CN115169683A - Analysis method for urban pollution automobile exhaust emission prediction - Google Patents
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Abstract
The invention relates to the technical field of smart cities, and provides an analysis method for predicting urban pollution automobile exhaust emission, which comprises the following steps: calculating the attribution of the monitoring equipment, and acquiring monitoring data through the monitoring equipment to form a data set A; acquiring vehicle information data, and setting pollution emission weights of different vehicles to form a data set B; calculating the total pollution emission weight of all vehicles passing each time interval of each district and county according to the data set A and the data set B to form a data set D; calculating the pollution discharge amount corresponding to the unit weight according to the annual discharge statistical data of the polluted gas in each district and county and the data set D to form a data set E; and predicting future traffic flow, and predicting a future pollution emission value according to the predicted future traffic flow. The invention can predict the harmful gas emission of the future tail gas in advance; based on the prediction result of the future, the auxiliary decision of environmental prevention and traffic management can be made in advance, and a decision basis is provided for smart city services.
Description
Technical Field
The invention relates to the technical field of smart cities, in particular to an analysis method for predicting urban pollution automobile exhaust emission.
Background
With the development of big data and artificial intelligence technology, the demand based on data intelligence is generated. The smart city and the brain concept of the city are provided, the technical system of data and AI algorithm fusion is promoted to be based on the GreenPlum ecological big data technology, and a Postgis spatial geographic information data calculation engine and a Madlib complete machine learning engine are seamlessly connected, so that the AI method based on big data has the possibility of high-efficiency realization.
The motor vehicle exhaust emission gradually becomes one of the main sources of urban atmospheric pollutants in China, and the motor vehicle exhaust emission level is evaluated by combining an intelligent transportation means with an emission model, so that convenience and high-efficiency evaluation of urban motor vehicle exhaust emission control measures and improvement of urban atmospheric environment are facilitated. At present, the digital city field can collect the data related to the ring inspection bureau, the vehicle information data of the traffic bureau and the like, but no existing solution and a specific landing method exist.
Therefore, how to provide an analysis method for predicting the emission of urban polluted automobile exhaust becomes a technical problem to be solved urgently.
Disclosure of Invention
In view of the above, the present invention aims to provide a method compatible with big data and Gis analysis and machine learning by collecting data of relevant business scenes, and the method uses environmental monitoring statistical data, traffic network data and traffic management organization vehicle information data to perform predictive analysis of exhaust emission, so as to solve the technical problem of motor vehicle exhaust emission evaluation in relevant intelligent city environment detection analysis.
The invention provides an analysis method for predicting the emission of urban polluted automobile exhaust, which comprises the following steps:
step S1: calculating attribution of monitoring equipment, and acquiring monitoring data through the monitoring equipment to form a data set A, wherein the data set A comprises: monitoring equipment identification, time interval, traffic flow, license plate number and region county which are monitored by the monitoring equipment in real time;
step S2: acquiring vehicle information data, setting pollution emission weights of different vehicles, and forming a data set B, wherein the data set B comprises: vehicle type, license plate number, displacement, city holding capacity of each type of vehicle;
and step S3: according to the data set A and the data set B, calculating the total pollution emission weight of all vehicles passing each time interval in each district and county to form a data set D, wherein the data set D comprises: time interval, region and county, total pollution emission weight;
and step S4: according to the annual pollutant gas emission statistical data and the data set D of each district and county, calculating the pollution emission amount corresponding to the unit weight to form a data set E, wherein the data set E comprises: the emission of various pollutant gases corresponding to the unit weight;
step S5: and predicting the future traffic flow, and predicting the future pollution emission value according to the predicted future traffic flow.
Further, in step S1, calculating the monitoring device attribution includes: and acquiring longitude and latitude coordinates, road network data and high-precision map data of the monitoring equipment, and calculating the attribution of the monitoring equipment by using a Postgis function.
Further, in step S2, setting the pollutant emission weights of different vehicles includes: and acquiring the discharge capacity information of the vehicle according to different license plate numbers, and setting the pollution discharge weight of the vehicle according to different discharge capacities.
