CN115170248B - Big data-based analysis and management method for car rental credit information - Google Patents
Big data-based analysis and management method for car rental credit information Download PDFInfo
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Abstract
The invention discloses a car rental credit information analysis and management method based on big data, which comprises the following steps: the method is used for analyzing whether the user pays fees on time on the platform, behaviors of the user on the rental car, traffic violations and damage degree of the rental car, analyzing dimensionality is comprehensive, further solving the problem that the analysis of the credit of the rental car of the user is inaccurate, and further improving the safety of the user on keeping the rental car during the rental period.
Description
Technical Field
The invention relates to the technical field of car leasing, in particular to a car leasing credit information analysis and management method based on big data.
Background
With the rapid development of economy, the development of shared economy is also more and more rapid, and common shared economy projects are as follows: in many economic projects, the shared automobile has great development potential, is helpful for relieving traffic jams and road abrasion, reduces air pollution, and when the automobile currently owned by people cannot meet the current demand, the automobile needs to be leased, a certain deposit needs to be paid when the automobile is leased, and the credit of the user has great influence on the payment deposit of the leased automobile, so that the credit of the leased automobile of the user needs to be analyzed.
The existing analysis of the rented car credits of the user is mostly carried out according to whether the user pays the fees on time on the platform or not, the influence of the behavior of the user on the rented car credits of the user, traffic violation conditions and car damage degree on the rented car credits of the user is ignored, the analysis dimension is single, and the analysis of the rented car credits of the user is inaccurate, so that on one hand, a reliable reference value cannot be provided for subsequent deposit evaluation, the credit coefficient of the subsequent user is not matched with the deposit to be paid, on the other hand, the behavior of the user during renting cannot be well restrained, and further the safety of the user on keeping the rented car during renting is reduced, and the maintenance cost of the rented car platform on the rented car is increased.
Disclosure of Invention
In order to overcome the defects in the background art, the embodiment of the invention provides a big data-based analysis and management method for car rental credit information, which can effectively solve the problems related to the background art.
The aim of the invention can be achieved by the following technical scheme:
A car rental credit information analysis and management method based on big data comprises the following steps:
Step 1, extracting historical rental car orders of a target user: extracting historical rental car orders of a target user in a detection period from a car rental platform, and numbering each historical rental car order as 1, 2.
Step 2, extracting lease parameters in historical lease car orders of target users: extracting lease parameters corresponding to each historical lease car order from the historical lease car orders of the target user;
step 3, analyzing the integrity of the leased car: analyzing the ideal coefficient of the rented car according to the renting parameters corresponding to the historical rented car orders of the target user;
Step 4, target user driving behavior specification analysis: analyzing a comprehensive driving behavior specification coefficient corresponding to the target user according to lease parameters corresponding to each historical lease car order of the target user;
Step 5, target user driving normalization analysis: analyzing a driving normative coefficient corresponding to the target user according to lease parameters corresponding to each historical lease car order of the target user;
step 6, target user lease payment time rationality analysis: analyzing lease payment time reasonable coefficients corresponding to the target user according to lease parameters corresponding to each historical lease car order of the target user;
step 7, target user leasing car credit analysis: and analyzing the credit coefficient of the rental car corresponding to the target user according to the perfect coefficient of the rental car, the comprehensive specification coefficient of the driving behavior corresponding to the target user, the driving specification coefficient and the reasonable coefficient of the rental payment time.
Further, the rental parameters include rental car information and target user information.
Further, the rental car information comprises exterior and interior images of the rental car, a driving operation behavior record video and a rental car surface area, and the target user information comprises a target user violation record during the rental car, a rental return time point and a rental fee payment time point.
