value aggregation areas of carbon emission intensity.

[0009] Preferably, the evaluation of the carbon emission substance diffusion capacity of the high-value aggregation areas of carbon emission intensity includes: Obtain the urban spatial distribution data, meteorological data, and regional point source carbon emission data of the high-value aggregation areas of carbon emission intensity; Construct a carbon emission substance diffusion model based on the atmospheric pollutant diffusion model, and input the urban spatial distribution data, meteorological data, and regional point source carbon emission data into the carbon emission substance diffusion model to obtain the carbon emission substance diffusion concentration. The atmospheric pollutant diffusion model is any one of the Gaussian plume model, Lagrangian particle diffusion model, and computational fluid dynamics model; Obtain the cumulative diffusion amount of carbon emission substances during the monitoring period by performing a time integral on the carbon emission substance diffusion concentration, and obtain the regional cumulative diffusion amount of carbon emission substances in the high-value aggregation areas of carbon emission intensity by performing a spatial integral on the cumulative diffusion amount of carbon emission substances.

[0010] Preferably, the evaluation of the carbon absorption capacity of the high-value aggregation areas of carbon emission intensity includes: Obtain the ecological resource data of the high-value aggregation areas of carbon emission intensity; Construct a vegetation productivity model based on the ecosystem productivity model and estimate the net primary productivity of vegetation in the high-value aggregation areas of carbon emission intensity by using the vegetation productivity model combined with the ecological resource data. The ecosystem model includes any one of the ecological process model and the light use efficiency model; The net primary productivity of vegetation is corrected by introducing a defoliation correction coefficient, and the product of the net primary productivity of vegetation and the defoliation correction coefficient is used as the corrected net primary productivity. The calculation formula for the corrected net primary productivity is as follows: ; In the formula is the net primary productivity before correction, is the leaf area index of the current vegetation, is the maximum leaf area index of the vegetation, is the corrected net primary productivity; The product of the corrected net primary productivity and the carbon conversion coefficient is used as the vegetation carbon absorption amount in the high-value aggregation area of carbon emission intensity; The regional vegetation cumulative carbon absorption amount in the high-value aggregation area of carbon emission intensity during the monitoring period is obtained by integrating the vegetation carbon absorption amount over time.

[0011] Preferably, evaluating the regional carbon budget balance ability of the high-value aggregation area of carbon emission intensity and optimizing the regional carbon budget includes: Obtaining the regional carbon emissions, the cumulative diffusion amount of regional carbon emissions, and the regional vegetation cumulative carbon absorption amount in the high-value aggregation area of carbon emission intensity; The ratio of the sum of the cumulative diffusion amount of regional carbon emissions and the regional vegetation cumulative carbon absorption amount to the regional carbon emissions is used as the regional carbon budget balance index; When the regional carbon budget balance index of the high-value aggregation area of carbon emission intensity is less than 1, the ecological resource coverage level within the region is improved. When the regional carbon budget balance index of the high-value aggregation area of carbon emission intensity is greater than or equal to 1, the ecological resource coverage level within the region is maintained.

[0012] In a second aspect, the present application provides a dynamic monitoring and control system for carbon emissions in cold-region cities based on big data, including: A data acquisition module for acquiring regional point-source carbon emission data, urban spatial distribution data, meteorological data, and ecological resource data in different monitoring regions of cold-region cities; A spatial aggregation evaluation module for evaluating the spatial aggregation of carbon emission intensity in the monitoring region through regional point-source carbon emission data; A carbon emission aggregation area evaluation module for evaluating high-value aggregation areas of carbon emission intensity based on the spatial aggregation of carbon emission intensity; A diffusion ability evaluation module for evaluating the diffusion ability of carbon emissions in high-value aggregation areas of carbon emission intensity by combining urban spatial distribution data with meteorological data; A carbon absorption ability evaluation module for evaluating the carbon absorption ability of high-value aggregation areas of carbon emission intensity through ecological resource data; A regional carbon budget optimization module is used to evaluate the regional carbon budget balance ability of high-value aggregation areas of carbon emission intensity and optimize the regional carbon budget according to the carbon emission substance diffusion ability combined with the carbon absorption ability.

