CN113902499A - Method, device, equipment and medium for pushing station information of shared vehicles - Google Patents
Method, device, equipment and medium for pushing station information of shared vehicles Download PDFInfo
- Publication number
- CN113902499A CN113902499A CN202010574598.0A CN202010574598A CN113902499A CN 113902499 A CN113902499 A CN 113902499A CN 202010574598 A CN202010574598 A CN 202010574598A CN 113902499 A CN113902499 A CN 113902499A
- Authority
- CN
- China
- Prior art keywords
- user
- station
- destination
- target station
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0645—Rental transactions; Leasing transactions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
- G06Q30/0637—Approvals
Abstract
The invention discloses a method, a device, equipment and a medium for pushing station information of shared vehicles, wherein the method comprises the following steps: acquiring a target site with the residual capacity smaller than a preset capacity threshold; predicting the probability that a first recommended parking point of a destination of a user is a target station according to a pre-established prediction model; judging whether a target station with the probability exceeding a preset probability threshold exists or not; and when the target station with the probability exceeding the preset probability threshold exists, pushing prompt information of the target station to the user, wherein the prompt information is used for prompting the user that the target station has the risk that the vehicle cannot be returned. The method and the device have the advantages that the probability that the first recommended parking point of the destination of the user is the target station is predicted by the aid of the pre-established prediction model, so that the user can timely know the capacity information of the station, the problem that the user cannot park when the first recommended parking point of the destination is the target station is avoided, and the problem that shared vehicles are stacked on the target station is further avoided.
Description
Technical Field
The invention relates to the technical field of shared economy, in particular to a method, a device, equipment and a medium for pushing station information of shared vehicles.
Background
The sharing economy is a current social hotspot, generally refers to a new economic model based on strangers and temporary transfer of the use right of articles, and the essence of the sharing economy is to integrate spare articles, labor force and educational medical resources under the line, with the main purpose of obtaining a certain reward.
One resource that is particularly prominent in the shared economy is that vehicles, such as shared bicycles, shared mopeds (shared electric vehicles), shared automobiles and the like, are new vehicles, and can be unlocked through code scanning and cyclically shared, which is a promoting result of the shared economy in a new era.
In the field of shared travel such as shared bicycles or shared mopeds, in order to provide convenient service for users, the users can pick up vehicles at any starting point and return vehicles at any starting point within the operation range limited by a shared service provider. Great convenience is brought to the travel of the user. However, the riding destination of the user is not controllable, and the continuous inflow of vehicles exists at some stations in part of the time period, so that the accumulation occurs. Especially during morning and evening peak hours, vehicles may be piled up at the gate of a subway station, near a bus station, near an office, or at the gate of a cell. The accumulation of vehicles reduces the circulation efficiency of the vehicles on the one hand, and more importantly, the vehicles possibly block traffic, influence the normal travel of other people and influence the appearance of the city. Thus, stack management is an important challenge and necessity for shared vehicles.
The inventor finds that, in the prior art, although the number of shared vehicles at the corresponding station position can be checked through the APP on the terminal, the main role is to facilitate the user to actively know the position of the shared vehicle; on the other hand, the viewing mode in the prior art can only display the number of the shared vehicles at the station position, and the user cannot know the real-time capacity of the station in time, so that the problem that the shared vehicles are stacked or the user cannot return vehicles at the destination is easily caused.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is that a user cannot know the real-time capacity of a station in time, and the problem of vehicle accumulation sharing or the problem that the user cannot return to a vehicle at a destination is easily caused.
In order to achieve the above object, the present invention provides a method for pushing station information of shared vehicles, including: acquiring a target site with the residual capacity smaller than a preset capacity threshold; predicting the probability that a first recommended parking point of a destination of a user is the target station according to a pre-established prediction model; judging whether a target station with the probability exceeding a preset probability threshold exists or not; and when the target station with the probability exceeding a preset probability threshold exists, pushing prompt information of the target station to the user, wherein the prompt information is used for prompting the user that the target station has the risk that the vehicle cannot be returned.
In a preferred embodiment of the present invention, the acquiring a target station with a remaining capacity smaller than a preset capacity threshold includes: acquiring the current position of a user using a shared vehicle; acquiring capacity information of all stations within a preset distance range around the current position; judging whether a target station with the residual capacity smaller than a preset capacity threshold exists or not based on the capacity information of all stations; when the target station exists, the step of predicting the probability that the first recommended parking point of the destination of the user is the target station according to a pre-established prediction model is executed.
