CN111832597A - Vehicle type determination method and device - Google Patents

Vehicle type determination method and device Download PDF

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CN111832597A
CN111832597A CN201910708336.6A CN201910708336A CN111832597A CN 111832597 A CN111832597 A CN 111832597A CN 201910708336 A CN201910708336 A CN 201910708336A CN 111832597 A CN111832597 A CN 111832597A
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vehicle
preset time
time period
target
type
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CN111832597B (en
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艾建伍
俞开先
朱宏图
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Beijing Didi Infinity Technology and Development Co Ltd
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Beijing Didi Infinity Technology and Development Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
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Abstract

The application provides a vehicle type judging method and device, which comprises the steps of obtaining running track information of a target vehicle in each preset time period in a plurality of preset time periods and service characteristic information of a service providing end corresponding to the target vehicle in each preset time period; respectively determining vehicle type attribute information corresponding to each preset time period based on the running track information and the service characteristic information corresponding to each preset time period; and determining whether the target vehicle is the vehicle of the target type or not based on the vehicle type attribute information corresponding to each preset time period. According to the method and the device, the running track information of the target vehicle in the preset time period and the corresponding service characteristic information are used for calculation, whether the target vehicle is the target type vehicle in the preset time period is determined according to the calculation result, whether the target vehicle is the target type vehicle can be automatically and quickly determined, and the identification efficiency of the target type vehicle is improved.

Description

Vehicle type determination method and device
Technical Field
The application relates to the technical field of vehicles and automation, in particular to a method and a device for judging vehicle types.
Background
The new energy vehicle refers to a vehicle using an unconventional vehicle fuel as a power source, such as a hybrid electric vehicle, a pure electric vehicle, and the like. With the popularization of new energy vehicles, the total demand of charging piles is estimated to be an inevitable problem, and the total demand of charging piles directly depends on the number of the new energy vehicles in the market, so that the number of the new energy vehicles needs to be accurately estimated.
Currently, the vehicle type entered by the vehicle, for example, the vehicle is a new energy vehicle, may be obtained in a network appointment platform. However, due to the reasons of recording errors, non-timely updating and the like, the accurate number of new energy vehicles cannot be determined in the prior art, and the number of new energy vehicles is checked manually, so that the efficiency is extremely low.
Disclosure of Invention
In view of the above, an object of the embodiments of the present application is to provide a method and an apparatus for determining a vehicle type, an electronic device, and a storage medium, which can automatically and quickly determine whether a target vehicle is a target type vehicle, and have high timeliness, efficiency, and intelligence degree.
In a first aspect, an embodiment of the present application provides a method for determining a vehicle type, where the method includes:
acquiring running track information of a target vehicle in each preset time period in a plurality of preset time periods and service characteristic information of a service providing end corresponding to the target vehicle in each preset time period;
respectively determining vehicle type attribute information corresponding to each preset time period based on the running track information and the service characteristic information corresponding to each preset time period;
and determining whether the target vehicle is a target type vehicle or not based on the vehicle type attribute information corresponding to each preset time period.
With reference to the first aspect, an embodiment of the present application provides a first possible implementation manner of the first aspect, where the travel track information includes coordinate information of the target vehicle at each preset time within a corresponding preset time period;
the determining the vehicle type attribute information corresponding to each preset time period respectively based on the running track information and the service characteristic information corresponding to each preset time period comprises the following steps:
respectively determining the acceleration information of the target vehicle corresponding to each preset time period based on a plurality of pieces of coordinate information corresponding to each preset time period;
and respectively determining vehicle type attribute information corresponding to each preset time period based on the acceleration information and the service characteristic information corresponding to each preset time period.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present application provides a second possible implementation manner of the first aspect, where the determining, based on the acceleration information and the service characteristic information corresponding to each preset time period, the vehicle type attribute information corresponding to each preset time period respectively includes:
determining an acceleration vector corresponding to each preset time period based on the acceleration information corresponding to each preset time period;
determining a service characteristic vector corresponding to each preset time period based on the service characteristic information corresponding to each preset time period;
aiming at each preset time period, splicing the acceleration vector corresponding to the preset time period and the service characteristic vector corresponding to the preset time period to obtain a vehicle classification vector corresponding to the preset time period;
and respectively determining the vehicle type attribute information corresponding to each preset time period based on the vehicle classification vector corresponding to each preset time period.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present application provides a third possible implementation manner of the first aspect, where before the acceleration information of the target vehicle corresponding to each preset time period is respectively determined based on a plurality of pieces of coordinate information corresponding to each preset time period, the method further includes:
for each preset time period, determining the position of the target vehicle at each preset time within the preset time period based on the coordinate information of each preset time within the preset time period;
and determining the position of which the distance between the adjacent positions is greater than the preset distance, and deleting the coordinate information corresponding to the determined position.
With reference to the first aspect, an embodiment of the present application provides a fourth possible implementation manner of the first aspect, where the vehicle type attribute information includes a probability value that the target vehicle is a target type vehicle.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present application provides a fifth possible implementation manner of the first aspect, where the determining, based on the vehicle type attribute information corresponding to each preset time period, whether the target vehicle is a target type vehicle includes:
for each preset time period, when the probability value corresponding to the preset time period is greater than a preset probability, determining the vehicle type corresponding to the preset time period as a target type;
and if the vehicle type is the preset time period of the target type, and the occupation ratio in all the preset time periods is greater than the preset occupation ratio, determining that the target vehicle is the vehicle of the target type.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present application provides a sixth possible implementation manner of the first aspect, where the method further includes:
if the target vehicle is a vehicle of a target type, acquiring the vehicle type calibrated in a storage device by the target vehicle;
judging whether the vehicle type calibrated in the storage device by the target vehicle is the target type;
if not, the vehicle type of the target vehicle in the storage device is calibrated to be the target type.
With reference to the first aspect, an embodiment of the present application provides a seventh possible implementation manner of the first aspect, where the determining, based on the travel track information and the service characteristic information corresponding to each preset time period, vehicle type attribute information corresponding to each preset time period respectively includes:
and obtaining a vehicle classification model by utilizing pre-training, and processing the running track information and the service characteristic information corresponding to each preset time period to obtain vehicle type attribute information corresponding to each preset time period.
With reference to the seventh possible implementation manner of the first aspect, this application provides an eighth possible implementation manner of the first aspect, where the method further includes a step of training the obtained vehicle classification model:
the method comprises the steps of obtaining the vehicle type of each sample vehicle in a plurality of sample vehicles, the running track information of each sample vehicle in each preset time period and the service characteristic information of a service providing end corresponding to each sample vehicle in each preset time period;
for each sample vehicle, determining acceleration information of the sample vehicle in each preset time period based on the running track information of the sample vehicle in each preset time period;
and training the initial vehicle classification model by utilizing the service characteristic information of the service providing end corresponding to each sample vehicle in each preset time period, the acceleration information of each sample vehicle in each preset time period and the vehicle type of each sample vehicle to obtain a final vehicle classification model.
With reference to the eighth possible implementation manner of the first aspect, this application example provides a ninth possible implementation manner of the first aspect, where the method further includes a step of selecting a sample vehicle:
obtaining vehicle types of candidate sample vehicles stored in a storage device;
if the stored vehicle type is the target type and preset operation is performed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a positive sample for training the vehicle classification model;
and if the stored vehicle type is a preset type and the preset operation is not executed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a negative sample for training the vehicle classification model.
In a second aspect, an embodiment of the present application further provides a vehicle type determination device, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring the running track information of a target vehicle in each preset time period in a plurality of preset time periods and the service characteristic information of a service providing end corresponding to the target vehicle in each preset time period;
the first determining module is used for respectively determining vehicle type attribute information corresponding to each preset time period based on the running track information and the service characteristic information corresponding to each preset time period;
and the second determination module is used for determining whether the target vehicle is a target type vehicle or not based on the vehicle type attribute information corresponding to each preset time period.
With reference to the second aspect, the present embodiments provide a first possible implementation manner of the second aspect, where the travel track information includes coordinate information of the target vehicle at each preset time within a corresponding preset time period;
the first determining module respectively determines vehicle type attribute information corresponding to each preset time period based on the running track information and the service characteristic information corresponding to each preset time period, and the first determining module comprises the following steps:
respectively determining the acceleration information of the target vehicle corresponding to each preset time period based on a plurality of pieces of coordinate information corresponding to each preset time period;
and respectively determining vehicle type attribute information corresponding to each preset time period based on the acceleration information and the service characteristic information corresponding to each preset time period.
With reference to the first possible implementation manner of the second aspect, an embodiment of the present application provides a second possible implementation manner of the second aspect, where the first determining module, when determining the vehicle type attribute information corresponding to each preset time period respectively based on the acceleration information and the service characteristic information corresponding to each preset time period, includes:
determining an acceleration vector corresponding to each preset time period based on the acceleration information corresponding to each preset time period;
determining a service characteristic vector corresponding to each preset time period based on the service characteristic information corresponding to each preset time period;
aiming at each preset time period, splicing the acceleration vector corresponding to the preset time period and the service characteristic vector corresponding to the preset time period to obtain a vehicle classification vector corresponding to the preset time period;
and respectively determining the vehicle type attribute information corresponding to each preset time period based on the vehicle classification vector corresponding to each preset time period.
With reference to the first possible implementation manner of the second aspect, the present application provides a third possible implementation manner of the second aspect, where the method further includes:
the position determining module is used for determining the position of the target vehicle at each preset time in each preset time period based on the coordinate information of each preset time in each preset time period;
and the deleting module is used for determining the position of which the distance between the adjacent positions is greater than the preset distance and deleting the coordinate information corresponding to the determined position.
With reference to the second aspect, embodiments of the present application provide a fourth possible implementation manner of the second aspect, where the vehicle type attribute information includes a probability value that the target vehicle is a target type vehicle.
With reference to the fourth possible implementation manner of the second aspect, an embodiment of the present application provides a fifth possible implementation manner of the second aspect, where the second determining module, when determining whether the target vehicle is a target type vehicle based on the vehicle type attribute information corresponding to each preset time period, includes:
for each preset time period, when the probability value corresponding to the preset time period is greater than a preset probability, determining the vehicle type corresponding to the preset time period as a target type;
and if the vehicle type is the preset time period of the target type, and the occupation ratio in all the preset time periods is greater than the preset occupation ratio, determining that the target vehicle is the vehicle of the target type.
With reference to the fourth possible implementation manner of the second aspect, an embodiment of the present application provides a sixth possible implementation manner of the second aspect, where the sixth possible implementation manner further includes a calibration module, configured to:
if the target vehicle is a vehicle of a target type, acquiring the vehicle type calibrated in a storage device by the target vehicle;
judging whether the vehicle type calibrated in the storage device by the target vehicle is the target type;
if not, the vehicle type of the target vehicle in the storage device is calibrated to be the target type.
With reference to the second aspect, the present application provides a seventh possible implementation manner of the second aspect, where the determining, by the first determining module, when determining the vehicle type attribute information corresponding to each preset time period respectively based on the acceleration information and the service characteristic information corresponding to each preset time period, includes:
and obtaining a vehicle classification model by utilizing pre-training, and processing the running track information and the service characteristic information corresponding to each preset time period to obtain vehicle type attribute information corresponding to each preset time period.
With reference to the seventh possible implementation manner of the second aspect, an embodiment of the present application provides an eighth possible implementation manner of the second aspect, where the apparatus further includes a training module, configured to:
the method comprises the steps of obtaining the vehicle type of each sample vehicle in a plurality of sample vehicles, the running track information of each sample vehicle in each preset time period and the service characteristic information of a service providing end corresponding to each sample vehicle in each preset time period;
for each sample vehicle, determining acceleration information of the sample vehicle in each preset time period based on the running track information of the sample vehicle in each preset time period;
and training the initial vehicle classification model by utilizing the service characteristic information of the service providing end corresponding to each sample vehicle in each preset time period, the acceleration information of each sample vehicle in each preset time period and the vehicle type of each sample vehicle to obtain a final vehicle classification model.
With reference to the eighth possible implementation manner of the second aspect, the present application provides a ninth possible implementation manner of the second aspect, where the apparatus further includes a sample selection module, configured to:
obtaining vehicle types of candidate sample vehicles stored in a storage device;
if the stored vehicle type is the target type and preset operation is performed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a positive sample for training the vehicle classification model;
and if the stored vehicle type is a preset type and the preset operation is not executed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a negative sample for training the vehicle classification model.
In a third aspect, an embodiment of the present application further provides an electronic device, where the electronic device includes: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate with each other through the bus when the electronic device is running, and the machine-readable instructions are executed by the processor to perform the steps of the vehicle type determination method according to any one of the first aspect, the first possible implementation manner of the first aspect, and the eighth possible implementation manner of the first aspect.
In a fourth aspect, the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores thereon a computer program, and the computer program is executed by a processor to perform the steps of the method for determining a vehicle type according to any one of the first aspect, the first possible implementation manner of the first aspect, and the eighth possible implementation manner of the first aspect.
The method for judging the vehicle type comprises the steps of obtaining running track information of a target vehicle in each preset time period in a plurality of preset time periods and service characteristic information of a service providing end corresponding to the target vehicle in each preset time period; respectively determining vehicle type attribute information corresponding to each preset time period based on the running track information and the service characteristic information corresponding to each preset time period; and determining whether the target vehicle is the vehicle of the target type or not based on the vehicle type attribute information corresponding to each preset time period. The method and the device have the advantages that the running track information of the target vehicle in the preset time period and the corresponding service characteristic information are used for calculation, whether the target vehicle is the target type vehicle in the preset time period is determined according to the calculation result, whether the target vehicle is the target type vehicle can be determined in time, the problem that the number of the target type vehicles is inaccurate due to the fact that the vehicles are recorded wrongly and not updated in time is solved, and timeliness is high; meanwhile, the problem that the efficiency is low due to the fact that target vehicles need to be checked manually is solved, and the efficiency and the intelligent degree are high.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart illustrating a vehicle type determination method according to an embodiment of the present application;
fig. 2 is a flowchart illustrating a method for determining a vehicle type according to an embodiment of the present application, where vehicle type attribute information corresponding to each preset time period is determined;
fig. 3 is a flowchart illustrating a method for determining a plurality of coordinate information corresponding to each preset time period in a vehicle type determination method according to an embodiment of the present application;
fig. 4 is a flowchart illustrating a vehicle type of a target vehicle calibrated in a storage device in a vehicle type determination method provided in an embodiment of the present application;
FIG. 5 is a flowchart illustrating a training method of a vehicle classification model provided in a method for determining a vehicle type according to an embodiment of the present application;
fig. 6 is a schematic structural diagram illustrating a vehicle type determination device according to an embodiment of the present application;
fig. 7 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
At present, the information of the vehicle type recorded by the service provider can be obtained in the network appointment platform, wherein the information includes whether the vehicle is a new energy vehicle. However, due to the reasons of recording errors, non-timely updating and the like, the accurate number of new energy vehicles cannot be determined, and the number of new energy vehicles is checked manually, so that the efficiency is extremely low. In view of the above problems, the method and the device for determining the vehicle type, the electronic device, and the storage medium provided in the embodiments of the present application can quickly determine whether the target vehicle is the target type vehicle, for example, can quickly determine whether the target vehicle is a new energy vehicle, and are high in timeliness and high in efficiency and intelligence.
For the purpose of facilitating understanding of the embodiments of the present application, a method for determining a vehicle type disclosed in the embodiments of the present application will be described in detail first.
As shown in fig. 1, a flowchart of a method for determining a vehicle type when a server is used as an execution subject according to an embodiment of the present application is shown, and the specific steps are as follows:
s101, acquiring the running track information of the target vehicle in each preset time period in a plurality of preset time periods and the service characteristic information of the service providing end corresponding to the target vehicle in each preset time period.
In a specific implementation, after each driving of the target vehicle is completed or in the driving process, the target vehicle may upload the driving track information of the target vehicle to the database in real time, and the database performs classification storage according to the time information uploaded by the target vehicle, the identification information of the target vehicle, and the like, which is not specifically limited in this embodiment of the application.
The server may directly obtain the travel track information of the target vehicle in each preset time period from the database storing the travel track information of the target vehicle, for example, the preset time period is 24 hours, that is, from 00: 00 to 24: 00, the server searches the corresponding running track information of the target vehicle from the database according to the time information (namely 00: 00 to 24: 00).
The driving track information includes coordinate information, speed information, and the like of the target vehicle at each preset time.
Of course, the database may further store service characteristic information of the service provider corresponding to the target vehicle in each preset time period, where the service characteristic information includes an online time, a working time, a driving receiving distance, and the like of the service provider corresponding to the target vehicle.
And S102, respectively determining vehicle type attribute information corresponding to each preset time period based on the running track information and the service characteristic information corresponding to each preset time period.
In a specific implementation, a plurality of preset times may be set, and coordinate information and speed information of the target vehicle at each preset time may be collected, for example, from 00: 00 starts, every 3 seconds as a preset time. And respectively determining the acceleration information of the target vehicle corresponding to each preset time period based on the coordinate information corresponding to each preset time period. Specifically, a Long Short-Term Memory network (LSTM) may be used to calculate based on the coordinate information and the speed information of each preset time within each preset time period, so as to obtain the acceleration information of the target vehicle corresponding to each preset time period; the acceleration information of the target vehicle corresponding to each preset time period can be obtained by calculating by using a formula between the speed and the acceleration, wherein when the calculation is performed by using the formula, one acceleration information can be obtained by calculating for every two adjacent coordinate information, and after a plurality of acceleration information are obtained, the acceleration information of the target vehicle corresponding to each preset time period can be obtained by performing averaging calculation, mean square error calculation and the like on the plurality of accelerations.
Further, after obtaining the acceleration information of the target vehicle corresponding to each preset time period, based on the acceleration information and the service characteristic information corresponding to each preset time period, the vehicle type attribute information corresponding to each preset time period may be respectively determined, where a specific determination process is described in detail below, and is not described herein in detail.
And S103, determining whether the target vehicle is the vehicle of the target type or not based on the vehicle type attribute information corresponding to each preset time period.
In a particular implementation, the vehicle type attribute information includes a probability value that the target vehicle is a target type of vehicle.
Specifically, a preset probability is preset, for each preset time period, after determining vehicle type attribute information corresponding to the preset time period, that is, a probability value of a target vehicle being a target type vehicle, the probability value corresponding to the preset time period is compared with the preset probability, and if the probability value corresponding to the preset time period is greater than the preset probability, the vehicle type corresponding to the preset time period is determined to be the target type. The vehicle type may include a fuel type, a new energy type, a hybrid type, and the like, and in the embodiment of the present application, the target type may be a new energy type.
For example, the preset probability is 6, and for each preset time period, if the probability value corresponding to the preset time period determined by using the acceleration information and the service feature information corresponding to the preset time period is 6.5, it is seen that the probability value 6.5 corresponding to the preset time period is greater than the preset probability 6, it is determined that the vehicle type corresponding to the target vehicle in the preset time period is the new energy type.
Further, the vehicle type of the target vehicle in each preset time period in the preset time periods is determined, after the vehicle type of the target vehicle corresponding to each preset time period in the preset time periods is determined, the vehicle type is judged to be the preset time period of the target type, the proportion in all the preset time periods is larger than the preset proportion, and the target vehicle is determined to be the vehicle of the target type.
For example, in the embodiment of the present application, the travel track information in each preset time period of 100 preset time periods and the service feature information of the service provider corresponding to the target vehicle in each preset time period are obtained, and the vehicle type of the target vehicle corresponding to each preset time period is obtained by calculating according to the above method, where the vehicle types of the target vehicles corresponding to 89 preset time periods are target types, that is, new energy types, and therefore 89 is used as a dividend, 100 is used as a divisor, and the percentage of the preset time periods corresponding to the new energy types in all the preset time periods is calculated to be 89%, and the percentage 89% is greater than the preset percentage 80%, so that the target vehicle is determined to be a vehicle of the new energy type.
The method and the device have the advantages that the running track information of the target vehicle in the preset time period and the corresponding service characteristic information are used for calculation, whether the target vehicle is the target type vehicle in the preset time period is determined according to the calculation result, whether the target vehicle is the target type vehicle can be determined in time, the problem that the number of the target type vehicles is inaccurate due to the fact that the vehicles are recorded wrongly and not updated in time is solved, and timeliness is high; meanwhile, the problem that the efficiency is low due to the fact that target vehicles need to be checked manually is solved, and the efficiency and the intelligent degree are high.
As shown in fig. 2, a specific method for respectively determining vehicle type attribute information corresponding to each preset time period based on acceleration information and service characteristic information corresponding to each preset time period is provided, wherein the specific steps are as follows:
s201, determining an acceleration vector corresponding to each preset time period based on the acceleration information corresponding to each preset time period;
s202, determining a service characteristic vector corresponding to each preset time period based on the service characteristic information corresponding to each preset time period;
s203, aiming at each preset time period, splicing the acceleration vector corresponding to the preset time period and the service characteristic vector corresponding to the preset time period to obtain a vehicle classification vector corresponding to the preset time period;
and S204, respectively determining the vehicle type attribute information corresponding to each preset time period based on the vehicle classification vector corresponding to each preset time period.
In specific implementation, in the embodiment of the application, the vehicle type attribute information corresponding to each preset time period is determined by using a vehicle classification model trained in advance. Here, the vehicle classification model may be used to process the travel track information and the service feature information corresponding to each preset time period to obtain vehicle type attribute information corresponding to each preset time period.
Specifically, the acceleration information corresponding to each preset time period is obtained by calculating the running track information corresponding to each preset time period, and vector extraction or vector conversion is performed on the acceleration information corresponding to each preset time period to obtain an acceleration vector corresponding to each preset time period; and carrying out vector extraction or vector conversion on the service characteristic information corresponding to each preset time period to obtain the service characteristic vector corresponding to each preset time period.
Then, aiming at each preset time period, splicing the acceleration vector corresponding to the preset time period and the service characteristic vector corresponding to the preset time period to obtain a vehicle classification vector corresponding to the preset time period; and inputting the vehicle classification vector corresponding to the preset time period into a vehicle classification model, and outputting vehicle type attribute information corresponding to each preset time period.
In a specific implementation, before determining the acceleration information of the target vehicle corresponding to each preset time period respectively based on a plurality of pieces of coordinate information corresponding to each preset time period, a plurality of pieces of coordinate information corresponding to each preset time period is determined according to the method shown in fig. 3, wherein the specific steps are as follows:
s301, aiming at each preset time period, determining the position of the target vehicle at each preset time in the preset time period based on the coordinate information of each preset time in the preset time period;
s302, determining the position of which the distance between the adjacent positions is greater than the preset distance, and deleting the coordinate information corresponding to the determined position.
In specific implementation, the position information acquired by the positioning system of the vehicle cannot be always accurate, and a situation of position information jump may exist, that is, the difference between the position information acquired by the positioning system and the position information where the vehicle is actually located is large.
Therefore, for each preset time period, after the coordinate information of each preset time is collected, the position of the target vehicle at each preset time within the preset time period is determined according to the coordinate information of each preset time. And judging whether the distance between the middle position and the adjacent position is greater than the position with the preset distance or not for the three continuous positions, and if so, deleting the coordinate information corresponding to the middle position.
In an implementation, if the vehicle type calibrated in the storage device by the target vehicle may be different from the actual vehicle type of the target vehicle, the vehicle type of the target vehicle may be re-calibrated according to the method shown in fig. 4, which includes the following specific steps:
s401, if the target vehicle is a vehicle of a target type, acquiring the vehicle type calibrated in the storage device by the target vehicle;
s402, judging whether the vehicle type calibrated in the storage device by the target vehicle is the target type;
and S403, if not, calibrating the vehicle type of the target vehicle in the storage device as the target type.
Here, after determining that the target vehicle is a vehicle of the target type, the server may obtain the vehicle type calibrated by the target vehicle from the storage device; and matching the vehicle type calibrated in the storage device by the target vehicle with the target type, and calibrating the vehicle type of the target vehicle in the storage device as the target type if the matching result is inconsistent, namely the vehicle type calibrated in the storage device by the target vehicle is not the target type.
Of course, after determining that the target vehicle is a vehicle of another type other than the target type, the server may obtain the vehicle type calibrated by the target vehicle from the storage device; and matching the vehicle type calibrated in the storage device by the target vehicle with other types, and calibrating the vehicle type of the target vehicle in the storage device into other types if the matching result is inconsistent, namely the vehicle type calibrated in the storage device by the target vehicle is not the other type.
In the embodiment of the present application, with reference to fig. 5, there are further provided specific steps of training to obtain a vehicle classification model:
s501, obtaining the vehicle type of each sample vehicle in a plurality of sample vehicles, the running track information of each sample vehicle in each preset time period, and the service characteristic information of a service providing end corresponding to each sample vehicle in each preset time period;
s502, for each sample vehicle, determining acceleration information of the sample vehicle in each preset time period based on the running track information of the sample vehicle in each preset time period;
s503, training the initial vehicle classification model by using the service characteristic information of the service providing end corresponding to each sample vehicle in each preset time period, the acceleration information of each sample vehicle in each preset time period and the vehicle type of each sample vehicle to obtain a final vehicle classification model.
Specifically, the vehicle type of each sample vehicle in the plurality of sample vehicles, the travel track information of each sample vehicle in each preset time period, and the service characteristic information of the service provider corresponding to each sample vehicle in each preset time period are used as a training sample set, and the training sample set comprises a positive sample pair and a negative sample pair. And for each sample vehicle, determining the acceleration information of the sample vehicle in each preset time period based on the running track information of the sample vehicle in each preset time period. Thus, the positive sample pair includes a plurality of first sample vehicles, for each of which the first sample vehicle corresponds to a first sample of acceleration information, the first sample vehicle corresponds to first service characteristic information, and the first sample vehicle corresponds to a first specific tag (the first specific tag indicates a vehicle type of the first sample vehicle); the negative sample pairs include a plurality of second sample vehicles, for each of which there is a second sample of acceleration information corresponding to the vehicle, second service characteristic information corresponding to the vehicle, and a second specific tag corresponding to the vehicle (the second specific tag indicating the vehicle type of the second sample vehicle).
Selecting a first preset number of positive sample pairs and a second preset number of negative sample pairs from the training sample set; for each positive sample pair, vector extraction is respectively carried out on the first acceleration information sample and the first service characteristic sample, and a first acceleration characteristic vector sample and a first service characteristic vector sample corresponding to each first sample vehicle are obtained; performing vector conversion on each first specific label to obtain a first label vector sample; vector extraction is carried out on each negative sample pair from each second acceleration information sample and each second service characteristic sample respectively to obtain each second acceleration characteristic vector sample and each second service characteristic vector sample; and performing vector conversion on each second specific label to obtain a second label vector sample.
And respectively inputting a first acceleration characteristic vector sample, a first service characteristic vector sample and a first label vector sample which respectively correspond to different positive sample pairs, and a second acceleration characteristic vector sample, a second service characteristic vector sample and a second label vector sample which respectively correspond to different negative sample pairs into a vehicle classification model to be trained to obtain a first detection result for each positive sample pair and a second detection result for each negative sample pair.
And calculating an error value of the training round based on the first detection result of each positive sample pair and a preset first theoretical result, the second detection result of each negative sample pair and a preset second theoretical result.
And when the calculated error value is greater than the set value, adjusting the parameters of the vehicle classification model to be detected, and performing the next training process by using the adjusted vehicle classification model to be trained until the calculated error value is not greater than the set value, determining that the training is finished, thereby obtaining the final vehicle classification model.
Before a training sample set is obtained, the samples need to be screened and classified, and specifically, vehicle types of candidate sample vehicles stored in a storage device are obtained; if the stored vehicle type is the target type and preset operation is performed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a positive sample of the training vehicle classification model;
and if the stored vehicle type is a preset type and the preset operation is not executed on the candidate sample vehicle, taking the candidate sample vehicle as the sample vehicle and taking the candidate sample vehicle as a negative sample of the training vehicle classification model.
Here, the preset operation may include a charging operation of, when it is determined that the vehicle type of the candidate sample vehicle in the storage device is the target type, that is, the new energy type, determining whether the candidate sample vehicle performs an overcharge operation, and if the overcharge operation is performed, regarding the candidate sample vehicle as the sample vehicle and regarding the candidate sample vehicle as a positive sample for training the vehicle classification model. The executing operation may include not only the charging operation, but also a limited number of times that the charging operation is performed within a certain time period, that is, if the number of times that the candidate sample vehicle performs the charging operation within the certain time period is greater than a preset threshold number of times, the candidate sample vehicle is used as the sample vehicle, and the candidate sample vehicle is used as a positive sample for training the vehicle classification model.
Similarly, after determining that the stored vehicle type is a preset type, namely a fuel type, a hybrid type and the like, it is determined whether the candidate sample vehicle performs an overcharge operation, and if it is determined that the preset operation is not performed on the candidate sample vehicle, the candidate sample vehicle is taken as the sample vehicle and the candidate sample vehicle is taken as a negative sample of the training vehicle classification model.
Based on the same inventive concept, the embodiment of the present application further provides a vehicle type determination device corresponding to the vehicle type determination method, and since the principle of solving the problem of the device in the embodiment of the present application is similar to that of the vehicle type determination method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and repeated details are omitted.
Referring to fig. 6, a vehicle type determination device according to a further embodiment of the present application includes:
an obtaining module 601, configured to obtain travel track information of a target vehicle in each preset time period of multiple preset time periods, and service feature information of a service provider corresponding to the target vehicle in each preset time period;
the first determining module 602 is configured to determine vehicle type attribute information corresponding to each preset time period respectively based on the travel track information and the service feature information corresponding to each preset time period;
a second determining module 603, configured to determine whether the target vehicle is a target type vehicle based on the vehicle type attribute information corresponding to each preset time period.
In one embodiment, the driving track information includes coordinate information of the target vehicle at each preset time within a corresponding preset time period;
the first determining module 602, when determining the vehicle type attribute information corresponding to each preset time period respectively based on the travel track information and the service characteristic information corresponding to each preset time period, includes:
respectively determining the acceleration information of the target vehicle corresponding to each preset time period based on a plurality of pieces of coordinate information corresponding to each preset time period;
and respectively determining vehicle type attribute information corresponding to each preset time period based on the acceleration information and the service characteristic information corresponding to each preset time period.
In another embodiment, the first determining module 602, when determining the vehicle type attribute information corresponding to each preset time period respectively based on the acceleration information and the service characteristic information corresponding to each preset time period, includes:
determining an acceleration vector corresponding to each preset time period based on the acceleration information corresponding to each preset time period;
determining a service characteristic vector corresponding to each preset time period based on the service characteristic information corresponding to each preset time period;
aiming at each preset time period, splicing the acceleration vector corresponding to the preset time period and the service characteristic vector corresponding to the preset time period to obtain a vehicle classification vector corresponding to the preset time period;
and respectively determining the vehicle type attribute information corresponding to each preset time period based on the vehicle classification vector corresponding to each preset time period.
In still another embodiment, the above vehicle type determination device further includes:
a position determining module 604, configured to determine, for each preset time period, a position of the target vehicle at each preset time within the preset time period based on the coordinate information of each preset time within the preset time period;
and the deleting module 605 is configured to determine a position where a distance between adjacent positions is greater than a preset distance, and delete coordinate information corresponding to the determined position.
In still another embodiment, the vehicle type attribute information in the above vehicle type determination device includes a probability value that the target vehicle is a target type of vehicle.
In still another embodiment, the above vehicle type determination device further includes: the second determining module 603, when determining whether the target vehicle is a target type vehicle based on the vehicle type attribute information corresponding to each preset time period, includes:
for each preset time period, when the probability value corresponding to the preset time period is greater than a preset probability, determining the vehicle type corresponding to the preset time period as a target type;
and if the vehicle type is the preset time period of the target type, and the occupation ratio in all the preset time periods is greater than the preset occupation ratio, determining that the target vehicle is the vehicle of the target type.
In still another embodiment, the above vehicle type determination apparatus further includes a calibration module 606 for:
if the target vehicle is a vehicle of a target type, acquiring the vehicle type calibrated in a storage device by the target vehicle;
judging whether the vehicle type calibrated in the storage device by the target vehicle is the target type;
if not, the vehicle type of the target vehicle in the storage device is calibrated to be the target type.
In another embodiment, the first determining module 602, when determining the vehicle type attribute information corresponding to each preset time period respectively based on the acceleration information and the service characteristic information corresponding to each preset time period, includes:
and obtaining a vehicle classification model by utilizing pre-training, and processing the running track information and the service characteristic information corresponding to each preset time period to obtain vehicle type attribute information corresponding to each preset time period.
In still another embodiment, the above vehicle type determination apparatus further comprises a training module 607 for:
the method comprises the steps of obtaining the vehicle type of each sample vehicle in a plurality of sample vehicles, the running track information of each sample vehicle in each preset time period and the service characteristic information of a service providing end corresponding to each sample vehicle in each preset time period;
for each sample vehicle, determining acceleration information of the sample vehicle in each preset time period based on the running track information of the sample vehicle in each preset time period;
and training the initial vehicle classification model by utilizing the service characteristic information of the service providing end corresponding to each sample vehicle in each preset time period, the acceleration information of each sample vehicle in each preset time period and the vehicle type of each sample vehicle to obtain a final vehicle classification model.
In another embodiment, the apparatus for determining a vehicle type further includes a sample selecting module 608 for:
obtaining vehicle types of candidate sample vehicles stored in a storage device;
if the stored vehicle type is the target type and preset operation is performed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a positive sample for training the vehicle classification model;
and if the stored vehicle type is a preset type and the preset operation is not executed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a negative sample for training the vehicle classification model.
Fig. 7 illustrates a structure of an electronic device 700 according to an embodiment of the present invention, where the electronic device 700 includes: at least one processor 701, at least one network interface 704 or other user interface 703, memory 705, at least one communication bus 702. A communication bus 702 is used to enable connective communication between these components. The electronic device 700 optionally contains a user interface 703 including a display (e.g., touchscreen, LCD, CRT, Holographic (Holographic) or projection (Projector), etc.), a keyboard or a pointing device (e.g., mouse, trackball (trackball), touch pad or touchscreen, etc.).
Memory 705 may include both read-only memory and random access memory, and provides instructions and data to processor 701. A portion of the memory 705 may also include non-volatile random access memory (NVRAM).
In some embodiments, memory 705 stores the following elements, executable modules or data structures, or a subset thereof, or an expanded set thereof:
an operating system 7051, which contains various system programs for implementing various basic services and for processing hardware-based tasks;
the application module 7052 contains various applications, such as a desktop (launcher), a media player (MediaPlayer), a Browser (Browser), and the like, for implementing various application services.
In an embodiment of the present invention, the processor 701 is configured to, by calling a program or instructions stored in the memory 705:
acquiring running track information of a target vehicle in each preset time period in a plurality of preset time periods and service characteristic information of a service providing end corresponding to the target vehicle in each preset time period;
respectively determining vehicle type attribute information corresponding to each preset time period based on the running track information and the service characteristic information corresponding to each preset time period;
and determining whether the target vehicle is a target type vehicle or not based on the vehicle type attribute information corresponding to each preset time period.
Optionally, the running track information includes coordinate information of the target vehicle at each preset time within a corresponding preset time period;
in the method executed by the processor 701, the determining, based on the travel track information and the service characteristic information corresponding to each preset time period, vehicle type attribute information corresponding to each preset time period respectively includes:
respectively determining the acceleration information of the target vehicle corresponding to each preset time period based on a plurality of pieces of coordinate information corresponding to each preset time period;
and respectively determining vehicle type attribute information corresponding to each preset time period based on the acceleration information and the service characteristic information corresponding to each preset time period.
Optionally, in the method executed by the processor 701, the determining, based on the acceleration information and the service characteristic information corresponding to each preset time period, vehicle type attribute information corresponding to each preset time period respectively includes:
determining an acceleration vector corresponding to each preset time period based on the acceleration information corresponding to each preset time period;
determining a service characteristic vector corresponding to each preset time period based on the service characteristic information corresponding to each preset time period;
aiming at each preset time period, splicing the acceleration vector corresponding to the preset time period and the service characteristic vector corresponding to the preset time period to obtain a vehicle classification vector corresponding to the preset time period;
and respectively determining the vehicle type attribute information corresponding to each preset time period based on the vehicle classification vector corresponding to each preset time period.
Optionally, before determining the acceleration information of the target vehicle corresponding to each preset time period respectively based on the plurality of pieces of coordinate information corresponding to each preset time period, the processor 701 may further perform:
for each preset time period, determining the position of the target vehicle at each preset time within the preset time period based on the coordinate information of each preset time within the preset time period;
and determining the position of which the distance between the adjacent positions is greater than the preset distance, and deleting the coordinate information corresponding to the determined position.
Optionally, the processor 701 executes a method in which the vehicle type attribute information includes a probability value that the target vehicle is a target type vehicle.
Optionally, in the method executed by the processor 701, the determining whether the target vehicle is a target type vehicle based on the vehicle type attribute information corresponding to each preset time period includes:
for each preset time period, when the probability value corresponding to the preset time period is greater than a preset probability, determining the vehicle type corresponding to the preset time period as a target type;
and if the vehicle type is the preset time period of the target type, and the occupation ratio in all the preset time periods is greater than the preset occupation ratio, determining that the target vehicle is the vehicle of the target type.
Optionally, the processor 701 executes a method further including:
if the target vehicle is a vehicle of a target type, acquiring the vehicle type calibrated in a storage device by the target vehicle;
judging whether the vehicle type calibrated in the storage device by the target vehicle is the target type;
if not, the vehicle type of the target vehicle in the storage device is calibrated to be the target type.
Optionally, in the method executed by the processor 701, the determining, based on the travel track information and the service characteristic information corresponding to each preset time period, vehicle type attribute information corresponding to each preset time period respectively includes:
and obtaining a vehicle classification model by utilizing pre-training, and processing the running track information and the service characteristic information corresponding to each preset time period to obtain vehicle type attribute information corresponding to each preset time period.
Optionally, in the method executed by the processor 701, the method further includes a step of training the vehicle classification model:
the method comprises the steps of obtaining the vehicle type of each sample vehicle in a plurality of sample vehicles, the running track information of each sample vehicle in each preset time period and the service characteristic information of a service providing end corresponding to each sample vehicle in each preset time period;
for each sample vehicle, determining acceleration information of the sample vehicle in each preset time period based on the running track information of the sample vehicle in each preset time period;
and training the initial vehicle classification model by utilizing the service characteristic information of the service providing end corresponding to each sample vehicle in each preset time period, the acceleration information of each sample vehicle in each preset time period and the vehicle type of each sample vehicle to obtain a final vehicle classification model.
Optionally, the processor 701 executes a method, wherein the method further includes the step of selecting a sample vehicle:
obtaining vehicle types of candidate sample vehicles stored in a storage device;
if the stored vehicle type is the target type and preset operation is performed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a positive sample for training the vehicle classification model;
and if the stored vehicle type is a preset type and the preset operation is not executed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a negative sample for training the vehicle classification model.
The computer program product of the method and the device for determining a vehicle type provided in the embodiment of the present application includes a computer readable storage medium storing a program code, and instructions included in the program code may be used to execute the method in the foregoing method embodiment.
Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, or the like, and when the computer program on the storage medium is executed, the method for determining the vehicle type can be executed, so that whether the target vehicle is a target type vehicle can be determined in time, the timeliness is high, and the efficiency and the intelligence degree are high.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (13)

1. A method of determining a vehicle type, characterized by comprising:
acquiring running track information of a target vehicle in each preset time period in a plurality of preset time periods and service characteristic information of a service providing end corresponding to the target vehicle in each preset time period;
respectively determining vehicle type attribute information corresponding to each preset time period based on the running track information and the service characteristic information corresponding to each preset time period;
and determining whether the target vehicle is a target type vehicle or not based on the vehicle type attribute information corresponding to each preset time period.
2. The determination method according to claim 1, wherein the travel track information includes coordinate information of the target vehicle at each preset time within a corresponding preset time period;
the determining the vehicle type attribute information corresponding to each preset time period respectively based on the running track information and the service characteristic information corresponding to each preset time period comprises the following steps:
respectively determining the acceleration information of the target vehicle corresponding to each preset time period based on a plurality of pieces of coordinate information corresponding to each preset time period;
and respectively determining vehicle type attribute information corresponding to each preset time period based on the acceleration information and the service characteristic information corresponding to each preset time period.
3. The determination method according to claim 2, wherein the determining the vehicle type attribute information corresponding to each preset time period, respectively, based on the acceleration information and the service characteristic information corresponding to each preset time period, includes:
determining an acceleration vector corresponding to each preset time period based on the acceleration information corresponding to each preset time period;
determining a service characteristic vector corresponding to each preset time period based on the service characteristic information corresponding to each preset time period;
aiming at each preset time period, splicing the acceleration vector corresponding to the preset time period and the service characteristic vector corresponding to the preset time period to obtain a vehicle classification vector corresponding to the preset time period;
and respectively determining the vehicle type attribute information corresponding to each preset time period based on the vehicle classification vector corresponding to each preset time period.
4. The determination method according to claim 2, wherein before the acceleration information of the target vehicle corresponding to each preset time period is respectively determined based on a plurality of pieces of coordinate information corresponding to each preset time period, the method further comprises:
for each preset time period, determining the position of the target vehicle at each preset time within the preset time period based on the coordinate information of each preset time within the preset time period;
and determining the position of which the distance between the adjacent positions is greater than the preset distance, and deleting the coordinate information corresponding to the determined position.
5. The determination method according to claim 1, wherein the vehicle type attribute information includes a probability value that a target vehicle is a target type of vehicle.
6. The determination method according to claim 5, wherein the determining whether the target vehicle is a target type vehicle based on the vehicle type attribute information corresponding to each preset time period includes:
for each preset time period, when the probability value corresponding to the preset time period is greater than a preset probability, determining the vehicle type corresponding to the preset time period as a target type;
and if the vehicle type is the preset time period of the target type, and the occupation ratio in all the preset time periods is greater than the preset occupation ratio, determining that the target vehicle is the vehicle of the target type.
7. The determination method according to claim 5, further comprising:
if the target vehicle is a vehicle of a target type, acquiring the vehicle type calibrated in a storage device by the target vehicle;
judging whether the vehicle type calibrated in the storage device by the target vehicle is the target type;
if not, the vehicle type of the target vehicle in the storage device is calibrated to be the target type.
8. The determination method according to claim 1, wherein the determining the vehicle type attribute information corresponding to each preset time period based on the travel track information and the service characteristic information corresponding to each preset time period, respectively, comprises:
and obtaining a vehicle classification model by utilizing pre-training, and processing the running track information and the service characteristic information corresponding to each preset time period to obtain vehicle type attribute information corresponding to each preset time period.
9. The determination method according to claim 8, characterized in that the method further comprises a step of training the vehicle classification model:
the method comprises the steps of obtaining the vehicle type of each sample vehicle in a plurality of sample vehicles, the running track information of each sample vehicle in each preset time period and the service characteristic information of a service providing end corresponding to each sample vehicle in each preset time period;
for each sample vehicle, determining acceleration information of the sample vehicle in each preset time period based on the running track information of the sample vehicle in each preset time period;
and training the initial vehicle classification model by utilizing the service characteristic information of the service providing end corresponding to each sample vehicle in each preset time period, the acceleration information of each sample vehicle in each preset time period and the vehicle type of each sample vehicle to obtain a final vehicle classification model.
10. The determination method according to claim 9, characterized by further comprising the step of selecting a sample vehicle:
obtaining vehicle types of candidate sample vehicles stored in a storage device;
if the stored vehicle type is the target type and preset operation is performed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a positive sample for training the vehicle classification model;
and if the stored vehicle type is a preset type and the preset operation is not executed on the candidate sample vehicle, taking the candidate sample vehicle as a sample vehicle and taking the candidate sample vehicle as a negative sample for training the vehicle classification model.
11. A determination device of a vehicle type, characterized by comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring the running track information of a target vehicle in each preset time period in a plurality of preset time periods and the service characteristic information of a service providing end corresponding to the target vehicle in each preset time period;
the first determining module is used for respectively determining vehicle type attribute information corresponding to each preset time period based on the running track information and the service characteristic information corresponding to each preset time period;
and the second determination module is used for determining whether the target vehicle is a target type vehicle or not based on the vehicle type attribute information corresponding to each preset time period.
12. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of the method of determining a type of vehicle according to any one of claims 1 to 10.
13. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when being executed by a processor, performs the steps of the method for determining a type of vehicle according to any one of claims 1 to 10.
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