Multi-dimensional carbon emission monitoring system and method based on big data

Abstract

The invention relates to the field of carbon emission monitoring, in particular to a multi-dimensional carbon emission monitoring system and method based on big data. The method comprises the following steps: acquiring real-time data, and calculating initial carbon emission through a multi-source carbon emission dynamic calculation algorithm; carrying out regional division on the initial carbon emission according to geographic positions, and calculating weighted carbon emission; and calculating a multi-dimensional carbon emission index through a regional diffusion emission calculation algorithm based on the weighted carbon emission, and carrying out visual display on the multi-dimensional carbon emission index. The problems that a traditional carbon emission monitoring method mostly depends on static data or periodic statistics, and dynamic changes of energy consumption and emission in production activities cannot be reflected in real time are solved; the influence of the geographic position, the area density and the boundary effect on emission distribution is ignored, so that the calculation result lacks space refinement; a traditional carbon emission model rarely comprehensively considers the influence of meteorological conditions on emission diffusion, so that an emission index cannot truly reflect a dynamic propagation rule in an environment.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission monitoring, and in particular to a multi-dimensional carbon emission monitoring system and method based on big data. Background Art

[0002] Traditional carbon emission monitoring methods have many limitations in terms of coverage, data accuracy and real-time performance, and are unable to meet the current complex and changing environmental governance needs.

[0003] The above-mentioned traditional carbon emission monitoring methods mostly rely on static data or periodic statistics (such as monthly reports and annual reports), which cannot reflect the dynamic changes of energy consumption and emissions in production activities in real time, limiting the timely adjustment of strategies; the overall total emissions are used as the calculation target, ignoring the impact of geographical location, regional density and boundary effects on emission distribution, resulting in a lack of spatial refinement in the calculation results and an inability to accurately reflect the emission characteristics of different regions; traditional carbon emission models rarely comprehensively consider the impact of meteorological conditions (such as wind speed and wind direction) on emission diffusion, resulting in the emission index being unable to truly reflect the dynamic propagation laws in the environment, limiting the accuracy of cross-regional monitoring. Summary of the invention

[0004] The present invention provides a multi-dimensional carbon emission monitoring system and method based on big data to solve the problems that traditional carbon emission monitoring methods mostly rely on static data or periodic statistics (such as monthly reports, annual reports), cannot reflect the dynamic changes of energy consumption and emissions in production activities in real time, and limit the timely adjustment of strategies; take the overall total emissions as the calculation target, ignore the influence of geographical location, regional density and boundary effects on emission distribution, resulting in a lack of spatial refinement in the calculation results, and cannot accurately reflect the emission characteristics of different regions; traditional carbon emission models rarely comprehensively consider the influence of meteorological conditions (such as wind speed and wind direction) on emission diffusion, resulting in the emission index cannot truly reflect the dynamic propagation laws in the environment, limiting the accuracy of cross-regional monitoring.

[0005] The present invention provides a multi-dimensional carbon emission monitoring system and method based on big data, which specifically includes the following technical solutions: A multi-dimensional carbon emission monitoring method based on big data, comprising the following steps: S1. Obtain real-time data and construct a data set; based on the data set, introduce energy emission factors through a multi-source carbon emission dynamic calculation algorithm, multiply the energy consumption in the real-time data by the corresponding energy emission factor, obtain the energy carbon emission, and accumulate the carbon emission of each energy source to generate the basic carbon emission value of the data point; introduce a periodic adjustment item as a time dynamic weight, adjust the basic carbon emission value of the data point, and calculate the preliminary carbon emission; S2. Divide the preliminary carbon emissions into regions according to geographical location, and calculate the weighted carbon emissions by combining the distance between the data point and the regional center, the density distribution within the region, and the boundary effect; the calculation formula for the weighted carbon emissions is: , in, Indicates In the region Data points at time Weighted carbon emissions; Indicates In the region Data points at time Initial carbon emissions; Indicates the number of coordinate dimensions; represents the distance term; Indicates In the region Data points at time No. Coordinate values; Indicates Regional centers at a time No. Coordinate values; represents the adjustment coefficient of the density factor; Indicates Regions in time The density distribution of data points; represents the boundary effect reflected by the exponential function; represents the adjustment coefficient of the boundary effect; Indicates at time No. In the region The distance from a data point to the region boundary; S3. Based on weighted carbon emissions, the regional diffusion emission calculation algorithm is used to extract the wind speed and direction information of the target area from the meteorological data in the real-time data, and the diffusion factor is calculated in combination with the regional area and the distance between regions; based on the weighted carbon emissions, combined with the diffusion factor, the multi-dimensional carbon emission index is calculated, and the multi-dimensional carbon emission index is visualized.