Further, in step S3, calculating a total weight of pollutant emissions of all vehicles passing through each time interval in each district and county according to the data set a and the data set B includes: summing the products of the various types of vehicles obtained by monitoring in each time interval of each district and county and the corresponding pollution emission weights to obtain the total pollution emission weight Ky of each monitoring, summing the total pollution emission weights Ky of all monitoring equipment to obtain the total pollution emission value K obtained by monitoring in each time interval of each district and county.
Further, in step S4, calculating the pollution discharge amount corresponding to the unit weight according to the annual discharge statistical data of the polluted gas in each district and county and the data set D, includes:
s41: acquiring annual emission statistical data of the polluted gas in each district and county to form a data set C, wherein the data set C comprises: the annual statistical value of the emission of each polluted gas belongs to the county;
s42: determining the total pollution emission amount T of each gas in each time interval of each district and county according to the annual pollution gas emission statistical data of each district and county;
s43: associating the data set C with the data set D, and inquiring and obtaining the total weight K of the pollution emission values monitored and obtained in each time interval of each district and county;
s44: and (4) dividing the total pollution emission amount T of each gas in each time interval of each district and county in the step (S42) by the total pollution emission value K acquired by all monitoring in each time interval of each district and county inquired in the step (S43) to obtain each pollution gas emission amount E corresponding to the unit weight.
Further, in step S5, predicting a future traffic flow includes: performing machine learning model training and reasoning of time series prediction on the traffic flow of each monitoring device by using an ARIMA method of Madlib/Plpython through historical data of a data set A, calculating to obtain the traffic flow of each time interval in the future, and obtaining a data set Af of each monitoring device, wherein the data set Af comprises: traffic flow, license plate number, and region of the country.
Further, in step S5, predicting a future pollutant emission value according to the predicted future vehicle flow includes: and replacing the data set A in the step S3 with the predicted future traffic data set Af, and calculating the total pollutant emission weight of all vehicles passing through each time interval in each district and county according to the data set Af and the data set B.
Further, in step S5, calculating a total weight of pollutant emissions of all vehicles passing through each time interval in each county according to the data set Af and the data set B includes: summing products of various types of vehicles acquired by each monitoring in each future time interval of each county and corresponding pollution emission weights to obtain a total pollution emission weight Kf of each monitoring, summing the total pollution emission weights Kf of all monitoring equipment to obtain a total pollution emission value Kfs of all monitoring in each future time interval of each county.
Further, in step S5, predicting a future pollutant emission value includes: the future pollution emission value Pf is the product of the total weight Kfs of the pollution emission values obtained by all monitoring in each time interval in the future of each county and each pollutant gas emission amount E corresponding to the unit weight.
Further, in step S1, calculating the monitoring device attribution, further includes: and acquiring longitude and latitude coordinates of the monitoring equipment and longitude and latitude coordinates of centers of all the counties, calculating the distance between the monitoring equipment and the centers of all the counties through st _ distance, sequencing the calculated distances, and determining the attribution of the monitoring equipment according to the minimum distance.
The analysis method for predicting the emission of the tail gas of the urban polluted vehicle can predict the emission of harmful gases in the future tail gas in advance; based on the prediction result of the future, the auxiliary decision of environmental prevention and traffic management can be made in advance, and a decision basis is provided for smart city services; service data of different departments are combined, and data value and intelligent solutions are excavated.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for analyzing a prediction of exhaust emissions from a city-polluted vehicle according to an exemplary first embodiment of the present invention.
FIG. 2 is a flowchart illustrating a method for predicting and analyzing an emission of a pollutant car according to a fourth exemplary embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below with reference to the accompanying drawings.
