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

Abstract

The present invention relates to the field of carbon emission monitoring, and particularly to a multi-dimensional carbon emission monitoring system and method based on big data. The content includes: obtaining real-time data, and calculating preliminary carbon emissions through a multi-source carbon emission dynamic calculation algorithm; dividing the preliminary carbon emissions into regions according to geographical locations, and calculating weighted carbon emissions; based on the weighted carbon emissions, calculating a multi-dimensional carbon emission index through a regional diffusion emission calculation algorithm, and visually displaying the multi-dimensional carbon emission index. It solves the problems that traditional carbon emission monitoring methods mostly rely on static data or periodic statistics and cannot reflect the dynamic changes of energy consumption and emissions in production activities in real time; the influence of geographical location, regional density, and boundary effects on emission distribution is ignored, resulting in a lack of spatial refinement in calculation results; traditional carbon emission models rarely comprehensively consider the influence of meteorological conditions on emission diffusion, resulting in the emission index being unable to truly reflect the dynamic propagation law in the environment.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission monitoring, and particularly 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 difficult to meet the current complex and changeable environmental governance requirements.

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

[0005] A multi-dimensional carbon emission monitoring system and method based on big data of the present invention specifically includes the following technical solutions:

[0006] A multi-dimensional carbon emission monitoring method based on big data includes the following steps:

[0007] S1. Obtain real-time data and construct a data set; based on the data set, through a multi-source carbon emission dynamic calculation algorithm, introduce an energy emission factor, multiply the energy consumption in the real-time data by the corresponding energy emission factor to obtain the carbon emission of the energy, and accumulate the carbon emissions of each energy to generate the basic carbon emission value of the data point; introduce a periodic adjustment term as a time dynamic weight to adjust the basic carbon emission value of the data point and calculate the preliminary carbon emission;

[0008] 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:

[0009] ,

[0010] where, represents the weighted carbon emissions of the th data point in the th region at time ; represents the preliminary carbon emissions of the th data point in the th region at time ; represents the number of coordinate dimensions; represents the distance term; represents the th coordinate value of the th data point in the th region at time ; represents the th coordinate value of the regional center at time ; represents the adjustment coefficient of the density factor; represents the density distribution of the data points in the th region at time ; represents the boundary effect reflected by the exponential function; represents the adjustment coefficient of the boundary effect; represents the distance from the th data point to the regional boundary in the th region at time ; represents the distance from the

[0011] S3. Based on the weighted carbon emissions, extract the wind speed and wind direction information of the target region from the meteorological data in the real-time data through the regional diffusion emission calculation algorithm, and calculate the diffusion factor by combining the regional area and the distance between regions. Based on the weighted carbon emissions, combine the diffusion factor, calculate the multi-dimensional carbon emission index, and visually display the multi-dimensional carbon emission index.

[0012] Preferably, the S1 specifically includes:

[0013] The calculation formula for the preliminary carbon emissions is:

[0014] ,

[0015] Among them, represents the preliminary carbon emissions of the th data point at time ; represents the emission factor of the th type of energy; represents the th data point at time using the th type of energy; represents the total number of energy types; represents the periodic adjustment term; represents the period adjustment coefficient; represents the radian of a complete period; represents the time period parameter; represents the phase adjustment parameter.

[0016] Preferably, the S3 specifically includes:

[0017] The calculation formula of the multi-dimensional carbon emission index is:

[0018] ,

[0019] Among them, represents the multi-dimensional carbon emission index of the th region at time ; represents the weighted carbon emissions of the th data point in the th region at time ; represents the diffusion factor; represents the th region at time the number of data points; represents the th region at time wind speed; is the th region at time wind direction; represents the th region at time area; represents at time the th average distance from the center of a region to the center of neighboring regions.

[0020] A multi-dimensional carbon emission monitoring system based on big data includes the following parts:

[0021] Data acquisition module, preliminary emission calculation module, regional emission calculation module, regional diffusion emission calculation module, data output and visualization module;

[0022] Data acquisition module: Acquire real-time data, organize the acquired real-time data into a structured data set; output the data set to the preliminary emission calculation module;

[0023] Preliminary emission calculation module: Based on the data set, calculate the preliminary carbon emissions through a multi-source carbon emission dynamic calculation algorithm; output the preliminary carbon emissions to the regional emission calculation module;

[0024] Regional emission calculation module: Divide the preliminary carbon emissions according to geographical location, and combine the distance between the data points and the regional center, the density distribution within the region, and the boundary effect to calculate the weighted carbon emissions; output the weighted carbon emissions to the regional diffusion emission calculation module;

[0025] Regional diffusion emission calculation module: Based on the weighted carbon emissions, calculate the multi-dimensional carbon emission index through the regional diffusion emission calculation algorithm; output the multi-dimensional carbon emission index to the data output and visualization module;

[0026] Data output and visualization module: Provide a real-time monitoring interface, display the multi-dimensional carbon emission index of each region, and present the multi-dimensional carbon emission index in a visual form.

