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CN108389072B - Method for managing fees in p2p vehicle renting process - Google Patents

Method for managing fees in p2p vehicle renting process Download PDF

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CN108389072B
CN108389072B CN201810062340.5A CN201810062340A CN108389072B CN 108389072 B CN108389072 B CN 108389072B CN 201810062340 A CN201810062340 A CN 201810062340A CN 108389072 B CN108389072 B CN 108389072B
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CN108389072A (en
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钟迪
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Shenzhen Zhizun Automobile Service Co ltd
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Shenzhen Zhizun Automobile Service Co Ltd
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    • G06Q30/0283—Price estimation or determination
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0645—Rental transactions; Leasing transactions
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
    • G07F17/0042—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for hiring of objects
    • G07F17/0057—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for hiring of objects for the hiring or rent of vehicles, e.g. cars, bicycles or wheelchairs

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Abstract

A method for managing fees during renting a p2p vehicle, comprising the steps of: s01, generating an owner personalized vehicle driving evaluation model; s02, evaluating the driving information of the vehicle borrower to generate an evaluation result; and S03, configuring a fee settlement coefficient, and generating the vehicle rental fee according to the evaluation result and the fee settlement coefficient. Compared with the prior art, the invention can realize that: according to the information of the actual condition, road condition and the like of the vehicle owner, particularly the individualized driving style of the vehicle owner, whether the driving style of a vehicle user meets the expectation of the vehicle owner or not is accurately judged, and a basis is provided for the vehicle owner to continuously consider lending to the same person or not. And moreover, the fee settlement coefficient can be configured, and the vehicle rental fee is generated according to the evaluation result and the fee settlement coefficient, so that the charging is more personalized.

Description

Method for managing fees in p2p vehicle renting process
Technical Field
The invention relates to the technical field of vehicle safety, in particular to a cost management method in a p2p vehicle renting process.
Background
In the prior art, although a large-scale sharing scene such as sharing an automobile exists, the automobile may be borrowed among friends or acquaintances.
In the scene of vehicle borrowing, the vehicle owner usually has great attention to the behavior of other people using the vehicle, and then the vehicle owner cannot master the information of other people using the vehicle, so whether the vehicle is carefully cared in the borrowing process or whether serious illegal behaviors such as drunk driving and drunk driving exist or not only influences the borrowing intention of the vehicle owner and even possibly brings serious consequences of undertaking legal responsibility.
In addition, during the process of lending a vehicle to an individual, each individual is often charged at random, which is not conducive to the promotion of a true p2p vehicle rental.
Disclosure of Invention
In view of the above, the present invention provides a method for managing fees during a p2p vehicle rental process.
A method for managing fees during renting a p2p vehicle, comprising the steps of:
s01, generating an owner personalized vehicle driving evaluation model;
s02, evaluating the driving information of the vehicle borrower to generate an evaluation result;
and S03, configuring a fee settlement coefficient, and generating the vehicle rental fee according to the evaluation result and the fee settlement coefficient.
In the method for managing the fee in the p2p vehicle renting process, the method comprises the following steps:
the step S01 includes:
s1, acquiring configuration information and vehicle condition information of the vehicle, high-precision map information and road condition information in the vehicle movement range;
s2, acquiring high-precision map information and road condition information of the vehicle owner driving within a past preset time range, and corresponding historical driving information; and generating an owner personalized vehicle driving evaluation model according to the historical driving information of the vehicle owner, high-precision map information and road condition information corresponding to the historical driving information.
In the method for managing fees during renting p2p vehicles according to the present invention, the step S02 specifically includes:
and S3, obtaining the driving information of the vehicle borrower, high-precision map information and road condition information corresponding to the driving information of the vehicle borrower, evaluating the driving information of the vehicle borrower through the vehicle owner personalized vehicle driving evaluation model, and generating an evaluation result.
In a p2p vehicle rental process fee management method according to the present invention,
the step S1 includes:
acquiring configuration information of a vehicle, wherein the configuration information comprises variable box information, engine displacement information and oil product information; acquiring vehicle condition information, wherein the vehicle condition information comprises no-load information, service life information and maintenance information;
obtaining high-precision map information in a vehicle movement range, wherein the high-precision map information comprises: road type information, lane information, road marking information, intersection information, gradient information, lane curvature information;
and acquiring road condition information, wherein the road condition information comprises the corresponding relation between each position in the high-precision map information, corresponding traffic flow, pedestrians, non-motor vehicles and time.