Further, the specific implementation manner of analyzing the integrity coefficient of the rental car in the step 3 includes the following steps:
Step 31: extracting exterior images and interior images of the rental car from rental car information in rental parameters in each historical rental car order of the target user, and comparing the exterior images and interior images of the rental car with exterior images and interior images of the rental car before the rental, thereby identifying exterior defect parameters and interior defect parameters of the rental car, wherein the exterior defect parameters comprise exterior defect types and exterior defect areas, and the interior defect parameters comprise interior defect types and interior defect areas;
step 32: extracting an external defect type from external defect parameters of the leased automobile, and matching the external defect type with the duty ratio coefficients corresponding to various external defect types stored in a database, so as to match the duty ratio coefficients corresponding to the external defect types of the leased automobile;
Step 33: extracting an internal defect type from the internal defect parameters of the leased automobile, and matching the internal defect type with the proportionality coefficients corresponding to various internal defect types stored in a database, so as to match the proportionality coefficients corresponding to the internal defect types of the leased automobile;
Step 34: extracting the surface area of the rented car from rented car information in rented parameters in each historical rented car order of the target user;
step 35: analyzing the exterior sound coefficient of the rental car according to the exterior defect area and the surface area corresponding to the rental car and the duty ratio coefficient corresponding to the exterior defect type in each historical rental car order of the target user, wherein the calculation formula is as follows: Wherein/> The method comprises the steps of representing the external sound coefficients of the rental cars, wherein s i and s' respectively represent the external defect areas and the surface areas corresponding to the rental cars in the ith historical rental car order of a target user, and lambda i represents the duty ratio coefficients of the external defect types corresponding to the rental cars in the ith historical rental car order;
Step 36: analyzing the internal sound coefficient of the rental car according to the internal defect area, the surface area and the proportional coefficient corresponding to the internal defect type of the rental car in each historical rental car order of the target user, wherein the calculation formula is as follows: Wherein/> Representing the internal sound coefficients of the rental cars, s i ', s ' respectively representing the internal defect areas and the surface areas corresponding to the rental cars in the ith historical rental car order of the target user, and lambda i ' represents the proportionality coefficients of the internal defect types corresponding to the rental cars in the ith historical rental car order;
Step 37: analyzing the rental car sound coefficient based on the exterior sound coefficient and the interior sound coefficient of the rental car, wherein the calculation formula is as follows: Wherein/> Indicating rental car health coefficients.
Further, the specific implementation manner of analyzing the driving behavior comprehensive specification coefficient corresponding to the target user in the step 4 includes the following steps:
Step 41: extracting driving operation behavior record videos from rental car information in rental car parameters in each historical rental car order of a target user;
step 42: dividing a driving operation behavior recording video into a plurality of behavior pictures according to a preset frame number, and numbering each behavior picture as 1, 2.
Step 43: acquiring the skin exposed parts of the target users in each behavior picture, counting the number of the skin exposed parts, and further matching the skin exposed parts of the target users in each behavior picture with the skin allowable exposed parts stored in the database one by one, so as to count the successful number of the skin exposed parts;
Step 44: according to the number of successful skin exposure parts of the target user and the number of skin exposure parts in each behavior picture, analyzing the corresponding dressing risk coefficient of the target user in each historical leasing car order, wherein the calculation formula is as follows: Wherein eta i represents the corresponding dressing risk coefficient of the target user in the ith historical rental car order, alpha im represents the successful matching number of the skin exposed parts of the target user in the mth behavior picture in the ith historical rental car order, and alpha im' represents the number of the skin exposed parts of the target user in the mth behavior picture in the ith historical rental car order;
Step 45: identifying hand motion characteristics corresponding to each historical rental car order of a target user based on each behavior picture, comparing the hand motion characteristics with hand characteristics corresponding to throwing behaviors stored in a database, judging whether the throwing behaviors exist in the target user in the behavior pictures, and extracting throwing object parameters from the behavior pictures if the throwing behaviors exist, wherein the throwing object parameters comprise throwing object types and throwing object volumes;
step 46: extracting a throwing object type from corresponding throwing object parameters in each historical rental car order of the target user, and matching the throwing object type with weight factors corresponding to unit volumes of various throwing object types stored in a database, so as to match the weight factors corresponding to the unit volumes of the throwing object types of each historical rental car order of the target user;
Step 47: according to the weight factors corresponding to the throwing object volume and the throwing object type unit volume of the target user in each historical rental car order, the throwing danger coefficient corresponding to each historical rental car order of the target user is analyzed, and the calculation formula is as follows: mu i＝vi*γi, wherein mu i represents a throwing danger coefficient corresponding to the ith historical rental car order of the target user, v i represents a throwing object volume of the ith historical rental car order of the target user, and gamma i represents a weight factor corresponding to a throwing object type unit volume of the ith historical rental car order of the target user;
Step 48: based on the dressing risk coefficient and throwing object risk coefficient corresponding to the historical rental car orders of the target user, the driving behavior specification coefficient corresponding to the historical rental car orders of the target user is analyzed, and the calculation formula is as follows: Wherein kappa i' represents a driving behavior specification coefficient corresponding to the ith historical rental car order of the target user;
Step 49: according to the driving behavior standard coefficient and standard driving behavior standard coefficient corresponding to each historical leased car order of the target user, analyzing the driving behavior comprehensive standard coefficient corresponding to the target user, wherein the calculation formula is as follows Where κ represents the driving behavior comprehensive specification coefficient corresponding to the target user, and κ "represents the standard driving behavior specification coefficient.
