CN120655390A

CN120655390A - Method, device, equipment and medium for identifying rental business opportunities based on vehicle positioning

Info

Publication number: CN120655390A
Application number: CN202510703933.5A
Authority: CN
Inventors: 孙振
Original assignee: Ping An International Financial Leasing Co Ltd
Current assignee: Ping An International Financial Leasing Co Ltd
Priority date: 2025-05-28
Filing date: 2025-05-28
Publication date: 2025-09-16

Abstract

The invention relates to the technical field of data processing, and discloses a rental business machine identification method, a device, equipment and a medium based on vehicle positioning, according to the method, a first characteristic value representing the running characteristic of a vehicle is calculated according to first vehicle positioning data of a target client history renting the vehicle, and a second characteristic value of the running characteristic of the vehicle is obtained according to the type of the client. The first feature matching degree of the first feature value and the second feature value is calculated through a feature matching algorithm, and the comparison result of the first feature matching degree and a preset matching degree threshold value is used as a basis for judging whether a renting request initiated by a target client is a real request or not, so that accuracy and reliability of judging the authenticity of the renting request of the target client are improved, the technical problems of accurately identifying the real business opportunity of the renting request of a self-thawing client and improving the renting business volume of a vehicle in the financial field are solved, risks can be controlled, potential clients can be prevented from being lost, and the business benefit of a renting company is improved.

Description

Rental business machine identification method, device, equipment and medium based on vehicle positioning

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a medium for identifying a rental business machine based on vehicle positioning.

Background

In the vehicle marketing business, since the vehicle selling price is high, the vehicle purchasing person can choose to purchase the vehicle from the finance company in a manner of payment by stage (including bank credit payment stage, financing lease monthly payment and the like). Taking the financing rental company as an example, the vehicle purchases the vehicle by providing funds to the vehicle purchaser (i.e., lessee) at a time, and the lessee needs to pay a certain amount and pay the lease monthly.

At present, the conventional mode of commercial vehicle financing and renting is B2B2C (business to business to customer) service, namely, a customer has a vehicle purchasing requirement, a channel (such as a dealer, a leaning company, a carrier, and the like, hereinafter referred to as a channel) is found, and then the channel recommends the customer to a renting company for renting. The mode of financing leasing company is that when customers buy cars, the leasing company provides a fund for qualified customers to the channel, and the channel sells cars to the customers. Therefore, the channel is that the full money of the vehicle financing amount is received at the first time, and the clients pay in stages, which is called as 'real renting'.

One business model is that the channel itself is also a customer, i.e., the customer is the same person as the boss/actual person of the channel, which is referred to as "self-thawing". It is difficult to identify the authenticity of the rental business. Since it is likely that this customer will not have enough funds to transfer for a short period of time, and will not be borrowed from the bank, vehicle financing funds are withdrawn from the rental company by way of financing lease. In this case, after the rental, the channel can take the financing of the vehicle at one time, and if the channel refuses the repayment, the rental company suffers a relatively large loss, which is called "self-thawing fraud".

In the conventional mode, the leasing company can only reject the leasing for controlling the risk, aiming at the fact that the leasing company is difficult to identify the authenticity of the leasing merchant under the condition that the client and the channel are the same real person. However, some of the actual "real renting" customers are actually used for business, and the way in which the customers are directly abandoned due to the control risk affects the renting traffic of the rental company, losing some of the potential customers.

Therefore, how to accurately identify the authenticity of the renting request of the self-thawing client and improve the renting traffic becomes a technical problem to be solved.

Disclosure of Invention

The invention provides a method, a device, computer equipment and a medium for identifying a renting business machine based on vehicle positioning, which are used for solving the technical problem that a renting business is potentially lost due to inaccurate identification of authenticity of a renting request of a self-thawing client.

In a first aspect, a method for identifying a rental business machine based on vehicle positioning is provided, including:

When a renting request initiated by a target client is received, acquiring first vehicle positioning data corresponding to historical renting vehicles of the target client;

determining a first characteristic value of at least one vehicle operating characteristic of the historic rental vehicle based on the first vehicle positioning data;

acquiring a second characteristic value corresponding to each vehicle running characteristic based on the client type of the target client;

Calculating a first feature matching degree of the first feature value and the second feature value based on a feature matching algorithm;

and when the first feature matching degree is greater than or equal to a preset matching degree threshold value, determining that the renting request initiated by the target client is a real request.

In a second aspect, there is provided a rental business machine identification device based on vehicle positioning, including:

The first data acquisition module is used for acquiring first vehicle positioning data corresponding to historical renting vehicles of the target client when a renting request initiated by the target client is received;

A first feature determination module for determining a first feature value of at least one vehicle operating feature of the historic rental vehicle based on the first vehicle positioning data;

the second characteristic acquisition module is used for acquiring second characteristic values corresponding to the vehicle running characteristics based on the client type of the target client;

the feature matching calculation module is used for calculating a first feature matching degree of the first feature value and the second feature value based on a feature matching algorithm;

The real request judging module is used for determining that the renting request initiated by the target client is a real request when the first feature matching degree is larger than or equal to a preset matching degree threshold value.

In a third aspect, a computer device is provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor executing the computer program to perform the steps of the rental business machine identification method based on vehicle positioning.

In a fourth aspect, a computer readable storage medium is provided, the computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the vehicle positioning-based rental business machine identification method described above.

