CN115743143A - Vehicle running condition analysis method and system based on Internet of vehicles - Google Patents
Vehicle running condition analysis method and system based on Internet of vehicles Download PDFInfo
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- CN115743143A CN115743143A CN202211460519.9A CN202211460519A CN115743143A CN 115743143 A CN115743143 A CN 115743143A CN 202211460519 A CN202211460519 A CN 202211460519A CN 115743143 A CN115743143 A CN 115743143A
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Abstract
The invention discloses a vehicle running condition analysis method based on an internet of vehicles, which comprises the following steps: responding to a vehicle running condition analysis request instruction, and acquiring historical vehicle running data to be evaluated; calculating index values of a plurality of indexes of the vehicle to be evaluated according to the historical driving data of the vehicle to be evaluated; the plurality of indicators comprise: average vehicle speed, overspeed times, overspeed percentage, rapid deceleration times, rapid acceleration times and idle percentage; and carrying out weighted summation on the index values of a plurality of indexes of the vehicle to be evaluated through preset weight, outputting driving behavior scores, and outputting a vehicle running condition analysis result according to the driving behavior scores.
Description
Technical Field
The invention relates to the technical field of automobile data analysis, in particular to a vehicle driving condition analysis method and system based on an internet of vehicles.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The driving behavior and the driving state of the driver are monitored and evaluated in real time, the driver is reminded of changing the driving habit in time, and traffic accidents are avoided. For fleet management and insurance companies (UBI), the effects of safety, energy conservation, efficiency improvement and the like can be improved.
The existing vehicle running condition analysis is mostly based on subjective identification of experienced workers, manpower is wasted in the evaluation process, the evaluation result is mixed with the subjective opinion of the workers, and objective and accurate evaluation means are lacked.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a vehicle driving condition analysis method and system based on an internet of vehicles;
in a first aspect, the invention provides a vehicle driving condition analysis method based on the internet of vehicles;
the vehicle driving condition analysis method based on the Internet of vehicles comprises the following steps:
responding to a vehicle running condition analysis request instruction, and acquiring historical running data of a vehicle to be evaluated;
calculating index values of a plurality of indexes of the vehicle to be evaluated according to the historical driving data of the vehicle to be evaluated; the plurality of indicators comprise: average vehicle speed, overspeed times, overspeed percentage, rapid deceleration times, rapid acceleration times and idle percentage;
and weighting and summing index values of a plurality of indexes of the vehicle to be evaluated through preset weight, outputting driving behavior scores, and outputting a vehicle running condition analysis result according to the driving behavior scores.
In a second aspect, the invention provides a vehicle driving condition analysis system based on the internet of vehicles;
vehicle driving condition analysis system based on internet of vehicles includes:
an acquisition module configured to: responding to a vehicle running condition analysis request instruction, and acquiring historical vehicle running data to be evaluated;
a computing module configured to: calculating index values of a plurality of indexes of the vehicle to be evaluated according to the historical driving data of the vehicle to be evaluated; the plurality of indicators comprise: average vehicle speed, overspeed times, overspeed percentage, rapid deceleration times, rapid acceleration times and idling percentage;
an output module configured to: and carrying out weighted summation on the index values of a plurality of indexes of the vehicle to be evaluated through preset weight, outputting driving behavior scores, and outputting a vehicle running condition analysis result according to the driving behavior scores.
In a third aspect, the present invention further provides an electronic device, including:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer readable instructions,
wherein the computer readable instructions, when executed by the processor, perform the method of the first aspect.
In a fourth aspect, the present invention also provides a storage medium storing non-transitory computer readable instructions, wherein the non-transitory computer readable instructions, when executed by a computer, perform the instructions of the method of the first aspect.
In a fifth aspect, the invention also provides a computer program product comprising a computer program for implementing the method of the first aspect when run on one or more processors.
Compared with the prior art, the invention has the beneficial effects that:
the latitude and granularity of data collected by the Internet of vehicles are richer than those of the prior analysis technology, because the data collected by the Internet of vehicles can be collected in advance through a buried point and various sensors and transmitted simultaneously in real time; through the system, based on different purpose analysis, an index can be calculated by carefully calculating each driving behavior with different weights, so as to measure the driving behavior or mark various labels on the driving behavior of the user; and is closer to the customer, so that the customer uses the auxiliary driving behaviors more frequently.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are included to illustrate an exemplary embodiment of the invention and not to limit the invention.
FIG. 1 is a flowchart of a method according to a first embodiment;
FIG. 2 is a map showing the running speed of the vehicle according to the first embodiment;
FIG. 3 is a schematic view of the vehicle speed for a first driving trip in accordance with a first embodiment;
FIG. 4 is an average vehicle speed versus idle duration (in percent) for the first embodiment;
FIG. 5 shows the number of overspeed times for the entire driving trip according to the first embodiment;
FIG. 6 is an acceleration profile of a first driving stroke according to the first embodiment;
FIG. 7 is a profile of rapid acceleration and rapid deceleration for a first driving maneuver according to the first embodiment;
FIG. 8 is a diagram illustrating a relationship between the idle speed and the rapid acceleration/deceleration times according to the first embodiment;
FIG. 9 is a graph of a fitted relationship between the number of idling times and the number of rapid acceleration and rapid deceleration times according to the first embodiment;
fig. 10 is a driving behavior index value statistical table according to the first embodiment.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise, and furthermore, it should be understood that the terms "comprises" and "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the invention may be combined with each other without conflict.
All data are legally applied to the data on the basis of meeting laws and regulations and user consent.
Example one
The embodiment provides a vehicle running condition analysis method based on the Internet of vehicles;
as shown in fig. 1, the method for analyzing vehicle driving conditions based on internet of vehicles includes:
s101: responding to a vehicle running condition analysis request instruction, and acquiring historical running data of a vehicle to be evaluated;
s102: calculating index values of a plurality of indexes of the vehicle to be evaluated according to the historical driving data of the vehicle to be evaluated; the plurality of indicators comprise: average vehicle speed, overspeed times, overspeed percentage, rapid deceleration times, rapid acceleration times and idling percentage;
s103: and carrying out weighted summation on the index values of a plurality of indexes of the vehicle to be evaluated through preset weight, outputting driving behavior scores, and outputting a vehicle running condition analysis result according to the driving behavior scores.
Further, the historical driving data of the vehicle to be evaluated specifically includes:
the method comprises the steps of obtaining total mileage data of a vehicle through a vehicle odometer, collecting speed data in the driving process of the vehicle through a speed sensor, collecting braking data in the driving process of the vehicle through a force sensor, and collecting total driving time of the vehicle through a timer.
Vehicle history driving data of the internet of vehicles: data acquisition signals of original production equipment manufacturer OEM and aftermarket solutions are acquired 365 by 2 by 360 by 262800 times a year, assuming that the data acquisition frequency is 1 sampling all signals for 10 seconds, and the home car is used for 2 hours (data acquisition starts upon start-up) on average, depending on the acquisition frequency.
Further, the average vehicle speed = total mileage/(total time-parking time);
the number of overspeed times is judged by the vehicle speed and the road speed limit, and if the vehicle speed exceeds the speed of the road speed limit, the overspeed is regarded as one overspeed;
percent overspeed = [ (actual speed-speed limit)/speed limit ]. 100%;
the number of rapid deceleration times is that vehicle running data is recorded through the internet of vehicles, the average acceleration of the vehicle in a set time period is calculated, a threshold value is set, if the value of the average acceleration is larger than the set threshold value, one-time rapid acceleration is counted, and the number of rapid deceleration times of the vehicle is obtained;
the number of rapid acceleration is obtained by recording vehicle running data through the internet of vehicles, calculating the average acceleration of the vehicle in a set time period, setting a threshold value, and counting one-time rapid deceleration if the average acceleration is smaller than the set threshold value;
percentage of idle = total idle duration/total driving duration 100%.
Further, the calculation formula of the overspeed percentage is as follows:
overspeed percentage = (actual speed-specified speed)/specified speed × 100%.
Further, the sharp deceleration is defined as: and taking 10 km/h.s as a threshold value for judging rapid deceleration, and taking rapid deceleration below-10.
Further, the sharp acceleration is defined as: the threshold value for judging the rapid acceleration is 10 km/h.s, and the rapid acceleration is determined when the threshold value exceeds + 10.
Further, the outputting the analysis result of the vehicle driving condition according to the driving behavior score includes:
based on different purposes, a score is calculated by weighting each driving behavior component, and the score is used for measuring the driving behavior of the user or marking various labels on the driving behaviors in different score intervals.
The driving behavior score is calculated in different formulas for different business/research purposes. Such as: when an insurance company designs a UBI product, the more stable the driving, the higher the score, the less the driving times/mileage/time, and the higher the score.
Taking the data of the first driving trip, it has 1854 records, the time span is 2479 seconds, namely 41.3 minutes. Indicating some data loss in the middle. First, a distribution diagram of the vehicle speed is shown in fig. 2.
The general case of the vehicle speed distribution can be seen from fig. 2: the median vehicle speed reaches 63.63 km/h, which shows that the traffic condition is good. The maximum speed is about 120 km/h, which shows that the vehicle owner does not overspeed during the journey (the speed limit is assumed to be 120 km/h). The vehicle speed is plotted in time sequence as shown in fig. 3.
As shown in fig. 3, the horizontal line indicates 120 km/h, which is a general speed limit value of the expressway. Has the following characteristics: in the previous time of about 2/3, the vehicle is started and stopped, and the average speed is lower; the latter traffic conditions become better, substantially above 60 km/h. From the distance between two adjacent points, it can be inferred that the larger the distance, the larger the acceleration (including acceleration and deceleration), the more rapid acceleration, or rapid deceleration, may exist. The points with larger adjacent distances are often close to the vehicle speed of 0, i.e. before and after "idling".
As shown in fig. 4, the percentage of the average vehicle speed to the idle speed (the total idle speed period/the total driving period) for each driving stroke is calculated. As shown in fig. 4, the relationship of negative correlation: the smaller the idle time period (percentage), the larger the corresponding average vehicle speed. The correlation is not particularly strict, indicating that the average vehicle speed is related to more factors. Of particular note, the correlation between the average vehicle speed and the idle time period is observed here, and there is no causal relationship between the two. The true primary cause should be traffic conditions, congestion, weather, etc. external causes (if there is no vehicle or the driver's own cause). In fig. 3 it is shown that there may be an overspeed (actually not). The number of speeding (> 120 km/h) per trip is calculated for all 142 trips and figure 5 is plotted, as shown in figure 5. Most trips, the user is not speeding.
Acceleration calculation formula:
a = (Vt-Vo)/t { with Vo as the positive direction, a is the same direction (acceleration) with Vo, a >0; in the reverse direction, a <0}
Where v represents a vehicle speed (m/s 2) and t represents time(s).
Acceleration is the amount of change in velocity per unit time. This unit time is 1 second.
The data used is in the order of seconds, but because partial data is missing, the time interval between two adjacent records is sometimes longer than 1 second, and the acceleration can be obtained by the adjacent records (otherwise, if more data is missing, the calculation result has larger deviation).
The data unit for vehicle speed is kilometer per hour and the acceleration thus calculated is kilometer per hour. There is only one multiple relationship between the two. Taking the data of the first driving stroke as an example, the acceleration at each time point is calculated and plotted on the graph, as shown in fig. 5.
As shown in fig. 6, the data points falling on the axes of 0 indicate an acceleration of 0; the data points in the positive and negative directions appear to be comparable. The vertical axis is acceleration (km/h.s), which is converted into standard units (m/s 2), and the size of the number and intuition in daily life are difficult to establish intuitive connection. Taking Tesla Model3 as an example, its hundred kilometers acceleration number only needs 3.5 seconds, and the average acceleration (not considering that the actual acceleration in this 3.5 seconds is changed) is calculated in terms of acceleration units kilometers per hour. 100/3.5=28.57 km/hour. The running is the level of overtravel, mainly shows in the test run, the racing car field, etc., and most cars can not reach in the market. For a common family car, the acceleration time of a hundred kilometers is generally 8 seconds away. The average acceleration is calculated at an acceleration level of one hundred kilometres for 8 seconds: 100/8=12.5 km/h.sec.
And taking 10 km/h.s as a threshold value for judging rapid acceleration/rapid deceleration, wherein the rapid acceleration is carried out when the threshold value exceeds +10, and the rapid deceleration is carried out when the threshold value is lower than-10. Taking the first driving trip as an example, fig. 6 is redrawn, and the thresholds for rapid acceleration and rapid deceleration are identified, resulting in fig. 7.
As is clear from fig. 7, there are two rapid accelerations and two rapid decelerations in the first driving trip.
It was observed that there may be a correlation between rapid acceleration and rapid deceleration and the number of idling times when analyzing fig. 3.
Referring back to fig. 3, before and after each idling (vehicle speed = 0), the distance between two adjacent points is relatively large, but the distance is somewhat different from the above-defined rapid acceleration/rapid deceleration, so that the acceleration threshold is intentionally relaxed to 5 km/h. The number of accelerations exceeding this threshold for each trip is counted and a graph 8 is drawn.
As can be seen from fig. 8, the idle speed number has a strong positive correlation with the rapid acceleration and rapid deceleration number.
By a linear fit, as shown in fig. 9. The corrected R-square (Adjusted R-Squared) is 0.72, and the regression diagnosis shows that the model is relatively appropriate, so that the linear correlation between the two is basically determined.
Combining the above analysis, the statistical values of each index are listed in fig. 10.
After all the required driving behavior KPIs are calculated, a driving behavior score can be obtained by referring to a predefined calculation formula, and then the user (driver) can be labeled. Such as: novice drivers, sedate old drivers, etc.
Example two
The embodiment provides a vehicle running condition analysis system based on the Internet of vehicles;
vehicle driving condition analysis system based on internet of vehicles includes:
an acquisition module configured to: responding to a vehicle running condition analysis request instruction, and acquiring historical running data of a vehicle to be evaluated;
a computing module configured to: calculating index values of a plurality of indexes of the vehicle to be evaluated according to the historical driving data of the vehicle to be evaluated; the plurality of indicators comprise: average vehicle speed, overspeed times, overspeed percentage, rapid deceleration times, rapid acceleration times and idling percentage;
an output module configured to: and weighting and summing index values of a plurality of indexes of the vehicle to be evaluated through preset weight, outputting driving behavior scores, and outputting a vehicle running condition analysis result according to the driving behavior scores.
It should be noted here that the above-mentioned obtaining module, calculating module and output module correspond to steps S101 to S103 in the first embodiment, and the above-mentioned modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the description of each embodiment has an emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions in other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be another division, for example, a plurality of modules may be combined or may be integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Example four
The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The vehicle running condition analysis method based on the Internet of vehicles is characterized by comprising the following steps:
responding to a vehicle running condition analysis request instruction, and acquiring historical running data of a vehicle to be evaluated;
calculating index values of a plurality of indexes of the vehicle to be evaluated according to the historical driving data of the vehicle to be evaluated; the plurality of indicators comprise: average vehicle speed, overspeed times, overspeed percentage, rapid deceleration times, rapid acceleration times and idling percentage;
and carrying out weighted summation on the index values of a plurality of indexes of the vehicle to be evaluated through preset weight, outputting driving behavior scores, and outputting a vehicle running condition analysis result according to the driving behavior scores.
2. The vehicle networking-based vehicle driving condition analysis method according to claim 1, wherein the historical driving data of the vehicle to be evaluated specifically comprises:
the method comprises the steps of acquiring total mileage data of a vehicle through a vehicle odometer, acquiring speed data in the driving process of the vehicle through a speed sensor, acquiring braking data in the driving process of the vehicle through a force sensor, and acquiring total driving duration of the vehicle through a timer.
3. The internet-of-vehicles-based vehicle driving condition analysis method of claim 1, wherein the average vehicle speed = total mileage/(total time-parking time);
the number of times of overspeed is judged by the speed of the vehicle and the speed limit of the road, and if the speed of the vehicle exceeds the speed of the speed limit of the road, the vehicle is considered to be overspeed for one time;
percent overspeed = [ (actual speed-speed limit)/speed limit ] × 100%.
4. The internet of vehicles-based vehicle driving condition analysis method of claim 1, wherein the number of rapid deceleration is obtained by recording vehicle driving data through the internet of vehicles, calculating the average acceleration of the vehicle within a set time period, setting a threshold, and counting a rapid acceleration if the average acceleration is greater than the threshold to obtain the number of rapid deceleration of the vehicle.
5. The method as claimed in claim 1, wherein the number of rapid acceleration is obtained by recording vehicle driving data through the internet of vehicles, calculating average acceleration of the vehicle within a set time period, setting a threshold, and counting a rapid deceleration if the average acceleration is less than the threshold.
6. The internet-of-vehicles-based vehicle behavior analysis method of claim 1, wherein idle percentage = idle total duration/total driving duration 100%.
7. The internet-of-vehicles-based vehicle driving condition analysis method according to claim 1, wherein outputting the vehicle driving condition analysis result according to the driving behavior score comprises:
based on different purposes, a score is calculated by weighting each driving behavior component, and the score is used for measuring the driving behavior of the user or marking various labels on the driving behaviors in different score intervals.
8. Vehicle driving condition analysis system based on internet of vehicles, characterized by including:
an acquisition module configured to: responding to a vehicle running condition analysis request instruction, and acquiring historical vehicle running data to be evaluated;
a computing module configured to: calculating index values of a plurality of indexes of the vehicle to be evaluated according to the historical driving data of the vehicle to be evaluated; the plurality of indicators comprise: average vehicle speed, overspeed times, overspeed percentage, rapid deceleration times, rapid acceleration times and idling percentage;
an output module configured to: and weighting and summing index values of a plurality of indexes of the vehicle to be evaluated through preset weight, outputting driving behavior scores, and outputting a vehicle running condition analysis result according to the driving behavior scores.
9. An electronic device, comprising:
a memory for non-transitory storage of computer readable instructions; and
a processor for executing the computer readable instructions,
wherein the computer readable instructions, when executed by the processor, perform the method of any of claims 1-7.
10. A storage medium storing non-transitory computer-readable instructions, wherein the non-transitory computer-readable instructions, when executed by a computer, perform the instructions of the method of any one of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116499772A (en) * | 2023-06-28 | 2023-07-28 | 天津所托瑞安汽车科技有限公司 | Vehicle braking performance evaluation method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116499772A (en) * | 2023-06-28 | 2023-07-28 | 天津所托瑞安汽车科技有限公司 | Vehicle braking performance evaluation method and device, electronic equipment and storage medium |
| CN116499772B (en) * | 2023-06-28 | 2023-10-03 | 天津所托瑞安汽车科技有限公司 | Vehicle braking performance evaluation method and device, electronic equipment and storage medium |
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