A vehicle service processing method and vehicle service platform system based on user portrait

Abstract

This invention discloses a vehicle management method and platform system based on user profiles. It acquires real-time vehicle driving behavior and vehicle body change information; aggregates a large amount of vehicle risk control information to establish a vehicle risk control database and a statistical model; sets user profile preferences based on the statistical model and vehicle risk control information; selects specific vehicle risk control information from the vehicle risk control database based on user profile preferences; and provides specific vehicle risk control information. By comprehensively considering both user driving behavior and vehicle body changes, it helps screen users, thereby accurately matching vehicle management product positioning, enabling competitor comparison, revenue risk control, user recommendation, and precise matching. This truly reflects the matching of car insurance with accident rates, accurately reflects customer segmentation, helps avoid homogenization of car insurance products, helps optimize the relative ratio of operating costs and charges, and can also help users improve their driving habits to a certain extent.

Description

Technical Field

[0001] This invention relates to data processing for auto insurance business, and more particularly to a method for processing vehicle-related data and a vehicle-related platform system based on user profiles. Background Technology

[0002] With technological advancements and improved living standards, cars have become commonplace in households, leading to a booming auto insurance business. However, many property insurance companies often incur losses in their auto insurance operations. The reasons for this include: firstly, insurance fraud and unreasonable parts-to-vehicle ratios result in high payout rates, such as insuring commercial vehicles as non-commercial vehicles; secondly, the comprehensive expense ratio in the auto insurance industry is between 30% and 40%, with over 30% of premiums allocated to channel fees, which in reality can reach over 50%.

[0003] User profiling is a virtual representation of a real user. It's a target user model built upon a series of real data, used for product demand mining and interaction design. Its core is the virtualized representation of common characteristics among a large number of real users. For example, it involves labeling users' habits, behaviors, and attributes to abstract a complete picture of a virtual user, providing possibilities for many internet businesses such as target positioning, user selection, advertising recommendations, content distribution, and event marketing. It is the foundation of big data technologies such as computational advertising, personalized recommendations, and intelligent marketing, and also the cornerstone of big data businesses and technologies. The core of user profiling is setting and categorizing tags for a large number of users. Tags are usually highly refined, artificially defined characteristic identifiers, such as age, gender, region, interests, purchasing preferences, and behavioral habits. From these tag sets, a complete picture of a user's information can be abstracted. Each tag describes one dimension of the user, and these dimensions are interconnected, collectively forming a holistic description of the user. User profiling technology has been applied to many online shopping platforms such as Taobao and JD.com. However, in the car insurance business, which has similar user profiles (i.e., similar user information tags), it has not yet been applied to car insurance data processing. Summary of the Invention

[0004] This invention provides a vehicle service processing method and vehicle service platform system based on user profiles. The technical problems to be solved include: how to screen users to match vehicle service product positioning, and realize competitor comparison, revenue risk control, user recommendation and accurate matching.

[0005] The technical solution of the present invention is as follows:

[0006] A vehicle service processing method based on user profiles includes the following steps:

[0007] Real-time acquisition of vehicle driving behavior information and body change information, and use of vehicle driving behavior information and body change information as vehicle risk control information;

[0008] A large amount of vehicle risk control information was collected to establish a vehicle risk control database and a statistical model.

[0009] Based on the statistical model and its vehicle risk control information, user profile preferences are set;

[0010] Based on user profile preferences, select specific vehicle risk control information from the vehicle risk control database that has the same user profile;

[0011] Provides risk control information for specific vehicles.

[0012] Preferably, after establishing the statistical model, compensation data is used to validate the model.

[0013] Preferably, during operation, updated compensation data is used to adjust the statistical model.

[0014] Preferably, after summarizing the data, before establishing the vehicle risk control database, data preprocessing is also performed.

[0015] Preferably, user profile preferences are set based on customer needs, statistical models, and vehicle risk control information.

[0016] Preferably, specific vehicle risk control information is provided only to the specific customer who made the request, based on the customer's needs.

[0017] Preferably, driving behavior information is acquired through active data acquisition, while vehicle body change information is acquired through passive sensing.

[0018] Preferably, before acquiring the vehicle's driving behavior information and body change information in real time, the method further includes: acquiring vehicle registration information and user registration information to establish a connection between the user and the vehicle; and using the vehicle registration information, user registration information, driving behavior information, and body change information as its vehicle risk control information.