[0013] In a third aspect, the present application provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes the dynamic monitoring and control method for carbon emissions in cold-region cities based on big data by calling the computer program stored in the memory.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which when run on a computer, cause the computer to execute the dynamic monitoring and control method for carbon emissions in cold-region cities based on big data.

[0015] Compared with the prior art, the present application has the following advantages and beneficial effects: By integrating the regional point-source carbon emission data, urban spatial distribution data, and vegetation coverage data of cold-region cities, the present application realizes the quantitative evaluation of the spatial aggregation of carbon emission intensity, the carbon emission substance diffusion ability, and the carbon absorption ability, effectively improving the accuracy and reliability of carbon emission evaluation in cold-region cities. At the same time, by optimizing the layout of ecological resources and giving full play to the carbon sink effect of ecological resources, the carbon budget balance ability of cold-region cities is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objectives, and advantages of the present application will become more obvious by reading the detailed description of the non-limiting embodiments with reference to the following drawings: Figure 1 is the overall flowchart of the dynamic monitoring and control method for carbon emissions in cold-region cities based on big data provided by the embodiments of the present application; Figure 2 is the structural schematic diagram of the dynamic monitoring and control system for carbon emissions in cold-region cities based on big data provided by the embodiments of the present application; Figure 3 is the structural schematic diagram of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The technical solution of the present application will be described in detail below through the drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present application and the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0018] Please refer to Figure 1 , Figure 1It is a schematic diagram of the overall process of the method for dynamically monitoring and controlling carbon emissions in cold-region cities based on big data provided by an embodiment of this application, specifically including the following steps: S110: Obtain the regional point-source carbon emission data, urban spatial distribution data, meteorological data, and ecological resource data of different monitoring regions in cold-region cities.

[0019] S120: Evaluate the spatial aggregation of carbon emission intensity in the monitoring region through the regional point-source carbon emission data; Urban carbon emission sources often show uneven spatial distribution. By evaluating the spatial aggregation of carbon emission intensity, high-value aggregation areas of carbon emission intensity can be accurately identified. As the main contributors to carbon emissions, high-value aggregation areas of carbon emission intensity will also be key areas for carbon emission reduction. Furthermore, targeted carbon emission monitoring and control can be carried out, which helps to improve the efficiency of carbon emission management. Evaluating the spatial aggregation of carbon emission intensity in the monitoring region includes: Obtain the regional point-source carbon emission data of different monitoring regions, and the regional point-source carbon emission data includes the regional location and the corresponding regional carbon emissions; Construct a spatial weight matrix according to the regional location, with a weight value of 1 for adjacent monitoring regions and a weight value of 0 for non-adjacent monitoring regions; Perform local spatial autocorrelation analysis based on the regional carbon emissions in combination with the spatial weight matrix to obtain the local spatial autocorrelation index, and evaluate the spatial aggregation of carbon emission intensity in the monitoring region through the local spatial autocorrelation index. Among them, local spatial autocorrelation analysis is a statistical method used to study the correlation and its significance between local regional data values and their neighboring regions in geographical space, which can reveal the spatial clustering or dispersion characteristics of data at the local scale, helping to identify spatial patterns such as high-value aggregation areas, low-value aggregation areas, and high-low outliers. For example, in urban carbon emission research, local spatial autocorrelation analysis can effectively identify whether the areas with high carbon emission intensity form an aggregation phenomenon with adjacent regions. The calculation formula of the local spatial autocorrelation index is: ; In the formula and are the regional carbon emissions of the th monitoring region and the th monitoring region respectively, is the average value of the regional carbon emissions of all monitoring regions, is the number of monitoring regions, is the weight value of the th monitoring region and the th monitoring region in the spatial weight matrix, is the local spatial autocorrelation index of the th monitoring region.

[0020] S130: Evaluate the high-value aggregation area of carbon emission intensity according to the spatial aggregation of carbon emission intensity; The local spatial autocorrelation index can quantify the spatial aggregation characteristics of regional carbon emissions and avoid subjective judgment deviation. Comparing the carbon emission amount with the average value of carbon emission amounts further screens out the areas where the carbon emission intensity significantly exceeds the standard, ensuring that the evaluation of the high-value aggregation area has statistical significance. Evaluating the high-value aggregation area of carbon emission intensity includes: Obtain the local spatial autocorrelation index of the monitoring area; Regard the monitoring area with a positive local spatial autocorrelation index and a regional carbon emission amount greater than the average value of the regional carbon emission amounts of all monitoring areas as the high-value aggregation area of carbon emission intensity.

