A dual-carbon data analysis method based on big data

Abstract

The present invention discloses a dual-carbon data analysis method based on big data, which belongs to the field of carbon emission technology, and specifically includes: obtaining road vehicle data in real time through on-board electronic license plates; extracting vehicle information of upstream sections of target road sections from a database, and calculating the expected vehicle flow of the current urban road section based on the vehicle information; obtaining historical vehicle speed-vehicle flow data of the current urban road section, establishing a fitting function based on the historical vehicle speed-vehicle flow data, and calculating the carbon emission value of the future target road section based on the vehicle flow and the average vehicle speed. If the future urban road carbon emission value exceeds the risk threshold, the road limit value is changed; if it does not exceed, the monitoring is continued. The present invention realizes speed limit decision-making by comprehensively considering carbon emission indicators, which can effectively reduce carbon emissions on the road.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission technology, and in particular to a dual-carbon data analysis method based on big data. Background Art

[0002] In recent years, the transportation industry has been consuming more and more oil, and road traffic accounts for more than 70% of the consumption among various modes of transportation, with the fastest growth rate. It is the primary source of air pollutants in large cities. Although new energy vehicles are being vigorously developed, the number of new energy vehicles is difficult to increase rapidly in a short period of time and replace the status of fuel vehicles. Therefore, for the complex and huge road traffic system, building an intelligent transportation system and management method with the goal of reducing carbon emission intensity has become one of the important ways to reduce carbon emissions.

[0003] In the prior art, although the patent application with publication number CN108921383B provides a method and system for obtaining a carbon emission index based on big data technology, the solution only obtains the vehicle condition and does not consider the type of energy used by the vehicle. The data accuracy is low. The patent application with publication number US20220057373A1 only considers the data of diesel vehicles, which is relatively one-sided and not suitable for macro-control of carbon emissions. With the development of science and technology, the application of intelligent transportation facilities such as electronic license plates has gradually become popular, providing new technical means for achieving carbon emission control. Traditional road variable speed limit control is only aimed at road safety and efficiency issues, and does not make variable speed limit decisions from the perspective of reducing carbon emissions. Summary of the invention

[0004] The purpose of the present invention is to provide a dual-carbon data analysis method based on big data to solve the following technical problems:

[0005] With the development of science and technology, the application of intelligent transportation facilities such as electronic license plates has gradually become popular, providing new technical means for achieving carbon emission control. Traditional variable speed limit control is only aimed at road safety and efficiency issues, and does not make variable speed limit decisions from the perspective of reducing carbon emissions.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A dual-carbon data analysis method based on big data includes the following steps:

[0008] S1, obtaining vehicle data of current urban roads in real time through the on-board electronic license plate, the vehicle data including vehicle position, average speed and vehicle flow, and storing the vehicle data in a database;

[0009] S2, extracting vehicle data of the upstream section of the target road section from the database, calculating the mean flow rate of the vehicle flow of the target road section according to the vehicle data, and calculating the expected vehicle flow rate of the target road section according to the mean flow rate;

[0010] S3, obtaining historical vehicle speed-vehicle flow data of the current target road segment, establishing a fitting function based on the historical vehicle speed-vehicle flow data, wherein the fitting function is used to fit the relationship between the vehicle speed and the vehicle flow; substituting the expected vehicle flow into the fitting function to obtain the corresponding vehicle average speed;

[0011] S4, calculate the future carbon emission value of the target road section based on the expected vehicle flow and the average vehicle speed. If the future carbon emission value of the target road section exceeds the risk threshold, calculate the average vehicle speed limit under the expected vehicle flow so that the carbon emission of the target road section is less than or equal to the risk threshold, and correct the current urban road limit; if it does not exceed, continue monitoring.

[0012] As a further solution of the present invention: the average vehicle speed is the average speed of all vehicles passing through several sections of the road in unit time, and the traffic flow is the average number of vehicles passing through several sections of the road in unit time.

