Vehicle abnormal departure identification method and device, electronic equipment and medium

Abstract

The embodiment of the invention provides a vehicle abnormal departure identification method and device, electronic equipment and a medium, and belongs to the field of financial science and technology. The method comprises the following steps: acquiring related information of a to-be-identified vehicle and GPS data of the to-be-identified vehicle; under the condition that the related information of the to-be-identified vehicle and the GPS data of the to-be-identified vehicle meet preset conditions, calculating a score of the to-be-identified vehicle according to a preset scoring rule and the GPS data of the to-be-identified vehicle, and obtaining a score value of the to-be-identified vehicle, the preset condition is used for determining that the to-be-identified vehicle is abnormal in a border city, and the border city is a city in a preset border city set; and when the score value is greater than a preset threshold value, determining that the to-be-identified vehicle is an abnormal departure vehicle. The embodiment of the invention can be suitable for a vehicle only loaded with a wireless GPS, and the applicability of the vehicle abnormal departure identification method is improved.

Description

Technical Field

[0001] The present application relates to the field of fintech, and particularly to a method, device, electronic device and medium for identifying abnormal vehicle departure from the country. Background Art

[0002] With the wide application of financial leasing business in the field of automobile sales, as the core leased asset in financial leasing transactions, effective supervision of vehicles has become a key link to ensure rent recovery and asset disposal. However, the phenomenon of malicious lessees illegally disposing of leased vehicles through reselling or pledging is not uncommon. Among them, the most difficult problem is that the vehicle is maliciously taken out of the country, because once the vehicle leaves the country, it is extremely difficult to recover, causing significant economic losses to the leasing company.

[0003] Existing methods for identifying abnormal vehicle departure from the country use in-vehicle GPS positioning data to obtain the vehicle position in real time and set up an electronic fence at the port of entry and exit for early warning. Although this method is effective for in-vehicle GPS devices with high-frequency updated positioning information, it is difficult to work for vehicles equipped with only wireless GPS devices. And wireless GPS devices are exactly the type of devices widely used by leasing companies due to their advantages such as concealed installation and low cost. Therefore, the existing methods for identifying abnormal vehicle departure from the country have low applicability. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a method, device, electronic device and medium for identifying abnormal vehicle departure from the country, aiming to solve the problem of low applicability of existing methods for identifying abnormal vehicle departure from the country.

[0005] To achieve the above object, in the first aspect of the embodiments of the present application, a method for identifying abnormal vehicle departure from the country is proposed, and the method includes:

[0006] Obtain relevant information of the vehicle to be identified and GPS data of the vehicle to be identified, where the relevant information includes user information of the lease user who leases the vehicle to be identified and lease information of the vehicle to be identified;

[0007] When the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet preset conditions, calculate a score of the vehicle to be identified according to a preset scoring rule and the GPS data of the vehicle to be identified to obtain a score value of the vehicle to be identified, where the preset conditions are used to determine that the vehicle to be identified is abnormal in a border city, and the border city is a city where preset border cities are concentrated;

[0008] When the score value is greater than a preset threshold, determine that the vehicle to be identified is an abnormal departure vehicle.

[0009] In some embodiments, the preset conditions include that the GPS offline time of the vehicle to be identified is greater than a first threshold, and at least one of the following:

[0010] The pick-up location of the vehicle to be identified does not belong to a border city;

[0011] Among the three addresses in the user information, none belongs to a border city. The three addresses include at least one of the work address of the rental user, the residential address of the rental user, the household register address of the rental user, the work address of the relative of the rental user, the residential address of the relative of the rental user, and the household register address of the relative of the rental user.

[0012] In some embodiments, the preset scoring rules include at least one of the following:

[0013] The difference between the date when the vehicle enters a border city and the vehicle pick-up date is less than a first threshold;

[0014] The number of administrative regions the vehicle has passed through within a preset historical period is greater than or equal to a second threshold;

[0015] The average daily mileage of the vehicle within a preset historical period is greater than a third threshold;

[0016] The distances between the stop points of the vehicle and the three addresses in the user information within a preset historical period all exceed a fourth threshold. The three addresses include at least one of the work address of the rental user, the residential address of the rental user, the household register address of the rental user, the work address of the relative of the rental user, the residential address of the relative of the rental user, and the household register address of the relative of the rental user.

[0017] In some embodiments, the preset scoring rules include at least one preset rule;

[0018] Before calculating the score of the vehicle to be identified according to the preset scoring rules and the GPS data of the vehicle to be identified to obtain the score value of the vehicle to be identified when the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet the preset conditions, the method further includes:

[0019] Obtain the GPS data of historical abnormally departing vehicles within a preset historical period;

[0020] Perform eigenvalue statistics on the GPS data of each historical abnormally departing vehicle within a preset historical period to obtain the GPS eigenvalue of each historical abnormally departing vehicle. The GPS eigenvalue includes the average daily mileage of the vehicle, the number of administrative regions the vehicle has passed through, and the GPS status of the vehicle;

[0021] Match the GPS eigenvalue of each of the multiple historical vehicles with abnormal departures with each preset rule in the preset scoring rule to obtain the score corresponding to each preset rule in the preset scoring rule;

[0022] When the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet the preset conditions, calculate the score of the vehicle to be identified according to the preset scoring rule and the GPS data of the vehicle to be identified to obtain the score value of the vehicle to be identified, including:

[0023] When the GPS data of the vehicle to be identified meets one or more preset rules in the preset scoring rule, obtain the score corresponding to the met preset rule;

[0024] Add up the scores corresponding to the met preset rules to obtain the score value of the vehicle to be identified.

[0025] In some embodiments, the GPS data of the historical vehicle with abnormal departure includes the longitude and latitude data of the historical vehicle with abnormal departure within a preset historical period and the data acquisition time;

[0026] Perform eigenvalue statistics on the GPS data of each historical vehicle with abnormal departure within a preset historical period to obtain the GPS eigenvalue of each historical vehicle with abnormal departure, including:

[0027] For each historical vehicle with abnormal departure, calculate the distance between the position points indicated by the longitude and latitude data of the historical vehicle with abnormal departure at two adjacent data acquisition times according to the GPS data of the historical vehicle with abnormal departure to obtain the driving mileage of the historical vehicle with abnormal departure between two adjacent data acquisition times;

[0028] Add up the driving mileage of the historical vehicle with abnormal departure between each two adjacent data acquisition times and divide by the total number of days to obtain the average daily mileage of the historical vehicle with abnormal departure, and the total number of days is determined according to the preset historical period;

[0029] According to the position points where the longitude and latitude data of the historical vehicle with abnormal departure fall on the administrative region map, count the number of administrative regions passed by the historical vehicle with abnormal departure;

[0030] If the duration between two adjacent acquisition times in the data acquisition times included in the GPS data of the historical vehicle with abnormal departure is greater than the preset duration, determine that the GPS status of the historical vehicle with abnormal departure is the GPS disconnection status.