[0021] S140: Evaluate the diffusion ability of carbon emissions in the high-value aggregation area of carbon emission intensity through urban spatial distribution data combined with meteorological data; By evaluating the diffusion ability of carbon emissions in the high-value aggregation area of carbon emission intensity, the diffusion range and influence degree of carbon emissions in the high-value aggregation area in space and time are further quantified. By combining urban spatial distribution data, meteorological data and regional point-source carbon emission data, and based on the atmospheric pollutant diffusion model, the diffusion process of carbon emissions can be accurately simulated. Evaluating the diffusion ability of carbon emissions in the high-value aggregation area of carbon emission intensity includes: Obtain the urban spatial distribution data, meteorological data and regional point-source carbon emission data of the high-value aggregation area of carbon emission intensity; Construct a carbon emission diffusion model based on the atmospheric pollutant diffusion model. Input the urban spatial distribution data, meteorological data and regional point-source carbon emission data into the carbon emission diffusion model to obtain the carbon emission diffusion concentration. The atmospheric pollutant diffusion model is any one of the Gauss Mist-Plume Model (GMM), Lagrangian Particle Dispersion Models (LPDM) and Computational Fluid Dynamics (CFD); Obtain the cumulative diffusion amount of carbon emissions during the monitoring period by integrating the carbon emission diffusion concentration over time and obtain the cumulative diffusion amount of regional carbon emissions in the high-value aggregation area of carbon emission intensity by integrating the cumulative diffusion amount of carbon emissions over space.

[0022] S150: Evaluate the carbon absorption ability of the high-value aggregation area of carbon emission intensity through ecological resource data; The carbon absorption capacity of high-value aggregation areas of carbon emission intensity is used to quantify the offset capacity of ecological resources such as vegetation in the region for carbon emissions, helps to identify the contribution of ecological resources in the carbon cycle, and provides data support for formulating targeted ecological restoration and carbon emission reduction strategies. Evaluating the carbon absorption capacity of high-value aggregation areas of carbon emission intensity includes: Obtain ecological resource data of high-value aggregation areas of carbon emission intensity; Based on the ecosystem productivity model, construct a vegetation productivity model and use the vegetation productivity model combined with ecological resource data to estimate the net primary productivity of vegetation in high-value aggregation areas of carbon emission intensity. The ecosystem model includes any one of the ecological process models (such as the BIOME-BGC model, BEPS model) and the light use efficiency models (such as the CASA model); Modify the net primary productivity of vegetation by introducing a leaf litter correction coefficient, and take the product of the net primary productivity of vegetation and the leaf litter correction coefficient as the corrected net primary productivity. The calculation formula for the corrected net primary productivity is: ; In the formula is the net primary productivity before correction, is the leaf area index of the current vegetation, is the maximum leaf area index of the vegetation, is the corrected net primary productivity. Among them, the leaf area index (Leaf Area Index, LAI) refers to the multiple of the total area of plant leaves per unit land area to the land area, which is related to the density, structure (single layer or multi-layer) of the vegetation, the biological characteristics of the trees (branching angle, leaf attachment angle, shade tolerance, etc.) and environmental conditions (light, water, soil nutrient status). It is a comprehensive index representing the light energy utilization status and canopy structure of the vegetation. The leaf area index can be measured by using an optical instrument (such as LAI-2200, AccuPAR) to obtain the leaf area index by measuring the light transmittance of the vegetation canopy; Take the product of the corrected net primary productivity and the carbon conversion coefficient as the vegetation carbon absorption amount in high-value aggregation areas of carbon emission intensity. Among them, the net primary productivity (NPP) refers to subtracting the amount consumed by plant respiration from the total energy fixed by the vegetation community or the total amount of organic matter produced, which directly reflects the production capacity of the vegetation community under natural environmental conditions. The carbon conversion coefficient is the proportional coefficient for converting the vegetation net primary productivity into the carbon absorption amount, and its general value range is 0.45 - 0.50; Obtain the regional vegetation cumulative carbon absorption amount in high-value aggregation areas of carbon emission intensity during the monitoring period by performing time integration on the vegetation carbon absorption amount.