[0006] Preferably, the S1 specifically includes: The calculation formula for the preliminary carbon emissions is: , in, Indicates Data points at time Initial carbon emissions; Indicates Emission factors for each energy source; Indicates Data points at time Use The number of energy sources; Indicates the total number of energy types; Represents a periodic adjustment item; represents the period adjustment coefficient; The arc degree representing one complete cycle; Indicates the time period parameter; Indicates the phase adjustment parameter.

[0007] Preferably, the S3 specifically includes: The calculation formula of the multi-dimensional carbon emission index is: , in, Indicates Regions in time Multi-dimensional carbon emission index; Indicates In the region Data points at time Weighted carbon emissions; represents the diffusion factor; Indicates Regions in time The number of data points; Indicates Regions in time wind speed; It is Regions in time Wind direction at the time; Indicates Regions in time area; Indicates at time No. The average distance from the center of a region to the centers of neighboring regions.

[0008] A multi-dimensional carbon emission monitoring system based on big data, including the following parts: Data acquisition module, preliminary emission calculation module, regional emission calculation module, regional diffuse emission calculation module, data output and visualization module; Data acquisition module: acquires real-time data, organizes the acquired real-time data into a structured data set, and outputs the data set to the preliminary emission calculation module; Preliminary emission calculation module: Based on the data set, the preliminary carbon emissions are calculated through the multi-source carbon emission dynamic calculation algorithm; the preliminary carbon emissions are output to the regional emission calculation module; Regional emission calculation module: divide the preliminary carbon emissions into regions according to geographical location, and calculate the weighted carbon emissions by combining the distance between the data point and the regional center, the density distribution within the region, and the boundary effect; output the weighted carbon emissions to the regional diffusion emission calculation module; Regional diffusion emission calculation module: Based on the weighted carbon emissions, the multi-dimensional carbon emission index is calculated through the regional diffusion emission calculation algorithm; the multi-dimensional carbon emission index is output to the data output and visualization module; Data output and visualization module: provides a real-time monitoring interface, displays the multi-dimensional carbon emission index of each region, and presents the multi-dimensional carbon emission index in a visual form.

[0009] The beneficial effects of the technical solution of the present invention are: 1. By collecting real-time data from multiple data sources such as industrial sensors, energy networks and third-party meteorological data providers, and organizing it into a structured data set containing geographic location and time information, the preliminary carbon emissions are calculated using a multi-source carbon emission dynamic calculation algorithm. Compared with the traditional static carbon emission calculation method, it can reflect changes in energy use in real time and capture the dynamic characteristics of emissions in the time dimension (such as the difference between day and night) through periodic adjustment items, thereby improving the accuracy and real-time performance of carbon emission monitoring, providing enterprises or governments with more timely emission data support, and is suitable for dynamically adjusting production plans or energy use strategies, thereby optimizing resource allocation and reducing unnecessary carbon emissions.