In a p2p vehicle rental process fee management method according to the present invention,
the step S2 includes:
s21, acquiring high-precision map information and road condition information of the vehicle owner running within a past preset time range;
s22, acquiring high-precision map information of driving within a past preset time range of a vehicle owner and historical driving information of the vehicle owner corresponding to road condition information, wherein the historical driving information comprises instantaneous oil consumption, accelerator opening, vehicle speed information, accelerator switching speed, braking frequency and braking switching speed;
s23, performing typing processing on high-precision map information and historical driving information of the vehicle owner corresponding to the road condition information, wherein the high-precision map information and the historical driving information are driving by the vehicle owner in the past preset time range, and acquiring the classification type of the high-precision map information and the road condition information corresponding to the driving habit of the vehicle owner;
s24, performing feature calibration on the high-precision map within the vehicle movement range according to the classification type result;
s25, acquiring preference setting information of the owner on the historical driving information of the owner;
and S26, generating an owner personalized vehicle driving evaluation model according to the high-precision map, the preference setting information, the historical driving information of the vehicle owner and the road condition information after the characteristic calibration.
In a p2p vehicle rental process fee management method according to the present invention,
the step S1 is preceded by:
s11, configuring a vehicle management pool in a vehicle ECU, wherein the vehicle management pool comprises legal user type information and temporary user management information;
s12, acquiring initial configuration information of a vehicle owner to a vehicle borrower, and writing the initial configuration information of the vehicle borrower into temporary user management information corresponding to a vehicle management pool in a vehicle ECU; the temporary user management information comprises authentication information and service life information of a vehicle borrower;
correspondingly, the step S3 is followed by:
pre-updating the temporary user management information according to the evaluation result, sending the pre-updating result to the vehicle owner, acquiring evaluation information of the vehicle owner, and formally updating the temporary user management information according to the adjustment information when the vehicle owner adjusts the pre-updating result; and when the vehicle owner does not adjust the pre-updating result, formally updating the temporary user management information according to the pre-updating information.
The beneficial technical effects are as follows: compared with the prior art, the invention can realize that: according to the information of the actual condition, road condition and the like of the vehicle owner, particularly the individualized driving style of the vehicle owner, whether the driving style of a vehicle user meets the expectation of the vehicle owner or not is accurately judged, and a basis is provided for the vehicle owner to continuously consider lending to the same person or not. The system can also encourage the lender to develop good driving habits when the lender agrees with the borrower by setting a set of complete personalized charging system, and is beneficial to energy conservation and emission reduction.
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FIG. 1 is a flow chart of a method for managing fees during a p2p vehicle rental process according to an embodiment of the present invention.
Detailed Description
In some existing technical schemes for sharing vehicles, a large amount of driving preference information of each person is mostly counted, and driving behavior portrayal of users is mainly obtained aiming at oil consumption and the like. However, the above solutions of the prior art have some drawbacks: 1. the scheme is usually to analyze and count a large number of users to set different charging or other behaviors for each person for a sharing platform company, and the problem to be solved is not to evaluate the vehicle borrowing; 2. in the prior art, statistics is usually carried out simply through some oil consumption and road conditions, for example, the congestion degree is judged through map navigation, so that the driving behavior of a user is not accurately drawn. 3. In the case of borrowing automobiles among friends or acquaintances, personal preferences of users are often mixed, and each person is different in the points of attention, for example, some people consider that rapid acceleration or rapid braking cannot be endured, but some people may pay more attention to oil consumption, which are concerns affecting whether an owner borrows the automobiles for others. 4. In the process of lending a vehicle to an individual, each individual is often charged at random, which is not conducive to the promotion of a true p2p vehicle rental. In view of the above problems, embodiments of the present invention provide the following solutions.
As shown in fig. 1, in an embodiment of the present invention, a method for managing fees during renting a p2p vehicle includes the steps of:
s01, generating an owner personalized vehicle driving evaluation model;
s02, evaluating the driving information of the vehicle borrower to generate an evaluation result;
and S03, configuring a fee settlement coefficient, and generating the vehicle rental fee according to the evaluation result and the fee settlement coefficient.
Optionally, the step S01 includes:
and S1, acquiring configuration information and vehicle condition information of the vehicle, high-precision map information and road condition information in the vehicle movement range.
Optionally, the step S1 includes:
acquiring configuration information of a vehicle, wherein the configuration information comprises variable box information, engine displacement information and oil product information; acquiring vehicle condition information, wherein the vehicle condition information comprises no-load information, service life information and maintenance information; in this step, the acquired configuration information of the vehicle is an important factor related to the use of the vehicle, for example, when the vehicle is empty load information and load information, the braking distance or the fuel consumption may change. In the step, different influences of the vehicle in the use process are judged by selecting no-load information, use duration information and maintenance information. In addition, what is more critical is that the configuration information of the vehicle acquired here is the configuration information of the vehicle owner, so that the information is more targeted for the subsequent judgment of the driving behavior of the vehicle borrower, which is also different from the place where the driving performance of a certain vehicle type is judged by selecting a large amount of vehicle information statistics in other technical solutions.
Obtaining high-precision map information in a vehicle movement range, wherein the high-precision map information comprises: road type information, lane information, road marking information, intersection information, gradient information, lane curvature information. The embodiment of the invention is different from other technical schemes in that the high-precision map information in the vehicle activity range is obtained, and the embodiment of the invention is very important because the prior art usually only focuses on the road type at most, such as high speed, national road or urban road, and the actual driving is more complicated, such as more or less lanes and the frequency of red and green color setting (too short red and green colors may need frequent acceleration, deceleration and parking); the lanes are straight or have a certain curvature of curvature; the road marking information comprises pedestrian crosswalks, intersection guiding information, turning directions and the like; these are critical to the performance of a driver driving a vehicle, i.e. one of the reasons why the performance of different drivers in the same vehicle is completely different.
And acquiring road condition information, wherein the road condition information comprises the corresponding relation between each position in the high-precision map information, corresponding traffic flow, pedestrians, non-motor vehicles and time.
In this step, the acquisition of the road condition information is set, which is different from other existing technical schemes in that: each position in the high-precision map information corresponds to the corresponding relation between each traffic flow, pedestrian and non-motor vehicle and time, so that (a) each position in the high-precision map information (the high-precision map information comprises road type information, lane information, road marking information, intersection information, gradient information and lane curvature information) exists; (b) each position corresponds to information of each traffic flow, pedestrian and non-motor vehicle, because the traffic flow, the number of the pedestrian and the non-motor vehicle and the information influence the driving behavior. The information acquisition can be achieved in various ways, such as through a navigation system, a monitoring center camera, or a camera of the vehicle, and the like, and the improvement point of the embodiment of the invention is not focused on this point; in the prior art, each position information in a high-precision map is not combined with each traffic flow, pedestrians and non-motor vehicles, so that the performance of judging the driving of a driver on a vehicle is not accurate enough; (c) in addition, by introducing a correspondence relationship with time, actual integrated travel information of each intersection or each position in a high-precision map can be formed. In addition, the high-precision map in the embodiment of the invention refers to an electronic map with the precision of 1 meter or less.
S2, acquiring high-precision map information and road condition information of the vehicle owner driving within a past preset time range, and corresponding historical driving information; and generating an owner personalized vehicle driving evaluation model according to the historical driving information of the vehicle owner, high-precision map information and road condition information corresponding to the historical driving information.
In the step red of the embodiment of the invention, because the high-precision map information, the road condition information and the corresponding historical driving information which are driven by the vehicle owner within the past preset time range are obtained, the generated vehicle driving evaluation model is provided with the vehicle owner personalized information, is a vehicle owner personalized vehicle driving evaluation model, and is different from the prior art that the average oil consumption of a certain vehicle type is obtained through mass data.
Optionally, the step S2 includes:
s21, acquiring high-precision map information and road condition information of the vehicle owner running within a past preset time range;
s22, acquiring high-precision map information of driving within a past preset time range of a vehicle owner and historical driving information of the vehicle owner corresponding to road condition information, wherein the historical driving information comprises instantaneous oil consumption, accelerator opening, vehicle speed information, accelerator switching speed, braking frequency and braking switching speed;
by implementing the step, the driving habits of the vehicle owner under the high-precision map information and road condition information are obtained, and the vehicle owner personalized vehicle driving evaluation model can be generated only if the driving habits of the vehicle owner are obtained.
S23, performing typing processing on the high-precision map information and the historical driving information of the vehicle owner corresponding to the road condition information, wherein the high-precision map information and the historical driving information are driving by the vehicle owner in the past preset time range, and acquiring the classification type of the high-precision map information and the road condition information corresponding to the driving habit of the vehicle owner.
Since the driving position and road condition of the vehicle owner cannot cover all areas that the vehicle borrower may go to, the problem to be solved in this step is how to predict all areas that the vehicle borrower may go to through the limited driving position and road condition of the vehicle owner. Therefore, the step carries out typing processing through the corresponding historical driving information of the vehicle owner to obtain the classification type of the high-precision map information and the road condition information corresponding to the driving habit of the vehicle owner. One embodiment is that the existing high-precision map information comprises: the road type information, the lane information, the road marking information, the intersection information, the gradient information and the lane curvature information, and the road condition information comprises the corresponding relation between each position in the high-precision map information, corresponding traffic flow, pedestrians, non-motor vehicles and time, so that the result of the type processing can be matched with the high-precision map information and the road condition information, and the driving habits of car owners can be predicted according to the high-precision map information and the road condition information in other areas.
In a preferred embodiment, high-precision map information and historical driving information of a vehicle owner corresponding to road condition information, which are driven by the vehicle owner within a past preset time range, can be selected as input of a neural network algorithm, the influence of each element in the high-precision map information and the road condition information on the historical driving information of the vehicle owner is obtained through the neural network algorithm, and each element in the high-precision map information and the road condition information is classified according to the influence to realize typing processing.
S24, performing feature calibration on the high-precision map within the vehicle movement range according to the classification type result;
s25, acquiring preference setting information of the owner on the historical driving information of the owner;
by implementing the steps, the individual driving habits of the vehicle owners are considered when the vehicle owner personalized vehicle driving evaluation model is generated, and the preference of the user can be adapted. As an optional scheme, preference setting information of the vehicle owner on historical driving information of the vehicle owner can be embodied by obtaining weight setting of classification type results of high-precision map information and road condition information corresponding to driving habits of the vehicle owner.
More preferably, the influence indexes of the vehicle owner on the vehicle wear and the fuel consumption are generated and displayed to the user through the acquired historical driving information including the instantaneous fuel consumption, the accelerator opening, the vehicle speed information, the accelerator switching speed, the brake frequency and the brake switching speed, and then the preference setting information of the vehicle owner on the historical driving information of the vehicle owner is acquired. By implementing the preferred embodiment, the purpose is to enable the user to know various indexes of the influence of the driving behavior concerned by the user on the vehicle, so that the user can perform preference setting again, and the setting result can reflect the preference of the user.
And S26, generating an owner personalized vehicle driving evaluation model according to the high-precision map, the preference setting information, the historical driving information of the vehicle owner, the road condition information and the vehicle configuration information after the characteristic calibration.
Preferably, the embodiment of the present invention provides a preferred implementation: the driving evaluation model of the vehicle owner personalized vehicle is as follows:
Figure BDA0001555694970000071
wherein E0Scoring a driving of the user; e1Scoring the driving of the vehicle borrower, wherein the higher the score is, the user is not advised to loan out; the lower the score, the higher the likelihood of lending.
Figure BDA0001555694970000072
Wherein f (m) is a high-precision map function, and f (r) is a road condition evaluation function; f (h) is a driving assessment function; and sigma is an objective evaluation coefficient and is a vehicle compensation coefficient, and the two coefficients are obtained through data statistical analysis.
Figure BDA0001555694970000073
Wherein p is slope data; r is lane curvature data; l is a road marking influence coefficient; w is a road type influence coefficient; m lane width influence coefficients; and x is an intersection influence coefficient.Wherein the values of l, w, m and x are obtained by data statistical analysis.
Figure BDA0001555694970000074
Wherein c is a traffic flow influence coefficient; p is a radical ofrThe pedestrian flow influence coefficient; b is a non-motor vehicle flow influence coefficient; mu is a time influence coefficient; wherein c and prThe values of b and mu can be obtained by data statistical analysis.
Figure BDA0001555694970000075
Wherein v is vehicle speed information; c. C1Instantaneous fuel consumption data; f is the braking frequency; v. of1Switching speed for the throttle; v. of2Switching speed for braking; k is the accelerator opening; lambda [ alpha ]1Is a braking frequency preference coefficient; lambda [ alpha ]2A preference factor is switched for the throttle; lambda [ alpha ]3A preference coefficient for brake switching; lambda [ alpha ]4Is the accelerator opening preference coefficient; lambda [ alpha ]1、λ2、λ3、λ4The vehicle owner can set the vehicle according to personal preference.
It can be understood that the vehicle owner personalized vehicle driving evaluation model can be in various forms, the preferred embodiment only provides one implementation mode, individual preference can be accurately matched, information such as actual vehicle conditions and road conditions is combined, parameter value settings are personalized, and scientification and personalization are met.
Optionally, the step S02 includes:
and S3, obtaining the driving information of the vehicle borrower, high-precision map information and road condition information corresponding to the driving information of the vehicle borrower, evaluating the driving information of the vehicle borrower through the vehicle owner personalized vehicle driving evaluation model, and generating an evaluation result to feed back to the vehicle owner.
Preferably, the first and second electrodes are formed of a metal,
the step S1 is preceded by:
s11, configuring a vehicle management pool in a vehicle ECU, wherein the vehicle management pool comprises legal user type information and temporary user management information;
s12, acquiring initial configuration information of a vehicle owner to a vehicle borrower, and writing the initial configuration information of the vehicle borrower into temporary user management information corresponding to a vehicle management pool in a vehicle ECU; the temporary user management information comprises authentication information and service life information of a vehicle borrower;
the authentication information of the vehicle borrower may generate information for controlling the vehicle by acquiring identification information of the vehicle borrower.
Correspondingly, the step S3 is followed by:
pre-updating the temporary user management information according to the evaluation result, sending the pre-updating result to the vehicle owner, acquiring evaluation information of the vehicle owner, and formally updating the temporary user management information according to the adjustment information when the vehicle owner adjusts the pre-updating result; and when the vehicle owner does not adjust the pre-updating result, formally updating the temporary user management information according to the pre-updating information.
Alternatively, the fee settlement factor in the step S03 is inversely proportional to the age of the car.
The beneficial technical effects are as follows: compared with the prior art, the invention can realize that: according to the information of the actual condition, road condition and the like of the vehicle owner, particularly the individualized driving style of the vehicle owner, whether the driving style of a vehicle user meets the expectation of the vehicle owner or not is accurately judged, and a basis is provided for the vehicle owner to continuously consider lending to the same person or not. The system can also encourage the lender to develop good driving habits when the lender agrees with the borrower by setting a set of complete personalized charging system, and is beneficial to energy conservation and emission reduction.
It is understood that various other changes and modifications may be made by those skilled in the art based on the technical idea of the present invention, and all such changes and modifications should fall within the protective scope of the claims of the present invention.