Further, the specific implementation manner of analyzing the driving specification coefficient corresponding to the target user in the step 5 includes the following steps:
step 51: extracting violation records of the target user during the car renting process from target user information in renting parameters in each historical car renting order of the target user, wherein the violation records comprise violation types and violation times;
Step 52: extracting the violation types from the violation records of the target user during the period of renting the automobile, further counting the types of the violation types corresponding to the target user, and numbering the types as 1,2, j, k, and k respectively, so as to count the occurrence times of the types of the violations;
step 53: matching the types of the corresponding violation types of the target user with the violation values corresponding to the single violations of the types of the violations stored in the database, and further matching the violation values corresponding to the single violations of the types of the violations of the target user;
step 54: according to the single violation value of each violation type and the occurrence frequency of each violation type, the corresponding running specification coefficient of the target user is analyzed, and the calculation formula is as follows: Wherein phi represents a driving standard coefficient corresponding to the target user, sigma j represents a violation value corresponding to a single violation of a jth violation type category of the target user, and v Indicating the number of times the jth offending type category of the target user has occurred.
Further, the specific implementation manner of analyzing the reasonable rental payment time coefficient corresponding to the target user in the step 6 includes the following steps:
step 61: extracting a lease return time point and a lease fee payment time point from target user information in lease parameters in each historical lease car order of the target user;
Step 62: according to the lease return time point, lease fee payment time point and allowable fee payment duration of each historical lease car order of a target user, analyzing lease payment time reasonable coefficients corresponding to the target user, wherein the calculation formula is as follows: wherein θ represents a reasonable coefficient of rental payment time corresponding to the target user, t i′、ti represents a rental return time point and a rental fee payment time point corresponding to the i-th historical rental car order of the target user, and t "represents a time length of payment permission.
Further, the specific calculation formula for analyzing the credit coefficient of the rental car corresponding to the target user in the step 7 is as follows: Wherein, psi represents the credit coefficient of the leased car corresponding to the target user.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
According to the invention, when the credit analysis of the rented automobile of the user is performed, the payment cost of the user on time of the platform is analyzed, and the behavior, traffic violation condition and rented automobile damage degree of the user during renting the automobile are analyzed, so that the problem that the analysis dimension is single is solved, and the problem that the credit analysis of the rented automobile of the user is inaccurate is solved, on one hand, a reliable reference value can be provided for the subsequent deposit evaluation, the matching degree of the credit coefficient of the subsequent user and the deposit to be paid is improved, on the other hand, the behavior of the user during renting can be well restrained, and the safety of the user for keeping the rented automobile during renting is further improved, so that the maintenance cost of the rented automobile platform for the rented automobile is reduced.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
Fig. 1 is a schematic diagram of a method for analyzing and managing car rental credit information based on big data.
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, the invention provides a car rental credit information analysis and management method based on big data, which comprises the following steps:
Step 1, extracting historical rental car orders of a target user: and extracting historical rental car orders of the target user in a detection period from the car rental platform, and numbering each historical rental car order as 1, 2.
Step 2, extracting lease parameters in historical lease car orders of target users: and extracting lease parameters corresponding to each historical lease car order from the historical lease car orders of the target user.
