CN108986447B

CN108986447B - Vehicle management method, server and system

Info

Publication number: CN108986447B
Application number: CN201810765435.3A
Authority: CN
Inventors: 张岩; 田超; 李贤恩
Original assignee: Beijing Mobike Technology Co Ltd
Current assignee: Hanhai Information Technology Shanghai Co Ltd
Priority date: 2018-07-12
Filing date: 2018-07-12
Publication date: 2020-11-03
Anticipated expiration: 2038-07-12
Also published as: CN108986447A

Abstract

The invention discloses a vehicle management method, a server and a system. The method comprises the following steps: acquiring a plurality of position coordinate samples according to the acquired plurality of pieces of vehicle position data and the plurality of pieces of reference position data; determining the spatial distribution of the position errors according to the plurality of position coordinate samples; and triggering to implement vehicle management according to the position error spatial distribution. According to the invention, the corresponding management is triggered for the vehicle with larger position error, the vehicle management efficiency is improved, and the vehicle using experience of the user is improved.

Description

Vehicle management method, server and system

Technical Field

The invention relates to the technical field of vehicles, in particular to a vehicle management method, a server and a system.

Background

With the rapid development of manufacturing technology and the popularization of the internet, the travel of shared vehicles such as shared bicycles and shared automobiles becomes a new travel mode in cities, the travel demand of urban people can be effectively met, and the method is green and environment-friendly.

As the size of the users of the shared vehicles becomes larger and the demand for vehicles increases explosively, the number of shared vehicles put into operation in the market increases greatly, and due to the moving liquidity of the shared vehicles, if the shared vehicles put into circulation use are not managed, the urban traffic management is greatly stressed, so that a service provider operating the shared vehicles usually carries out corresponding vehicle management after positioning the shared vehicles. Meanwhile, a user who desires to use a shared vehicle generally needs to find a corresponding shared vehicle for use after positioning an available shared vehicle. Thus, accurate positioning of the shared vehicle is very important.

However, currently, a positioning module (e.g., a Global positioning system, GPS) provided on a shared vehicle is usually relied on to position the shared vehicle, but the positioning module of the shared vehicle usually has a large error, especially in an area with a serious high-rise shelter, the error may reach several hundred meters, and great difficulty is brought to an operator of a service provider who desires to use the shared vehicle to find the shared vehicle or to find the shared vehicle to implement vehicle management.

Disclosure of Invention

It is an object of the present invention to provide a new solution for vehicle positioning.

According to a first aspect of the present invention, there is provided a vehicle management method, implemented by a server, comprising:

acquiring a plurality of corresponding position coordinate samples according to the acquired vehicle position data and the reference position data;

the reference position data is data which is acquired through other positioning modules outside the positioning module of the vehicle and is related to the geographic position of the vehicle, and each position coordinate sample comprises a vehicle position coordinate and a reference position coordinate corresponding to the vehicle position coordinate;

determining the spatial distribution of the position errors according to a plurality of position coordinate samples;

the position error spatial distribution is the distribution of position coordinate samples with position errors in a geographic space;

and triggering to implement corresponding vehicle management according to the position error spatial distribution.

Alternatively,

the reference location data comprises user location data; the method further comprises the following steps:

when the vehicle is used, vehicle position data of the vehicle are obtained;

when the user starts to use the vehicle, acquiring corresponding user position data;

and/or the presence of a gas in the gas,

the reference location data comprises device location data of the road locating device; the method further comprises the following steps:

when the vehicle is finished being used, vehicle position data of the vehicle and device position data of a road positioning device corresponding to the vehicle are acquired.

Optionally, the step of obtaining position coordinate samples of the corresponding multiple position coordinate samples includes:

determining reference position data with correlation for each piece of vehicle position data;

and respectively acquiring a vehicle position coordinate according to each piece of vehicle position data, acquiring a reference position coordinate corresponding to the vehicle position coordinate according to reference position data associated with the vehicle position data, and acquiring a corresponding position coordinate sample so as to acquire a plurality of position coordinate samples.