It should be noted that, in the case of no conflict, the features in the following embodiments and examples may be combined with each other; moreover, based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
Fig. 1 is a flowchart of an analysis method for predicting the exhaust emission of a city-polluted vehicle according to an exemplary first embodiment of the present invention, as shown in fig. 1, the method of the present embodiment includes:
step S1: calculating attribution of monitoring equipment, and acquiring monitoring data through the monitoring equipment to form a data set A, wherein the data set A comprises: monitoring equipment identification, a time interval, traffic flow, license plate number and a region county which the monitoring equipment monitors in real time;
step S2: acquiring vehicle information data, setting pollution emission weights of different vehicles, and forming a data set B, wherein the data set B comprises: vehicle type, license plate number, displacement, city holding capacity of each type of vehicle;
and step S3: according to the data set A and the data set B, calculating the total pollution emission weight of all vehicles passing each time interval in each district and county to form a data set D, wherein the data set D comprises: time interval, region and county, total pollution emission weight;
and step S4: according to the annual emission statistical data and the data set D of the polluted gas in each district and county, calculating the pollution emission corresponding to the unit weight to form a data set E, wherein the data set E comprises: the discharge amount of each pollutant gas corresponding to the unit weight;
step S5: and predicting the future traffic flow, and predicting the future pollution emission value according to the predicted future traffic flow.
In step S1 of this embodiment, calculating the attribution of the monitoring device includes: and acquiring longitude and latitude coordinates, road network data and high-precision map data of the monitoring equipment, and calculating the attribution of the monitoring equipment by using a Postgis function.
In step S1 of this embodiment, calculating the attribution of the monitoring device further includes: and acquiring longitude and latitude coordinates of the monitoring equipment and longitude and latitude coordinates of centers of all the counties, calculating the distance between the monitoring equipment and the centers of all the counties through st _ distance, sequencing the calculated distances, and determining the attribution of the monitoring equipment according to the minimum distance.
The second embodiment of the present invention provides an analysis method for predicting urban pollutant automobile exhaust emission, where this embodiment is a preferred embodiment of the method shown in fig. 1, and in step S2 of this embodiment, setting pollutant emission weights of different vehicles includes: the method comprises the steps of obtaining discharge capacity information of vehicles according to different license plate numbers, and setting pollution emission weights of the vehicles according to different discharge capacities, specifically, setting a tail gas pollution emission weight of a 1.0T vehicle to be 1, setting a tail gas pollution emission weight of a 2.0T vehicle to be 2, setting a tail gas pollution emission weight of a 3.0T vehicle to be 3, setting a pollution emission weight of a new energy pure electric vehicle to be 0, and setting an emission weight of a plug-in hybrid 1.5T vehicle to be 0.75.
A third exemplary embodiment of the present invention provides an analysis method for predicting urban pollutant automobile exhaust emission, where this embodiment is a preferred embodiment of the method shown in fig. 1, and in step S3 of this embodiment, the calculating a total pollutant emission weight of all vehicles passing through each time interval in each district and county according to the data set a and the data set B includes: summing the products of various types of vehicles obtained by monitoring in each time interval of each district and county and the corresponding pollution emission weight to obtain the total pollution emission weight Ky of each monitoring, summing the total pollution emission weights Ky of all monitoring equipment to obtain the total pollution emission value K obtained by monitoring in each time interval of each district and county.
Fig. 2 is a flowchart of an analysis method for predicting urban pollutant automobile exhaust emission according to an exemplary fourth embodiment of the present invention, where this embodiment is a preferred embodiment of the method shown in fig. 1, and in step S4 of the method of this embodiment, the calculating of the pollutant emission amount corresponding to the unit weight value according to the annual pollutant emission statistical data and the data set D in each district and county includes:
s41: acquiring annual emission statistical data of polluted gas in each district and county to form a data set C, wherein the data set C comprises: the annual statistical value of the emission of each polluted gas belongs to the county;
s42: determining the total pollution emission amount T of each gas in each time interval of each district and county according to the annual emission statistical data of the polluted gas in each district and county;
s43: correlating the data set C with the data set D, and inquiring and obtaining the total weight K of all monitored and obtained pollution emission values of each time interval of each district and county;
s44: and (4) dividing the total pollutant emission amount T of each gas in each time interval of each district and county in the step (S42) by the total pollutant emission value weight K obtained by all monitoring in each time interval of each district and county inquired in the step (S43) to obtain each pollutant gas emission amount E corresponding to the unit weight.