[0019] Preferably, the vehicle processing method based on user profile further includes the step of: obtaining the user's online credit information based on the user registration information, and the vehicle risk control information also includes online credit information.

[0020] A vehicle service platform system based on user profiles, comprising:

[0021] The real-time acquisition module acquires real-time information on the vehicle's driving behavior and body changes, and uses this information as vehicle risk control information.

[0022] The model aggregation module aggregates a large amount of vehicle risk control information, establishes a vehicle risk control database, and builds statistical models.

[0023] The user profiling module sets user profile preferences based on statistical models and vehicle risk control information.

[0024] The user selection module selects specific vehicle risk control information from the vehicle risk control database based on user profile preferences;

[0025] The information provision module provides risk control information for specific vehicles.

[0026] By adopting the above-mentioned solution, this invention comprehensively considers two factors: user driving behavior and vehicle body changes. This helps to screen users, thereby accurately matching the positioning of car insurance products, enabling competitor comparison, revenue risk control, user recommendation, and precise matching. It truly reflects the matching between car insurance and the claim rate, accurately reflects customer segmentation, helps avoid the homogenization of car insurance products, helps optimize the relative ratio of operating costs and charges, and can also help users improve their driving habits to a certain extent. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the first embodiment of the vehicle management method based on user profiles according to the present invention;

[0028] Figure 2 This is a schematic diagram of the second embodiment of the vehicle processing method based on user profiles according to the present invention;

[0029] Figure 3 This is a schematic diagram of the third embodiment of the vehicle processing method based on user profiles described in this invention;

[0030] Figure 4 This is a schematic diagram of the fourth embodiment of the vehicle management method based on user profiles according to the present invention;

[0031] Figure 5 This is a schematic diagram of the fifth embodiment of the vehicle management method based on user profiles described in this invention;

[0032] Figure 6 This is a schematic diagram of the sixth embodiment of the vehicle management method based on user profiles according to the present invention;

[0033] Figure 7 This is a schematic diagram of the seventh embodiment of the vehicle management method based on user profiles according to the present invention;

[0034] Figure 8 This is a schematic diagram of the eighth embodiment of the vehicle management method based on user profiles described in this invention;

[0035] Figure 9 This is a schematic diagram of the ninth embodiment of the vehicle processing method based on user profiles described in this invention;

[0036] Figure 10 This is a schematic diagram of the tenth embodiment of the vehicle service platform system based on user profiles described in this invention. Detailed Implementation

[0037] To facilitate understanding of the present invention, a more detailed description is provided below with reference to the accompanying drawings and specific embodiments. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in this specification. It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element present.

[0038] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0039] User profiling, or user information tagging, helps build the underlying data foundation to serve upper-layer applications. The final form of user profiling involves analyzing user behavior and ultimately tagging each user. The main purpose of this invention is to adapt to technological and societal changes by employing user profiling technology to provide a platform, services, and data to numerous vehicle service companies. This enables these companies to select suitable customers based on their needs and provide customized services—services that precisely meet those customers' requirements. Car insurance is the most important service for vehicle service companies; others include map services, route services, resource replenishment services, and information services. For ease of description, the following explanation of vehicle services primarily uses car insurance as an example, but it should be understood that the user profiling-based vehicle service processing method of this invention can be used in car insurance business as well as other vehicle services.

[0040] like Figure 1As shown, one embodiment of the present invention is a vehicle service processing method based on user profiles, which includes the following steps: real-time acquisition of vehicle driving behavior information and vehicle body change information, using the vehicle driving behavior information and vehicle body change information as its vehicle risk control information; summarizing a large amount of vehicle risk control information, establishing a vehicle risk control database and establishing a statistical model; setting user profile preferences based on the statistical model and its vehicle risk control information; selecting specific vehicle risk control information with the same user profile from the vehicle risk control database based on user profile preferences; and providing specific vehicle risk control information. User profile preferences are the customer's needs or choices for users. Setting user profile preferences involves customizing user profile tags and classifying them into appropriate categories based on customer preferences. By adopting the above solution, the present invention comprehensively considers two factors: user driving behavior and vehicle body changes, which can help screen users, thereby accurately matching the positioning of vehicle service products, realizing competitor comparison, revenue risk control, user recommendation and precise connection, truly reflecting the matching of car insurance and accident rate, accurately reflecting customer segmentation, helping to avoid homogenization of car insurance products, helping to optimize the relative ratio of operating costs and charges, and also helping users improve their driving habits to a certain extent.