[0010] 2. Based on the preliminary carbon emissions, the data set is divided into multiple regions according to geographical location, and the weighted carbon emissions are calculated by comprehensively considering the distance between the data point and the regional center, the density distribution within the region and the boundary effect through the spatial weighting factor. This breaks through the limitation of traditional carbon emission calculations that ignores spatial distribution differences, realizes the spatial heterogeneity analysis of emissions, and truly reflects the emission distribution law within the region. It provides a scientific basis for regionalized carbon emission management, enabling enterprises or regulatory agencies to formulate differentiated strategies based on the emission characteristics of different regions (such as industrial areas and suburbs), thereby improving resource allocation efficiency and the effectiveness of environmental policy implementation.

[0011] 3. Based on weighted carbon emissions, a regional diffusion emission calculation algorithm is introduced. Combined with meteorological conditions (wind speed, wind direction) and spatial characteristics (regional area, distance between regions), a multi-dimensional carbon emission index is calculated. It not only calculates the total emissions in the region, but also simulates the diffusion behavior of carbon emissions, providing a comprehensive emission portrait. The generated multi-dimensional carbon emission index supports cross-regional comparative analysis and long-term trend forecasting, providing data-driven decision-making support for the government to formulate regional coordinated emission reduction policies or for enterprises to optimize supply chain layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a structural diagram of a multi-dimensional carbon emission monitoring system based on big data according to the present invention; Figure 2 This is a flow chart of a multi-dimensional carbon emission monitoring method based on big data described in the present invention. DETAILED DESCRIPTION

[0013] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0015] The following is a detailed description of a multi-dimensional carbon emission monitoring system and method based on big data provided by the present invention in conjunction with the accompanying drawings.

[0016] See attached Figure 1 , which shows a structure diagram of a multi-dimensional carbon emission monitoring system based on big data provided by an embodiment of the present invention, the system includes the following parts: Data acquisition module, preliminary emission calculation module, regional emission calculation module, regional diffuse emission calculation module, data output and visualization module; Data acquisition module: acquires real-time data from multiple data sources, organizes the acquired real-time data into a structured data set, and outputs the data set to the preliminary emission calculation module; Preliminary emission calculation module: Based on the data set, the preliminary carbon emissions are calculated through the multi-source carbon emission dynamic calculation algorithm; the preliminary carbon emissions are output to the regional emission calculation module; Regional emission calculation module: divides the preliminary carbon emissions into multiple regions according to geographical location, and calculates the weighted carbon emissions by comprehensively considering the distance between the data point and the regional center, the density distribution within the region, and the boundary effect; outputs the weighted carbon emissions to the regional diffusion emission calculation module; Regional diffusion emission calculation module: Based on the weighted carbon emissions, the multi-dimensional carbon emission index is calculated through the regional diffusion emission calculation algorithm; the multi-dimensional carbon emission index is output to the data output and visualization module; Data output and visualization module: provides a real-time monitoring interface, displays the multi-dimensional carbon emission index of each region, and presents the multi-dimensional carbon emission index in a visual form (such as tables, heat maps, curve charts, etc.) to facilitate user understanding and analysis. It supports data export function and provides support for corporate carbon footprint management or carbon trading.

