CN113280830B - Data-driven specific driving scene vehicle screening and mileage checking method - Google Patents

Abstract

The present invention provides a data-driven specific driving scene vehicle screening and mileage checking method, which uses the vehicle-mounted big data of new energy vehicles to consider various vehicle operating states and information, and realizes the big data including a large number of non-teaching vehicles. Hierarchical and precise screening of specific driving scenarios (such as new energy vehicles specially used to practice parking in the driving school), combined with the vehicle speed and mileage information displayed on the vehicle instrument and the vehicle speed and mileage calculated from other angles, the screened out The vehicle mileage realizes comprehensive and accurate verification, thereby having many beneficial effects that are not available in the prior art.

Description

Data-driven vehicle screening and mileage verification method for specific driving scenarios

technical field

The invention belongs to the technical field of new energy vehicle big data, and in particular relates to a method for screening and checking mileage of vehicles in a driving school based on the big data of new energy vehicles.

Background technique

In some specific driving scenarios (such as new energy vehicles specially used for parking training in teaching scenarios such as driving schools), due to its small range of motion and the existence of GPS accuracy errors, the existing GPS positioning information is used to calculate its driving mileage. method is not applicable. Depot storage occupies a relatively high proportion in the use scene, and is one of the important considerations affecting the performance and life of the vehicle. Therefore, it is necessary to monitor and count the accumulated mileage of the teaching vehicles in time and ensure normal teaching and training. Compared with traditional fuel vehicles, new energy vehicles have significant advantages in on-board data collection and processing, which is more conducive to the batch processing of the above-mentioned out-of-stock vehicles screening and mileage verification, avoiding the need to regularly and repeatedly screen out all vehicles for transfer The heavy work of checking the mileage of the vehicles for training in the warehouse and checking the mileage of the bicycles separately. However, most of the existing new energy vehicle mileage verification methods are to remove abnormal data by online mileage (such as dashboard mileage) and compare it with the GPS mileage of vehicle positioning information to obtain the effective mileage of the vehicle. However, for the new energy vehicles that have been transferred from the driving school training, due to their relatively small effective range of motion and relatively large GPS error accuracy, it is still impossible to accurately complete the above screening and verification work.

Contents of the invention

In view of this, the present invention aims to solve the technical problem of the lack of means for screening and mileage verification of new energy vehicles in specific driving scenarios in this field, and provides a data-driven specific driving scene vehicle screening and mileage verification method, through Execute steps S1-S5 to complete the screening of vehicles that have been moved out of the warehouse, and perform mileage checks on the screened vehicles by performing steps S6-S8. The method is specifically based on the following steps:

S1. The database platform collects the following new energy vehicle data including new energy vehicles in special driving scenarios: motor speed, motor torque, instrument panel speed, battery pack current, vehicle location information (longitude, latitude), and Form vehicle data set Q1;

S2. Preliminarily select the data in Q1, and screen the vehicles according to the proportion of the number of backward frames in the data frame to obtain the vehicle data set Q2;

S3. Filter out the running vehicle data set Q3 from Q2 according to the current of the battery pack, and obtain the vehicle data set Q4 based on the speed of the instrument panel;

S4. Select the data corresponding to the vehicle in the special driving scene from Q4 based on the motor torque to obtain the vehicle data set Q5;

S5. Obtain the vehicle data set Q6 based on the screening of the vehicle activity range in Q5.

S6. The database platform collects relevant data of the vehicle corresponding to Q6, calculates the mileage according to the motor speed and motor torque, and determines the initial calculation mileage by considering the result and the dashboard mileage;

S7. Calculate the mileage change caused by the abnormality according to the abnormal situation in the running of the vehicle;

S8. Exclude the mileage change caused by the abnormality in the initial calculation mileage, and complete the final mileage check.

Further, the process of obtaining the vehicle data set Q2 in step S2 specifically includes:

From Q1, determine the ratio i _n between the number of historical frames n _reversing of each vehicle in the reversing state and the total number of historical frames n _reversing :

The data component Q2 corresponding to the vehicle whose i _n is greater than a predetermined value is screened out.

Further, the process of obtaining vehicle data sets Q3 and Q4 in step S3 specifically includes:

a) According to the current information i of the battery pack, filter out the data set Q3 that is in the normal operating state, that is, the discharging state; wherein, i<0 is the charging state, i>0 is the discharging state, and i=0 is the static state;

b) Calculating the ratio of the data frame V _{predetermined value} less than the predetermined value in the historical dashboard speed information of the vehicle corresponding to Q3 to the total frame number V _{total frame number} :

The data corresponding to the vehicles whose i _V is greater than the predetermined value is composed into Q4.

Further, the process of obtaining the vehicle data set Q5 in step S4 specifically includes:

For each vehicle corresponding to the data set Q4, calculate the average value T _average in the data whose motor torque T is not equal to 0 in one day, and filter the data corresponding to the vehicle whose T _average > T1 to form Q5;

Among them, T1 can be calculated according to the motor torque of a sample vehicle in normal operation at n different periods t ₁ , t ₂ ,..., t _n :

Further, the process of obtaining the vehicle data set Q6 in step S5 specifically includes:

Calculate the center latitude and longitude of the activity range of each vehicle corresponding to Q5 within a certain period of time:

Among them, n represents the number of time periods in this period, t ₁ , t ₂ ,..., t _n represent different time periods;

的车辆所对应数据组成Q6。For each vehicle with the center longitude and latitude as the center of the circle and set a predetermined radius, the number of frames in the historical data frame within the range X _{frame number} ratio within the range X is greater than the predetermined value:

The data corresponding to the vehicle of the group constitutes Q6.

Further, the process of determining the initial calculation mileage in step S6 specifically includes:

The database platform collects the tire radius r and transmission ratio i _g of the vehicle corresponding to Q6, and calculates the vehicle speed u _a in combination with the motor speed ω:

Calculate the mileage of the vehicle per second:

Sum to get the total computed mileage:

S _{calculated mileage} = S _{calculated mileage (t1)} + S _{calculated mileage (t2)} + ... + S _{calculated mileage (tn)}

Take the smaller value between the total calculated mileage and the dashboard mileage as the initial calculation mileage.

Further, the process of calculating the mileage change caused by the abnormality in step S7 specifically includes:

a) Calculate the mileage change caused by the abnormal speed:

Determine whether the motor speed in the data is constant for multiple frames and not equal to 0, and record the number of frames: from the t _1th to the t _xth frame, calculate the vehicle's speed during the period from t ₁ to t _x frame number The mileage change information on the instrument panel, assuming that the odometer reading in frame t ₁ is S _ω1 , and the odometer reading in frame t _x is S _ωx , then the mileage change between two frames is recorded as S _{speed abnormality 1} :

_{Abnormal S speed 1} ＝S _ωx -S _ω1

Then the total speed abnormal mileage including n times of abnormal speed is recorded as S _{speed abnormality} :

_{Abnormal S speed = abnormal} S _{speed 1} + _{abnormal S speed 2} +...+ _{abnormal S speed n} ;

b) Calculate the mileage change caused by the abnormal current:

Judging whether the current data in the data has a continuous multi-frame value that remains unchanged and is not equal to 0, and records its continuous frame number: from the t _1th to the t _xth frame, calculate the vehicle’s temperature during the period from t ₁ to t _x frame number The mileage change information on the instrument panel, assuming that the odometer reading in frame t ₁ is S _I1 , and the odometer reading in frame t _x is S _Ix , then the mileage change between the two frames is recorded as S _{current abnormality 1} :

S _{current abnormality 1} ＝S _tx —S _I1

Then the total speed abnormal mileage including n times of current abnormality is recorded as S _{current abnormality} :

S _{current abnormality} =S _{current abnormality 1} +S _{current abnormality 2} +...+S _{current abnormality n} ;

c) Calculate the mileage jump:

Determine whether the mileage change in the data exceeds the predetermined distance between two consecutive frames, record the mileage between two frames of data as W _n (n=1, 2, 3, ...), then the total mileage jump is recorded as W:

W=W ₁ +W ₂ + . . . +W _n .

Further, the mileage changes caused by excluding abnormalities in step S7 are effectively calculated as follows:

S _{effective calculation mileage} = S _{initial calculation mileage} - S _{speed abnormality} - S _{current abnormality} - W

The smaller of the effective calculated mileage S _{effective calculated mileage} and S _{instrument panel mileage} is taken as the final mileage verification result.

The above-mentioned method provided by the present invention utilizes the vehicle-mounted big data of new energy vehicles to consider various vehicle operating states and information, and realizes the hierarchical and accurate screening of vehicles that have been relocated from the driving school from the big data that includes a large number of non-teaching vehicles. Combining the vehicle speed and mileage information displayed by the vehicle instrument and the vehicle speed and mileage calculated from other angles, the screened vehicle mileage is fully and accurately checked, thus having many beneficial effects that are not available in the prior art.

Description of drawings

Fig. 1 is a schematic flow diagram of the overall process of the method provided by the present invention.

detailed description

The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

The data-driven specific driving scene vehicle screening and mileage checking method provided by the present invention, as shown in Figure 1, is specifically based on the following steps:

S1. The database platform collects the following new energy vehicle data including new energy vehicles in special driving scenarios: motor speed, motor torque, dashboard speed, battery pack current, vehicle location information (longitude, latitude), and forms Vehicle data set Q1;

S2. Preliminarily select the data in Q1, and screen the vehicles according to the proportion of the number of backward frames in the data frame to obtain the vehicle data set Q2;

S3. Filter out the running vehicle data set Q3 from Q2 according to the current of the battery pack, and obtain the vehicle data set Q4 based on the speed of the instrument panel;

S4. Select a vehicle data set Q5 in a special driving scenario from Q4 based on the motor torque;

S5. Obtain the vehicle data set Q6 based on the screening of the vehicle activity range in Q5.

S6. The database platform collects relevant data of the vehicle corresponding to Q6, calculates the mileage according to the motor speed and motor torque, and determines the initial calculation mileage by considering the result and the dashboard mileage;

S7. Calculate the mileage change caused by the abnormality according to the abnormal situation in the running of the vehicle;

S8. Exclude the mileage change caused by the abnormality in the initial calculation mileage, and complete the final mileage check.

In a preferred embodiment of the present invention, the process of obtaining the vehicle data set Q2 in step S2 specifically includes:

Determine the ratio i _n between the historical frame number n of each vehicle in the _reverse state and the historical frame number n from Q1:

For driving school vehicles, on the premise of large sample data, the ratio of the number of frames in the forward state to the number of frames in the reverse state in the data is significantly different from the ratio of other normal driving vehicles, so it is possible to filter out i _n >40% The data corresponding to the vehicle constitutes Q2.

The process of obtaining vehicle data sets Q3 and Q4 in step S3 specifically includes:

a) According to the current information i of the battery pack, filter out the data set Q3 that is in the normal operating state, that is, the discharging state; wherein, i<0 is the charging state, i>0 is the discharging state, and i=0 is the static state;

b) Calculate the proportion of the data frame v _{predetermined value} less than 30km/h in the historical dashboard speed information of the vehicle corresponding to Q3 to the total frame number v _{total frame number} :

The data corresponding to the vehicles with i _V >70% is composed into Q4.

The process of obtaining the vehicle data set Q5 in step S4 specifically includes:

For driving school vehicles, due to long-term low-speed driving and frequent start and stop, the torque should be greater than the average value of other vehicles. Therefore, for each vehicle corresponding to the data set Q4, the calculated motor torque T in a day is not equal to The average value in the data of 0 is T _average , and the data corresponding to the vehicles with T _average > T1 is selected to form Q5;

Among them, T1 can be calculated according to the motor torque of a sample vehicle in normal operation at n different periods t ₁ , t ₂ ,..., t _n :

Further, the process of obtaining the vehicle data set Q6 in step S5 specifically includes:

Calculate the center latitude and longitude of the activity range of each vehicle corresponding to Q5 within a certain period of time:

Among them, n represents the number of time periods in this period, t ₁ , t ₂ ,..., t _n represent different time periods;

的车辆所对应数据组成Q6。For each vehicle, take the center longitude and latitude as the center of the circle and set a radius of 20 meters, and the positioning information in the historical data frame within a day is in the frame number X _{within the range. The frame number} ratio in the range is greater than the predetermined value, that is,

The data corresponding to the vehicle of the group constitutes Q6.

The process of determining the initial calculation mileage in step S6 specifically includes:

The database platform collects the tire radius r and transmission ratio i _g of the vehicle corresponding to Q6, and calculates the vehicle speed u _a in combination with the motor speed ω:

Calculate the mileage of the vehicle per second:

Sum to get the total computed mileage:

S _{calculated mileage} = S _{calculated mileage (t1)} + S _{calculated mileage (t2)} + ... + S _{calculated mileage (tn)}

Take the smaller value between the total calculated mileage and the dashboard mileage as the initial calculation mileage.

Further, the process of calculating the mileage change caused by the abnormality in step S7 specifically includes:

a) Calculate the mileage change caused by the abnormal speed:

Determine whether the motor speed in the data has a constant value for 20 consecutive frames (20 seconds) and is not equal to 0, and record the number of continuous frames: from t ₁ to t _x frame, calculate from t ₁ to t _x frame The vehicle’s dashboard mileage change information during the counting period, assuming that the odometer reading in frame t ₁ is S _ω1 , and the odometer reading in frame t _x is S _ωx , then the mileage change between two frames is recorded as S _{speed abnormality 1} :

_{Abnormal S speed 1} ＝S _ωx -S _ω1

Then the total speed abnormal mileage including n times of abnormal speed is recorded as S _{speed abnormality} :

_{Abnormal S speed = abnormal} S _{speed 1} + _{abnormal S speed 2} +...+ _{abnormal S speed n} ;

b) Calculate the mileage change caused by the abnormal current:

Determine whether the current data in the data has 20 consecutive frames (20 seconds) of constant value and not equal to 0, and record the number of continuous frames: from t ₁ to t _x frame, calculate from t ₁ to t _x frame The vehicle’s dashboard mileage change information during the counting period, assuming that the odometer reading in frame t ₁ is S _I1 , and the odometer reading in frame t _x is S _Ix , then the mileage change between two frames is recorded as S _{current abnormality 1} :

S _{current abnormality 1} ＝S _lx -S _I1

Then the total speed abnormal mileage including n times of current abnormality is recorded as S _{current abnormality} :

S _{current abnormality} =S _{current abnormality 1} +S _{current abnormality 2} +...+S _{current abnormality n} ;

c) Calculate the mileage jump:

Determine whether the mileage change in the data exceeds 0.01km between two consecutive frames, and record the mileage between two frames of data as W _n (n=1, 2, 3, ...), then the total mileage jump is recorded as W:

W=W ₁ +W ₂ + . . . +W _n .

Further, the mileage changes caused by excluding abnormalities in step S7 are effectively calculated as follows:

S _{effective calculation mileage} = S _{initial calculation mileage} - S _{speed abnormality} - S _{current abnormality} - W

The smaller of the effective calculated mileage S _{effective calculated mileage} and S _{instrument panel mileage} is taken as the final mileage verification result.

Among them, for the instrument panel mileage information, the mileage information corresponding to the first frame can be recorded as S _t1 , the mileage information corresponding to the nth frame is S _tn , and the instrument panel mileage of the vehicle is calculated as S _{Instrument panel mileage} =S _tn -S _t1 .

It should be understood that the sequence number of each step in the embodiment of the present invention does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention .

Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications and substitutions can be made to these embodiments without departing from the principle and spirit of the present invention. and modifications, the scope of the invention is defined by the appended claims and their equivalents.

Claims (7)

1. The data-driven method for screening and checking the mileage of the vehicle in the specific driving scene is characterized in that: the method is specifically based on the following steps:

s1, a database platform collects the following new energy vehicle data including new energy vehicles in a special driving scene: the method comprises the following steps of (1) forming a vehicle data set Q1 by motor rotating speed, motor torque, instrument panel speed, battery pack current and longitude and latitude position information of a vehicle;

s2, initially selecting data in the Q1, and screening out data corresponding to vehicles with the ratio between the number of the historical frames in the reverse state and the total number of the historical frames larger than a preset value to obtain a vehicle data set Q2;

s3, screening a vehicle data set Q3 in a running state from the Q2 according to the current of the battery pack, and screening to obtain a vehicle data set Q4 based on the speed of an instrument panel;

s4, selecting a vehicle data set Q5 under a special driving scene from the Q4 based on the motor torque;

s5, screening a vehicle data set Q6 from the Q5 based on the vehicle activity range;

s6, collecting relevant data of the vehicle corresponding to the Q6 by the database platform, calculating mileage according to the motor rotating speed and the motor torque, and determining initial accounting mileage by considering the calculated mileage and instrument panel mileage;

s7, calculating mileage change caused by abnormality according to the abnormal condition of the running vehicle;

and S8, removing the mileage change caused by the abnormality from the initial accounting mileage, and finishing the final mileage check.

2. The method of claim 1, wherein: the process of obtaining the vehicle data sets Q3 and Q4 in step S3 specifically includes:

a) Screening out a data set Q3 in a normal operation state, namely a discharge state according to the current information i of the battery pack; wherein i <0 is a charging state, i >0 is a discharging state, and i =0 is a rest state;

b) Calculating a data frame V smaller than a preset value in historical instrument panel speed information of the vehicle corresponding to the Q3 _{Predetermined value} Total number of frames V _{Total frame number} The proportion of (A):

will i _V The data corresponding to vehicles greater than the predetermined value constitutes Q4.

3. The method of claim 1, wherein: the process of obtaining the vehicle data set Q5 in step S4 specifically includes:

for each vehicle corresponding to the data set Q4, an average value T in the data that the motor torque T is not equal to 0 in one day is calculated _Average Screening of T _Average >The data corresponding to the vehicle of T1 form Q5;

wherein T1 can be used for n different time periods T according to a sample vehicle which normally runs ₁ ，t ₂ ，…，t _n The motor torque of (a) is calculated to obtain:

4. the method of claim 1, wherein: the process of obtaining the vehicle data set Q6 in step S5 specifically includes:

and (3) calculating the central longitude and latitude of each vehicle corresponding to the Q5 in the moving range in a certain period:

where n represents the number of time periods in the period, t ₁ ，t ₂ ，…，t _n Representing different time periods;

setting a range with a preset radius for each vehicle by taking the longitude and latitude of the center as the center of the circle, and setting the number X of frames with the position information in one day in the range in the historical data frames _{Number of frames in range} In proportion of

Data composition corresponding to vehicle larger than preset valueQ6。

5. The method of claim 1, wherein: the process of determining the initial mileage accounting in step S6 specifically includes:

the database platform collects the tire radius r and the transmission ratio i of the vehicle corresponding to the Q6 _g Calculating the speed u of the vehicle by combining the motor speed omega _a ：

Calculating the mileage per second of the vehicle:

summing to obtain the total calculated mileage:

S _{calculating mileage} ＝S _{Calculating mileage (t 1)} +S _{Calculating mileage (t 2)} +…+S _{Calculating mileage (tn)}

And taking the smaller value of the total calculated mileage and the instrument panel mileage as the initial calculated mileage.

6. The method of claim 1, wherein: the process of calculating the mileage change caused by the abnormality in step S7 specifically includes:

a) Calculating the total mileage change caused by the abnormal rotating speed:

judging whether the rotating speed of the motor in the data has the condition that the continuous multi-frame numerical value is not changed and is not equal to 0, and recording the continuous frame number: from t th ₁ To t _x Frame, computing from t ₁ To t _x Meter Panel mileage Change information of a vehicle during frame number, let t ₁ Frame odometer reading is S _ω1 ，t _x Frame odometer reading is S _ωx If the mileage change between two frames is recorded as S _{Abnormal rotational speed 1} ：

S _{Abnormal rotational speed 1} ＝S _ωx -S _ω1

The total abnormal revolution speed mileage including n times of abnormal revolution speeds is recorded as S _{Abnormal rotational speed} ：

S _{Abnormal rotational speed} ＝S _{Abnormal rotational speed 1} +S _{Abnormal rotational speed 2} +…+S _{Abnormal speed n} ；

b) Calculating the total mileage change caused by the current abnormality:

judging whether the current data in the data has the condition that the continuous multi-frame numerical value is unchanged and is not equal to 0, and recording the continuous frame number: from t ₁ To t th _x Frame, computing from t ₁ To t _x Meter Panel mileage Change information of a vehicle during frame number, let t ₁ Frame odometer reading S _I1 ，t _x Frame odometer reading is S _Ix And the change of the mileage between two frames is recorded as S _{Current anomaly 1} ：

S _{Current anomaly 1} ＝S _Ix -S _I1

The total abnormal rotating speed mileage including n times of current abnormality is marked as S _{Abnormality of current} ：

S _{Abnormality of current} ＝S _{Current anomaly 1} +S _{Current anomaly 2} +…+S _{Abnormal current n} ；

c) Calculating total mileage jump:

judging whether the range change in the data exceeds the preset distance between 2 continuous frames, and recording the range between two frames of data as W _n N =1, 2, 3, \8230, then the total mileage jump is marked as W:

W＝W ₁ +W ₂ +…+W _n 。

7. the method of claim 6, wherein: and step 7, removing the mileage change caused by the abnormality to obtain an effective calculated mileage:

S _{effectively calculating mileage} ＝S _{Initial accounted mileage} -S _{Abnormality of rotational speed} -S _{Abnormality of current} -W

Effective calculated mileage S _{Effectively calculating mileage} And S _{Mileage of instrument panel} The smaller the two is as the final mileage check result.

Images