In a preferred embodiment of the present invention, the method for pushing site information further includes: acquiring available sites with residual capacity greater than or equal to the preset capacity threshold; and pushing the position information of the available sites to the user.
In a preferred embodiment of the present invention, the predicting, according to a pre-established prediction model, a probability that a first recommended parking spot of a destination of a user is the target station includes: acquiring the identity information of the user and the current time at the current position; inputting the identity information, the target station, the current time and the current position into the prediction model, performing calculation through the prediction model, and outputting the probability that the first recommended parking point of the destination of the user is the target station.
In a preferred embodiment of the present invention, the step of establishing the prediction model comprises: counting parking data of each destination in the historical order data of the user; acquiring a station with historical accumulation, and counting order data of an order track passing through a preset range near the station and taking the station as a destination; and establishing an initial training model, and training the training model by using the parking data and the order data to obtain the prediction model.
In a preferred embodiment of the present invention, the parking data includes: the frequency of parking for all destinations, the number of times each destination parks on different dates and different periods of time.
In a preferred embodiment of the present invention, the acquiring a target station with a remaining capacity smaller than a preset capacity threshold includes: acquiring the current position of a user using a shared vehicle; acquiring all sites corresponding to destinations matched with the current position in the user historical order data; judging whether the residual capacity of the station is smaller than a preset capacity threshold value or not; and taking the station with the residual capacity smaller than the preset capacity threshold value as the target station.
In a preferred embodiment of the present invention, the acquiring all sites corresponding to destinations matching the current location in the user historical order data includes: and acquiring a site corresponding to the destination of the order track passing through the current position from the historical order data.
In a preferred embodiment of the present invention, the acquiring all sites corresponding to destinations matching the current location in the user historical order data includes: determining an order track matched with a travel track of the user before the user reaches the current position from the historical order data; and acquiring a site corresponding to the destination of the order track.
In a preferred embodiment of the present invention, the acquiring all sites corresponding to destinations matching the current location in the user historical order data includes: obtaining order data of a time period matched with the current time from the historical order data; and determining a station corresponding to the destination of the order track passing through the current position from the order data.
In order to achieve the above object, the present invention provides a station information pushing device for sharing a vehicle, including: the acquisition module is used for acquiring a target site with the residual capacity smaller than a preset capacity threshold; the prediction module is used for predicting the probability that a first recommended parking point of a destination of a user is the target station according to a pre-established prediction model; the judging module is used for judging whether a target station with the probability exceeding a preset probability threshold exists or not; and the pushing module is used for pushing prompt information of the target station to the user when the target station with the probability exceeding a preset probability threshold exists, and the prompt information is used for prompting the user that the target station has the risk that the vehicle cannot return.
In order to achieve the above object, the present invention provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor implements the steps of the station information pushing method of the shared vehicle when executing the computer program.
To achieve the above object, the present invention provides a readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the steps of the station information pushing method of the shared vehicle.
The device or the method provided by the invention has the following technical effects:
1. according to the method and the device, the target station with the residual capacity smaller than the preset capacity threshold is obtained, the probability that the first recommended parking point of the destination of the user is the target station is predicted according to the pre-established prediction model, and when the probability exceeds the preset probability threshold, the prompt information which is pushed to the target station is sent to the user, so that the user can know the capacity information of the station in time, the problem that the user cannot park when the first recommended parking point of the destination of the user is the target station is avoided, and the problem of accumulation of shared transportation tools on the target station is further avoided.
2. After the capacity information of the station is acquired, the accumulation condition of the station can be judged through the capacity information, specifically, the accumulation condition is judged through judging the residual capacity of the station, and when the residual capacity of the station is smaller than a preset capacity threshold value, the station is determined to be a target station, which indicates that the station has certain accumulation or is full. And triggering the prediction step when the target station is judged to exist in the distance range. If the target site does not exist, prediction is not triggered, so that the operation pressure of the back-end server can be reduced, and the expense of the back-end server is reduced.
