CN113505955A - User driving behavior scoring method based on TSP system - Google Patents
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Abstract
The invention relates to the technical field of driving behavior analysis and discloses a user driving behavior scoring method based on a TSP system, which comprises the following steps: s1: acquiring information, namely acquiring driving data, and acquiring the driving data of the vehicle through a vehicle-mounted TSP system; s2: the method comprises the steps of stroke segmentation, wherein the stroke is segmented through acquired time data and vehicle engine data; s3: acquiring basic data of a journey, processing the journey data, and acquiring related data of behavior scoring; s4: and clustering and classifying the travel data. The message data collected by the TSP system are used for comprehensively evaluating the driving behaviors and habits of the user, realizing the risk control of the driving of the driver, enhancing the accuracy and stability of the subsequent service of the travel analysis, improving the data processing effect and enhancing the awareness of the driver on avoiding the driving risk by dividing the data, thereby reducing the traffic accident occurrence rate and improving the traffic safety level.
Description
Technical Field
The invention relates to the technical field of driving behavior analysis, in particular to a user driving behavior scoring method based on a TSP system.
Background
Along with the development of the Internet of things and the automobile industry, the number of automobiles in the Internet of vehicles is greatly increased, the quality of life of people is greatly improved, the daily life of people is facilitated, and the supervision of the vehicles and the guarantee of the safety of vehicle driving are still one of the problems which are extremely wanted to be solved in the modern society.
Through retrieval, the patent with the authorization publication number of CN104699955B discloses a driving behavior scoring method and a system thereof, wherein the driving behavior scoring method comprises the following steps: the VCU acquires driving behavior related information of a driver, and uploads the driving behavior related information and a vehicle identifier to a server through a wireless communication network; the server compares the driving behavior related information with a data corpus stored by the server, and scores the driving behavior of the driver in real time according to a comparison result; however, the above patents cannot comprehensively and effectively assess the driving style and habit of the driver, and also influence the accuracy of the subsequent business analysis of the driving behavior, and cannot meet the requirements of people.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a user driving behavior scoring method based on a TSP system, and solves the problems that the driving style and habit of a driver cannot be comprehensively and effectively evaluated by the conventional driving behavior analysis method, and the accuracy of subsequent service of driving behavior analysis is influenced.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme:
a user driving behavior scoring method based on a TSP system comprises the following steps:
s1: acquiring information, namely acquiring driving data, and acquiring the driving data of the vehicle through a vehicle-mounted TSP system;
s2: the method comprises the steps of stroke segmentation, wherein the stroke is segmented through acquired time data and vehicle engine data;
s3: acquiring basic data of a journey, processing the journey data, and acquiring related data of behavior scoring;
s4: clustering and classifying the travel data, and segmenting the vehicle driving data;
s5: and scoring the travel data, and judging the total score of the driving behaviors of the vehicle.
2. The method as recited in claim 1, wherein said method comprises a step of using a TSP system to score driving behavior of a user.
As a further aspect of the present invention, the acquiring of the vehicle driving data by the TSP system in S1 includes: the method comprises the steps of acquiring the direction and the angle of a vehicle running, the transverse acceleration, the longitudinal acceleration, the speed, the vehicle identification code, the engine ignition state, the speed of a code table, a timestamp and the mileage by using a terminal during the running of the vehicle.
Further, in S2, the real-time data is stored through the message queue, and the data is divided according to the vehicle identification code, the engine ignition state, the code table speed and the time stamp.
On the basis of the foregoing solution, in S3, data in the trip is filtered when the trip data is processed, so as to screen out error data and abnormal data, and retain intermediate valid data, and in addition, a required data field needs to be analyzed from the data packet of the trip, and part of the data is processed, including the driving duration, the highest speed, the driving distance, and each acceleration index of the current trip, and a corresponding "three-urgency" index.
Further, the step S4 of segmenting the vehicle driving data includes clustering the data processed in the previous step, that is, calculating a distance formula between each data feature, dividing the points with a short distance into the same class of points, adjusting the inter-class distance according to the iteration class center with a continuous number of turns, classifying the data according to the attributes of the data, and finally labeling the points in the same class with the same label to complete the classification of the sample points, and subsequently placing the sample points into the history sample when the number of new sample points reaches a certain number, and performing the overall clustering.
On the basis of the foregoing solution, when the total score of the driving behavior of the vehicle is judged in S5, after the marking is completed according to the above historical data, the historical data is placed in a decision tree model for training, the accuracy of the training model is tested, when new user travel data follows, the model is directly input and a category label is marked, a corresponding relationship between the category label and the score is made, assuming that the category grades are A, B, C, D, E, F from high to low, the score of a is 100, the score of B, C, D, E is between a1 and a2, and the score of F is between a1 and a 3.
