CN113158141B - New energy automobile overload detection method based on big data - Google Patents
New energy automobile overload detection method based on big data Download PDFInfo
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Abstract
The invention provides a new energy automobile overload detection method based on big data, which can determine whether a target new energy automobile is overloaded or not by calculating the overload energy consumption probability of similar automobile types on the same road section and comparing the overload energy consumption probability with processed traffic management historical data, overcomes the defect of a complicated single-vehicle single-time vehicle inspection mode in the prior art, fully exerts the advantages of new energy vehicles and a big data platform, obviously improves the overload inspection efficiency and reduces the cost. The calculation of the average speed probability of the target vehicle is combined, the overload is divided into different conditions of overload and overspeed-free, so that the overload target vehicle in a low-speed and low-energy-consumption state can be effectively found, the omission of the inspection result is avoided or the target only overspeed is mistakenly identified as overload, and the accuracy of the method is ensured. Those skilled in the art can more flexibly choose whether to adjust the accuracy of the result in conjunction with the subsequent detection of the average velocity based on the teachings provided by the present invention.
Description
Technical Field
The invention belongs to the technical field of big data of new energy automobiles, and particularly relates to a method for detecting an overload condition of a vehicle by using the big data of the new energy automobile.
Background
Due to continuous improvement of road conditions and improvement of various performances of the new energy automobile, the overload problem of the new energy automobile is also getting worse. The overload problem not only damages public roads, but also damages the life and property safety of people. The overload problem has become one of the main causes of traffic accidents.
In order to solve the problem of vehicle overload, the traffic management department and the related departments consume a large amount of manpower, material resources and financial resources, however, under the current conditions, the method for detecting the overload of the traditional fuel vehicle or the new energy vehicle mainly comprises the following steps: the method of setting an overrun station for detection, additionally installing an acceleration sensor for a vehicle for detection, carrying out routine inspection on vehicle overload by personnel in a traffic control department and the like is still limited to single detection of a single vehicle, has serious defects in the aspects of real-time performance and detection efficiency, and has extremely high cost of consumed manpower, material resources and financial resources.
Disclosure of Invention
In view of this, the present invention aims to solve the technical problems of low overload detection efficiency and high cost in the prior art, and provides a new energy vehicle overload detection method based on big data by using the advantages of new energy vehicles and big data, internet of vehicles and other technologies, and specifically includes the following steps:
(1) Acquiring running condition data corresponding to a plurality of continuous segments of a new energy automobile type running on a specific road section;
(2) Calculating the energy consumed by the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total energy consumed by the target vehicle on the road section based on the energy consumed on each segment;
(3) Repeating the steps (1) and (2) for other vehicles of the same vehicle type, and counting to obtain a probability density function of total energy consumed by the vehicle type on the road section;
(4) And calculating the probability of the total energy consumed by the target vehicle to be detected of the vehicle type when the target vehicle runs on the road section based on the probability density function of the total energy, and comparing the probability with an overload energy probability threshold determined according to historical statistical data of a traffic control department so as to determine whether the target vehicle is overloaded or not.
Further, the step (2) specifically comprises: acquiring voltage and current data in driving condition data corresponding to a plurality of continuous segments, and calculating the total energy E consumed by a certain specific vehicle type on the road section by the following formula:
wherein n is the number of times of fragment collection; u shape k Is the voltage sampled at the kth time and has the unit of V; i is k The current sampled at the kth time is in A; delta t is the time interval of two adjacent sampling, and the unit is s; the unit of E is J.
Further, the specific calculation method for determining the overload energy probability threshold according to the historical statistical data of the traffic management department is as follows:
wherein n is 1 The total number of the vehicles which are checked on the same road section and are of the same type as the target vehicle; n is 2 The total number of overloaded and overspeed vehicles of the same type which are checked;
comparing the probability P (E) of the total energy consumed by the target vehicle when traveling on the road section Measuring ) And said P 1 If P (E) Measuring )>P 1 It is confirmed that overload occurs.
Further, after performing the steps (1) to (3), the following steps (5) to (6) are also performed:
(5) Calculating the average speed of the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total average speed of the target vehicle on the road section based on the average speed on each segment;
(6) Repeating the step (5) for other vehicles of the same vehicle type, and counting to obtain a probability density function of the total average speed of the vehicle type on the road section;
when the content is 50 percent<P(E Measuring )<P 1 And if so, calculating the probability of the average speed of the target vehicle by using the probability density function of the total average speed, and comparing the probability with a speed probability threshold determined according to historical statistical data of the traffic control department so as to determine whether the target vehicle is overloaded or not.