In the scheme realized by the method, the device, the computer equipment and the storage medium for identifying the renting merchant based on the vehicle positioning, the first vehicle positioning data of the historical renting vehicle of the target client is obtained so as to analyze the actual use and the operation characteristics of the vehicle and facilitate judging the authenticity of the renting request. According to the first vehicle positioning data, calculating a first characteristic value representing the vehicle running characteristic, and obtaining a second characteristic value of the vehicle running characteristic according to the type of the client as a judging basis. And calculating a first feature matching degree of the first feature value and the second feature value through a feature matching algorithm, and comparing the first feature matching degree with a preset matching degree threshold value so as to judge whether the renting request initiated by the target client is a real request. When the first feature matching degree is greater than or equal to a preset matching degree threshold value, the vehicle running feature of the target user for the use of the rented vehicle is determined to be in accordance with the client type of the target user, so that the renting request of the target client is determined to be a real request, and the accuracy and reliability of judging the authenticity of the renting request of the target client are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic illustration of an application environment of a rental business machine identification method based on vehicle positioning in accordance with one embodiment of the present invention;

FIG. 2 is a schematic flow chart of a first embodiment of a method for identifying a rental business machine based on vehicle positioning according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a first vehicle positioning data source structure for first rental and multiple rentals according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a rental business machine identification device based on vehicle positioning according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a computer device according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of another embodiment of a computer device according to the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. 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.

The recognition method of the renter on the basis of the vehicle positioning, provided by the embodiment of the invention, can be applied to an application environment as shown in fig. 1, wherein a client communicates with a server through a network. The method comprises the steps of receiving a renting request initiated by a client through a client by a server, analyzing the renting request, inquiring historical renting vehicle first vehicle positioning data corresponding to the client from a preset database according to client information in the renting request, calculating a first characteristic value of at least one vehicle running characteristic according to the first vehicle positioning data, calculating a second characteristic value corresponding to each vehicle running characteristic according to vehicle positioning data corresponding to the client type of the client in the preset database, and calculating first characteristic matching degrees of the first characteristic value and the second characteristic value through a characteristic matching algorithm to determine that the renting request initiated by the client is a real request when the first characteristic matching degree is larger than or equal to a preset matching degree threshold value. In the invention, aiming at a renting request of a self-fusion client in a vehicle renting service scene in the financial field, first vehicle positioning data of a target client history renting vehicle is acquired so as to analyze actual use and operation characteristics of the vehicle and facilitate judging the authenticity of the renting request. According to the first vehicle positioning data, calculating a first characteristic value representing the vehicle running characteristic, and obtaining a second characteristic value of the vehicle running characteristic according to the type of the client as a judging basis. And calculating a first feature matching degree of the first feature value and the second feature value through a feature matching algorithm, and comparing the first feature matching degree with a preset matching degree threshold value so as to judge whether the renting request initiated by the target client is a real request. When the first feature matching degree is larger than or equal to a preset matching degree threshold value, the vehicle operation feature of the target user for the use of the rented vehicle can be determined to be in accordance with the client type of the target user, so that the renting request of the target user is determined to be a real request, the accuracy and reliability of judging the authenticity of the renting request of the target user are improved, the technical problems of accurately identifying the real business opportunity of the renting request of the self-thawing client and improving the renting business volume are solved, risks can be controlled, potential clients can be prevented from being lost, and the business benefit of a renting company is improved. The clients may be, but are not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server may be implemented by a stand-alone server or a server cluster formed by a plurality of servers. The present invention will be described in detail with reference to specific examples.

Referring to fig. 2, fig. 2 is a flowchart of a first embodiment of a rental business machine identification method based on vehicle positioning according to an embodiment of the present invention, including the following steps:

s101, when a renting request initiated by a target client is received, acquiring first vehicle positioning data corresponding to historical renting vehicles of the target client;

In an embodiment, the target client may initiate a rental request to the service end of the renter through the client, where the rental request may include client information, requirement information, and the like of the target client.

In one embodiment, the customer information may include customer base information, financial information, credit information, and the like.

For example, in the field of financial leasing, for individual customers, the customer is required to provide personal basic information such as name, age, sex, identification card number, contact phone (for convenience in receiving leasing related information such as contract, vehicle status notification, etc.), home address (for related matters such as credit evaluation and vehicle return), income information, asset information, liability information, etc. For business clients, the clients are required to provide business basic information such as business names, business unified social credit codes (for business operations of business identity recognition and legal compliance), business legal person names, business contact person names, contact phones, business addresses, business bank account information (for rent payment and settlement), financial reports, bank running water, tax cases, business credit ratings and the like.

Similarly, credit information also requires the customer to provide corresponding credit information, such as personal credit scores of the individual customer (e.g., credit scores of a central credit system, etc.), personal credit reports of the individual customer, including credit card usage records, loan repayment records, etc., depending on the type of customer. And the credit records of the enterprise clients, such as the credit rating of the enterprise at a bank, whether the enterprise has a litigation dispute, whether the enterprise has delinquent money and the like. Such information may help the rental company determine if the business is crediting, is able to pay a lease on time, etc.

In one embodiment, the demand information may include vehicle information, rental time, and the like.

The vehicle information may include, among other things, vehicle make, model, configuration requirements (e.g., engine displacement, interior configuration, whether special equipment is needed, etc.), quantity requirements, etc. For example, vehicle models may include specific vehicle model requirements such as economy, luxury, SUV, MPV, vans, etc., depending on the customer's purpose of use (e.g., personal daily commute, transportation, passenger transport, home tour, business reception, etc.). For example, if the customer is an enterprise customer such as a carrier, multiple vehicles may be required to conduct the business, and the rental company may be required to arrange funds and rental schemes based on the number of vehicles.

The rental time may include a rental start time, a rental end time. The length of the lease period affects the way the lease is calculated and the total amount. For example, long-term renting may have a lease offer, but also requires the customer to assume longer usage responsibilities, while short-term renting may have a relatively high lease offer, but the customer may be flexibly adjusted according to business needs.

Illustratively, the rental request may also include channel information, vehicle usage information, warranty information, and the like.

The channel information may be a recommended channel name (e.g., dealer, affiliated company, carrier, etc.). This helps the rental company to know the customer source, assess channel reliability and quality of service. For example, if a dealer recommended a higher quality customer, the rate of breach was lower, the rental company might increase the credit rating of the target customer recommended by the dealer.

The vehicle usage information is used to describe what service the vehicle is used for (e.g., cargo transportation, passenger transport, engineering vehicles, etc.). Different uses may be at different risks, and rental companies may adjust risk control measures based on vehicle use. For example, vehicles for passenger transport may require more stringent security checks and insurance requirements, whereby rental companies may require customers to provide additional insurance or purchase specific insurance.

