CN110659754A - Vehicle information determination method and device - Google Patents
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Abstract
The embodiment of the invention relates to the technical field of vehicles, in particular to a vehicle information determining method, which comprises the following steps: identifying a driving license of a vehicle, and acquiring characteristic data related to price in the driving license; and predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model. According to the embodiment of the invention, because the prediction model is obtained by training according to a large amount of historical data, the corresponding characteristic data can be set as required, so that the price of the vehicle can be ensured to be obtained according to all the characteristic data corresponding to the prediction model every time the price of the vehicle is determined, and the comprehensiveness of the reference characteristic data is ensured. And the characteristic data is obtained by identifying the driving license, and is more objective and more accurate compared with manual determination. Based on this, the price information of the vehicle can be determined more accurately.
Description
Technical Field
Embodiments of the present invention relate to the field of vehicle technologies, and in particular, to a vehicle information determination method, a vehicle information determination apparatus, an electronic device, and a computer-readable storage medium.
Background
At present, various information of the vehicle mainly needs to be determined by manual measurement and recording. For example, for price information of a vehicle, it is necessary to manually determine information of the age of the vehicle, the model of the vehicle, and the like, and to manually estimate the price.
However, the manually collected feature data is not accurate enough, and the kinds of the feature data collected may be different each time the same information is determined, that is, the kinds of the feature data referred to each time the vehicle information is determined are different, so that it is difficult to accurately determine the information of the vehicle.
Disclosure of Invention
Embodiments of the present invention provide a vehicle information determination method, a vehicle information determination apparatus, an electronic device, and a computer-readable storage medium to solve the deficiencies in the related art.
According to a first aspect of embodiments of the present invention, there is provided a vehicle information determination method including:
identifying a driving license of a vehicle, and acquiring characteristic data related to price in the driving license;
and predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
According to a second aspect of the embodiments of the present invention, there is provided a vehicle information determination device including:
the driving license identification module is used for identifying the driving license of the vehicle and acquiring characteristic data related to price in the driving license;
and the price information generation module is used for predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
identifying a driving license of a vehicle, and acquiring characteristic data related to price in the driving license;
and predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
identifying a driving license of a vehicle, and acquiring characteristic data related to price in the driving license;
and predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
According to the embodiment, the prediction model is obtained by training according to a large amount of historical data, and the corresponding characteristic data can be set as required, so that the price of the vehicle can be ensured to be obtained according to all the characteristic data corresponding to the prediction model every time, and the comprehensiveness of the reference characteristic data is ensured. And the characteristic data is obtained by identifying the driving license, and is more objective and more accurate compared with manual determination. Based on this, the price information of the vehicle can be determined more accurately.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flowchart illustrating a vehicle information determination method according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart illustrating another vehicle information determination method according to an embodiment of the invention.
FIG. 3 is a schematic flow chart diagram illustrating a method for determining driving range according to an embodiment of the present invention.
Fig. 4 is a schematic flowchart illustrating still another vehicle information determination method according to an embodiment of the present invention.
Fig. 5 is a schematic flowchart illustrating still another vehicle information determination method according to an embodiment of the present invention.
Fig. 6 is a schematic block diagram showing a vehicle information determination apparatus according to an embodiment of the present invention.
Fig. 7 is a schematic block diagram showing another vehicle information determination apparatus according to an embodiment of the invention.
Fig. 8 is a schematic block diagram showing still another vehicle information determination apparatus according to an embodiment of the present invention.
Fig. 9 is a schematic block diagram showing still another vehicle information determination apparatus according to an embodiment of the present invention.
Fig. 10 is a schematic block diagram showing still another vehicle information determination apparatus according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Fig. 1 is a schematic flowchart illustrating a vehicle information determination method according to an embodiment of the present invention. The method of the embodiment can be applied to mobile terminals such as mobile phones and tablet computers, and can also be applied to servers. As shown in fig. 1, the vehicle information determination method includes the steps of:
and step S1, identifying the driving license of the vehicle, and acquiring characteristic data related to the price in the driving license.
