CN116798234A

CN116798234A - Method, device, computer equipment and storage medium for determining station parameter information

Info

Publication number: CN116798234A
Application number: CN202311084086.6A
Authority: CN
Inventors: 赵鹏; 刘永威
Original assignee: Beijing Apoco Blue Technology Co ltd
Current assignee: Beijing Apoco Blue Technology Co ltd
Priority date: 2023-08-28
Filing date: 2023-08-28
Publication date: 2023-09-22
Anticipated expiration: 2043-08-28
Also published as: CN116798234B

Abstract

The application relates to a method, a device, computer equipment and a storage medium for determining station parameter information. The method comprises the following steps: when a target subarea meets preset site optimization conditions, determining a user trial vehicle returning position corresponding to each vehicle-returning-difficulty order of the target subarea, and determining a parking penalty position corresponding to each parking penalty order of the target subarea; determining target positions according to the vehicle returning trial positions of the users corresponding to the vehicle returning difficulty orders and the parking penalty positions corresponding to the parking penalty orders, and clustering the target positions to obtain clustered clusters; and determining station parameter information of the target subarea based on the cluster and station information of the target subarea. The method can improve the efficiency of determining the station parameter information.

Description

Method, device, computer equipment and storage medium for determining station parameter information

Technical Field

The present application relates to the field of artificial intelligence technology, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for determining station parameter information.

Background

In the city for operating the shared vehicle, if the site setting is unreasonable, the situation that the user is difficult to find the vehicle, park, even be fine without reason and the like can be caused, the complaint of the user is excessive, the order is prevented from growing, and the vehicle using experience is greatly influenced. Therefore, it is very important to adjust the station of the shared vehicle. And the premise of adjusting the station of the shared vehicle is to determine the station parameter information of the shared vehicle.

The traditional method for determining the station parameter information is that related technicians determine the station parameter information of the operation city according to personal experience. Therefore, the conventional method for determining the station parameter information consumes a long time and is inefficient.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer-readable storage medium, and a computer program product for determining station parameter information that can improve efficiency.

In a first aspect, the present application provides a method for determining station parameter information. The method comprises the following steps:

when a target subarea meets preset site optimization conditions, determining a user trial vehicle returning position corresponding to each vehicle-returning-difficulty order of the target subarea, and determining a parking penalty position corresponding to each parking penalty order of the target subarea;

Determining target positions according to the vehicle returning trial positions of the users corresponding to the vehicle returning difficulty orders and the parking penalty positions corresponding to the parking penalty orders, and clustering the target positions to obtain clustered clusters;

and determining station parameter information of the target subarea based on the cluster and station information of the target subarea.

In one embodiment, the determining the station parameter information of the target sub-area based on the cluster and the station information of the target sub-area includes:

for each cluster, taking a minimum convex closure formed by each target position point in the cluster as a coverage area of the cluster;

determining station parameter sub-information corresponding to the cluster based on the coverage area of the cluster and station information of the target sub-area;

and constructing station parameter information of the target sub-area by the station parameter sub-information corresponding to each cluster.

In one embodiment, the station information includes station ranges of each station, and determining station parameter sub-information corresponding to the cluster based on the coverage area of the cluster and the station information of the target sub-area includes:

If the station range of the station is overlapped with the coverage range of the cluster, determining the optimized station range of the station based on the coverage range of the cluster and the station range of the station, and generating station parameter sub-information corresponding to the cluster containing the optimized station range of the station;

if the station range of the station does not exist and the coverage area of the cluster is overlapped, the coverage area of the cluster is used as the station range of the newly-built station, and station parameter sub-information corresponding to the cluster containing the station range of the newly-built station is generated.

In one embodiment, the coverage area includes a plurality of boundary longitude and latitude coordinate point arrays of a cluster, the station range includes a plurality of boundary longitude and latitude coordinate point arrays of a station, and determining the optimized station range of the station based on the coverage area of the cluster and the station range of the station includes:

inputting a plurality of boundary longitude and latitude coordinate point arrays of the cluster and a plurality of boundary longitude and latitude coordinate point arrays of the station to a preset convex hull detection function to obtain a plurality of optimized boundary longitude and latitude coordinate point arrays of the station;

And using the longitude and latitude coordinate point arrays of the plurality of optimized boundaries of the station as an optimized station range of the station.

In one embodiment, the method further comprises:

when the preset station evaluation conditions are met, the station number, the service area information, the order total amount, the difficult-to-return vehicle order amount and the parking penalty order amount of the target area are obtained, and the station number, the service area information, the order total amount, the difficult-to-return vehicle order amount and the parking penalty order amount of the target sub-area are obtained; the target area comprises a plurality of sub-areas, and the target sub-area is one of the plurality of sub-areas;

calculating the site density, the difficult-to-return order ratio and the parking penalty order ratio of the target area, and the site density, the difficult-to-return order ratio and the parking penalty order ratio of the target subarea based on the number of stations, the service area information, the number of difficult-to-return orders and the parking penalty orders of the target area, and the number of stations, the service area information, the total number of orders, the number of difficult-to-return orders and the number of parking penalty orders of the target subarea;

and if the site density of the target subarea is smaller than the site density of the target area, or the difficult-to-return vehicle order ratio of the target subarea is larger than the difficult-to-return vehicle order ratio of the target area, or the parking penalty order ratio of the target subarea is larger than the parking penalty order ratio of the target area, determining that the target subarea meets the preset site optimization condition.

In one embodiment, the calculating the site density, the difficult-to-return order ratio, and the parking penalty order ratio of the target area, and the site density, the difficult-to-return order ratio, and the parking penalty order ratio of the target subarea based on the station number, the service area information, the order total amount, the difficult-to-return order amount, and the parking penalty order amount of the target subarea, and the station number, the service area information, the order total amount, the difficult-to-return order amount, and the parking penalty order amount of the target subarea includes:

calculating the service area of the target area based on the service area information of the target area, and calculating the service area of the target sub-area based on the service area information of the target sub-area;

taking the ratio of the number of stations in the target area to the service area of the target area as the site density of the target area, and taking the ratio of the number of stations in the target sub-area to the service area of the target sub-area as the site density of the target sub-area;

taking the ratio of the number of the difficult-to-return orders in the target area to the total order amount in the target area as the difficult-to-return order duty ratio of the target area, and taking the ratio of the number of the difficult-to-return orders in the target sub-area to the total order amount in the target sub-area as the difficult-to-return order duty ratio of the target sub-area;

And taking the ratio of the number of the parking penalty orders of the target area to the total order amount of the target area as the ratio of the parking penalty orders of the target area, and taking the ratio of the number of the parking penalty orders of the target subarea to the total order amount of the target subarea as the ratio of the parking penalty orders of the target subarea.

In one embodiment, the method further comprises:

acquiring the clicking times of a user attempting to return the vehicle corresponding to a target order and user feedback information of the target order;

and if the clicking times of the user corresponding to the target order for attempting to return the vehicle is greater than or equal to a preset threshold value of the clicking times of the vehicle which is difficult to return, or the user feedback information corresponding to the target order indicates that the vehicle is difficult to return, determining that the target order is the vehicle which is difficult to return.

In a second aspect, the application further provides a device for determining station parameter information. The device comprises:

the first determining module is used for determining the position of a user attempting to return a vehicle corresponding to each difficult-to-return vehicle order of the target subarea when the target subarea meets a preset site optimization condition, and determining the parking penalty position corresponding to each parking penalty order of the target subarea;

The clustering module is used for determining target positions according to the parking trial positions of the users corresponding to the difficult-to-return orders and the parking penalty positions corresponding to the parking penalty orders, and clustering the target positions to obtain clusters;

and the second determining module is used for determining station parameter information of the target subarea based on the cluster and the station information of the target subarea.

In one embodiment, the second determining module is specifically configured to:

In one embodiment, the station information includes a station range of each station, and the second determining module is specifically configured to:

In one embodiment, the coverage area includes a plurality of boundary longitude and latitude coordinate point arrays of the cluster, the station area includes a plurality of boundary longitude and latitude coordinate point arrays of the station, and the second determining module is specifically configured to:

In one embodiment, the apparatus further comprises:

the first acquisition module is used for acquiring the number of stations, the service area information, the total number of orders, the number of orders difficult to return and the number of parking penalty orders of the target area, and the number of stations, the service area information, the total number of orders, the number of orders difficult to return and the number of parking penalty orders of the target subarea when the preset station evaluation condition is met; the target area comprises a plurality of sub-areas, and the target sub-area is one of the plurality of sub-areas;

The calculation module is used for calculating the site density, the difficult-to-return vehicle order ratio and the parking penalty order ratio of the target area and the site density, the difficult-to-return vehicle order ratio and the parking penalty order ratio of the target subarea based on the station number, the service area information, the order total amount, the difficult-to-return vehicle order amount and the parking penalty order amount of the target area and the station number, the service area information, the order total amount, the difficult-to-return vehicle order amount and the parking penalty order amount of the target subarea;

and the third determining module is used for determining that the target subarea meets the preset site optimization condition if the site density of the target subarea is smaller than the site density of the target area, or the difficult-to-return vehicle order ratio of the target subarea is larger than the difficult-to-return vehicle order ratio of the target area, or the parking penalty order ratio of the target subarea is larger than the parking penalty order ratio of the target area.

In one embodiment, the computing module is specifically configured to:

In one embodiment, the apparatus further comprises:

the second acquisition module is used for acquiring the clicking times of the user attempting to return the vehicle corresponding to the target order and the user feedback information of the target order;

And the fourth determining module is used for determining that the target order is a difficult-to-return vehicle order if the clicking time of the user corresponding to the target order for attempting to return the vehicle is greater than or equal to a preset difficult-to-return vehicle clicking time threshold or the user feedback information corresponding to the target order indicates that the vehicle returning is difficult.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the first aspect described above when the processor executes the computer program.

In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the first aspect described above.

In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, carries out the steps of the first aspect described above.

The method, the device, the computer equipment, the storage medium and the computer program product for determining the station parameter information are used for determining the position of a user attempting to return the vehicle corresponding to each difficult-to-return vehicle order in the target subarea and determining the position of parking penalty corresponding to each parking penalty order in the target subarea when the target subarea meets the preset station optimization conditions; determining target positions according to the vehicle returning trial positions of the users corresponding to the vehicle returning difficulty orders and the parking penalty positions corresponding to the parking penalty orders, and clustering the target positions to obtain clustered clusters; and determining station parameter information of the target subarea based on the cluster and station information of the target subarea. When the target subarea meets site optimization conditions, the target positions are determined according to the user trial-return positions corresponding to the difficult-to-return orders and the parking penalty positions corresponding to the parking penalty orders, the target positions are clustered, the station parameter information of the target subarea is automatically determined based on the clustered clusters and the station information of the target subarea, the consumed time is short, and the efficiency of determining the station parameter information can be improved.

Drawings

FIG. 1 is a flow chart of a method for determining station parameter information in one embodiment;

FIG. 2 is a flowchart illustrating steps for determining station parameter information for a target sub-area in one embodiment;

FIG. 3 is a flowchart illustrating steps for determining station parameter sub-information corresponding to a cluster in one embodiment;

FIG. 4 is a flow diagram of the steps for determining an optimized station scope for a station in one embodiment;

FIG. 5 is a flowchart illustrating a determination that a target sub-region satisfies a preset site optimization condition in an embodiment;

FIG. 6 is a flow chart of steps for calculating the site density, hard-to-return order ratio and parking penalty order ratio for a target area, and site density, hard-to-return order ratio and parking penalty order ratio for a target sub-area, in one embodiment;

FIG. 7 is a flow diagram of determining a hard-to-return order in one embodiment;

FIG. 8 is a block diagram showing the construction of a device for determining station parameter information in one embodiment;

fig. 9 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

In one embodiment, as shown in fig. 1, a method for determining station parameter information is provided, where this embodiment is applied to a terminal for illustration, it is understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and implemented through interaction between the terminal and the server. The terminal can be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things equipment and portable wearable equipment, and the internet of things equipment can be smart speakers, smart televisions, smart air conditioners, smart vehicle-mounted equipment and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers. In this embodiment, the method includes the steps of:

and 101, when the target subarea meets the preset site optimization conditions, determining the position of the vehicle which is tried to be returned by the user corresponding to each vehicle-difficult order of the target subarea, and determining the position of the vehicle-stop penalty corresponding to each vehicle-stop penalty order of the target subarea.

In the embodiment of the application, the target subarea is a subarea to be subjected to station adjustment. The target sub-area operates the shared vehicle. The station is a shared vehicle station. The target sub-area may be a city, may be a city service area, or may be an area of other forms of shared vehicle operation. The shared vehicle is a vehicle sharing economy, and can be a shared bicycle, a shared electric bicycle and a shared automobile. The site optimization condition is used for measuring whether the station of the sub-area needs to be optimized or adjusted, and can be a time condition. For example, if the time distance of last determining station parameter information of the target sub-area has now exceeded a preset first time threshold, the terminal determines that the target sub-area meets a preset station optimization condition.

The hard-to-return order contains an order number, an order start-stop time, and a user's attempted return location. The difficult-to-return order is an order which is difficult for a user to return successfully. The user attempting to return the vehicle is a position where the user attempts to return the vehicle, but the vehicle is not returned successfully. If the difficult-to-return vehicle order has a plurality of positions where the user tries to return the vehicle but does not return the vehicle successfully, the terminal determines the center point of the positions where the user tries to return the vehicle but does not return the vehicle successfully, and takes the center point as the position where the user tries to return the vehicle corresponding to the difficult-to-return vehicle order. Specifically, the terminal may cluster positions where the plurality of users try to get back but do not get back successfully to the difficult car-returning order, obtain a clustering result, and use a center point of the clustering result as a center point of the positions where the plurality of users try to get back but do not get back successfully. The parking penalty order includes an order number, an order start-stop time, and a parking penalty location. The parking penalty order is an order that is penalized after the user parks. Parking penalties refer to a system penalty caused by a user not returning a car in a station. The parking penalty location is the user's parking location for the parking penalty order.

Step 102, determining target positions according to the trial-and-return vehicle positions of the users corresponding to the difficult-to-return vehicle orders and the parking penalty positions corresponding to the parking penalty orders, and clustering the target positions to obtain clustered clusters.

In the embodiment of the application, the terminal clusters each target position by adopting a density clustering algorithm based on the preset maximum distance and minimum sample number among samples to obtain a cluster. The maximum distance between samples is the maximum distance between target positions, for example, the maximum distance between samples is 50 meters. The minimum number of samples is the minimum number of target locations within the cluster, e.g., the minimum number of samples is 90. It will be appreciated that the maximum distance between samples and the minimum number of samples may be set by the skilled person based on actual conditions or experience. The Density clustering algorithm may be a Density-based, noise-robust spatial clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN). The average of all target locations within a cluster is represented as the center point of the cluster.

In one example, the terminal uses, as target positions, a user trial return vehicle position corresponding to each difficult return vehicle order and a parking penalty position corresponding to each parking penalty order.

In another example, the terminal takes, as the target location, a user trial-and-return vehicle position corresponding to each difficult-to-return vehicle order and a parking penalty position corresponding to each parking penalty order located within the service area of the target sub-area.

And step 103, determining station parameter information of the target subarea based on the cluster and station information of the target subarea.

In the embodiment of the application, the station parameter information is used for indicating to adjust stations in the target subarea. For example, the station parameter information may include a station range of the newly created station. The station scope includes position information of the station boundary. For example, the station parameter information may also include an optimized station range of the original station. The optimized station range is the adjusted station range.

And the terminal judges whether the coverage area of each cluster is overlapped with the station range of the station of the target subarea or not, and an overlapping judgment result is obtained. Then, the terminal determines station parameter information of the target sub-area based on the overlapping judgment result.

In the method for determining station parameter information, when the target subarea meets the preset station optimization conditions, determining the positions of the vehicle returning attempts of the users corresponding to the difficult-to-return vehicle orders of the target subarea, and determining the parking penalty positions corresponding to the parking penalty orders of the target subarea; determining target positions according to the trial-return positions of the users corresponding to the difficult-return orders and the parking penalty positions corresponding to the parking penalty orders, and clustering the target positions to obtain clustered clusters; and determining station parameter information of the target subarea based on the cluster and station information of the target subarea. When the target subarea meets site optimization conditions, the target positions are determined according to the user trial-return positions corresponding to the difficult-to-return orders and the parking penalty positions corresponding to the parking penalty orders, the target positions are clustered, the station parameter information of the target subarea is automatically determined based on the clustered clusters and the station information of the target subarea, the consumed time is short, and the efficiency of determining the station parameter information can be improved. In addition, the method determines the station parameter information through the user trial returning position corresponding to the difficult returning vehicle order and the parking penalty position corresponding to the parking penalty order, considers the actual experience of the user, is more in line with the actual situation, and can improve the accuracy of determining the station parameter information. In addition, the method indicates the shared vehicle operators to adjust and optimize the station by determining the station parameter information, so that the method is more reasonable, the user experience can be improved, the problem of obstructing the increase of orders can be solved, and the increase of the orders is promoted.

In one embodiment, as shown in fig. 2, the specific process of determining the station parameter information of the target sub-area based on the cluster and the station information of the target sub-area includes the following steps:

step 201, regarding each cluster, taking the minimum convex closure formed by each target position point in the cluster as the coverage of the cluster.

In the embodiment of the application, for each cluster, the terminal determines the minimum convex closure formed by each target position point in the cluster. And then, the terminal takes the minimum convex closure formed by each target position point in the cluster as the coverage area of the cluster.

In one example, the terminal inputs the longitude and latitude coordinate point arrays of each target position included in the cluster to a preset convex hull detection function to obtain a plurality of boundary longitude and latitude coordinate point arrays of the cluster. And then, the terminal takes the plurality of boundary longitude and latitude coordinate point arrays of the cluster as the minimum convex closure. The plurality of boundary longitude and latitude coordinate point arrays of the cluster are the plurality of longitude and latitude coordinate point arrays of the boundary of the cluster.

Step 202, determining station parameter sub-information corresponding to the cluster based on the coverage area of the cluster and station information of the target sub-area.

In the embodiment of the application, the terminal judges whether the coverage area of the cluster is overlapped with the station range of the target sub-area based on the coverage area of the cluster and the station information of the target sub-area, and obtains an overlapping judgment sub-result of the cluster. And then, the terminal determines station parameter sub-information corresponding to the cluster based on the overlapping judgment sub-result of the cluster.

And 203, constructing station parameter information of the target sub-region by using station parameter sub-information corresponding to each cluster.

In the method for determining station parameter information, for each cluster, the minimum convex closure formed by each target position point in the cluster is used as the coverage area of the cluster; determining station parameter sub-information corresponding to the cluster based on the coverage area of the cluster and station information of the target sub-area; and constructing station parameter information of the target subarea by the station parameter sub-information corresponding to each cluster. In this way, the minimum convex closure formed by each target position point in the cluster is used as the coverage of the cluster, and then the station parameter information of the target sub-area is determined based on the coverage of the cluster and the station information of the target sub-area.

In one embodiment, as shown in fig. 3, the station information includes a station range of each station, and the specific process of determining station parameter sub-information corresponding to the cluster based on the coverage area of the cluster and the station information of the target sub-area includes the following steps:

step 301, if there is an overlap between the station range of the station and the coverage of the cluster, determining an optimized station range of the station based on the coverage of the cluster and the station range of the station, and generating station parameter sub-information corresponding to the cluster including the optimized station range of the station.

In the embodiment of the application, for each station in the target sub-area, the terminal determines whether the station range of the station and the coverage of the cluster overlap based on a preset overlap determination function, the station range of the station and the coverage of the cluster, and obtains an overlap determination sub-result of the station. The overlap determination function may be a function for calculating an intersection, for example, an inters function of a shape library. The optimized station range is the adjusted station range.

In one example, the coverage area includes a plurality of boundary longitude and latitude coordinate point arrays of the cluster, and the station area includes a plurality of boundary longitude and latitude coordinate point arrays of the station. Aiming at each station in the target subarea, the terminal inputs a plurality of boundary longitude and latitude coordinate point arrays of the station and a plurality of boundary longitude and latitude coordinate point arrays of the cluster into a preset intersectional function to obtain an overlapping judgment sub-result of the station.

Step 302, if the station range of the station does not exist and the coverage area of the cluster is overlapped, the coverage area of the cluster is used as the station range of the newly-built station, and station parameter sub-information corresponding to the cluster containing the station range of the newly-built station is generated.

In the method for determining the station parameter information, if the station range of the station overlaps with the coverage area of the cluster, determining the optimized station range of the station based on the coverage area of the cluster and the station range of the station, and generating station parameter sub-information corresponding to the cluster containing the optimized station range of the station; if the station range of the station does not exist and the coverage range of the cluster is overlapped, the coverage range of the cluster is used as the station range of the newly-built station, and station parameter sub-information corresponding to the cluster containing the station range of the newly-built station is generated. In this way, under the condition that the coverage area of the cluster and the station ranges of stations in the target subarea are not overlapped, a station is newly built according to the coverage area of the cluster, and the problem of insufficient station number in the target subarea can be solved; under the condition that the coverage area of the cluster and the station range of each station in the target subarea are overlapped, the adjusted station range is determined based on the coverage area of the cluster and the station range of the station, and the range of the existing station is adjusted, so that the method is more reasonable, the problem that the station area in the target subarea is too small can be solved, the method is more in line with the actual situation, and the accuracy of determining the station parameter information is comprehensively improved.

In one embodiment, as shown in fig. 4, the coverage area includes a plurality of boundary longitude and latitude coordinate point arrays of a cluster, the station area includes a plurality of boundary longitude and latitude coordinate point arrays of a station, and the specific process of determining the optimized station area of the station based on the coverage area of the cluster and the station area of the station includes the following steps:

step 401, inputting a plurality of boundary longitude and latitude coordinate point arrays of the cluster and a plurality of boundary longitude and latitude coordinate point arrays of the station to a preset convex hull detection function to obtain a plurality of optimized boundary longitude and latitude coordinate point arrays of the station.

In the embodiment of the application, the longitude and latitude coordinate point arrays of the boundaries of the cluster are the longitude and latitude coordinate point arrays of the boundaries of the cluster. The longitude and latitude coordinate point arrays of the boundaries of the station are the longitude and latitude coordinate point arrays of the boundaries of the station. The longitude and latitude coordinate point arrays of the optimized boundaries of the station are the longitude and latitude coordinate point arrays of the adjusted station boundaries.

In one embodiment, the terminal determines a plurality of sets of longitude and latitude coordinate points of the optimized boundary of the station, which may be expressed as: new_points_array=cv2.convexhull (points_array). The new_points_array is a plurality of optimized boundary longitude and latitude coordinate point arrays of the station, the points_array is a plurality of boundary longitude and latitude coordinate point arrays of the cluster and a plurality of boundary longitude and latitude coordinate point arrays of the station, and cv2.ConvexHull is a convex hull detection function. The input is a longitude and latitude coordinate point array of two polygons, the output is a longitude and latitude coordinate point array [ (lon 1, lat 1), (lon 2, lat 2) of a minimum containing area formed by splicing, wherein lon1 and lon2 are longitudes of a plurality of optimized boundary longitude and latitude coordinate points of a station, and lat1 and lat2 are latitudes of a plurality of optimized boundary longitude and latitude coordinate points of the station. The cv2.Convexhull function is a function in the OpenCV library that is used to compute the Convex Hull (Convex Hull) for a given set of points. A convex hull is the smallest convex polygon that encloses a given set of points, i.e., the smallest outer polygon that will not be concave. The main principle of the cv2.ConvexHull function is to compute the convex hull of the point set by either the Graham scan algorithm (Graham Scan Algorithm) or the fast convex hull algorithm (Quick Hull Algorithm). Both algorithms are common convex hull calculation methods. The specific principle of the Graham scanning algorithm is as follows: first, the point with the smallest ordinate in the point set is selected as the starting point, and then the rest points are ordered according to the polar angle. After sorting, starting from the starting point, traversing the points in turn, and maintaining a stack. In the traversal process, for each point, the relation between the point and the stack top point and the last stack top point is judged. If the angle of the three points makes a concave shape, the top of the stack is popped up until the concave shape is no longer formed. Finally, the points in the stack form a convex hull. The concrete principle of the QuickHull algorithm is as follows: first, two points of minimum and maximum abscissa in the point set are selected, and the point set is divided into two parts: points above and below the straight line. These two points must be on the convex hull. The QuickHull algorithm is then recursively applied to both sets of points. In each recursion, the point furthest from the line is selected, the set of points is divided into points on both sides of the line, and the above steps are repeated. And finally, merging all convex hull boundaries to obtain the whole convex hull. Whichever algorithm is used, the cv2.convexHull function computes the convex hull vertices of the point set, which are arranged in a counterclockwise or clockwise order to form the complete convex hull boundary.

And step 402, using a plurality of optimized boundary longitude and latitude coordinate point arrays of the station as an optimized station range of the station.

In the method for determining the station parameter information, a plurality of boundary longitude and latitude coordinate point arrays of the cluster and a plurality of boundary longitude and latitude coordinate point arrays of the station are input into a preset convex hull detection function to obtain a plurality of optimized boundary longitude and latitude coordinate point arrays of the station; and using the longitude and latitude coordinate point arrays of the multiple optimized boundaries of the station as the optimized station range of the station. Therefore, the cluster and the existing station are spliced into an optimized station range capable of containing the cluster and the existing station through the convex hull detection function, the plurality of boundary longitude and latitude coordinate point arrays of the cluster and the plurality of boundary longitude and latitude coordinate point arrays of the station, so that a user who optimizes and adjusts the station can cover a vehicle returning position and a parking penalty position of a parking penalty order which are tried by a user who is difficult to return the vehicle order, actual experience of the user is considered, actual conditions are met, and accuracy of determining station parameter information can be improved.

In one embodiment, as shown in fig. 5, the method for determining station parameter information further includes the steps of:

and step 501, when preset station evaluation conditions are met, acquiring the station number, service area information, order total amount, difficult-to-return vehicle order amount and parking penalty order amount of the target area, and the station number, service area information, order total amount, difficult-to-return vehicle order amount and parking penalty order amount of the target sub-area.

The target area comprises a plurality of subareas, and the target subarea is one of the subareas.

In the embodiment of the application, the station evaluation condition is used for measuring whether the station of the subarea needs to be evaluated, and the evaluation is whether the station of the subarea needs to be optimized or adjusted, and can be a time condition. For example, if the last time the time distance that determines whether the target sub-area meets the preset station optimization condition has now exceeded the preset second time threshold, the terminal determines that the target sub-area meets the preset station evaluation condition. The service area is a geographic area defined in a city in which a shared vehicle may be operated. The service area information includes boundary position information of the service area. The target area may be an area where all operations of a certain geographical area share vehicles. The total order quantity, the difficult-to-return order quantity and the parking penalty order quantity are all the total order quantity, the difficult-to-return order quantity and the parking penalty order quantity in the historical time period. For example, the historical period of time may be the last 30 days.

Step 502, calculating the site density, the difficult-to-return order ratio and the parking penalty order ratio of the target area, and the site density, the difficult-to-return order ratio and the parking penalty order ratio of the target subarea based on the number of stations, the service area information, the total number of orders, the number of difficult-to-return orders and the parking penalty order of the target area, and the number of stations, the service area information, the total number of orders, the number of difficult-to-return orders and the number of parking penalty orders of the target subarea.

In the embodiment of the application, the terminal calculates the station density, the difficult-to-return order ratio and the parking penalty order ratio of the target area based on the station number, the service area information, the order total amount, the difficult-to-return order number and the parking penalty order number of the target area. Meanwhile, the terminal calculates the site density, the difficult-to-return order ratio and the parking penalty order ratio of the target subarea based on the station number, the service area information, the order total amount, the difficult-to-return order number and the parking penalty order number of the target subarea.

In step 503, if the site density of the target sub-area is less than the site density of the target area, or the difficult-to-return vehicle order ratio of the target sub-area is greater than the difficult-to-return vehicle order ratio of the target area, or the parking penalty order ratio of the target sub-area is greater than the parking penalty order ratio of the target area, then determining that the target sub-area meets the preset site optimization condition.

In the embodiment of the application, the terminal compares the site density of the target subarea with the site density of the target area, compares the difficult-to-return vehicle order ratio of the target subarea with the difficult-to-return vehicle order ratio of the target area, and compares the parking penalty order ratio of the target subarea with the parking penalty order ratio of the target area.

In the method for determining station parameter information, when preset station evaluation conditions are met, station number, service area information, order total quantity, difficult-to-return order quantity and parking penalty order quantity of a target area, and station number, service area information, order total quantity, difficult-to-return order quantity and parking penalty order quantity of a target sub-area are obtained; calculating the site density, the vehicle-difficult-to-return order ratio and the parking penalty order ratio of the target area, and the site density, the vehicle-difficult-to-return order ratio and the parking penalty order ratio of the target subarea based on the number of stations, the service area information, the total order quantity, the vehicle-difficult-to-return order quantity and the parking penalty order quantity of the target area, and the number of stations, the service area information, the total order quantity, the vehicle-difficult-to-return order quantity and the parking penalty order quantity of the target subarea; if the site density of the target subarea is smaller than the site density of the target area, or the difficult-to-return vehicle order ratio of the target subarea is larger than the difficult-to-return vehicle order ratio of the target area, or the parking penalty order ratio of the target subarea is larger than the parking penalty order ratio of the target area, determining that the target subarea meets the preset site optimization condition. In this way, the station setting of the target subarea is unreasonable and the station parameter information of the target subarea is determined by calculating the station density, the difficult-to-return order ratio and the parking penalty order ratio of the target subarea, and when the station density of the target subarea is lower than the average value of the target area, or the difficult-to-return order ratio of the target subarea is higher than the average value of the target area, or the parking penalty order ratio of the target subarea is higher than the average value of the target area, namely, at least one of the three indexes is abnormal, so that the station of the target subarea is optimally adjusted, the subarea needing to be adjusted can be timely determined by considering the actual experience of a user, and the timeliness of the station parameter information determination can be improved.

In one embodiment, as shown in fig. 6, the specific process of calculating the site density, the difficulty-returning order ratio and the parking penalty order ratio of the target area, and the site density, the difficulty-returning order ratio and the parking penalty order ratio of the target subarea based on the number of stations, the service area information, the total order amount, the difficulty-returning order amount and the parking penalty order amount of the target subarea, and the number of stations, the service area information, the total order amount, the difficulty-returning order amount and the parking penalty order amount of the target subarea includes the following steps:

step 601, calculating the service area of the target area based on the service area information of the target area, and calculating the service area of the target sub-area based on the service area information of the target sub-area.

In the embodiment of the application, the terminal calculates the service area of the target area based on the boundary position information of the service area of the target area. Meanwhile, the terminal calculates the service area of the target sub-area based on the boundary position information of the service area of the target sub-area.

Step 602, taking the ratio of the number of stations in the target area to the service area of the target area as the site density of the target area, and taking the ratio of the number of stations in the target sub-area to the service area of the target sub-area as the site density of the target sub-area.

In the embodiment of the present application, the station density is the number of stations covered evenly per unit area, for example, the station density may be the number of stations covered evenly per square kilometer.

And 603, taking the ratio of the number of the difficult-to-return orders in the target area to the total order amount in the target area as the difficult-to-return order duty ratio of the target area, and taking the ratio of the number of the difficult-to-return orders in the target sub-area to the total order amount in the target sub-area as the difficult-to-return order duty ratio of the target sub-area.

In the embodiment of the application, the difficult-to-return vehicle order is the ratio of the number of the difficult-to-return vehicle orders to the number of all orders.

Step 604, taking the ratio of the number of the parking penalty orders in the target area to the total order amount in the target area as the ratio of the parking penalty orders in the target area, and taking the ratio of the number of the parking penalty orders in the target sub-area to the total order amount in the target sub-area as the ratio of the parking penalty orders in the target sub-area.

In the embodiment of the application, the parking penalty ratio is the ratio of the number of parking penalty orders to the number of all orders.

In the above method for determining station parameter information, the service area of the target area is calculated based on the service area information of the target area, and the service area of the target sub-area is calculated based on the service area information of the target sub-area; taking the ratio of the number of stations in the target area to the service area of the target area as the site density of the target area, and taking the ratio of the number of stations in the target sub-area to the service area of the target sub-area as the site density of the target sub-area; taking the ratio of the number of the difficult-to-return orders in the target area to the total order amount in the target area as the difficult-to-return order duty ratio of the target area, and taking the ratio of the number of the difficult-to-return orders in the target sub-area to the total order amount in the target sub-area as the difficult-to-return order duty ratio of the target sub-area; taking the ratio of the number of the parking penalty orders in the target area to the total order amount in the target area as the parking penalty order duty ratio of the target area, and taking the ratio of the number of the parking penalty orders in the target subarea to the total order amount in the target subarea as the parking penalty order duty ratio of the target subarea. In this way, the ratio of the number of stations in the unit service area, the number of orders difficult to return to the vehicle to the number of all orders and the ratio of the number of orders to the number of orders for stopping and penalizing are taken as the conditions for measuring whether to determine the station parameter information of the target subarea, the actual experience of the user is considered, the actual situation is more met, the accuracy for judging whether to determine the station parameter information of the target subarea can be improved, and the accuracy for determining the station parameter information is further improved.

In one embodiment, as shown in fig. 7, the method for determining station parameter information further includes the steps of:

step 701, obtaining the clicking times of the user attempting to return the car corresponding to the target order and the user feedback information of the target order.

In the embodiment of the application, the number of clicks of the user for attempting to return the vehicle is the number of clicks of the user for attempting to return the vehicle and not successfully returning the vehicle. The target order is any one of all orders.

Step 702, if the number of clicks of the user corresponding to the target order for attempting to return the vehicle is greater than or equal to the preset threshold of the number of clicks of difficult to return the vehicle, or if the user feedback information corresponding to the target order indicates that the vehicle is difficult to return, determining that the target order is the difficult to return vehicle order.

In the embodiment of the application, the threshold value of the hard-to-return vehicle clicking times is used for measuring whether the order is a hard-to-return vehicle order or not. For example, the hard-to-return number of clicks threshold may be 3.

In the method for determining the station parameter information, the clicking times of the user attempting to return the vehicle corresponding to the target order and the user feedback information of the target order are obtained; if the clicking times of the user corresponding to the target order for attempting to return the vehicle is greater than or equal to a preset threshold value of the clicking times of the vehicle which is difficult to return, or the user feedback information corresponding to the target order indicates that the vehicle is difficult to return, determining that the target order is the vehicle which is difficult to return. Therefore, whether the order is a difficult-to-return order is judged by whether the user tries to return the car for many times and not returning the car successfully and whether the user explicitly feeds back the car returning difficulty, the actual experience of the user is considered, the actual situation is more met, and the accuracy of determining the station parameter information can be improved.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a device for determining station parameter information for realizing the above-mentioned method for determining station parameter information. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiment of the determining device for one or more station parameter information provided below may refer to the limitation of the determining method for station parameter information hereinabove, and will not be repeated herein.

In one embodiment, as shown in fig. 8, there is provided a station parameter information determining apparatus 800, including: a first determination module 810, a clustering module 820, and a second determination module 830, wherein:

the first determining module 810 is configured to determine a position of a user attempting to return a vehicle corresponding to each difficult-to-return vehicle order in the target sub-area and determine a parking penalty position corresponding to each parking penalty order in the target sub-area when the target sub-area meets a preset site optimization condition;

the clustering module 820 is configured to determine a target position according to the parking trial-and-error position of the user corresponding to the difficult-to-return vehicle order and the parking penalty position corresponding to the parking penalty order, and cluster each target position to obtain a cluster;

a second determining module 830, configured to determine station parameter information of the target sub-area based on the cluster and station information of the target sub-area.

Optionally, the second determining module 830 is specifically configured to:

Optionally, the station information includes a station range of each station, and the second determining module 830 is specifically configured to:

Optionally, the coverage area includes a plurality of boundary longitude and latitude coordinate point arrays of the cluster, the station area includes a plurality of boundary longitude and latitude coordinate point arrays of the station, and the second determining module 830 is specifically configured to:

Optionally, the apparatus 800 further includes:

Optionally, the computing module is specifically configured to:

Optionally, the apparatus 800 further includes:

The above-mentioned respective modules in the determination device of station parameter information may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 9. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile 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 the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by the processor, implements a method of determining station parameter information. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the 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 stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as Static Random access memory (Static Random access memory AccessMemory, SRAM) or dynamic Random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. A method for determining station parameter information, the method comprising:

2. The method of claim 1, wherein the determining station parameter information for the target sub-area based on the cluster and the station information for the target sub-area comprises:

3. The method according to claim 2, wherein the station information includes station ranges of respective stations, and the determining station parameter sub-information corresponding to the cluster based on the coverage area of the cluster and the station information of the target sub-area includes:

4. The method of claim 3, wherein the coverage area comprises a plurality of boundary longitude and latitude coordinate point arrays of a cluster, the station range comprises a plurality of boundary longitude and latitude coordinate point arrays of a station, and the determining the optimized station range for the station based on the coverage area of the cluster and the station range for the station comprises:

5. The method according to claim 1, wherein the method further comprises:

6. The method of claim 5, wherein calculating the site density, the difficult to return order ratio, and the parking penalty order ratio for the target area, and the site density, the difficult to return order ratio, and the parking penalty order ratio for the target sub-area based on the number of stations, the service area information, the total number of orders, the number of difficult to return orders, and the number of parking penalty orders for the target area, and the number of stations, the service area information, the total number of orders, the number of difficult to return orders, and the number of parking penalty orders for the target sub-area comprises:

7. The method according to claim 1, wherein the method further comprises:

8. A station parameter information determining apparatus, the apparatus comprising:

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.