[0027] The beneficial effects of the technical solution of the present invention are:

[0028] 1. By collecting real-time data from multiple data sources such as industrial sensors, energy networks, and third-party meteorological data providers in real time, and organizing it into a structured data set containing geographical location and time information, and using a multi-source carbon emission dynamic calculation algorithm to calculate the preliminary carbon emissions, compared with the traditional static carbon emission calculation method, it can reflect the 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 terms, improving the accuracy and real-time performance of carbon emission monitoring, providing more timely emission data support for enterprises or governments, being applicable to dynamically adjusting production plans or energy use strategies, and thus optimizing resource allocation and reducing unnecessary carbon emissions.

[0029] 2. Based on the preliminary carbon emissions, the dataset is divided into multiple regions according to geographical locations. By comprehensively considering the distance between data points and the regional center, the density distribution within the region, and the boundary effect through a spatial weighting factor, the weighted carbon emissions are calculated, breaking through the limitation of traditional carbon emission calculations that ignore spatial distribution differences, realizing the spatial heterogeneity analysis of emissions, truly reflecting the emission distribution law within the region, providing a scientific basis for regional carbon emission management, enabling enterprises or regulatory agencies to formulate differentiated strategies according to the emission characteristics of different regions (such as industrial areas and suburbs), thereby improving the efficiency of resource allocation and the effectiveness of environmental policy implementation.

[0030] 3. Based on the weighted carbon emissions, an algorithm for calculating regional diffusive emissions is introduced. Combining 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 within the region but also simulates the diffusive behavior of carbon emissions, providing a comprehensive emission profile. The generated multi-dimensional carbon emission index supports cross-regional comparative analysis and long-term trend prediction, providing data-driven decision-making support for the government to formulate regional collaborative emission reduction policies or for enterprises to optimize the supply chain layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a structural diagram of a multi-dimensional carbon emission monitoring system based on big data according to the present invention;

[0032] Figure 2 It is a flowchart of a multi-dimensional carbon emission monitoring method based on big data according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0033] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0035] The following specifically describes the specific solutions of a multi-dimensional carbon emission monitoring system and method based on big data provided by the present invention with reference to the accompanying drawings.

[0036] Refer to the attached Figure 1 , which shows a structural 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:

[0037] Data acquisition module, preliminary emission calculation module, regional emission calculation module, regional diffusion emission calculation module, data output and visualization module;

[0038] Data acquisition module: Obtain real-time data from multiple data sources, organize the obtained real-time data into a structured data set; output the data set to the preliminary emission calculation module;

[0039] Preliminary emission calculation module: Based on the data set, calculate the preliminary carbon emissions through a multi-source carbon emission dynamic calculation algorithm; output the preliminary carbon emissions to the regional emission calculation module;

[0040] Regional emission calculation module: Divide the preliminary carbon emissions into multiple regions according to geographical locations, and calculate the weighted carbon emissions by comprehensively considering the distance between data points 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;

[0041] Regional diffusion emission calculation module: Based on the weighted carbon emissions, calculate the multi-dimensional carbon emission index through the regional diffusion emission calculation algorithm; output the multi-dimensional carbon emission index to the data output and visualization module;

[0042] Data output and visualization module: Provide a real-time monitoring interface, display the multi-dimensional carbon emission index of each region, present the multi-dimensional carbon emission index in a visual form (such as tables, heat maps, line graphs, etc.) for easy understanding and analysis by users, support the data export function, and provide support for enterprise carbon footprint management or carbon trading.

[0043] Refer to Appendix Figure 2 , which shows a flowchart of a multi-dimensional carbon emission monitoring method provided by an embodiment of the present invention. The method includes the following steps:

[0044] 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 emission dynamic calculation algorithm;

[0045] Obtain real-time data from multiple data sources, including but not limited to recording the consumption of various energies (such as natural gas, coal) during the production process through industrial sensors, obtaining real-time usage data of energies such as gas through the energy network, and obtaining meteorological data by calling the APIs of third-party meteorological data providers (such as the National Meteorological Administration, Open Weather Data Service, etc.). Organize the obtained real-time data into a structured data set. The data set contains multiple data points, each data point corresponding to a specific monitoring location or device, recording the quantities of various energies used at the current moment, as well as geographical location information;

[0046] Based on the data set, the multi-source carbon emission dynamic calculation algorithm is used to calculate the preliminary carbon emissions;

[0047] 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;

[0048] 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.