An exemplary fifth embodiment of the present invention provides an analysis method for predicting an emission of an automobile with urban pollution, where this embodiment is a preferred embodiment of the method shown in fig. 1, and in step S5, the predicting a future traffic flow includes: through historical data of the data set A, machine learning model training and reasoning of time series prediction is carried out on the traffic flow of each monitoring device by using an ARIMA method of Madlib/Plpython, the traffic flow of each future time interval is calculated, and a data set Af of each monitoring device is obtained, wherein the data set Af comprises: traffic flow, license plate number, and region of the country.
In this embodiment, predicting a future pollutant emission value according to the predicted future vehicle flow includes: and replacing the data set A in the step S3 with the predicted future traffic data set Af, and calculating the total pollutant emission weight of all vehicles passing through each time interval in each district and county according to the data set Af and the data set B.
In this embodiment, calculating a total pollutant discharge weight of all vehicles passing each time interval in each county according to the data set Af and the data set B includes: summing products of various types of vehicles obtained by monitoring in each time interval of each district and county and corresponding pollution emission weights to obtain total pollution emission weights Kf of each monitoring, summing the total pollution emission weights Kf of all monitoring equipment to obtain total pollution emission values Kfs obtained by monitoring in each time interval of each district and county in the future.
In this embodiment, predicting the future pollutant emission value includes: the future pollution emission value Pf is the product of the total weight Kfs of the pollution emission values obtained by all monitoring in each time interval in the future of each county and each pollutant gas emission amount E corresponding to the unit weight.
A fifth exemplary embodiment of the present invention provides a method for analyzing an emission prediction of an automobile with urban pollution,
the above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An analysis method for urban polluted automobile exhaust emission prediction is characterized by comprising the following steps:
step S1: calculating attribution of monitoring equipment, and acquiring monitoring data through the monitoring equipment to form a data set A, wherein the data set A comprises: monitoring equipment identification, time interval, traffic flow, license plate number and region county which are monitored by the monitoring equipment in real time;
step S2: acquiring vehicle information data, setting pollution emission weights of different vehicles, and forming a data set B, wherein the data set B comprises: vehicle type, license plate number, displacement, city holding capacity of each type of vehicle;
and step S3: according to the data set A and the data set B, calculating the total pollution emission weight of all vehicles passing each time interval in each district and county to form a data set D, wherein the data set D comprises: time interval, region and county, total pollution emission weight;
and step S4: according to the annual pollutant gas emission statistical data and the data set D of each district and county, calculating the pollution emission amount corresponding to the unit weight to form a data set E, wherein the data set E comprises: the emission of various pollutant gases corresponding to the unit weight;
step S5: and predicting future traffic flow, and predicting a future pollution emission value according to the predicted future traffic flow.
2. The method for analyzing the prediction of urban pollutant automobile exhaust emission according to claim 1, wherein in step S1, calculating the attribution of the monitoring equipment comprises: and acquiring longitude and latitude coordinates, road network data and high-precision map data of the monitoring equipment, and calculating the attribution of the monitoring equipment by using a Postgis function.
3. The method of claim 1, wherein in step S2, the setting of the emission weight of different vehicles comprises: and acquiring the displacement information of the vehicle according to different license plate numbers, and setting the pollution discharge weight of the vehicle according to different displacements.
4. The method as claimed in claim 1, wherein in step S3, the step of calculating the total pollutant emission weight of all vehicles passing through each time interval in each district and county according to the data set a and the data set B comprises: summing the products of the various types of vehicles obtained by monitoring in each time interval of each district and county and the corresponding pollution emission weights to obtain the total pollution emission weight Ky of each monitoring, summing the total pollution emission weights Ky of all monitoring equipment to obtain the total pollution emission value K obtained by monitoring in each time interval of each district and county.