[0031] In some embodiments, the matching of the GPS feature values of the multiple historical abnormally departing vehicles with each preset rule in the preset scoring rule to obtain the score corresponding to each preset rule in the preset scoring rule includes:

[0032] For each preset rule, perform:

[0033] Determine the score corresponding to the preset rule according to the number of GPS feature values of the multiple historical departing vehicles that meet the preset rule.

[0034] In some embodiments, after determining that the vehicle to be identified is an abnormally departing vehicle when the score value is greater than a preset threshold, the method further includes:

[0035] Generate an anti-fraud task order according to the relevant information of the vehicle to be identified;

[0036] Send the anti-fraud task order to the monitoring end;

[0037] Receive the investigation result fed back by the monitoring end according to the anti-fraud task order, where the investigation result is used to indicate whether the rental user is a fraudulent user;

[0038] When the investigation result indicates that the rental user is a fraudulent user, increase the score corresponding to the preset rule that the vehicle to be identified meets in the preset scoring rule;

[0039] When the investigation result indicates that the rental user is not a fraudulent user, decrease the score corresponding to the preset rule that the vehicle to be identified meets in the preset scoring rule, and / or adjust the threshold corresponding to the preset rule that the vehicle to be identified meets in the preset scoring rule.

[0040] To achieve the above object, a second aspect of the embodiments of the present application proposes a vehicle abnormal departure identification device, and the device includes:

[0041] An acquisition module, configured to acquire relevant information of the vehicle to be identified and GPS data of the vehicle to be identified, where the relevant information includes user information of the rental user who rents the vehicle to be identified and rental information of the vehicle to be identified;

[0042] A scoring module, configured to calculate a score of the vehicle to be identified according to a preset scoring rule and the GPS data of the vehicle to be identified when the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet preset conditions, to obtain a score value of the vehicle to be identified, where the preset conditions are used to determine that the vehicle to be identified has an abnormality in a border city, and the border city is a city with a concentrated preset border city;

[0043] An identification module, configured to determine the vehicle to be identified as an abnormally departing vehicle when the scoring value is greater than a preset threshold.

[0044] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the vehicle abnormal departure identification method described in the first aspect above is implemented.

[0045] To achieve the above object, a fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the vehicle abnormal departure identification method described in the first aspect above is implemented.

[0046] The vehicle abnormal departure identification method, device, electronic device and medium provided by the present application obtain relevant information of the vehicle to be identified and GPS data of the vehicle to be identified, screen the vehicle to be identified through preset conditions, score the vehicle to be identified that meets the preset conditions according to preset rules to obtain a scoring value of the vehicle to be identified, so as to improve the scoring efficiency of the vehicle to be identified, and improve the accuracy of scoring the vehicle to be identified through preset rules. The vehicle to be identified with a scoring value greater than the preset threshold is determined as an abnormally departing vehicle. Through preset conditions and preset rules, any vehicle equipped with GPS can be identified for abnormal departure of the vehicle, that is, it includes vehicles equipped with only wireless GPS devices, improving the applicability of the vehicle abnormal departure identification method. Description of the Drawings

[0047] Figure 1 is a flowchart of the vehicle abnormal departure identification method provided by the embodiments of the present application;

[0048] Figure 2 is another flowchart of the vehicle abnormal departure identification method provided by the embodiments of the present application;

[0049] Figure 3 is Figure 2 a flowchart of step S203 in

[0050] Figure 4 is another flowchart of the vehicle abnormal departure identification method provided by the embodiments of the present application;

[0051] Figure 5 is a structural diagram of the vehicle abnormal departure identification device provided by the embodiments of the present application;

[0052] Figure 6 is a hardware structural diagram of the electronic device provided by the embodiments of the present application. Detailed Embodiments

[0053] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] It should be noted that although functional module division is carried out in the device schematic diagram and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in a different module division in the device or a different sequence in the flowchart. Terms such as "first" and "second" in the description, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0056] With the wide application of financial leasing business in the field of automobile sales, as the core leased asset in financial leasing transactions, the effective supervision of vehicles has become a key link to ensure rent recovery and asset disposal. However, the phenomenon that malicious lessees illegally dispose of leased vehicles through reselling or pledging is not uncommon, especially the situation where vehicles are maliciously taken out of the country, which is particularly difficult because once the vehicle leaves the country, it is extremely difficult to recover, causing significant economic losses to leasing companies.

[0057] Existing methods for identifying abnormal vehicle departure use in-vehicle GPS positioning data to obtain the vehicle's position in real time and set up an electronic fence at the entry and exit ports for early warning. Although this method is effective for in-vehicle GPS devices with high-frequency updated positioning information, it is difficult to work for vehicles equipped with only wireless GPS devices. And wireless GPS devices, due to their advantages such as concealed installation and low cost, are exactly the types of devices widely used by leasing companies. Therefore, the existing methods for identifying abnormal vehicle departure have low applicability.

[0058] Based on this, the embodiments of the present application provide a method, device, electronic device and medium for identifying abnormal vehicle departure, aiming to solve the problem of low applicability of existing methods for identifying abnormal vehicle departure.

[0059] The method, device, electronic device and medium for identifying abnormal vehicle departure provided by the embodiments of the present application will be specifically described through the following embodiments. First, the method for identifying abnormal vehicle departure in the embodiments of the present application will be described.

[0060] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0061] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0062] The vehicle abnormal departure recognition method provided by the embodiments of the present application relates to the field of financial technology. The vehicle abnormal departure recognition method provided by the embodiments of the present application can be applied to a terminal, or to a server side, or can also be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the vehicle abnormal departure recognition method, etc., but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet-type devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] It should be noted that in each specific embodiment of the present application, when it comes to relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or separate consent will be obtained through methods such as pop-up windows or redirecting to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0065] Figure 1 is a schematic flowchart of the vehicle abnormal departure identification method provided by the embodiments of the present application. Please refer to Figure 1 The vehicle abnormal departure identification method provided by the embodiments of the present application may include, but is not limited to, steps S101 to S103.

[0066] Step S101: Obtain the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified. The relevant information includes the user information of the rental user who rents the vehicle to be identified and the rental information of the vehicle to be identified.

[0067] In this step, the relevant information of the vehicle to be identified includes the user information of the rental user who rents the vehicle to be identified and the rental information of the vehicle to be identified. The user information of the rental user includes, but is not limited to, the basic information of the rental user, driver's license information, payment information, information of the user's relatives, historical rental records, and credit records. The rental information of the vehicle to be identified includes, but is not limited to, the basic information of the vehicle, rental details, vehicle status, rental terms, and vehicle usage records.

[0068] Among them, the basic information of the rental user includes, but is not limited to, the three-address information of the rental user, that is, the residential address, work address, and household registration address of the rental user. The basic information of the vehicle includes, but is not limited to, the license plate number, vehicle identification code, etc. The rental details of the vehicle include, but is not limited to, the rental start date and end date, and rental duration.

[0069] Step S102: When the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet the preset conditions, calculate the score of the vehicle to be identified according to the preset scoring rules and the GPS data of the vehicle to be identified, and obtain the score value of the vehicle to be identified. The preset conditions are used to determine that the vehicle to be identified is abnormal in the border city, and the border city is a city where preset border cities are concentrated.

[0070] In this step, the preset conditions are used to determine whether there is an abnormality in the vehicle to be identified. In the embodiments of the present application, the preset conditions can be set as follows: when the vehicle enters a border city, the time when the GPS device installed in the vehicle loses signal exceeds the set system threshold, the pick-up point of the vehicle is not in the border city, and the three-address information of the user renting the vehicle and the three-address information of the relatives of the user renting the vehicle are not in the border city. The system threshold can be set according to the actual situation and will not be limited here; the preset conditions can also be set to meet one or more of the following: when the vehicle enters a border city, the time when the GPS device installed in the vehicle loses signal exceeds the set system threshold, the pick-up point of the vehicle is not in the border city, the three-address information of the user renting the vehicle and the three-address information of the relatives of the user renting the vehicle are not in the border city; the preset conditions can also be set according to the actual situation and will not be limited here.

[0071] The border city set can be constructed by querying the list of cities with border ports in each region according to publicly available Internet materials, or the city where the last offline point of the vehicle is located before losing contact of the abnormal departure vehicle identified in this embodiment can be added to the border city set to update the border city set.

[0072] The preset scoring rule can be set according to the actual situation or can be set by analyzing historical abnormal departure vehicles, and will not be limited here.

[0073] Exemplarily, the preset conditions are set to meet the following: the time when the GPS device installed in the vehicle loses signal exceeds the set system threshold, the pick-up point of the vehicle is not in the border city, and the three-address information of the user renting the vehicle and the three-address information of the relatives of the user renting the vehicle are not in the border city; the system threshold is set to 30 minutes. If the GPS signal of vehicle A is offline for 40 minutes after entering the border city, the pick-up location of vehicle A is a non-border city, and the three-address information of the user renting vehicle A and the user's relatives does not belong to the border city, then it is considered that vehicle A has an abnormality, and vehicle A is scored through the preset scoring rule to obtain the scoring value of vehicle A.

[0074] Step S103, when the scoring value is greater than the preset threshold, determine that the vehicle to be identified is an abnormal departure vehicle.

[0075] In this step, the preset threshold can be set by analyzing historical abnormal departure vehicles or can be set according to the actual situation, and will not be limited here; when the scoring value of the vehicle to be identified is greater than the preset threshold, determine that the vehicle to be identified is an abnormal departure vehicle.

[0076] In this implementation, by obtaining the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified, screening the vehicle to be identified through preset conditions, scoring the vehicle to be identified that meets the preset conditions according to preset rules, obtaining the score value of the vehicle to be identified, so as to improve the scoring efficiency of the vehicle to be identified, and improving the accuracy of scoring the vehicle to be identified through the preset rules. Determine the vehicle to be identified as an abnormal departure vehicle for the vehicle to be identified with a score value greater than the preset threshold. Through the preset conditions and preset rules, any vehicle equipped with GPS can be identified for abnormal vehicle departure, that is, it includes vehicles equipped with only wireless GPS devices, improving the applicability of the vehicle abnormal departure identification method.

[0077] In some embodiments, the preset conditions include that the GPS offline time of the vehicle to be identified is greater than a first threshold, and at least one of the following:

[0078] The pick-up location of the vehicle to be identified does not belong to a border city;

[0079] At least one of the three addresses in the user information does not belong to a border city, and the three addresses include at least one of the work address of the rental user, the residential address of the rental user, the household registration address of the rental user, the work address of the relative of the rental user, the residential address of the relative of the rental user, and the household registration address of the relative of the rental user.

[0080] In some embodiments, the preset scoring rules include at least one of the following:

[0081] The difference between the date when the vehicle enters the border city and the pick-up date of the vehicle is less than the first threshold;

[0082] The number of administrative regions the vehicle has passed through within a preset historical period is greater than or equal to a second threshold;

[0083] The average daily mileage of the vehicle within a preset historical period is greater than a third threshold;

[0084] The distances between the stop points of the vehicle and the three addresses in the user information within a preset historical period all exceed a fourth threshold, and the three addresses include at least one of the work address of the rental user, the residential address of the rental user, the household registration address of the rental user, the work address of the relative of the rental user, the residential address of the relative of the rental user, and the household registration address of the relative of the rental user.

[0085] It should be noted that the first threshold, the second threshold, the third threshold, and the fourth threshold can be set according to the actual situation and are not limited here.

[0086] In another implementation, a setting scheme for a first threshold, a second threshold, a third threshold, and a fourth threshold is provided. Among them, the first threshold can be set to 30 days, the second threshold can be set to N, where N = (the date when the vehicle enters the border city - the date when the vehicle first crosses administrative regions + 1) / 2, the third threshold can be set to 3 times the average daily mileage of the vehicle obtained by dividing the total mileage of the vehicle as a rental vehicle to date by the number of rented days, and the fourth threshold can be set to 50 km.

[0087] As Figure 2 shown, an embodiment of the present application also provides a method for identifying abnormal vehicle departure, which may include but is not limited to steps S201 to S206.

[0088] Step S201: Obtain relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified. The relevant information includes user information of the rental user who rents the vehicle to be identified and rental information of the vehicle to be identified.