Claims (2)

1. A method for managing fees during renting p2p vehicles, comprising the steps of:
s01, generating an owner personalized vehicle driving evaluation model;
s02, evaluating the driving information of the vehicle borrower to generate an evaluation result;
s03, configuring a fee settlement coefficient, and generating a vehicle rental fee according to the evaluation result and the fee settlement coefficient;
the step S01 includes:
s1, acquiring configuration information and vehicle condition information of the vehicle, high-precision map information and road condition information in the vehicle movement range;
s2, acquiring high-precision map information and road condition information of the vehicle owner driving within a past preset time range, and corresponding historical driving information; generating an owner personalized vehicle driving evaluation model according to the historical driving information of the vehicle owner, high-precision map information and road condition information corresponding to the historical driving information;
the step S02 specifically includes:
s3, obtaining driving information of the vehicle borrower, high-precision map information and road condition information corresponding to the driving information of the vehicle borrower, evaluating the driving information of the vehicle borrower through the vehicle owner personalized vehicle driving evaluation model, and generating an evaluation result;
the step S1 includes:
acquiring configuration information of a vehicle, wherein the configuration information comprises variable box information, engine displacement information and oil product information; acquiring vehicle condition information, wherein the vehicle condition information comprises no-load information, service life information and maintenance information;
obtaining high-precision map information in a vehicle movement range, wherein the high-precision map information comprises: road type information, lane information, road marking information, intersection information, gradient information, lane curvature information;
acquiring road condition information, wherein the road condition information comprises corresponding relations between each position in the high-precision map information and each traffic flow, pedestrian and non-motor vehicle and time respectively;
the step S2 includes:
s21, acquiring high-precision map information and road condition information of the vehicle owner running within a past preset time range;
s22, acquiring high-precision map information of driving within a past preset time range of a vehicle owner and historical driving information of the vehicle owner corresponding to road condition information, wherein the historical driving information comprises instantaneous oil consumption, accelerator opening, vehicle speed information, accelerator switching speed, braking frequency and braking switching speed;
s23, performing typing processing on high-precision map information and historical driving information of the vehicle owner corresponding to the road condition information, wherein the high-precision map information and the historical driving information are driving by the vehicle owner in the past preset time range, and acquiring the classification type of the high-precision map information and the road condition information corresponding to the driving habit of the vehicle owner;
s24, performing feature calibration on the high-precision map within the vehicle movement range according to the classification type result;
s25, acquiring preference setting information of the owner on the historical driving information of the owner;
s26, generating an owner personalized vehicle driving evaluation model according to the high-precision map, the preference setting information, the historical driving information of the vehicle owner and the road condition information after the characteristic calibration;
the driving evaluation model of the vehicle owner personalized vehicle is as follows:
Figure FDA0002658344580000021
wherein E0Scoring a driving of the user; e1Scoring the driving of the vehicle borrower, wherein the higher the score is, the user is not advised to loan out; the lower the score, the higher the likelihood of lending;
Figure FDA0002658344580000022
wherein f (m) is a high-precision map function, and f (r) is a road condition evaluation function; f (h) is a driving assessment function; sigma is an objective evaluation coefficient and is a vehicle compensation coefficient, and the two coefficients are obtained through data statistical analysis;
Figure FDA0002658344580000023
wherein p is slope data; r is lane curvature data; l is a road marking influence coefficient; w is a road type influence coefficient; m lane width influence coefficients; x is the crossing influence coefficient; wherein the values of l, w, m and x are obtained by data statistical analysis;
Figure FDA0002658344580000024
wherein c is a traffic flow influence coefficient; p is a radical ofrThe pedestrian flow influence coefficient; b is a non-motor vehicle flow influence coefficient; mu is a time influence coefficient; wherein c and prB, mu values can be obtained through data statistical analysis;
Figure FDA0002658344580000025
wherein v is vehicle speed information; c. C1Instantaneous fuel consumption data; f is the braking frequency; v. of1Switching speed for the throttle; v. of2Switching speed for braking; k is the accelerator opening; lambda [ alpha ]1Is a braking frequency preference coefficient; lambda [ alpha ]2A preference factor is switched for the throttle; lambda [ alpha ]3A preference coefficient for brake switching; lambda [ alpha ]4Is the accelerator opening preference coefficient; lambda [ alpha ]1、λ2、λ3、λ4The vehicle owner can set the vehicle according to personal preference.
2. The method for managing fees during the rental of p2p vehicles according to claim 1,
the step S1 is preceded by:
s11, configuring a vehicle management pool in a vehicle ECU, wherein the vehicle management pool comprises legal user type information and temporary user management information;
s12, acquiring initial configuration information of a vehicle owner to a vehicle borrower, and writing the initial configuration information of the vehicle borrower into temporary user management information corresponding to a vehicle management pool in a vehicle ECU; the temporary user management information comprises authentication information and service life information of a vehicle borrower;
correspondingly, the step S3 is followed by:
pre-updating the temporary user management information according to the evaluation result, sending the pre-updating result to the vehicle owner, acquiring evaluation information of the vehicle owner, and formally updating the temporary user management information according to the adjustment information when the vehicle owner adjusts the pre-updating result; and when the vehicle owner does not adjust the pre-updating result, formally updating the temporary user management information according to the pre-updating information.
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