CN111538752A - Vehicle use identification method and system based on Internet of vehicles and storage medium - Google Patents
Vehicle use identification method and system based on Internet of vehicles and storage medium Download PDFInfo
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- 238000012546 transfer Methods 0.000 claims description 8
- 238000007477 logistic regression Methods 0.000 claims description 4
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- 238000007781 pre-processing Methods 0.000 claims description 3
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- 238000012549 training Methods 0.000 claims description 3
- 238000003064 k means clustering Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 5
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- 230000000284 resting effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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Abstract
The invention discloses a vehicle use identification method, a system and a storage medium based on Internet of vehicles, which comprise a vehicle networking database and a vehicle use record database, wherein the vehicle networking database comprises user charging and discharging behavior data, the vehicle use record database comprises vehicle use record data, and the method comprises the following steps: extracting user charging and discharging behavior data from a vehicle networking database; extracting vehicle use record data from a vehicle use record database; and identifying the use of the vehicle based on the user charging and discharging behavior data and the vehicle use record data. The invention can identify the use of the vehicle based on the charge-discharge behavior data of the Internet of vehicles and the use record data of the vehicle.
Description
Technical Field
The invention belongs to the technical field of automobile vehicle networks, and particularly relates to a vehicle use identification method and system based on an automobile network and a storage medium.
Background
With the rise of a new technological revolution and industry transformation and inoculation, the new energy automobile industry is entering a new stage of accelerated development. The development of the new energy automobile industry not only brings unprecedented new revolution and new kinetic energy for the automobile industry, but also is beneficial to reducing the emission of greenhouse gases, responding to the challenge of climate change and improving the global ecological environment. However, the new energy automobile market is still in the initial development stage at present, the new energy automobile market is not perfect in the aspects of industry standards, specifications and the like, and users have higher expectations for the quality and safety problems of new energy automobiles. In order to relieve the user's carelessness about the quality and safety of the new energy automobile and to improve the user's vehicle use specification, it is necessary to perform monitoring analysis on new energy automobiles with different purposes.
Therefore, there is a need to develop a new vehicle usage identification method, system and storage medium based on the internet of vehicles.
Disclosure of Invention
The invention aims to provide a vehicle purpose identification method, a vehicle purpose identification system and a storage medium based on an internet of vehicles, which can identify the purpose of a vehicle based on charge and discharge behavior data and vehicle purpose record data of the internet of vehicles.
In a first aspect, the vehicle usage identification method based on the internet of vehicles comprises a vehicle networking database and a vehicle usage record database, wherein the vehicle networking database comprises user charging and discharging behavior data, and the vehicle usage record database comprises vehicle usage record data, and the method comprises the following steps:
extracting user charging and discharging behavior data from a vehicle networking database;
extracting vehicle use record data from a vehicle use record database;
and identifying the use of the vehicle based on the user charging and discharging behavior data and the vehicle use record data.
Further, the vehicle is classified into a commercial vehicle, a non-commercial vehicle and a non-commercial transfer vehicle.
Further, the identifying the use of the vehicle based on the user charging and discharging behavior data and the vehicle use record data specifically includes:
constructing a characteristic variable according to the user charging and discharging behavior data;
preprocessing the characteristic variable data set, deleting abnormal data and extreme value data, complementing or deleting missing data according to the data quantity condition, and performing z-score standardization processing on the data set;
dividing the data set into two blocks by using a k _ means clustering algorithm;
training and learning the two pieces of data by using logistic regression to identify the purpose of the vehicle;
if the identified vehicle usage is a non-commercial vehicle, marking the vehicle as a non-commercial vehicle; if the identified vehicle is used as a commercial vehicle and the extracted vehicle use record data is the commercial vehicle, marking the vehicle as the commercial vehicle; and if the identified vehicle application is a commercial vehicle and the extracted vehicle application record data is a non-commercial vehicle, matching the extracted vehicle unique identification number of the non-commercial vehicle with the identified unique identification number of the commercial vehicle, and if the matching is successful, marking the vehicle as the non-commercial vehicle transfer commercial vehicle.