3. And judging whether the target site is the target site by using the destination of the matched order data in the historical order data as basic data for judgment, wherein the destination of the matched order data in the historical order data is more in line with the behavior habit of the user and improves the accuracy of prediction and the accuracy of information pushing compared with the method of using sites in the range around the current position as judgment bases.
4. And matching the order track in the historical order data with the trip track of the user, and when the order track and the trip track are matched, taking the station of the destination of the order track as a judgment basis of the target station, wherein the accuracy is higher compared with the mode of covering the current position.
5. The information of available stations is provided for the user to select while the information of the target station is pushed to the user, so that the user can select other stations to return the vehicle or return the vehicle in advance, the problem that the user cannot stop and return the vehicle when reaching the destination is avoided, and the user experience is improved.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of a method for pushing station information of a shared vehicle according to the present invention;
FIG. 2 is a flow chart of another preferred embodiment of the method for pushing station information of a shared vehicle according to the present invention;
FIG. 3 is a flow chart of another preferred embodiment of the station information pushing device of the shared vehicle according to the present invention;
FIG. 4 is an internal block diagram of a computer device, provided in one embodiment.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated. The drawings provided in the following embodiments are only for illustrating the basic idea of the invention in a schematic way, and the sequence relation of the steps is only for clearly describing the processing logic of the embodiments of the invention, and the steps need not limit the sequence of the steps when there is no obvious sequential logic between the steps.
Some exemplary embodiments of the invention have been described for illustrative purposes, and it is to be understood that the invention may be practiced otherwise than as specifically described.
The embodiment of the invention provides a method for pushing station information of a shared vehicle, which is mainly executed by a back-end server of the shared vehicle, and pushes corresponding prompt information to a user in time through prediction of a user destination and judgment of destination station capacity. The step of pushing the prompt information to the user may be that the back-end server pushes corresponding prompt information to a terminal device (a mobile phone, a tablet computer, etc.) held by the user; the method can also mean that the back-end server pushes prompt information to a vehicle-mounted terminal on a shared vehicle rented by the user, and the user can obtain corresponding prompt information in the process of using the shared vehicle. For example, when the user uses a shared automobile, and the back-end server predicts that the capacity of the destination station of the user is full, the back-end server pushes prompt information for prompting that the capacity of the destination station is full to a central control terminal on the shared automobile, so that the user can timely know the capacity condition of the destination station for returning the vehicle in advance.
As shown in fig. 1, a method for pushing station information of a shared vehicle according to an embodiment of the present invention includes:
step S101, acquiring a target station with the residual capacity smaller than a preset capacity threshold.
The stations described in the embodiments of the present invention may be stations with fixed parking spaces or stations with fixed parking areas, such as pile-inserting type shared bicycles, each station has a fixed number of piles, which is the total capacity of the station; for a shared car, each station has a fixed number of parking spaces, i.e. the total number of stations. The remaining capacity of a station refers to the number of remaining parkable stations, and when the remaining capacity is smaller than a preset capacity threshold, the station is defined as a target station. The preset capacity threshold may be set according to an empirical value or a statistical value, for example, 98% of the total capacity is used as the preset capacity threshold. In the embodiment of the present invention, the target station actually refers to a station with a very small remaining capacity or a full station.
According to the embodiment of the invention, the back-end server can perform subsequent probability prediction by acquiring the target site, and timely inform the user of the site information of the predicted destination. Specifically, the identification information of the target station may be obtained, and the identification information is used to uniquely identify the corresponding target station. The target site can be all sites with the residual capacity smaller than a preset capacity threshold value at the current moment, and can also be the target site in a fixed position area; but also based on the target sites near the user's current location.
And step S102, predicting the probability that the first recommended parking point of the destination of the user is the target station according to a pre-established prediction model.
In an embodiment of the invention, the destination of the user may be an intended return station for using the shared vehicle. Typically, users will have a destination to which they will reach when using a shared vehicle. The user returns to the vehicle after reaching the destination and then does his or her own things. Since the destination of the user may be a no-parking spot or a no-parking spot, the embodiment of the present invention predicts the first recommended parking spot of the destination, that is, the optimal parking spot, and predicts the probability that the destination is the target stop. The prediction model of the embodiment of the invention is used for predicting the probability that the first recommended parking point of the destination of the user is the target station. Since the destination of the user is subjectively determined by the user, but the destination can be predicted according to the historical behavior data of the user, the predicted probability represents the possibility that the first recommended parking point of the destination is the destination actually desired by the user, and the greater the value, the greater the possibility. Therefore, the probability that all the stations are the first recommended stop points of the user destination can be predicted, and the probability of each station is obtained, wherein the probability of the target station is included. When the probability that the first recommended stop of the destination is the target station is high, it indicates that there is a high possibility that the user returns to the target station. The prediction model of the embodiment of the invention can be obtained by training according to the parking data in the user historical order data and the data of the accumulated stations.