In one embodiment, the driving license of the vehicle may be identified by an OCR (Optical Character Recognition) technique, for example, data such as a VIN code (i.e., a vehicle identification code), a number plate number, usage properties, and a registration date in the driving license may be identified. The data of the vehicle type, brand, manufacturer and the like can be further determined by analyzing the VIN code.
For the above data, characteristic data related to price, such as vehicle type, usage property, registration date, number plate number, etc., may be obtained, wherein the vehicle age may be further determined based on the registration date and the current time, the region to which the vehicle belongs may be determined based on the number plate number, the mileage per unit time of the vehicle may be estimated based on the usage property, and the mileage per unit time of the vehicle may be estimated based on the mileage per unit time of the vehicle and the vehicle.
Step S2, predicting price information of the vehicle based on the feature data and a prediction model generated in advance.
In one embodiment, the prediction model may be obtained by training a training set composed of a large amount of historical data through a machine learning algorithm, wherein the input quantity of the prediction model is the characteristic data, the output quantity is the price of the vehicle, and the price of the vehicle corresponding to the driving license from which the characteristic data is derived can be obtained by inputting the characteristic data into the prediction model.
In one embodiment, because the prediction model is obtained by training according to a large amount of historical data, the corresponding characteristic data (namely, the input quantity) can be set as required, so that the price of the vehicle can be ensured to be obtained according to all the characteristic data corresponding to the prediction model every time the price of the vehicle is determined, and the comprehensiveness of the reference characteristic data is ensured. And the characteristic data is obtained by identifying the driving license, and is more objective and more accurate compared with manual determination. Based on this, the price information of the vehicle can be determined more accurately.
Fig. 2 is a schematic flow chart illustrating another vehicle information determination method according to an embodiment of the invention. As shown in fig. 2, the method further comprises:
step S3, before identifying the license of the vehicle, acquiring historical transaction prices in historical transaction information of sample vehicles and historical characteristic data associated with the historical transaction prices aiming at a preset number of sample vehicles, and taking training data formed by the historical characteristic data and the historical transaction prices as a training set;
and step S4, learning by using the training set through a machine learning algorithm to obtain the prediction model.
In one embodiment, the sample vehicle may be any vehicle, preferably, a vehicle belonging to the same vehicle type as the vehicle corresponding to the driving license, and the appropriate prediction model may be obtained relatively quickly by performing machine learning based on a vehicle composition training set in which the vehicle belonging to the same vehicle type as the vehicle corresponding to the driving license is included.
In one embodiment, for each sample vehicle, training data consisting of historical trading prices and historical feature data in information for the past month, two months, or longer, e.g., half a year, may be collected as a training set. The training process of the training set containing the historical trading price and the historical characteristic data can be a repeated iteration process, and weights of different characteristic data are obtained through learning a large amount of data in the training set. And then after the characteristic data are input into the prediction model, weighting corresponding characteristic data through each weight value, and finally outputting price information of the vehicle.
In one embodiment, in order to reduce the amount of data to be calculated during training, part of the training data may be randomly selected from all the training data for training, for example, 50 pieces of training data may be randomly selected from all the training data, and then 10 pieces of training data may be randomly selected from all the training data for training.
Optionally, the machine learning algorithm comprises at least one of:
a linear regression algorithm, a regression decision tree algorithm, an iterative decision tree algorithm, or a random forest algorithm.
In one embodiment, the prediction model is generated according to a training set, and various specific training algorithms may be adopted, including a linear Regression algorithm, an RDT (Regression Decision Tree) algorithm, a GBDT (iterative Decision Tree) algorithm, and the like.
In one embodiment, the accuracy of the predictions is different due to the predictive models generated by the various training algorithms described above. However, the embodiment of the present invention provides a method with more feature data, even dozens of feature data, so that the relationship between the feature data and the predicted price multiple may be non-linear, and the linear regression algorithm is mainly suitable for linear conditions, so that the accuracy of the predicted price multiple of the prediction model generated by the linear regression algorithm is relatively low.
The RDT algorithm has many good characteristics, such as low complexity of training time, fast prediction speed, easy display of the training model (easy display of the obtained decision tree in a picture), and the like. However, single decision trees may suffer from overfitting, which can be reduced by some methods, such as pruning, but still present problems.
The GBDT is also called MART (multiple Additive Regression Tree), and is an iterative decision tree algorithm which is composed of a plurality of decision trees, and the final answer is made by accumulating the conclusions of all the trees. GBDT can be used for almost all regression problems, including linear regression and also non-linear regression, and relative logistic regression can only be used for linear regression, and GBDT has a very wide application range. The core of the GBDT is that each arborescence is the residual sum of all previous tree conclusions, and the residual sum is an accumulated amount of real values obtained after adding predicted values. The final result of the GBDT algorithm is to generate N (in this embodiment, N may be greater than 100) trees, which can greatly reduce the disadvantages of the single decision tree.
Because GBDT generates models with higher accuracy than RDT, embodiments of the present invention may preferably employ the GBDT algorithm to generate the prediction model.
Optionally, the characteristic data comprises at least one of:
vehicle type, vehicle age, nature of use, and region of belonged.
In one embodiment, the price of the vehicle is positively correlated with the grade of the vehicle type, and the higher the vehicle type is, the higher the price is; the price of the vehicle is inversely related to the age of the vehicle, and the larger the age of the vehicle is, the lower the price is; the use property determines the driving range of the vehicle, for example, the use property is non-operation, the driving range is relatively short, and the price is relatively high, for example, the use property is operation, the driving range is relatively long, and the price is relatively low; the price of the vehicle is positively correlated with the prosperity degree of the area, and the more prosperity the area is, the higher the price is.
It should be noted that the feature data is not limited to the above four types, and for example, the engine type of the vehicle can be further obtained by parsing the VIN code, and the engine type also belongs to the feature data related to the price of the vehicle.
FIG. 3 is a schematic flow chart diagram illustrating a method for determining driving range according to an embodiment of the present invention. The characteristic data further comprises a usage property, the mileage being determined by:
step S11, determining the mileage of the vehicle per unit time according to the use property;
and step S12, determining the driving mileage of the vehicle according to the vehicle age and the driving mileage per unit time.
In one embodiment, the mileage driven per unit time varies for vehicles of different usage properties, e.g., the mileage driven per day is greater for operating vehicles versus non-operating vehicles due to the higher frequency of usage. The driving range of the vehicle can be determined according to the driving range and the age of the vehicle, for example, the driving range of the operating vehicle per unit time is 100 km/day, the age of the vehicle is 3 years, and the driving range of the vehicle is 365 × 3 × 100-109500 km; and the mileage of the non-operating vehicle per unit time is 30 kilometers per day, the age of the vehicle is 3 years, and the mileage of the vehicle is 365 multiplied by 3 multiplied by 30 which is 32850 kilometers.
Fig. 4 is a schematic flowchart illustrating still another vehicle information determination method according to an embodiment of the present invention. As shown in fig. 4, the method further comprises:
step S5, determining maintenance items and maintenance cycles of the vehicle according to the vehicle type;
step S6, determining maintenance time according to the vehicle age or the driving mileage and the maintenance period;
step S7, generating maintenance information from the maintenance time and the maintenance items.
In one embodiment, on the basis of the characteristic data, in addition to the price information of the vehicle, other information of the vehicle can be determined, for example maintenance information of the vehicle.
In one embodiment, the maintenance period and the maintenance item of the vehicle of different vehicle types are different, so the maintenance item and the maintenance period of the vehicle can be determined according to the vehicle type, for example, the maintenance item is engine oil, the corresponding maintenance period is 5000 kilometers, for example, the maintenance item is cleaning an oil filter, and the corresponding maintenance period is 8000 kilometers.
Taking the oil as an example, for example, the driving distance is 14000 km, and since the oil needs to be replaced when the vehicle is 15000 km, it can be determined that the oil needs to be replaced when the vehicle is driven 1000 km again, and therefore, a prompt message can be generated when the vehicle approaches 15000 km, for example, the driving distance is 14900 km, so as to prompt the user to replace the oil.
It should be noted that the maintenance items are not limited to the two examples, and the maintenance period may be measured by a characteristic other than the mileage, such as time.
In one embodiment, the maintenance time is determined according to the vehicle age or the driving mileage and the maintenance period, and corresponding maintenance information is generated, so that a user does not need to manually record when each maintenance item needs maintenance, on one hand, the content of manual recording required by the user is favorably reduced, on the other hand, the maintenance information can be timely generated when maintenance is required, the service life of the vehicle is favorably prolonged, and the driving safety can be improved.
Fig. 5 is a schematic flowchart illustrating still another vehicle information determination method according to an embodiment of the present invention. As shown in fig. 5, the method further comprises:
step S8, determining insurance price according to the vehicle type;
step S9, determining insurance payment time according to the vehicle age;
and step S10, generating insurance information according to the insurance payment time and the insurance price.
In one embodiment, based on the characteristic data, in addition to the price information of the vehicle, other information of the vehicle may be determined, such as insurance information of the vehicle.
In one embodiment, the insurance (e.g., business insurance) price may be positively correlated with the grade of the vehicle model, the higher the insurance price; insurance (e.g., intensity of delivery) prices may correspond to vehicle models, e.g., the more vehicle seats, the higher the insurance price.
In one embodiment, since the vehicle age can be determined according to the certification date in the driving license, and generally before and after the certification date, the user (vehicle owner) will make insurance for the vehicle, and the insurance is generally paid according to the year, the time length of paying the distance can be determined according to the vehicle age and the current time, for example, the vehicle age is 1 year and 11 months, that is, the time length of paying the insurance is 1 month left, and the current time is 2017, 8 and 1 days, then the time length of paying the insurance is 2017, 9 and 1 days. And then can be when approaching insurance time of paying, for example when the time is week apart from insurance time of paying, generate insurance information for the user looks up, guarantee that the user can in time pay the insurance expense, avoid the vehicle to get on the road and cause the loss to the user under the circumstances of not paying the insurance.
Corresponding to the embodiments of the vehicle information determination method described above, embodiments of the invention also relate to a vehicle information determination apparatus.
Fig. 6 is a schematic block diagram showing a vehicle information determination apparatus according to an embodiment of the present invention. As shown in fig. 6, the vehicle information determination device includes:
the driving license identification module 1 is used for identifying the driving license of the vehicle and acquiring characteristic data related to price in the driving license;
and the price information generation module 2 is used for predicting the price information of the vehicle according to the characteristic data and a prediction model generated in advance.
Fig. 7 is a schematic block diagram showing another vehicle information determination apparatus according to an embodiment of the invention. As shown in fig. 7, the apparatus further includes:
the training set generating module 3 is configured to, before a driving license of a vehicle is identified, acquire, for a preset number of sample vehicles, historical transaction prices in historical transaction information of the sample vehicles and historical feature data associated with the historical transaction prices, and use training data formed by the historical feature data and the historical transaction prices as a training set;
and the machine learning module 4 is used for learning to obtain the prediction model by utilizing the training set through a machine learning algorithm.
Optionally, the characteristic data comprises at least one of:
vehicle type, vehicle age, nature of use, and region of belonged.
Fig. 8 is a schematic block diagram showing still another vehicle information determination apparatus according to an embodiment of the present invention. As shown in fig. 8, the feature data further includes a usage property, and the apparatus further includes:
a mileage determining module 5, configured to determine a mileage of the vehicle per unit time according to the usage property; and determining the driving mileage of the vehicle according to the vehicle age and the mileage driven per unit time.
Fig. 9 is a schematic block diagram showing still another vehicle information determination apparatus according to an embodiment of the present invention. As shown in fig. 9, the method further includes:
a maintenance determining module 6, configured to determine a maintenance item and a maintenance period of the vehicle according to the vehicle type;
the maintenance time determining module 7 is used for determining maintenance time according to the vehicle age or the driving mileage and the maintenance period;
and the maintenance information generating module 8 is used for generating maintenance information according to the maintenance time and the maintenance items.
Fig. 10 is a schematic block diagram showing still another vehicle information determination apparatus according to an embodiment of the present invention. As shown in fig. 10, the apparatus further includes:
the insurance price determining module 9 is used for determining the insurance price according to the vehicle type;
the payment time determining module 10 is used for determining insurance payment time according to the vehicle age;
and the insurance information generating module 11 is used for generating insurance information according to the insurance payment time and the insurance price.
With regard to the apparatus in the above embodiments, the specific manner in which each module performs operations has been described in detail in the related method embodiments, and will not be described in detail here.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the invention. One of ordinary skill in the art can understand and implement it without inventive effort.
An embodiment of the present invention further provides an electronic device, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
identifying a driving license of a vehicle, and acquiring characteristic data related to price in the driving license;
and predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
Embodiments of the present invention also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
identifying a driving license of a vehicle, and acquiring characteristic data related to price in the driving license;
and predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (14)
1. A vehicle information determination method characterized by comprising:
identifying a driving license of a vehicle, and acquiring characteristic data related to price in the driving license;
and predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
2. The method of claim 1, further comprising:
before identifying a driving license of a vehicle, acquiring historical transaction prices in historical transaction information of sample vehicles and historical feature data associated with the historical transaction prices aiming at a preset number of sample vehicles, and taking training data formed by the historical feature data and the historical transaction prices as a training set;
and learning by utilizing the training set through a machine learning algorithm to obtain the prediction model.
3. The method of claim 1, wherein the characterization data comprises at least one of:
vehicle type, vehicle age, nature of use, and region of belonged.
4. The method of claim 3, wherein the characterization data further comprises the mileage determined by:
determining the mileage of the vehicle per unit time according to the usage property;
and determining the driving mileage of the vehicle according to the vehicle age and the mileage driven per unit time.
5. The method of claim 3, further comprising:
determining maintenance items and maintenance periods of the vehicle according to the vehicle type;
determining maintenance time according to the vehicle age or the driving mileage and the maintenance period;
and generating maintenance information according to the maintenance time and the maintenance items.
6. The method of claim 3, further comprising:
determining an insurance price according to the vehicle type;
determining insurance payment time according to the vehicle age;
and generating insurance information according to the insurance payment time and the insurance price.
7. A vehicle information determination device characterized by comprising:
the driving license identification module is used for identifying the driving license of the vehicle and acquiring characteristic data related to price in the driving license;
and the price information generation module is used for predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
8. The apparatus of claim 7, further comprising:
the training set generation module is used for acquiring historical transaction prices in historical transaction information of sample vehicles and historical characteristic data associated with the historical transaction prices aiming at a preset number of sample vehicles before identifying the driving license of the vehicle, and taking the training data formed by the historical characteristic data and the historical transaction prices as a training set;
and the machine learning module is used for learning to obtain the prediction model by utilizing the training set through a machine learning algorithm.
9. The apparatus of claim 7, wherein the characterization data comprises at least one of:
vehicle type, vehicle age, nature of use, and region of belonged.
10. The apparatus of claim 9, wherein the characterization data further comprises a usage property, the apparatus further comprising:
the mileage determining module is used for determining the mileage of the vehicle running in unit time according to the use property; and determining the driving mileage of the vehicle according to the vehicle age and the mileage driven per unit time.
11. The apparatus of claim 9, further comprising:
the maintenance determining module is used for determining maintenance items and maintenance periods of the vehicle according to the vehicle type;
the maintenance time determining module is used for determining maintenance time according to the vehicle age or the driving mileage and the maintenance period;
and the maintenance information generating module is used for generating maintenance information according to the maintenance time and the maintenance items.
12. The apparatus of claim 9, further comprising:
the insurance price determining module is used for determining insurance price according to the vehicle type;
the payment time determining module is used for determining insurance payment time according to the vehicle age;
and the insurance information generating module generates insurance information according to the insurance payment time and the insurance price.
13. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
identifying a driving license of a vehicle, and acquiring characteristic data related to price in the driving license;
and predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
14. A computer-readable storage medium, on which a computer program is stored, which program, when executed by a processor, carries out the steps of:
identifying a driving license of a vehicle, and acquiring characteristic data related to price in the driving license;
and predicting the price information of the vehicle according to the characteristic data and a pre-generated prediction model.
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| CN113911051A (en) * | 2021-11-04 | 2022-01-11 | 艾尔卡(北京)科技有限公司 | Method and device for obtaining current driving mileage of vehicle and electronic equipment |
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| CN113911051A (en) * | 2021-11-04 | 2022-01-11 | 艾尔卡(北京)科技有限公司 | Method and device for obtaining current driving mileage of vehicle and electronic equipment |
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