Carbon emission intelligent monitoring management system based on big data

Abstract

The invention discloses a carbon emission intelligent monitoring management system based on big data, and relates to the technical field of carbon emission monitoring, and the system comprises the steps: calculating to obtain a carbon emission initial measurement value, calculating to obtain a carbon emission actual amount through the consumption of combusted fossil fuel, and calculating to obtain a carbon dioxide concentration correction coefficient; using an electrochemical sensor detector to measure the carbon dioxide concentration of a discharge outlet, obtaining a carbon dioxide concentration correction coefficient at a corresponding temperature, calculating to obtain a real-time carbon emission value, calculating to obtain a trend carbon emission value of a carbon emission monitoring area, calculating to obtain a carbon emission value change rate, and calculating to obtain a carbon emission predicted value; and obtaining continuous carbon emission duration and carbon emission standard exceeding early warning times in the region, calculating to obtain carbon emission trend duration and trend carbon emission standard exceeding early warning times, calculating to obtain a region carbon emission index, and calculating to obtain a system carbon emission index.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring, and specifically to an intelligent monitoring and management system for carbon emissions based on big data. Background Art

[0002] With the concept of sustainable development becoming increasingly popular, the accuracy requirements for carbon emission control are also increasing day by day. Traditional carbon emission monitoring relies on manual sampling and laboratory analysis, which has problems such as high data lag, limited coverage, and high costs. Existing digital monitoring systems mostly use single-sensor data and lack the ability to fuse multi-source heterogeneous data, resulting in insufficient prediction accuracy. Therefore, it is particularly important to develop a more intelligent and accurate carbon emission monitoring and management system.

[0003] In the Chinese invention application with the publication number CN119147708A, a vehicle carbon emission monitoring and early warning system based on Internet of Things technology is disclosed, including a processor and a memory. The processor executes the computer program stored in the memory, and respectively obtains the standard environmental parameter interval and the carbon emission correction factor of each remaining environmental parameter interval by according to the carbon emissions, vehicle speed, and environmental parameters with preset weather type labels at each historical sampling moment of the vehicle; obtains the real-time carbon emissions and real-time environmental parameters, obtains the target carbon emission correction factor corresponding to the real-time carbon emissions according to the real-time environmental parameters, corrects the real-time carbon emissions by using the target carbon emission correction factor to obtain the corrected carbon emissions, and conducts carbon emission monitoring and early warning on the vehicle according to the corrected carbon emissions.

[0004] In the above invention application, the carbon emissions are analyzed and calculated by the carbon emissions, vehicle speed, and environmental parameters at the historical sampling moment of the vehicle. However, due to the lack of processing and transformation of historical data, there may be problems with data timeliness, resulting in inaccurate calculation results.

[0005] Therefore, the present invention provides an intelligent monitoring and management system for carbon emissions based on big data. Summary of the Invention

[0006] (I) Technical Problems to be Solved In view of the deficiencies of the prior art, the present invention provides an intelligent monitoring and management system for carbon emissions based on big data. By calculating the initial carbon emission measurement value, calculating the actual carbon emission amount based on the consumption of fossil fuels burned, calculating the carbon dioxide concentration correction coefficient to reduce the error of calculating the carbon emission value, calculating the carbon emission prediction value to predict the trend of the monitoring area, obtaining the continuous carbon emission duration and the number of carbon emission over-standard warning times within the area, calculating the carbon emission trend duration and the number of trend carbon emission over-standard warning times, calculating the regional carbon emission index, and calculating the system carbon emission index to judge the overall carbon emission situation of the system monitoring area, thus solving the technical problems recorded in the background art.

[0007] (II)Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: An intelligent monitoring and management method for carbon emissions based on big data, including the following steps: An intelligent monitoring and management system for carbon emissions based on big data, including: A carbon emission correction analysis module, which obtains the measured wind speed and the measured carbon dioxide concentration , calculates the initial carbon emission measurement value , and calculates the actual carbon emission amount , and comprehensively calculates the carbon dioxide concentration correction coefficient based on the initial carbon emission measurement value and the actual carbon emission amount ; ; A short-term carbon emission prediction module, which calculates the real-time carbon emission value based on the real-time temperature , real-time wind speed , real-time carbon dioxide concentration at the emission port and the carbon dioxide concentration correction coefficient , and calculates the trend carbon emission value and the carbon emission value change rate of the carbon emission monitoring area, and calculates the carbon emission prediction value through the trend carbon emission value and the carbon emission value change rate ; A long-term carbon emission analysis module, which obtains the continuous carbon emission duration and the number of carbon emission over-standard warning times within the area, calculates the carbon emission trend duration and the number of trend carbon emission over-standard warning times , and calculates the regional carbon emission index by combining the carbon emission trend duration and the number of trend carbon emission over-standard warning times , the system carbon emission index is calculated .