Step S103, judging whether a target site with the probability exceeding a preset probability threshold exists;
and step S104, when the target station with the probability exceeding the preset probability threshold exists, pushing prompt information of the target station to a user, wherein the prompt information is used for prompting the user that the target station has the risk that the vehicle cannot be returned.
The preset probability threshold is used as a criterion for judging the probability, and it can be considered that when the probability exceeds the preset probability threshold, the corresponding target station has a very high possibility of being a destination of the user, and at this time, the back-end server needs to push corresponding prompt information to the user to prompt the user that the target station is full or close to the full state, so that a risk that the user cannot return the vehicle exists. The predetermined probability threshold may be determined based on empirical values or statistical values, such as 50%. The embodiment of the invention can reduce the data volume of the pushed prompt information through the preset probability threshold, thereby avoiding unnecessary emotional feeling of the user caused by repeating or pushing a large amount of invalid information. In the embodiment of the invention, the back-end server pushes the corresponding prompt information to the user by judging the station with the probability exceeding the preset probability threshold, so that the user can timely acquire the information of the station according to the pushed information and adjust the station for changing the car, and the problem that the user cannot change the car after reaching the destination is avoided.
It should be noted that, in the embodiment of the present invention, the number of the acquired target stations may be one or multiple, and therefore, the number of the target stations whose probability exceeds the preset probability threshold may also be zero, one, or multiple. When the number is zero, not pushing information to the user; when the number is one or more, then prompt information of all targeted sites is pushed to the user, and the prompt information may include the location of the targeted sites, capacity information of the targeted sites (e.g., sites full or nearly full), and the like.
According to the embodiment of the invention, the target station with the residual capacity smaller than the preset capacity threshold is obtained, the probability that the first recommended stop point of the destination of the user is the target station is predicted by using the pre-established prediction model, and when the probability exceeds the preset probability threshold, the prompt information which is sent to the target station is pushed to the user, so that the user can timely know the capacity information of the station, the problem that the user cannot stop when the first recommended stop point of the destination of the user is the target station is avoided, and the problem of accumulation of shared transportation tools on the target station is further avoided.
As an optional implementation manner, as shown in fig. 2, in the embodiment of the present invention, the acquiring a target station whose remaining capacity is smaller than a preset capacity threshold includes:
in step S1011, the current position where the user uses the shared vehicle is acquired.
In the embodiment of the invention, the current position of the user can be obtained by monitoring the user terminal in real time, or can be obtained by monitoring the vehicle-mounted terminal or the positioning module on the shared vehicle. In the embodiment of the invention, the rear-end server can predict the destination of the user in time by acquiring the current position of the user in real time, and gives the probability that the first recommended parking point of the destination is the target station, so that the prediction result can be adjusted in time, and the prediction accuracy is improved.
Step S1012, acquiring capacity information of all stations within a preset distance range around the current position.
The preset distance range around the current location may refer to an area within 500 meters from the current location. And the back-end server acquires the capacity information of all the sites in the area, and can know the accumulation condition of the sites through further judgment.
Step S1013, determining whether there is a target station whose remaining capacity is smaller than a preset capacity threshold based on the capacity information of all the stations.
And step S102, when the target station exists, a step of predicting the probability that the first recommended parking point of the destination of the user is the target station according to a pre-established prediction model is executed. When the target site does not exist, no other operation can be performed.
Specifically, the predicting, according to a pre-established prediction model, the probability that a first recommended parking spot of a destination of a user is the target station includes: acquiring the identity information of the user and the current time at the current position; inputting the identity information, the target station, the current time and the current position into the prediction model, performing calculation through the prediction model, and outputting the probability that the first recommended parking point of the destination of the user is the target station. The identity information may include account information of the user, and the like, and when the user uses the shared vehicle, the user usually needs to register an account and log in corresponding application software, so that the user can obtain the identity information after using the shared vehicle.