In a specific embodiment, the rental parameters include rental car information and target user information.
In a specific embodiment, the rental car information includes exterior and interior images of the rental car, a driving operation behavior record video, and a rental car surface area, and the target user information includes a target user's violation record during the rental car, a rental return time point, and a rental fee payment time point.
Step 3, analyzing the integrity of the leased car: and analyzing the ideal coefficient of the rented car according to the renting parameters corresponding to the historical rented car orders of the target user.
In a specific embodiment, the specific implementation manner of analyzing the health coefficient of the rental car in the step 3 includes the following steps:
Step 31: and extracting an external image and an internal image of the rental car from rental car information in rental parameters in each historical rental car order of the target user, and comparing the external image and the internal image with the external image and the internal image of the rental car before the rental respectively, thereby identifying external defect parameters and internal defect parameters of the rental car, wherein the external defect parameters comprise an external defect type and an external defect area, and the internal defect parameters comprise an internal defect type and an internal defect area.
The external defect types include scratches, dirt, paint and the like, and the internal defect types include scratches, peeling, cracking and the like.
It should be noted that the invention analyzes the perfect coefficient of the rental car from two aspects of external and internal defects of the rental car, and the analysis is relatively comprehensive.
Step 32: and extracting the external defect type from the external defect parameters of the leased automobile, and matching the external defect type with the duty ratio coefficients corresponding to the external defect types stored in the database, so as to match the duty ratio coefficients corresponding to the external defect types of the leased automobile.
Step 33: and extracting the internal defect type from the internal defect parameters of the leased automobile, and matching the internal defect type with the proportionality coefficients corresponding to the internal defect types stored in the database, so as to match the proportionality coefficients corresponding to the internal defect types of the leased automobile.
Step 34: and extracting the surface area of the rented car from the rented car information in the rented parameters in each historical rented car order of the target user.
Step 35: analyzing the exterior sound coefficient of the rental car according to the exterior defect area and the surface area corresponding to the rental car and the duty ratio coefficient corresponding to the exterior defect type in each historical rental car order of the target user, wherein the calculation formula is as follows: Wherein/> And s i and s' respectively represent the external defect area and the surface area corresponding to the rental car in the ith historical rental car order of the target user, and lambda i represents the duty ratio coefficient of the corresponding external defect type of the rental car in the ith historical rental car order.
Step 36: analyzing the internal sound coefficient of the rental car according to the internal defect area, the surface area and the proportional coefficient corresponding to the internal defect type of the rental car in each historical rental car order of the target user, wherein the calculation formula is as follows: Wherein/> And s i ' and s ' respectively represent the internal defect area and the surface area corresponding to the rental car in the ith historical rental car order of the target user, and lambda i ' represents the proportionality coefficient of the internal defect type corresponding to the rental car in the ith historical rental car order.
Step 37: analyzing the rental car sound coefficient based on the exterior sound coefficient and the interior sound coefficient of the rental car, wherein the calculation formula is as follows: Wherein/> Indicating rental car health coefficients.
If the integrity factor of the rental car is not qualified, it is indicated that the target user does not well protect the rental car during the rental car, and the aesthetic appearance and the service time of the rental car are affected.
Step 4, target user driving behavior specification analysis: and analyzing the comprehensive driving behavior specification coefficient corresponding to the target user according to the lease parameters corresponding to the target user in each historical lease car order.
In a specific embodiment, the specific implementation manner of analyzing the driving behavior comprehensive specification coefficient corresponding to the target user in the step 4 includes the following steps:
Step 41: and extracting driving operation behavior recording videos from rental car information in rental car parameters in each historical rental car order of the target user.
Step 42: dividing the driving operation behavior recording video into a plurality of behavior pictures according to a preset frame number, and respectively numbering each behavior picture as 1, 2.
Step 43: the method comprises the steps of obtaining the skin exposed parts of target users in each behavior picture, counting the number of the skin exposed parts, and further matching the skin exposed parts of the target users in each behavior picture with the skin allowable exposed parts stored in a database one by one, so that the number of successful skin exposed parts is counted.
