CN110648244A

CN110648244A - Block chain-based vehicle insurance scheme generation method and device and driving data processing system

Info

Publication number: CN110648244A
Application number: CN201910836090.0A
Authority: CN
Inventors: 文振湘; 江勇; 冯智泉
Original assignee: Guangzhou Yamei Information Science & Technology Co Ltd
Current assignee: Yamei Zhilian Data Technology Co.,Ltd.
Priority date: 2019-09-05
Filing date: 2019-09-05
Publication date: 2020-01-03

Abstract

The application relates to a block chain-based vehicle insurance scheme generation method and device and a driving data processing system. The method comprises the following steps: acquiring driving data; the driving data is data reflecting driving behaviors and/or vehicle conditions; analyzing the driving data to obtain a driving risk factor; the driving risk factor is used for reflecting the driving accident risk of the driving behavior and/or reflecting the driving accident risk of the vehicle condition; and uploading the driving risk factors to a block chain system for block chain evidence storage, wherein the driving risk factors stored in the block chain system are used for generating a vehicle insurance scheme of an insurance user. By adopting the method, the personal privacy of the user can be effectively protected while the data authenticity of the driving risk factors is ensured.

Description

Block chain-based vehicle insurance scheme generation method and device and driving data processing system

Technical Field

The present application relates to the field of car networking technologies, and in particular, to a block chain-based car insurance scheme generation method and apparatus, a driving data processing system, a computer device, and a computer-readable storage medium.

Background

Currently, more and more users participate in car insurance applications through various insurance companies. The insurance company usually firstly evaluates the risk of driving accidents when the user drives the vehicle, and calculates the vehicle insurance amount required to be paid by the user according to the risk, so as to generate a vehicle insurance scheme according to the vehicle insurance amount. In order to more accurately evaluate the risk of driving accidents when a user drives a vehicle, a risk category of UBI (use Based Insurance) is derived.

When the user applies the UBI car insurance, the user needs to provide personal driving data for an insurance company, or the vehicle enterprise to which the user belongs provides the personal driving data for the insurance company, and the insurance company calculates the amount of the UBI car insurance to be paid according to the driving data (such as driving time, driving place and mileage) reflecting the driving behavior of the user.

However, since the driving data of the user usually includes personal privacy such as a trip location and a trip time of the user, the driving data including the personal privacy is delivered to an insurance company and stored by the user or the vehicle company, and there is a risk of revealing the personal privacy of the user.

Therefore, the current car insurance scheme generation method has the risk of revealing the personal privacy of the user.

Disclosure of Invention

In view of the above, it is necessary to provide a block chain-based car insurance scheme generating method, a block chain-based car insurance scheme generating apparatus, a driving data processing system, a computer device and a computer readable storage medium.

A block chain-based vehicle insurance scheme generation method comprises the following steps:

acquiring driving data; the driving data is data reflecting driving behaviors and/or vehicle conditions;

analyzing the driving data to obtain a driving risk factor; the driving risk factor is used for reflecting the driving accident risk of the driving behavior and/or reflecting the driving accident risk of the vehicle condition;

and uploading the driving risk factors to a block chain system for block chain evidence storage, wherein the driving risk factors stored in the block chain system are used for generating a vehicle insurance scheme of an insurance user.

collecting driving data through a vehicle-mounted diagnosis system; the driving data is data reflecting driving behaviors and/or vehicle conditions;

uploading the driving data to a server; the server is used for analyzing the driving data to obtain a driving risk factor; uploading the driving risk factors to a block chain system for block chain storage, wherein the driving risk factors stored in the block chain system are used for generating a vehicle insurance scheme of an insurance user; the driving risk factor is used for reflecting the driving accident risk of the driving behavior and/or reflecting the driving accident risk of the vehicle condition.

A driving data processing system comprising:

the system comprises a vehicle end and a driving data server;

the vehicle end is used for acquiring driving data through a vehicle-mounted diagnosis system and uploading the driving data to the driving data server; the driving data is data reflecting driving behaviors and/or vehicle conditions;

the driving data server is used for analyzing the driving data to obtain driving risk factors; uploading the driving risk factors to a block chain system for block chain storage, wherein the driving risk factors stored in the block chain system are used for generating a vehicle insurance scheme of an insurance user; the driving risk factor is used for reflecting the driving accident risk of the driving behavior and/or reflecting the driving accident risk of the vehicle condition.

A block chain-based vehicle insurance scheme generation apparatus includes:

the data acquisition module is used for acquiring driving data; the driving data is data reflecting driving behaviors and/or vehicle conditions;

the data analysis module is used for analyzing the driving data to obtain driving risk factors; the driving risk factor is used for reflecting the driving accident risk of the driving behavior and/or reflecting the driving accident risk of the vehicle condition;

and the evidence storage module is used for uploading the driving risk factors to a block chain system for block chain evidence storage, and the driving risk factors stored in the block chain system are used for generating a vehicle insurance scheme of the insurance user.

A block chain-based vehicle insurance scheme generation apparatus includes:

the data acquisition module is used for acquiring driving data through the vehicle-mounted diagnosis system; the driving data is data reflecting driving behaviors and/or vehicle conditions;

the data uploading module is used for uploading the driving data to a server; the server is used for analyzing the driving data to obtain a driving risk factor; uploading the driving risk factors to a block chain system for block chain storage, wherein the driving risk factors stored in the block chain system are used for generating a vehicle insurance scheme of an insurance user; the driving risk factor is used for reflecting the driving accident risk of the driving behavior and/or reflecting the driving accident risk of the vehicle condition.

A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:

A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

The block chain-based vehicle insurance scheme generation method, the block chain-based vehicle insurance scheme generation device, the driving data processing system, the computer equipment and the computer-readable storage medium, the driving risk factor reflecting the driving accident risk of the driving behavior and/or the vehicle condition is obtained by analyzing the driving data reflecting the driving behavior and/or the vehicle condition, then the driving risk factor is uploaded to the block chain system for block chain evidence storage, when generating the car insurance scheme, the user or the car enterprise only needs to provide the driving risk factors for the insurance service provider, the insurance service provider can generate the car insurance scheme by adopting the driving risk factors, because the user or the vehicle enterprise does not need to directly provide the driving data for the insurance service provider, the personal privacy of the user is ensured, and the risk of the personal privacy disclosure of the user is reduced. Moreover, the driving risk factors are uploaded to a block chain system for block chain storage certification, the data authenticity of the driving risk factors is guaranteed by using the non-tamper property of the block chain, when the car insurance scheme is generated, the insurance service provider does not need to spend time for conducting authenticity verification on the driving risk factors, and the efficiency of generating the car insurance scheme is improved. Therefore, through the block chain-based vehicle insurance scheme generation method, the data authenticity of the driving risk factors is ensured, and meanwhile, the individual privacy of the user is effectively protected.