After the back-end server acquires the capacity information of the station, the accumulation condition of the station can be judged through the capacity information, specifically, the accumulation condition is judged through judging the remaining capacity of the station, and when the remaining capacity of the station is smaller than a preset capacity threshold, the station is determined to be a target station, which indicates that the station has a certain accumulation or is full. And triggering the prediction step when the target station is judged to exist in the distance range. If the target site does not exist, prediction is not triggered, so that the operation pressure of the back-end server can be reduced, and the expense of the back-end server is reduced.
Steps S103-S104 are the same as those in fig. 1, and are not described herein.
As an alternative implementation manner, in an embodiment of the present invention, the acquiring a target station whose remaining capacity is smaller than a preset capacity threshold includes: acquiring the current position of a user using a shared vehicle; acquiring all sites corresponding to destinations matched with the current position in the user historical order data; judging whether the residual capacity of the station is smaller than a preset capacity threshold value or not; and taking the station with the residual capacity smaller than a preset capacity threshold value as the target station.
In the embodiment of the present invention, the historical order data refers to order data for previously using shared vehicles, where the shared vehicles in the order data are the same type of shared vehicles as the current shared vehicles. The order data may include data such as the unique identification of the shared vehicle used by the user, the start and end of the user's rental, and the order track.
In the embodiment of the invention, historical order data is matched with the current position, when the current position is matched, the destination in the historical order data is used as the current destination of a user, then whether the residual capacity of the site of the destination is smaller than a preset capacity threshold value or not is judged, and if yes, the site of the destination is marked as a target site.
In the embodiment, the destination of the matched order data in the historical order data is used as the basic data for judgment, and whether the order data is the target site or not is judged.
Further optionally, in the embodiment of the present invention, the acquiring all sites corresponding to destinations that match the current location in the user historical order data includes: and acquiring a site corresponding to the destination of the order track passing through the current position from the historical order data. That is, in the embodiment of the present invention, the current location and the order track are directly adopted for matching, and when the order track covers the current location, the destination of the order track can be considered to be the destination of the shared vehicle currently used by the user to a great extent, and the corresponding station is used as the basis for determining that the matched station is used as the target station.
In the embodiment of the invention, because the area division is usually performed by adopting the grids on the map, a certain error exists in the positioned position, and the order track can not completely cover the actual position point, the order track covers the current position, which can mean that the current position is in the grids covered by the order track.
Further alternatively, in the embodiment of the present invention, the acquiring all sites corresponding to destinations that match the current location in the user historical order data includes: determining an order track matched with a travel track of the user before the user reaches the current position from the historical order data; and acquiring a site corresponding to the destination of the order track.
In the embodiment of the invention, as the user uses the shared vehicle to travel to the current position and has a travel track, namely a track from the starting point to the current position, the track is matched with the order track in the historical order data, when the track is matched with the order track, the similarity between the travel track of the current user and the historical order track is shown, and according to the behavior habit of the user, the destination of the order track can be considered to be the current destination of the user to a great extent, so that the destination station is used as the judgment basis of the target station. Where track matching may be judged by the overlap ratio of the tracks, for example, when the overlapping paths of the tracks reach a certain threshold (80%), the two are considered to match.
In the embodiment of the invention, the order track in the historical order data is matched with the travel track of the user, and when the order track and the travel track are matched, the station of the destination of the order track is used as the judgment basis of the target station, so that the accuracy is higher compared with the mode of covering the current position.
Further alternatively, in the embodiment of the present invention, the acquiring all sites corresponding to destinations that match the current location in the user historical order data includes: obtaining order data of a time period matched with the current time from the historical order data; and determining a station corresponding to the destination of the order track passing through the current position from the order data.
In the embodiment of the present invention, the site is determined by using historical order data in the same time period, and based on the behavior habits of the user, it is known that the similarity of things done in the same time period is higher, in this embodiment, the current time is used as a matching condition, the order data in the same time period is determined from the historical order data, and then matching is performed by using the current position, specifically, the order trajectory in the order data passes through the current position, and the method may be the above-mentioned manner of covering the current position, and specifically, refer to the above-mentioned embodiment, and no further description is given here.
In the embodiment, the increased time is used as a judgment basis, so that the behavior habit of the user is better met, the final prediction result is accurate, and the pushed information is more accurate.
As an optional implementation manner, in an embodiment of the present invention, the station information pushing method further includes: acquiring available sites with residual capacity greater than or equal to the preset capacity threshold; and pushing the position information of the available sites to the user. The available site is the available site closest to the destination, preferably the available site between the current position and the destination.
In the embodiment of the invention, the information of the available station is provided for the user to select while pushing the pushing information of the target station to the user, so that the user can select other stations to return the car or return the car in advance, the problem that the user cannot stop and return the car when reaching the destination is avoided, and the user experience is improved.
As an alternative implementation manner, in an embodiment of the present invention, the step of establishing the prediction model includes:
and S1, counting the parking data of each destination in the historical order data of the user. The parking data includes: the frequency of parking for all destinations, the number of times each destination parks on different dates and different periods of time.
Specifically, the historical order data of the user may be acquired offline, and then:
a) counting the frequency of all destinations of a user;
b) the number of stops per destination on weekdays/non-weekdays;
c) the number of stops of each destination in a time period of 6-10 points, 10-14 points, 14-17 points, 17-21 points, 21-6 points and the like;
d) according to the type of the main information point (poi) near the destination (e.g.: bus stations, subway stations, shopping malls, office buildings, tourist areas, schools, residential areas, etc.), the number of times the user parks for the type poi on weekdays/non-weekdays, and for five periods in c, is counted.
And S2, acquiring a station with historical accumulation, and counting order data of which the order track passes through a preset range near the station and takes the station as a destination. Specifically, sites with historical piles can be acquired offline, and the number of orders passing through the grid in the nearly 30-day order track of all the geohash8 grids with 500 meters nearby and the number of orders with the current pile sites as the destination after passing through the grid can be counted (working day/non-working day, time interval differentiation).
S3, establishing an initial training model, and training the training model by using the parking data and the order data to obtain the prediction model.
Using the data, a model such as LR, xgboost, lightgbm, etc. is used to perform modeling to obtain a training model, and then a prediction model is obtained by training to predict the probability that the user will arrive at the destination at a certain place on a certain date and a certain time.
The model and modeling process used are prior art, and the embodiment of the invention mainly lies in the difference of the used training data, and the data are trained by utilizing the existing initial model to obtain a prediction model for probability prediction of the destination.
An embodiment of the present invention provides a station information pushing device for a shared vehicle, which may be disposed on a backend server and configured to execute a station information pushing method for the shared vehicle shown in fig. 1, and as shown in fig. 3, the device includes:
an obtaining module 301, configured to obtain a target station whose remaining capacity is smaller than a preset capacity threshold;
the prediction module 302 is configured to predict, according to a pre-established prediction model, a probability that a first recommended parking spot of a destination of a user is the target station;
a judging module 303, configured to judge whether there is a target station with the probability exceeding a preset probability threshold;
the pushing module 304 is configured to, when there is a target station with the probability exceeding a preset probability threshold, push prompt information of the target station to the user, where the prompt information is used to prompt the user that the target station has a risk that the vehicle cannot be returned.
According to the embodiment of the invention, the target station with the residual capacity smaller than the preset capacity threshold is obtained, the probability that the first recommended stop point of the destination of the user is the target station is predicted by using the pre-established prediction model, and when the probability exceeds the preset probability threshold, the prompt information which is sent to the target station is pushed to the user, so that the user can timely know the capacity information of the station, the problem that the user cannot stop when the first recommended stop point of the destination of the user is the target station is avoided, and the problem of accumulation of shared transportation tools on the target station is further avoided.
In an embodiment of the present invention, a computer device is further provided, where the computer device may be a backend server in the foregoing embodiments, and an internal structure diagram of the computer device may be as shown in fig. 4. The computer device comprises a processor, a memory and a network interface which are connected through a system bus, and also comprises a display screen and an input device. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external computer device through a network connection. The computer program is executed by a processor to realize a method for pushing station information of a shared vehicle, the computer device may further include a display screen and an input device, the display screen may be a liquid crystal display screen or an electronic ink display screen, the input device of the computer device may be a touch layer covered on the display screen, may also be a key, a track ball or a touch pad arranged on a casing of the computer device, and may also be an external keyboard, a touch pad or a mouse, etc.
On the other hand, the computer device may not include a display screen and an input device, and those skilled in the art will understand that the structure shown in fig. 4 is only a block diagram of a part of the structure related to the present application, and does not constitute a limitation to the computer device to which the present application is applied, and a specific computer device may include more or less components than those shown in the figure, or combine some components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring a target site with the residual capacity smaller than a preset capacity threshold;
predicting the probability that a first recommended parking point of a destination of a user is the target station according to a pre-established prediction model;
judging whether a target station with the probability exceeding a preset probability threshold exists or not;
and when the target station with the probability exceeding a preset probability threshold exists, pushing prompt information of the target station to the user, wherein the prompt information is used for prompting the user that the target station has the risk that the vehicle cannot be returned.
In one embodiment, a readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring a target site with the residual capacity smaller than a preset capacity threshold;
predicting the probability that a first recommended parking point of a destination of a user is the target station according to a pre-established prediction model;
judging whether a target station with the probability exceeding a preset probability threshold exists or not;
and when the target station with the probability exceeding a preset probability threshold exists, pushing prompt information of the target station to the user, wherein the prompt information is used for prompting the user that the target station has the risk that the vehicle cannot be returned.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (12)
1. A method for pushing station information of shared vehicles is characterized by comprising the following steps:
acquiring a target site with the residual capacity smaller than a preset capacity threshold;
predicting the probability that a first recommended parking point of a destination of a user is the target station according to a pre-established prediction model;
judging whether a target station with the probability exceeding a preset probability threshold exists or not;
and when the target station with the probability exceeding a preset probability threshold exists, pushing prompt information of the target station to the user, wherein the prompt information is used for prompting the user that the target station has the risk that the vehicle cannot be returned.
2. The method for pushing the station information of the shared vehicle according to claim 1, wherein the obtaining of the target station with the remaining capacity less than the preset capacity threshold comprises:
acquiring the current position of a user using a shared vehicle;
acquiring capacity information of all stations within a preset distance range around the current position;
judging whether a target station with the residual capacity smaller than a preset capacity threshold exists or not based on the capacity information of all stations;
when the target station exists, the step of predicting the probability that the first recommended parking point of the destination of the user is the target station according to a pre-established prediction model is executed.
3. The station information pushing method of a shared vehicle according to claim 2, characterized in that the station information pushing method further comprises:
acquiring available sites with residual capacity greater than or equal to the preset capacity threshold;
and pushing the position information of the available sites to the user.
4. The method for pushing the station information of the shared vehicle according to claim 2, wherein the predicting the probability that the first recommended stop of the destination of the user is the target station according to a pre-established prediction model comprises:
acquiring the identity information of the user and the current time at the current position;
inputting the identity information, the target station, the current time and the current position into the prediction model, performing calculation through the prediction model, and outputting the probability that the first recommended parking point of the destination of the user is the target station.
5. The method for pushing the station information of the shared vehicle according to claim 1, wherein the predictive model building step includes:
counting parking data of each destination in the historical order data of the user;
acquiring a station with historical accumulation, and counting order data of an order track passing through a preset range near the station and taking the station as a destination;
and establishing an initial training model, and training the training model by using the parking data and the order data to obtain the prediction model.
6. The method for pushing the station information of the shared vehicle according to claim 1, wherein the obtaining of the target station with the remaining capacity less than the preset capacity threshold comprises:
acquiring the current position of a user using a shared vehicle;
acquiring all sites corresponding to destinations matched with the current position in the user historical order data;
judging whether the residual capacity of the station is smaller than a preset capacity threshold value or not;
and taking the station with the residual capacity smaller than the preset capacity threshold value as the target station.
7. The method for pushing the station information of the shared vehicle according to claim 6, wherein the obtaining of all stations corresponding to the destination matching the current location in the user historical order data comprises:
and acquiring a site corresponding to the destination of the order track passing through the current position from the historical order data.
8. The method for pushing the station information of the shared vehicle according to claim 6, wherein the obtaining of all stations corresponding to the destination matching the current location in the user historical order data comprises:
determining an order track matched with a travel track of the user before the user reaches the current position from the historical order data;
and acquiring a site corresponding to the destination of the order track.
9. The method for pushing the station information of the shared vehicle according to claim 6, wherein the obtaining of all stations corresponding to the destination matching the current location in the user historical order data comprises:
obtaining order data of a time period matched with the current time from the historical order data;
and determining a station corresponding to the destination of the order track passing through the current position from the order data.
10. A station information pushing apparatus for a shared vehicle, comprising:
the acquisition module is used for acquiring a target site with the residual capacity smaller than a preset capacity threshold;
the prediction module is used for predicting the probability that a first recommended parking point of a destination of a user is the target station according to a pre-established prediction model;
the judging module is used for judging whether a target station with the probability exceeding a preset probability threshold exists or not;
and the pushing module is used for pushing prompt information of the target station to the user when the target station with the probability exceeding a preset probability threshold exists, and the prompt information is used for prompting the user that the target station has the risk that the vehicle cannot return.
11. A computer arrangement comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the steps of the method according to any of claims 1-9 are implemented when the computer program is executed by the processor.
12. A readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010574598.0A CN113902499A (en) | 2020-06-22 | 2020-06-22 | Method, device, equipment and medium for pushing station information of shared vehicles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010574598.0A CN113902499A (en) | 2020-06-22 | 2020-06-22 | Method, device, equipment and medium for pushing station information of shared vehicles |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113902499A true CN113902499A (en) | 2022-01-07 |
Family
ID=79186469
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010574598.0A Pending CN113902499A (en) | 2020-06-22 | 2020-06-22 | Method, device, equipment and medium for pushing station information of shared vehicles |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113902499A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116346905A (en) * | 2023-05-30 | 2023-06-27 | 广东鑫兴科技有限公司 | Shared parking method, executing device, electronic equipment and storage medium |
-
2020
- 2020-06-22 CN CN202010574598.0A patent/CN113902499A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116346905A (en) * | 2023-05-30 | 2023-06-27 | 广东鑫兴科技有限公司 | Shared parking method, executing device, electronic equipment and storage medium |
| CN116346905B (en) * | 2023-05-30 | 2023-10-20 | 广东鑫兴科技有限公司 | Shared parking method, executing device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2019237522A1 (en) | Vehicle leasing method and apparatus, and computer device and storage medium | |
| CN107609067B (en) | Big data-based intelligent parking space recommendation method and system | |
| Xu et al. | Real-time street parking availability estimation | |
| Karbassi et al. | Vehicle route prediction and time of arrival estimation techniques for improved transportation system management | |
| CN108120999B (en) | Vehicle-mounted navigation equipment and parking lot guiding method | |
| CN106548246B (en) | Real-time charging recommendation method and system for electric vehicle | |
| WO2014059208A2 (en) | Method and system to determine auto insurance risk | |
| CN111275296A (en) | One-way shared automobile system site selection optimization method based on trajectory data | |
| EP3035275B1 (en) | Giving a user access to a third party service | |
| CN105844951A (en) | IoV parking management method | |
| CN110796496A (en) | Setting method and device of excitation area, computer equipment and storage medium | |
| CN109102093B (en) | Method and device for determining single hot spot area under taxi appointment and electronic equipment | |
| CN113159907B (en) | Private pile sharing method and device, computer equipment and storage medium | |
| CN111091215A (en) | Vehicle identification method and device, computer equipment and storage medium | |
| CN111256720A (en) | Navigation method and device | |
| CN113902499A (en) | Method, device, equipment and medium for pushing station information of shared vehicles | |
| CN110599301A (en) | Vehicle management method, device, computer equipment and storage medium | |
| CN106382036A (en) | Parking space management method and system and terminal | |
| CN111626589A (en) | Siltation area determination method and device and vehicle scheduling method and device | |
| WO2020254418A1 (en) | System and method for populating a database with occupancy data of parking facilities | |
| CN110826889A (en) | Vehicle management method, device, computer equipment and storage medium | |
| CN111126752A (en) | Vehicle station management method and device, computer equipment and storage medium | |
| CN110969299A (en) | Traffic route generation method and device, computer equipment and storage medium | |
| CN110674962A (en) | Vehicle journey prediction processing method and device and storage medium | |
| CN115985131A (en) | Parking lot parking space reservation method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |