CN106411998A - Prediction method for UBI (Usage-Based Insurance) system based on internet of vehicles big data - Google Patents
Prediction method for UBI (Usage-Based Insurance) system based on internet of vehicles big data Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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Abstract
The invention discloses a prediction method for a UBI (Usage-Based Insurance) system based on internet of vehicles big data. The method mainly comprises the steps of S1, carrying out collection storage on vehicle driving data and driving behavior information by applying a smart on-board diagnostics OBD terminal, and carrying out data processing; S2, carrying out analysis modeling; S3, providing a reasonable vehicle insurance prediction scheme on the basis of analysis modeling, and carrying out modular system analysis and processing for individual service demands of users; and S4, providing a vehicle insurance prediction model and a UBI vehicle insurance pricing strategy on the basis of driving behavior analysis research. According to the prediction method for the internet of vehicles insurance system, namely the usage-based insurance UBI system, based on the big data provided by the invention, the reasonable vehicle insurance prediction scheme is provided on the basis of application of the smart on-board diagnostics OBD terminal, vehicle data collection, driving behavior information storage and processing, and data analysis modeling, and the modular system analysis and processing for the individual service of the users can be provided.
Description
Technical field
The present invention relates to the UBI systematic study under the big data epoch, i.e. data source, the process of data, the analysis of data and
The parts such as forecast model, especially give vehicle insurance forecast model and UBI vehicle insurance pricing strategy.
Background technology
The property insurance industry of China in 2013 breached hundreds of millions yuan of high pointes, increased 21.3% than 2009, even so, but
The profit of insurance industry is still undesirable.Because traditional automobile insurance only considers that vehicle purchases valency, purchase car type etc., car
Insurance model is extremely single, does not account for the impact to automobile insurance for the driving behavior, leads to the vehicle insurance of most of high-quality
User be minority because severe driving behavior cause great number Claims Resolution user check so that insurer vehicle insurance premium set
There is seriously irrational phenomenon.
By contrast, external insurance premium rate is more flexible, the U.S.'s unmarried low age insurance premium rate highest (lack a sense of responsibility,
Car accident easily occurs)；German new hand's rate high (probability that is in danger is high)；Canadian weekend (is gone out with car is lower than working fare rate
Dangerous probability is low).Abroad actively promote UBI (Usage-Based Insurance, UBI, the car insurance system based on driving behavior
System) insurance, and achieve certain effect, the car networking insurance model of following UBI also will persistently be promoted and application.
With Internet era arrival and techno-globalism development, mobile Internet constantly penetrating into society,
Economic every field, similarly the car networking under the Internet is also just towards car insurance industry penetration, thus based on car networking
Car insurance industry has huge development prospect.Wherein, car networking technology, big data technology etc. are following insurance industry development
Core driver.
Publication No. CN105389864A, the patent of invention disclosure of entitled " a kind of method of automobile UBI information retrieval "
A kind of data extraction method based on automobile UBI, belongs to automobile UBI insurance system field, including automobile UBI data acquisition system,
Gathering method, preprocessing method, work flow, cloud computing and data mining etc..But this patent only relates to intelligent vehicle-carried perception eventually
The collection to some parameters in car running process for the end, and to UBI systematic research, without reference to the prediction of vehicle insurance expense.
Content of the invention
Present invention aim at solution car insurance pattern is extremely single, do not account for driving behavior to automobile insurance
Impact, propose the big data epoch under UBI system, data source, the process of data, the analysis of data and forecast model, to each
Individual module has carried out detailed elaboration analysis.And the analysis result with reference to data, formulate rational vehicle insurance predictive mode.
For solving the above problems, the present invention combines to car networking insurance and the UBI systematic research under the big data epoch
UBI system prediction method based on car networking big data is proposed.Concrete technical scheme is as follows：
Based on the UBI system prediction method under big data technology, comprise the steps：
Step 1：Application intelligent vehicle mounted terminal OBD, is collected to vehicle operation data and driving behavior information storing, and
Carry out data processing；
Step 2：Analysis modeling；
Step 3：Rational vehicle insurance prediction scheme, and the personalized service for user is given on the basis of analysis modeling
Requirement carries out modular systematic analysiss and process；
Step 4：On the basis of driving behavior analysis research, provide vehicle insurance forecast model and UBI vehicle insurance pricing strategy.
Further, data processing described in step 1 comprises data prediction data and stores two parts, and data prediction can
To obtain to car insurance prediction scheme valuable data message, data storage is by the data solution relevant to driving behavior
Analysis, filters out the data needed for UBI system, then these data is carried out classifying, merges, and store distributed data base
In, collecting storage is to utilize car networking, by devices such as OBD, GPS, completes adopting of vehicle oneself state and environmental information data
Collection, and pass through the Internet by the data transfer gathering to central processing unit.
Further, analysis modeling described in step 2 processes the data characteristicses being to extract for pretreatment, to different driving
Behavior gives different insurance premium rates.
Beneficial effect：
1. the present invention is by processing to big data, having drawn the UBI system in car networking big data epoch.
2. in the presence of this system, labor big data processing procedure, in conjunction with real income data from different perspectives
The impact to vehicle insurance for the driving behavior is described.
3. the result according to analysis, provides easy analysis model and the suggestion of rational vehicle insurance predictive mode.
Brief description
Fig. 1 is the car networking model under OBD pattern.
Fig. 2 is the UBI system in the car networking big data epoch of the present invention.
Fig. 3 is to the result figure collecting Data Analysis Services.
Fig. 4 is the flow chart of the present invention.
Specific embodiment
In conjunction with accompanying drawing, specific embodiments of the present invention are further described in detail.The present invention proposes based on car connection
The UBI system prediction method of net big data, is studied to car networking insurance, and novelty was proposed under the big data epoch
UBI system prediction method.The method is from the driving behavior custom of car owner, type of mileage, the price purchased and vehicle etc.
Aspect carries out comprehensive analysis, is based in the first generation of car networking insurance and pays (PAYD, Pay As You Drive) by mileage
Car insures the second filial generation and considers to propose car and people's phase on the car safe basis of driving safety (PHYD, Pay How You Drive)
Determine vehicle insurance scheme in conjunction with multi-mode, only single to the car or people analytical model breaking traditions.Present invention analyzing and processing
Data is all the true driving behavior data collected by car-mounted terminal OBD.
As shown in figure 1, car networking (Internet of Vehicles, IOV) is by devices such as OBD, GPS, complete car
Oneself state and environmental information data collection, by the Internet by the data transfer gathering to central processing unit and to data
It is analyzed processing, and the vehicle of different demands is effectively supervised and the system of integrated service is provided, realize the intelligence of vehicle
Energyization controls.
OBD (OBD, On-Board Diagnostics) is the core technology of car networking, has merged automobile intelligent sense
Know the monitoring mould of the link block, automotive system and part (electromotor, emission control systems etc.) of module, automobile and the Internet
Block, realizes real time record and the report of vehicle condition.The car networking system of OBD pattern, is by OBD terminal, background system, mobile phone
These three major parts of APP form, the car networking model under OBD pattern, and the sensor of built-in vehicle has Intellisense function,
OBD OBD passes through to control local area network (CAN, Controller Aver Network) to be connected with bus, obtains ECU
Car status information in (ECU, Engine Control Unit).This modular system is similar with the logic composition of Internet of Things, by
Data acquisition, Data Analysis Services, data report etc. forms.
As shown in Fig. 2 the UBI system in big data epoch mainly has data source, the process of data, the analysis of data and prediction
The part such as model forms.The following is the labor summary of the UBI vehicle insurance system to big data.
The OBD installing in motor vehicles carries out real-time monitoring to each system of vehicle, the application of car networking achieve from
Client-server (Client/Server) is successfully connected, and server is the resource center of whole application system, and client is sent out
The data sent is sent to database server, and client can also conduct interviews to data base.Data source of the present invention is stored in pass
It is in data base MySQL, be transferred in distributed data base management system (DDBMS) by data gateway.MySQL has small volume, speed
Hurry up, low cost the features such as it is adaptable to quick in vehicle condition produce data, the data upgrading in time in data base, eliminate redundancy
Data message, decreases the waste of Internet resources.
Data processing comprises data prediction data and stores two parts, and data prediction can obtain and insure prediction to car
The valuable data message of scheme.By the data parsing relevant to driving behavior, filter out UBI system institute proposed by the present invention
The data needing, such as daily four anxious (bring to a halt, anxious accelerate, anxious slow down, zig zag) number of times, distance travelled, the travel time, hypervelocity time
Then these data are carried out classifying, merge, and store in distributed data base HBase by the data such as number.HBase is a kind of
Based on the project of Hadoop, also referred to as Hadoop distributed file system (HDFS, Hadoop Distributed File
System).It is the distributed data base of a unstructured data storage, manages cluster using Zookeeper, in framework layer
Master (leader in Zookeeper) and multiple region server (RS, RegionServer) are divided on face.Basic framework
As follows, RS is one of cluster node, and each RS can be responsible for multiple Region, and each Region can only be by a RS
Service is provided, in HBase, needs multiple Region to carry out data storage, HBase defines certain limit to each Region, falls on rule
Determine the data of scope, the Region of regulation will be distributed to, thus load is assigned on each node, here it is distributed storage
Process and advantage YARN (Yet Another Resource Negotiator) be cloth cluster explorer.
MapReduce1 framework is to execute Map and Reduce task on whole cluster and report result, but in large construction cluster, works as collection
When group node exceedes a certain amount of, arise that cascading failure, cascading failure leads to whole cluster serious by network flood form
Deteriorate.In order to overcome this defect of MapReduce1, it is layered the technology of cluster Governance framework using YARN, cluster can be made altogether
Enjoy, scalable and more reliable.YARN hierarchy is that each several part resource is passed to base by resource manager ResourceManager
Plinth node-agent program NodeManager, NodeManager starts and monitors base application execution and resource management
(resource allocation such as CPU, internal memory).
Spark be one based on internal memory calculate cluster computing system, its core be elasticity distribution formula data set (RDD,
Resilient Distributed Datasets), all operations of Spark are based on RDD, and RDD is fault-tolerant, parallel data
Structure, RDD is the object set of not revisable distribution.Each RDD is made up of multiple subregions, and each subregion can be simultaneously
Calculate on different nodes in the cluster, the graded properties of RDD are with computation capability so that Spark can better profit from
Telescopic hardware resource.If combine subregion with both persistences, just can more efficiently process mass data.
The present invention is collected for 1000 car datas, and analyzes and processes driving behavior related data information, and such as four
The time of urgency, distance travelled, maximum instantaneous velocity and trip.As shown in figure 3, being to be driven from daily respectively based on driving behavior
The block diagram of distance, the number of times summation of daily four urgency, maximal rate and four aspect the data obtaineds of travel time the latest, by this
The analysis of a little data, draws corresponding driving behavior result, is the UBI car insurance scheme under the big data epoch in the present invention
Strong evidence is provided.
Modeling analysis are the data characteristicses extracted for pretreatment, obtain the result wanted.After data is extracted, often
Use Spark algorithm.The application that Spark commonly uses has Spark SQL, Spark Streaming, MLLib, Graph etc..
Spark SQL realizes SQL query using RDD；Spark Streaming streaming calculates, and provides real-time computing function；GraphX schemes
Computational frame is it is achieved that basic figure computing function, conventional nomography and pregel figure programming framework；MLLib machine learning storehouse,
Common classification, cluster, recurrence, the machine learning algorithm Parallel Implementation such as crosscheck are provided, as naive Bayesian, logistic regression,
Decision tree, neutral net, TFIDF, collaborative filtering scheduling algorithm, have existed it is only necessary to bring data into tune inside ML lib
With more convenient.
UBI system proposed by the present invention gives different insurance premium rates to different driving behaviors, and provides personalization
Value-added service.After big data analyzing and processing, the embodiment of the automobile insurance that this system provides is as follows：To each user
Such as 100 points of daily one base total score value of setting, four anxious/when travelling the night running of the hypervelocity number of times of total kilometrage/daily/daily daily
Between press 5:2:2:1 distribution total score, that is, 50 points/20 points/20 points/10 points.
If table 1 below lattice are the code of points formulated according to driving behavior, how many by accumulative score, judge a people's
The Optimality of driving behavior.
Table 1
The scoring event of 1 year is added up according to scheme and is designated as Sum, the natural law of driving is counted and calculated the natural law of fraction and be
Day, average is designated as Avg：
Avg=Sum/Day
For the behavior that prevents from maliciously rehearsing, natural law Day has certain regulation：If Day<100 days, it is considered as the lowest class, 100
≤Day<250, then in the original superior certain proportion of Sum 50%, if Day >=250, calculate according to former Sum.
Being analyzed according to Avg to overcome to difference is divided into different grade Avg >=80 to be for five-star client, 60≤Avg<80 4
Star, 40≤Avg<60 Samsungs, 20≤Avg<40 2 stars, 0≤Avg<20 1 star clients.The client of different stars can undertake not
With car insurance premium rate, insurance company should reward top-tier customer (i.e. the high client of star), gives preferential in the insurance of next year
Activity, meanwhile, punishment user inferior (i.e. the low client of star), can improve the coming year and insure the insurance premium rate of vehicle.Additionally, obtaining
The data taking can also be provided personalized service for client, such as according to driving habit with through haunt, in good time for its recommendation
Local characteristic and shop action message, the service such as give to remind in time for driving behavior bad user.
Fig. 4 is the flow chart based on the UBI system prediction method under big data technology for the present invention, there it can be seen that this
The Forecasting Methodology of invention is under the technological frame based on car networking big data first, applies intelligent vehicle mounted terminal OBD, to vehicle row
Sail data and driving behavior information is collected storing, and carry out data processing；It is analyzed afterwards modeling, and build in analysis
Provide rational vehicle insurance prediction scheme on the basis of mould, and carry out modular system for the personalized service requirement of user and divide
Analysis and process.On the basis of the last research in driving behavior analysis, provide vehicle insurance forecast model and UBI vehicle insurance pricing strategy.
Claims (3)
1. based on the UBI system prediction method under big data technology it is characterised in that comprising the steps：
Step 1：Application intelligent vehicle mounted terminal OBD, is collected to vehicle operation data and driving behavior information storing, and carries out
Data processing；
Step 2：Analysis modeling；
Step 3：Provide rational vehicle insurance prediction scheme on the basis of analysis modeling, and require for the personalized service of user
Carry out modular systematic analysiss and process；
Step 4：On the basis of driving behavior analysis research, provide vehicle insurance forecast model and UBI vehicle insurance pricing strategy.
2. the UBI system prediction method under the technology based on big data according to claim 1 is it is characterised in that in step 1
Described data processing comprises data prediction data and stores two parts, and data prediction can obtain to car insurance prediction side
The valuable data message of case, data storage is by the data parsing relevant to driving behavior, filters out needed for UBI system
Then these data are carried out classifying, merge, and store in distributed data base by data, and collecting storage is to utilize car networking,
By devices such as OBD, GPS, complete vehicle oneself state and the collection of environmental information data, and the number that will gather by the Internet
According to being transferred to central processing unit.
3. the UBI system prediction method under the technology based on big data according to claim 1 is it is characterised in that in step 2
Described analysis modeling processes the data characteristicses being to extract for pretreatment, gives different insurances to different driving behaviors
Rate.
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| CN107203945A (en) * | 2017-06-12 | 2017-09-26 | 清华大学苏州汽车研究院（吴江） | Vehicle insurance grading evaluation method and device |
| CN107292528A (en) * | 2017-06-30 | 2017-10-24 | 阿里巴巴集团控股有限公司 | Vehicle insurance Risk Forecast Method, device and server |
| CN107564280A (en) * | 2017-08-22 | 2018-01-09 | 王浩宇 | Driving behavior data acquisition and analysis system and method based on environment sensing |
| CN107657537A (en) * | 2017-09-14 | 2018-02-02 | 青岛车盟信息技术服务有限公司 | UBI collecting vehicle informations analysis system and car owner's classification and premium discount method |
| CN107977896A (en) * | 2017-12-21 | 2018-05-01 | 江西爱驰亿维实业有限公司 | The accounting method and device that car insurance is taken |
| CN108171430A (en) * | 2017-12-29 | 2018-06-15 | 深圳市轱辘车联数据技术有限公司 | Data processing method, mobile unit and UBI analysis centers server |
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| CN109255440A (en) * | 2017-07-11 | 2019-01-22 | 上海有孚网络股份有限公司 | The method that predictive maintenance is carried out to Electric Power Generating Equipment based on recurrent neural network (RNN) |
| CN109460964A (en) * | 2018-09-29 | 2019-03-12 | 中国平安财产保险股份有限公司 | Method, apparatus and computer equipment based on the more newly-generated vehicle insurance price list of data |
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| CN110599620A (en) * | 2019-07-26 | 2019-12-20 | 广州亚美信息科技有限公司 | Data processing method and device, computer equipment and readable storage medium |
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| CN107203945A (en) * | 2017-06-12 | 2017-09-26 | 清华大学苏州汽车研究院（吴江） | Vehicle insurance grading evaluation method and device |
| CN107292528A (en) * | 2017-06-30 | 2017-10-24 | 阿里巴巴集团控股有限公司 | Vehicle insurance Risk Forecast Method, device and server |
| US11244402B2 (en) | 2017-06-30 | 2022-02-08 | Advanced New Technologies Co., Ltd. | Prediction algorithm based attribute data processing |
| CN109255440A (en) * | 2017-07-11 | 2019-01-22 | 上海有孚网络股份有限公司 | The method that predictive maintenance is carried out to Electric Power Generating Equipment based on recurrent neural network (RNN) |
| CN107564280A (en) * | 2017-08-22 | 2018-01-09 | 王浩宇 | Driving behavior data acquisition and analysis system and method based on environment sensing |
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| CN107977896A (en) * | 2017-12-21 | 2018-05-01 | 江西爱驰亿维实业有限公司 | The accounting method and device that car insurance is taken |
| CN108171430A (en) * | 2017-12-29 | 2018-06-15 | 深圳市轱辘车联数据技术有限公司 | Data processing method, mobile unit and UBI analysis centers server |
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| CN108268309A (en) * | 2018-02-06 | 2018-07-10 | 广东暨通信息发展有限公司 | The batch processing method of computer big data |
| CN108734592A (en) * | 2018-05-11 | 2018-11-02 | 深圳市图灵奇点智能科技有限公司 | Car insurance business datum analysis method and system |
| CN109460964A (en) * | 2018-09-29 | 2019-03-12 | 中国平安财产保险股份有限公司 | Method, apparatus and computer equipment based on the more newly-generated vehicle insurance price list of data |
| CN109766217A (en) * | 2018-12-20 | 2019-05-17 | 北京梧桐车联科技有限责任公司 | A kind of vehicle system fault repairing method and device |
| CN109784586A (en) * | 2019-03-07 | 2019-05-21 | 上海赢科信息技术有限公司 | The prediction technique and system of the situation of being in danger of vehicle insurance |
| CN109784586B (en) * | 2019-03-07 | 2023-08-29 | 上海赢科信息技术有限公司 | Prediction method and system for danger emergence condition of vehicle danger |
| CN110599620A (en) * | 2019-07-26 | 2019-12-20 | 广州亚美信息科技有限公司 | Data processing method and device, computer equipment and readable storage medium |
| CN110599620B (en) * | 2019-07-26 | 2021-12-24 | 广州亚美信息科技有限公司 | Data processing method and device, computer equipment and readable storage medium |
| CN111323535A (en) * | 2019-12-30 | 2020-06-23 | 江苏徐工信息技术股份有限公司 | Method for detecting vehicle exhaust emission qualification |
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| CN111131014A (en) * | 2020-01-22 | 2020-05-08 | 北方工业大学 | Internet of things gateway |
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| CN111833194A (en) * | 2020-06-20 | 2020-10-27 | 南京易科姆智能科技有限公司 | Vehicle insurance mode for charging based on rated usage in insurance period |
| CN113554518A (en) * | 2021-04-01 | 2021-10-26 | 成都雅信安科技服务有限公司 | System for pricing car insurance through vehicle-mounted equipment |
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