Step 44: according to the number of successful skin exposure parts of the target user and the number of skin exposure parts in each behavior picture, analyzing the corresponding dressing risk coefficient of the target user in each historical leasing car order, wherein the calculation formula is as follows: Wherein η i represents a corresponding dressing risk coefficient of the target user in the ith historical rental car order, α im represents the number of successful skin exposure parts of the target user in the mth behavioral picture in the ith historical rental car order, and α im' represents the number of skin exposure parts of the target user in the mth behavioral picture in the ith historical rental car order.
Step 45: identifying hand motion characteristics corresponding to each historical rental car order of a target user based on each behavior picture, comparing the hand motion characteristics with hand characteristics corresponding to throwing behaviors stored in a database, judging whether the throwing behaviors exist in the target user in the behavior pictures, and extracting throwing object parameters from the behavior pictures if the throwing behaviors exist, wherein the throwing object parameters comprise throwing object types and throwing object volumes.
Step 46: and extracting the projectile type from the projectile parameters corresponding to each historical rental car order of the target user, and matching the projectile type with the weight factors corresponding to the unit volumes of the projectile types stored in the database, so as to match the weight factors corresponding to the unit volumes of the projectile types of each historical rental car order of the target user.
Step 47: according to the weight factors corresponding to the throwing object volume and the throwing object type unit volume of the target user in each historical rental car order, the throwing danger coefficient corresponding to each historical rental car order of the target user is analyzed, and the calculation formula is as follows: mu i＝vi*γi, wherein mu i represents a throwing danger coefficient corresponding to the ith historical rental car order of the target user, v i represents a throwing object volume of the ith historical rental car order of the target user, and gamma i represents a weight factor corresponding to a throwing object type unit volume of the ith historical rental car order of the target user.
Step 48: based on the dressing risk coefficient and throwing object risk coefficient corresponding to the historical rental car orders of the target user, the driving behavior specification coefficient corresponding to the historical rental car orders of the target user is analyzed, and the calculation formula is as follows: wherein κ i' represents the driving behavior specification coefficient corresponding to the i-th historical rental car order of the target user.
Step 49: according to the driving behavior standard coefficient and standard driving behavior standard coefficient corresponding to each historical leased car order of the target user, analyzing the driving behavior comprehensive standard coefficient corresponding to the target user, wherein the calculation formula is as followsWhere κ represents the driving behavior comprehensive specification coefficient corresponding to the target user, and κ "represents the standard driving behavior specification coefficient.
The purpose of analyzing the driving behavior of the target user is to: detecting whether the target user has an unclean behavior, such as exposing an upper body, taking off shoes, and throwing an article out of a window, during driving of the rental car, further ensures normalization of the driving behavior of the target user, and thus can improve behavior restriction force on a driver.
Step 5, target user driving normalization analysis: and analyzing the running specification coefficient corresponding to the target user according to the lease parameters corresponding to the target user in each history lease car order.
In a specific embodiment, the specific implementation manner of analyzing the driving specification coefficient corresponding to the target user in the step 5 includes the following steps:
Step 51: and extracting the violation records of the target user during the car renting process from the target user information in the renting parameters of each historical car renting order of the target user, wherein the violation records comprise the violation types and the number of violations.
Step 52: and extracting the violation types from the violation records of the target user during the period of renting the automobile, counting the types of the violation types corresponding to the target user, and numbering the types as 1, 2.
Step 53: and matching the types of the corresponding violation types of the target user with the violation values corresponding to the single violations of the types of the violations stored in the database, so as to match the violation values corresponding to the single violations of the types of the violations of the target user.
Step 54: according to the single violation value of each violation type and the occurrence frequency of each violation type, the corresponding running specification coefficient of the target user is analyzed, and the calculation formula is as follows: Wherein phi represents a driving standard coefficient corresponding to the target user, sigma j represents a violation value corresponding to a single violation of a jth violation type category of the target user, and v Indicating the number of times the jth offending type category of the target user has occurred.
If traffic violation occurs during the process of driving the rental car by the target user, the traffic violation not only threatens the safety of the target user, but also the rental platform needs to bear certain responsibility, and meanwhile, the rental car may become an accident car, further use of the rental car is affected, and the cost of putting the rental car on the rental platform is increased.