Drawings

FIG. 1 is a schematic flow chart diagram of a block chain-based car insurance scheme generation method, according to an embodiment;

FIG. 2 is an application environment diagram of a block chain based car insurance scheme generation method of an embodiment;

FIG. 3 is a flow chart illustrating a driving data verification step according to one embodiment;

FIG. 4 is a flow chart illustrating another driving data verification step according to one embodiment;

fig. 5 is a schematic flowchart of a driving record video encryption uploading step according to an embodiment;

FIG. 6 is a flow diagram of another block chain based car insurance scheme generation method, according to an embodiment;

FIG. 7 is a block diagram of an embodiment of a block chain based car insurance scheme generation apparatus;

FIG. 8 is a block diagram of another block chain based car insurance scheme generation apparatus according to an embodiment;

FIG. 9 is a block diagram of a driving data processing system, according to an embodiment;

FIG. 10 is an internal block diagram of a computer device of an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In one embodiment, as shown in FIG. 1, a block chain based car insurance scheme generation method is provided. The block chain-based car insurance scheme generation method provided by the embodiment can be applied to the application environment shown in fig. 2.

In the application environment shown in fig. 2, a vehicle end 210, a driving data server 220, a blockchain system 230, an insurance service server 240 and a user end 250 can be included.

The vehicle end 210 may be a terminal on the vehicle for controlling the operation of the vehicle, and is also commonly referred to as an on-board center control platform. An OBD (On-Board Diagnostics) device may be included in the vehicle end 210. The vehicle end 210 can acquire various driving data of the vehicle through the OBD device.

The driving data server 220 may be a server for receiving and analyzing driving data. In practical applications, the driving data server 220 may be composed of one server or a server cluster.

The insurance service server 240 may be a server of an insurance service provider for processing an insurance service. In practical applications, the insurance service server 240 may be composed of one server or a server cluster.

The blockchain system 230 may be a system composed of a plurality of server nodes and certified by using a blockchain technology. The blockchain system 230 may achieve non-tamper-resistance of the blockchain by decentralization. More specifically, a plurality of server nodes in the blockchain system 230 record data and encrypt the data into a data block of one block, where the data block is also used to generate a data block of another block by encryption, when verifying the data, the blockchain system 230 verifies the data through a common identification mechanism, if the data needs to be tampered, the data in more than half of the server nodes in the blockchain system 230 needs to be tampered, and the data blocks of consecutive blocks need to be tampered, so as to control more than half of the server nodes and modify consecutive data, which is difficult to be implemented in practice, and thus the blockchain system 230 implements non-tamper of the blockchain storage through a blockchain technique.

In practical applications, the system architecture of the blockchain system 230 may be specifically a public blockchain, an industry alliance blockchain, or a private blockchain, and those skilled in the art can determine the system architecture of the blockchain system according to actual needs.

The user end 250 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.

The vehicle insurance scheme can be a property insurance scheme for paying the vehicle insurance to obtain the compensation when the vehicle has an accident. One of the common vehicle insurance is UBI vehicle insurance, which is a kind of insurance for determining the amount of the vehicle insurance to be paid according to the actual driving behaviors (such as driving time, driving place and mileage) of the user. Unlike the traditional vehicle insurance, the vehicle insurance amount of the UBI vehicle insurance is not uniform, and the UBI vehicle insurance has the characteristic of individuation, for example, if the risk of driving accidents caused by the actual driving behaviors of the users is lower, the relative vehicle insurance amount is lower, and otherwise, the risk is higher.

In this embodiment, a method for determining a car insurance scheme based on a block chain is described by taking the example that the method is applied to the driving data server 220 in fig. 2, and includes the following steps:

step S110, obtaining driving data; the driving data is data reflecting driving behavior and/or vehicle conditions.

The driving data can be data generated by driving the vehicle by a user and collected by OBD equipment on the vehicle. The driving data may reflect driving behavior of the user while driving the vehicle and/or vehicle conditions of the vehicle. For example, the speed of the vehicle is decelerated from a high speed to a stop, reflecting the braking behavior of the user; as another example, a vehicle fault record for a vehicle reflects the aging condition of the vehicle.

The driving behaviors can be behaviors of braking, turning, accelerating, idling, illegal driving and the like when the user drives the vehicle.

The vehicle condition may be a driving mileage, a real-time location, a battery level, a vehicle fault, a vehicle fuel consumption, a vehicle damage record, a vehicle estimation, and the like of the vehicle.

In a specific implementation, the vehicle ends 210 of the multiple vehicles can acquire data generated when the user drives the vehicle through the OBD device, and the data is used as driving data. The driving data is transmitted to the driving data server 220 through communication modules such as WIFI and Bluetooth built in the OBD device of the vehicle end 210. Thus, the driving data server 220 can obtain driving data of a plurality of vehicles. The driving data server 220 usually records users to which the vehicle belongs, so that the users corresponding to the driving data can be determined.

For example, a mileage counter of the vehicle may record the driving mileage of the vehicle all the time, and the OBD device of the vehicle end 210 may periodically collect the driving mileage recorded by the mileage counter and transmit the driving mileage to the driving data server 220 through the communication module.

The driving data server 220 may receive the driving data from a plurality of vehicle terminals 210 belonging to different users, thereby obtaining the driving data of the plurality of users.

The driving data server 220 obtains driving data of a plurality of users to perform data analysis on the driving data.

Step S120, analyzing the driving data to obtain a driving risk factor; the driving risk factor is used to reflect the risk of a driving accident of the driving behaviour and/or to reflect the risk of a driving accident of the vehicle condition.

The driving risk factor may reflect the level of the risk of the driving accident caused by the driving behavior of the user, and/or the level of the risk of the driving accident caused by the vehicle condition of the vehicle.

In a specific implementation, the driving data server 220 may analyze the driving data in various analysis manners to obtain the driving risk factor.

In one analysis method, the driving data server 220 analyzes the driving data of the individual user to obtain the individual driving risk factor of the individual user.