[0089] The implementation manner of step S201 is the same as that of step S101. For details, refer to the description in step S101 and will not be elaborated here.

[0090] Step S202: Obtain the GPS data of historical abnormally departing vehicles within a preset historical period.

[0091] In this step, collect the GPS data of multiple historical abnormally departing vehicles within a preset historical period. The preset historical period can be set according to actual situations and is not limited here.

[0092] Step S203: Perform eigenvalue statistics on the GPS data of each historical abnormally departing vehicle within the preset historical period to obtain the GPS eigenvalue of each historical abnormally departing vehicle. The GPS eigenvalue includes the average daily mileage of the vehicle, the number of administrative regions the vehicle has passed through, and the GPS status of the vehicle.

[0093] In this step, perform eigenvalue statistics on the GPS data of each historical departing vehicle within the preset historical period to obtain the GPS eigenvalue of each historical departing vehicle. The GPS eigenvalue includes the average daily mileage of the vehicle, the number of administrative regions the vehicle has passed through, and the GPS status of the vehicle. The GPS status of the vehicle is divided into a normal communication status and a GPS loss of contact status.

[0094] Exemplarily, within 7 days before the loss of contact of historical abnormally departing vehicle A (the preset historical period is 7 days), the total driving mileage is 2800 kilometers, and it has passed through four administrative regions b, c, d, and e. By performing eigenvalue statistics on the GPS data of vehicle A, it can be obtained that the average daily mileage of vehicle A is 400 kilometers, the number of administrative regions vehicle A has passed through is 4, and the GPS status of the vehicle is a loss of contact status.

[0095] Step S204: Match the GPS feature values of multiple historical abnormally departing vehicles with each preset rule in the preset scoring rule to obtain the scores corresponding to each preset rule in the preset scoring rule.

[0096] In this step, match the GPS feature values of multiple historical abnormally departing vehicles with each preset rule in the preset scoring rule. For each preset rule: count the number of historical abnormally departing vehicles that meet the rule, and based on the number of historical abnormally departing vehicles that meet each preset rule, obtain the scores corresponding to each preset rule in the preset scoring rule.

[0097] Step S205: When the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet the preset conditions, calculate the score of the vehicle to be identified according to the preset scoring rule and the GPS data of the vehicle to be identified to obtain the score value of the vehicle to be identified. The preset conditions are used to determine that the vehicle to be identified has an abnormality in the border city, and the border city is a city where preset border cities are concentrated.

[0098] The implementation manner of step S205 is the same as that of step S102. For details, refer to the description in step S102 and will not be elaborated here.

[0099] Step S206: When the score value is greater than the preset threshold, determine that the vehicle to be identified is an abnormally departing vehicle.

[0100] The implementation manner of step S206 is the same as that of step S103. For details, refer to the description in step S102 and will not be elaborated here.

[0101] When the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet the preset conditions in step S205, calculating the score of the vehicle to be identified according to the preset scoring rule and the GPS data of the vehicle to be identified to obtain the score value of the vehicle to be identified may include, but is not limited to, steps S2051 to S2052.

[0102] Step S2051: When the GPS data of the vehicle to be identified meets one or more preset rules in the preset scoring rule, obtain the scores corresponding to the preset rules that are met.

[0103] Step S2052: Add up the scores corresponding to the preset rules that are met to obtain the score value of the vehicle to be identified.

[0104] In this implementation manner, the preset scoring rule may include one preset rule or multiple preset rules. The GPS of the vehicle to be identified is matched with the preset rules in the preset scoring rule. When the vehicle to be identified meets a certain preset rule, the vehicle to be identified obtains the score corresponding to the preset rule. Herein, the preset rule can be set according to the actual situation or can be set by statistically analyzing the behavioral characteristics of historical abnormally departing vehicles, and no limitation is made herein.

[0105] Exemplarily, the preset scoring rule includes: a first preset rule, a second preset rule, a third preset rule, and a fourth preset rule. Among them, the first preset rule is that the difference between the date when the vehicle enters the border city and the vehicle's lease start date is less than the first threshold, and the first threshold is set to 30 days; the second preset rule is that the number of administrative regions passed through by the vehicle within the preset historical period is greater than or equal to the second threshold, and the second threshold is set to N, where N = (the date when the vehicle enters the border city - the date when the vehicle first has a cross - administrative - region behavior + 1) / 2; the third preset rule is that the average daily mileage of the vehicle within the preset historical period is greater than the third threshold, and the third threshold is set to 3 times the historical average daily mileage of the vehicle obtained by dividing the total mileage of the vehicle as a leased vehicle to date by the number of days leased; the fourth preset rule is that the distances between the vehicle's stop points and the three addresses in the user information all exceed the fourth threshold, and the fourth threshold is set to 50 km. The three addresses include at least one of the work address of the leased user, the residential address of the leased user, the household register address of the leased user, the work address of the relative of the leased user, the residential address of the relative of the leased user, and the household register address of the relative of the leased user.

[0106] The lease start date of Vehicle B is January 1, 2023, the date of entering the border city is January 20, 2023, the preset historical period is 7 days, the date of the first cross - administrative - region behavior is January 10, 2023, the number of administrative regions passed through within 7 days is 8, the total mileage within 7 days is 3,850 kilometers, the total mileage to date is 60,000 kilometers, the total number of leased days is 300 days, the three addresses of the user and relatives are City A, City B, and City C, the stop points within 7 days are City D, with a distance of 100 km from City A, 150 km from City B, and 200 km from City C. The score corresponding to the first preset rule is 20 points, the score of the second preset rule is 30 points, the score of the third preset rule is 40 points, and the score of the fourth preset rule is 50 points.

[0107] Match the GPS data of vehicle B with the first preset rule. The difference between the date when vehicle B enters the border city and the lease start date = 20 days, 20 days < 30 days, which meets the first preset rule, and record 20 points; match the GPS data of vehicle B with the second preset rule, N = (2023.1.20 - 2023.1.10 + 1) / 2 = 6, the number of administrative regions vehicle B has passed through is 8 > 6, which meets the second preset rule, and record 30 points; match the GPS data of vehicle B with the third preset rule. The average daily mileage of vehicle B's total historical days: 60,000 km / 300 days = 200 km / day, the third threshold is 200 km / day * 3 = 600 km / day, and the average daily mileage of vehicle B within 7 days is 3850 km / 7 days = 550 km / day, which does not meet the third preset rule, so no points are recorded; match the GPS data of vehicle B with the fourth preset rule. The distances between city D and the three addresses in the user information all exceed 50 km, which meets the fourth preset rule, and record 50 points; in summary, the score value of vehicle B is 100 points.