[0049] The preliminary carbon emissions calculation formula is:

[0050] ,

[0051] 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;

[0052] S2. Divide the preliminary carbon emissions into regions according to geographical locations and calculate the weighted carbon emissions;

[0053] Divide the preliminary carbon emissions into multiple regions according to geographical locations, record the positions of the data points and the coordinates of the regional centers, and comprehensively consider the distance between the data points and the regional centers, the density distribution within the regions, and the boundary effects to calculate the weighted carbon emissions of the data points within each region;

[0054] 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 longitude and latitude coordinates of the data point and the longitude and latitude coordinates of the regional center, calculate the square root form of the Euclidean distance to obtain the straight-line distance between the two. To avoid the influence of too large or too small distances on the results, normalize the distance value, take the reciprocal of the distance value and make a smooth adjustment, 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 correspondingly reduced, ensuring that the emission activities near the regional center contribute more to the regional emissions and making it conform to the actual spatial distribution law;

[0055] The density distribution within the region is calculated based on the number of data points within the region and the area of the region; by dividing the number of data points within the region by the area of the region, an index reflecting the density of the data point distribution is obtained to reflect the concentration degree of the emission activities within the region, thereby affecting the weight distribution of the weighted calculation;

[0056] The boundary effect is reflected through an exponential function based on the perpendicular distance from the data point to the nearest boundary line (i.e., the boundary distance); the emission points near the boundary may have an overflow effect on the adjacent regions, while the data points far from the boundary contribute more to the total emissions of the region; by introducing the boundary effect, the spatial characteristics of the emissions can be more realistically simulated;

[0057] The calculation formula for the weighted carbon emissions is:

[0058] ,

[0059] where, represents the weighted carbon emissions of the th data point in the th region at time ; represents the preliminary carbon emissions of the th data point in the th region at time ; represents the spatial weighting factor used to adjust the preliminary carbon emissions; represents 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 regional center. The closer the distance, the larger the spatial weighting factor; the farther the distance, the smaller the spatial weighting factor. Represents the th data point in the th coordinate value in time, such as longitude or latitude, representing the geographical location of the data point. Represents the th regional center's th coordinate value in time, such as longitude or latitude, representing the central location of the region. Represents the density and boundary effect term, which adjusts the carbon emissions through the density factor and the boundary distance. The larger the spatial weighting factor when the density is higher and the distance from the boundary is farther. Represents the adjustment coefficient of the density factor, which is used to control the contribution of the density distribution to the spatial weighting factor and can be specifically set according to the specific implementation scenario and is not limited here. Represents the th region's data point density distribution in time, reflecting the degree of data point concentration within the region. The higher the density, the larger the spatial weighting factor. The calculation formula for the density distribution is: , Represents the th region's data point quantity in time. Represents the th region's area in time. Represents the boundary effect reflected by the exponential function. Represents the adjustment coefficient of the boundary effect, which is used to control the influence speed of the boundary distance on the spatial weighting factor and can be specifically set according to the specific implementation scenario and is not limited here. Represents the th data point's distance to the regional boundary in the

[0060] The calculation of weighted carbon emissions can more realistically reflect the spatial heterogeneity of emissions, such as the difference in emission contributions between industrial-intensive regions and marginal regions.

[0061] S3. Based on the weighted carbon emissions, calculate the multi-dimensional carbon emission index through the regional diffusion emission calculation algorithm and visualize the multi-dimensional carbon emission index.

[0062] Based on the weighted carbon emissions, use the regional diffusion emission calculation algorithm to calculate the multi-dimensional carbon emission index.

[0063] The diffusion factor is the core part of the regional diffusion emission calculation algorithm, which is used to simulate the influence of meteorological conditions and spatial characteristics on the carbon emission diffusion process;

[0064] Wind speed and wind direction information of the target area are extracted from meteorological data. Wind speed represents the speed of air flow, while wind direction indicates the possible movement direction of carbon emission substances. In order to quantify the influence of wind direction, the regional diffusion emission calculation algorithm performs angle processing on the wind direction and calculates 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 considering the influence 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 more uniform the diffusion effect may be. Specifically, taking the square root of the regional area can weaken the extreme influence 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 force factor, enabling the diffusion calculation of carbon emissions to adapt to regions of different scales;

[0065] 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 emission substances spreading to the adjacent area. The farther the distance, the smaller the influence of diffusion. In order to simulate the attenuation effect, an exponential attenuation factor based on distance and wind speed is introduced, and the attenuation speed is adjusted in combination with the wind speed to make the diffusion factor more in line with physical laws;

[0066] The calculation formula for the multi-dimensional carbon emission index is:

[0067] ,

[0068] where, represents the multi-dimensional carbon emission index of the th region at time ; represents the weighted carbon emission of the th data point in the th region at time ; represents the diffusion factor, reflecting the spatial diffusion effect of carbon emissions between regions; represents the contribution of wind speed and wind direction to carbon emission diffusion, which is an adjustment term affected by the regional area; represents the wind force factor; represents the wind speed of the th region at time , reflecting the promoting effect of wind force on carbon emission diffusion. The greater the wind speed, the more significant the diffusion; is the wind direction at time for the th area; represents the cosine value of the wind direction at time , which converts the wind direction into a directional influence factor with a value range of and is used to reflect the directional influence of the wind direction on diffusion. For example, diffusion is greater with the wind and smaller against the wind. The wind direction is expressed in angles, e.g., 0° is the north wind and 90° is the east wind; represents the th area at time . The area of the area affects the dilution degree of diffusion. The larger the area, the lower the carbon emission concentration per unit area; represents the square root of the area of the th area at time . The square root is used to normalize the influence of the area of the area; represents an exponential decay factor based on distance and wind speed, which combines the wind speed to adjust the decay rate and is used to simulate the decay law of carbon emissions with distance, making the diffusion factor more in line with physical laws; represents the average distance from the center of the th area at time to the center of the neighboring area;

[0069] Based on weighted carbon emissions and real-time meteorological conditions, a multi-dimensional carbon emission index is generated that can reflect the true diffusion behavior of carbon emissions. It is not only applicable to the emission monitoring of a single area but also supports cross-regional comparative analysis and long-term trend prediction, providing data-driven decision support for enterprises, governments, or other institutions;

[0070] Provide a real-time monitoring interface to display the multi-dimensional carbon emission index of each area, presenting the multi-dimensional carbon emission index in a visual form (such as tables, heat maps, curves, etc.) for easy user understanding and analysis, and supporting the data export function to provide support for enterprise carbon footprint management or carbon trading.

[0071] The above embodiments will be further explained below in combination with specific experimental data:

[0072] (1) Data point setting:

[0073] In this experiment, two areas are selected:

[0074] Area 1: 3 data points (A, B, C);

[0075] Area 2: 3 data points (D, E, F);

[0076] The time point is set to 08:00 on March 1, 2025 (t = 8 hours).

[0077] (2)Parameter setting:

[0078] Table 1

[0079] ,

[0080] (3)Data point information:

[0081] Table 2

[0082] ,

[0083] Center coordinates of Region 1 (3, 4);

[0084] Center coordinates of Region 2 (12, 11);

[0085] (4)Experimental process and calculation:

[0086] S1: Calculate the preliminary carbon emissions :

[0087] ,

[0088] Calculation of the cycle adjustment term: , , π / 2 rad, ,

[0089] ,

[0090] ,

[0091] Region 1:

[0092] Data point A: ,

[0093] Data point B: ,

[0094] Data point C: ,

[0095] Region 2:

[0096] Data point D: ,

[0097] Data point E: ,

[0098] Data point F: ,

[0099] Table 3

[0100] ,

[0101] S2: Calculate the weighted carbon emissions :

[0102] ,

[0103] Density distribution:

[0104] Region 1: ,

[0105] Region 2: ,

[0106] Region 1:

[0107] Data point A:

[0108] Distance term: ,

[0109] Boundary effect: ,

[0110] Spatial weighting factor: ,

[0111] ,

[0112] Table 4

[0113] ,

[0114] S3: Calculate the multi-dimensional carbon emission index :

[0115] ,

[0116] Region 1:

[0117] Wind force factor: ,

[0118] Area adjustment: ,

[0119] Exponential decay: ,

[0120] Diffusion factor: ,

[0121] Total weighted carbon emissions: ,

[0122] ,

[0123] Table 5

[0124] ,

[0125] (5) Result analysis:

[0126] Initial carbon emissions:

[0127] The highest emission point in Region 1 is B , and the highest in Region 2 is , reflecting the difference in energy consumption;

[0128] Weighted carbon emissions:

[0129] Due to the large area and low density in Region 1 , the boundary effect has a significant impact on Point B (6 km) ; Region 2 has a higher density , and Point D has a higher weight due to its proximity to the center (distance 1 km) ;

[0130] Multi-dimensional carbon emission index:

[0131] Region 1 is slightly higher than Region 2 , because the higher wind speed (5 m / s) promotes diffusion, while the wind direction in Region 2 has no contribution to diffusion.