[0008] Furthermore, the test wind speed is measured by a wind speed sensor , and the test carbon dioxide concentration at the emission port is measured by an electrochemical sensor detector , and the initial carbon emission measurement value is calculated :

[0009] Among them, represents the number measured in different temperature environments, represents the carbon dioxide standard value in the atmospheric environment, represents the angle between the wind direction and the monitoring section, represents the cross-sectional area of the emission perpendicular to the wind direction, - represents the time from the start to the end of fuel combustion.

[0010] Furthermore, the actual carbon emission amount is calculated based on the consumption of fossil fuels burned :

[0011] Among them, b is the type number of fossil fuels, b = 1, 2, …, x , x is the total number of fossil fuel types, is the consumption of the b th type of fossil fuel, represents the carbon content of the b th type of fossil fuel as received, represents the carbon oxidation rate of the b th type of fossil fuel, and 12 / 44 is the ratio of the mass of carbon atoms to the mass of carbon dioxide atoms.

[0012] Furthermore, the carbon dioxide concentration correction coefficient is calculated by integrating the initial carbon emission measurement value and the actual carbon emission amount : :

[0013] The carbon dioxide concentration correction coefficient is calculated as above.

[0014] Furthermore, based on the real-time wind speed in the carbon emission monitoring area , the real-time carbon dioxide concentration at the emission port and the carbon dioxide concentration correction coefficient the real-time carbon emission value is calculated :

[0015] Among them, represents the number of each monitoring area, = 1, 2, …, m， represents the chronological number of each data, j = 1, 2, …, n , m, n is a positive integer.

[0016] Furthermore, obtain the carbon emission value , and obtain the trend carbon emission value of the carbon emission monitoring area through exponentially weighted moving average calculation:

[0017] Among them, j represents the chronological number of each data, j = 1, 2, …, n , n is a positive integer, the monitoring time is taken at 0:00 every day, and the latest monitoring result .

[0018] Furthermore, obtain the trend carbon emission value , and calculate the carbon emission value change rate :

[0019] The carbon emission value change rate has the above calculation formula.

[0020] Furthermore, obtain the trend carbon emission value and the carbon emission value change rate , and calculate the carbon emission prediction value :

[0021] When > , it indicates that the carbon emissions of the enterprises in this area exceed the standard during this period, and a warning is sent out; Among them, represents the standard carbon emission limit value per unit product of the products produced in this area, represents the quantity of products produced in this area, represents the number of each product, = 1, 2, …, y , y is a positive integer.

[0022] Further, obtain the continuous carbon emission duration within the area and the number of carbon emission over - standard warning times , and calculate the carbon emission trend duration and the number of trend carbon emission over - standard warning times :

[0023] Among them, represents the number of each monitoring area, = 1, 2, …, m， represents the time - sequence number of each data, g = 1, 2, …, k , m, k is a positive integer, and the monitoring time is taken at 0:00 every Monday.

[0024] Further, obtain the carbon emission index of each area , and calculate the system carbon emission index :

[0025] When ≥ , it indicates that there is a problem of carbon emission over - standard in the total monitoring area of the carbon emission monitoring system.

[0026] (III) Beneficial effects The present invention provides a big - data - based intelligent carbon emission monitoring and management system, which has the following beneficial effects: 1. By using a wind speed sensor to measure the test wind speed and an electrochemical sensor detector to measure the test carbon dioxide concentration at the emission port , calculate the initial carbon emission measurement value , calculate the actual carbon emission amount through the consumption of fossil fuels , and calculate the carbon dioxide concentration correction coefficient by integrating the initial carbon emission measurement value and the actual carbon emission amount . It can effectively eliminate the carbon dioxide concentration error caused by temperature changes, contribute to obtaining accurate data on the total carbon emission, improve the calculation accuracy and obtain accurate data.

[0027] 2. By using a temperature sensor to measure the real - time temperature of the carbon emission monitoring area , a wind speed sensor to measure the real - time wind speed , and an electrochemical sensor detector to measure the real - time carbon dioxide concentration at the emission port , obtain the carbon dioxide concentration correction coefficient at the corresponding temperature , calculate the real-time carbon emission value , calculate the trend carbon emission value of the carbon emission monitoring area , calculate the carbon emission value change rate , through the trend carbon emission value and the carbon emission value change rate calculate the carbon emission prediction value , the carbon emission value and carbon emission trend of the monitoring area can be accurately calculated, which helps to understand the trend of carbon emissions in the area. Predicting the carbon emission value in the monitoring area can timely detect the situation of excessive carbon emissions.

[0028] 3. By obtaining the continuous carbon emission duration in the area and the number of carbon emission over-limit warning times , calculate the carbon emission trend duration through exponentially weighted moving average and the number of trend carbon emission over-limit warning times , perform dimensionless processing on the carbon emission trend duration and the number of trend carbon emission over-limit warning times to calculate and obtain the regional carbon emission index , calculate the system carbon emission index , it can effectively judge the overall carbon emission situation in the control area of the carbon emission monitoring system, which helps to timely detect the situation of excessive carbon emissions in the area. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic structural diagram of an intelligent carbon emission monitoring and management system based on big data according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] Please refer to Figure 1 , the present invention provides an intelligent carbon emission monitoring and management system based on big data, including: A carbon emission correction analysis module, which obtains the test wind speed and the test carbon dioxide concentration , calculates the initial carbon emission measurement value , and calculates the actual carbon emission amount , and comprehensively combines the initial carbon emission measurement value and the actual carbon emission volume Calculate the carbon dioxide concentration correction coefficient .

[0032] Measure the test wind speed through a wind speed sensor and measure the test carbon dioxide concentration at the emission port through an electrochemical sensor detector to calculate the initial measured value of carbon emission :

[0033] Among them, represents the number measured in different temperature environments, represents the carbon dioxide standard value in the atmospheric environment, represents the angle between the wind direction and the monitoring section, represents the emission cross-sectional area perpendicular to the wind direction, - represents the time from the start of fuel combustion to the end of combustion.

[0034] Calculate the actual carbon emission volume through the consumption of combusted fossil fuels :

[0035] Among them, b is the type number of fossil fuels, b = 1, 2, …, x , x is the total number of fossil fuel types, is the consumption of the b th type of fossil fuel, represents the carbon content of the b th type of fossil fuel as received, represents the carbon oxidation rate of the b th type of fossil fuel. 12 / 44 is the ratio of the mass of carbon atoms to the mass of carbon dioxide atoms.

[0036] Comprehensive initial measured value of carbon emission and the actual carbon emission volume to calculate the carbon dioxide concentration correction coefficient :

[0037] Measure the test wind speed through the use of a wind speed sensor and measure the test carbon dioxide concentration at the emission port through an electrochemical sensor detector to calculate the initial measured value of carbon emission , and calculate the actual carbon emission volume through the consumption of combusted fossil fuels to calculate the actual carbon emission volume , comprehensive initial measured value of carbon emission and the actual carbon emission amount Calculate the carbon dioxide concentration correction coefficient , which can effectively eliminate the carbon dioxide concentration error caused by temperature changes, help obtain accurate data on the total carbon emission amount, improve the calculation accuracy, and obtain accurate data.

[0038] The short-term carbon emission prediction module calculates the real-time carbon emission value based on the real-time temperature , real-time wind speed , real-time carbon dioxide concentration at the emission port and the carbon dioxide concentration correction coefficient Calculate the real-time carbon emission value , and calculate the trend carbon emission value and the carbon emission value change rate in the carbon emission monitoring area . Through the trend carbon emission value and the carbon emission value change rate calculate the carbon emission prediction value .

[0039] Use a temperature sensor to measure the real-time temperature in the carbon emission monitoring area , a wind speed sensor to measure the real-time wind speed , and an electrochemical sensor detector to measure the real-time carbon dioxide concentration at the emission port .

[0040] Obtain the real-time temperature in the carbon emission monitoring area The temperature environment carbon dioxide concentration correction coefficient corresponding to the average value is recorded as the carbon dioxide concentration correction coefficient of this carbon emission monitoring area .

[0041] Based on the real-time wind speed in the carbon emission monitoring area , real-time carbon dioxide concentration at the emission port and the carbon dioxide concentration correction coefficient calculate the real-time carbon emission value :

[0042] Among them, represents the number of each monitoring area = 1, 2,..., m， represents the time sequence number of each data j = 1, 2,..., n , m, n is a positive integer

[0043] Obtain the real-time carbon emission value Calculate the trend carbon emission value in the carbon emission monitoring area :

[0044] Among them, j represents the chronological number of each real-time data, j = 1, 2, …, n , n is a positive integer.

[0045] Obtain the trend carbon emission value , and calculate the change rate of the carbon emission value :

[0046] Obtain the trend carbon emission value and the change rate of the carbon emission value , and calculate the carbon emission prediction value :

[0047] When > , it indicates that the carbon emissions of enterprises in this region exceed the standard during this period, and a warning is sent out.

[0048] Among them, represents the standard carbon emission limit value per unit product of the products produced in this region, represents the quantity of products produced in this region, represents the number of each product, = 1, 2, …, y , y is a positive integer.

[0049] Measure the real-time temperature of the carbon emission monitoring area by using a temperature sensor , measure the real-time wind speed by using a wind speed sensor , measure the real-time carbon dioxide concentration at the emission port by using an electrochemical sensor detector , obtain the carbon dioxide concentration correction coefficient at the corresponding temperature , calculate the real-time carbon emission value , calculate the trend carbon emission value of the carbon emission monitoring area , and the change rate of the carbon emission value , through the trend carbon emission value and the change rate of the carbon emission value calculate the carbon emission prediction value , it is possible to accurately calculate the carbon emission value and carbon emission trend of the monitoring area, which helps to understand the trend of carbon emissions in the area. Predicting the carbon emission value of the monitoring area can timely detect the situation of carbon emission exceeding the standard.

[0050] Long-term carbon emission analysis module, obtaining the continuous carbon emission duration within the region and the number of carbon emission over-standard warning times to calculate the carbon emission trend duration and the number of trend carbon emission over-standard warning times Combined with the carbon emission trend duration and the number of trend carbon emission over-standard warning times to calculate and obtain the regional carbon emission index to calculate the system carbon emission index .

[0051] Obtain the continuous carbon emission duration within the region and the number of carbon emission over-standard warning times to calculate the carbon emission trend duration and the number of trend carbon emission over-standard warning times :

[0052] Among them, represents the number of each monitoring area, = 1, 2, …, m， represents the time sequence number of each data, g = 1, 2, …, k , m, k is a positive integer.

[0053] Obtain the carbon emission trend duration and the number of trend carbon emission over-standard warning times to calculate and obtain the regional carbon emission index :

[0054] Obtain the carbon emission index of each region to calculate the system carbon emission index :

[0055] When ≥ , it indicates that there is a problem of carbon emission over-standard in the total monitoring area of the carbon emission monitoring system, and it is necessary to control the carbon emission of each area monitored by the system and investigate and solve the areas with carbon emission over-standard problems.

[0056] By obtaining the continuous carbon emission duration within the region and the number of carbon emission over-standard warning times , through the exponentially weighted moving average to calculate the carbon emission trend duration and the number of trend carbon emission over-standard warning times , for the duration of the carbon emission trend and the number of early warning times for excessive trend carbon emissions are dimensionless processed, and the regional carbon emission index is calculated and obtained , and the system carbon emission index is calculated , which can effectively judge the overall carbon emission situation in the control area of the carbon emission monitoring system, and helps to timely discover the situation of excessive carbon emissions in the area.

[0057] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution.

[0058] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0059] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application.

Claims

1. A carbon emission intelligent monitoring and management system based on big data, characterized in that: include: Carbon emission correction analysis module, obtain test wind speed and test carbon dioxide concentration , calculate the initial value of carbon emissions , and calculate the actual amount of carbon emissions , preliminary value of comprehensive carbon emissions and actual carbon emissions Calculate the CO2 concentration correction factor ; Short-term carbon emission prediction module, based on the real-time temperature of the carbon emission monitoring area , Real-time wind speed , Real-time carbon dioxide concentration at the emission outlet and CO2 concentration correction factor Calculate real-time carbon emissions , and calculate the trend carbon emission value of the carbon emission monitoring area and carbon emission value change rate , through the trend carbon emission value and carbon emission value change rate Calculate the predicted carbon emissions ; Long-term carbon emission analysis module, obtains the continuous carbon emission duration in the region and carbon emission exceeding the standard warning number , calculate the duration of carbon emission trends and trend of carbon emission exceeding standard warning times , combined with the duration of carbon emission trends and trend of carbon emission exceeding standard warning times Calculate the regional carbon emission index , calculate the system carbon emission index .

2. According to claim 1, a carbon emission intelligent monitoring and management system based on big data is characterized in that: The wind speed sensor measures the test wind speed , electrochemical sensor detector measures the carbon dioxide concentration at the discharge port , calculate the initial value of carbon emissions : in, Indicates the number of different temperature environment measurements, Indicates the standard value of carbon dioxide in the atmospheric environment. Indicates the angle between wind direction and monitoring section, represents the discharge cross-sectional area perpendicular to the wind direction, - Indicates the time from the start to the end of fuel combustion.

3. According to the big data-based intelligent carbon emission monitoring and management system of claim 1, it is characterized by: The actual amount of carbon emissions is calculated by the consumption of fossil fuels burned : Where b is the type number of fossil fuels, b =1, 2, …, x , x is the total number of fossil fuel types, For the b The consumption of fossil fuels, Indicates b The elemental carbon content of the fossil fuels, Indicates b The carbon oxidation rate of this fossil fuel is 12 / 44, which is the ratio of the atomic mass of carbon to the atomic mass of carbon dioxide.

4. According to claim 1, a carbon emission intelligent monitoring and management system based on big data is characterized in that: Preliminary value of comprehensive carbon emissions and actual carbon emissions Calculate the CO2 concentration correction factor : The CO2 concentration correction factor The calculation formula is as above.

5. According to the big data-based intelligent carbon emission monitoring and management system of claim 1, it is characterized by: Based on the real-time wind speed in the carbon emission monitoring area , Real-time carbon dioxide concentration at the emission outlet and CO2 concentration correction factor Calculate real-time carbon emissions : in, Indicates the number of each monitoring area, =1, 2, …, m， Indicates the time sequence number of each data. j =1, 2, …, n , m、n Is a positive integer.

6. The carbon emission intelligent monitoring and management system based on big data according to claim 1 is characterized by: Get carbon emission value , the trend carbon emission value of the carbon emission monitoring area is obtained by exponentially weighted moving average calculation : in, j Indicates the time sequence number of each data. j =1, 2, …, n , n It is a positive integer. The monitoring time is 0:00 every day. The latest monitoring result .

7. The carbon emission intelligent monitoring and management system based on big data according to claim 1 is characterized by: Get trending carbon emissions values , calculate the carbon emission value change rate : The rate of change of carbon emission value The calculation formula is as above.

8. The carbon emission intelligent monitoring and management system based on big data according to claim 1 is characterized by: Get trending carbon emissions values and carbon emission value change rate , calculate the predicted carbon emission value : when > When the carbon emissions of enterprises in the area exceed the standard during the period, an early warning will be issued; in, Indicates the unit product standard carbon emission limit value of products produced in this area. represents the number of products produced in the region, Indicates the number of each product. =1, 2, …, y , y Is a positive integer.

9. The carbon emission intelligent monitoring and management system based on big data according to claim 1 is characterized by: Get the continuous carbon emission duration in the region and carbon emission exceeding the standard warning number , calculate the duration of carbon emission trend and trend of carbon emission exceeding standard warning times : in, Indicates the number of each monitoring area, =1, 2, …, m， Indicates the time sequence number of each data. g =1, 2, …, k , m, k It is a positive integer, and the monitoring time is 0:00 every Monday.

10. The carbon emission intelligent monitoring and management system based on big data according to claim 1 is characterized by: Get the carbon emission index of each region , calculate the system carbon emission index : when ≥ When the carbon emission monitoring system detects excessive carbon emissions in the total area, it indicates that the carbon emission monitoring system has excessive carbon emissions in the total area.

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