[0108] In this embodiment, corresponding scores are assigned to each scoring rule in the preset scoring rules based on historical abnormally departing vehicles in advance to improve the accuracy and reliability of scoring. And based on multiple characteristic values such as the average daily mileage, the number of administrative regions passed through, and the GPS status in the vehicle GPS data, the vehicle behavior is comprehensively evaluated to more accurately identify abnormally departing vehicles. And the preset scoring rules can be adjusted and extended according to the actual situation to adapt to different scenarios and requirements, improving the flexibility and adaptability of the vehicle abnormally departing identification method.

[0109] In some embodiments, as Figure 3 shown, the statistical calculation of the characteristic values of the GPS data of each of the historical abnormally departing vehicles within the preset historical period in step S203 to obtain the GPS characteristic values of each of the historical abnormally departing vehicles may include, but is not limited to, steps S2031 to S2034.

[0110] Step S2031: For each of the historical abnormally departing vehicles, calculate the distance between the position points indicated by the longitude and latitude data of the historical abnormally departing vehicle at two adjacent data acquisition times according to the GPS data of the historical abnormally departing vehicle, and obtain the driving mileage of the historical departing vehicle between two adjacent data acquisition times;

[0111] Step S2032: Add up the driving mileage of the historical departing vehicle between each two adjacent data acquisition times and divide by the total number of days to obtain the average daily mileage of the historical departing vehicle, and the total number of days is determined according to the preset historical period;

[0112] Step S2033: According to the position points where the latitude and longitude data of the historical departing vehicles fall on the administrative region map, count the number of administrative regions passed by the historical departing vehicles.

[0113] Step S2034: If there are two adjacent data collection times in the GPS data of the historical abnormal departing vehicle with a time duration greater than a preset duration, determine that the GPS status of the historical departing vehicle is the GPS loss-of-contact status.

[0114] In this implementation, the GPS data of the historical abnormal departing vehicle includes the latitude and longitude data of the historical abnormal departing vehicle and the data collection time within a preset historical period. Calculate the distance between the position points indicated by the latitude and longitude data of the historical abnormal departing vehicle at two adjacent data collection times to obtain the driving mileage of the historical departing vehicle between two adjacent data collection times. Add up the driving mileage of the historical departing vehicle between each two adjacent data collection times and divide by the total number of days in the preset historical period to obtain the average daily mileage of the historical departing vehicle. It is also possible to calculate the weekly mileage, average weekly mileage, monthly mileage, average monthly mileage, total mileage, etc. of the vehicle according to requirements.

[0115] In this implementation, it is possible to count the number of administrative regions passed by the historical departing vehicle according to the position points where the latitude and longitude data of the historical departing vehicle fall on the administrative region map; it is also possible to convert the latitude and longitude data of the historical departing vehicle into vehicle positioning points corresponding to the latitude and longitude data through an electronic map, and count the number of administrative regions passed by the historical departing vehicle according to the administrative regions to which the vehicle positioning points belong.

[0116] In this implementation, it is possible to determine that the GPS status of the historical departing vehicle is the GPS loss-of-contact status by the fact that there are two adjacent data collection times in the GPS data of the historical abnormal departing vehicle with a time duration greater than a preset duration, or determine that the GPS status of the historical departing vehicle is the GPS loss-of-contact status according to the historical departing vehicle whose GPS fails to report data for more than a preset duration. After determining that the GPS status of the historical passing vehicle is the GPS loss-of-contact status, record the GPS device number, the start time of the GPS loss-of-contact status, the latitude and longitude coordinates of the last positioning point of the vehicle before the GPS loss-of-contact status, and the administrative region information where the last positioning point of the vehicle before the GPS loss-of-contact status is located.

[0117] In this embodiment, by combining multiple dimensions such as the driving mileage of the vehicle, the average daily mileage, the number of administrative regions passed through, and the GPS status, etc., to achieve a comprehensive risk assessment of the vehicle behavior, so as to timely discover the abnormal behavior of the vehicle and improve the accuracy of identifying abnormal vehicles.

[0118] In some other implementation manners, when matching the vehicle to be identified with the scoring rules in the preset scoring rules, the above-mentioned method of calculating the GPS eigenvalue of the historical departure vehicle's GPS data can be adopted to calculate the GPS eigenvalue of the vehicle to be identified, so as to facilitate the matching with the scoring rules in the preset scoring rules.

[0119] In some implementation manners, in the case that the GPS data of the vehicle to be identified conforms to one or more preset rules in the preset scoring rules in step S1024, obtaining the score corresponding to the conforming preset rule may include but is not limited to the following:

[0120] For each preset rule, execute:

[0121] Determine the score corresponding to the preset rule according to the number of GPS eigenvalues of the multiple historical departure vehicles that meet the preset rule.

[0122] In this implementation manner, for each preset rule, count the number of GPS eigenvalues of multiple historical departure vehicles that meet the preset rule, and determine the score corresponding to the preset rule according to the weight ratio of the historical departure vehicles that meet the preset rule among the multiple historical departure vehicles.

[0123] In some other implementation manners, corresponding scores can be configured for each preset rule according to the actual situation.

[0124] Exemplarily, there are 100 historical abnormally departing vehicles. The preset rules include the first preset rule, the second preset rule, the third preset rule, and the fourth preset rule. The number of historical abnormally departing vehicles that meet the first preset rule is 20, the number of historical abnormally departing vehicles that meet the second preset rule is 30, the number of historical abnormally departing vehicles that meet the first preset rule is 25, and the number of historical abnormally departing vehicles that meet the first preset rule is 15. Then the score corresponding to the first preset rule is 20 points, the score corresponding to the second preset rule is 30 points, the score corresponding to the third preset rule is 25 points, and the score corresponding to the fourth preset rule is 15 points.

[0125] In this implementation manner, counting the number of historical departure vehicles that meet each preset rule and converting the number into a score realizes the quantitative assessment of risks, avoids the deviation of subjective judgment, makes the scores configured for each preset rule more objective and scientific. By assigning scores to each rule, the scoring results of multiple rules can be integrated to comprehensively evaluate the departure risk of the vehicle, and the scores corresponding to each rule can also be dynamically adjusted according to the actual situation to make the scoring of each rule more in line with the actual scenario.