The guaranty information may include guaranty information (guaranty name, identification number, contact, guaranty capability, etc.) or guaranty information (kind, value, rights instance, etc.) of the guaranty. The guaranty may reduce the risk of the rental company, which needs to record the guaranty-related information in detail if the client provides the guaranty.

It can be appreciated that when the target client initiates the rental request, if the target client is a self-thawing client, that is, the boss/real control of the purchase channel (such as dealer, affiliated company, carrier, etc.) of the target client is the target client itself, the current rental request is determined to be a self-thawing service, and the self-thawing service has a certain risk for the finance party providing financing. In order to control risks, the authenticity of the renting request needs to be judged through the renting business machine identification method based on vehicle positioning.

In order to ensure the safety of the renting assets, the renting company can install GPS positioning equipment on the vehicle on the premise of agreeing of the lessee, and continuously track the positions and the service conditions of the vehicle assets. The leasing company can analyze vehicle positioning, vehicle running track, vehicle running mileage, time length and the like through the positioning device, so that abnormal vehicle use behaviors or industry integrity risks are found, corresponding measures are taken in advance, and risks are avoided in modes of requiring lessees to pay in advance, for example.

Specifically, when the self-thawing channel applies for financing lease for the first time, financing is provided for the channel according to the upper limit of half of the line of a conventional non-self-thawing transport company, after renting a customer, a vehicle-mounted positioning component (such as a GPS) regularly monitors the characteristics of running mileage and the like, and when the customer continuously pays for a certain period and meets the characteristics of the running mileage and the like, financing is provided for the company according to the line of the normal non-self-thawing transport company, so that self-thawing fraud and real customers can be distinguished, and the problems that whether the customer has rented or not is refused due to self-thawing fraud cannot be identified and a business opportunity is lost are solved.

In one embodiment, when a first rental request is initiated by the target client, at least one rental vehicle is rented to the target client based on a preset rental amount, and vehicle positioning information of the rental vehicle is acquired based on a preset data acquisition period, so as to obtain the first vehicle positioning data.

In a traditional rental business, for self-thawing customers, to circumvent risk, rental companies are typically only able to refuse to rent. But in fact some are truly "real renting" customers for business, and the way customers are directly relinquished because of the control risk affects renting of the rental company. Therefore, based on the method provided by the application, when the self-thawing client initiates the renting request, the self-thawing client is subjected to vehicle operation data examination so as to judge whether the renting request is a real business opportunity or not.

Specifically, when receiving a lease request initiated by a target client, firstly judging whether the lease request is a first lease request, namely judging whether the target client is a first lease application for financing lease to a current lease party. If the target client initiates a first lease request, a small amount of lease vehicle financing cost can be provided for the target client according to a preset lease limit, and positioning monitoring of a preset data acquisition period is carried out on the lease vehicles so as to acquire first vehicle positioning data of a first set of lease vehicles.

In one embodiment, a small preset start rent degree grant start lease may be set for the first time a start lease request is initiated from a converged target client. For example, a specific preset rent degrees may refer to a certain proportion of the amount of a conventional non-self-thawing transport company, and the proportion may be set to 30% -50% according to the bearing capacity of the rental company to risks, so as to control the overall risks.

Specifically, the self-thawing client initiating the first renting request can first rent a batch of vehicles, because the vehicle ownership belongs to the renting company, the renting company can install GPS positioning equipment on the vehicles, the asset security of the renting company is ensured, and the renting company can acquire the GPS positioning information of the vehicles through the authorization of the user, so that the first vehicle positioning data corresponding to the target client initiating the first renting request is acquired.

As shown in fig. 3, the first rental and the second rental (multiple rentals, referred to as second rentals, and the same applies hereinafter) involve a customer rental account system, a car networking system, and a credit system, except that the first rental is to connect the vehicle information after the customer rents to the car networking system, and the car networking system monitors the rented vehicles periodically. The secondary renting will take the data of the internet of vehicles (the first vehicle positioning data of historical renting of the rented clients) as one of decision conditions, and introduce the data into a letter judgment link.

Further, based on the client information of the target client, at least one piece of renting vehicle information corresponding to the historical renting vehicles is obtained, and based on the renting vehicle information, the first vehicle positioning data corresponding to the historical renting vehicles is queried in a preset database.

In one embodiment, the rental request may include customer information, which may be information for uniquely marking and identifying the customer's identity, such as a pin number or a corporate unified social credit code as described above. For the individual customer, the identification card number is used as the unique identification mark and can be used as the customer information of the individual customer. For enterprise clients, the unified social credit code covers a plurality of key attributes such as legal identity, credit information and the like of the enterprise, and has uniqueness, so that the enterprise client can be used as client information of the enterprise clients.

In a rental business scenario, one customer may have multiple rental activities, each of which may involve different vehicles and items. Therefore, when obtaining the client information, besides the basic identity, the login account information of the client, various value-added service registration information related to lease historically and the like need to be considered.

In an embodiment, according to customer information, rental vehicle information of historical rental vehicles corresponding to the customer information is queried, and the rental vehicle information can comprise vehicle identification information, such as vehicle types, brands, license plates or frame numbers, and the like, which can be used for identifying the vehicles.

For example, when the customer information is an identification card number, all leasing contracts associated with the identification card number can be searched according to a preset association rule. Each lease contract generally includes fields such as start and stop time of the lease, vehicle details (e.g., license plate number, frame number, vehicle model number, vehicle identification code, etc.), and lease price. For example, in the data table structure design, the customer information and the vehicle information are connected in an associated mode as external keys, so that when multiple tables are queried, the relevant vehicle lease records can be quickly located through the main keys (such as customer IDs) of the customers.

In one embodiment, according to the information of the rented vehicles, first vehicle positioning data of historical rented vehicles of the target clients are searched in a preset database.

Wherein the preset database is a storage unit for storing vehicle positioning information at the server side, and the vehicle positioning information is usually derived from GPS positioning equipment installed on a rented vehicle.