Step 6, target user lease payment time rationality analysis: and analyzing the lease payment time reasonable coefficient corresponding to the target user according to lease parameters corresponding to the target user in each historical lease car order.
In a specific embodiment, the specific implementation manner of analyzing the reasonable rental payment time coefficient corresponding to the target user in the step 6 includes the following steps:
Step 61: and extracting a lease return time point and a lease fee payment time point from target user information in lease parameters in each historical lease car order of the target user.
Step 62: according to the lease return time point, lease fee payment time point and allowable fee payment duration of each historical lease car order of a target user, analyzing lease payment time reasonable coefficients corresponding to the target user, wherein the calculation formula is as follows: wherein θ represents a reasonable coefficient of rental payment time corresponding to the target user, t i′、ti represents a rental return time point and a rental fee payment time point corresponding to the i-th historical rental car order of the target user, and t "represents a time length of payment permission.
It should be noted that, the sign of θ may be positive or negative, and when the sign of θ is positive, it indicates that the rental payment time corresponding to the target user is reasonable, and the larger the value of θ is, the more reasonable the rental payment time corresponding to the target user is, and when the sign of θ is negative, it indicates that the rental payment time corresponding to the target user is unreasonable, and the smaller the value of θ is, the more unreasonable the rental payment time corresponding to the target user is.
If the rental payment time of the target user is long, the target user is not shown to be high in timekeeping, and the credit of the target user is reflected from the side surface to be low, so that the reasonable coefficient of the rental payment time corresponding to the target user needs to be analyzed.
Step 7, target user leasing car credit analysis: and analyzing the credit coefficient of the rental car corresponding to the target user according to the perfect coefficient of the rental car, the comprehensive specification coefficient of the driving behavior corresponding to the target user, the driving specification coefficient and the reasonable coefficient of the rental payment time.
In a specific embodiment, a specific calculation formula for analyzing the credit coefficient of the rental car corresponding to the target user in the step 7 is as follows: Wherein, psi represents the credit coefficient of the leased car corresponding to the target user.
According to the invention, when the credit analysis of the rented automobile of the user is performed, the payment cost of the user on time of the platform is analyzed, and the behavior, traffic violation condition and rented automobile damage degree of the user during renting the automobile are analyzed, so that the problem that the analysis dimension is single is solved, and the problem that the credit analysis of the rented automobile of the user is inaccurate is solved, on one hand, a reliable reference value can be provided for the subsequent deposit evaluation, the matching degree of the credit coefficient of the subsequent user and the deposit to be paid is improved, on the other hand, the behavior of the user during renting can be well restrained, and the safety of the user for keeping the rented automobile during renting is further improved, so that the maintenance cost of the rented automobile platform for the rented automobile is reduced.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (7)
1. The analysis and management method for the car rental credit information based on the big data is characterized by comprising the following steps:
Step 1, extracting historical rental car orders of a target user: extracting historical rental car orders of a target user in a detection period from a car rental platform, and numbering each historical rental car order as 1, 2.
Step 2, extracting lease parameters in historical lease car orders of target users: extracting lease parameters corresponding to each historical lease car order from the historical lease car orders of the target user;
step 3, analyzing the integrity of the leased car: analyzing the ideal coefficient of the rented car according to the renting parameters corresponding to the historical rented car orders of the target user;
Step 4, target user driving behavior specification analysis: analyzing a comprehensive driving behavior specification coefficient corresponding to the target user according to lease parameters corresponding to each historical lease car order of the target user;
Step 5, target user driving normalization analysis: analyzing a driving normative coefficient corresponding to the target user according to lease parameters corresponding to each historical lease car order of the target user;
step 6, target user lease payment time rationality analysis: analyzing lease payment time reasonable coefficients corresponding to the target user according to lease parameters corresponding to each historical lease car order of the target user;
step 7, target user leasing car credit analysis: analyzing the credit coefficient of the rental car corresponding to the target user according to the sound coefficient of the rental car, the comprehensive specification coefficient of the driving behavior corresponding to the target user, the driving specification coefficient and the reasonable coefficient of the rental payment time;
The specific implementation manner of analyzing the sound coefficient of the rental car in the step 3 comprises the following steps:
Step 31: extracting exterior images and interior images of the rental car from rental car information in rental parameters in each historical rental car order of the target user, and comparing the exterior images and interior images of the rental car with exterior images and interior images of the rental car before the rental, thereby identifying exterior defect parameters and interior defect parameters of the rental car, wherein the exterior defect parameters comprise exterior defect types and exterior defect areas, and the interior defect parameters comprise interior defect types and interior defect areas;
step 32: extracting an external defect type from external defect parameters of the leased automobile, and matching the external defect type with the duty ratio coefficients corresponding to various external defect types stored in a database, so as to match the duty ratio coefficients corresponding to the external defect types of the leased automobile;
Step 33: extracting an internal defect type from the internal defect parameters of the leased automobile, and matching the internal defect type with the proportionality coefficients corresponding to various internal defect types stored in a database, so as to match the proportionality coefficients corresponding to the internal defect types of the leased automobile;
Step 34: extracting the surface area of the rented car from rented car information in rented parameters in each historical rented car order of the target user;
step 35: analyzing the exterior sound coefficient of the rental car according to the exterior defect area and the surface area corresponding to the rental car and the duty ratio coefficient corresponding to the exterior defect type in each historical rental car order of the target user, wherein the calculation formula is as follows: Wherein/> Representing the exterior integrity factor of a rental car,/>、/>Respectively representing the external defect area and the surface area corresponding to the rental car in the ith historical rental car order of the target user,/>Representing the duty ratio coefficient of the corresponding external defect type of the leased car in the ith historical leased car order;
Step 36: analyzing the internal sound coefficient of the rental car according to the internal defect area, the surface area and the proportional coefficient corresponding to the internal defect type of the rental car in each historical rental car order of the target user, wherein the calculation formula is as follows: Wherein/> Representing the inside health coefficient of the rental car,/>、/>Respectively representing the internal defect area and the surface area corresponding to the rental car in the ith historical rental car order of the target user,/>The proportional coefficient of the corresponding internal defect type of the leased car in the ith historical leased car order is represented;
Step 37: analyzing the rental car sound coefficient based on the exterior sound coefficient and the interior sound coefficient of the rental car, wherein the calculation formula is as follows: Wherein/> Indicating rental car health coefficients.
2. The big data-based analysis and management method for car rental credit information, as set forth in claim 1, is characterized in that: the rental parameters include rental car information and target user information.
3. The big data-based car rental credit information analysis and management method according to claim 2, wherein: the rental car information comprises exterior and interior images of the rental car, a driving operation behavior record video and a rental car surface area, and the target user information comprises a target user violation record, a rental return time point and a rental fee payment time point during the rental car.
4. The big data-based analysis and management method for car rental credit information, as set forth in claim 1, is characterized in that: the specific implementation manner of analyzing the comprehensive driving behavior specification coefficient corresponding to the target user in the step4 comprises the following steps:
Step 41: extracting driving operation behavior record videos from rental car information in rental car parameters in each historical rental car order of a target user;
step 42: dividing a driving operation behavior recording video into a plurality of behavior pictures according to a preset frame number, and numbering each behavior picture as 1, 2.
Step 43: acquiring the skin exposed parts of the target users in each behavior picture, counting the number of the skin exposed parts, and further matching the skin exposed parts of the target users in each behavior picture with the skin allowable exposed parts stored in the database one by one, so as to count the successful number of the skin exposed parts;
Step 44: according to the number of successful skin exposure parts of the target user and the number of skin exposure parts in each behavior picture, analyzing the corresponding dressing risk coefficient of the target user in each historical leasing car order, wherein the calculation formula is as follows: Wherein/> Representing the corresponding dressing risk factor for the target user in the ith historical rental car order,Representing successful matching number of skin bare parts of target user in mth behavior picture in ith historical rental car order,/>The number of the exposed parts of the skin in the mth action picture of the target user in the ith historical leasing car order is represented;
Step 45: identifying hand motion characteristics corresponding to each historical rental car order of a target user based on each behavior picture, comparing the hand motion characteristics with hand characteristics corresponding to throwing behaviors stored in a database, judging whether the throwing behaviors exist in the target user in the behavior pictures, and extracting throwing object parameters from the behavior pictures if the throwing behaviors exist, wherein the throwing object parameters comprise throwing object types and throwing object volumes;
step 46: extracting a throwing object type from corresponding throwing object parameters in each historical rental car order of the target user, and matching the throwing object type with weight factors corresponding to unit volumes of various throwing object types stored in a database, so as to match the weight factors corresponding to the unit volumes of the throwing object types of each historical rental car order of the target user;
step 47: according to the weight factors corresponding to the throwing object volume and the throwing object type unit volume of the target user in each historical rental car order, the throwing danger coefficient corresponding to each historical rental car order of the target user is analyzed, and the calculation formula is as follows: Wherein/> Representing throwing danger coefficient corresponding to ith historical leasing automobile order of target user,/>Representing the projectile volume of the target user at the ith historical rental car order,/>Representing a weight factor corresponding to a unit volume of a throwing object type of the i-th historical rental car order of the target user;
Step 48: based on the dressing risk coefficient and throwing object risk coefficient corresponding to the historical rental car orders of the target user, the driving behavior specification coefficient corresponding to the historical rental car orders of the target user is analyzed, and the calculation formula is as follows: Wherein/> Representing a driving behavior specification coefficient corresponding to the i-th historical leased automobile order of the target user;
Step 49: according to the driving behavior standard coefficient and standard driving behavior standard coefficient corresponding to each historical leased car order of the target user, analyzing the driving behavior comprehensive standard coefficient corresponding to the target user, wherein the calculation formula is as follows Wherein/>Representing the comprehensive specification coefficient of the driving behavior corresponding to the target user,/>Representing standard driving behavior normative coefficients.
5. The big data-based car rental credit information analysis and management method according to claim 4, wherein: the specific implementation manner of analyzing the running specification coefficient corresponding to the target user in the step 5 comprises the following steps:
step 51: extracting violation records of the target user during the car renting process from target user information in renting parameters in each historical car renting order of the target user, wherein the violation records comprise violation types and violation times;
Step 52: extracting the violation types from the violation records of the target user during the period of renting the automobile, further counting the types of the violation types corresponding to the target user, and numbering the types as 1,2, j, k, and k respectively, so as to count the occurrence times of the types of the violations;
step 53: matching the types of the corresponding violation types of the target user with the violation values corresponding to the single violations of the types of the violations stored in the database, and further matching the violation values corresponding to the single violations of the types of the violations of the target user;
step 54: according to the single violation value of each violation type and the occurrence frequency of each violation type, the corresponding running specification coefficient of the target user is analyzed, and the calculation formula is as follows: Wherein/> Representing the corresponding driving normative coefficient of the target user,/>Indicating the value of the violation corresponding to the j-th violation type single violation of the target user,Indicating the number of times the jth offending type category of the target user has occurred.
6. The big data-based car rental credit information analysis and management method according to claim 5, wherein the method comprises the following steps: the specific implementation manner of analyzing the reasonable lease payment time coefficient corresponding to the target user in the step 6 includes the following steps:
step 61: extracting a lease return time point and a lease fee payment time point from target user information in lease parameters in each historical lease car order of the target user;
Step 62: according to the lease return time point, lease fee payment time point and allowable fee payment duration of each historical lease car order of a target user, analyzing lease payment time reasonable coefficients corresponding to the target user, wherein the calculation formula is as follows: Wherein/> Representing reasonable coefficient of lease payment time corresponding to target user,/>、/>Respectively representing the lease return time point and the lease fee payment time point corresponding to the ith historical lease car order of the target user,/>Indicating the time period in which the payment of the fee is allowed.
7. The big data-based car rental credit information analysis and management method as claimed in claim 6, wherein: the specific calculation formula for analyzing the credit coefficient of the rental car corresponding to the target user in the step 7 is as follows: Wherein/> And representing the credit coefficient of the leased car corresponding to the target user.
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