In a further aspect of the present invention, the step of determining the score of the driving vehicle in S5 further includes:
s501: judging whether the classification result of the vehicle driving data is A or not, if so, scoring the vehicle driving behavior as 100, and if not, executing a step S502;
s502: judging whether the classification result of the vehicle driving data is B, C, D, E, if so, judging that the vehicle driving behavior score is in a (a 1, a 2) section, and if not, executing a step S503;
s503: and judging whether the classification result of the vehicle driving data is F or not, if so, judging that the vehicle driving behavior is in an interval (a 2, a 3).
(III) advantageous effects
Compared with the prior art, the invention provides a user driving behavior scoring method based on a TSP system, which has the following beneficial effects:
1. the invention uses the message data collected by the TSP system to comprehensively evaluate the driving behaviors and habits of the user and realizes the risk control of driving of the driver.
2. According to the invention, through accurate evaluation of the driving behavior of the user, the risk identification of the subsequent ubi service is further facilitated, the accuracy and stability of the subsequent service of the travel analysis are enhanced, and the data processing effect can be improved by dividing the data.
3. According to the invention, the travel is segmented through the acquired time data and the vehicle engine data, the travel data is processed, the related data of behavior scoring is acquired, the vehicle driving data is segmented, the total scoring of the vehicle driving behaviors is carried out, and the awareness of avoiding driving risks of a driver is enhanced, so that the traffic accident occurrence rate is reduced, and the traffic safety level is improved.
Drawings
Fig. 1 is a schematic flow chart illustrating a driving behavior scoring method for a user based on a TSP system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a vehicle-mounted data trip division process of a user driving behavior scoring method based on a TSP system according to the present invention;
fig. 3 is a schematic flow chart illustrating a cleaning and reprocessing total score of vehicle driving data according to a user driving behavior scoring method based on a TSP system;
fig. 4 is a schematic flow chart of a vehicle trip segment of a user driving behavior scoring method based on a TSP system according to the present invention;
fig. 5 is a schematic flow chart of vehicle trip segment scoring of the user driving behavior scoring method based on the TSP system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1 to 5, a user driving behavior scoring method based on a TSP system includes the steps of:
s1: acquiring information, acquiring driving data, acquiring the driving data of a vehicle through a vehicle-mounted TSP system, and comprehensively evaluating the driving behaviors and habits of a user through message data collected by the TSP system to realize risk control on driving of the driver;
s2: the method comprises the steps of stroke segmentation, wherein the stroke is segmented through acquired time data and vehicle engine data;
s3: acquiring basic data of a journey, processing the journey data, and acquiring related data of behavior scoring;
s4: clustering and classifying the travel data, and segmenting the vehicle driving data;
s5: the travel data are scored, the total score of the driving behaviors of the vehicles is judged, the risks of subsequent ubi services are further identified through accurate evaluation of the driving behaviors of the users, and accuracy and stability of the subsequent services of travel analysis are enhanced.
The TSP system in S1 of the invention acquires the vehicle running data and acquires the data, which means that: acquiring the direction and the angle of rotation, the transverse acceleration, the longitudinal acceleration, the speed, the vehicle identification code, the engine ignition state, the code table speed, the timestamp and the mileage of the running vehicle, acquiring the direction and the transverse acceleration, the longitudinal acceleration, the speed, the vehicle identification code, the engine ignition state, the code table speed, the mileage by a terminal, storing the real-time data by a message queue in S2, and according to the vehicle identification code, the engine ignition state, the code table speed and the timestamp, the data processing effect can be improved by dividing the data into the data, the data in the process of processing the travel data is filtered in S3 to screen out error data and abnormal data, the middle effective data is reserved, in addition, the required data field needs to be analyzed from the data message of the travel, and processing partial data including driving duration, maximum speed, driving distance, various acceleration indexes and corresponding 'three-urgency' indexes of the current trip.
Particularly, the step S4 is to segment the vehicle driving data, which includes clustering the data processed in the previous step, that is, calculating the distance formula between the data features, dividing the points with a short distance into the same class of points, adjusting the inter-class distance according to the iterative class center with a continuous number of turns, classifying the data according to the attributes of the data, marking the points in the same class with the same label to complete the classification of the sample points, putting the sample points into the historical sample when the new sample points reach a certain number, performing the integral clustering to complete the revision of the historical label and improve the label calibration accuracy, and when the total evaluation of the vehicle driving behavior is judged in step S5, putting the historical data into the decision tree model to train and test the accuracy of the training model, and when the travel data of the new user is subsequently provided, the method comprises the steps of directly inputting a model, marking category labels, making a corresponding relation between the category labels and scores, assuming that the category grades are A, B, C, D, E, F from high to low in sequence, the score of A is 100, the score belongs to B, C, D, E, the score is between a1 and a2, the score is F, the score is between a1 and a3, segmenting a journey through obtained time data and vehicle engine data, processing journey data, obtaining relevant data of behavior scores, segmenting vehicle driving data, scoring the total driving behaviors of a vehicle, and enhancing the driver' S awareness of avoiding driving risks, so that the traffic accident occurrence rate is reduced, the traffic safety grade is improved, and the step of judging the vehicle driving score in S5 further comprises the following steps:
s501: judging whether the classification result of the vehicle driving data is A or not, if so, scoring the vehicle driving behavior as 100, and if not, executing a step S502;
s502: judging whether the classification result of the vehicle driving data is B, C, D, E, if so, judging that the vehicle driving behavior score is in a (a 1, a 2) section, and if not, executing a step S503;
s503: and judging whether the classification result of the vehicle driving data is F or not, if so, judging that the vehicle driving behavior is in an interval (a 2, a 3).
In the description herein, it is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
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 (7)
1. A user driving behavior scoring method based on a TSP system is characterized by comprising the following steps:
s1: acquiring information, namely acquiring driving data, and acquiring the driving data of the vehicle through a vehicle-mounted TSP system;
s2: the method comprises the steps of stroke segmentation, wherein the stroke is segmented through acquired time data and vehicle engine data;
s3: acquiring basic data of a journey, processing the journey data, and acquiring related data of behavior scoring;
s4: clustering and classifying the travel data, and segmenting the vehicle driving data;
s5: and scoring the travel data, and judging the total score of the driving behaviors of the vehicle.
2. The method as claimed in claim 1, wherein the step of collecting the driving data of the TSP system in S1 is that: the method comprises the steps of acquiring the direction and the angle of a vehicle running, the transverse acceleration, the longitudinal acceleration, the speed, the vehicle identification code, the engine ignition state, the speed of a code table, a timestamp and the mileage by using a terminal during the running of the vehicle.
3. The method as claimed in claim 1, wherein the step S2 stores the real-time data through message queue, and divides the data into trip according to vehicle identification code, engine ignition status, code table speed and time stamp.
4. The method as claimed in claim 1, wherein the step S3 is implemented by filtering data in the trip during processing the trip data, screening out error data and abnormal data, retaining valid data in the middle, parsing required data fields from data messages in the trip, and processing part of data, including driving duration, maximum speed, driving distance, acceleration indexes, and "three-urgency" indexes corresponding to the driving duration, the maximum speed, the driving distance, and the acceleration indexes of the current trip.
5. The user driving behavior scoring method based on the TSP system of claim 1, wherein the step S4 of segmenting the vehicle driving data includes clustering the data processed in the previous step, i.e., calculating a distance formula between each data feature, dividing the points with a short distance into the same class of points, adjusting the inter-class distance according to the iterative class center with the number of wheels, classifying the data according to the data' S own attributes, marking the same label on the points in the same class, completing the classification of the sample points, and subsequently placing the sample points into the history sample when the number of new sample points reaches a certain number, thereby performing the overall clustering.
6. The method as claimed in claim 1, wherein the total score of the driving behavior of the vehicle is judged in S5, the marking is completed according to the history data, the history data is placed in a decision tree model for training, the accuracy of the training model is tested, when new trip data of the user is available, the model is directly input to tag categories, and the corresponding relationship between the category tags and the score is established, assuming that the category grade is A, B, C, D, E, F from high to low, the score is 100, the score is B, C, D, E, the score is between a1 and a2, and the score is F, the score is between a1 and a 3.
7. The method as recited in claim 6, wherein the step of determining the driving score of the vehicle at S5 further comprises:
s501: judging whether the classification result of the vehicle driving data is A or not, if so, scoring the vehicle driving behavior as 100, and if not, executing a step S502;
s502: judging whether the classification result of the vehicle driving data is B, C, D, E, if so, judging that the vehicle driving behavior score is in a (a 1, a 2) section, and if not, executing a step S503;
s503: and judging whether the classification result of the vehicle driving data is F or not, if so, judging that the vehicle driving behavior is in an interval (a 2, a 3).
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| CN114973670A (en) * | 2022-05-23 | 2022-08-30 | 斑马网络技术有限公司 | Method, device and equipment for determining travel |
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