Taking car rental as an example, when the vehicle is in the process of renting, the GPS device records accurate position information of the vehicle at set time intervals (for example, every 15s or every hundred meters), including longitude and latitude coordinates, record time stamps, running speed and the like. The data are stored in a cloud database or a structured database of a local server after encrypted transmission.

For example, when storing the vehicle positioning information, the vehicle positioning information may be stored in a separate table or a separate area according to the vehicle identification (such as license plate number) and the time dimension, so as to improve the query efficiency. When the first vehicle positioning data of the taxi from the history is required to be queried from the preset database, vehicle information of the taxi, such as license plate numbers and time range of the taxi, can be used for constructing SQL query sentences or other data query sentences. For example, the positioning data records are filtered through license plate numbers, and all positioning information in the period of renting the client is screened out in combination with the time field, so that all driving position tracks of the client in the period of renting the vehicle are completely presented.

In one embodiment, the self-fusing target customer is both a channel and a dealer, and its manner of use of the vehicle may be used to determine its intent to initiate a rental request.

For example, if the customer is in actual transport, operating revenue is obtained by running a certain mileage per day. However, if the self-thawing fraud is performed, only in order to obtain funds without using the vehicle, keeping the vehicle in a quasi-new vehicle state, and when someone finds the channel to buy the vehicle, reselling the vehicle to a third party, so that a good price can be sold. Therefore, self-fusion fraud is significantly different from customers of real customers in terms of the mileage operating rate, the former is basically 0, and the latter mileage is not less than 2000km. The leasing company can judge whether the client is self-thawing fraud according to the daily operation mileage and the like after the client purchases the vehicle.

Therefore, the positioning component (such as GPS) is arranged in the renting vehicle to collect positioning data of the vehicle rented from the melting target client, so as to obtain first vehicle positioning data of the renting vehicle, and relevant vehicle operation characteristics for judging the renting intention of the melting client are calculated to further judge whether the current renting request is a real business opportunity.

Further, vehicle positioning information sent by a positioning component installed in the historical taxi starting vehicle based on a preset interval period is received, vehicle operation mileage of the historical taxi starting vehicle is calculated based on the vehicle positioning information, first vehicle positioning data of the historical taxi starting vehicle is generated based on the vehicle positioning information, the vehicle operation mileage and the receiving time of the vehicle positioning information, and the taxi starting vehicle information and the first vehicle positioning data corresponding to the historical taxi starting vehicle are stored based on the preset database.

Specifically, a positioning assembly is installed in a history rented vehicle, and the positioning assembly has at least positioning and communication functions. The positioning component is set to send vehicle positioning information to the server once every preset interval period (such as 15 seconds), namely vehicle GPS information (namely vehicle positioning information) is reported to the longitude and latitude of the positioning point once every 15 seconds, and the data are stored and analyzed by the server (such as a car networking network system of a leasing company). The server receives and records the vehicle positioning information sent by the positioning component, wherein the recorded content comprises the corresponding client information of the vehicle, the vehicle identification information, the vehicle positioning coordinates, the vehicle positioning time and the like.

For example, the vehicle positioning information may be stored in the following form:

The system comprises a leasing company, a target client, a frame number, a device reporting time, a device locating component and a device locating coordinate, wherein the contract number is a contract tag number signed by the leasing company and the target client, the frame number is used for uniquely identifying vehicle identification information of the leasing vehicle, the device number is used for uniquely identifying the device number of the locating component installed on the leasing vehicle, the device reporting time is the time of the vehicle locating information reported by the locating component or the time of the server side receiving the vehicle locating information reported by the locating component, and the longitude and latitude of the device reporting time is the vehicle locating coordinate collected by the appointed bit component. It will be appreciated that the vehicle positioning information may record other relevant data, such as monitoring duration, fixed stay coordinates, etc., according to actual needs.

Further, vehicle operation mileage of the historical taxi-starting vehicle is calculated based on the vehicle positioning information, first vehicle positioning data of the historical taxi-starting vehicle is generated based on the vehicle positioning information, the vehicle operation mileage and the receiving time of the vehicle positioning information, and the taxi-starting vehicle information and the first vehicle positioning data corresponding to the historical taxi-starting vehicle are stored based on the preset database.

In an embodiment, the positioning points are connected into a smooth track according to a time sequence, so that the running mileage of the vehicle can be obtained. Corresponding to customers with the property of transportation production data such as commercial vehicles, the concept of starting work is provided, the transportation mileage of the vehicles exceeds 20 km per day, the daily starting work of the customers is defined, and the number of days/natural days of the starting work of the customers in one month is the monthly starting work rate of the customers.

On the basis of acquiring the vehicle running mileage, the generation of positioning data is a process of integrating and recording discrete vehicle positioning information and journey data. Meanwhile, the reception time is also an important reference element for generating positioning data. For example, anchor points of a certain historical rental vehicle are arranged in time sequence as T1, T2, and the numbers of the first and second, tn, and each Ti corresponds to a latitude and longitude coordinate and a time stamp. When the first vehicle positioning data is generated, not only the time and position of each Ti are recorded, but also the accumulated driving range from T1 to the current Ti is attached. Taking the example of the vehicle from the departure point to the stop point, the data generation stage builds a data table, wherein the accurate mileage change condition of each time the vehicle moves to one positioning point is recorded.

For example, the source of vehicle positioning information is typically an onboard GPS or a satellite positioning component such as beidou, which transmits data such as longitude, latitude, and time stamp of the vehicle at preset intervals (e.g., every 15 seconds or every 100 meters). For example, a taxi company background receives anchor point a (30.5 ° north latitude, 104.0 ° east longitude, and time stamp 09:00:00) from 9:00 am during running, and receives anchor point B (30.5001 ° north latitude, 104.0001 ° east longitude, and time stamp 09:00:10) after a certain period of time. To calculate the range between points a and B, a distance calculation formula under a geographic coordinate system, such as a classical HAVERSINE formula or a Vincenty algorithm based on the ground level, needs to be applied. Taking HAVERSINE formula as an example, it considers the spherical approximation of the earth, calculates the great circle distance between two points on the sphere through two sets of longitude and latitude coordinates, and the unit of the result can be kilometers or miles.