Further, still include:
after identifying the usage of the vehicle, setting a threshold value of a statistical index of the vehicle based on the usage of the vehicle;
dividing the statistical indexes into trigger indexes and non-trigger indexes;
for the non-trigger indexes, pushing related information to the user side or the vehicle side regularly;
and for the trigger type index, pushing related information to the user terminal or the vehicle terminal when the trigger condition is met.
Further, the user charging and discharging behavior data comprises automobile driving mileage, charging state, time, vehicle state and fast and slow charging frequency.
In a second aspect, the invention provides a vehicle usage identification system based on internet of vehicles, comprising a processor and a memory;
the memory having stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, may perform the steps of the internet of vehicles based vehicle usage identification method of the present invention.
In a third aspect, the present invention provides a storage medium storing one or more computer readable programs, which are executable by one or more processors to implement the steps of the internet of vehicles based vehicle usage identification method according to the present invention.
The invention has the following advantages: the vehicle is identified by a commercial vehicle, a non-commercial vehicle and a non-commercial vehicle based on the charge and discharge behavior data of the internet of vehicles and the vehicle use record data, the three vehicles are monitored and analyzed by means of a big data platform, various index thresholds are respectively set, and message prompts such as vehicle conditions, driving behaviors and the like are provided for users at a user end/a vehicle end regularly or irregularly, so that the user is helped to improve the vehicle specification and safety consciousness.
Drawings
Fig. 1 is a schematic view of connection of components required for vehicle use identification in the present embodiment.
Fig. 2 is a flowchart of the present embodiment.
Detailed Description
The invention will be further explained with reference to the drawings.
In this embodiment, a vehicle use identification method based on the internet of vehicles includes a vehicle networking database and a vehicle use record database, where the vehicle networking database includes user charging and discharging behavior data, and the vehicle use record database includes vehicle use record data, and the method includes the following steps:
extracting user charging and discharging behavior data from a vehicle networking database;
extracting vehicle use record data from a vehicle use record database;
and identifying the use of the vehicle based on the user charging and discharging behavior data and the vehicle use record data.
Referring to fig. 1, in this embodiment, the required components include a vehicle-mounted terminal, a big data analysis platform, and a user access interface, where the vehicle-mounted terminal and the big data analysis platform can establish a communication connection, and the big data analysis platform and the user access interface can establish a communication connection.
The vehicle-mounted terminal is used for acquiring and transmitting the data of the Internet of vehicles.
The big data analysis platform is used for storing various data sources, calculating various indexes and models, and pushing analysis results and messages.
The user access interface is used for displaying the statistical analysis result and receiving the message prompt.
In this embodiment, the vehicle is classified into a commercial vehicle, a non-commercial vehicle, and a non-commercial transfer vehicle.
Referring to fig. 2, in this embodiment, the identifying the use of the vehicle based on the user charging and discharging behavior data and the vehicle use record data specifically includes:
(1) constructing characteristic variables according to user charging and discharging behavior data
For example: the types of the user charging and discharging behavior data comprise automobile driving mileage, charging state, time, vehicle state, high and low charging frequency and the like. And classifying the user charging and discharging behavior data.
For example:
the duration class: the average daily driving time, the average daily charging time, the average working day and day driving time, the average resting day and day driving time and the like;
mileage category: daily average driving mileage, working daily average driving mileage, resting daily average driving mileage and the like;
frequency class: the average daily mileage, the average daily use frequency of fast charging, the average daily use frequency of slow charging and the like;
other classes: daily average charge capacity, daily average discharge capacity, and the like.
And increasing and decreasing the data fields used in the modeling process according to the actual data.
(2) Cleaning data
And preprocessing the characteristic variable data set, deleting abnormal data and extreme value data, complementing or deleting missing data according to the data quantity condition, and performing z-score standardization processing on the data set.
(3) Classifying datasets with k _ means
The feature variables are first classified algorithmically using k _ means, whose core idea is to partition the data objects into different clusters by iteration to minimize the objective function, so that the resulting clusters are as compact and independent as possible. Randomly selecting K objects as the centroids of the initial K clusters; then distributing the other objects to the nearest cluster according to the distance between the other objects and the centroid of each cluster; and finding the centroid of the newly formed cluster. This process of iterative relocation is repeated until the objective function is minimized.
For example:
inputting: the number k =2 of the expected clusters, the data size n =5 ten thousand, and the number of iterations is 10000;
and (3) outputting: 2 clusters that minimize the squared error criterion function, i.e. the data set is divided into two blocks;
and classifying the two pieces of identified data into a commercial vehicle and a non-commercial vehicle.
(4) Commercial vehicle and non-commercial vehicle identification
And training and learning the two pieces of data by using logistic regression, and identifying the purpose of the vehicle by using the logistic regression to train and learn the two pieces of data. The core of the method is to describe the influence degree of an independent variable X on a dependent variable Y and predict the dependent variable.
For example:
inputting: the independent variable X is the daily average driving time, the working daily average driving time, the daily average peak driving time, the daily average driving mileage and the daily average driving distance, Y is a commercial vehicle and a non-commercial vehicle, and the cross validation K = 5;
and outputting that Y is a commercial vehicle or a non-commercial vehicle, and the accuracy of the prediction result is = 98%.
If the identified vehicle usage is a non-commercial vehicle, marking the vehicle as a non-commercial vehicle; and if the identified vehicle purpose is a commercial vehicle and the extracted vehicle purpose record data is the commercial vehicle, marking the vehicle as the commercial vehicle.
(6) Identification of non-commercial transport vehicles
And if the identified vehicle application is a commercial vehicle and the extracted vehicle application record data is a non-commercial vehicle, matching the extracted vehicle unique identification number of the non-commercial vehicle with the identified unique identification number of the commercial vehicle, and if the matching is successful, marking the vehicle as the non-commercial vehicle transfer commercial vehicle.
Referring to fig. 2, in this embodiment, the vehicle usage identification method based on the internet of vehicles further includes:
after identifying the usage of the vehicle, setting a threshold value of a statistical index of the vehicle based on the usage of the vehicle;
dividing the statistical indexes into trigger indexes and non-trigger indexes;
for the non-trigger indexes, pushing related information to the user side or the vehicle side regularly;
and for the trigger type index, pushing related information to the user terminal or the vehicle terminal when the trigger condition is met.
In the present embodiment, the threshold value of the statistical index of the vehicle is set based on the use of the vehicle; the following is illustrated with reference to examples:
(1) building various statistical indexes and relevant models
For example:
batteries: the method comprises the following steps of analyzing indexes such as current, voltage, differential pressure and battery temperature, and predicting the health degree of the battery and other algorithm models;
class of driving behavior: the method comprises the following steps of (1) carrying out index analysis on a driving mode, SOC consumption, a vehicle speed, mileage and the like, and carrying out an algorithm model on driving habits, driving safety scores and the like;
fault class: temperature difference alarm, battery maximum temperature alarm, driving motor controller temperature alarm, DC-DC temperature alarm and other fault statistics, fault early warning and other algorithm models.
The statistical indexes and the relevant models can be increased or decreased according to actual requirements.
(2) Threshold setting
(2.1) commercial vehicle threshold setting
Various indexes or models are selected as the data of the commercial vehicle, and threshold values are set, such as: when the battery temperature during charging > J, assume J = 100.
(2.2) non-commercial vehicle threshold setting
The various indicators or models are selected as non-commercial data, thresholds are set, for example: when the battery temperature during charging > J, assume J = 80.
(2.3) non-commercial vehicle transfer-commercial vehicle threshold setting
Selecting each index or model as data of a non-commercial vehicle to a commercial vehicle, and setting a threshold value, such as: when the battery temperature during charging > J, assume J = 100.
(3) Message push (taking trigger type index as an example)
(3.1) push of user messages for commercial vehicles
And when the temperature of the battery is more than 100 ℃ in the charging process, sending a message prompt to the user of the commercial vehicle.
In this embodiment, the message prompt may be edited to have different speech rates according to different temperature values.
(3.2) non-commercial vehicle user message push
And when the temperature of the battery is more than 80 ℃ in the charging process, sending a message prompt to a non-commercial vehicle user.
In this embodiment, the message prompt may be edited to have different speech rates according to different temperature values.
(3.3) push of user message for non-commercial vehicle transfer to commercial vehicle
And when the temperature of the battery is more than 100 ℃ in the charging process, sending a message prompt to a user of the non-commercial vehicle transfer commercial vehicle.
In this embodiment, the message prompt may be edited to have different speech rates according to different temperature values.
In this embodiment, a vehicle usage identification system based on internet of vehicles includes a processor and a memory;
the memory having stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, may implement the steps of the internet of vehicles based vehicle usage identification method as described in this embodiment.
In this embodiment, a storage medium stores one or more computer readable programs, which are executable by one or more processors to implement the steps of the internet-of-vehicles based vehicle usage identification method as described in this embodiment.
Claims (7)
1. A vehicle use identification method based on Internet of vehicles is characterized by comprising a vehicle networking database and a vehicle use record database, wherein the vehicle networking database comprises user charging and discharging behavior data, and the vehicle use record database comprises vehicle use record data, and the method comprises the following steps:
extracting user charging and discharging behavior data from a vehicle networking database;
extracting vehicle use record data from a vehicle use record database;
and identifying the use of the vehicle based on the user charging and discharging behavior data and the vehicle use record data.
2. The internet-of-vehicles based vehicle use identification method of claim 1, wherein: the vehicle is classified into a commercial vehicle, a non-commercial vehicle and a non-commercial transfer vehicle.
3. The internet-of-vehicles-based vehicle usage identification method of claim 2, wherein: the method for identifying the use of the vehicle based on the user charging and discharging behavior data and the vehicle use record data specifically comprises the following steps:
constructing a characteristic variable according to the user charging and discharging behavior data;
preprocessing the characteristic variable data set, deleting abnormal data and extreme value data, complementing or deleting missing data according to the data quantity condition, and performing z-score standardization processing on the data set;
dividing the data set into two blocks by using a k _ means clustering algorithm;
training and learning the two pieces of data by using logistic regression to identify the purpose of the vehicle;
if the identified vehicle usage is a non-commercial vehicle, marking the vehicle as a non-commercial vehicle; if the identified vehicle is used as a commercial vehicle and the extracted vehicle use record data is the commercial vehicle, marking the vehicle as the commercial vehicle; and if the identified vehicle application is a commercial vehicle and the extracted vehicle application record data is a non-commercial vehicle, matching the extracted vehicle unique identification number of the non-commercial vehicle with the identified unique identification number of the commercial vehicle, and if the matching is successful, marking the vehicle as the non-commercial vehicle transfer commercial vehicle.
4. The internet-of-vehicles-based vehicle usage identification method according to any one of claims 1 to 3, further comprising:
after identifying the usage of the vehicle, setting a threshold value of a statistical index of the vehicle based on the usage of the vehicle;
dividing the statistical indexes into trigger indexes and non-trigger indexes;
for the non-trigger indexes, pushing related information to the user side or the vehicle side regularly;
and for the trigger type index, pushing related information to the user terminal or the vehicle terminal when the trigger condition is met.
5. The Internet of vehicles-based vehicle use identification method of claim 4, wherein: the user charging and discharging behavior data comprises automobile driving mileage, charging state, time, vehicle state and fast and slow charging frequency.
6. A vehicle purpose identification system based on internet of vehicles is characterized in that: comprises a processor and a memory;
the memory having stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, may implement the steps of the internet of vehicles based vehicle usage identification method of any of claims 1 to 5.
7. A storage medium, characterized by: the storage medium stores one or more computer readable programs executable by one or more processors to perform the steps of the internet of vehicles based vehicle use identification method of any of claims 1-5.
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Cited By (1)
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