[0041] Preferably, after establishing the statistical model, compensation data is used to validate the model. For example... Figure 2 As shown, one embodiment of the present invention is a vehicle management method based on user profiles, which includes the following steps: acquiring vehicle driving behavior information and vehicle body change information in real time, and using the vehicle driving behavior information and vehicle body change information as its vehicle risk control information; summarizing a large amount of vehicle risk control information, establishing a vehicle risk control database and establishing a statistical model, and using compensation data to verify the model; setting user profile preferences according to the statistical model and its vehicle risk control information; selecting specific vehicle risk control information with the same user profile from the vehicle risk control database according to the user profile preferences; and providing specific vehicle risk control information. Validating the model using claims data is essential. However, unlike the validation methods commonly understood by those skilled in the art, it involves not only verifying the model's feasibility but also its authenticity and robustness. As mentioned in the background section, a significant reason for losses in the auto insurance business of many property insurance companies is insurance fraud and unreasonable parts-to-vehicle ratios leading to high payout rates. Therefore, we establish a vehicle risk control database and a statistical model to help clients screen users. We then verify the authenticity of the statistical model by inputting a large amount of existing claims data. Specific statistical models can employ principal component analysis and cluster analysis, implemented using specialized software such as IBM SPSS Statistics or statistical analysis systems. These are not the inventive points of this invention; they can simply be used appropriately.

[0042] Preferably, during operation, updated claims data is used to adjust the statistical model. Updating claims data means continuously using new claims data to adjust the statistical model, ensuring that the model accurately reflects current claims data. This is because times are changing, and fraudulent methods are also evolving, so it's necessary to adapt to these changes. For example... Figure 3 As shown, one embodiment of the present invention is a vehicle management method based on user profiles, which includes the following steps: acquiring vehicle driving behavior information and vehicle body change information in real time, and using the vehicle driving behavior information and vehicle body change information as its vehicle risk control information; summarizing a large amount of vehicle risk control information, establishing a vehicle risk control database and establishing a statistical model, and using compensation data to verify the model; adjusting the statistical model using updated compensation data during operation; setting user profile preferences according to the statistical model and its vehicle risk control information; selecting specific vehicle risk control information with the same user profile from the vehicle risk control database according to the user profile preferences; and providing specific vehicle risk control information. This allows the statistical model to accurately reflect compensation data, minimizing the occurrence of insurance fraud, and also helps customers identify malicious users.

[0043] Preferably, after aggregation and before establishing a vehicle risk control database, data preprocessing is performed. For example... Figure 4 As shown, one embodiment of the present invention is a vehicle management method based on user profiles, which includes the following steps: real-time acquisition of vehicle driving behavior information and vehicle body change information, using the vehicle driving behavior information and vehicle body change information as its vehicle risk control information; summarizing a large amount of vehicle risk control information, preprocessing the data, establishing a vehicle risk control database and establishing a statistical model; setting user profile preferences based on the statistical model and its vehicle risk control information; selecting specific vehicle risk control information with the same user profile from the vehicle risk control database based on the user profile preferences; and providing specific vehicle risk control information. Data preprocessing has many methods, mainly including data cleaning, data integration, data reduction, and data transformation, used to preprocess the collected raw data to improve data quality, etc. These are not the inventive points of this invention; they can be used reasonably.

[0044] Preferably, user profile preferences are set based on customer needs, statistical models, and vehicle risk control information. For example... Figure 5As shown, one embodiment of the present invention is a vehicle management method based on user profiles, which includes the following steps: real-time acquisition of vehicle driving behavior information and vehicle body change information, using the vehicle driving behavior information and vehicle body change information as its vehicle risk control information; summarizing a large amount of vehicle risk control information, establishing a vehicle risk control database and establishing a statistical model; setting user profile preferences according to customer needs, the statistical model and its vehicle risk control information; selecting specific vehicle risk control information with the same user profile from the vehicle risk control database according to the user profile preferences; and providing specific vehicle risk control information. Preferably, setting user profile preferences includes extracting keywords from customer needs, combining the keywords to obtain at least two keyword groups, using each keyword group to poll the statistical model, combining the polling results with vehicle risk control information to obtain the user profile preferences for the customer needs, and establishing an association between the user profile preferences and the customer needs, wherein the user profile preferences include: customer name, customer needs, keyword groups, user profile and vehicle risk control information. Vehicle risk control information is a key element of this invention. It can not only be used in conjunction with user profiles to define tags, but also provide feedback quickly and efficiently, meeting the needs of various customers for rapid search of the users they require.

[0045] Preferably, risk control information for specific vehicles is provided only to the specific customer who requests it, based on that customer's needs. For example... Figure 6 As shown, one embodiment of the present invention is a vehicle management method based on user profiles, which includes the following steps: acquiring vehicle driving behavior information and vehicle body change information in real time, and using the vehicle driving behavior information and vehicle body change information as its vehicle risk control information; summarizing a large amount of vehicle risk control information, establishing a vehicle risk control database and establishing a statistical model; setting user profile preferences according to customer needs, statistical models and vehicle risk control information; selecting specific vehicle risk control information with the same user profile from the vehicle risk control database according to user profile preferences; and providing specific vehicle risk control information only to the specific customer who made the request. This enables a rapid and efficient response to customer needs, achieving same-day feedback.

[0046] Preferably, driving behavior information is acquired through active data acquisition; vehicle body change information is acquired through passive sensing. For example... Figure 7As shown, one embodiment of the present invention is a vehicle management method based on user profiles, which includes the following steps: real-time acquisition of vehicle driving behavior information and vehicle body change information, using the vehicle driving behavior information and vehicle body change information as its vehicle risk control information; wherein, driving behavior information is acquired by actively acquiring data; vehicle body change information is acquired by passive sensing; a large amount of vehicle risk control information is summarized to establish a vehicle risk control database and establish a statistical model; user profile preferences are set according to the statistical model and its vehicle risk control information; specific vehicle risk control information with the same user profile is selected from the vehicle risk control database according to the user profile preferences; and specific vehicle risk control information is provided. Driving behavior information can be actively obtained through vehicle data. Preferably, driving behavior information includes driving behavior information, parking behavior information, driving environment information, and traffic violation records.

[0047] Ideally, driving behavior information includes: average speed, speeding status, speeding duration, engine RPM, rapid acceleration, sudden braking, sharp turns, lane change frequency, gear shift frequency, seatbelt status, warm-up time, headlight status, high beam status, turn signal status during turns, turn signal status during lane changes, idling time, mobile phone usage, and fatigue driving. This driving behavior information can reveal a user's driving habits, which is crucial. A fatigued driver or someone with severe road rage is far more likely to cause an accident than a normal driver. Current designers often perceive young people as more dangerous than middle-aged people, and their first car as more dangerous than others. However, this is a survivor bias. Driving behavior information reflects driving habits; drivers with good driving habits are far safer than those with poor driving habits. These drivers with good driving habits can be targeted for user profile preferences. In other words, customers can express their needs, which include their choices, such as their user profile preferences—the users they want to select. For example, his user profile preferences include tags such as: 30 to 40 years old, more than 3 years of driving experience, speeding less than 10%, speeding time less than 5 seconds, continuous driving time less than 2 hours, and using turn signals when turning or changing lanes. Or, similarly, select some low-risk target users, and provide them with more preferential services.

[0048] Ideally, parking behavior information includes: parking selection, parking speed, handbrake, parking gear, closing doors, closing windows, and turning off lights. This parking behavior information can reveal a user's parking habits. Good parking habits not only help prevent theft and ensure parking safety, but also help avoid accidental losses due to improper parking, such as forgetting to turn off lights leading to battery damage requiring replacement or jumper ignition, or scratches caused by poor parking locations.

[0049] The advantage is that driving environment information includes: driving time, driving duration, road conditions, mileage for each terrain type, driving time for each terrain type, and parking time for each terrain type. Driving under different road conditions or terrain environments carries significantly different risks. Continuous driving or driving in the early morning also carries significantly different risks. Similarly, the driving risk of someone with a high number of traffic violations differs from that of someone with fewer violations. Therefore, by comprehensively considering driving behavior information, parking behavior information, driving environment information, and traffic violation records, it is possible to use big data to screen customer user profiles and preferences, match them with specific vehicle risk control information, and then customize vehicle services individually, forming unique vehicle processing methods. This can also create a vehicle service platform system that provides unique customized services to different customers based on big data.

[0050] Actively acquiring data can be done by proactively obtaining it during the transmission of current driving data. In other words, by utilizing the large amount of driving data from the vehicle's infotainment system or onboard computer system, driving behavior information, parking behavior information, driving environment information, and traffic violation records can be proactively obtained. Of course, if traffic violation records are not transmitted to the vehicle's infotainment system or onboard computer system, they can also be proactively obtained from the network. The reason it is called proactive data acquisition is that most of this data can be obtained directly or through statistical analysis. Some special data may require additional technical collection methods to obtain, but these are basically mature technologies, so they can be obtained proactively.

[0051] Ideally, the vehicle body change information includes, but is not limited to, the following: changes in acceleration, vehicle posture, and distance to surrounding obstacles. Acceleration changes are relatively easy to obtain; for example, they can be calculated using vehicle data or obtained through sensor or lidar ranging. Vehicle posture changes can be obtained using one or more position sensors, multiple cameras combined with multiple lidar ranging devices, or by using multiple cameras to create a three-dimensional spatial transformation for calculation. Distance changes to surrounding obstacles can be obtained using lidar ranging. Passive sensing of vehicle body change information primarily determines the safety or safety factor of vehicle driving based on objective factors. For example, very rapid acceleration changes indicate poor driving habits; large changes in vehicle posture indicate a harsh driving environment, safety risks, or potential accidents; small or large changes in the distance to surrounding obstacles indicate a dangerous driving style or environment. Passive sensing of vehicle body change information mainly involves sensing changes in position, speed, and distance using devices such as sensors, cameras, and lidar.

[0052] Better still, it generates multiple curves for driving behavior information and multiple curves for vehicle body change information based on driving behavior information and vehicle body change information, respectively. The multiple curves for driving behavior information include driving behavior information curves, parking behavior information curves, driving environment information curves, and fitted curves or line graphs for traffic violation records. Driving behavior information curves include average speed change curves, fitted curves or line graphs for speeding status, fitted curves or line graphs for speeding time, engine speed change curves, fitted curves or line graphs for rapid acceleration, fitted curves or line graphs for emergency braking, fitted curves or line graphs for sharp turns, fitted curves or line graphs for lane change frequency, fitted curves or line graphs for gear shift frequency, fitted curves or line graphs for seatbelt status, fitted curves or line graphs for warm-up time, fitted curves or line graphs for on-headlight status, fitted curves or line graphs for high beam status, fitted curves or line graphs for turn signal status during turns, fitted curves or line graphs for turn signal status during lane changes, fitted curves or line graphs for idling time, fitted curves or line graphs for mobile phone use, and fitted curves or line graphs for fatigued driving, etc. Parking behavior information curves include fitting curves or polylines for parking selection, parking speed variation, handbrake status, parking gear status, door closing status, window closing status, and light-off status. Driving environment information curves include: driving time variation curves, driving duration variation curves, road condition fitting curves or polylines, mileage variation curves for different terrains, driving time variation curves for different terrains, and parking time variation curves for different terrains. Furthermore, vehicle driving behavior information and body change information are used as vehicle risk control information, including: using average and extreme data from multiple curves of vehicle driving behavior information and multiple curves of vehicle body change information as vehicle risk control information. For curves or fitted curves, such as average speed curves, engine speed curves, driving time curves, and driving duration curves, the average value can be easily calculated, and extreme values ​​can be easily extracted. If necessary, statistical methods can be used to obtain other reference indicators. For line graphs, such as those for rapid acceleration, emergency braking, sharp turns, lane change frequency, gear shift frequency, handbrake status, and parking gear status, the average value can also be easily calculated, and the line graphs themselves have direct extreme values. If necessary, statistical methods can be used to obtain other reference indicators. This method of implementing curves, fitted curves, and line graphs mainly utilizes a large amount of data, combined with computers and their processing programs, to easily and conveniently obtain a large number of required values ​​for vehicle risk control information. It has a sufficiently clear labeling effect on user profile preferences, and it is easy to directly or indirectly select specific vehicle risk control information with the same user profile from the vehicle risk control database, making it very convenient to apply.

[0053] As described above, the vehicle service processing method based on user profiles is not limited to premiums, but can also be applied to loss ratios, timeliness of claims investigation, rapid case closure, business analysis, and business structure selection. In particular, its application in business structure selection can help customers design new insurance products and their combinations, identify users and improve the quality of claims settlement, and significantly reduce the average car insurance rate while also addressing personnel costs.

[0054] The advantage is that, for different types of driving behavior information and vehicle body change information, the same or different weighting coefficients are set in the statistical model. These weighting coefficients include a personal safety coefficient, a vehicle property coefficient, a social harm coefficient, and a weighted summation coefficient, which is obtained by weighting and summing the personal safety coefficient, vehicle property coefficient, and social harm coefficient. The weighting coefficients involving driving behavior information and vehicle body change information are a unique feature of this invention, never seen in other vehicle service processing methods, and can help customers quickly and accurately identify the required users. The weighted summation coefficient mainly provides a very intuitive result; for example, if the weighted summation coefficient exceeds 10, then this user may be rejected by most customers, or this user may need to bear a greater burden in terms of expenses, or a secondary protection mechanism may need to be introduced to reduce risk.

[0055] For example, for dangerous driving behaviors, such as speeding by 50% or more, a weighting coefficient of 5 to 20 times can be set and labeled as a social harm coefficient, meaning a social harm coefficient of 5 to 20. This can also be labeled as a personal safety coefficient of 3 to 5 times and a vehicle property coefficient of 2 to 4 times. For instance, for speeding by 120%, the total weighting coefficient could be (12+5+4) / 3 = 7; for speeding by 50%, the total weighting coefficient could be (5+3+2) / 3 = 3.33; for speeding by 80%, the total weighting coefficient could be (8+4+3) / 3 = 5, and so on.

[0056] For driving behaviors that have little impact on safety but may significantly affect the wear and tear on certain components, such as warm-up time, a vehicle property coefficient of 1.5 to 2 can be set. A personal safety coefficient can be omitted, defaulting to 1, or set to 1.1 to 1.4 depending on the strategy. A social hazard coefficient is also omitted, defaulting to 1. The weighted summation coefficient can be (1.5 + 1.1 + 1) / 3 = 1.2. And so on. Driving behavior information while parked generally shows a low correlation with the social hazard coefficient and a high correlation with the vehicle property coefficient.

[0057] Traffic violation records will show higher social harm coefficients, personal safety coefficients, and vehicle property coefficients.

[0058] The frequency of sudden acceleration and braking is highly correlated with personal safety and vehicle property safety. Therefore, a higher personal safety and vehicle property safety coefficient reflects a higher frequency of sudden acceleration and braking. Other indicators follow the same logic.

[0059] Moreover, each indicator can be set with reference to the above implementation examples and combined with the actual situation. It can also be changed or adjusted according to changes or new needs during application.

[0060] As described above, the personal safety coefficient, vehicle property coefficient, social harm coefficient, and weighted summation coefficient can help accurately define and identify users based on driving behavior. Furthermore, they can achieve tag-based user definition through user profiling. Distinguishing between the personal safety coefficient, vehicle property coefficient, and social harm coefficient allows customers to identify and filter users from three perspectives: personal safety, vehicle property, and social harm. The weighted summation coefficient also provides a direct view of vehicle risk control information reflected in different driving behaviors and vehicle body changes, thereby accurately matching vehicle insurance product positioning, enabling competitor comparison, revenue risk control, user recommendation, and precise matching. This truly reflects the match between car insurance and claim rates, accurately reflects customer segmentation, helps avoid homogenization of car insurance products, and helps optimize the relative ratio of operating costs to charges.

[0061] Preferably, before acquiring real-time vehicle driving behavior information and vehicle body change information, the method further includes: acquiring vehicle registration information and user registration information to establish a connection between the user and the vehicle; and using the vehicle registration information, user registration information, driving behavior information, and vehicle body change information as its vehicle risk control information. Figure 8As shown, one embodiment of the present invention is a vehicle management method based on user profiles, which includes the following steps: acquiring vehicle registration information and user registration information to establish a connection between users and vehicles; acquiring vehicle driving behavior information and vehicle body change information in real time, and using vehicle registration information, user registration information, driving behavior information, and vehicle body change information as vehicle risk control information; summarizing a large amount of vehicle risk control information to establish a vehicle risk control database and a statistical model; setting user profile preferences based on the statistical model and vehicle risk control information; selecting specific vehicle risk control information with the same user profile from the vehicle risk control database based on user profile preferences; and providing specific vehicle risk control information. When the vehicle management method based on user profiles accesses a customer or accesses customer data, it can often obtain vehicle registration information and user registration information. This expands the database of the vehicle management method based on user profiles, better demonstrating the role of big data. Furthermore, using vehicle registration information, user registration information, driving behavior information, and vehicle body change information as vehicle risk control information can help the database of the vehicle management method based on user profiles to create user profiles. It can also help customers set user profile preferences to obtain specific vehicle risk control information.