Alternatively,

the reference location data comprises user location data;

the step of determining, for each piece of vehicle position data, that there is associated reference position data, respectively, includes:

for each piece of vehicle position data, selecting corresponding user position data of which the acquisition time accords with a preset time association condition from user position data of a user of the vehicle corresponding to the vehicle position data as reference position data associated with the vehicle position data;

and/or, the reference location data comprises device location data;

and for each piece of vehicle position data, selecting the equipment position data of the road positioning equipment with the closest vehicle distance corresponding to the vehicle position data as the reference position data associated with the vehicle position data.

Alternatively,

the time-correlation condition is that the acquisition timing of the reference position data is after the acquisition timing of the vehicle position data, and a time difference therebetween is within a preset time period.

Optionally, the step of determining the spatial distribution of the position error according to the plurality of position coordinate samples includes:

for each position coordinate sample, obtaining the error distance of the position coordinate sample;

determining the position coordinate sample with the error distance larger than the error distance threshold value as a position coordinate sample with a position error;

and determining the spatial distribution of the position errors according to the vehicle position coordinates of the position coordinate samples with the position errors.

Alternatively,

for each position coordinate sample, the step of obtaining the error distance of the position coordinate sample comprises:

determining a position deviation parameter according to the vehicle position coordinate and the reference position coordinate of the position coordinate sample;

determining a corresponding error distance according to the position deviation parameter and the radius of the earth;

and/or the presence of a gas in the gas,

and acquiring a Manhattan distance or a Chebyshev distance between the vehicle position coordinate and the reference position coordinate according to the vehicle position coordinate of the position coordinate sample and the reference position coordinate, and taking the Manhattan distance or the Chebyshev distance as an error distance.

Alternatively,

the vehicle position coordinates and the user position coordinates of the position coordinate sample are position coordinates based on different coordinate systems;

before the step of obtaining the error distance of the position coordinate sample, the method further comprises the following steps:

and processing the vehicle position coordinates and the user position coordinates according to a preset target coordinate system to obtain the vehicle position coordinates and the user position coordinates based on the preset target coordinate system so as to obtain the error distance of the position coordinate sample.

Alternatively,

vehicle management includes at least vehicle position calibration;

according to the position error spatial distribution, the step of triggering the implementation of the corresponding vehicle management comprises the following steps:

determining a vehicle calibration area with vehicle positioning calibration requirements in a geographic space corresponding to the spatial distribution of the position errors;

selecting a target vehicle in a vehicle calibration area;

and according to the vehicle position data of the target vehicle and the adjacent vehicles of the target vehicle, performing vehicle position calibration on the target vehicle to obtain the calibration position coordinates of the target vehicle.

Alternatively,

the step of determining the vehicle calibration area where the vehicle positioning calibration requirement exists in the geographic space corresponding to the position error spatial distribution comprises the following steps:

dividing a geographic space to obtain a plurality of geographic areas;

acquiring the number of samples of position coordinate samples with position errors in each geographic area;

and determining the geographic area with the number of samples larger than a preset number threshold value as a vehicle calibration area.

Optionally, the step of selecting the target vehicle in the vehicle calibration area comprises:

and selecting the vehicle corresponding to the position coordinate sample with the maximum error distance in the vehicle calibration area as the target vehicle.

Optionally, the step of calibrating the vehicle position data of the target vehicle according to the vehicle position data of the target vehicle and the vehicle position data of the neighboring vehicle of the target vehicle to obtain the calibrated vehicle position data includes:

acquiring a corresponding position sample set comprising a plurality of position samples according to the vehicle position data of the target vehicle and the adjacent vehicles of the target vehicle;

wherein each position sample comprises vehicle position coordinates obtained from one piece of vehicle position data;

determining position samples with abnormality in the position sample set and filtering to obtain a calibration sample set;

from the set of calibration samples, calibration position coordinates of the target vehicle are determined.

Alternatively,

each piece of vehicle position data at least comprises a unique vehicle identification of the vehicle, a vehicle position coordinate and the time for acquiring the vehicle position coordinate;

each piece of reference location data includes user location data including at least a unique vehicle identification of a vehicle used by the user, user location coordinates, and a time at which the user location coordinates were obtained.

According to a second aspect of the present invention, there is provided a server, comprising:

a memory for storing executable instructions;

and a processor, configured to execute the server to perform the vehicle management method according to the control of the executable instruction.

According to a third aspect of the present invention, there is provided a vehicle management system, comprising:

a vehicle;

a client;

and a server according to the second aspect of the invention.