[0126] Such as Figure 4As shown in the figure, an embodiment of the present application further provides a method for identifying abnormal vehicle departure, which may include but is not limited to steps S101 to S108.

[0127] Step S101: Obtain the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified. The relevant information includes the user information of the rental user who rents the vehicle to be identified and the rental information of the vehicle to be identified.

[0128] Step S102: When the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet the preset conditions, calculate the score of the vehicle to be identified according to the preset scoring rules and the GPS data of the vehicle to be identified, and obtain the score value of the vehicle to be identified. The preset conditions are used to determine that the vehicle to be identified is abnormal in the border city, and the border city is a city where preset border cities are concentrated.

[0129] Step S103: When the score value is greater than the preset threshold, determine that the vehicle to be identified is an abnormal departure vehicle.

[0130] Step S104: Generate an anti-fraud task order according to the relevant information of the vehicle to be identified.

[0131] Step S105: Send the anti-fraud task order to the monitoring end.

[0132] Step S106: Receive the investigation result feedback by the monitoring end according to the anti-fraud task order. The investigation result is used to indicate whether the rental user is a fraudulent user.

[0133] Step S107: When the investigation result indicates that the rental user is a fraudulent user, increase the score value corresponding to the preset rule that the vehicle to be identified meets in the preset scoring rules.

[0134] Step S108: When the investigation result indicates that the rental user is not a fraudulent user, decrease the score value corresponding to the preset rule that the vehicle to be identified meets in the preset scoring rules, and / or adjust the threshold value corresponding to the preset rule that the vehicle to be identified meets in the preset scoring rules.

[0135] In this implementation manner, an anti-fraud task order is generated according to the relevant information of the vehicle to be identified. The anti-fraud task order may include but is not limited to the basic information of the vehicle (such as license plate number, vehicle model), rental user information (such as name, contact information), GPS trajectory data of the vehicle, and description of abnormal behavior (such as departure time, departure location). The monitoring end determines whether there is fraud behavior of the rental user through on-site investigation, telephone verification, data analysis, etc. according to the anti-fraud task order to generate an investigation result.

[0136] Receive the investigation results from the monitoring terminal. If the leased user is confirmed as a fraudulent user in the investigation results, increase the score corresponding to the preset rule that the vehicle to be identified conforms to. For example, if the vehicle to be identified conforms to the rule of "excessively high average daily mileage", increase the score of this rule to enhance the weight of this rule in subsequent risk assessments. If the leased user is confirmed as a non-fraudulent user in the investigation results, decrease the score corresponding to the preset rule that the vehicle to be identified conforms to, and / or adjust the threshold of this rule. If the vehicle to be identified conforms to the rule of "too far distance between stop points", but the investigation results show that the user's behavior is normal, then decrease the score of this rule or increase the distance threshold to reduce misjudgment.

[0137] Exemplarily, the GPS eigenvalue of the vehicle C to be identified is scored 85 points, which is greater than the preset threshold of 80 points. It is determined that the vehicle C to be identified is an abnormally departing vehicle. An anti-fraud task order is generated based on the relevant information of vehicle C and sent to the monitoring terminal. After receiving the anti-fraud task order, the monitoring terminal contacts the leased user by phone and verifies the purpose of their itinerary. If the investigation results show that the leased user is determined to have fraudulent behavior, that is, the leased user is a fraudulent user, according to the investigation results, increase the score corresponding to the preset rule that this vehicle conforms to, such as the rule of "excessively high average daily mileage", from the original 20 points to 25 points. If the investigation results show that the leased user is a non-fraudulent user, then decrease the score corresponding to the preset rule that this vehicle conforms to, such as the score corresponding to "too far distance between stop points", from the original 15 points to 10 points, or adjust the distance threshold from 50 km to 60 km.

[0138] In this embodiment, by generating an anti-fraud task order and dynamically adjusting the rule scores and thresholds, it is possible to effectively identify and handle fraudulent behaviors in vehicle departure risks, while optimizing the preset scoring rules and improving the accuracy and adaptability of scoring for vehicles to be identified.

[0139] In some other implementation manners, when the investigation results indicate that the leased user is a fraudulent user, by analyzing data such as leased user information, vehicle information, and contract signing time, obtain other contracts related to the vehicle to be identified (i.e., associated contracts), and verify whether the vehicles in the associated contracts are abnormally departing vehicles. If they are abnormally departing vehicles, extract the most recent positioning data before the vehicle in the associated contract loses contact, determine the city where it is located. If this city is not in the concentrated border cities, add it to the concentrated border cities to update the concentrated border cities, so as to provide early warning capabilities for vehicles that lose contact in this city in the future.

[0140] Figure 5 This is the structural schematic diagram of the vehicle abnormal departure identification device provided by the embodiments of the present application. Please refer to Figure 5, An embodiment of the present application further provides a vehicle abnormal departure recognition device 800, which can implement the above-mentioned vehicle abnormal departure recognition method. The vehicle abnormal departure recognition device 800 includes:

[0141] An acquisition module 801, configured to acquire relevant information of the vehicle to be recognized and GPS data of the vehicle to be recognized. The relevant information includes user information of the rental user who rents the vehicle to be recognized and rental information of the vehicle to be recognized;

[0142] A scoring module 802, configured to calculate a score of the vehicle to be recognized according to a preset scoring rule and the GPS data of the vehicle to be recognized when the relevant information of the vehicle to be recognized and the GPS data of the vehicle to be recognized meet a preset condition, so as to obtain a score value of the vehicle to be recognized. The preset condition is used to determine that the vehicle to be recognized has an abnormality in a border city, and the border city is a city where preset border cities are concentrated;

[0143] An identification module 803, configured to determine that the vehicle to be recognized is an abnormal departure vehicle when the score value is greater than a preset threshold.

[0144] In some embodiments, the preset condition includes that the GPS offline time of the vehicle to be recognized is greater than a first threshold, and at least one of the following:

[0145] The pick-up location of the vehicle to be recognized does not belong to a border city;

[0146] At least one of the three addresses in the user information does not belong to a border city. The three addresses include at least one of the rental user's work address, the rental user's residential address, the rental user's household registration address, the rental user's relative's work address, the rental user's relative's residential address, and the rental user's relative's household registration address.

[0147] In some embodiments, the preset scoring rule includes at least one of the following:

[0148] The difference between the date when the vehicle enters the border city and the vehicle rental start date is less than a first threshold;

[0149] The number of administrative regions the vehicle has passed through within a preset historical period is greater than or equal to a second threshold;

[0150] The average daily mileage of the vehicle within a preset historical period is greater than a third threshold;

[0151] During a preset historical period, the distances between the stop points of the vehicle and the three addresses in the user information all exceed a fourth threshold, where the three addresses include at least one of the work address of the rental user, the residential address of the rental user, the household register address of the rental user, the work address of the relative of the rental user, the residential address of the relative of the rental user, and the household register address of the relative of the rental user.

[0152] In some embodiments, the preset scoring rule includes at least one preset rule;

[0153] The vehicle abnormal departure recognition device 800 further includes:

[0154] A historical acquisition module, configured to acquire the GPS data of historical abnormally departing vehicles during a preset historical period;

[0155] A feature value calculation module, configured to perform feature value statistics on the GPS data of each historical abnormally departing vehicle during a preset historical period to obtain the GPS feature value of each historical abnormally departing vehicle, where the GPS feature value includes the average daily mileage of the vehicle, the number of administrative regions passed by the vehicle, and the GPS status of the vehicle;

[0156] A score determination module, configured to match the GPS feature values of multiple historical abnormally departing vehicles with each preset rule in the preset scoring rule to obtain the score corresponding to each preset rule in the preset scoring rule;

[0157] The scoring module 802 includes:

[0158] A score acquisition sub-module, configured to acquire the score corresponding to the preset rule that is met when the GPS data of the vehicle to be recognized meets one or more preset rules in the preset scoring rule;

[0159] A score calculation sub-module, configured to add up the scores corresponding to the preset rules that are met to obtain the score value of the vehicle to be recognized.

[0160] In some embodiments, the GPS data of the historical abnormally departing vehicle includes the longitude and latitude data of the historical abnormally departing vehicle during a preset historical period and the data acquisition time;

[0161] The feature value calculation module includes:

[0162] A mileage calculation unit, configured to, for each historical abnormally departing vehicle, calculate the distance between the position points indicated by the longitude and latitude data of the historical abnormally departing vehicle at two adjacent data acquisition times according to the GPS data of the historical abnormally departing vehicle to obtain the driving mileage of the historical departing vehicle between two adjacent data acquisition times;

[0163] A mean calculation unit, configured to add up the driving mileage of the historical departing vehicles between every two adjacent data collection times, and divide the sum by the total number of days to obtain the daily mileage mean of the historical departing vehicles, where the total number of days is determined according to the preset historical period;

[0164] An administrative region statistics unit, configured to count the number of administrative regions passed through by the historical departing vehicles according to the position points where the longitude and latitude data of the historical departing vehicles fall on the administrative region map;

[0165] A GPS status determination unit, configured to determine that the GPS status of the historical departing vehicle is in a GPS disconnection state if there is a time period between two adjacent data collection times in the GPS data of the historical abnormally departing vehicle that is greater than a preset time period.

[0166] In some embodiments, the score determination module includes:

[0167] A score assignment unit, configured to, for each preset rule, perform:

[0168] Determine the score corresponding to the preset rule according to the number of GPS feature values of the multiple historical departing vehicles that meet the preset rule.

[0169] In some embodiments, the vehicle abnormal departure identification device 800 further includes:

[0170] A task order generation module, configured to generate an anti-fraud task order according to the relevant information of the vehicle to be identified;

[0171] A sending module, configured to send the anti-fraud task order to the monitoring end;

[0172] A receiving module, configured to receive the investigation result feedback by the monitoring end according to the anti-fraud task order, where the investigation result is used to indicate whether the rental user is a fraudulent user;

[0173] An adjustment module, configured to, when the investigation result indicates that the rental user is a fraudulent user, increase the score corresponding to the preset rule that the vehicle to be identified meets in the preset scoring rule;

[0174] When the investigation result indicates that the rental user is not a fraudulent user, decrease the score corresponding to the preset rule that the vehicle to be identified meets in the preset scoring rule, and / or adjust the threshold corresponding to the preset rule that the vehicle to be identified meets in the preset scoring rule.

[0175] The specific implementation manner of the vehicle abnormal departure identification device 800 is basically the same as the specific embodiments of the above vehicle abnormal departure identification method, and will not be elaborated here.

[0176] An embodiment of the present application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above-mentioned vehicle abnormal departure recognition method is implemented. The electronic device can be any intelligent terminal including a desktop computer, a tablet computer, a mobile phone, a vehicle-mounted computer, etc.

[0177] Please refer to Figure 6 , Figure 6 which is a schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present application. The electronic device includes:

[0178] A processor 901, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present application;

[0179] A memory 902, which can be implemented in forms such as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 902 and are called by the processor 901 to execute the vehicle abnormal departure recognition method of the embodiments of the present application;

[0180] An input / output interface 903, which is used to implement information input and output;

[0181] A communication interface 904, which is used to implement communication interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0182] A bus 905, which transmits information between the various components of the device (such as the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0183] Among them, the processor 901, the memory 902, the input / output interface 903, and the communication interface 904 are communicatively connected to each other inside the device through the bus 905.

[0184] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned vehicle abnormal departure recognition method is implemented.

[0185] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0186] The vehicle abnormal departure recognition method, device, electronic device and medium provided by the embodiments of the present application obtain relevant information of the vehicle to be recognized and the GPS data of the vehicle to be recognized, screen the vehicle to be recognized through preset conditions, score the vehicle to be recognized that meets the preset conditions according to preset rules, and obtain the score value of the vehicle to be recognized, so as to improve the scoring efficiency of the vehicle to be recognized, and improve the accuracy of scoring the vehicle to be recognized through preset rules. Determine the vehicle to be recognized with a score value greater than the preset threshold as an abnormal departure vehicle. Through preset conditions and preset rules, any vehicle equipped with GPS can be recognized for abnormal departure of the vehicle, that is, it includes vehicles equipped with only wireless GPS devices, improving the applicability of the vehicle abnormal departure recognition method.

[0187] The embodiments described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0188] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or combine certain steps, or different steps.

[0189] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0190] Those of ordinary skill in the art will understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or a suitable combination thereof.

[0191] As used in the description of the present application and the above-mentioned drawings, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0192] It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Here, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or a similar expression means any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0193] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above-mentioned unit division is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0194] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0195] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0196] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of this application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs and other various media that can store programs.

[0197] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, and thus do not limit the scope of rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall fall within the scope of rights of the embodiments of this application.

Claims

1. A method for identifying abnormal departure of a vehicle, characterized in that, The method includes: Obtaining relevant information of the vehicle to be identified and GPS data of the vehicle to be identified, where the relevant information includes user information of the rental user who rents the vehicle to be identified and rental information of the vehicle to be identified; When the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet preset conditions, calculating a score of the vehicle to be identified according to a preset scoring rule and the GPS data of the vehicle to be identified to obtain a score value of the vehicle to be identified. The preset conditions are used to determine that the vehicle to be identified is abnormal in a border city, and the border city is a city where preset border cities are concentrated; When the score value is greater than a preset threshold, determining that the vehicle to be identified is an abnormal departure vehicle.

2. The method according to claim 1, characterized in that, The preset conditions include that the GPS offline time of the vehicle to be identified is greater than a first threshold, and at least one of the following: The pick-up location of the vehicle to be identified does not belong to a border city; None of the three addresses in the user information belong to a border city. The three addresses include at least one of the rental user's work address, the rental user's residential address, the rental user's household registration address, the rental user's relative's work address, the rental user's relative's residential address, and the rental user's relative's household registration address.

3. The method according to claim 1, characterized in that, The preset scoring rule includes at least one of the following: The difference between the date when the vehicle enters the border city and the vehicle's pick-up date is less than a first threshold; The number of administrative regions the vehicle has passed through within a preset historical period is greater than or equal to a second threshold; The average daily mileage of the vehicle within a preset historical period is greater than a third threshold; The distances between the vehicle's stop points and the three addresses in the user information within a preset historical period all exceed a fourth threshold. The three addresses include at least one of the rental user's work address, the rental user's residential address, the rental user's household registration address, the rental user's relative's work address, the rental user's relative's residential address, and the rental user's relative's household registration address.

4. The method according to claim 1, wherein The preset scoring rule includes at least one preset rule; Before calculating a score of the vehicle to be identified according to a preset scoring rule and the GPS data of the vehicle to be identified to obtain a score value of the vehicle to be identified when the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet preset conditions, the method further includes: Obtaining GPS data of historical abnormal departure vehicles within a preset historical period; Performing eigenvalue statistics on the GPS data of each historical abnormal departure vehicle within a preset historical period to obtain GPS eigenvalues of each historical abnormal departure vehicle. The GPS eigenvalues include the average daily mileage of the vehicle, the number of administrative regions the vehicle has passed through, and the GPS status of the vehicle; Matching the GPS eigenvalues of multiple historical abnormal departure vehicles with each preset rule in the preset scoring rule to obtain a score corresponding to each preset rule in the preset scoring rule; When the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet the preset conditions, the score of the vehicle to be identified is calculated according to the preset scoring rules and the GPS data of the vehicle to be identified to obtain the score value of the vehicle to be identified, including: When the GPS data of the vehicle to be identified meets one or more preset rules in the preset scoring rules, obtaining the score corresponding to the preset rules that meet the score; The scores corresponding to the preset rules that are met are added together to obtain the score value of the vehicle to be identified.

5. The method according to claim 4, wherein The GPS data of the historical abnormal departure vehicles includes the latitude and longitude data of the historical abnormal departure vehicles within a preset historical period and the data collection time; The characteristic value statistics of the GPS data of each of the historical abnormal departure vehicles within a preset historical period are performed to obtain the GPS characteristic value of each of the historical abnormal departure vehicles, including: For each of the historical abnormal departure vehicles, the distance between the position points indicated by the latitude and longitude data of the historical abnormal departure vehicle at two adjacent data collection moments is calculated based on the GPS data of the historical abnormal departure vehicle, so as to obtain the mileage of the historical departure vehicle between the two adjacent data collection moments; The mileage of the historical departure vehicles between each two adjacent data collection moments is added up, and the result is divided by the total number of days to obtain the average daily mileage of the historical departure vehicles, where the total number of days is determined according to the preset historical period; According to the location points where the latitude and longitude data of the historically departing vehicles fall on the administrative district map, the number of administrative districts that the historically departing vehicles have passed through is obtained by counting; If the duration between two adjacent data collection moments included in the GPS data of the historical abnormal departure vehicle is greater than a preset duration, it is determined that the GPS state of the historical departure vehicle is a GPS lost connection state.

6. The method according to claim 4, wherein The step of matching the GPS characteristic values ​​of the plurality of historical abnormal departure vehicles with each preset rule in the preset scoring rules to obtain the score corresponding to each preset rule in the preset scoring rules includes: For each preset rule, execute: The score corresponding to the preset rule is determined according to the number of GPS characteristic values ​​of the plurality of historical departure vehicles that satisfy the preset rule.

7. The method according to claim 1, characterized in that, When the score value is greater than a preset threshold, after determining that the vehicle to be identified is an abnormal departure vehicle, the method further includes: Generate an anti-fraud task list based on the relevant information of the vehicle to be identified; Sending the anti-fraud task sheet to the monitoring terminal; Receiving the investigation result fed back by the monitoring end according to the anti-fraud task sheet, wherein the investigation result is used to indicate whether the rental user is a fraudulent user; In the case where the investigation result indicates that the rental user is a fraudulent user, increasing the score corresponding to the preset rule that the to-be-identified vehicle complies with in the preset scoring rule; When the investigation result indicates that the rental user is not a fraudulent user, the score corresponding to the preset rule that the vehicle to be identified complies with in the preset scoring rule is reduced, and / or the threshold corresponding to the preset rule that the vehicle to be identified complies with in the preset scoring rule is adjusted.

8. A vehicle abnormal departure recognition device, characterized in that, The device includes: An acquisition module, configured to acquire relevant information of the vehicle to be identified and GPS data of the vehicle to be identified, where the relevant information includes user information of the rental user who rents the vehicle to be identified and rental information of the vehicle to be identified; A scoring module, configured to calculate a score of the vehicle to be identified according to a preset scoring rule and the GPS data of the vehicle to be identified to obtain a score value of the vehicle to be identified when the relevant information of the vehicle to be identified and the GPS data of the vehicle to be identified meet preset conditions, where the preset conditions are used to determine that the vehicle to be identified is abnormal in a border city, and the border city is a city where preset border cities are concentrated; An identification module, configured to determine that the vehicle to be identified is an abnormal departure vehicle when the score value is greater than a preset threshold.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the vehicle abnormal departure identification method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the vehicle abnormal departure identification method according to any one of claims 1 to 7 is implemented.

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