[0023] S160: Evaluate the regional carbon budget balance capacity of high-value aggregation areas of carbon emission intensity based on the carbon emission substance diffusion ability combined with the carbon absorption ability and optimize the regional carbon budget; By combining the diffusion amount of carbon emissions and the carbon absorption amount of vegetation, the actual situation of regional carbon budget can be comprehensively reflected. Based on the carbon budget balance index as the judgment basis, it is possible to intuitively identify whether the carbon budget is out of balance, and then take targeted measures to improve or maintain the ecological resource coverage level, thereby effectively optimizing the regional carbon budget, evaluating the regional carbon budget balance ability of high-value aggregation areas of carbon emission intensity, and optimizing the regional carbon budget, including: Obtain the regional carbon emissions, the cumulative diffusion amount of regional carbon emissions, and the cumulative carbon absorption amount of regional vegetation in the high-value aggregation area of carbon emission intensity; Take the ratio of the sum of the cumulative diffusion amount of regional carbon emissions and the cumulative carbon absorption amount of regional vegetation to the regional carbon emissions as the regional carbon budget balance index; When the regional carbon budget balance index of the high-value aggregation area of carbon emission intensity is less than 1, improve the ecological resource coverage level within the region; when the regional carbon budget balance index of the high-value aggregation area of carbon emission intensity is greater than or equal to 1, maintain the ecological resource coverage level within the region.

[0024] Please refer to Figure 2 , Figure 2 FIG. is a schematic structural diagram of a dynamic monitoring and control system for carbon emissions in cold-region cities based on big data provided by an embodiment of the present application. The present embodiment provides a dynamic monitoring and control system for carbon emissions in cold-region cities based on big data, including: A data acquisition module 210, configured to acquire regional point-source carbon emission data, urban spatial distribution data, meteorological data, and ecological resource data of different monitoring regions in cold-region cities; A spatial aggregation evaluation module 220, configured to evaluate the spatial aggregation of carbon emission intensity in the monitoring region through the regional point-source carbon emission data; A carbon emission aggregation area evaluation module 230, configured to evaluate the high-value aggregation area of carbon emission intensity according to the spatial aggregation of carbon emission intensity; A diffusion ability evaluation module 240, configured to evaluate the diffusion ability of carbon emissions in the high-value aggregation area of carbon emission intensity by combining the urban spatial distribution data with the meteorological data; A carbon absorption ability evaluation module 250, configured to evaluate the carbon absorption ability of the high-value aggregation area of carbon emission intensity through the ecological resource data; A regional carbon budget optimization module 260, configured to evaluate the regional carbon budget balance ability of the high-value aggregation area of carbon emission intensity according to the diffusion ability of carbon emission substances combined with the carbon absorption ability, and optimize the regional carbon budget.

[0025] In an embodiment of the present application, the spatial aggregation evaluation module 220 is configured to evaluate the spatial aggregation of carbon emission intensity in the monitoring region through the regional point-source carbon emission data. Evaluating the spatial aggregation of carbon emission intensity in the monitoring region includes: Obtain the regional point-source carbon emission data for different monitoring regions, where the regional point-source carbon emission data includes the regional location and the corresponding regional carbon emissions; Construct a spatial weight matrix based on the regional location, where the weight value corresponding to adjacent monitoring regions is 1 and the weight value corresponding to non-adjacent monitoring regions is 0; Perform a local spatial autocorrelation analysis based on the regional carbon emissions in combination with the spatial weight matrix to obtain a local spatial autocorrelation index, and evaluate the spatial aggregation of the carbon emission intensity in the monitoring region through the local spatial autocorrelation index.

[0026] In an embodiment of the present application, the carbon emission aggregation area evaluation module 230 is used to evaluate the high-value aggregation area of the carbon emission intensity according to the spatial aggregation of the carbon emission intensity. Evaluating the high-value aggregation area of the carbon emission intensity includes: Obtain the local spatial autocorrelation index of the monitoring region; Regard the monitoring region where the local spatial autocorrelation index is positive and the regional carbon emissions are greater than the average value of the regional carbon emissions of all monitoring regions as the high-value aggregation area of the carbon emission intensity.

[0027] In an embodiment of the present application, the diffusion ability evaluation module 240 is used to evaluate the diffusion ability of the carbon emissions in the high-value aggregation area of the carbon emission intensity by combining the urban spatial distribution data with the meteorological data. Evaluating the diffusion ability of the carbon emissions in the high-value aggregation area of the carbon emission intensity includes: Obtain the urban spatial distribution data, meteorological data, and regional point-source carbon emission data of the high-value aggregation area of the carbon emission intensity; Construct a carbon emission diffusion model based on the atmospheric pollutant diffusion model, and input the urban spatial distribution data, meteorological data, and regional point-source carbon emission data into the carbon emission diffusion model to obtain the carbon emission diffusion concentration. The atmospheric pollutant diffusion model is any one of the Gaussian plume model, Lagrangian particle diffusion model, and computational fluid dynamics model; Obtain the cumulative diffusion amount of the carbon emissions during the monitoring period by performing a time integration on the carbon emission diffusion concentration and obtain the regional cumulative diffusion amount of the carbon emissions in the high-value aggregation area of the carbon emission intensity by performing a spatial integration on the cumulative diffusion amount of the carbon emissions.

[0028] In an embodiment of the present application, the carbon absorption ability evaluation module 250 is used to evaluate the carbon absorption ability of the high-value aggregation area of the carbon emission intensity through the ecological resource data. Evaluating the carbon absorption ability of the high-value aggregation area of the carbon emission intensity includes: Obtain the ecological resource data of the high-value aggregation area of the carbon emission intensity; Construct a vegetation productivity model based on the ecosystem productivity model and estimate the net primary productivity of the vegetation in the high-value aggregation area of the carbon emission intensity by using the vegetation productivity model in combination with the ecological resource data. The ecosystem model includes any one of the ecological process model and the light use efficiency model; The net primary productivity of vegetation is corrected by introducing a leaf litter correction coefficient, and the product of the net primary productivity of vegetation and the leaf litter correction coefficient is used as the corrected net primary productivity. The calculation formula for the corrected net primary productivity is as follows: ; In the formula is the net primary productivity before correction, is the leaf area index of the current vegetation, is the maximum leaf area index of the vegetation, is the corrected net primary productivity; The product of the corrected net primary productivity and the carbon conversion coefficient is used as the vegetation carbon absorption amount in the high-value aggregation area of carbon emission intensity; The regional vegetation cumulative carbon absorption amount in the high-value aggregation area of carbon emission intensity during the monitoring period is obtained by performing a time integral on the vegetation carbon absorption amount.

[0029] In an embodiment of the present application, the regional carbon budget optimization module 260 is used to evaluate the regional carbon budget balance ability of the high-value aggregation area of carbon emission intensity and optimize the regional carbon budget according to the carbon emission substance diffusion ability combined with the carbon absorption ability. Evaluating the regional carbon budget balance ability of the high-value aggregation area of carbon emission intensity and optimizing the regional carbon budget includes: Obtaining the regional carbon emissions, the cumulative diffusion amount of regional carbon emissions, and the cumulative carbon absorption amount of regional vegetation in the high-value aggregation area of carbon emission intensity; Taking the ratio of the sum of the cumulative diffusion amount of regional carbon emissions and the cumulative carbon absorption amount of regional vegetation to the regional carbon emissions as the regional carbon budget balance index; When the regional carbon budget balance index of the high-value aggregation area of carbon emission intensity is less than 1, the ecological resource coverage level in the region is improved. When the regional carbon budget balance index of the high-value aggregation area of carbon emission intensity is greater than or equal to 1, the ecological resource coverage level in the region is maintained.

[0030] For the steps of each parameter and each unit module in the above-mentioned big data-based cold region city carbon emission dynamic monitoring and control system of the present application to achieve corresponding functions, reference can be made to the parameters and steps in the embodiments of the big data-based cold region city carbon emission dynamic monitoring and control method in the above text, which will not be elaborated here.

[0031] Please refer to Figure 3 , an embodiment of the present invention also provides an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected through the communication bus 330. The memory 310 stores thereon a big data-based cold region city carbon emission dynamic monitoring and control method that can be loaded and executed by the processor 320 as provided in the above embodiments.

[0032] The memory 310 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 310 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function, and instructions for implementing the big data-based dynamic monitoring and control method for carbon emissions in cold-region cities provided in the above embodiments, etc.; the data storage area can store data involved in the big data-based dynamic monitoring and control method for carbon emissions in cold-region cities provided in the above embodiments, etc.

[0033] The processor 320 may include one or more processing cores. By running or executing the instructions, programs, code sets or instruction sets stored in the memory 310, the processor 320 calls the data stored in the memory 310 to perform various functions of this application and process data. The processor 320 can be at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller and a microprocessor. It can be understood that for different devices, the electronic devices for implementing the functions of the above-mentioned processor 320 may also be others, and the embodiments of this application do not make specific limitations.

[0034] The communication bus 330 may include a path for transmitting information between the above components. The communication bus 330 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 330 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 3 only a double arrow is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0035] The embodiments of this application provide a computer-readable storage medium, storing a computer program that can be loaded and executed by a processor and that is for the big data-based dynamic monitoring and control method for carbon emissions in cold-region cities provided in the above embodiments.

[0036] In the embodiments of the present application, a computer-readable storage medium may be a tangible device that retains and stores instructions used by an instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, an optical disc, a magnetic disc, a mechanical encoding device, and any combination of the foregoing.

[0037] The term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus.

[0038] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions applied in the present application.

Claims

1. A dynamic monitoring and control method for carbon emissions in cold-region cities based on big data, characterized in that, It includes the following steps: Obtain the regional point-source carbon emission data, urban spatial distribution data, meteorological data, and ecological resource data of different monitoring areas in cold-region cities; Evaluate the spatial aggregation of carbon emission intensity in the monitoring area through the regional point-source carbon emission data; Evaluate the high-value aggregation areas of carbon emission intensity based on the spatial aggregation of carbon emission intensity; Evaluate the carbon emission diffusion capacity of the high-value aggregation areas of carbon emission intensity by combining the urban spatial distribution data with the meteorological data; Evaluate the carbon absorption capacity of the high-value aggregation areas of carbon emission intensity through the ecological resource data; Evaluate the regional carbon budget balance capacity of the high-value aggregation areas of carbon emission intensity based on the carbon emission diffusion capacity combined with the carbon absorption capacity and optimize the regional carbon budget.

2. The dynamic monitoring and control method for carbon emissions in cold-region cities based on big data according to claim 1, characterized in that The evaluation of the spatial aggregation of carbon emission intensity in the monitoring area includes: Obtain the regional point-source carbon emission data of different monitoring areas, and the regional point-source carbon emission data includes the regional location and the corresponding regional carbon emissions; Construct a spatial weight matrix according to the regional location, with a weight value of 1 for adjacent monitoring areas and a weight value of 0 for non-adjacent monitoring areas; Conduct a local spatial autocorrelation analysis based on the regional carbon emissions combined with the spatial weight matrix to obtain the local spatial autocorrelation index, and evaluate the spatial aggregation of carbon emission intensity in the monitoring area through the local spatial autocorrelation index.

3. The method for dynamically monitoring and controlling carbon emissions in cold-region cities based on big data according to claim 1, characterized in that, The evaluation of the high-value aggregation areas of carbon emission intensity includes: Obtain the local spatial autocorrelation index of the monitoring area; Regard the monitoring areas with a positive local spatial autocorrelation index and regional carbon emissions greater than the average of the regional carbon emissions of all monitoring areas as the high-value aggregation areas of carbon emission intensity.

4. The method for dynamically monitoring and controlling carbon emissions in cold-region cities based on big data according to claim 1, wherein The evaluation of the carbon emission diffusion capacity of the high-value aggregation areas of carbon emission intensity includes: Obtain the urban spatial distribution data, meteorological data, and regional point-source carbon emission data of the high-value aggregation areas of carbon emission intensity; Construct a carbon emission diffusion model based on the atmospheric pollutant diffusion model, and input the urban spatial distribution data, meteorological data, and regional point-source carbon emission data into the carbon emission diffusion model to obtain the carbon emission diffusion concentration. The atmospheric pollutant diffusion model is any one of the Gaussian plume model, Lagrangian particle diffusion model, and computational fluid dynamics model; Obtain the cumulative carbon emission diffusion amount during the monitoring period by performing a time integral on the carbon emission diffusion concentration and obtain the regional carbon emission cumulative diffusion amount of the high-value aggregation areas of carbon emission intensity by performing a spatial integral on the cumulative carbon emission diffusion amount.

5. The dynamic monitoring and control method for carbon emissions in cold-region cities based on big data according to claim 1, characterized in that, The evaluation of the carbon absorption capacity of the high-value aggregation areas of carbon emission intensity includes: Obtain the ecological resource data of the high-value aggregation areas of carbon emission intensity; Construct a vegetation productivity model based on the ecosystem productivity model and estimate the net primary productivity of the vegetation in the high-value aggregation areas of carbon emission intensity by combining the vegetation productivity model with the ecological resource data. The ecosystem model includes any one of the ecological process model and the light use efficiency model; Correct the net primary productivity of the vegetation by introducing a leaf litter correction coefficient, and take the product of the net primary productivity of the vegetation and the leaf litter correction coefficient as the corrected net primary productivity. The calculation formula for the corrected net primary productivity is: ； In the formula is the net primary productivity before correction, is the leaf area index of the current vegetation, is the maximum leaf area index of the vegetation, is the net primary productivity after correction; Take the product of the corrected net primary productivity and the carbon conversion coefficient as the vegetation carbon absorption amount of the high-value aggregation areas of carbon emission intensity; The regional cumulative carbon absorption of the high-value aggregation area of carbon emission intensity during the monitoring period is obtained by integrating the vegetation carbon absorption over time.

6. The dynamic monitoring and control method for carbon emissions in cold-region cities based on big data according to claim 1, characterized in that, Evaluating the regional carbon budget balance ability of the high-value aggregation area of carbon emission intensity and optimizing the regional carbon budget includes: Obtaining the regional carbon emissions, the cumulative diffusion amount of regional carbon emissions, and the regional cumulative carbon absorption of the high-value aggregation area of carbon emission intensity; Taking the ratio of the sum of the cumulative diffusion amount of regional carbon emissions and the regional cumulative carbon absorption to the regional carbon emissions as the regional carbon budget balance index; When the regional carbon budget balance index of the high-value aggregation area of carbon emission intensity is less than 1, improving the ecological resource coverage level within the region, and when the regional carbon budget balance index of the high-value aggregation area of carbon emission intensity is greater than or equal to 1, maintaining the ecological resource coverage level within the region.

7. A dynamic monitoring and control system for carbon emissions in cold-region cities based on big data, which is applied to the dynamic monitoring and control method for carbon emissions in cold-region cities based on big data according to any one of claims 1-6, characterized in that, The system includes: A data acquisition module for acquiring regional point-source carbon emission data, urban spatial distribution data, meteorological data, and ecological resource data of different monitoring areas in cold-region cities; A spatial aggregation evaluation module for evaluating the spatial aggregation of carbon emission intensity in the monitoring area through the regional point-source carbon emission data; A carbon emission aggregation area evaluation module for evaluating the high-value aggregation area of carbon emission intensity based on the spatial aggregation of carbon emission intensity; A diffusion ability evaluation module for evaluating the diffusion ability of carbon emissions in the high-value aggregation area of carbon emission intensity by combining the urban spatial distribution data with the meteorological data; A carbon absorption ability evaluation module for evaluating the carbon absorption ability of the high-value aggregation area of carbon emission intensity through the ecological resource data; A regional carbon budget optimization module for evaluating the regional carbon budget balance ability of the high-value aggregation area of carbon emission intensity and optimizing the regional carbon budget by combining the carbon emission diffusion ability with the carbon absorption ability.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the dynamic monitoring and control method for carbon emissions in cold-region cities based on big data as described in any one of claims 1-6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, Stored with instructions that, when run on a computer, cause the computer to execute the dynamic monitoring and control method for carbon emissions in cold-region cities based on big data as described in any one of claims 1-6.

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