According to one embodiment of the disclosure, a plurality of corresponding position coordinate samples are determined through a plurality of pieces of vehicle position data and reference position data, position error spatial distribution is determined according to the plurality of position coordinate samples, and corresponding vehicle management can be triggered and implemented aiming at vehicles with large position errors according to the position error spatial distribution, so that vehicle management efficiency is correspondingly improved, and vehicle using experience of users is improved. .

Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.

FIG. 1 is a block diagram showing an example of a hardware configuration of a computing system that may be used to implement an embodiment of the invention.

Fig. 2 shows a flowchart of a vehicle management method of the first embodiment of the invention.

Fig. 3 shows a flowchart of the step of obtaining a sample of location coordinates of the first embodiment of the present invention.

Fig. 4 shows a flow chart of the step of determining the spatial distribution of errors according to the first embodiment of the present invention.

Fig. 5 shows a flow chart of the step of obtaining the error distance according to the first embodiment of the present invention.

Fig. 6 shows a flowchart of the triggering vehicle management steps of the first embodiment of the present invention.

FIG. 7 shows a flowchart of the steps for determining the calibration area of the vehicle in the first embodiment of the present invention.

Fig. 8 shows a flowchart of the step of obtaining calibration position coordinates according to the first embodiment of the present invention.

Fig. 9 shows a block diagram of a server of the first embodiment of the present invention.

Fig. 10 shows a block diagram of a vehicle management system of a second embodiment of the invention.

Detailed Description

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

< hardware configuration >

As shown in fig. 1, the vehicle system 100 includes a server 1000, a client 2000, a vehicle 3000, and a network 4000.

The server 1000 provides a service point for processes, databases, and communications facilities. The server 1000 may be a unitary server or a distributed server across multiple computers or computer data centers. The server may be of various types, such as, but not limited to, a web server, a news server, a mail server, a message server, an advertisement server, a file server, an application server, an interaction server, a database server, or a proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported or implemented by the server. For example, a server, such as a blade server, a cloud server, etc., or may be a server group consisting of a plurality of servers, which may include one or more of the above types of servers, etc.

In one example, the server 1000 may be as shown in fig. 1, including a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600. Although the server may also include speakers, microphones, etc., these components are not relevant to the present invention and are omitted here.

The processor 1100 may be, for example, a central processing unit CPU, a microprocessor MCU, or the like. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a serial interface, an infrared interface, and the like. Communication device 1400 is capable of wired or wireless communication, for example. The display device 1500 is, for example, a liquid crystal display, an LED display touch panel, or the like. The input device 1600 may include, for example, a touch screen, a keyboard, and the like.

In the present embodiment, the client 2000 is an electronic device having a communication function and a service processing function. The client 2000 may be a mobile terminal, such as a mobile phone, a laptop, a tablet, a palmtop, etc. In one example, the client 2000 is a device that performs management operations on the vehicle 3000, such as a mobile phone installed with an Application (APP) that supports operation and management of the vehicle.

As shown in fig. 1, the client 2000 may include a processor 2100, a memory 2200, an interface device 2300, a communication device 2400, a display device 2500, an input device 2600, an output device 2700, a camera device 2800, and the like. The processor 2100 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 2200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 2300 includes, for example, a USB interface, a headphone interface, and the like. Communication device 2400 is capable of wired or wireless communication, for example. The display device 2500 is, for example, a liquid crystal display panel, a touch panel, or the like. The input device 2600 may include, for example, a touch screen, a keyboard, or a microphone. The output device 2700 is for outputting information, and may be, for example, a speaker for outputting voice information to a user. The image pickup device 2800 is used for image pickup of acquisition information, and is, for example, a camera or the like.

The vehicle 3000 is any vehicle that can give the right to share the use by different users in time or separately, for example, a shared bicycle, a shared moped, a shared electric vehicle, a shared vehicle, and the like. The vehicle 3000 may be a bicycle, a tricycle, an electric scooter, a motorcycle, a four-wheeled passenger vehicle, or the like.

As shown in fig. 1, vehicle 3000 may include a processor 3100, a memory 3200, an interface device 3300, a communication device 3400, an output device 3500, an input device 3600, a positioning device 3700, sensors 3800, and so forth. The processor 3100 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 3200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface 3300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 3400 can perform wired or wireless communication, for example. The output device 3500 may be, for example, a device that outputs a signal, may be a display device such as a liquid crystal display panel or a touch panel, or may be a speaker or the like that outputs voice information or the like. The input device 3600 may include, for example, a touch panel, a keyboard, or the like, and may input voice information through a microphone. The positioning device 3700 is used to provide positioning function, and may be, for example, a GPS positioning module, a beidou positioning module, etc. The sensor 3800 is used for acquiring vehicle attitude information, and may be, for example, an accelerometer, a gyroscope, or a three-axis, six-axis, nine-axis micro-electro-mechanical system (MEMS), or the like.

The network 4000 may be a wireless communication network or a wired communication network, and may be a local area network or a wide area network. In the article management system shown in fig. 1, a vehicle 3000 and a server 1000, and a client 2000 and the server 1000 can communicate with each other via a network 4000. The vehicle 3000 may be the same as the server 1000, and the network 4000 through which the client 2000 communicates with the server 1000 may be different from each other.

It should be understood that although fig. 1 shows only one server 1000, client 2000, vehicle 3000, it is not meant to limit the corresponding number, and multiple servers 1000, clients 2000, vehicles 3000 may be included in the vehicle system 100.

Taking the vehicle 3000 as an example of a shared bicycle, the vehicle system 100 is a shared bicycle system. The server 1000 is used to provide all the functionality necessary to support shared bicycle use. The client 2000 may be a mobile phone on which a shared bicycle application is installed, which may help a user to obtain a corresponding function using the vehicle 3000, and the like.

The vehicle system 100 shown in FIG. 1 is illustrative only and is not intended to limit the invention, its application, or uses in any way.

In an embodiment of the present invention, the memory 1200 of the server 1000 is used for storing instructions for controlling the processor 1100 to operate so as to execute the vehicle positioning method provided by the embodiment of the present invention.

Although a number of devices are shown in fig. 1 for server 1000, the present invention may relate to only some of the devices, for example, server 1000 may relate to only memory 1200 and processor 1100.

In an embodiment of the present invention, the memory 2200 of the client 2000 is configured to store instructions for controlling the processor 2100 to operate the client 2000 to execute the vehicle management method according to the embodiment of the present invention.

Although a number of devices are shown in fig. 1 for client 2000, the present invention may relate to only some of the devices, for example, client 2000 may relate to only memory 2200 and processor 2100.

In an embodiment of the present invention, the memory 3200 of the vehicle 3000 is configured to store instructions for controlling the processor 3100 to operate so as to perform the vehicle localization method provided by the embodiment of the present invention.

Although a plurality of devices are shown for the vehicle 3000 in fig. 1, the present invention may relate only to some of the devices, for example, the vehicle 3000 relates only to the memory 3200 and the processor 3100.

In the above description, the skilled person will be able to design instructions in accordance with the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.

< first embodiment >

< method >

In the embodiment, the vehicle is a transportation device which is released for a user to obtain a use right in modes of time-sharing lease, local lease and the like, and the vehicle may be a two-wheeled or three-wheeled bicycle, a moped, an electric vehicle, or a motor vehicle with more than four wheels.

The vehicle positioning method is implemented by a server, and the server can be in various entity forms. For example, the server may be a cloud server, or may also be the server 1000 shown in fig. 1.

As shown in fig. 2, the vehicle management method includes: steps S2100-S2300.

In step S2100, a plurality of position coordinate samples are acquired based on the plurality of acquired vehicle position data and the plurality of reference position data.

Each position coordinate sample includes vehicle position coordinates and reference position coordinates corresponding to the vehicle position coordinates.

Vehicle location data is data relating to the geographic location where the vehicle is located. In one example, each piece of vehicle location data includes at least a unique vehicle identification of the vehicle, vehicle location coordinates, and a time at which the vehicle location coordinates were obtained.

The unique vehicle identification of the vehicle is used to uniquely identify the vehicle, and may be, for example, a vehicle ID.

The vehicle position coordinates are coordinate information for identifying the position of the vehicle. The vehicle location coordinates may include the longitude of the geographic location of the vehicle and the latitude of the vehicle. The vehicle position coordinates can be acquired by the vehicle when reporting the vehicle position data according to a preset period, or can be acquired by triggering vehicle reporting after requesting the vehicle. In this embodiment, the vehicle may acquire the vehicle Position coordinates through a positioning module (e.g., GPS) provided in the vehicle, which is not listed here.

The time when the vehicle position coordinates are acquired is the time when the server according to the present embodiment acquires the corresponding vehicle position coordinates from the vehicle. In one example, the time at which the vehicle position coordinates are obtained may be when the vehicle is finished being used, such as when the vehicle is a bicycle, and the time at which the vehicle position coordinates are obtained may be when the vehicle is locked.

The reference position data is data related to the geographical position where the vehicle is located, which is acquired by other positioning modules than the vehicle's own positioning module.

In one example, the reference location data may include user location data. The user location data may be obtained by a client held by a user using the vehicle. For example, the user location data may include at least a unique vehicle identification of the vehicle used by the user, user location coordinates, and a time at which the user location coordinates were obtained.

The user position coordinates are coordinate information for identifying the user position. The user location coordinates may include the longitude of the geographic location of the user and the latitude of the vehicle. The user position coordinates may be obtained by a positioning module (e.g., GPS) included in the client used by the user, and then reported to the server implementing the embodiment.

The time when the user position coordinates are acquired is the time when the server implementing the embodiment acquires the corresponding user position coordinates from the user. In one example, the time when the user position coordinates are acquired may be when the user starts using the vehicle, for example, when the vehicle is a bicycle, and the time when the user position coordinates are acquired may be when the user scans a two-dimensional code of the vehicle or inputs a vehicle ID to perform an unlocking operation using the client used by the user.

In one example, the reference location data includes user location data, and correspondingly, the vehicle management method provided in this embodiment further includes:

when the vehicle is used, vehicle position data of the vehicle are obtained;

and acquiring corresponding user position data when the user starts to use the vehicle.

In this example, the vehicle position data of the vehicle is acquired when the vehicle is finished being used, and the vehicle position data is acquired after the vehicle is determined to be stopped, so that the data stability of the acquired vehicle position data can be ensured. For example, when the vehicle is a bicycle, the vehicle position data is acquired when the user finishes using the bicycle lock, and the data stability of acquiring the vehicle position data is ensured.

And when the user starts to use the vehicle, the vehicle enters a use state from a static state at the moment, and corresponding user position data are acquired and used as reference position data of the vehicle position, so that the reference performance of the reference position data is ensured to be high. In this example, the vehicle may have various forms, and the specific time when the user starts using the vehicle may correspond to different times, for example, when the vehicle is a bicycle, when the user starts using the vehicle, the time when the user scans the two-dimensional code of the vehicle or inputs the vehicle ID to perform the unlocking operation using the client used, or when the user starts using the vehicle when the vehicle is a motor vehicle, the time when the user starts starting using the vehicle engine. When the user starts to use the vehicle, the user position data can be obtained through a positioning module configured by the client used by the user, and then reported to the server implementing the embodiment.

Alternatively, the reference position data may also comprise device position data of the road locating device.

The road positioning device is a device which is arranged on a road and used for positioning a specific geographic position of the road, for example, a device with a positioning function, which is realized by a short-distance wireless communication mode, such as a bluetooth spike and the like. Correspondingly, the device location data is data relating to the specific geographical location at which the road locating device is located. In one example, the reference location data includes device location data for a road locating device. Correspondingly, the vehicle management method provided by the embodiment further includes:

In this example, the vehicle may be in various forms, and the specific time when the corresponding vehicle is finished being used may be different. For example, when the vehicle is a bicycle, when the bicycle is locked by a user when the vehicle is used, or when the vehicle is a motor vehicle, when the vehicle is used, when the engine of the vehicle is turned off, and the like. How to acquire the vehicle position data of the vehicle has been described in detail above, and will not be described in detail here.

The road positioning device corresponding to the vehicle is a device for positioning a specific geographic position of a road, which is arranged on the road near the geographic position where the vehicle is located when the vehicle is used, for example, a device with a positioning function, which is realized by a short-distance wireless communication mode, such as a bluetooth spike.

Once the road positioning device is set on the corresponding road, the corresponding device location data is correspondingly determined, in this example, the device location data corresponding to the vehicle may be obtained by obtaining the device identifier of the road positioning device corresponding to the vehicle, and querying from the database storing the device location data of the road positioning device according to the device identifier.

Use road positioning equipment with the bluetooth spike, the vehicle is the bicycle that is provided with the intelligent lock that supports bluetooth communication as an example, when the bicycle lock was closed, the bluetooth signal that bluetooth spike sent around it was received to the intelligence lock through the bicycle, select the bluetooth signal that bluetooth signal strength is the biggest among all bluetooth signals received (when bluetooth signal strength is big more, this bluetooth spike that corresponds is more close apart from the vehicle), the bluetooth spike that this bluetooth signal that this intensity is the biggest is the bluetooth spike that corresponds with the bicycle, the equipment sign that bluetooth spike can be obtained to this bluetooth signal of analysis, can inquire the equipment data that obtain this bluetooth spike according to the equipment sign.

In one example, the step of obtaining position coordinate samples of the corresponding plurality of position coordinate samples is shown in fig. 3, and may include: steps S2110-S2120.

Step S2110 of determining that there is associated reference position data for each piece of vehicle position data, respectively.

The vehicle position data is data which is reported by the vehicle through a positioning module of the vehicle and is related to the geographic position of the vehicle, and the reference position data is data which is reported by other positioning modules except the positioning module of the vehicle and is related to the geographic position of the vehicle. The reference position data determined to be associated with the vehicle position data may be determined by association with the vehicle ID or may be determined by association with the vehicle distance from the vehicle.

The method comprises the steps of determining the reference position data which are associated with each piece of vehicle position data respectively, so that a plurality of corresponding position coordinate samples are determined in combination with the subsequent steps, determining position error spatial distribution according to the plurality of position coordinate samples, and triggering and implementing corresponding vehicle management according to the position error spatial distribution.

In this example, the reference location data may include user location data, and correspondingly, step S2210 includes:

for each piece of vehicle position data, selecting user position data corresponding to the acquisition time and meeting a preset time association condition from user position data of a user of the vehicle corresponding to the vehicle position data as reference position data associated with the vehicle position data.

The time of acquiring the user location data may be recorded by the server in the embodiment when the server acquires the user location data. The vehicle used by the user corresponding to the user location data may be obtained by the server according to the query of the vehicle usage record in the embodiment.

Or the time of acquiring the user position data and the vehicle used by the user corresponding to the user position data can be directly determined according to the vehicle position data and the user position data. For example, the vehicle position data includes at least a unique vehicle identification of the vehicle, vehicle position coordinates, and a time at which the vehicle position coordinates were obtained; the user location data includes at least a unique vehicle identification of the vehicle used by the user, user location coordinates, and a time at which the user location coordinates were obtained. And determining a user using the unique vehicle identifier of the vehicle in the user position data according to the unique vehicle identifier of the vehicle, and taking the user position data of which the corresponding acquisition time meets the preset time association condition as reference position data associated with the vehicle position data.

The time-related condition may be set according to a specific application scenario or application requirements. For example, the time-correlation condition may be that the acquisition timing of the reference position data is after the acquisition timing of the vehicle position data, and the time difference therebetween is within a preset time period. The preset time duration may be set according to a specific application scenario or application requirements, for example, when the obtaining time of the vehicle position data is when the vehicle is finished being used, the obtaining time of the reference position data is when the user starts using the vehicle, the preset time duration is set to 1 hour, and correspondingly, when the user uses the vehicle within 1 hour after the vehicle is finished being used last time, the time correlation condition is met.

In addition, if the vehicle is in the area where the manager often carries the adjustment (whether the area where the manager often carries the adjustment can be obtained by adopting the existing technical means, which is not described herein again), the obtaining time of the vehicle position data can be set to be 1 hour when the vehicle is finished using, and correspondingly, the obtaining time of the reference position data must be within 1 hour after the vehicle is finished using, so as to prevent the possibility that the vehicle is carried due to too long time of the set time difference; whereas, if the vehicle is not in the area where the manager often carries the adjustment, the preset time period may be suitably set to a time period longer than 1 hour.

In this example, the reference location data may also include device location data, and correspondingly, step S2210 includes:

And selecting the equipment position data of the road positioning equipment with the closest vehicle distance corresponding to the vehicle position data as the reference position data associated with the vehicle position data, so as to ensure that the referential property of the reference position data is higher. Use road positioning equipment to use the bluetooth spike, the vehicle is the bicycle that is provided with the intelligent lock that supports bluetooth communication, and the acquisition of vehicle position data is the bicycle when closing the lock constantly for the example: when the bicycle is locked, the Bluetooth signals sent by the Bluetooth spikes around the bicycle are received through the intelligent bicycle lock of the bicycle, the Bluetooth signals with the maximum Bluetooth signal intensity are screened out from all the received Bluetooth signals (when the Bluetooth signal intensity is larger, the corresponding Bluetooth spikes are closer to the bicycle), the Bluetooth spikes corresponding to the Bluetooth signals with the maximum intensity are the Bluetooth spikes closest to the bicycle, the Bluetooth signals are analyzed to obtain the equipment identification of the Bluetooth spikes, and according to the equipment identification, the equipment data of the Bluetooth spikes can be inquired and obtained from the database recording the equipment position data of the Bluetooth spikes and serve as the reference position data associated with the vehicle position data of the bicycle.

Step S2120, obtaining vehicle position coordinates according to each piece of vehicle position data, obtaining reference position coordinates corresponding to the vehicle position coordinates according to reference position data associated with the vehicle position data, obtaining corresponding position coordinate samples, and obtaining multiple position coordinate samples.

For example, the vehicle position data may include vehicle position coordinates, the reference position data may include user position data, and the user position data includes user position coordinates, and the vehicle position coordinates may be directly obtained from each piece of vehicle position data, and the user position coordinates may be taken out from the user position data corresponding to the piece of vehicle position data as the reference position coordinates, so as to obtain corresponding position coordinate samples, thereby obtaining multiple pieces of position coordinate samples.

For another example, the vehicle position data may include vehicle position coordinates, the reference position data may include device position data, the device position data includes device position coordinates, the vehicle position coordinates may be directly obtained from each piece of vehicle position data, the device position coordinates may be taken out from the device position data corresponding to the piece of vehicle position data as the reference position coordinates, and corresponding position coordinate samples may be obtained, so as to obtain a plurality of position coordinate samples.

In this embodiment, according to the presence of the associated vehicle position data and the reference position data, the obtained corresponding multiple position coordinate samples are determined, the corresponding position error spatial distribution can be obtained by combining the subsequent steps, and according to the position error spatial distribution, the corresponding vehicle management can be triggered and implemented for the vehicle with the larger position error, so that the vehicle management efficiency is correspondingly improved, and the vehicle use experience of the user is improved.

After step S2100, the flow proceeds to:

step S2200, according to the multiple position coordinate samples, determining the spatial distribution of the position error; wherein the spatial distribution of the position errors is the distribution of the position coordinate samples with the position errors in the geographic space.

The spatial distribution of the position errors may be the vehicle position coordinates of the corresponding position coordinate samples with the position errors marked on the corresponding map, and the corresponding position coordinate samples with the position errors are distributed in the geographic space.

In one example, determining a spatial distribution of position errors from a plurality of samples of position coordinates as shown in fig. 4 may include: steps S2210-S2230.

Step S2210, for each position coordinate sample, an error distance of the position coordinate sample is obtained.

The position coordinate sample includes vehicle position coordinates and reference position coordinates corresponding to the vehicle position coordinates.

The error distance of the position coordinate sample is used for measuring the error between the position coordinate of the vehicle and the corresponding reference position coordinate. The error distance may be in various forms, and correspondingly, in this example, the corresponding error distance may be obtained in various ways.

For example, as shown in FIG. 5, steps S2211-S2212 may be included:

step S2211, determining a position deviation parameter according to the vehicle position coordinates and the reference position coordinates of the position coordinate sample.

The position deviation parameter is a parameter for calculating an error distance of the position coordinate sample.