[0132] (6) Conclusion

[0133] The experiment verified that the multi-dimensional carbon emission monitoring system can comprehensively consider time, space, and meteorological factors, accurately reflect the spatial heterogeneity and diffusion characteristics of carbon emissions, and is applicable to real-time monitoring and cross-regional comparative analysis.

[0134] In summary, a multi-dimensional carbon emission monitoring system and method based on big data have been completed.

[0135] The order of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0136] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0137] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all 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, It includes the following steps: S1. Obtain real-time data and construct a data set; based on the data set, through a multi-source carbon emission dynamic calculation algorithm, introduce energy emission factors, multiply the energy consumption in the real-time data by the corresponding energy emission factors to obtain the carbon emissions of energy, and accumulate the carbon emissions of each type of energy to generate the basic carbon emission value of the data point; introduce a periodic adjustment term as a time dynamic weight to adjust the basic carbon emission value of the data point and calculate the preliminary carbon emissions; S2. Divide the preliminary carbon emissions into regions according to geographical locations, and calculate the weighted carbon emissions in combination with 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: ， Among them, represents the weighted carbon emission of the th data point in the th area at time ; represents the preliminary carbon emission of the th data point in the th area at time ; represents the number of coordinate dimensions; represents the distance term; represents the th coordinate value of the th data point in the th area at time ; represents the th coordinate value of the center of the th area at time ; represents the adjustment coefficient of the density factor; represents the density distribution of the data points in the th area at time ; represents the boundary effect reflected by the exponential function; represents the adjustment coefficient of the boundary effect; represents the distance from the th data point in the th area to the boundary of the area at time ; S3. Based on the weighted carbon emissions, through a regional diffusion emission calculation algorithm, extract the wind speed and wind direction information of the target region from the meteorological data in the real-time data, and calculate the diffusion factor in combination with the regional area and the distance between regions; Based on the weighted carbon emissions, in combination with the diffusion factor, calculate the multi-dimensional carbon emission index and visually display the multi-dimensional carbon emission index.

2. The multi-dimensional carbon emission monitoring method based on big data according to claim 1, wherein, The S1 specifically includes: The calculation formula for the preliminary carbon emissions is: ， Among them, represents the preliminary carbon emission of the th data point at time ; represents the emission factor of the th type of energy; represents the quantity of the th data point at time using the th type of energy; represents the total number of energy types; represents the periodic adjustment term; represents the period adjustment coefficient; represents the radian of a complete period; represents the time period parameter; represents the phase adjustment parameter.

3. A multi-dimensional carbon emission monitoring method based on big data according to claim 1, characterized in that, The S3 specifically includes: The calculation formula for the multi-dimensional carbon emission index is: ， Among them, represents the multi-dimensional carbon emission index of the th region at time ; represents the weighted carbon emission of the th data point in the th region at time ; represents the diffusion factor; represents the number of data points of the th region at time ; represents the wind speed of the th region at time ; is the wind direction of the th region at time ; represents the area of the th region at time ; represents the average distance from the center of the th region to the center of the neighboring region at time .

4. A multi-dimensional carbon emission monitoring system based on big data, which is applied to a multi-dimensional carbon emission monitoring method based on big data as described in claim 1, and is characterized in that, It includes the following parts: Data acquisition module, preliminary emission calculation module, regional emission calculation module, regional diffusion emission calculation module, data output and visualization module; Data acquisition module: Obtain real-time data and organize the obtained real-time data into a structured data set; Output the data set to the preliminary emission calculation module; Preliminary emission calculation module: Based on the data set, calculate the preliminary carbon emissions through a multi-source carbon emission dynamic calculation algorithm; output the preliminary carbon emissions to the regional emission calculation module; Regional emission calculation module: Divide the preliminary carbon emissions into regions according to geographical locations, and calculate the weighted carbon emissions in combination with 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, calculate the multi-dimensional carbon emission index through a regional diffusion emission calculation algorithm; output the multi-dimensional carbon emission index to the data output and visualization module; Data output and visualization module: Provide a real-time monitoring interface, display the multi-dimensional carbon emission index of each region, and present the multi-dimensional carbon emission index in a visual form.

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