CN110674962A - Vehicle journey prediction processing method and device and storage medium - Google Patents
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Abstract
The invention provides a method, a device and a storage medium for estimating and processing a vehicle journey, wherein the method comprises the following steps: receiving POI information sent by a client; determining a candidate target matched with the POI information according to the POI information; acquiring characteristic information of a candidate target; according to the characteristic information of the candidate target, acquiring estimated target information of the candidate target in a period of time in the future by adopting a pre-trained prediction model; and feeding back the estimated target information to the client. The method can accurately estimate the vehicle travel condition in the future time period, and can provide reliable reference for making a trip decision of a user.
Description
Technical Field
The invention relates to the technical field of navigation, in particular to a method and a device for estimating and processing a vehicle journey and a storage medium.
Background
The parking difficulty is a travel problem generally faced by car owners in current large and medium-sized cities, the occupation situation of parking spaces in the parking lot is mainly collected in real time through hardware equipment pre-arranged in the parking lot at present, the parking lot occupation ratio is calculated according to the total number of the parking spaces, and then the relation parameter between the occupation ratio and the parking time is estimated by utilizing linear regression.
In addition, along with the popularization of new energy vehicles, many parking lots still have been equipped with for the new energy vehicles and have filled electric pile, but fill electric pile's quantity limited, and the time that different vehicles need consume that charges also is different, therefore the user still urgently hopes to obtain the idle condition who fills electric pile in the parking lot in advance when looking for the parking lot.
However, the existing parking space information acquisition mode has high requirements on hardware equipment, is poor in applicability, is not easy to popularize in a large area, and has low accuracy of estimated results.
Disclosure of Invention
The invention provides a vehicle travel prediction processing method, a vehicle travel prediction processing device and a storage medium, which can accurately predict the vehicle travel situation in the future time period and can provide reliable reference for making a trip decision of a user.
In a first aspect, the present invention provides a method for estimating and processing a vehicle journey, comprising:
receiving POI (point of interest) information sent by a client;
determining a candidate target matched with the POI information according to the POI information;
acquiring characteristic information of a candidate target;
according to the characteristic information of the candidate target, acquiring estimated target information of the candidate target in a period of time in the future by adopting a pre-trained prediction model;
and feeding back the estimated target information to the client.
In one possible design, determining a candidate target matching the POI information according to the POI information includes:
and taking the position corresponding to the POI information as a center to acquire all candidate targets in a preset radius range.
In one possible design, the feature information of the candidate target includes: current time information, navigation travel information, weather information, date information of the day, traffic flow information of a navigation path, and retrieval heat of POI information;
the candidate target is at least one of a candidate parking lot and a candidate charging station, wherein when the candidate target is the candidate charging station, the feature information of the candidate target further includes distribution information of other charging stations within a preset range of the candidate charging station.
In a possible design, before obtaining the estimated target information of the candidate target in a future period of time by using a pre-trained prediction model according to the feature information of the candidate target, the method further includes:
acquiring feature information of different candidate targets at different moments and actual target information corresponding to the feature information in a future period of time from log buried point data uploaded by a client;
constructing a training sample data set according to the feature information of different targets at different moments;
constructing a prediction model based on a machine learning method;
and taking the training sample data set as the input of the prediction model, taking actual target information of a period of time in the future as the output, and iteratively training the prediction model until a preset iteration number is reached to obtain a pre-trained prediction model.
In one possible design, further comprising:
when the candidate object is the candidate parking lot, the estimated object information includes: estimating parking time, adding the estimated parking time and the estimated time spent by the user from the departure place to the candidate parking lot, and feeding back to the client;
when the candidate target is the candidate charging station, the estimated target information includes: and estimating the vacancy rate of the charging pile, and feeding the estimated vacancy rate of the charging pile back to the client.
In a second aspect, the present invention provides a trip prediction processing apparatus for a vehicle, comprising:
the receiving module is used for receiving the POI information sent by the client;
the determining module is used for determining a candidate target matched with the POI information according to the POI information;
the acquisition module is used for acquiring the characteristic information of the candidate target;
the estimation module is used for acquiring estimation target information of the candidate target in a period of time in the future by adopting a pre-trained prediction model according to the characteristic information of the candidate target;
and the feedback module is used for feeding back the estimated target information to the client.
In one possible design, the determining module is specifically configured to:
and taking the position corresponding to the POI information as a center to acquire all candidate targets in a preset radius range.
In one possible design, the feature information of the candidate target includes: current time information, navigation travel information, weather information, date information of the day, traffic flow information of a navigation path, and retrieval heat of POI information;
the candidate target is at least one of a candidate parking lot and a candidate charging station, wherein when the candidate target is the candidate charging station, the feature information of the candidate target further includes distribution information of other charging stations within a preset range of the candidate charging station.
In one possible design, further comprising:
the training module is used for acquiring the feature information of different candidate targets at different moments and the actual target information corresponding to the feature information in a future period from the log buried point data uploaded by the client before acquiring the estimated target information of the candidate targets in the future period by adopting a pre-trained prediction model according to the feature information of the candidate targets;
constructing a training sample data set according to the feature information of different targets at different moments;
constructing a prediction model based on a machine learning method;
and taking the training sample data set as the input of the prediction model, taking actual target information of a period of time in the future as the output, and iteratively training the prediction model until a preset iteration number is reached to obtain a pre-trained prediction model.
In one possible design, the feedback module is specifically configured to:
when the candidate object is the candidate parking lot, the estimated object information includes: estimating parking time, adding the estimated parking time and the estimated time spent by the user from the departure place to the candidate parking lot, and feeding back to the client;
when the candidate target is the candidate charging station, the estimated target information includes: and estimating the vacancy rate of the charging pile, and feeding the estimated vacancy rate of the charging pile back to the client.
In a third aspect, an embodiment of the present invention provides a server, including: a processor and a memory, the memory having stored therein executable instructions of the processor; wherein the processor is configured to perform the trip prediction processing method of the vehicle of any of the first aspect via execution of the executable instructions.
In a fourth aspect, an embodiment of the present invention provides a system for estimating and processing a journey of a vehicle, including: a client and a server; the client side carries out data interaction with the server through the loaded map application;
the server is the server according to the third aspect, or the server includes the trip estimation processing device of the vehicle according to any one of the second aspects.
In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the journey estimation processing method for a vehicle according to any one of the first aspects.
According to the parking time-consuming statistical method, the device and the storage medium, the POI information sent by the client side is received; determining a candidate target matched with the POI information according to the POI information; acquiring characteristic information of a candidate target; according to the characteristic information of the candidate target, acquiring estimated target information of the candidate target in a period of time in the future by adopting a pre-trained prediction model; and feeding back the estimated target information to the client. The method can accurately estimate the vehicle travel condition in the future time period, and can provide reliable reference for making a trip decision of a user.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of an application scenario of the present invention;
fig. 2 is a flowchart of a method for estimating a vehicle travel according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a vehicle travel estimation processing device according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a travel estimation processing device of a vehicle according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a server according to a fourth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
In the following, some terms in the present application are explained to facilitate understanding by those skilled in the art:
1) ETL, an abbreviation of Extract-Transform-Load, is used to describe the process of extracting (Extract), Transform, and Load (Load) data from a source to a destination. The term ETL is more commonly used in data warehouses, but its objects are not limited to data warehouses.
2) Points of Interest (POIs) are also called points of information (points of information). The electronic map is generally represented by bubble icons, and the positions of destinations and departure places positioned on the electronic map can correspond to scenic spots, government agencies, companies, malls, restaurants and the like, and the positions are all POIs.
Fig. 1 is a schematic structural diagram of an Application scenario of the present invention, as shown in fig. 1, a client 10 (which may be a terminal such as a mobile phone, a tablet computer, or a navigator) is pre-loaded with a map Application (APP), the client 10 sends POI information of a point of interest to a server 20, and the server 20 determines a candidate parking lot and/or a candidate charging station matching the POI information according to the POI information. The server 20 then acquires the characteristic information of the parking lot candidate, and/or the characteristic information of the charging station candidate. Specifically, the server 20 may perform ETL processing on the log buried point data uploaded by the clients of the vehicles entering and exiting the parking lot and the charging stations to obtain feature information of candidate parking lots and feature information of candidate charging stations; the characteristic information of the candidate parking lot and the characteristic information of the candidate charging station may be acquired by the parking lot management platform and the charging station management platform. The server 20 is loaded with a trained first prediction model 21 and a trained second prediction model 22 (the first prediction model and the second prediction model may be independent from each other or may be coupled into one prediction model); specifically, a pre-trained first prediction model 21 may be adopted to obtain the estimated parking time of the candidate parking lot in a future period of time; the estimated charging post availability of the candidate charging stations in a future period of time may also be obtained through a pre-trained second predictive model 22. And finally, the server 20 feeds back the estimated parking time and/or the estimated charging pile vacancy rate to the client 10. Therefore, the parking time consumption of different parking lots in a future period and/or the charging pile vacancy rate of the charging station can be accurately estimated, reliable reference is provided for making a trip decision of a user, and user experience is good.
The following describes the technical solutions of the present invention and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of a method for estimating a vehicle travel according to an embodiment of the present invention, and as shown in fig. 2, the method in this embodiment may include:
s101, receiving POI information sent by a client.
In this embodiment, the client may be a common portable terminal, such as a smart phone, a tablet computer, a navigator, and the like. The map application is pre-loaded in the client, and not only can realize the navigation function, but also can perform data interaction with the server. The server may be a management platform of an existing map application, or may be an independent data management platform, which is not limited in this embodiment. Specifically, when a user uses a map application in a client to navigate, POI information is displayed on a search box and a map interface of the map application; the client then sends the POI information to the server.
And S102, determining a candidate target matched with the POI information according to the POI information.
In this embodiment, the server may obtain all candidate targets within a preset radius range by using the position corresponding to the POI information as a center, where the candidate targets include at least one of a parking lot and a candidate charging station. In this embodiment, the value of the preset radius is not limited, and in practical application, the preset radius may be set according to user definition.
And S103, acquiring characteristic information of the candidate target.
In this embodiment, the feature information of the candidate target may include: current time information, navigation trip information, weather information, date of the day information, traffic flow information of a navigation route, retrieval heat of POI information, and the like. The candidate target is at least one of a candidate parking lot and a candidate charging station, wherein when the candidate target is the candidate charging station, the feature information of the candidate target further includes distribution information of other charging stations within a preset range of the candidate charging station.
And S104, acquiring estimated target information of the candidate target in a future period of time by adopting a pre-trained prediction model according to the characteristic information of the candidate parking lot.
In this embodiment, the feature information of different candidate targets at different times and the actual target information corresponding to the feature information in a future period of time can be obtained from the log buried point data uploaded by the client; constructing a training sample data set according to the feature information of different targets at different moments; constructing a prediction model based on a machine learning method; and taking the training sample data set as the input of the prediction model, taking actual target information of a period of time in the future as the output, and iteratively training the prediction model until a preset iteration number is reached to obtain a pre-trained prediction model.
In an alternative embodiment, the predictive model may employ a Recurrent Neural Network (RNN) model. Specifically, the characteristic information of different parking lots at different moments and the actual average parking time corresponding to the characteristic information in a future period of time can be obtained from log buried point data uploaded from a client, where the characteristic information includes: current time information, navigation travel information, weather information, date information of the day, traffic flow information of a navigation path, and retrieval heat of POI information; constructing a first training sample data set according to the characteristic information of different parking lots at different moments; constructing a prediction model based on a machine learning method; and taking the first training sample data set as the input of the prediction model, taking the actual average parking time consumption of a period of time in the future as the target output, and iteratively training the prediction model until a preset iteration number is reached to obtain a pre-trained prediction model.
Specifically, in an optional manner, in order to improve the accuracy of the prediction model, periodic feature information and proximity feature information may be introduced into the first training sample data set; the periodic characteristic information refers to characteristic information acquired on different dates and at the same time; the proximity characteristic information refers to selecting characteristic information within N hours from the current time (the periodic characteristic generally takes data of a plurality of dates at the same time to characterize the periodicity). In the present embodiment, the specific value of N is not limited.
In an alternative embodiment, the predictive model may employ a Recurrent Neural Network (RNN) model. Specifically, the characteristic information of different charging stations at different moments and the actual charging pile vacancy rate of a future period of time corresponding to the characteristic information can be obtained from log buried point data uploaded by a client, wherein the characteristic information includes: current time information, navigation travel information, weather information, current date information, traffic flow information of a navigation path, distribution information of other charging stations in a preset range of candidate charging stations, and retrieval heat of POI information; constructing a second training sample data set by using the characteristic information of different charging stations at different moments; constructing a prediction model based on a machine learning method; and taking the second training sample data set as the input of the prediction model, taking the actual charging pile idle rate in a period of time in the future as the target output, and iteratively training the prediction model until a preset iteration number is reached to obtain a pre-trained prediction model.
