CN111598347B - Ultra-long travel segmentation optimization method for road transport vehicle - Google Patents
Ultra-long travel segmentation optimization method for road transport vehicle Download PDFInfo
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- 238000007781 pre-processing Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000013450 outlier detection Methods 0.000 claims description 3
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Abstract
The invention discloses an ultra-long travel segmentation optimization method for a road transport vehicle, which comprises the steps of obtaining track data of satellite positioning of the vehicle; detecting, filtering and preprocessing abnormal points of data; performing travel segmentation on the track data; ultra-long travel screening; and (5) cutting in an ultra-long stroke. According to the invention, the track data acquired by the terminal are used, the data are subjected to stroke segmentation after being cleaned and filtered, the segmented strokes are subjected to ultra-long stroke screening, and finally the ultra-long strokes are segmented again. The invention can be applied to various applications related to driving behaviors, and the ultra-long travel is screened and then re-segmented, so that the accuracy of travel information can be ensured by reasonable travel segmentation, and the driving behaviors and driving risks of users reflected in the travel information can be more real and reliable.
Description
Technical Field
The invention relates to a vehicle ultra-long travel segmentation optimization method, in particular to a road transport vehicle ultra-long travel segmentation optimization method, and belongs to the technical field of vehicle travel segmentation optimization.
Background
With the general installation of the road transport vehicle satellite positioning system terminal, the collection and storage capacity of road vehicle track data is obviously improved. The vehicle track journey processing is a necessary means for analyzing the driving behavior of the user and evaluating the driving risk part. The information obtained by analyzing the vehicle track journey data can provide reliable basis for the insurance company to draw user figures and for government traffic supervision. With the development of big data, the application scenes of the vehicle journey information are more and more with the promotion of artificial intelligence and 5G technology.
Because the vehicle track is generated by the position point information obtained by the satellite positioning system, the satellite positioning device can be influenced by factors such as self precision and environment when acquiring data, and certain difference exists between the acquired data and actual data. The existing vehicle track data stroke segmentation method is suitable for segmentation of a plurality of strokes, but the problem of overlength of the strokes after partial segmentation is found. The ultra-long travel can cause travel mileage, travel time and other travel information of the travel to be abnormal, and the abnormal phenomenon can cause the analysis result to be deviated, so that misleading is caused to an information user. According to the invention, the track data of the road transportation vehicle is obtained, the abnormal data is filtered, preprocessed and subjected to travel segmentation, the ultra-long travel is screened according to the start-stop time of the travel, and the ultra-long travel is subjected to travel segmentation.
Disclosure of Invention
The invention aims to solve the problems and provide an ultra-long travel cutting optimization method for a road transport vehicle.
The invention realizes the above purpose through the following technical scheme: an ultra-long travel cut optimization method for a road transport vehicle comprises the following steps:
(1) Acquiring track data of satellite positioning of the vehicle;
(2) Detecting, filtering and preprocessing abnormal points of data;
(3) Performing travel segmentation on the track data;
(4) Screening the ultra-long travel;
(5) And carrying out stroke segmentation on the ultra-long stroke.
As a further scheme of the invention: the acquiring the track data of the satellite positioning of the vehicle comprises the following steps:
satellite positioning latitude, satellite positioning longitude, satellite positioning time, satellite positioning direction, satellite positioning speed, satellite positioning accuracy, and satellite number.
As a further scheme of the invention: the data outlier detection filtering and preprocessing comprises the following steps:
the track data of the satellite positioning of the vehicle is filtered, and data points with abnormal longitude and latitude, abnormal satellite positioning time, abnormal satellite positioning direction, abnormal satellite positioning speed and poor satellite positioning precision are removed.
As a further scheme of the invention: the performing travel segmentation on the track data comprises the following steps:
and obtaining the cut road transport vehicle journey data according to the road transport vehicle journey cutting rule through satellite positioning track data. From the above calculation, 2 information indexes of the trip start-stop time can be obtained.
As a further scheme of the invention: the ultra-long travel screening includes:
and acquiring the start-stop time information of the journey, and calculating the driving time of the journey. Screening the ultra-long travel according to travel duration of the travel, and judging that the travel of the vehicle is not the ultra-long travel if the travel duration of the travel is less than a certain value; if the travel time length is greater than a certain value, judging that the travel time is an ultra-long travel, and carrying out the next stage segmentation. And executing the operation, and screening out the ultra-long travel distance.