Further, the step (6) is specifically: obtaining average speed v corresponding to a plurality of continuous segments k Calculating the total average speed of a specific vehicle on the road section by the following formulaDegree v:
further, the specific calculation method for determining the overspeed probability threshold according to the historical statistical data of the traffic management department is as follows:
wherein n is 1 The total number of the vehicles which are checked on the same road section and are of the same type as the target vehicle; n is 3 The total number of overloaded but not overspeed vehicles of the same type that have been checked;
comparing the probability P (v) of the total average speed of the target vehicle when traveling on the road section Measuring ) And said P 2 If P (v) Measuring )<P 2 It is confirmed that overload occurs, otherwise, overload does not occur.
According to the method provided by the invention, whether the target new energy automobile is overloaded or not can be determined by calculating the overload energy consumption probability of the same type of automobile on the same road section and comparing the overload energy consumption probability with the processed traffic management historical data, so that the defect that in the prior art, a complicated single-automobile single-time inspection mode is overcome, the advantages of the new energy automobile and a large data platform are fully exerted, the overload inspection efficiency is obviously improved, and the cost is reduced. The calculation of the overspeed probability of the target vehicle is combined, the overload is divided into different conditions of overload and overspeed-free overload, so that the overload target vehicle in a low-speed and low-energy-consumption state can be effectively found, the omission of the inspection result is avoided or the overspeed-only target is mistakenly identified as overload, and the accuracy of the method is ensured. Those skilled in the art with the benefit of the teachings of the present invention can more flexibly choose whether to adjust the accuracy of the results in conjunction with the subsequent detection of the average velocity.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a new energy automobile overload detection method based on big data, which specifically comprises the following steps:
(1) Acquiring running condition data corresponding to a plurality of continuous segments of a new energy automobile type running on a specific road section;
(2) Calculating the energy consumed by the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total energy consumed by the target vehicle on the road section based on the energy consumed on each segment;
(3) Repeating the steps (1) and (2) for other vehicles of the same vehicle type, and counting to obtain a probability density function of total energy consumed by the vehicle type on the road section;
(4) And calculating the probability of the total energy consumed by the target vehicle to be detected of the vehicle type when the target vehicle runs on the road section based on the probability density function of the total energy, and comparing the probability with an overload energy probability threshold determined according to historical statistical data of a traffic control department so as to determine whether the target vehicle is overloaded.
In a preferred embodiment of the present invention, step (2) is specifically: acquiring voltage and current data in the driving condition data corresponding to the plurality of continuous segments, and calculating the total energy E consumed by a vehicle of a certain vehicle type on the road section by the following formula:
wherein n is the number of times of fragment collection; u shape k Is the voltage sampled at the kth time and has the unit of V; i is k Is the current sampled at the kth time in units ofA; delta t is the time interval of two adjacent sampling, and the unit is s; the unit of E is J.
The specific calculation method for determining the overload energy probability threshold according to the historical statistical data of the traffic management department comprises the following steps:
wherein n is 1 The total number of the vehicles which are checked on the same road section and are of the same type as the target vehicle; n is a radical of an alkyl radical 2 The total number of overloaded and overspeed vehicles of the same type which are checked;
using probability density function f of total energy E (x) By the formula
The probability of the total energy of the target vehicle can be obtained. Comparing the probability P (E) of the total energy consumed by the target vehicle when traveling on the road Measuring ) And said P 1 If P (E) Side survey )＞P 1 It is confirmed that overload occurs.
When the target vehicle is in a low speed state, because the energy consumed at this time is less, it may not be able to correctly judge whether the target vehicle is overloaded according to the energy probability alone. Since the overloaded vehicle is less harmful at low speed, the inspection can be terminated after the inspection for the more dangerous condition of overload and overspeed is realized in the above steps, and the technical problem proposed by the present invention is solved. And for the scene needing to more comprehensively check the overload, the following steps (5) to (6) can be synchronously executed when the steps (1) to (3) are executed:
(5) Calculating the average speed of the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total average speed of the target vehicle on the road section based on the average speed on each segment;
(6) Repeating the step (5) for other vehicles of the same vehicle type, and counting to obtain a probability density function of the total average speed of the vehicle type on the road section;
using probability of total average velocityDensity function f v (x) By the formula
The probability of the overall average speed of the target vehicle can be obtained. When the content is 50 percent<P(E Measuring )<P 1 And calculating the average speed probability of the target vehicle by using the probability density function of the total average speed because the consumed energy is still higher, and comparing the average speed probability with an overspeed probability threshold determined according to historical statistical data of a traffic control department so as to determine whether the target vehicle is overloaded or not.
In step (6), the average speed v corresponding to a plurality of continuous segments is obtained k Calculating the total average speed v of the vehicle of a specific vehicle type on the road section by the following formula:
the specific calculation method for determining the overspeed probability threshold according to the historical statistical data of the traffic management department comprises the following steps:
wherein n is 1 The total number of the vehicles which have been checked on the same road section and are of the same type as the target vehicle; n is a radical of an alkyl radical 3 The total number of overloaded but not overspeed vehicles of the same type which have been checked;
comparing the probability P (v) of the total average speed of the target vehicle when traveling on the road section Measuring ) And said P 2 If P (v) Measuring )＜P 2 It means that the target vehicle still consumes a high amount of energy in a low speed state, and thus it can be confirmed that overload has occurred, otherwise it can be confirmed that overload has not occurred.
It should be understood that, the sequence numbers of the steps in the embodiment of the present invention do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. Big data-based new energy automobile overload detection method is characterized in that: the method specifically comprises the following steps:
(1) Acquiring running condition data corresponding to a plurality of continuous segments of a certain new energy automobile type running on a certain specific road section;
(2) Calculating the energy consumed by the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total energy consumed by the target vehicle on the road section based on the energy consumed on each segment;
(3) Repeating the steps (1) and (2) for other vehicles of the same vehicle type, and counting to obtain a probability density function of total energy consumed by the vehicle type on the road section;
(4) Calculating the average speed of the target vehicle on each segment by using the acquired running condition data corresponding to each segment; obtaining the total average speed of the target vehicle on the road section based on the average speed on each segment;
(5) Repeating the step (4) for other vehicles of the same vehicle type, and counting to obtain a probability density function of the total average speed of the vehicle type on the road section;
(6) And calculating the probability of the total energy consumed by the target vehicle to be detected of the vehicle type when the target vehicle runs on the road section and the probability of the average speed based on the probability density function of the total energy and the probability density function of the total average speed, and comparing the probability with an overload energy probability threshold value and a speed probability threshold value determined according to historical statistical data of traffic control departments respectively, thereby determining whether the target vehicle is overloaded or not.
2. The method of claim 1, wherein: the step (2) is specifically as follows: acquiring voltage and current data in the driving condition data corresponding to the plurality of continuous segments, and calculating the total energy E consumed by a target vehicle of a specific vehicle type on the road section by the following formula:
wherein n is the number of times of fragment collection; u shape k Is the voltage sampled at the kth time and has the unit of V; i is k Is the current sampled at the kth time and has the unit of A; delta t is the time interval between two adjacent sampling times, and the unit is s; the unit of E is J.
3. The method of claim 1, wherein: the specific calculation method for determining the overload energy probability threshold according to the historical statistical data of the traffic management department comprises the following steps:
wherein n is 1 The total number of the vehicles which have been checked on the same road section and are of the same type as the target vehicle; n is 2 The total number of overloaded and overspeed vehicles of the same type which have been checked;
comparing the probability P (E) of the total energy consumed by the target vehicle when traveling on the road section Measuring ) And said P 1 If P (E) Side survey )>P 1 It is confirmed that overload occurs.
4. The method of claim 3, wherein: the concrete content is 50%<P(E Side survey )<P 1 And then, calculating the probability of the average speed of the target vehicle by using the probability density function of the total average speed, and comparing the probability with a speed probability threshold determined according to historical statistical data of the traffic control department so as to determine whether the target vehicle is overloaded or not.
5. The method of claim 4, wherein: the step (6) is specifically as follows: obtaining average speed v corresponding to a plurality of continuous segments k The total average speed v of a particular vehicle type over the route is calculated by the following formula:
6. the method of claim 5, wherein: the specific calculation method for determining the speed probability threshold according to the historical statistical data of the traffic management department comprises the following steps:
wherein n is 1 The total number of the vehicles which are checked on the same road section and are of the same type as the target vehicle; n is 3 The total number of overloaded but not overspeed vehicles of the same type that have been checked;
comparing the probability P (v) of the total average speed of the target vehicle when traveling on the road section Measuring ) And said P 2 If P (v) Measuring )<P 2 It is confirmed that overload has occurred, otherwise no overload has occurred.
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| CN202110372170.2A CN113158141B (en) | 2021-04-07 | 2021-04-07 | New energy automobile overload detection method based on big data |
| PCT/CN2021/129476 WO2022213596A1 (en) | 2021-04-07 | 2021-11-09 | Big data-based new energy vehicle overload detection method |
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| WO2016074608A2 (en) * | 2014-11-11 | 2016-05-19 | 冯春魁 | Methods and systems for vehicle operation monitoring and control, video monitoring, data processing, and overload monitoring and control |
| CN108860011B (en) * | 2018-04-17 | 2020-04-28 | 北京理工大学 | Vehicle overload identification method and system |
| CN111027146B (en) * | 2019-12-30 | 2023-11-24 | 行蜂科技(深圳)有限责任公司 | Dynamic real-time calculation method for vehicle load |
| CN111762096A (en) * | 2020-06-27 | 2020-10-13 | 王亚鹏 | New energy automobile safety early warning method and system based on artificial intelligence |
| CN111833604B (en) * | 2020-07-10 | 2021-10-29 | 北京交通大学 | Vehicle load state identification method and device based on driving behavior feature extraction |
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