[0062] Preferably, the vehicle processing method based on user profiles further includes the step of: obtaining the user's online credit information based on the user's registration information, and the vehicle risk control information also includes online credit information. Figure 9As shown, one embodiment of the present invention is a vehicle management method based on user profiles, which includes the following steps: obtaining vehicle registration information and user registration information to establish a connection between users and vehicles; obtaining the user's online credit information based on the user registration information; obtaining the vehicle's driving behavior information and vehicle body change information in real time, and using the vehicle registration information, user registration information, driving behavior information, vehicle body change information, and online credit information as its vehicle risk control information; summarizing a large amount of vehicle risk control information to establish a vehicle risk control database and a statistical model; setting user profile preferences based on the statistical model and its vehicle risk control information; selecting specific vehicle risk control information with the same user profile from the vehicle risk control database based on the user profile preferences; and providing specific vehicle risk control information. In the era of big data, online credit information represents a user's integrity and is a highly valuable indicator. The vehicle service processing method based on user profiles often requires users to provide information directly or indirectly through customers. This information typically includes personal details such as name, ID number, ID card number, and mobile phone number, as well as authorization information for online credit information. Therefore, by obtaining a user's online credit information based on their registration information and then determining the user's integrity based on that information, and using it as one of the vehicle risk control information, customers can be helped to screen users.

[0063] Preferably, a vehicle service platform system based on user profiles employs the vehicle service processing method based on user profiles described in any of the above embodiments; preferably, a vehicle service platform system based on user profiles employs each step of the vehicle service processing method based on user profiles described in any of the above embodiments, or sets up functional modules to implement each step; preferably, a vehicle service platform system based on user profiles has functional modules corresponding to the execution of each step of the vehicle service processing method based on user profiles. For example... Figure 10As shown, one embodiment of the present invention is a vehicle service platform system based on user profiles, comprising: a real-time acquisition module for acquiring real-time vehicle driving behavior information and vehicle body change information, using the vehicle driving behavior information and vehicle body change information as its vehicle risk control information; a model aggregation module for aggregating a large amount of vehicle risk control information, establishing a vehicle risk control database and a statistical model; a user profile module for setting user profile preferences based on the statistical model and its vehicle risk control information; a user selection module for selecting specific vehicle risk control information with the same user profile from the vehicle risk control database based on user profile preferences; and an information provision module for providing specific vehicle risk control information. And so on. Using the above scheme, the present invention comprehensively considers two factors: user driving behavior and vehicle body changes, which helps to screen users, thereby accurately matching the positioning of vehicle service products, realizing competitor comparison, revenue risk control, user recommendation, and precise matching, truly reflecting the matching of car insurance and accident rates, accurately reflecting customer segmentation, helping to avoid homogenization of car insurance products, helping to optimize the relative ratio of operating costs and charges, and also helping users improve their driving habits to a certain extent.

[0064] Furthermore, embodiments of the present invention also include a vehicle service processing method and a vehicle service platform system based on user profiles, formed by combining the technical features of the above embodiments.

[0065] It should be noted that the above-mentioned technical features can be combined with each other to form various embodiments not listed above, all of which are considered to be within the scope of this invention specification; and, for those skilled in the art, improvements or modifications can be made based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A vehicle management method based on user profiles, characterized in that, Includes the following steps: Real-time acquisition of vehicle driving behavior information and body change information, and use of vehicle driving behavior information and body change information as vehicle risk control information; A large amount of vehicle risk control information was collected to establish a vehicle risk control database and a statistical model. Based on the statistical model and its vehicle risk control information, user profile preferences are set; Based on user profile preferences, select specific vehicle risk control information from the vehicle risk control database that has the same user profile; The user profile preferences refer to the customer's needs or choices. Setting user profile preferences involves customizing user profile tags and categorizing them appropriately based on customer preferences. Specifically, setting user profile preferences includes extracting keywords from customer needs, combining these keywords to obtain at least two keyword groups, using a polling statistical model for each keyword group, combining the polling results with vehicle risk control information to obtain the user profile preferences for that customer's needs, and establishing a correlation between the user profile preferences and the customer's needs. The user profile preferences include: customer name, customer needs, keyword groups, user profile, and vehicle risk control information. Provide risk control information for specific vehicles; after establishing a statistical model, use compensation data to validate the model; During operation, updated compensation data is used to adjust the statistical model; After the data is aggregated, and before establishing the vehicle risk control database, data preprocessing is also performed. The vehicle processing method based on user profiles also includes the step of generating multiple curves for driving behavior information and multiple curves for vehicle body change information based on driving behavior information and vehicle body change information, respectively. Among them, the multiple curves of driving behavior information include driving behavior information curve, parking behavior information curve, driving environment information curve, and traffic violation record fitting curve or traffic violation record broken line; Driving behavior information curves include average speed change curves, overspeed status fitting curves or broken lines, overspeed time fitting curves or broken lines, engine speed change curves, rapid acceleration fitting curves or broken lines, emergency braking fitting curves or broken lines, sharp turning fitting curves or broken lines, lane change frequency fitting curves or broken lines, gear shift frequency fitting curves or broken lines, seat belt status fitting curves or broken lines, warm-up time change curves, headlight status fitting curves or broken lines, high beam status fitting curves or broken lines, turn signal status fitting curves or broken lines when turning, turn signal status fitting curves or broken lines when changing lanes, idling time change curves, mobile phone usage fitting curves or broken lines, and fatigue driving fitting curves or broken lines. The parking behavior information curves include the parking selection fitting curve or polyline, the parking rate change curve, the handbrake status fitting curve or polyline, the parking gear status fitting curve or polyline, the door closing status fitting curve or polyline, the window closing status fitting curve or polyline, and the light turning off status fitting curve or polyline. The driving environment information curves include: driving time variation curve, driving duration variation curve, road condition fitting curve or broken line, driving mileage variation curve for each terrain, driving time variation curve for each terrain, and parking time variation curve for each terrain. The vehicle's driving behavior information and body change information are used as its vehicle risk control information, including: using the average data and extreme value data from multiple curves of the vehicle's driving behavior information and multiple curves of the body change information as vehicle risk control information.

2. The vehicle service processing method based on user profiles according to claim 1, characterized in that, Based on customer needs, statistical models, and vehicle risk control information, user profile preferences are set. For different driving behavior information and vehicle body change information, the same or different weight coefficients are set in the statistical model. These weight coefficients include personal safety coefficient, vehicle property coefficient, social harm coefficient, and weighted summation coefficient. The weighted summation coefficient is obtained by weighting and summing the personal safety coefficient, vehicle property coefficient, and social harm coefficient.

3. The vehicle processing method based on user profiles according to claim 2, characterized in that, Provide specific vehicle risk control information only to the specific customer who makes the request, based on the customer's needs.

4. The vehicle service processing method based on user profiles according to claim 1, characterized in that, It acquires driving behavior information by actively gathering data and acquires vehicle body change information by passively sensing data.

5. The vehicle service processing method based on user profiles according to claim 1, characterized in that, Before acquiring real-time information on vehicle driving behavior and body changes, the process also includes: acquiring vehicle registration information and user registration information to establish a connection between the user and the vehicle; and using the vehicle registration information, user registration information, driving behavior information, and body change information as vehicle risk control information.

6. The vehicle processing method based on user profiles according to claim 5, characterized in that, It also includes the following steps: obtaining the user's online credit information based on the user's registration information, and the vehicle risk control information also includes online credit information.

7. A vehicle service platform system based on user profiles, characterized in that, The vehicle service processing method based on user profiles as described in any one of claims 1 to 6 is adopted, wherein the vehicle service platform system based on user profiles comprises: The real-time acquisition module acquires real-time information on the vehicle's driving behavior and body changes, and uses this information as vehicle risk control information. The model aggregation module aggregates a large amount of vehicle risk control information, establishes a vehicle risk control database, and builds statistical models. The user profiling module sets user profile preferences based on statistical models and vehicle risk control information. The user selection module selects specific vehicle risk control information from the vehicle risk control database based on user profile preferences; The information provision module provides risk control information for specific vehicles.

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