[0013] As a further solution of the present invention: In S3, the specific process of calculating the road carbon emission value of the future city is:

[0014] S11, obtaining historical vehicle speed-vehicle flow data of the current urban road section, and establishing a fitting function based on the historical vehicle speed-vehicle flow data, wherein the fitting function is used to fit the relationship between the vehicle speed and the vehicle flow;

[0015] S12, substituting the expected vehicle flow of the current urban road section into the historical vehicle speed-vehicle flow data to establish a fitting function, and obtaining the average vehicle speed of the future urban road section;

[0016] S13, according to the carbon emission calculation formula , where V is the average vehicle speed, a and b are preset coefficients, ρ is the density of gasoline, N is the default net calorific value of gasoline, F is the net calorific value emission factor, n is the expected vehicle flow, and I is the characteristic factor;

[0017] As a further solution of the present invention: in S3, the fitting function is:

[0018] n=Kj(VV 2 / C);

[0019] V represents the expected average vehicle speed, n is the expected vehicle flow, C is the road restriction value, and Kj is the vehicle congestion density under road congestion conditions.

[0020] As a further solution of the present invention: in S4, the specific process of changing the speed limit is:

[0021] The average vehicle speed V of the future urban road section is obtained. If V is greater than the upper limit of the average vehicle speed, the road restriction value C of the urban road is reduced. If V is less than the upper limit of the average vehicle speed, the road restriction value C of the urban road is increased.

[0022] As a further solution of the present invention: in S4, the specific process of adjusting the road restriction value is:

[0023] Calculate the difference between the average vehicle speed V and the upper limit of the average vehicle speed to get ΔV. According to the fitting function n=Kj(VV 2 / C), calculate the road restriction value adjustment value ΔC, and correct the road restriction value of the future urban road section according to the road restriction value adjustment value ΔC to obtain the road restriction value C';

[0024] ΔC = ΔVC (ΔV + VC) / (V 2 -ΔVC);

[0025] C' = C + ΔC;

[0026] Wherein, C' is the urban road restriction value after correction, and C is the urban road restriction value before correction.

[0027] As a further solution of the present invention: the difference between the risk threshold and the carbon emission of the future target road section is calculated to obtain ΔE, and ΔE is substituted into the carbon emission calculation formula to calculate the average speed upper limit ΔV';

[0028]

[0029] The maximum value is selected from ΔV1' and ΔV2' and calibrated as the upper limit of the average vehicle speed ΔV', where a and b are preset coefficients, ρ is the density of gasoline, N is the default net calorific value of gasoline, F is the net calorific value emission factor, n is the expected vehicle flow, and I is the characteristic factor.

[0030] As a further solution of the present invention: the step S4 further includes: if the carbon emission value of the future target road segment does not exceed the risk threshold and the urban road limit value C' is less than the initial urban road limit value C 标 , then the current urban road limit value is corrected, and the specific correction process is:

[0031] Calculate the difference between the risk threshold and the carbon emission of the future target road section to obtain ΔE, substitute ΔE into the carbon emission calculation formula to obtain the average speed optimization value ΔV', substitute the average speed optimization value ΔV' into the fitting function, calculate the road restriction value adjustment value ΔC, and correct the road restriction value of the future urban road section according to the road restriction adjustment value ΔC to obtain the road restriction value C'. If C' is greater than the initial value C of the urban road restriction value, 标 , then C' is corrected to C 标 ; If C' is less than or equal to the initial value C of the urban road limit 标 , then maintain the road limit value C';

[0032] ΔC = ΔV'C (ΔV' + VC) / (V 2 -ΔV'C);

[0033] C' = C + ΔC;

[0034] Wherein, C' is the urban road restriction value after correction, and C is the urban road restriction value before correction.

[0035] Beneficial effects of the present invention:

[0036] The present invention establishes a fitting function model through a power function based on the historical vehicle speed-vehicle flow data of the current urban road, obtains the relationship between the speed of the passing lane and the vehicle flow, and calculates the expected vehicle flow of the current urban road based on the vehicle flow of real-time traffic data, and judges whether the carbon emission value of the urban road exceeds the risk threshold value based on the predicted vehicle flow rate. If it exceeds the risk threshold value, the urban road restriction value is corrected to obtain the average vehicle speed of the future urban road section. If the average speed is greater than a preset threshold value, the road restriction value of the urban road is reduced. If V is less than the preset threshold value, the road restriction value of the urban road is increased to reduce the carbon emission of the urban road. If it does not exceed the risk threshold value, monitoring is continued, so as to realize speed limit decision-making by comprehensively considering carbon emission indicators, effectively reduce the carbon emission in the road, and optimize traffic carbon emission. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below in conjunction with the accompanying drawings.

[0038] Figure 1 It is a schematic flow chart of a dual-carbon data analysis method based on big data of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of 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.

[0040] See also Figure 1 As shown, the present invention is a dual-carbon data analysis method based on big data, comprising the following steps:

[0041] S1, obtaining vehicle data of current urban roads in real time through the on-board electronic license plate, the vehicle data including vehicle position, average speed and vehicle flow, and storing the vehicle data in a database;

[0042] S2, extracting vehicle data of the upstream section of the target road section from the database, calculating the mean flow rate of the vehicle flow of the target road section according to the vehicle data, and calculating the expected vehicle flow rate of the target road section according to the mean flow rate;

[0043] S3, obtaining historical vehicle speed-vehicle flow data of the current target road segment, establishing a fitting function based on the historical vehicle speed-vehicle flow data, wherein the fitting function is used to fit the relationship between the vehicle speed and the vehicle flow; substituting the expected vehicle flow into the fitting function to obtain the corresponding vehicle average speed;

[0044] S4, calculate the future carbon emission value of the target road section based on the expected vehicle flow and the average vehicle speed. If the future carbon emission value of the target road section exceeds the risk threshold, calculate the average vehicle speed limit under the expected vehicle flow so that the carbon emission of the target road section is less than or equal to the risk threshold, and correct the current urban road limit; if it does not exceed, continue monitoring.

[0045] The present invention establishes a fitting function model through a power function based on the historical vehicle speed-vehicle flow data of the current urban road, obtains the relationship between the speed of the passing lane and the vehicle flow, and calculates the expected vehicle flow of the current urban road based on the vehicle flow of real-time traffic data, and judges whether the carbon emission value of the urban road exceeds the risk threshold value based on the predicted vehicle flow rate. If it exceeds the risk threshold value, the urban road restriction value is corrected to obtain the average vehicle speed of the future urban road section. If the average speed is greater than a preset threshold value, the road restriction value of the urban road is reduced. If V is less than the preset threshold value, the road restriction value of the urban road is increased to reduce the carbon emission of the urban road. If it does not exceed the risk threshold value, monitoring is continued, so as to realize speed limit decision-making by comprehensively considering carbon emission indicators, effectively reduce the carbon emission in the road, and optimize traffic carbon emission.

[0046] In a preferred embodiment of the present invention, the average vehicle speed is the average speed of all vehicles passing through several sections of the road in unit time, and the traffic flow is the average number of vehicles passing through several sections of the road in unit time.

[0047] In another preferred embodiment of the present invention, in S3, the specific process of calculating the carbon emission value of the road section of the future city is:

[0048] S11, obtaining historical vehicle speed-vehicle flow data of the current urban road section, and establishing a fitting function based on the historical vehicle speed-vehicle flow data, wherein the fitting function is used to fit the relationship between the vehicle speed and the vehicle flow;

[0049] S12, substituting the expected vehicle flow of the current urban road section into the historical vehicle speed-vehicle flow data to establish a fitting function, and obtaining the average vehicle speed of the future urban road section;

[0050] S13, according to the carbon emission calculation formula , where V is the average vehicle speed, a and b are preset coefficients, ρ is the density of gasoline, N is the default net calorific value of gasoline, F is the net calorific value emission factor, n is the expected vehicle flow, and I is the characteristic factor;

[0051] In another preferred embodiment of the present invention, in S3, the fitting function is:

[0052] n=Kj(VV 2 / C);

[0053] V represents the expected average vehicle speed, n is the expected vehicle flow, C is the road restriction value, and Kj is the vehicle congestion density under road congestion conditions.

[0054] In another preferred embodiment of the present invention, in S4, the specific process of changing the speed limit is:

[0055] The average vehicle speed V of the future urban road section is obtained. If V is greater than the upper limit of the average vehicle speed, the road restriction value C of the urban road is reduced. If V is less than the upper limit of the average vehicle speed, the road restriction value C of the urban road is increased.

[0056] Theoretically, if you want to minimize vehicle carbon emissions, the average vehicle speed should be maintained at b / 2a. However, in real life, the average speed of vehicles is usually limited by traffic volume and road speed limits. When the carbon emission value of urban roads exceeds the risk threshold, it means that there are too many vehicles on the road or the speed of road vehicles is too fast. Therefore, it is necessary to correct the limit value of urban roads. If the average speed V is greater than the upper limit of the average speed, it means that the current average speed of vehicles is too high, which will lead to serious road carbon emissions. Therefore, it is necessary to reduce the road limit value C of urban roads. If V is less than or equal to the upper limit of the average speed, it means that the current road traffic volume is too large and the vehicle speed is low, resulting in excessive carbon emissions on urban roads. Therefore, the road limit value C of urban roads is increased.

[0057] In another preferred embodiment of the present invention, in S4, the specific process of adjusting the road restriction value is:

[0058] Calculate the difference between the average vehicle speed V and the upper limit of the average vehicle speed to get ΔV. According to the fitting function n=Kj(VV 2 / C), calculate the road restriction value adjustment value ΔC, and correct the road restriction value of the future urban road section according to the road restriction value adjustment value ΔC to obtain the road restriction value C';

[0059] ΔC = ΔVC (ΔV + VC) / (V 2 -ΔVC);

[0060] C' = C + ΔC;

[0061] Wherein, C' is the urban road restriction value after correction, and C is the urban road restriction value before correction.

[0062] It is worth noting that when obtaining ΔC, ΔC needs to be rounded down, and if ΔC is less than the preset adjustment threshold, you can choose not to adjust the road restriction value of the future urban road section. It can be understood that when ΔC is too small, the speed limit will not change much when the road restriction value is corrected, and it will cause frequent changes in the speed limit, thereby causing instability in traffic flow and affecting the road's traffic capacity.

[0063] In another preferred embodiment of the present invention, the specific calculation process of the average vehicle speed upper limit is:

[0064] Calculate the difference between the risk threshold and the carbon emissions of the future target road section to get ΔE, and substitute ΔE into the carbon emission calculation formula to get the average speed limit ΔV';

[0065]

[0066] The maximum value is selected from ΔV1' and ΔV2' and calibrated as the upper limit of the average vehicle speed ΔV', where a and b are preset coefficients, ρ is the density of gasoline, N is the default net calorific value of gasoline, F is the net calorific value emission factor, n is the expected vehicle flow, and I is the characteristic factor.

[0067] Since the carbon emissions of road vehicles and vehicle speed present a quadratic relationship, theoretically, it is best when the preset threshold is calibrated to b / 2a. However, the preset threshold should take the actual situation into consideration. When b / 2a is too small and does not meet the actual situation, an optimal speed threshold should be determined based on the curve relationship. Therefore, the speed threshold in the present invention is predetermined based on actual road traffic needs. It can be understood that when the theoretical optimal speeds are 20km / h and 10km / h, it is necessary to shift the speed threshold to the right and determine 20km / h as the upper limit of the average speed based on the actual road traffic needs.

[0068] In another preferred embodiment of the present invention, the step S4 further includes: if the carbon emission value of the future target road segment does not exceed the risk threshold and the urban road limit value C' is less than the initial urban road limit value C 标 , then the current urban road limit value is corrected, and the specific correction process is:

[0069] Calculate the difference between the risk threshold and the carbon emission of the future target road section to obtain ΔE, substitute ΔE into the carbon emission calculation formula to obtain the average speed optimization value ΔV', substitute the average speed optimization value ΔV' into the fitting function, calculate the road restriction value adjustment value ΔC, and correct the road restriction value of the future urban road section according to the road restriction adjustment value ΔC to obtain the road restriction value C'. If C' is greater than the initial value C of the urban road restriction value, 标 , then C' is corrected to C 标 ; If C' is less than or equal to the initial value C of the urban road limit 标 , then maintain the road limit value C';

[0070] ΔC = ΔV'C (ΔV' + VC) / (V 2 -ΔV'C);

[0071] C' = C + ΔC;

[0072] Wherein, C' is the urban road restriction value after correction, and C is the urban road restriction value before correction.

[0073] It can be understood that when there is an expected predicted road carbon emissions exceeding the expected threshold, the limit value of the urban road will be adjusted to reduce the expected vehicle carbon emissions on the urban road. When the next expected road carbon emissions are monitored and do not exceed the expected threshold, the road limit value will be regressed and corrected, which not only ensures the carbon emissions of the road but also optimizes the traffic flow on the urban road.

[0074] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A dual-carbon data analysis method based on big data, characterized in that: The following steps are involved: S1, obtaining vehicle data of current urban roads in real time through the on-board electronic license plate, the vehicle data including vehicle position, average speed and vehicle flow, and storing the vehicle data in a database; S2, extracting vehicle data of the upstream section of the target road section from the database, calculating the mean flow rate of the vehicle flow of the target road section according to the vehicle data, and calculating the expected vehicle flow rate of the target road section according to the mean flow rate; S3, obtaining historical vehicle speed-vehicle flow data of the current target road segment, establishing a fitting function based on the historical vehicle speed-vehicle flow data, wherein the fitting function is used to fit the relationship between the vehicle speed and the vehicle flow; substituting the expected vehicle flow into the fitting function to obtain the corresponding vehicle average speed; S4, calculating the future carbon emission value of the target road section based on the expected vehicle flow and the average vehicle speed. If the future carbon emission value of the target road section exceeds the risk threshold, the average vehicle speed limit is calculated under the expected vehicle flow so that the carbon emission of the target road section is less than or equal to the risk threshold, and the current urban road limit is corrected; if it does not exceed, continue monitoring; The specific process of calculating the road carbon emission value of the future target road section is as follows: S11, acquiring historical vehicle speed-vehicle flow data of the target road segment, and establishing a fitting function according to the historical vehicle speed-vehicle flow data, wherein the fitting function is used to fit the relationship between the vehicle speed and the vehicle flow; S12, substituting the expected vehicle flow of the target road section into the historical vehicle speed-vehicle flow data to establish a fitting function, and obtaining the average vehicle speed of the future urban road section; S13, according to the carbon emission calculation formula , where V is the average vehicle speed, a and b are preset coefficients, ρ is the density of gasoline, N is the default net calorific value of gasoline, F is the net calorific value emission factor, n is the expected vehicle flow, and I is the characteristic factor; The fitting function is: n=Kj(V-V 2 / C); V represents the expected average vehicle speed, n is the expected vehicle flow, C is the road restriction value, and Kj is the vehicle blocking density under road congestion; The specific process of adjusting the road limit value is as follows: Calculate the difference between the average vehicle speed V and the upper limit of the average vehicle speed to get ΔV. According to the fitting function n=Kj(VV 2 / C), calculate the road restriction value adjustment value ΔC, and correct the road restriction value of the future urban road section according to the road restriction value adjustment value ΔC to obtain the road restriction value C'; ΔC=ΔVC(ΔV+V-C) / (V 2 -ΔVC); C' = C + ΔC; Wherein, C' is the urban road restriction value after correction, and C is the urban road restriction value before correction.

2. The method for analyzing dual carbon data based on big data according to claim 1, characterized in that: The average vehicle speed is the average speed of all vehicles passing through a number of road sections in a unit time, and the traffic flow is the average number of vehicles passing through a number of road sections in a unit time.

3. The dual-carbon data analysis method based on big data according to claim 1 is characterized in that: In S4, the specific process of changing the city restriction value is as follows: The average vehicle speed V of the future urban road section is obtained. If V is greater than the upper limit of the average vehicle speed, the road restriction value C of the urban road is reduced. If V is less than the upper limit of the average vehicle speed, the road restriction value C of the urban road is increased.

4. The method for analyzing dual-carbon data based on big data according to claim 1, characterized in that: The specific calculation process of the average speed limit is as follows: Calculate the difference between the risk threshold and the carbon emissions of the future target road section to get ΔE, and substitute ΔE into the carbon emission calculation formula to get the average speed limit ΔV'; The maximum value is selected from ΔV1' and ΔV2' and calibrated as the upper limit of the average vehicle speed ΔV', where a and b are preset coefficients, ρ is the density of gasoline, N is the default net calorific value of gasoline, F is the net calorific value emission factor, n is the expected vehicle flow, and I is the characteristic factor.

5. The method for analyzing dual carbon data based on big data according to claim 4 is characterized in that: In the step S4, if the carbon emission value of the future target road segment does not exceed the risk threshold and the urban road limit value C' is less than the initial urban road limit value C 标 , then the current urban road limit value is corrected, and the specific correction process is: Calculate the difference between the risk threshold and the carbon emission of the future target road section to obtain ΔE, substitute ΔE into the carbon emission calculation formula to obtain the average speed optimization value ΔV', substitute the average speed optimization value ΔV' into the fitting function, calculate the road restriction value adjustment value ΔC, and correct the road restriction value of the future urban road section according to the road restriction adjustment value ΔC to obtain the road restriction value C'. If C' is greater than the initial value C of the urban road restriction value, 标 , then C' is corrected to C 标 ; If C' is less than or equal to the initial value C of the urban road limit 标 , then maintain the road limit value C'; ΔC=ΔV'C(ΔV'+V-C) / (V 2 -ΔV'C); C' = C + ΔC; Wherein, C' is the urban road restriction value after correction, and C is the urban road restriction value before correction.

Images