In an embodiment, the first vehicle positioning data is stored in a preset database of the server, and the server may query and call the first vehicle positioning data stored in the preset database by accessing the preset database. The preset database can be a cloud database or a local storage database.

S102, determining a first characteristic value of at least one vehicle operation characteristic of the historical rented vehicle based on the first vehicle positioning data;

In one embodiment, the first vehicle location data may include vehicle location information such as vehicle range, vehicle location coordinates, vehicle location time, etc. The vehicle operating characteristics may include vehicle daily mileage, monthly work rate, etc.

Illustratively, vehicle positioning coordinates of the vehicle within a single day range are determined according to vehicle positioning time of the vehicle, and vehicle driving mileage of the vehicle within the single day range is accumulated according to the vehicle positioning coordinates of the vehicle within the single day range. And similarly, according to the vehicle positioning time, screening all vehicle positioning coordinates in a certain month, and further accumulating the total vehicle driving mileage of the vehicle in the month. And judging whether the vehicle is used for normal operation or not according to the single-day vehicle driving mileage of the vehicle in the month, identifying the total number of days of the vehicle for normal operation in the month, dividing the total number of days of operation by the total number of days of the month, and calculating to obtain the month operating rate of the vehicle in the month.

S103, acquiring a second characteristic value corresponding to each vehicle running characteristic based on the client type of the target client;

In general, reference may be made to the case of the same industry for specific criteria for determining whether to self-fuse fraud. For example, the month mileage and month operation rate of the same industry and the same vehicle type can be counted, the proportion of the average value of the client compared with the average value of the same industry and the same vehicle type is calculated, when the average value of the mileage of the client exceeds monthly of the average value of the mileage of the same industry and the same vehicle type, the client can be considered to be not a self-thawing fraudulent client, otherwise, the client can be considered to be a real client.

In one embodiment, in a financial rental business scenario, precisely determining the customer type of the target customer is a critical pre-step for subsequent data analysis. Typically, customer types are classified according to the use of the customer rental vehicle, which is closely related to the vehicle rental intent. For example, when a target customer rental vehicle is mainly used for cargo transportation, the vehicle type of the rental may be a medium-sized or small-sized van, a large-sized van, or the like, and such a customer may be classified as a freight-type customer, whereas if the customer rental vehicle is used for passenger transportation, such as renting a large/medium-sized passenger car or a small-sized taxi, the customer may be determined as a passenger-transport-type customer.

Further, second vehicle positioning data of at least one other client corresponding to the client type is obtained, and the second characteristic value corresponding to at least one vehicle running characteristic corresponding to the client type is calculated based on the second vehicle positioning data.

By way of example, the customer type of the target customer is taken as a benchmark, and vehicle positioning data of other same vehicle types and same industries are screened out from a preset database. For example, if the target customer is a freight type customer leasing a medium-sized truck for urban logistics distribution, then all medium-sized trucks in the database are screened out and the vehicle positioning data is also applied to the urban logistics distribution scenario. The screening process ensures the comparability of subsequent calculation and avoids data deviation caused by different vehicle types or different industry application scenes.

And calculating the numerical value of the vehicle operation characteristics such as the month mileage, the day mileage, the month work rate and the like of each vehicle in turn according to the screened vehicle positioning data. Taking the calculation of the mileage as an example, obtaining the mileage value of the vehicle by summarizing the mileage data corresponding to all positioning records of the vehicle in one month, wherein the daily mileage is the mileage of the vehicle per day, and the monthly operating rate is the ratio of the actual running days of the vehicle in one month to the total days.

And carrying out average calculation on all the values of the same vehicle running characteristics to obtain an average value of the running characteristics of each vehicle, namely a second characteristic value. For example, the month mileage values of all the screened medium trucks in the urban logistics distribution scene are added and divided by the total number of vehicles, the obtained average month mileage value is the second characteristic value of the running characteristic of the vehicles, such as the month mileage, the month operating rate and the like, and the second characteristic values of the characteristics such as the day mileage, the month operating rate and the like can be calculated in the same way. The second characteristic values represent the average level of the vehicles of the same industry and the same vehicle type under the normal operation condition, and can be used as the judgment basis for the follow-up judgment of self-fusion fraud.

S104, calculating a first feature matching degree of the first feature value and the second feature value based on a feature matching algorithm;

In one embodiment, the feature matching algorithm is an algorithm for comparing and determining the degree of similarity of two or more features. In the field of machine learning and data analysis, common feature matching algorithms include distance-based matching (e.g., euclidean distance, manhattan distance), similarity-metric-based matching (e.g., cosine similarity, correlation coefficients), and model-based matching (e.g., support vector machine, neural network, etc.). These algorithms may be used alone or in combination to improve the accuracy and reliability of feature matching.

In an embodiment, the first feature value and the second feature value for performing feature matching correspond to the same vehicle operation feature, for example, the vehicle operation feature is a month mileage, and the first feature value for performing feature matching, that is, the month mileage, is compared with the second feature value of the month mileage.

In an embodiment, feature matching calculation is performed on the first feature value and the second feature value corresponding to each vehicle running feature for comparison, so as to obtain feature matching results of different vehicle running features. And then, carrying out weighted calculation on feature matching results of the vehicle operation features according to feature weights of different vehicle operation features to obtain a comprehensive feature matching degree, namely a first feature matching degree, and further judging whether the vehicle operation features applied by a target client to the leased vehicle accord with the vehicle operation standard of the client type or not so as to judge whether the leasing request initiated by the target client is a true business or not.

Further, based on the feature matching algorithm, calculating second feature matching degrees of the first feature value and the second feature value corresponding to the same vehicle operation feature, and based on weighting weights corresponding to the vehicle operation features, performing weighting calculation on the second feature matching degrees to obtain the first feature matching degrees.

In one embodiment, an appropriate feature matching algorithm may be selected for a particular vehicle operating feature type. For example, if the vehicle is characterized by numerical and co-dimensional characteristics (e.g., month mileage, day mileage, month work rate, etc.), a distance-based matching algorithm, such as Euclidean distance, may be employed.

Illustratively, euclidean distance can intuitively reflect how far or near two values are in the value space. Taking a month mileage as an example, assuming that the month mileage of a certain customer vehicle is 8000 km, the average value (second characteristic value) of the same vehicle type in the same industry is 8500 km, and the Euclidean distance between the two is calculated as follows:

d=|8000-8500|=500

illustratively, to convert a distance to a degree of matching, the distance is typically mapped to a similarity range. A simple normalization method, for example, may be used to convert the distance into a similarity as the second feature matching degree:

Wherein s is the second feature matching degree, and d _max is a preset maximum distance threshold.

For example, assuming that the preset maximum distance threshold d _max is 2000 km, the similarity (i.e., the second feature matching degree) is:

And performing second feature matching degree calculation on each vehicle operation feature to form a second feature matching degree set. For example, the weighting weights may be assigned according to differences in importance of the operating characteristics of the respective vehicles. For example, the weight of the month mileage is 0.5, the daily mileage is 0.3, the month operating rate is 0.2, and the sum of the weights is 1. Multiplying each second feature matching degree by the corresponding weight, and summing to obtain a first feature matching degree:

f=ω₁·S₁+ω₂·S₂+ω₂·S₂+…ω_n·S_n

Where f represents the first feature matching degree obtained by the weighting calculation, ω _n represents the weighting weight of the nth vehicle operation feature, and S _n represents the second feature matching degree of the nth vehicle operation feature.

For example, vehicle operating characteristics include month mileage, day mileage, and month work rate. Wherein, the second feature matching degree of the month mileage is 0.8 (weight is 0.5), the second feature matching degree of the day mileage is 0.9 (weight is 0.3), and the second feature matching degree of the month operation rate is 0.7 (weight is 0.2), then the first feature matching degree of the vehicle is:

f=0.5×0.8+0.3×0.9+0.2×0.7=0.81

i.e., the first feature matching degree of the rental vehicle indicating the target client is 0.81.

S105, when the first feature matching degree is larger than or equal to a preset matching degree threshold, determining that the renting request initiated by the target client is a real request.

In one embodiment, different preset matching degree thresholds are set for different customer types to represent acceptable deviation ranges of business characteristics and industry average values of customers of the customer types in vehicle use.

For example, for freight customers, because of the high requirements on the efficiency of the operation of the vehicle due to the timeliness and cost control of freight transportation, the preset matching degree threshold may be set to a higher value, such as 0.7-0.8, which means that the running characteristics of the customers have a higher similarity with the average value of the same vehicle model in the same industry, and the customers are considered as real requests. For passenger transport clients, passenger transport demands have large fluctuation, are obviously influenced by holidays, traveling seasons and the like, and the preset matching degree threshold can be moderately relaxed, for example, 0.5-0.6, so that the tolerance of irregular vehicle operation is reflected.

In an embodiment, when the first feature matching degree is greater than or equal to a preset matching degree threshold, determining that the vehicle running feature of the target client for the rented vehicle accords with the operational feature, that is, the renting request initiated by the target client is a real request. The rental company can grant the customer a second amount of renting. If the customer needs to do so, the renting can be continued, and the rented vehicle still can be docked to the internet of vehicles system to monitor its transportation characteristics to verify whether there is a risk of self-thawing fraud for the next after-sales of the renting.

In an embodiment, when the first feature matching degree is smaller than the preset matching degree threshold, determining that the vehicle running feature of the target client for the rented vehicle does not accord with the operational feature, suspending the second renting limit of the client, and if the continuous multiple periods require that the rented contract be cleared in advance. In order to avoid risk loss, if the self-thawing fraud condition exists in the client, the leasing company can require the self-thawing fraud client to settle the vehicle in advance, so as to avoid overdue.

In another embodiment, the first feature matching degree obtained by weighting and calculating the second feature matching degrees of the plurality of vehicle operation features may be used as a basis for determining the self-fusion fraud, and the second feature matching degree corresponding to each vehicle operation feature may also be used as a basis for determining the self-fusion fraud. For example, taking the month operation rate as a judgment basis, if the month operation rate of the target client for the use of the taxi starting vehicle is lower than a preset matching degree threshold (such as 0.7 or 70%), the target client is considered to be suspected of self-fusion fraud, otherwise, if the month operation rate of the target client for the use of the taxi starting vehicle is higher than or equal to the preset matching degree threshold, the target client is considered to be a real demand client.

For example, when a single vehicle running characteristic is taken as a basis for judgment, whether the vehicle has self-fusion fraud characteristics can be stored by adopting the following fields:

Date of day	Customer ID	Frame number	Month mileage	Month operating rate	Whether or not to self-fuse fraud
						2024-8-9	00001	LAS00001	8000	80%	Whether or not
2024-8-10	00001	LAS00001	15	5%	Is that

In one embodiment, when at least two historical taxi starting vehicles exist, the first feature matching degree of the second feature value and the first feature value of each historical taxi starting vehicle is calculated in sequence, and when the first feature matching degree of any first feature value and the second feature value is smaller than the preset matching degree threshold value, the taxi starting request initiated by the target client is determined to be a fraud request.

If one customer purchases a plurality of vehicles, part of the self-fusion fraud and part of the real renting are carried out, the deviation of the self-fusion fraud of the customer is indicated, and the whole customer can be judged as the self-fusion fraud customer, so that the renting cannot be continued.

Specifically, when there are at least two history rental vehicles, the first feature matching degree of the second feature value with the first feature value of each history rental vehicle is sequentially calculated. The second characteristic value is the average value of the running characteristics of each vehicle obtained by screening the vehicle positioning data of other vehicles of the same vehicle type and the same industry according to the client type of the target client. The first characteristic value is a value of a vehicle running characteristic calculated based on vehicle positioning data of a history of a target customer. And calculating the matching degree between the two through a feature matching algorithm to obtain a plurality of first feature matching degrees.

If the first feature matching degree of any first feature value and any second feature value is smaller than the preset matching degree threshold, determining that the renting request initiated by the target client is a fraud request. The method shows that the vehicle running characteristics of the target clients have larger deviation from the normal running characteristics of the same industry and the same vehicle type, and the risk of self-fusion fraud exists. For example, if a customer has a history of renting vehicles, a vehicle has a month mileage far lower than the average value of the same vehicle type in the same industry, and the first feature matching degree is smaller than a preset threshold value, it may be determined that the customer has a fraud suspicion.

When the first feature matching degree is greater than or equal to a preset matching degree threshold value, determining that the vehicle running feature of the target client for the rented vehicle accords with the operational feature, namely the renting request initiated by the target client is a real request. This means that the vehicle running characteristics of the customer are consistent with the normal running characteristics of the same industry and the same vehicle model, and can be regarded as real leasing requirements.

For example, when the target customer is determined to be a real renting customer, that is, the vehicle running characteristics and the like thereof meet the operational characteristics, the customer is indicated to have actual vehicle use requirements and good running conditions. The rental company can grant the customer a secondary rental amount allowing it to continue renting vehicles when needed. For example, for a freight customer engaged in urban logistics distribution in a parental period, the indexes such as the vehicle mileage, the operating rate and the like of the freight customer are consistent with the average value of the same vehicle type in the same industry, and the freight customer has no abnormal operation condition, and the leasing company can provide a secondary leasing amount for the freight customer, so that the service expansion or temporary vehicle utilization requirement of the freight customer can be met.

The vehicles which are rented for the second time still can be connected to the Internet of vehicles system. The internet of vehicles system can monitor the transportation characteristics of the vehicle, including travel route, speed, residence time, etc. in real time. Through the continuous monitoring of the transportation characteristics, the leasing company can verify the vehicle use condition of the client after the second time of leasing and judge whether the client has self-fusion fraud risk. For example, if the vehicle transportation characteristics of the second rental of the customer are consistent with those of the first rental and the business operation mode is met, the customer is not at risk of self-fusion fraud, and the rental company can continue to reassurance cooperation.

It can be seen that in the above-mentioned scheme, the first vehicle positioning data of the target customer historical rental vehicle is obtained to analyze the actual use and operation characteristics of the vehicle so as to judge the authenticity of the rental request. According to the first vehicle positioning data, calculating a first characteristic value representing the vehicle running characteristic, and obtaining a second characteristic value of the vehicle running characteristic according to the type of the client as a judging basis. And calculating a first feature matching degree of the first feature value and the second feature value through a feature matching algorithm, and comparing the first feature matching degree with a preset matching degree threshold value so as to judge whether the renting request initiated by the target client is a real request. When the first feature matching degree is larger than or equal to a preset matching degree threshold value, the vehicle operation feature of the target user for the use of the rented vehicle can be determined to be in accordance with the client type of the target user, so that the renting request of the target user is determined to be a real request, the accuracy and reliability of judging the authenticity of the renting request of the target user are improved, the technical problems of accurately identifying the real business opportunity of the renting request of the self-thawing client and improving the renting business volume are solved, risks can be controlled, potential clients can be prevented from being lost, and the business benefit of a renting company is improved.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

In an embodiment, a vehicle positioning-based rental-service recognition device is provided, and the vehicle positioning-based rental-service recognition device corresponds to the vehicle positioning-based rental-service recognition method in the embodiment one by one. As shown in fig. 4, the rental business machine identification device based on vehicle positioning includes a first data acquisition module 301, a first feature determination module 302, a second feature acquisition module 303, a feature matching calculation module 304, and a real request judgment module 305. The functional modules are described in detail as follows:

The first data obtaining module 301 is configured to obtain first vehicle positioning data corresponding to a historical rental vehicle of a target client when receiving a rental request initiated by the target client;

a first feature determination module 302 for determining a first feature value of at least one vehicle operating feature of the historic rental vehicle based on the first vehicle positioning data;

a second feature obtaining module 303, configured to obtain second feature values corresponding to the vehicle running features based on the client type of the target client;

A feature matching calculation module 304, configured to calculate a first feature matching degree of the first feature value and the second feature value based on a feature matching algorithm;

the real request judging module 305 is configured to determine that the lease request initiated by the target client is a real request when the first feature matching degree is greater than or equal to a preset matching degree threshold.

In one embodiment, the first data acquisition module 301 includes:

a rental vehicle information acquisition unit configured to acquire rental vehicle information corresponding to at least one historical rental vehicle corresponding to client information based on the client information of a target client;

And the first vehicle positioning data inquiring unit is used for inquiring the first vehicle positioning data corresponding to the historical renting vehicles in a preset database based on the renting vehicle information.

In an embodiment, the first data acquisition module 301 further includes:

A positioning information receiving unit for receiving vehicle positioning information sent by a positioning component installed in the historical rented vehicle based on a preset interval period;

a vehicle running mileage calculation unit for calculating the vehicle running mileage of the history rented vehicle based on the vehicle positioning information;

A first vehicle positioning data generation unit configured to generate first vehicle positioning data of the historic rental vehicle based on the vehicle positioning information, the vehicle running mileage, and a reception time of the vehicle positioning information;

And the data storage unit is used for storing the renting vehicle information and the first vehicle positioning data corresponding to the historical renting vehicles based on the preset database.

In an embodiment, the second feature acquisition module 303 includes:

a second vehicle positioning data obtaining unit, configured to obtain second vehicle positioning data of at least one other client corresponding to the client type;

and a second feature value calculating unit configured to calculate, based on the second vehicle positioning data, the second feature value corresponding to at least one of the vehicle running features corresponding to the customer type.

In an embodiment, the rental-service machine identification device based on vehicle positioning further includes a fraud request judging module, including:

A first matching feature calculation unit configured to sequentially calculate, when there are at least two history rental vehicles, a first feature matching degree of the second feature value with the first feature value of each history rental vehicle;

And the fraud request judging unit is used for determining that the lease starting request initiated by the target client is a fraud request when the first feature matching degree of any first feature value and the second feature value is smaller than the preset matching degree threshold value.

In an embodiment, the first data acquisition module 301 further includes:

The first-time renting unit is used for renting at least one renting vehicle to the target client based on a preset renting limit when the renting request initiated by the target client is the first-time renting request;

the first vehicle positioning data acquisition unit is used for acquiring the vehicle positioning information of the rented vehicle based on a preset data acquisition period and obtaining the first vehicle positioning data.

In one embodiment, the feature matching calculation module 304 includes:

A second feature matching degree calculation unit configured to calculate a second feature matching degree of the first feature value and the second feature value corresponding to the same vehicle running feature based on the feature matching algorithm;

And the first feature matching degree calculation unit is used for carrying out weighted calculation on the second feature matching degree based on the weighted weight corresponding to each vehicle running feature to obtain the first feature matching degree.

The invention provides a renting merchant identification device based on vehicle positioning, which calculates a first characteristic value representing the running characteristic of a vehicle according to first vehicle positioning data of a target client for renting the vehicle, and acquires a second characteristic value of the running characteristic of the vehicle according to the type of the client as a judgment basis. And calculating a first feature matching degree of the first feature value and the second feature value through a feature matching algorithm, and comparing the first feature matching degree with a preset matching degree threshold value so as to judge whether the renting request initiated by the target client is a real request. When the first feature matching degree is greater than or equal to a preset matching degree threshold value, the vehicle running feature of the target user for the use of the rented vehicle is determined to be in accordance with the client type of the target user, so that the renting request of the target client is determined to be a real request, and the accuracy and reliability of judging the authenticity of the renting request of the target client are improved.

For specific limitations on the vehicle-positioning-based rental-business recognition device, reference is made to the above limitation on the vehicle-positioning-based rental-business recognition method, and no further description is given here. The above-described modules in the rental business machine identification device based on vehicle positioning may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes non-volatile and/or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is for communicating with an external client via a network connection. The computer program, when executed by a processor, performs a function or step of a rental business machine identification method service side based on vehicle positioning.

In one embodiment, a computer device is provided, which may be a client, the internal structure of which may be as shown in FIG. 6. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is for communicating with an external server via a network connection. The computer program, when executed by a processor, performs a function or step of a rental business machine identification method client side based on vehicle positioning

In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

It should be noted that, the functions or steps implemented by the computer readable storage medium or the computer device may correspond to the relevant descriptions of the server side and the client side in the foregoing method embodiments, and are not described herein for avoiding repetition.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.

The foregoing embodiments are merely illustrative of the technical solutions of the present invention, and not restrictive, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that modifications may still be made to the technical solutions described in the foregoing embodiments or equivalent substitutions of some technical features thereof, and that such modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A rental business machine identification method based on vehicle positioning, the method comprising:

2. The vehicle positioning-based rental business machine identification method of claim 1, wherein the obtaining the first vehicle positioning data corresponding to the historical rental vehicles of the target client comprises:

based on the client information of a target client, acquiring the information of at least one renting vehicle corresponding to the historical renting vehicle corresponding to the client information;

and inquiring the first vehicle positioning data corresponding to the historical renting vehicles in a preset database based on the renting vehicle information.

3. The method for identifying a rental business based on vehicle positioning according to claim 2, wherein the step of querying a preset database for the first vehicle positioning data corresponding to the historical rental vehicle based on the rental vehicle information further comprises:

receiving vehicle positioning information sent by a positioning component installed in the historical rented vehicle based on a preset interval period;

calculating the vehicle running mileage of the historical rented vehicle based on the vehicle positioning information;

generating first vehicle positioning data of the historical rented vehicle based on the vehicle positioning information, the vehicle running mileage and the receiving time of the vehicle positioning information;

And storing the renting vehicle information and the first vehicle positioning data corresponding to the historical renting vehicles based on the preset database.

4. The vehicle positioning-based rental business machine identification method of claim 1, wherein the obtaining, based on the client type of the target client, a second feature value corresponding to each of the vehicle operation features comprises:

acquiring second vehicle positioning data of at least one other client corresponding to the client type;

And calculating the second characteristic value corresponding to at least one vehicle running characteristic corresponding to the customer type based on the second vehicle positioning data.

5. The method for identifying a rental business machine based on vehicle positioning according to claim 1, wherein after obtaining the second feature value corresponding to each of the vehicle running features based on the client type of the target client, the method further comprises:

when at least two historical taxi-starting vehicles exist, sequentially calculating a first feature matching degree of the second feature value and the first feature value of each historical taxi-starting vehicle;

and when the first feature matching degree of any first feature value and the second feature value is smaller than the preset matching degree threshold value, determining that the renting request initiated by the target client is a fraud request.

6. The method for identifying a rental car as defined in claim 1, further comprising, prior to the step of obtaining the first vehicle positioning data corresponding to the historical rental car of the target customer:

when the initial renting request initiated by the target client is the initial renting request, renting at least one renting vehicle to the target client based on a preset renting amount;

And acquiring the vehicle positioning information of the rented vehicle based on a preset data acquisition period to obtain the first vehicle positioning data.

7. The rental business machine identification method based on vehicle positioning of claim 1, wherein the calculating a first feature matching degree of the first feature value and the second feature value based on a feature matching algorithm comprises:

Calculating a second feature matching degree of the first feature value and the second feature value corresponding to the same vehicle running feature based on the feature matching algorithm;

And carrying out weighted calculation on the second feature matching degree based on the weighted weight corresponding to each vehicle running feature to obtain the first feature matching degree.

8. A rental-service-opportunity identification device based on vehicle positioning, characterized in that the rental-service-opportunity identification device based on vehicle positioning comprises:

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the vehicle localization based rental business identification method of any one of claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the vehicle localization based rental business machine identification method of any one of claims 1 to 7.