As a further scheme of the invention: the ultra-long stroke segmentation includes:
the method comprises the steps of obtaining ultra-long travel track data, sequencing the gps point data from low to high according to the starting time, filtering and de-duplicating the sequenced gps point data, performing travel segmentation on the de-duplicated gps point data, and finally screening the segmented travel to obtain an effective travel.
The beneficial effects of the invention are as follows: the ultra-long travel segmentation optimization method for the road transport vehicle is reasonable in design, and filters and preprocesses the collected satellite positioning data, so that the influence of data quality on travel segmentation is reduced. Further, the vehicle trajectory data is subjected to journey slicing. Further, the invention screens ultra-long strokes. The invention cuts the ultra-long travel.
Drawings
FIG. 1 is a flow chart of an ultra-long travel cut optimization method for a road transportation vehicle according to an embodiment of the invention;
FIG. 2 is a flow chart of a method of row Cheng Qiefen according to an embodiment of the present invention;
fig. 3 is a flowchart of an ultra-long stroke segmentation optimization method according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 to 3, an ultra-long travel cutting optimization method for a road transport vehicle includes the following steps:
step A, satellite positioning track data are obtained:
in this example, a satellite positioning module (including a driving vehicle interior, a vehicle-mounted intelligent device, a smart phone, etc.) is started, and a data connection is established with the computing terminal through the satellite positioning module. The track data such as satellite positioning latitude, satellite positioning longitude, satellite positioning time, satellite positioning direction, satellite positioning speed, satellite positioning precision, satellite number and the like can be obtained;
step B, detecting, filtering and preprocessing abnormal points of data:
in this example, outlier detection and preprocessing are performed on the acquired data. And eliminating data points with abnormal longitude and latitude, abnormal satellite positioning time, abnormal satellite positioning direction, abnormal satellite positioning speed and poor satellite positioning precision. Because abnormal data points can cause deviation in feature calculation, accuracy is lost in judging illegal plugging conditions of the terminal;
step C, the travel segmentation of the track data:
by analyzing the travel of the road transport vehicle, the phenomenon that the part of travel is overlength is found, and the phenomenon can cause deviation on the travel division result. The track data of the road transport vehicle is large in data amount and may be out of order in arrangement order, so that the track data needs to be reordered in time. The quality of the track data of the road transport vehicle has an important influence on the journey data, so quality filtering of the track data is required. The track data of the road transport vehicle may have a repetitive phenomenon, so that the track data needs to be de-duplicated according to longitude and latitude information. The mode performance of adjacent time intervals of track data of the road transport vehicle is inconsistent, and the time interval of the cutting travel is required to be set according to the duty ratio of the mode; the specific steps described above refer to fig. 2.
Step D, ultra-long travel screening:
the travel time length of the travel can be calculated through the travel start-stop time information, and if the travel time length does not exceed a certain value, the travel is considered to be a non-ultra-long travel; if the travel time exceeds a certain value, the travel is considered to be an ultra-long travel. Acquiring the travel track data and carrying out next stage segmentation;
step E, ultra-long travel segmentation:
the ultra-long stroke cutting can be optimized through the ultra-long stroke re-cutting. The track data of the road transport vehicle may be similar, so the track data needs to be removed according to distance intervals. The mode performance of adjacent time intervals of track data of the road transport vehicle is not uniform, and the time interval of the slicing stroke needs to be set according to the duty ratio of the mode. The journey of the road transport vehicle needs to contain a certain number of track points, and the journey needs to be rejected according to the data of the track points in the journey. The travel information of the road transport vehicle can reflect the effectiveness of the travel, and the travel needs to be removed according to the longitude and latitude information of the travel; the specific steps described above refer to fig. 3.
As shown in fig. 2, in this embodiment, the method includes the following steps:
1) Ordering from low to high by st;
2) Calculating the number of all gps points of the vehicle track data, if the number is less than 2, not judging the vehicle, and if the number is more than or equal to 2, executing the next step;
3) Deleting gps points with acc (satellite positioning accuracy) of 0;
4) Deleting gps points with status (satellite positioning state) of 0;
5) Deleting gps points with sat less than or equal to 0;
6) If the ith gps point sat is earlier than the i-1 th gps point sat, deleting the ith gps point until the sequence of all gps points sat in the vehicle track data is increased;
7) The gps point with locationStatus (resolved value according to 808 protocol) of 0 (0 indicates unreliable satellite positioning) is pruned;
8) If the longitude and latitude values of the ith gps point are the same as the longitude and latitude of the ith-1 gps point, deleting the ith gps point until the longitude or latitude of two adjacent gps points are different;
9) Calculating the time interval between adjacent gps points of the vehicle track data, counting the duty ratio of the mode of the time interval, if the duty ratio of the mode is larger than x%, performing travel segmentation in t1min, and if the duty ratio of the mode is smaller than or equal to x%, performing travel segmentation in t2 min.
As shown in fig. 3, in this embodiment, the method includes the following steps:
1) Calculating the linear distance between adjacent gps points, and deleting the ith gps point if the linear distance between the ith gps point and the i-1 th gps point is smaller than the gps point of s1 m;
2) Calculating the time interval between adjacent gps points of the vehicle track data, and counting the duty ratio of the mode of the time interval, if the duty ratio of the mode is larger than x%, performing travel segmentation in t1min, and if the duty ratio of the mode is smaller than or equal to x%, performing travel segmentation in t2 min;
3) Deleting strokes with the number of gps points less than or equal to n;
4) And calculating the linear distance of the starting point and the ending point of the travel, and deleting the travel if the distance is smaller than s2 m.
Working principle: when the method for optimizing the ultra-long travel segmentation of the road transport vehicle is used, satellite positioning track data are firstly obtained, then filtering and preprocessing are detected according to abnormal points, travel segmentation is carried out on the track data, then the ultra-long travel is screened, and finally the ultra-long travel is segmented. According to the method, after related data are obtained, cleaning and preprocessing are carried out, travel segmentation is carried out according to track data. On the premise of cutting the ultra-long travel, the method is a reliable basis for analyzing the travel characteristics of the vehicle.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (1)
1. The ultra-long travel cutting optimization method for the road transport vehicle is characterized by comprising the following steps of: comprising the following steps:
(1) Acquiring track data of satellite positioning of the vehicle;
(2) Detecting, filtering and preprocessing abnormal points of data;
(3) Performing travel segmentation on the track data;
(4) Screening the ultra-long travel;
(5) Performing stroke segmentation on the ultra-long stroke;
the acquiring the track data of the satellite positioning of the vehicle comprises the following steps:
satellite positioning latitude, satellite positioning longitude, satellite positioning time, satellite positioning direction, satellite positioning speed, satellite positioning accuracy and satellite number;
the data outlier detection filtering and preprocessing comprises the following steps:
filtering the track data of the satellite positioning of the vehicle, and removing data points with abnormal longitude and latitude, abnormal satellite positioning time, abnormal satellite positioning direction, abnormal satellite positioning speed and poor satellite positioning precision;
the performing travel segmentation on the track data comprises the following steps:
the satellite positioning track data is used for acquiring the cut road transport vehicle journey data according to the road transport vehicle journey cutting rule; according to the calculation, 2 information indexes of the start and stop time of the journey can be obtained;
the ultra-long travel screening includes:
and acquiring the start-stop time information of the journey, and calculating the driving time of the journey. Screening the ultra-long travel according to travel duration of the travel, and judging that the travel of the vehicle is not the ultra-long travel if the travel duration of the travel is less than a certain value; if the travel time length is greater than a certain value, judging that the travel time length of the vehicle is an ultra-long travel, performing next stage segmentation, executing the operation, and screening out the ultra-long travel time length;
the ultra-long stroke segmentation includes:
the method comprises the steps of obtaining ultra-long travel track data, sequencing the gps point data from low to high according to the starting time, filtering and de-duplicating the sequenced gps point data, performing travel segmentation on the de-duplicated gps point data, and finally screening the segmented travel to obtain an effective travel.
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| CN112153573B (en) * | 2020-09-28 | 2023-04-07 | 平安国际融资租赁有限公司 | Segmentation method and device based on position track, computer equipment and storage medium |
| CN112380311B (en) * | 2020-11-27 | 2024-04-02 | 上海评驾科技有限公司 | POI (Point of interest) identification method based on travel track |
| CN114973670B (en) * | 2022-05-23 | 2024-04-09 | 斑马网络技术有限公司 | Stroke determination method, device and equipment |
| CN115080683B (en) * | 2022-08-24 | 2022-11-25 | 天津所托瑞安汽车科技有限公司 | Vehicle journey processing method, device and storage medium |
| CN116486639B (en) * | 2023-06-14 | 2023-09-29 | 眉山环天智慧科技有限公司 | Vehicle supervision method based on remote sensing and Beidou satellite data analysis |
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