[0017] See attached Figure 2 , which shows a flow chart of a multi-dimensional carbon emission monitoring method based on big data provided by an embodiment of the present invention, the method comprising the following steps: S1. Obtain real-time data and construct a data set; based on the data set, calculate the preliminary carbon emissions through a multi-source carbon emissions dynamic calculation algorithm; Acquire real-time data from multiple data sources, including but not limited to recording the consumption of various energy sources (such as natural gas and coal) in the production process through industrial sensors, acquiring real-time usage data of energy such as gas through energy networks, and acquiring meteorological data by calling APIs of third-party meteorological data providers (such as the National Meteorological Administration, Open Weather Data Services, etc.), and organize the acquired real-time data into a structured data set, which contains multiple data points, each of which corresponds to a specific monitoring location or device, and records the amount of various energy sources used at the current moment, as well as geographic location information; Based on the data set, the multi-source carbon emission dynamic calculation algorithm is used to calculate the preliminary carbon emissions; For each energy source involved in each data point, the multi-source carbon emission dynamic calculation algorithm will multiply the energy consumption by the corresponding emission factor to obtain the carbon emissions generated by the energy at the current moment, and accumulate the carbon emissions of each energy source to generate the basic carbon emission value of the data point; The multi-source carbon emission dynamic calculation algorithm introduces a periodic adjustment item as a time dynamic weight to adjust the basic carbon emission value to reflect the periodic changes in carbon emissions. Specifically, a sine function is used to simulate the periodic changes in carbon emissions. The sine function can generate a smooth fluctuation curve with a value ranging from -1 to 1, reflecting the high and low fluctuations of emissions within a day. A periodic adjustment coefficient is introduced to control the intensity of the fluctuation to ensure that the fluctuation is not too drastic, thereby maintaining the stability of the calculation results. In order to make the fluctuation more in line with the actual scenario, a phase adjustment parameter is introduced to offset the starting point of the cycle to a time point that is more in line with the actual situation. The preliminary carbon emissions calculation formula is: , in, Indicates Data points at time Initial carbon emissions; Indicates The emission factor of the energy source, such as coal is 2.5 tons / tons, which can be set according to the specific implementation scenario and is not limited here; Indicates Data points at time Use The amount of energy, that is, the amount of energy consumed; Indicates the total number of energy types, such as coal and natural gas; It represents the periodic adjustment term, reflecting the daily fluctuation of carbon emissions. It simulates the periodic changes of carbon emissions (such as the difference in emissions during the day and night) through the sine function to enhance the dynamics of the time dimension. Indicates the period adjustment coefficient, which can be set according to the specific implementation scenario and is not limited here; The arc degree representing one complete cycle; Indicates the time period parameter, which can be set according to the specific implementation scenario and is not limited here; Indicates the phase adjustment parameter, which can be set according to the specific implementation scenario and is not limited here; S2. Divide the preliminary carbon emissions into regions according to geographical locations and calculate weighted carbon emissions; Divide the preliminary carbon emissions into multiple regions based on geographical location, record the location of the data points and the coordinates of the regional center, comprehensively consider the distance between the data points and the regional center, the density distribution within the region, and the boundary effect, and calculate the weighted carbon emissions of the data points in each region; The distance between the data point and the regional center is an important basis for measuring the contribution of the data point to the overall regional emissions. Based on the latitude and longitude coordinates of the data point and the latitude and longitude coordinates of the regional center, the square root form of the Euclidean distance is calculated to obtain the straight-line distance between the two. In order to avoid the influence of too large or too small distance on the result, the distance value is normalized, the reciprocal of the distance value is taken and smoothed, so that the data points with closer distances obtain higher weights in the weighted calculation, while the weights of the data points with farther distances are reduced accordingly, ensuring that the emission activities near the regional center contribute more to the regional emissions, so that it conforms to the actual spatial distribution law; The density distribution in the region is calculated based on the number of data points in the region and the area of ​​the region; by dividing the number of data points in the region by the area of ​​the region, an indicator reflecting the density of data point distribution is obtained to reflect the concentration of emission activities in the region, thereby affecting the weight distribution of weighted calculation; The boundary effect is reflected by an exponential function based on the vertical distance from the data point to the nearest boundary line (i.e., boundary distance); emission points close to the boundary may have a spillover effect on the adjacent area, while data points far from the boundary contribute more to the total emission of the region; by introducing the boundary effect, the spatial characteristics of emissions can be simulated more realistically; The calculation formula for weighted carbon emissions is: , in, Indicates In the region Data points at time Weighted carbon emissions; Indicates In the region Data points at time Initial carbon emissions; represents the spatial weighting factor used to adjust the preliminary carbon emissions; Indicates the number of coordinate dimensions; Represents the distance term, which adjusts the carbon emissions by calculating the Euclidean distance between the data point and the center of the region. The closer the distance, the larger the spatial weighting factor, and the farther the distance, the smaller the spatial weighting factor; Indicates In the region Data points at time No. A coordinate value, such as longitude or latitude, indicating the geographic location of the data point; Indicates Regional centers at a time No. A coordinate value, such as longitude or latitude, indicating the center location of the area; It represents the density and boundary effect terms, and adjusts the carbon emissions by the density factor and boundary distance. The higher the density and the farther from the boundary, the greater the spatial weighting factor. The adjustment coefficient of the density factor is used to control the contribution of the density distribution to the spatial weighting factor. It can be set according to the specific implementation scenario and is not limited here. Indicates Regions in time The density distribution of data points reflects the density of data points in the region. The higher the density, the greater the spatial weighting factor. The calculation formula for density distribution is: , Indicates Regions in time The number of data points; Indicates Regions in time area; represents the boundary effect reflected by the exponential function; The adjustment coefficient representing the boundary effect is used to control the speed at which the boundary distance affects the spatial weighting factor. It can be set according to the specific implementation scenario and is not limited here. Indicates at time No. In the region The distance from a data point to the region boundary; The calculation of weighted carbon emissions can more truly reflect the spatial heterogeneity of emissions, such as the difference in emission contributions between industrially intensive areas and marginal areas; S3. Based on the weighted carbon emissions, the multi-dimensional carbon emission index is calculated through the regional diffusion emission calculation algorithm, and the multi-dimensional carbon emission index is visualized; Based on weighted carbon emissions, a multi-dimensional carbon emission index is calculated using a regional diffusion emission calculation algorithm; The diffusion factor is the core part of the regional diffusion emission calculation algorithm, which is used to simulate the impact of meteorological conditions and spatial characteristics on the carbon emission diffusion process; The wind speed and wind direction information of the target area is extracted from the meteorological data. The wind speed indicates the speed of air flow, while the wind direction indicates the possible movement direction of carbon emission substances. In order to quantify the impact of wind direction, the regional diffusion emission calculation algorithm will process the wind direction at an angle and calculate the cosine value of the wind direction angle to reflect the correlation between the wind direction and the emission diffusion direction. If the wind direction is consistent with the diffusion direction, the cosine value is close to 1, indicating that the wind direction has the greatest promoting effect on diffusion. If the wind direction is opposite, the cosine value may be negative, indicating that the diffusion is hindered to a certain extent. Further consider the impact of the area of ​​the region on diffusion. The size of the regional area reflects the distribution density of carbon emission substances within the region. The larger the area, the lower the emission concentration per unit area may be, and the diffusion effect may be more uniform. Specifically, taking the square root of the regional area can reduce the extreme impact of the regional area value being too large or too small on the result, while retaining the positive effect of the regional area on diffusion. The normalized regional area will be used to adjust the weight of the wind factor so that the diffusion calculation of carbon emissions can adapt to regions of different sizes. In the calculation of the diffusion factor, the average distance from the center of the target area to the center of the adjacent area reflects the difficulty of carbon emissions spreading to the adjacent area. The farther the distance, the smaller the impact of diffusion. In order to simulate the attenuation effect, an exponential attenuation factor based on distance and wind speed is introduced. The attenuation speed is adjusted in combination with wind speed to make the diffusion factor more in line with physical laws. The calculation formula of the multi-dimensional carbon emission index is: , in, Indicates Regions in time Multi-dimensional carbon emission index; Indicates In the region Data points at time Weighted carbon emissions; represents the diffusion factor, reflecting the spatial diffusion effect of carbon emissions among regions; It represents the contribution of wind speed and direction to the diffusion of carbon emissions, and is a regulation term affected by the area of ​​the region; represents the wind factor; Indicates Regions in time The wind speed reflects the driving effect of wind on the diffusion of carbon emissions. The greater the wind speed, the more significant the diffusion. It is Regions in time Wind direction at the time; Indicates at time The cosine value of wind direction converts wind direction into directional influence factor, with a value range of , which is used to reflect the directional effect of wind direction on diffusion, such as greater diffusion with the wind and less diffusion against the wind. Expressed as an angle, such as 0° for north wind and 90° for east wind; Indicates Regions in time The area of ​​the region affects the dilution degree of diffusion. The larger the area, the lower the carbon emission concentration per unit area. Indicates Regions in time The square root of the area of ​​, which is used to normalize the effect of regional area; Represents an exponential attenuation factor based on distance and wind speed. The attenuation speed is adjusted in combination with wind speed. It is used to simulate the attenuation law of carbon emissions with distance, making the diffusion factor more consistent with physical laws. Indicates at time No. The average distance from the center of a region to the center of the neighboring region; Based on weighted carbon emissions and real-time meteorological conditions, a multi-dimensional carbon emission index is generated that can reflect the actual diffusion behavior of carbon emissions. It is not only suitable for emission monitoring in a single region, but also supports cross-regional comparative analysis and long-term trend forecasting, providing data-driven decision support for enterprises, governments or other institutions. It provides a real-time monitoring interface, displays the multi-dimensional carbon emission index of each region, and presents the multi-dimensional carbon emission index in a visual form (such as tables, heat maps, curve charts, etc.) to facilitate user understanding and analysis. It supports data export function and provides support for corporate carbon footprint management or carbon trading.

[0018] The above embodiment is further explained below in conjunction with specific experimental data: (1) Data point setting: Two areas were selected for this experiment: Region 1: 3 data points (A, B, C); Region 2: 3 data points (D, E, F); The time point is set to 08:00 on March 1, 2025 (t=8 hours).

[0019] (2) Parameter setting: Table 1 , (3) Data point information: Table 2 , The center coordinates of area 1 are (3, 4); The center coordinates of area 2 are (12, 11); (4) Experimental process and calculation: S1: Calculate preliminary carbon emissions : , Periodic adjustment calculation: , , π / 2rad, , , , Region 1: Data Point A: , Data Point B: , Data Point C: , Region 2: Data point D: , Data point E: , Data point F: , Table 3 , S2: Calculate weighted carbon emissions : , Density distribution: Region 1: , Region 2: , Region 1: Data Point A: Distance term: , Boundary Effect: , Spatial weighting factor: , , Table 4 , S3: Calculate multi-dimensional carbon emission index : , Region 1: Wind Factor: , Area adjustment: , Exponential decay: , Diffusion Factor: , Total weighted carbon emissions: , , Table 5 , (5) Results analysis: Preliminary carbon emissions: The highest emission point in area 1 is B , the highest in area 2 is , reflecting differences in energy consumption; Weighted carbon emissions: Area 1 is larger and has lower density. The boundary effect has a significant impact on point B (6 km). ; Area 2 has a higher density Point D has a higher weight because it is close to the center (1 km away). ; Multi-dimensional carbon emission index: Region 1 Slightly higher than zone 2 , because the high wind speed (5 m / s) promotes diffusion, while the wind direction in area 2 has no contribution to diffusion.

[0020] (6) Conclusion The experiment verified that the multidimensional carbon emission monitoring system can integrate time, space and meteorological factors to accurately reflect the spatial heterogeneity and diffusion characteristics of carbon emissions, and is suitable for real-time monitoring and cross-regional comparative analysis.

[0021] In summary, a multi-dimensional carbon emission monitoring system and method based on big data has been completed.

[0022] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0023] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0024] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A multi-dimensional carbon emission monitoring method based on big data, characterized in that: The following steps are involved: S1. Obtain real-time data and construct a data set; based on the data set, introduce energy emission factors through a multi-source carbon emission dynamic calculation algorithm, multiply the energy consumption in the real-time data by the corresponding energy emission factor, obtain the energy carbon emission, and accumulate the carbon emission of each energy source to generate the basic carbon emission value of the data point; introduce a periodic adjustment item as a time dynamic weight, adjust the basic carbon emission value of the data point, and calculate the preliminary carbon emission; S2. Divide the preliminary carbon emissions into regions according to geographical location, and calculate the weighted carbon emissions by combining the distance between the data point and the regional center, the density distribution within the region, and the boundary effect; the calculation formula for the weighted carbon emissions is: ， in, Indicates In the region Data points at time Weighted carbon emissions; Indicates In the region Data points at time Initial carbon emissions; Indicates the number of coordinate dimensions; represents the distance term; Indicates In the region Data points at time No. Coordinate values; Indicates Regional centers at a time No. Coordinate values; represents the adjustment coefficient of the density factor; Indicates Regions in time The density distribution of data points; represents the boundary effect reflected by the exponential function; represents the adjustment coefficient of the boundary effect; Indicates at time No. In the region The distance from a data point to the region boundary; S3. Based on the weighted carbon emissions, the regional diffusion emission calculation algorithm is used to extract the wind speed and direction information of the target area from the meteorological data in the real-time data, and the diffusion factor is calculated by combining the regional area and the distance between regions; Based on weighted carbon emissions and combined with diffusion factors, the multidimensional carbon emission index is calculated and visualized.

2. According to the multi-dimensional carbon emission monitoring method based on big data in claim 1, it is characterized in that: The S1 specifically includes: The calculation formula for the preliminary carbon emissions is: ， in, Indicates Data points at time Initial carbon emissions; Indicates Emission factors for each energy source; Indicates Data points at time Use The number of energy sources; Indicates the total number of energy types; Represents a periodic adjustment item; represents the period adjustment coefficient; The arc degree representing one complete cycle; Indicates the time period parameter; Indicates the phase adjustment parameter.

3. According to the multi-dimensional carbon emission monitoring method based on big data in claim 1, it is characterized in that: The S3 specifically includes: The calculation formula of the multi-dimensional carbon emission index is: ， in, Indicates Regions in time Multi-dimensional carbon emission index; Indicates In the region Data points at time Weighted carbon emissions; represents the diffusion factor; Indicates Regions in time The number of data points; Indicates Regions in time wind speed; It is Regions in time Wind direction at the time; Indicates Regions in time area; Indicates at time No. The average distance from the center of a region to the centers of neighboring regions.

4. A multi-dimensional carbon emission monitoring system based on big data, applied to the multi-dimensional carbon emission monitoring method based on big data as claimed in claim 1, characterized in that: Includes the following sections: Data acquisition module, preliminary emission calculation module, regional emission calculation module, regional diffuse emission calculation module, data output and visualization module; Data acquisition module: acquires real-time data and organizes the acquired real-time data into structured data sets; Exporting the data set to the preliminary emission calculation module; Preliminary emission calculation module: Based on the data set, the preliminary carbon emissions are calculated through the multi-source carbon emission dynamic calculation algorithm; the preliminary carbon emissions are output to the regional emission calculation module; Regional emission calculation module: divide the preliminary carbon emissions into regions according to geographical location, and calculate the weighted carbon emissions by combining the distance between the data point and the regional center, the density distribution within the region, and the boundary effect; output the weighted carbon emissions to the regional diffusion emission calculation module; Regional diffusion emission calculation module: Based on the weighted carbon emissions, the multi-dimensional carbon emission index is calculated through the regional diffusion emission calculation algorithm; the multi-dimensional carbon emission index is output to the data output and visualization module; Data output and visualization module: provides a real-time monitoring interface, displays the multi-dimensional carbon emission index of each region, and presents the multi-dimensional carbon emission index in a visual form.

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