For example, the driving data of turning, braking, acceleration, turning time, braking time, acceleration time, etc. of a single user is analyzed to obtain dangerous driving behaviors such as sharp turning, sharp braking, sharp acceleration, etc. of the single user, the number of times of the dangerous driving behaviors is counted, and the frequency of the dangerous driving behaviors is obtained according to the number of times of the dangerous driving behaviors and the driving time of the single user. The dangerous driving behavior frequency can be used as an individual driving risk factor. The higher the frequency of dangerous driving behavior as an individual driving risk factor, the higher the risk of causing a driving accident and vice versa. In the subsequent vehicle insurance scheme generation, the vehicle insurance amount of the single user can be correspondingly calculated according to the magnitude of the driving risk factor, so that the vehicle insurance scheme is generated.

In another analysis mode, the driving data server 220 performs big data analysis on the driving data of multiple users to obtain macro driving risk factors of the multiple users.

For example, in the driving data of a plurality of users, a vehicle type with driving accidents is determined, then the number of users of the vehicle type and the number of times of driving accidents of the vehicle type are counted according to the driving data, and the accident occurrence frequency of the vehicle type is obtained according to the number of users of the vehicle type and the number of times of driving accidents. The accident occurrence frequency of the vehicle type can be used as a macroscopic driving risk factor. The higher the accident occurrence frequency as a macroscopic driving risk factor, the higher the risk of the vehicle type causing driving accidents, and vice versa. In the subsequent vehicle insurance scheme generation, the vehicle insurance amount of the user driving the vehicle type can be correspondingly calculated according to the macroscopic driving risk factor, so that the vehicle insurance scheme is generated.

Of course, in practical applications, a person skilled in the art may also analyze different driving risk factors for different driving data by using different analysis methods, and the embodiment of the present application does not limit a specific analysis method, specific driving data, and specific driving risk factors. For example, driving risk factors such as the accident occurrence frequency of a certain road section, the accident occurrence frequency of a certain time period, the number of times of driving violations, the vehicle failure rate and the like can be analyzed according to driving data such as the real-time position of the vehicle, the driving time, the illegal driving of the user, the vehicle failure records and the like.

Step S130, uploading the driving risk factor to the blockchain system for blockchain warranty storage, where the driving risk factor certified by the blockchain system is used to generate a car insurance scheme for the insurance user.

In a specific implementation, the driving data server 220 may upload the driving risk factor to the blockchain system 230, and store the driving risk factor by a plurality of server nodes in the blockchain system 230, thereby implementing the blockchain evidence storage of the driving risk factor.

In a specific implementation of generating the car insurance scheme, when a car insurance scheme of a certain insurance user needs to be generated, a practitioner of an insurance service can download one or more driving risk factors of the insurance user from the driving risk factors stored in the block chain system 230 through the insurance service server 240, evaluate the risk of a driving accident of the insurance user according to the driving risk factors of the insurance user, and correspondingly calculate the car insurance amount, thereby generating the car insurance scheme.

For example, the driving risk factors of the insurance user comprise sharp turning frequency, sharp braking frequency, sharp acceleration frequency and illegal driving times, the vehicle insurance amount of the insurance user is calculated according to the driving risk factors, and a vehicle insurance scheme comprising the vehicle insurance amount is generated.

For another example, the vehicle model of the insurance user is vehicle model a, the driving risk factor of the accident occurrence frequency of vehicle model a is downloaded from the driving risk factors stored in the blockchain system 230, the vehicle insurance amount of the insurance user is calculated according to the driving risk factor, and the vehicle insurance scheme including the vehicle insurance amount is generated.

In another implementation of generating a vehicle insurance scheme, a Smart Contract (Smart Contract) may be deployed at blockchain system 230, which may include a mathematical model for calculating an insurance amount based on driving risk factors. When a vehicle insurance scheme of a certain insurance user needs to be generated, the block chain system 230 runs an intelligent contract, one or more driving risk factors of the insurance user are called according to a mathematical model in the intelligent contract, the called driving risk factors are input into the mathematical model for operation, and then a vehicle insurance amount can be obtained, and the vehicle insurance scheme including the vehicle insurance amount is generated through the intelligent contract.

For example, an intelligent contract on blockchain system 230, whose mathematical model for calculating the amount of coverage, is: the vehicle risk amount S is the sharp turn frequency 400+ the sharp brake frequency 500+ the sharp acceleration frequency 300+ the number of times of driving violations 300. And (4) calling the driving risk factors of the sharp turning frequency, the sharp braking frequency, the sharp accelerating frequency and the times of illegal driving of the insurance user, and inputting the driving risk factors into the mathematical model to obtain the car insurance amount.

Certainly, in practical applications, a person skilled in the art can design a specific generation manner of the car insurance scheme according to actual needs, and the embodiment of the present application does not limit a specific implementation manner of generating the car insurance scheme according to the driving risk factor.

According to the block chain-based vehicle insurance scheme generation method, the driving risk factor reflecting the driving accident risk of the driving behaviors and/or the vehicle conditions is obtained by analyzing the driving data reflecting the driving behaviors and/or the vehicle conditions, and then the driving risk factor is uploaded to the block chain system to carry out block chain evidence storage. Moreover, the driving risk factors are uploaded to a block chain system for block chain storage certification, the data authenticity of the driving risk factors is guaranteed by using the non-tamper property of the block chain, when the car insurance scheme is generated, the insurance service provider does not need to spend time for conducting authenticity verification on the driving risk factors, and the efficiency of generating the car insurance scheme is improved. Therefore, through the block chain-based vehicle insurance scheme generation method, the data authenticity of the driving risk factors is ensured, and meanwhile, the individual privacy of the user is effectively protected.

Furthermore, after the driving risk factors are obtained by analyzing the driving data, the driving risk factors are uploaded to a block chain system for block chain evidence storage, so that the driving data with large data volume does not need to be uploaded to the block chain system, the driving data does not need to be analyzed by the block chain system, the problem of low data analysis efficiency caused by the limited throughput of the block chain system is solved by moving the processing logic of data analysis out of the block chain, and the analysis efficiency of the driving data is improved.

Furthermore, the insurance service provider can generate the vehicle insurance scheme by using the driving risk factors certified by the blockchain system, so that the driving data is collected without consuming resources and additionally deploying hardware equipment, and the driving data is maintained without consuming resources, thereby saving the cost for providing the insurance service.

Fig. 3 is a schematic flow chart of a driving data verification step according to an embodiment, in an embodiment, the method for generating a driving insurance scheme further includes the following steps:

step S310, calculating a first encryption value of the driving data, and uploading the first encryption value to the block chain system for block chain storage.

The encrypted value may be a value obtained by calculating driving data through an encryption algorithm. For example, the hash value of the driving data can be calculated by a hash algorithm as the encrypted value of the driving data.

In a specific implementation, the driving data server 220 may package the driving data into a hash value through a hash algorithm. For the purpose of illustration differentiation, the encrypted value obtained by the driving data server 220 calculating the driving data is named as the first encrypted value.

Of course, in practical applications, those skilled in the art may also calculate the encrypted value of the driving data through other various encryption algorithms, and the embodiment of the present application does not limit the specific encryption format of the encrypted value. For example, the encryption value of the traveling data may be calculated by a DEA (data encryption Algorithm) encryption Algorithm.

After obtaining the first encrypted value of the traveling data, the traveling data server 220 may upload the first encrypted value to the blockchain system 230, and perform blockchain verification by each server node in the blockchain system 230.

Step S320, receiving a data verification request from the data verification end.

The data verification terminal can be a terminal for vehicle data verification. In practical application, there may be a scene that needs to verify the authenticity of the driving data. For example, in one scenario, when an insurance service provider risks a driving accident, the authenticity of part of driving data needs to be verified; for another example, in a scenario where a user wants to make an annual check on a vehicle, a government department responsible for the annual check needs to verify the authenticity of part of the driving data. In a scene of verifying the authenticity of the driving data, a data verification request may be initiated to the driving data server 220 through the data verification terminal.

It should be noted that, for a scenario in which the insurance service provider verifies the driving data, the data verification end may be the insurance service server 240.

And step S330, sending the driving data to be verified to the data verification end, allowing the data verification end to calculate a second encryption value of the driving data to be verified, and comparing the second encryption value with the first encryption value stored in the block chain system so as to judge whether the driving data to be verified is real or not according to a comparison result.

In a specific implementation, the driving data server 220 may respond to the data verification request and return the requested driving data to be verified to the data verification end, and the data verification end may calculate the received encryption value of the driving data to be verified. For distinguishing and explaining, the data verification end calculates an encryption value obtained by the driving data to be verified and names the encryption value as a second encryption value. The data verification end may compare the calculated second encrypted value with the first encrypted value certified on the blockchain system 230, and if the two are matched, it may be determined that the driving data to be verified provided by the driving data server 220 is authentic, and if the two are not matched, it may be determined that the driving data to be verified provided by the driving data server 220 is not authentic.

According to the block chain-based vehicle insurance scheme generation method, the first encryption value of the vehicle data is calculated and uploaded to the block chain system for storage, when the authenticity of the vehicle data to be verified needs to be verified, the vehicle data to be verified requested by the data verification end is returned to the data verification end, the data verification end compares the received second encryption value of the vehicle data to be verified with the first credible encryption value stored in the block chain, and the authenticity of the vehicle data to be verified can be judged according to the comparison result. Therefore, the requirement for the verification of the driving data is met, and meanwhile, the driving data of all users do not need to be disclosed, so that the individual privacy of the users is guaranteed.

Fig. 4 is a schematic flow chart of another driving data verification step according to an embodiment, which further includes the following steps:

step S410, calculating a first encryption value of the driving data, and uploading the first encryption value to the blockchain system for blockchain storage.

Step S420, receiving a data verification request from the data verification end.

Step S430, sending a data authorization request to the user side.

In a specific implementation, when receiving a data verification request from the data verification end, the driving data server 220 generates a data authorization request for the requested driving data, and sends the data authorization request to the user end 250.

For example, the data verification request of the data verification end is a request for driving data at a driving location, and the driving data server 220 generates a data authorization request, where the data authorization request may include information that an object requesting data verification is an insurance service provider, and data content of the requested driving data is "driving location".

Step S440, when receiving the authorization confirmation notification fed back by the user end for the data authorization request, determining whether the block chain system successfully accounts for the first encrypted value.

In a specific implementation, the user can refer to the information in the data authorization request through the user end 250 and determine whether to authorize, and if the user agrees to authorize, the user can feed back an authorization confirmation notification to the driving data server 220 through the user end 250. After receiving the authorization confirmation notification, the driving data server 220 may further query the billing record of the blockchain system 230 for the first encrypted value to determine whether the billing of the blockchain system 230 for the first encrypted value is successful.

And S450, when the billing is successfully judged, the driving data to be verified is sent to the data verification end, the data verification end calculates a second encryption value of the driving data to be verified, the second encryption value is compared with the first encryption value of the traffic data to be verified stored in the block chain system, and whether the driving data are real is judged according to the comparison result.

In a specific implementation, when the driving data server 220 inquires that the record of the billing of the first encryption value by the blockchain system 230 exists, it can determine that the billing is successful, and return the driving data to be verified to the data verification end.

According to the block chain-based vehicle insurance scheme generation method, the data authorization request is sent to the user when the data verification request of the data verification end is received, and the driving data is returned after the authorization of the user is obtained, so that the individual privacy of the user is guaranteed.

And after the authorization of the user is obtained, the accounting of the first encrypted value successfully recorded by the blockchain system is further confirmed, so that the problem of data verification failure caused by unsuccessful recording of the encrypted value by the blockchain system is avoided.

Fig. 5 is a schematic flowchart of a driving record video encryption uploading step according to an embodiment, which further includes the following steps:

and step S510, receiving a driving recording video of the vehicle end.

The driving record video may be a video shot by a driving recorder of the vehicle end 210.

In one specific implementation, during the running process of the vehicle, the automobile data recorder can continuously shoot in front of the vehicle. When a vehicle has a driving accident such as a vehicle collision, a sensor of the vehicle for detecting the collision generates a collision signal correspondingly, the OBD device of the vehicle end 210 is triggered by the collision signal, and a video shot by the driving recorder is collected through the built-in communication module and uploaded to the driving data server 220 as a driving recording video. Thus, the driving data server 220 receives the driving record video of the vehicle end 210.

In another specific implementation, in the driving process of the vehicle, the vehicle event data recorder continuously shoots ahead of the vehicle, and the OBD device of the vehicle end 210 collects a video shot by the vehicle event data recorder through the built-in communication module, and uploads the video to the driving data server 220 as a driving recording video. Thus, the driving data server 220 receives the driving record video of the vehicle end 210.

And step S520, when the driving accident is recorded in the driving record video, calculating a third encryption value of the driving record video.

In a specific implementation, the driving data server 220 may determine whether a driving accident is recorded in the driving record video, and when it is determined that the driving accident is recorded in the driving record video, the driving data server 220 may calculate an encryption value of the driving record video. For example, the hash value of the driving record video is calculated through a hash algorithm to serve as the encryption value of the driving record video.

For the purpose of distinguishing the description, the encryption value calculated according to the vehicle record video is named as a third encryption value.

The driving accident recording method may include the steps of determining whether a driving accident is recorded in the driving recording video, for example, adding a driving accident tag to the driving recording video by the vehicle end, and determining that the driving accident is recorded in the driving recording video by the driving data server 220 according to the driving accident tag. For another example, the driving data server 220 may identify the driving record video, and when the vehicle collision picture is identified, determine that the driving record video records a driving accident.

Step S530, uploading the third encrypted value to a block chain system for block chain storage; and the third encryption value stored by the block chain system is used for verifying the authenticity of the driving record video.

In a specific implementation, the driving data server 220 may upload the calculated third encrypted value to the blockchain system 230, and the blockchain system 230 performs blockchain verification on the third encrypted value.

When the authenticity of the driving recording video needs to be verified, the encryption value of the driving recording video can be calculated, and when the calculated encryption value is matched with the third encryption value stored in the block chain system 230, it can be determined that the driving recording video is not tampered and has authenticity.

In practical application, there may be illegal users tampering the driving recording video to carry out vehicle insurance cheating. According to the embodiment of the application, the driving record video of the vehicle end is encrypted into the third encryption value and uploaded to the block chain for evidence storage, and due to the fact that the block chain cannot be tampered, authenticity of the driving record video is guaranteed, and illegal users cannot cheat to get a vehicle insurance by tampering the driving record video.

According to the block chain-based vehicle insurance scheme generation method, the driving record video recorded with the driving accident at the vehicle end is encrypted, and the encrypted value is uploaded to the block chain for block chain storage and certification, so that the driving record video cannot be tampered, and the authenticity of the driving record video is guaranteed.

In one embodiment, after step S510, the following steps are further included:

and judging whether the driving record video carries the driving accident label or not, and if so, judging that the driving record video records the driving accident.

In the specific implementation, when a vehicle has a driving accident such as a vehicle collision, the OBD device at the vehicle end 210 acquires a video shot by the driving recorder through the built-in communication module, and adds a driving accident tag to the video to obtain a driving recording video carrying the driving accident tag. The driving record video with the driving accident label is sent to the driving data server 220, and when the driving data server 220 receives the driving record video, it can be determined that the driving accident is recorded in the driving record video according to the driving accident label. Therefore, the driving data server 220 does not need to perform complex judgment processing, and processing resources of the server are saved.

In one embodiment, after step S510, the following steps are further included:

identifying a driving record video, and when an abnormal driving picture is identified, judging that the driving record video records a driving accident; the abnormal driving picture comprises at least one of a vehicle collision picture, a vehicle sudden braking picture and a vehicle sudden turning picture.

In a specific implementation, when the driving data server 220 receives the driving recording video, it may identify the driving recording video, and identify whether the driving recording video includes abnormal driving pictures such as a vehicle collision picture, a vehicle sudden braking picture, and a vehicle sudden turning picture. When the abnormal driving picture is identified, the driving accident of the vehicle is shown, and therefore the driving accident is judged to exist in the driving record video. Therefore, by identifying abnormal driving pictures such as a vehicle collision picture, a vehicle emergency braking picture, a vehicle emergency turning picture and the like in the driving recording video, the driving accident recorded in the driving recording video is judged according to the abnormal driving pictures, whether the driving accident occurs or not does not need to be judged by the vehicle end, the processing logic of one side of the vehicle end is simplified, and the processing resource of the vehicle end is saved.

In one embodiment, the driving data reflects driving behaviors of the insurance user, the driving risk factors include individual driving risk factors, and the step S120 may specifically include:

acquiring dangerous driving behavior times from driving data, and acquiring driving time from the driving data; the dangerous driving behavior times are times of dangerous driving behaviors of the user; the dangerous driving behaviors comprise at least one of sharp turning, sharp braking, sharp acceleration and illegal driving; obtaining dangerous driving behavior frequency according to the dangerous driving behavior frequency and the driving time; the dangerous driving behavior frequency is used as an individual driving risk factor.

In a specific implementation, the driving data server 220 may perform data analysis on the personal driving data of the insurance user to obtain a personal driving risk factor of the insurance user. More specifically, the driving data server 220 may search, from the driving data of the insurance user, dangerous driving behaviors of the insurance user such as sudden turning, sudden braking, sudden acceleration, and driving against regulations, and count the times of the dangerous driving behaviors of the insurance user, and according to the times of the dangerous driving behaviors and the driving time of the insurance user, may calculate the frequency of the dangerous driving behaviors of the insurance user as the personal driving risk factor of the insurance user.

For example, the insurance user performs the dangerous driving behavior of 12 sudden brakes within the driving time of 100 hours, and the frequency of the dangerous driving behavior of 12 times is obtained as the individual driving risk factor of 0.12 by dividing the driving time of 100 by the number of dangerous driving behaviors.

According to the block chain-based vehicle insurance scheme generation method, the dangerous driving behavior frequency of the insurance user is calculated according to the times and the driving time of dangerous driving behaviors such as sudden turning, sudden braking, sudden acceleration, illegal driving and the like of the insurance user during driving, the dangerous driving behavior frequency is used as an individual driving risk factor, and the vehicle insurance scheme is generated based on the individual driving risk factor, so that the vehicle insurance scheme matched with the risk of driving accidents caused by the individual driving behaviors of the insurance user can be generated.

In one embodiment, the driving data includes a plurality of driving data, the plurality of driving data respectively reflects vehicle conditions of a plurality of users, the driving risk factor includes a macroscopic driving risk factor, and the step S120 may specifically include:

determining the vehicle type of the driving accident in the plurality of driving data, determining the number of users of the vehicle type and the accident occurrence frequency of the vehicle type, and obtaining the accident occurrence frequency of the vehicle type as a macroscopic driving behavior factor according to the number of users of the vehicle type and the accident occurrence frequency of the vehicle type.

In a specific implementation, the driving data server 220 may obtain a large amount of driving data of multiple users to perform big data analysis. Specifically, the vehicle type with the driving accident can be searched in a large amount of driving data, then the number of users of the vehicle type is searched in the driving data, and the number of times of the driving accident of the vehicle type is counted to obtain the accident occurrence number. And obtaining the accident occurrence frequency of the vehicle type as a macro driving behavior factor according to the number of users of the vehicle type and the accident occurrence frequency of the vehicle type.

For example, the number of accidents is 500, the number of users is 20000, the number of accidents is 500 divided by the number of users 20000, the accident frequency of the vehicle type is 0.025, and the accident frequency of 0.025 is used as the macro driving behavior factor of the vehicle type.

When generating the car insurance scheme, if the insurance user drives the car model, the macro accident occurrence frequency of the car model can be adopted to calculate the car insurance amount.

In one embodiment, the driving data includes a plurality of driving data, the driving data respectively reflects driving behaviors of a plurality of users, the driving risk factor includes a macroscopic driving risk factor, and the step S120 may specifically include:

determining a road section with a driving accident in a plurality of driving data, determining the traffic flow of the road section and the accident occurrence frequency of the road section, and calculating the accident occurrence frequency of the road section according to the traffic flow of the road section and the accident occurrence frequency of the road section to be used as a macroscopic driving behavior factor.

In the specific implementation, a road section with driving accidents can be searched in a large amount of driving data, then the times of the driving accidents on the road section in the traveling vehicle data are counted, and the traffic flow of the road section is counted. And obtaining the accident occurrence frequency of the road section as a macro driving behavior factor according to the vehicle flow of the vehicle type and the accident occurrence frequency of the road section.

For example, the number of accidents is 500, the traffic flow is 10000, the number of accidents 500 is divided by the traffic flow 10000, the accident frequency of the road section is 0.05, and the accident frequency of 0.05 is used as the macro driving behavior factor of the road section.

and determining the time of the driving accident in the plurality of driving data, and counting the accident occurrence frequency of each time period according to the time of the driving accident as a macroscopic driving behavior factor.

In the specific implementation, the time of the driving accident can be searched in a large amount of driving data, then the time periods distributed by the time of the driving accident in the traveling vehicle data are counted to obtain the driving accident in each time period, and the times of the driving accident in each time period are counted to be used as the accident occurrence frequency in each time period and to be used as the macroscopic driving behavior factor.

For example, the number of driving accidents occurring at 10 to 11 points is counted as 50, and the number of driving accidents occurring at 11 to 12 points is counted as 70, whereby the accident occurrence frequencies of 10 to 11 points, 11 to 12 points are found as 50 and 70.

In practical application, driving accidents actually occur in the same time period due to time differences in different regions, and the recording time of the driving accidents may be different time periods due to the time differences. Therefore, the time zone of the vehicle can be determined through the recorded real-time position of the vehicle in the driving data, and the driving accident occurrence time in the driving data can be converted into the local time according to the time zone, so that the accident occurrence frequency of each time period can be calculated more accurately.

The block chain-based vehicle insurance scheme generation method obtains macroscopic driving risk factors which reflect the driving behaviors and the vehicle conditions in a macroscopic manner, such as the accident occurrence frequency of a vehicle type, the accident occurrence frequency of a road section, the accident occurrence frequency of each time period and the like, by performing big data analysis on the driving data which reflect the driving behaviors of a plurality of users and/or the vehicle conditions of a plurality of users, when generating the vehicle insurance scheme, the driving behavior of a certain user and the driving accident risk of the vehicle condition can be evaluated based on the macroscopic driving risk factor and the vehicle insurance scheme can be generated, the driving accident risk assessment method has the advantages that more accurate reference basis is provided for the evaluation of the driving accident risk caused by the individual driving behaviors and the vehicle conditions of the user, and the problem that the driving accident risk of the user is not matched with the driving accident risk of the user due to the fact that the driving insurance scheme is generated only on the basis of the driving data reflecting the individual driving behaviors and the vehicle conditions of the user in the prior art is solved.

And moreover, driving risk factors of driving accident risks based on multidimensional driving behaviors and vehicle conditions are uploaded to the block chain, and the insurance service provider can select the required driving risk factors to calculate the vehicle insurance amount and generate a vehicle insurance scheme, so that the flexibility of generating the vehicle insurance scheme is improved.

In one embodiment, the individual driving risk factors include a real-time risk factor and a historical risk factor, and further include:

calculating an evaluation value of the real-time risk factor, and calculating an evaluation value of the historical risk factor; when the evaluation value of the real-time risk factor is higher than that of the historical risk factor, recording an excitation integral aiming at the insurable user; the excitation integral is used to generate a car insurance deduction scheme.

In a specific implementation, the driving data server 220 may further calculate an evaluation value of the real-time risk factor and an evaluation value of the historical risk factor according to a preset evaluation algorithm. For example, the driving data of the user in the past 1 month is analyzed to obtain a historical risk factor; and analyzing the driving data of the user on the same day to obtain a real-time risk factor. And negating the historical risk factors and the real-time risk factors to obtain corresponding evaluation values.

Then, the real-time risk factor evaluation value is compared with the historical risk factor evaluation value, and when the real-time risk factor evaluation value is higher than the historical risk factor evaluation value, it indicates that the driving behavior and/or the vehicle condition of the user is improved, so the driving data server 220 can record the incentive points for the insured user. The above incentive points for the insured user may be used to deduct the car insurance amount. For example, a car insurance amount of 100 dollars can be deducted by adding 1 point to the insurance user.

By comparing the evaluation value of the real-time risk factor of the insurance user with the evaluation value of the historical risk factor, the incentive points for vehicle insurance deduction are recorded when the evaluation value of the real-time risk factor is higher than the evaluation value of the historical risk factor, so that the insurance user can be encouraged to improve the driving behavior and/or the vehicle condition.

In one embodiment, as shown in fig. 6, a block chain based car insurance scheme generation method is provided. The block chain-based car insurance scheme generation method provided by this embodiment may be applied to the application environment shown in fig. 2, and is described by taking the example that the method is applied to the vehicle end 210 in fig. 2, including the following steps:

step S610, collecting driving data through a vehicle-mounted diagnosis system; the driving data is data reflecting driving behaviors and/or vehicle conditions;

step S620, uploading the driving data to a server; the server is used for analyzing the driving data to obtain driving risk factors; uploading the driving risk factors to a block chain system to store a certificate of the block chain, wherein the driving risk factors stored by the block chain system are used for generating a vehicle insurance scheme of an insurance user; the driving risk factor is used to reflect the risk of a driving accident of the driving behaviour and/or to reflect the risk of a driving accident of the vehicle condition.

In a specific implementation, the vehicle end 210 may collect driving data reflecting driving behaviors and/or vehicle conditions through the OBD device, and upload the driving data to the server. The server may be the driving data server 220 in fig. 2.

After receiving the driving data, the driving data server 220 analyzes the driving data to obtain a driving risk factor, and uploads the driving risk factor to the block chain system 230. The process of analyzing the driving data by the driving data server 220 to obtain the driving risk factor and uploading the driving risk factor to the blockchain system 230 has been described in detail in the above embodiments, and is not described herein again.

According to the block chain-based vehicle insurance scheme generation method, the driving data are sent to the server, the server analyzes the driving data reflecting the driving behaviors and/or the vehicle conditions to obtain the driving risk factors reflecting the driving accident risks of the driving behaviors and/or the vehicle conditions, then the driving risk factors are uploaded to the block chain system to carry out block chain evidence storage, when the vehicle insurance scheme is generated, a user or a vehicle enterprise only needs to provide the driving risk factors to an insurance service provider, the insurance service provider can generate the vehicle insurance scheme by adopting the driving risk factors, and the user or the vehicle enterprise does not need to directly provide the driving data to the insurance service provider, so that the personal privacy of the user is guaranteed, and the risk of personal privacy leakage of the user is reduced. Moreover, the driving risk factors are uploaded to a block chain system for block chain storage certification, the data authenticity of the driving risk factors is guaranteed by using the non-tamper property of the block chain, when the car insurance scheme is generated, the insurance service provider does not need to spend time for conducting authenticity verification on the driving risk factors, and the efficiency of generating the car insurance scheme is improved. Therefore, through the block chain-based vehicle insurance scheme generation method, the data authenticity of the driving risk factors is ensured, and meanwhile, the individual privacy of the user is effectively protected.

In one embodiment, further comprising:

when a driving accident is detected, calling a video in an automobile data recorder as an automobile data recording video; uploading the driving recording video to a server; the server is used for calculating a third encryption value of the driving recording video and uploading the third encryption value to the block chain system for block chain storage.

In the concrete implementation, when the vehicle runs, the automobile data recorder can continuously shoot in front of the vehicle. When a vehicle has a driving accident such as a vehicle collision, a sensor of the vehicle for detecting the collision generates a collision signal correspondingly, the OBD device of the vehicle end 210 is triggered by the collision signal, and a video shot by the driving recorder is collected through the built-in communication module and uploaded to the driving data server 220 as a driving recording video. When the video shot by the automobile data recorder is collected, the current time for detecting the driving accident can be determined firstly, and the accident occurrence time is obtained. Then, a section of video with the video recording time being earlier than the accident occurrence time is searched in the collected videos shot by the automobile data recorder to serve as the automobile data recording video. Therefore, the video content irrelevant to the driving accident is prevented from being uploaded to the server, and transmission resources are saved.

It should be understood that although the various steps in the flow charts of fig. 1-6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise.

In one embodiment, as shown in fig. 7, there is provided a block chain-based car insurance scheme generating apparatus 700, including: data acquisition module 710, data analysis module 720 and deposit evidence module 730, wherein:

the data acquisition module 710 is used for acquiring driving data; the driving data is data reflecting driving behaviors and/or vehicle conditions;

the data analysis module 720 is used for analyzing the driving data to obtain driving risk factors; the driving risk factor is used for reflecting the driving accident risk of the driving behavior and/or reflecting the driving accident risk of the vehicle condition;

the evidence storage module 730 is configured to upload the driving risk factor to a blockchain system for blockchain evidence storage, where the driving risk factor stored in the blockchain system is used to generate a car insurance scheme for an insurance user.

In one embodiment, further comprising:

the encryption module is used for calculating a first encryption value of the driving data; uploading the first encryption value to the block chain system for block chain storage;

the request receiving module is used for receiving a data verification request of a data verification end;

and the data returning module is used for sending the driving data to be verified to the data verification end, so that the data verification end can calculate a second encryption value of the driving data to be verified, and compare the second encryption value with the first encryption value stored and verified by the block chain system, so as to judge whether the driving data to be verified is real according to a comparison result.

In one embodiment, further comprising:

the video receiving module is used for receiving a driving recording video of a vehicle end;

the encryption module is further used for calculating a third encryption value of the driving record video when the driving accident is recorded in the driving record video; uploading the third encryption value to the block chain system for block chain storage; and the third encryption value stored by the block chain system is used for verifying the authenticity of the driving record video.

In an embodiment, the driving data includes a plurality of driving data, the driving data respectively reflects driving behaviors of a plurality of users and/or vehicle conditions of the plurality of users, the driving risk factors include macro driving risk factors, and the data analysis module 720 is specifically configured to:

determining a vehicle type with a driving accident in the plurality of driving data, determining the number of users of the vehicle type and the accident occurrence frequency of the vehicle type, and obtaining the accident occurrence frequency of the vehicle type as the macroscopic driving behavior factor according to the number of users of the vehicle type and the accident occurrence frequency of the vehicle type;

and/or determining a road section with a driving accident in the plurality of driving data, determining the traffic flow of the road section and the accident occurrence frequency of the road section, and calculating the accident occurrence frequency of the road section according to the traffic flow of the road section and the accident occurrence frequency of the road section to serve as the macro driving behavior factor;

and/or determining the time of the driving accident in the plurality of driving data, and counting the accident occurrence frequency of each time period according to the time of the driving accident as the macroscopic driving behavior factor.

In one embodiment, the driving data reflects driving behavior of the insurance user, the driving risk factors include individual driving risk factors, and the data analysis module 720 is specifically configured to:

acquiring dangerous driving behavior times from the driving data, and acquiring driving time from the driving data; the dangerous driving behavior times are times of dangerous driving behaviors of the user; the dangerous driving behaviors comprise at least one of sharp turning, sharp braking, sharp acceleration and illegal driving; obtaining dangerous driving behavior frequency according to the dangerous driving behavior frequency and the driving time; and taking the dangerous driving behavior frequency as the individual driving risk factor.

In one embodiment, further comprising:

the authorization request sending module is used for sending a data authorization request to the user side;

the accounting judgment module is used for judging whether the accounting of the first encryption value by the block chain system is successful or not when an authorization confirmation notice fed back by the user side for the data authorization request is received; and when the billing is judged to be successful, the step of returning the driving data to the data verification end is executed.

In one embodiment, the individual driving risk factors include a real-time risk factor and a historical risk factor, further comprising:

the evaluation value calculation module is used for calculating the evaluation value of the real-time risk factor and calculating the evaluation value of the historical risk factor;

the integral recording module is used for recording an excitation integral aiming at the insurance user when the evaluation value of the real-time risk factor is higher than the evaluation value of the historical risk factor; the excitation integral is used for generating a vehicle insurance deduction scheme.

In one embodiment, further comprising:

the first accident judgment module is used for judging whether the driving record video carries a driving accident label or not, and if so, judging that the driving record video records a driving accident; or the second accident judgment module is used for identifying the driving record video, and when an abnormal driving picture is identified, judging that the driving record video records a driving accident; the abnormal driving picture comprises at least one of a vehicle collision picture, a vehicle sudden braking picture and a vehicle sudden turning picture.

In one embodiment, as shown in fig. 8, there is provided a block chain-based car insurance scheme generating apparatus 800, including: a data acquisition module 810 and a data upload module 820, wherein,

the data acquisition module 810 is used for acquiring driving data through the vehicle-mounted diagnosis system; the driving data is data reflecting driving behaviors and/or vehicle conditions;

a data uploading module 820, configured to upload the driving data to a server; the server is used for analyzing the driving data to obtain a driving risk factor; uploading the driving risk factors to a block chain system for block chain storage, wherein the driving risk factors stored in the block chain system are used for generating a vehicle insurance scheme of an insurance user; the driving risk factor is used for reflecting the driving accident risk of the driving behavior and/or reflecting the driving accident risk of the vehicle condition.

In one embodiment, further comprising:

the encryption uploading module is used for calling a video in the automobile data recorder as an automobile data recording video when a driving accident is detected; uploading the driving record video to a server; the server is used for calculating a third encryption value of the driving recording video and uploading the third encryption value to the block chain system for block chain storage.

For specific definition of the block chain-based vehicle insurance scheme generation apparatus, reference may be made to the above definition of the block chain-based vehicle insurance scheme generation method, which is not described herein again. The modules in the block chain-based vehicle insurance scheme generation device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

The block chain-based car insurance scheme generation device can be used for executing the block chain-based car insurance scheme generation method provided by any of the embodiments, and has corresponding functions and beneficial effects.

In one embodiment, as shown in fig. 9, a driving data processing system 900 is provided, and the driving insurance scheme generating system 900 specifically includes a vehicle end 910 and a driving data server 920; wherein the content of the first and second substances,

the vehicle end 910 is configured to collect driving data through a vehicle-mounted diagnosis system, and upload the driving data to the driving data server 920; the driving data is data reflecting driving behaviors and/or vehicle conditions;

the driving data server 920 is used for analyzing the driving data to obtain a driving risk factor; uploading the driving risk factors to a block chain system to store a certificate of the block chain, wherein the driving risk factors stored by the block chain system are used for generating a vehicle insurance scheme of an insurance user; the driving risk factor is used to reflect the risk of a driving accident of the driving behaviour and/or to reflect the risk of a driving accident of the vehicle condition.

The data processing procedures of the vehicle end 910 and the driving data server 920 have been described in detail in the above embodiments, and are not described herein again.

For specific definition of the driving data processing system, reference may be made to the above definition of the block chain-based vehicle insurance scheme generation method, and details are not described herein again. The driving data processing system can be used for executing the block chain-based vehicle insurance scheme generation method provided by any embodiment, and has corresponding functions and beneficial effects.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a block chain based car insurance scheme generation method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like. Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:

acquiring driving data; the driving data is data reflecting driving behaviors and/or vehicle conditions; analyzing the driving data to obtain a driving risk factor; the driving risk factor is used for reflecting the driving accident risk of the driving behavior and/or reflecting the driving accident risk of the vehicle condition; and uploading the driving risk factors to a block chain system for block chain evidence storage, wherein the driving risk factors stored in the block chain system are used for generating a vehicle insurance scheme of an insurance user.

In one embodiment, the processor, when executing the computer program, further performs the steps of:

collecting driving data through a vehicle-mounted diagnosis system; the driving data is data reflecting driving behaviors and/or vehicle conditions; and uploading the driving data to a server.

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

In one embodiment, the computer program when executed by the processor implements the steps of:

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

Claims

1. A block chain-based vehicle insurance scheme generation method is characterized by comprising the following steps:

2. The method of claim 1, further comprising:

calculating a first encryption value of the driving data;

uploading the first encryption value to the block chain system for block chain storage;

the method further comprises the following steps:

receiving a data verification request of a data verification end;

and sending the driving data to be verified to the data verification end, allowing the data verification end to calculate a second encryption value of the driving data to be verified, and comparing the second encryption value with the first encryption value stored in the block chain system so as to judge whether the driving data to be verified is real or not according to a comparison result.

3. The method of claim 1, further comprising:

receiving a driving recording video of a vehicle end;

when the driving accident is recorded in the driving record video, calculating a third encryption value of the driving record video;

uploading the third encryption value to the block chain system for block chain storage; and the third encryption value stored by the block chain system is used for verifying the authenticity of the driving record video.

4. The method of claim 1, wherein the driving data has a plurality of driving data respectively reflecting driving behaviors of a plurality of users and/or vehicle conditions of a plurality of users, the driving risk factor comprises a macroscopic driving risk factor, and the analyzing the driving data to obtain the driving risk factor comprises at least one of the following steps:

determining a road section with a driving accident in the plurality of driving data, determining the traffic flow of the road section and the accident occurrence frequency of the road section, and calculating the accident occurrence frequency of the road section according to the traffic flow of the road section and the accident occurrence frequency of the road section to serve as the macro driving behavior factor;

and determining the time of the driving accident in the plurality of driving data, and counting the accident occurrence frequency of each time period according to the time of the driving accident as the macroscopic driving behavior factor.

5. The method of claim 1, wherein the driving data reflects driving behavior of the insured user, the driving risk factors include individual driving risk factors, and the analyzing the driving data to derive driving risk factors comprises:

acquiring dangerous driving behavior times from the driving data, and acquiring driving time from the driving data; the dangerous driving behavior times are times of dangerous driving behaviors of the user; the dangerous driving behaviors comprise at least one of sharp turning, sharp braking, sharp acceleration and illegal driving;

obtaining dangerous driving behavior frequency according to the dangerous driving behavior frequency and the driving time;

and taking the dangerous driving behavior frequency as the individual driving risk factor.

6. The method of claim 5, wherein the individual driving risk factors include real-time risk factors and historical risk factors, further comprising:

calculating an evaluation value of the real-time risk factor, and calculating an evaluation value of the historical risk factor;

when the evaluation value of the real-time risk factor is higher than that of the historical risk factor, recording an excitation integral aiming at the insurable user; the excitation integral is used for generating a vehicle insurance deduction scheme.

7. The method according to claim 3, wherein after receiving the driving recording video at the vehicle end, the method further comprises:

judging whether the driving record video carries a driving accident label or not, if so, judging that the driving record video records a driving accident;

alternatively, the first and second electrodes may be,

identifying the driving record video, and when an abnormal driving picture is identified, judging that the driving record video records a driving accident; the abnormal driving picture comprises at least one of a vehicle collision picture, a vehicle sudden braking picture and a vehicle sudden turning picture.

8. A block chain-based vehicle insurance scheme generation method is characterized by comprising the following steps:

9. A block chain-based vehicle insurance scheme generation apparatus, comprising:

10. A block chain-based vehicle insurance scheme generation apparatus, comprising: