CN117423232A - Rental property vehicle identification method based on Internet of vehicles big data - Google Patents
Rental property vehicle identification method based on Internet of vehicles big data Download PDFInfo
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- CN117423232A CN117423232A CN202311357431.9A CN202311357431A CN117423232A CN 117423232 A CN117423232 A CN 117423232A CN 202311357431 A CN202311357431 A CN 202311357431A CN 117423232 A CN117423232 A CN 117423232A
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- 238000000034 method Methods 0.000 title claims abstract description 20
- 239000006185 dispersion Substances 0.000 claims abstract description 22
- 230000006399 behavior Effects 0.000 claims description 28
- 238000012549 training Methods 0.000 claims description 8
- 238000013500 data storage Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 230000003203 everyday effect Effects 0.000 claims description 3
- 230000001788 irregular Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000002372 labelling Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 3
- 230000006872 improvement Effects 0.000 description 5
- 238000007477 logistic regression Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000002354 daily effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
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- 239000000446 fuel Substances 0.000 description 1
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
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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
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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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
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
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Abstract
The invention relates to the technical field of vehicle identification, in particular to a rental property vehicle identification method based on internet of vehicles big data. S1, constructing a parking lot database commonly used by a car renting company; s2, comparing a common parking lot of the target vehicle with a common parking lot of a taxi company; s3, identifying a prepositive behavior of the target vehicle for leasing, and entering S4 after the prepositive behavior occurs; and S4, scoring by combining the dispersion degree D of the driving time and the dispersion degree L of the vehicle track. According to the invention, the identification of the rental property vehicle is realized by comparing the coordinates of the parking lot commonly used by private vehicles with the parking lot commonly used by rental vehicle companies and scoring the combination of the dispersion degree D of driving time and the dispersion degree L of vehicle tracks, the problem that the identification of non-operating vehicles is free from rental behavior technology is solved, and the identification efficiency and accuracy can be improved by adopting the prepositive behavior judgment.
Description
Technical Field
The invention relates to the technical field of vehicle identification, in particular to a rental property vehicle identification method based on internet of vehicles big data.
Background
With the increasing number of new energy vehicles rising year by year, the new energy vehicles have the characteristic of low use cost compared with fuel vehicles, and promote more and more new energy vehicles to engage in operation, wherein the leasing market also has a large number of new energy vehicles to engage in operation, so that a large number of non-operation vehicles also occur to engage in operation, and the vehicle risk cost of the vehicles is reduced; and the car insurance institutions and related government functional departments cannot effectively identify that the non-operating vehicles engage in leasing actions, which can cause great harm to society and masses.
Disclosure of Invention
The invention aims to provide a rental property vehicle identification method based on internet of vehicles big data, so as to solve the problems in the background art.
To achieve the above object, there is provided a rental property vehicle identification method based on internet of vehicles big data, comprising the method steps of:
s1, constructing a parking lot database commonly used by a car renting company;
s2, comparing a common parking lot of the target vehicle with a common parking lot of a taxi company;
s3, identifying a prepositive behavior of the target vehicle for leasing, and entering S4 after the prepositive behavior occurs;
s4, scoring according to the dispersity D of the driving time and the dispersity L of the vehicle track, comparing the final score with a preset judgment threshold, and when the final score is higher than the judgment threshold, the target vehicle is a rental-property vehicle; otherwise, not the rental property vehicle.
As a further improvement of the technical scheme, in S1, the parking place commonly used by the taxi company is marked by longitude and latitude of a WGS84 coordinate system, wherein:
the map electronic fence is used for enclosing the more regular parking lot;
and (5) circling the irregular parking lot by using a circular map electronic fence.
As a further improvement of the technical scheme, all parking lot location data are stored in the database according to the classification of the classification conditions, wherein the classification conditions comprise different cities and different companies.
As a further improvement of the technical scheme, in S2, a parking lot with a parking time longer than 5 hours in 30 days is searched for, and according to the coordinates of the common position of parking, a relevant interface of a Goldmap is called to determine whether the vehicle is in the parking lot commonly used by a renting company, wherein:
the common parking lot coincides with the common parking lot of the renting company, the common parking lot is marked as 1, otherwise, the common parking lot is marked as 0;
and 3, the vehicle with the front-end behavior in the step S3 is marked as 1, and when the model is involved, only the target vehicle marked as 1 is selected.
As a further improvement of the present technical solution, the driving time dispersion D is calculated as follows:
retrieving the driving time length data of the training model, the testing model and the verification model in the last 1 month according to the frame number through a big data storage platform Star-locks of the national platform; firstly, dividing the time into 24 driving time periods, wherein each time period is 1 hour, and then labeling the vehicle according to whether the vehicle runs in the time period every day, wherein the label with running behavior is 1, and the label without running behavior is 0; counting the days of driving behaviors in each period of 1 month; if the driving days of the period exceeds 20 days, the label f of the period is recorded as 1, and the sum of the labels of the 24 periods is counted to be the dispersion degree D of the driving time, namely:
as a further improvement of the present technical solution, the calculation process of the dispersion degree L of the vehicle track is as follows:
and retrieving vehicle position data of the training model, the testing model and the verification model for 1 month according to the frame number through a big data storage platform Star-locks of the national platform, finding the easiest, the easiest and the north positions in all the position data to enclose a rectangular area, placing the positions of each day into the rectangular area to form a track image, comparing the similarity of the track image for one month by using RMSE, wherein the RMSE is the dispersity L of the vehicle track.
Compared with the prior art, the invention has the beneficial effects that:
in the method for identifying the rental property vehicles based on the internet of vehicles big data, the rental property vehicles are identified by comparing the coordinates of the common parking lots of private vehicles with the common parking lots of rental vehicle companies and scoring the combination of the dispersion degree D of driving time and the dispersion degree L of vehicle tracks, so that the problem that the non-operating vehicles are identified to engage in the technical vacancy of rental behaviors is solved, and the efficiency and the accuracy of identification can be improved by adopting the prepositive behavior judgment.
Drawings
FIG. 1 is a schematic diagram of steps of a rental-property vehicle identification method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a method for identifying rental behaviors of non-operating vehicles based on internet of vehicles, which relates to technologies for combining clear internet of vehicles with big data, feature analysis and the like, and mainly aims at solving the problem that a vehicle insurance institution and related government functional departments cannot effectively identify the rental behaviors of the non-operating vehicles; based on the big data of the Internet of vehicles (GB/T32960 standard) and the driving behavior data of the known leased vehicles, the leased operation behavior of the vehicles is rapidly identified through a big data analysis technology, actual measurement of the operating vehicles is standardized, and an effective leased vehicle management and vehicle insurance underwriting system is constructed.
The method for identifying the rental property vehicles based on the big data of the Internet of vehicles comprises the following steps of:
s1, constructing a parking lot database commonly used by a car renting company:
the parking lot is studied commonly by the car renting companies in various cities around the country, and the position of the parking lot is marked by the longitude and latitude of the WGS84 coordinate system. For the more regular parking lot, the map electronic fence is used for enclosing, for the irregular or difficult-to-recognize parking lot, the circular map electronic fence is used for enclosing, and all data are classified and stored in the database according to different cities, different companies and other conditions.
S2, comparing the common parking lot of the vehicle with the common parking lot of a taxi company:
searching a parking lot with the parking time longer than 5 hours in the period of 30 days, calling a Goldmap related interface according to the coordinates of the common parking position, and judging whether the vehicle is in the parking lot common to a renting company. If the common parking lot coincides with the common parking lot of the renting company, the common parking lot is marked as 1, otherwise, the common parking lot is marked as 0. When the model is taken in, only the vehicle marked with 1 is selected. The vehicle marked 0 defaults to a normal private car.
S3, identifying the prepositive behavior of the private car for leasing:
and comparing the coordinates of the common parking lot of the private car with the common parking lot of the rental car company, if the two parking lots are dissimilar, considering that the private car is not used for renting, and if the two parking lots are similar, further dispersing the driving time D, dispersing the vehicle track L, and scoring the result to be higher, wherein the probability that the vehicle to be identified is the rental car is lower.
That is, a pre-behavior determination is made by S3, and it is determined whether to perform S4 based on the pre-behavior, which is a condition that the private car has rental properties when the parking lot of the private car is parked in the parking lot of the rental car company.
S4, scoring by combining the dispersion degree D of the driving time and the dispersion degree L of the vehicle track:
calculating the dispersion degree D of driving time:
and retrieving the driving time length data of the training model, the testing model and the verification model for the last 1 month according to the frame number through a Star-locks of a big data storage platform of the national platform. The time is first divided into 24 driving periods of 1 hour each. Then, according to whether the vehicle is running in the period of time every day, the label with running behavior is 1, and the label without running behavior is 0. Days in which driving behavior was present per period of time in the vicinity of 1 month were counted. If the driving days of the period exceeds 20 days, the label f of the period is recorded as 1, and the sum of the labels of the 24 periods is counted to obtain the dispersion degree D of the driving time.
Calculating the dispersion degree L of the vehicle track:
and retrieving vehicle position data of the training model, the test model and the verification model for the last 1 month according to the frame number through a big data storage platform Star-locks of the national platform. Among all the position data, the easiest, and the north-most positions are found to enclose a rectangular area. The daily positions are placed in a rectangular area to form a track image, and the similarity of the track image of the next month is compared by using the RMSE. RMSE is the dispersion L of the vehicle track.
And training a model through machine learning logistic regression, and putting all the constructed characteristics into the logistic regression model for training, so that whether the private car has leasing property or not is further determined through the final score.
The method is characterized in that slice report information of big data of the internet of vehicles (based on GB/T32960 standard) is acquired, and feature data of non-operating vehicles for leasing operation is obtained by cleaning and calculating the slice data of the internet of vehicles of the target internet of vehicles; determining driving behavior characteristics and corresponding thresholds of the leasing property vehicles by exploring driving behavior characteristic data of the existing leasing property vehicles, and calculating feature weights of the leasing property vehicles under different features; and then judging the dispersion degree of the driving track of the target vehicle in a period, the dispersion degree of the driving mileage, the dispersion degree of the time when the vehicle leaves the parking lot, the dispersion degree of the time when the vehicle returns to the parking lot and the like, judging whether the driving behavior characteristic of the vehicle is larger than a threshold value, obtaining a final score based on the threshold value of the driving behavior characteristic of the leased vehicle and the corresponding weight, judging whether the target is the vehicle with the leasing property according to the final score, setting a judgment threshold value in advance, and if the score is higher than the threshold value, the target is the vehicle with the leasing property, otherwise, the target is not the vehicle with the leasing property.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The method for identifying the rental property vehicles based on the big data of the Internet of vehicles is characterized by comprising the following steps of:
s1, constructing a parking lot database commonly used by a car renting company;
s2, comparing a common parking lot of the target vehicle with a common parking lot of a taxi company;
s3, identifying a prepositive behavior of the target vehicle for leasing, and entering S4 after the prepositive behavior occurs;
s4, scoring according to the dispersity D of the driving time and the dispersity L of the vehicle track, comparing the final score with a preset judgment threshold, and when the final score is higher than the judgment threshold, the target vehicle is a rental-property vehicle; otherwise, not the rental property vehicle.
2. The method for identifying rental property vehicles based on internet of vehicles big data according to claim 1, wherein the parking place commonly used by the rental company is marked with longitude and latitude of WGS84 coordinate system in S1, wherein:
the map electronic fence is used for enclosing the more regular parking lot;
and (5) circling the irregular parking lot by using a circular map electronic fence.
3. The method for rental property vehicle identification based on internet of vehicles big data according to claim 2, wherein all parking lot location data is stored in the database in a classification according to a distinguishing condition, the distinguishing condition including different cities and different companies.
4. The method for identifying rental property vehicles based on internet of vehicles according to claim 1, wherein in S2, a parking lot with a parking time longer than 5 hours in the near 30 days of the target vehicle is searched, and a german map related interface is called according to the coordinates of the common position of the parking, so as to determine whether the target vehicle is in the parking lot commonly used by the rental company, wherein:
the common parking lot coincides with the common parking lot of the renting company, the common parking lot is marked as 1, otherwise, the common parking lot is marked as 0;
and 3, the vehicle with the front-end behavior in the step S3 is marked as 1, and when the model is involved, only the target vehicle marked as 1 is selected.
5. The method for identifying rental property vehicles based on internet of vehicles according to claim 1, wherein the driving time dispersion D is calculated as follows:
retrieving the driving time length data of the training model, the testing model and the verification model in the last 1 month according to the frame number through a big data storage platform Star-locks of the national platform; firstly, dividing the time into 24 driving time periods, wherein each time period is 1 hour, and then labeling the vehicle according to whether the vehicle runs in the time period every day, wherein the label with running behavior is 1, and the label without running behavior is 0; counting the days of driving behaviors in each period of 1 month; if the driving days of the period exceeds 20 days, the label f of the period is recorded as 1, and the sum of the labels of the 24 periods is counted to be the dispersion degree D of the driving time, namely:
6. the rental property vehicle identification method based on internet of vehicles big data according to claim 1, wherein the calculation process of the dispersion degree L of the vehicle track is as follows:
and retrieving vehicle position data of the training model, the testing model and the verification model for 1 month according to the frame number through a big data storage platform Star-locks of the national platform, finding the easiest, the easiest and the north positions in all the position data to enclose a rectangular area, placing the positions of each day into the rectangular area to form a track image, comparing the similarity of the track image for one month by using RMSE, wherein the RMSE is the dispersity L of the vehicle track.
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