5. The method for analyzing the prediction of urban pollutant automobile exhaust emission according to claim 1, wherein in step S4, the calculating of the pollutant discharge amount corresponding to the unit weight according to the annual pollutant gas discharge statistical data and the data set D of each district and county comprises:
s41: acquiring annual emission statistical data of the polluted gas in each district and county to form a data set C, wherein the data set C comprises: the annual statistical value of the emission of each polluted gas belongs to the county;
s42: determining the total pollution emission amount T of each gas in each time interval of each district and county according to the annual pollution gas emission statistical data of each district and county;
s43: correlating the data set C with the data set D, and inquiring and obtaining the total weight K of all monitored and obtained pollution emission values of each time interval of each district and county;
s44: and (4) dividing the total pollution emission amount T of each gas in each time interval of each district and county in the step (S42) by the total pollution emission value K acquired by all monitoring in each time interval of each district and county inquired in the step (S43) to obtain each pollution gas emission amount E corresponding to the unit weight.
6. The method of claim 1, wherein the step S5 of predicting the future vehicle flow comprises: performing machine learning model training and reasoning of time series prediction on the traffic flow of each monitoring device by using an ARIMA method of Madlib/Plpython through historical data of a data set A, calculating to obtain the traffic flow of each time interval in the future, and obtaining a data set Af of each monitoring device, wherein the data set Af comprises: traffic flow, license plate number, and the region and county to which the license plate number belongs.
7. The method as claimed in claim 1, wherein the step S5 of predicting the future pollutant emission value according to the predicted future vehicle flow rate comprises: and replacing the data set A in the step S3 with the predicted future traffic data set Af, and calculating the total pollutant emission weight of all vehicles passing through each time interval in each district and county according to the data set Af and the data set B.
8. The method for analyzing the prediction of urban pollutant automobile exhaust emission according to claim 7, wherein in step S5, the step of calculating the total pollutant emission weight of all vehicles passing through each time interval in each county according to the data set Af and the data set B comprises: summing products of various types of vehicles acquired by each monitoring in each future time interval of each county and corresponding pollution emission weights to obtain a total pollution emission weight Kf of each monitoring, summing the total pollution emission weights Kf of all monitoring equipment to obtain a total pollution emission value Kfs of all monitoring in each future time interval of each county.
9. The method of claim 8, wherein the step S5 of predicting the future polluting emission values comprises: the future pollution emission value Pf is the product of the total weight Kfs of the pollution emission values obtained by monitoring in each time interval of each district and county and each pollutant gas emission value E corresponding to the unit weight.
10. The method for analyzing the prediction of urban pollutant automobile exhaust emission according to claim 1, wherein in step S1, the calculation of the attribution of the monitoring equipment further comprises: acquiring longitude and latitude coordinates of the monitoring equipment and longitude and latitude coordinates of centers of all the districts, calculating the distance between the monitoring equipment and the centers of all the districts through st _ distance, sequencing the calculated distances, and determining the attribution of the monitoring equipment according to the minimum distance.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115355447A (en) * | 2022-10-20 | 2022-11-18 | 成都秦川物联网科技股份有限公司 | Intelligent gas-fired gate station pressure regulating optimization method and system based on Internet of things |
| CN116910569A (en) * | 2023-09-13 | 2023-10-20 | 中电车联信安科技有限公司 | Vehicle carbon emission big data monitoring method, system, device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115355447A (en) * | 2022-10-20 | 2022-11-18 | 成都秦川物联网科技股份有限公司 | Intelligent gas-fired gate station pressure regulating optimization method and system based on Internet of things |
| US11893518B2 (en) | 2022-10-20 | 2024-02-06 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods and systems of optimizing pressure regulation at intelligent gas gate stations based on internet of things |
| CN116910569A (en) * | 2023-09-13 | 2023-10-20 | 中电车联信安科技有限公司 | Vehicle carbon emission big data monitoring method, system, device and storage medium |
| CN116910569B (en) * | 2023-09-13 | 2023-12-19 | 中电车联信安科技有限公司 | Vehicle carbon emission big data monitoring method, system, device and storage medium |
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