Specifically, in an optional manner, in order to improve the accuracy of the prediction model, periodic feature information and proximity feature information may be introduced into the second training data set; the periodic characteristic information refers to characteristic information acquired on different dates and at the same time; the proximity characteristic information refers to selecting characteristic information within N hours from the current time (the periodic characteristic generally takes data of a plurality of dates at the same time to characterize the periodicity). In the present embodiment, the specific value of N is not limited.
And S105, feeding back the estimated target information to the client.
In an alternative, when the candidate object is the candidate parking lot, the estimated object information includes: estimating parking time, adding the estimated parking time and the estimated time spent by the user from the departure place to the candidate parking lot, and feeding back to the client; when the candidate target is the candidate charging station, the estimated target information includes: and estimating the vacancy rate of the charging pile, and feeding the estimated vacancy rate of the charging pile back to the client.
In the embodiment, POI information sent by a client is received; determining a candidate target matched with the POI information according to the POI information; acquiring characteristic information of a candidate target; according to the characteristic information of the candidate target, acquiring estimated target information of the candidate target in a period of time in the future by adopting a pre-trained prediction model; and feeding back the estimated target information to the client. The method can accurately estimate the vehicle travel condition in the future time period, and can provide reliable reference for making a trip decision of a user.
Fig. 3 is a schematic structural diagram of a journey estimation processing device of a vehicle according to a second embodiment of the present invention, and as shown in fig. 3, the journey estimation processing device of the vehicle according to the present embodiment may include: a receiving module 31, a determining module 32, an obtaining module 33, a predicting module 34 and a feedback module 35.
The receiving module 31 is configured to receive POI information sent by a client.
And the determining module 32 is configured to determine, according to the POI information, a candidate target matched with the POI information.
And an obtaining module 33, configured to obtain feature information of the candidate target.
And the estimation module 34 is configured to acquire estimated target information of the candidate target in a future period of time by using a pre-trained prediction model according to the feature information of the candidate target.
And the feedback module 35 is configured to feed back the estimated target information to the client.
In one possible design, the determining module 32 is specifically configured to:
and taking the position corresponding to the POI information as a center to acquire all candidate targets in a preset radius range.
In one possible design, the feature information of the candidate parking lot includes: current time information, navigation travel information, weather information, date information of the day, traffic flow information of a navigation path, and retrieval heat of POI information; the candidate target is at least one of a candidate parking lot and a candidate charging station, wherein when the candidate target is the candidate charging station, the feature information of the candidate target further includes distribution information of other charging stations within a preset range of the candidate charging station.
In one possible design, the feedback module 35 is specifically configured to:
when the candidate object is the candidate parking lot, the estimated object information includes: estimating parking time, adding the estimated parking time and the estimated time spent by the user from the departure place to the candidate parking lot, and feeding back to the client; when the candidate target is the candidate charging station, the estimated target information includes: and estimating the vacancy rate of the charging pile, and feeding the estimated vacancy rate of the charging pile back to the client.
The device for estimating and processing the vehicle journey of the present embodiment may implement the technical solutions in the methods of any of the above method embodiments, and the implementation principles and technical effects are similar, and are not described herein again.
Fig. 4 is a schematic structural diagram of a travel estimation processing device of a vehicle according to a third embodiment of the present invention, as shown in fig. 4, the device in this embodiment may further include, on the basis of the device shown in fig. 3, in a possible implementation manner: a training module 36.
The training module 36 is configured to obtain feature information of different candidate targets at different times and actual target information corresponding to the feature information within a future period of time from log buried point data uploaded by a client before obtaining estimated target information of the candidate targets within the future period of time by using a pre-trained prediction model according to the feature information of the candidate targets;
constructing a training sample data set according to the feature information of different targets at different moments;
constructing a prediction model based on a machine learning method;
and taking the training sample data set as the input of the prediction model, taking actual target information of a period of time in the future as the output, and iteratively training the prediction model until a preset iteration number is reached to obtain a pre-trained prediction model.
The device for estimating and processing the vehicle journey of the present embodiment may implement the technical solutions in the methods of any of the above method embodiments, and the implementation principles and technical effects are similar, and are not described herein again.
The embodiment of the present invention further provides a system for estimating and processing a vehicle journey, including: a client and a server; and the client side performs data interaction with the server through the loaded map application. Fig. 5 is a schematic structural diagram of a server according to a fourth embodiment of the present invention, and as shown in fig. 5, the server 40 in this embodiment includes: a processor 41 and a memory 42.
The memory 42 is used for storing computer programs (such as application programs, functional modules, and the like for implementing the travel estimation processing method of the vehicle), computer instructions, and the like, which can be stored in one or more memories 42 in a partitioned manner. And the above-mentioned computer program, computer instructions, data, etc. can be called by the processor 41.
A processor 41 for executing the computer program stored in the memory 42 to implement the steps of the method according to the above embodiments. Reference may be made in particular to the description relating to the preceding method embodiment. The memory 42 and the processor 41 may be coupled by a bus 43.
The server of this embodiment may execute the technical solution in the method of any of the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
In addition, embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored, and when at least one processor of the user equipment executes the computer-executable instructions, the user equipment performs the above-mentioned various possible methods.
Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (12)
1. A method for estimating and processing a travel of a vehicle, comprising:
receiving POI (point of interest) information sent by a client;
determining a candidate target matched with the POI information according to the POI information;
acquiring characteristic information of a candidate target;
according to the characteristic information of the candidate target, acquiring estimated target information of the candidate target in a period of time in the future by adopting a pre-trained prediction model;
and feeding back the estimated target information to the client.
2. The method of claim 1, wherein determining, based on the POI information, candidate targets that match the POI information comprises:
and taking the position corresponding to the POI information as a center to acquire all candidate targets in a preset radius range.
3. The method of claim 1,
the feature information of the candidate target comprises: current time information, navigation travel information, weather information, date information of the day, traffic flow information of a navigation path, and retrieval heat of POI information;
the candidate target is at least one of a candidate parking lot and a candidate charging station, wherein when the candidate target is the candidate charging station, the feature information of the candidate target further includes distribution information of other charging stations within a preset range of the candidate charging station.
4. The method according to any one of claims 1 to 3, before obtaining the predicted target information of the candidate target in a future period of time by using a pre-trained prediction model according to the feature information of the candidate target, further comprising:
acquiring feature information of different candidate targets at different moments and actual target information corresponding to the feature information in a future period of time from log buried point data uploaded by a client;
constructing a training sample data set according to the feature information of different targets at different moments;
constructing a prediction model based on a machine learning method;
and taking the training sample data set as the input of the prediction model, taking actual target information of a period of time in the future as the output, and iteratively training the prediction model until a preset iteration number is reached to obtain a pre-trained prediction model.
5. The method of claim 3, further comprising:
when the candidate object is the candidate parking lot, the estimated object information includes: estimating parking time, adding the estimated parking time and the estimated time spent by the user from the departure place to the candidate parking lot, and feeding back to the client;
when the candidate target is the candidate charging station, the estimated target information includes: and estimating the vacancy rate of the charging pile, and feeding the estimated vacancy rate of the charging pile back to the client.
6. A travel estimation processing device for a vehicle, comprising:
the receiving module is used for receiving the POI information sent by the client;
the determining module is used for determining a candidate target matched with the POI information according to the POI information;
the acquisition module is used for acquiring the characteristic information of the candidate target;
the estimation module is used for acquiring estimation target information of the candidate target in a period of time in the future by adopting a pre-trained prediction model according to the characteristic information of the candidate target;
and the feedback module is used for feeding back the estimated target information to the client.
7. The apparatus of claim 6, wherein the determining module is specifically configured to:
and taking the position corresponding to the POI information as a center to acquire all candidate targets in a preset radius range.
8. The apparatus of claim 6,
the feature information of the candidate target comprises: current time information, navigation travel information, weather information, date information of the day, traffic flow information of a navigation path, and retrieval heat of POI information;
the candidate target is at least one of a candidate parking lot and a candidate charging station, wherein when the candidate target is the candidate charging station, the feature information of the candidate target further includes distribution information of other charging stations within a preset range of the candidate charging station.
9. The apparatus of any of claims 6-8, further comprising:
the training module is used for acquiring the feature information of different candidate targets at different moments and the actual target information corresponding to the feature information in a future period from the log buried point data uploaded by the client before acquiring the estimated target information of the candidate targets in the future period by adopting a pre-trained prediction model according to the feature information of the candidate targets;
constructing a training sample data set according to the feature information of different targets at different moments;
constructing a prediction model based on a machine learning method;
and taking the training sample data set as the input of the prediction model, taking actual target information of a period of time in the future as the output, and iteratively training the prediction model until a preset iteration number is reached to obtain a pre-trained prediction model.
10. The apparatus of claim 8, wherein the feedback module is specifically configured to:
when the candidate object is the candidate parking lot, the estimated object information includes: estimating parking time, adding the estimated parking time and the estimated time spent by the user from the departure place to the candidate parking lot, and feeding back to the client;
when the candidate target is the candidate charging station, the estimated target information includes: and estimating the vacancy rate of the charging pile, and feeding the estimated vacancy rate of the charging pile back to the client.
11. A server comprising a processor and a memory, the memory having stored therein executable instructions for the processor; wherein the processor is configured to perform the trip prediction processing method of the vehicle of any of claims 1-5 via execution of the executable instructions.
12. A computer-readable storage medium on which a computer program is stored, the program being characterized by implementing, when executed by a processor, a trip prediction processing method of a vehicle according to any one of claims 1 to 5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111882096A (en) * | 2020-08-03 | 2020-11-03 | 上海博泰悦臻电子设备制造有限公司 | Method for information processing, electronic device, and storage medium |
| CN115752503A (en) * | 2023-01-09 | 2023-03-07 | 徐工汉云技术股份有限公司 | Garden navigation path planning method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140207725A1 (en) * | 2013-01-22 | 2014-07-24 | Nec (China) Co., Ltd. | Device and method for destination prediction |
| CN107038488A (en) * | 2017-02-25 | 2017-08-11 | 浙江大学 | A kind of real-time berth reserving method in parking lot based on berth prediction and selection of stopping |
| CN107239846A (en) * | 2016-03-29 | 2017-10-10 | 中兴通讯股份有限公司 | parking lot berth prediction processing method and device |
| US20180150773A1 (en) * | 2016-11-30 | 2018-05-31 | Institute For Information Industry | Parking forecast and parking guidance planning system and method |
| CN108120999A (en) * | 2017-12-19 | 2018-06-05 | 大陆汽车投资（上海）有限公司 | In-vehicle navigation apparatus and parking lot guide method |
-
2018
- 2018-07-03 CN CN201810715985.4A patent/CN110674962A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140207725A1 (en) * | 2013-01-22 | 2014-07-24 | Nec (China) Co., Ltd. | Device and method for destination prediction |
| CN107239846A (en) * | 2016-03-29 | 2017-10-10 | 中兴通讯股份有限公司 | parking lot berth prediction processing method and device |
| US20180150773A1 (en) * | 2016-11-30 | 2018-05-31 | Institute For Information Industry | Parking forecast and parking guidance planning system and method |
| CN107038488A (en) * | 2017-02-25 | 2017-08-11 | 浙江大学 | A kind of real-time berth reserving method in parking lot based on berth prediction and selection of stopping |
| CN108120999A (en) * | 2017-12-19 | 2018-06-05 | 大陆汽车投资（上海）有限公司 | In-vehicle navigation apparatus and parking lot guide method |
Non-Patent Citations (2)
| Title |
|---|
| 《电子商务优秀设计方案与分析》编写组: "《电子商务优秀设计方案与分析》", 30 November 2015, 北京：中国铁道出版社 * |
| 贵州电网有限责任公司组 等编著: "《电动汽车充电管理云平台与智能充电技术》", 31 December 2017, 贵阳：贵州大学出版社 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111882096A (en) * | 2020-08-03 | 2020-11-03 | 上海博泰悦臻电子设备制造有限公司 | Method for information processing, electronic device, and storage medium |
| CN111882096B (en) * | 2020-08-03 | 2022-11-29 | 博泰车联网科技(上海)股份有限公司 | Method for information processing, electronic device, and storage medium |
| CN115752503A (en) * | 2023-01-09 | 2023-03-07 | 徐工汉云技术股份有限公司 | Garden navigation path planning method and device |
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