CN111144706A - Method for grading and classifying network taxi appointment drivers - Google Patents
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Abstract
The invention discloses a method for grading and classifying network car booking drivers, which comprises the following steps: the data acquisition and processing module is used for acquiring facial images and body state parameters of a network car booking driver, acquiring car condition information of the network car booking and preprocessing the acquired data; the construction module is used for constructing a network car booking driver evaluation index system; the evaluation module is used for establishing a network car booking driver scoring strategy and scoring the network car booking driver; the dividing module is used for dividing the category of the network car booking driver; and the user selection module is used for containing all the network car booking driver information in the area in a database, matching a certain number of network car booking drivers according to the travel demands input by the users in the terminal equipment, sending the scores and the categories of the network car booking drivers to the ordering users, and making final selection by the ordering users. The invention reduces the adverse effects of the driver on the road traffic safety caused by driving control, road rage emotion and physical conditions as much as possible, ensures the selection right of passengers and has important theoretical significance and application value.
Description
Technical Field
The invention belongs to the field of traffic safety driving, and particularly relates to a method for grading and classifying network car booking drivers.
Background
In recent years, network reservation vehicles are gradually known to the public, and reserving a vehicle for traveling through a network platform is one of the main traveling modes of people. The internet and the product sharing economy have certain problems while bringing convenience to people to go out. First, the passenger is not familiar with the net car appointment driver, and is not aware of the physical condition, driving behavior tendency, presence or absence of road rage, and the like. And secondly, after the passengers get on or off the order on the reservation platform, the networked car reservation driver takes the order, and the passengers have no selective right and can only passively accept the order.
Since the net appointment vehicle is an industry for providing transportation services, the driving safety directly determines the quality of the transportation services, and is also directly related to the personal safety of drivers and passengers. Therefore, the driving behavior safety score and the driver classification of the network car booking driver are of great significance for improving the traffic safety condition. Meanwhile, as a service industry, passengers are placed at the head and are given the option, thereby contributing to the stable and lasting development of the industry.
Disclosure of Invention
In order to solve the problems, the invention discloses a method for grading and classifying network car booking drivers, which reduces adverse effects of driving control, road rage emotion and physical conditions of the drivers on road traffic safety as much as possible.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a network appointment vehicle driver scoring and classifying method comprises the following steps:
s1, acquiring facial images of a network car booking driver, acquiring body state parameters of the network car booking driver, acquiring car condition information of the network car booking, and preprocessing acquired data;
s2, constructing a network car booking driver evaluation index system;
s3, establishing a scoring strategy of the network car booking driver, and scoring the network car booking driver according to the scoring strategy;
s4, dividing the category of the network car booking drivers;
and S5, including all the vehicle booking driver information in the area in a database, matching a certain number (less than or equal to 5) of vehicle booking drivers according to the travel demands input by the users on the terminal equipment, sending the scores and the categories of the vehicle booking drivers to the ordering users, and making final selections by the ordering users.
Further, in step S1, facial images of the net appointment driver are collected, including an eyebrow picking offset degree, a blinking frequency, and a mouth opening degree; the driver facial image information is acquired through an infrared high-speed camera; collecting body state parameters of a network taxi appointment driver, including pulse, heartbeat, blood oxygen content and body temperature; the body state parameters are collected through an intelligent bracelet worn on the wrist of a taxi appointment driver; the vehicle condition information of the networked appointment vehicle is collected and comprises the number of times of acceleration in unit time, the number of times of steering wheel angles in unit time and the number of times of operating an accelerator pedal and a brake pedal by a driver. The vehicle condition information is collected through the vehicle condition information monitoring module.
Further, in step S2, a net appointment driver evaluation index system is constructed, which includes: the target layer, i.e. the net car reservation driver score mu, and the criterion layer comprises the criterion muiThe evaluation factor layer includes criterion layers muiCorresponding evaluation factor
Wherein, i ═ {1, 2., ki}，kiIs a criterion of muiThe number of corresponding evaluation factors; the criteria
Further, in the step S3, a criterion layer weight vector a and an evaluation factor layer weight vector are calculated by a analytic hierarchy process
The criterion layer weight vector a comprises weights of all criteria, and the evaluation factor layer weight vector
Including the criterion muiThe weight of each corresponding evaluation factor; wherein, i ═ {1, 2., ki}，kiIs a criterion of muiThe number of corresponding evaluation factors; the criterion muiCorresponding weight vector
Scoring the network car booking drivers according to a scoring strategy and classifying the network car booking driver categories, wherein the scoring formula is
Wherein, i ═ {1, 2., ki}，kiIs a criterion of muiThe number of corresponding evaluation factors.
Further, in step S4, dividing the net car booking driver score into L grades, and assigning values to the net car booking driver score grades; the network appointment vehicle driver categories are divided into: aggressive, informal, and conservative.
Further, in step S5, the information of all car booking drivers in the area is contained in the database, a certain number of car booking drivers are matched according to the travel demand input by the user at the terminal device, and the scores and the categories of the car booking drivers are sent to the ordering user for final selection.
The method comprises the steps of collecting facial images of a network car booking driver, collecting body state parameters of the network car booking driver, collecting vehicle condition information of the network car booking, preprocessing the collected data, constructing a network car booking driver evaluation index system, establishing a network car booking driver grading strategy, and grading the security of the network car booking driver to obtain a network car booking driver security overall score. And dividing the net appointment drivers into an aggressive type, an informal type and a conservative type according to the overall scores. When a user gets an order from the online car booking device, the system automatically sends the safety total score of the online car booking driver within 5 matched with the requirement of the passenger to the passenger who gets the order, and the passenger can monitor the driving operation, road irritability emotion and physical condition of the online car booking driver in real time within 1 minute before finally making a decision to obtain the safety total score of the online car booking driver, so that the passenger can conveniently know the current driving state of the online car booking driver, and the safety total score is prevented from happening.
The invention has the beneficial effects that:
the invention can effectively extract the emotion evaluation index of the driver from the video image, the physical condition and the vehicle operation parameter, and quantize the emotion and the driving operation change state of the driver in real time.
The invention provides the method for effectively grading the driving safety of the network car booking driver based on the continuous emotional state time sequence data of the network car booking driver, the physical condition attribute of the driver and the vehicle operation parameter, dividing the category of the network car booking driver, giving the right of choice to passengers to make final decision, and being beneficial to reducing the road traffic safety problem of the network car booking.
Drawings
Fig. 1 is a general flowchart of a net appointment driver scoring and classification method.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
The invention discloses a method for grading and classifying network car booking drivers, which comprises the following five steps:
s1, acquiring facial images of a network car booking driver, acquiring body state parameters of the network car booking driver, acquiring car condition information of the network car booking, and preprocessing acquired data;
in a specific scheme, the current fatigue state of the net appointment driver can be known according to the eyebrow raising offset degree, the blink frequency and the mouth opening degree of the net appointment driver. Because the net car booking driver is in a high tension state all the time when driving on the road for a long time, and the problems of physical fatigue and chronic diseases can exist, the current physical condition of the net car booking driver can be known according to the pulse, the heartbeat, the blood oxygen content and the body temperature of the net car booking driver. The road traffic environment is complex and changeable, the processing mode of the net car booking driver to the emergent road condition is vital, the driving behavior of the net car booking driver needs to be monitored, and the current net car booking vehicle condition information can be reflected through the number of times of acceleration of the net car booking driver in unit time, the number of times of steering wheel turning in unit time and the number of times of the driver operating an acceleration pedal and a brake pedal.
S2, constructing a network car booking driver evaluation index system;
in a specific scheme, the network appointment vehicle driver evaluation index system comprises the following steps: the target layer, i.e. the net car reservation driver score mu, and the criterion layer comprises the criterion muiThe evaluation factor layer includes criterion layers muiCorresponding evaluation factor
Wherein, i ═ {1, 2., ki}，kiIs a criterion of muiThe number of corresponding evaluation factors; the criteria
S3, establishing a scoring strategy of the network car booking driver, and scoring the network car booking driver according to the scoring strategy;
calculating a criterion layer weight vector a and an evaluation factor layer weight vector by an analytic hierarchy process
The criterion layer weight vector a comprises weights of all criteria, and the evaluation factor layer weight vector
Including the criterion muiThe weight of each corresponding evaluation factor; wherein, i ═ {1, 2., ki}，kiIs a criterion of muiThe number of corresponding evaluation factors; the criterion muiCorresponding weight vector
Obtaining a network appointment vehicle according to the grading formulaThe rating of the driver is calculated by the formula
Wherein, i ═ {1, 2., ki}，kiIs a criterion of muiThe number of corresponding evaluation factors.
In the specific scheme, the scoring strategy has different settings according to different network car booking drivers, when the eyebrow picking range of the driver is larger, the fatigue of the driver is indicated, when the blinking frequency of the driver is lower, the fatigue of the driver is indicated, when the mouth opening degree of the driver is larger, the fatigue of the driver is indicated, and when the fatigue of the driver is higher, the safety score of the driver is lower; the safety score of the driver is lower when the pulse, the heartbeat, the blood oxygen content and the body temperature of the driver deviate from the corresponding health intervals. When the driver operates the net car, the more the number of times of acceleration per unit time, the more the number of times of steering wheel angle per unit time, and the more the number of times of operating the accelerator pedal and the brake pedal per unit time, the lower the safety score of the driver.
S4, dividing the category of the network car booking drivers;
in the specific scheme, the scoring of the network car booking drivers is divided into L grades, and the scoring grades of the network car booking drivers are assigned; the network appointment vehicle driver categories are divided into: aggressive, informal, and conservative.
And S5, including all the vehicle booking driver information in the area in a database, matching a certain number (less than or equal to 5) of vehicle booking drivers according to the travel demands input by the users on the terminal equipment, sending the scores and the categories of the vehicle booking drivers to the ordering users, and making final selections by the ordering users.
In a specific scheme, after a passenger orders at a network car booking reservation platform, the system automatically sends the safety overall score of the network car booking driver matched with the requirement of the passenger to the passenger who orders, and the passenger can monitor the network car booking driver in real time before finally making a decision to obtain the safety overall score of the network car booking driver, so that the passenger can conveniently know the current driving state of the network car booking driver, and the passenger can conveniently make a correct choice.
The method comprises the steps of collecting facial images of a network car booking driver, collecting body state parameters of the network car booking driver, collecting vehicle condition information of the network car booking, preprocessing the collected data, constructing a network car booking driver evaluation index system, establishing a network car booking driver grading strategy, and grading the security of the network car booking driver to obtain a network car booking driver security overall score. And dividing the net appointment drivers into an aggressive type, an informal type and a conservative type according to the overall scores. When the passenger orders in the online car booking platform, the system automatically sends the safety total score of the online car booking driver within 5 matched with the requirement of the passenger to the passenger who orders, and the passenger can monitor the driving operation, road irritability emotion and physical condition of the online car booking driver in real time within 1 minute before finally making a decision to obtain the safety total score of the online car booking driver, so that the passenger can conveniently know the current driving state of the online car booking driver, and the safety total score is prevented from happening.
The foregoing detailed description describes embodiments of the invention in order to illustrate the practice thereof. Other variations and modifications of the invention will be apparent to those skilled in the art, and it is intended that any variations, modifications or alterations which fall within the spirit and scope of the invention as disclosed and the present principles fall within the scope of the appended claims.
Claims (6)
1. A method for scoring and classifying network car booking drivers is characterized by comprising the following steps:
s1, acquiring facial images of a network car booking driver, acquiring body state parameters of the network car booking driver, acquiring car condition information of the network car booking, and preprocessing acquired data;
s2, constructing a network car booking driver evaluation index system;
s3, establishing a scoring strategy of the network car booking driver, and scoring the network car booking driver according to the scoring strategy;
s4, dividing the category of the network car booking drivers;
and S5, including all the network car booking driver information in the area in a database, matching the network car booking drivers with the number less than or equal to 5 according to the travel demands input by the users in the terminal equipment, sending the scores and the categories of the network car booking drivers to the ordering users, and making final selections by the ordering users.
2. The network car booking driver scoring and classifying method according to claim 1, wherein: in the step S1, facial images of the net appointment driver are collected, including eyebrow raising offset, blink frequency, and mouth openness; the driver facial image information is acquired through an infrared high-speed camera; collecting body state parameters of a network taxi appointment driver, including pulse, heartbeat, blood oxygen content and body temperature; the body state parameters are collected through an intelligent bracelet worn on the wrist of a taxi appointment driver; collecting vehicle condition information of the networked taxi appointment, wherein the vehicle condition information comprises the number of times of acceleration in unit time, the number of times of steering wheel angles in unit time and the number of times of operating an accelerator pedal and a brake pedal by a driver; the vehicle condition information is collected through the vehicle condition information monitoring module.
3. The network car booking driver scoring and classifying method according to claim 1, wherein: in step S2, a network appointment driver evaluation index system is constructed, which includes: a target layer, a criterion layer and an evaluation factor layer; the target layer, i.e. the net car reservation driver score mu, and the criterion layer comprises the criterion muiThe evaluation factor layer includes criterion layers muiCorresponding evaluation factor
Wherein, i ═ {1, 2., ki}，kiIs a criterion of muiThe number of corresponding evaluation factors; the criteria
4. The network car booking driver scoring and classifying method according to claim 1, wherein: in the step S3, a criterion layer weight vector a and an evaluation factor layer weight vector are calculated by an analytic hierarchy process
The criterion layer weight vector a comprises weights of all criteria, and the evaluation factor layer weight vector
Including the criterion muiThe weight of each corresponding evaluation factor; wherein, i ═ {1, 2., ki}，kiIs a criterion of muiThe number of corresponding evaluation factors; the criterion muiCorresponding weight vector
Obtaining the score of the net car booking driver according to the score formula, wherein the score formula is
Wherein, i ═ {1, 2., ki}，kiIs a criterion of muiThe number of corresponding evaluation factors.
5. The network car booking driver scoring and classifying method according to claim 1, wherein: in the step S4, dividing the net car booking driver score into L grades, and assigning values to the net car booking driver score grades; the network appointment vehicle driver categories are divided into: aggressive, informal, and conservative.
6. The network car booking driver scoring and classifying method according to claim 1, wherein: in step S5, the information of all networked car appointment drivers in the area is contained in the database, 2 to 5 networked car appointment drivers are matched according to the travel demand input by the user on the terminal device, and the scores and the categories of the networked car appointment drivers are sent to the ordering user for final selection.
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| CN111605556A (en) * | 2020-06-05 | 2020-09-01 | 吉林大学 | Road rage prevention recognition and control system |
| CN113222379A (en) * | 2021-04-29 | 2021-08-06 | 广州宸祺出行科技有限公司 | Method and system for matching network taxi appointment orders based on vehicle conditions |
| CN113415286A (en) * | 2021-07-14 | 2021-09-21 | 重庆金康赛力斯新能源汽车设计院有限公司 | Road rage detection method and equipment |
| CN113222379B (en) * | 2021-04-29 | 2024-05-31 | 广州宸祺出行科技有限公司 | Method and system for matching network vehicle order based on vehicle condition |
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