Assuming that the vehicle position coordinates in the position coordinate sample are (MLatA, MLonA), wherein MLatA is the latitude of the vehicle and MLonA is the longitude of the vehicle; the reference position coordinates are (MLatB, MLonB), MLatB is the latitude of the user or the road positioning device, MLonB is the longitude of the user or the road positioning device, and if the east longitude takes a positive value of the longitude, the west longitude takes a negative value of the longitude, the north latitude takes a 90-latitude value, and the south latitude takes a 90+ latitude value according to the reference of 0-degree longitude, the position deviation parameter C is:

C＝sin(MLatA)*sin(MLatB)*cos(MLonA-MLonB)

+cos(MLatA)*cos(MLatB)

step S2212, determining a corresponding error distance according to the position deviation parameter and the earth radius.

Assuming that the radius of the earth is R and the position deviation parameter is C, the corresponding error Distance is:

Distance＝R*arccos(C)*Pi/180

wherein Pi is the circumference ratio, the method for measuring the radius R of the earth can be realized by adopting the prior art, and is not described herein again.

In one example, the vehicle position coordinates and the user position coordinates of the position coordinate sample are position coordinates based on different coordinate systems; before the step of obtaining the error distance of the position coordinate sample, the method further comprises the following steps:

The vehicle position coordinates are coordinate information for identifying the vehicle position; the user position coordinates are coordinate information for identifying the user position. The coordinate information may be based on other coordinate systems such as the GCJ-02 coordinate system (Mars coordinate system), the WGS-84 coordinate system (original coordinate system), and the bd-09 coordinate system (Baidu coordinate system).

The preset target coordinate system is a coordinate system on which the vehicle position coordinates and the user position coordinates are based after the coordinate system on which the vehicle position coordinates and the user position coordinates are based is unified. The preset target coordinate system may be a coordinate system on which the vehicle position coordinates are based, may be a coordinate system on which the user position coordinates are based, or may be a third-party coordinate system that is not consistent with both the vehicle position coordinates and the coordinate system on which the user position coordinates are based.

When the vehicle position coordinates and the user position coordinates are position coordinates based on different coordinate systems, it is necessary to unify the coordinate systems on which the vehicle position coordinates and the user position coordinates are based. For example, the vehicle position coordinate is based on a GCJ-02 coordinate system (mars coordinate system), the user position coordinate is based on a WGS-84 coordinate system (original coordinate system), and the preset target coordinate system may be the GCJ-02 coordinate system adopted by the vehicle position coordinate, may also be the WGS-84 coordinate system adopted by the user position coordinate, and may also be another third-party coordinate system, which is not limited in this embodiment. ,

assuming that a vehicle position coordinate adopts a first coordinate system, the vehicle position coordinate is (LatA, LonA), wherein LatA is a vehicle latitude, LonA is a vehicle longitude, a user position coordinate adopts a second coordinate system, the user position coordinate is (MLatB, MLonB), mlataa is a user latitude, MLonA is a user longitude, and a preset target coordinate system is the second coordinate system.

Converting a first coordinate system adopted by the vehicle position coordinates into a second coordinate system, assuming alpha₁Is a conversion coefficient between a first coordinate system and a second coordinate system, alpha₂In terms of the degree of conversion between the first coordinate system and the second coordinate system, the converted vehicle position coordinates (MLatA, MLonA) are:

(MLatA,MLonA)＝(α₁LatA+α₂,α₁LonA+α₂)

wherein MLatA is the latitude of the vehicle behind the unified coordinate system, and MLonA is the longitude of the vehicle behind the unified coordinate system.

Assuming that the east longitude takes a positive value, the west longitude takes a negative value, the north latitude takes a 90-latitude value, and the south latitude takes a 90+ latitude value according to the reference of 0-degree longitude, the position deviation parameter C is:

C＝sin(MLatA)*sin(MLatB)*cos(MLonA-MLonB)

+cos(MLatA)*cos(MLatB)

the corresponding error Distance is:

Distance＝R*Arccos(C)*Pi/180

wherein Pi is the circumference ratio and the radius of the earth is R.

For example, a manhattan distance or a chebyshev distance between the vehicle position coordinates and the reference position coordinates of the position coordinate sample may be acquired as the error distance.

Acquiring a Manhattan Distance between the two as an error Distance:

Distance＝|x₁-x₂|+|y₁-y₂|

wherein (x)₁,y₁) To convert the latitude and longitude of the vehicle coordinate location to a planar coordinate, (x)₂,y₂) Is a plane coordinate after the longitude and latitude conversion of the reference position coordinate.

The longitude and latitude coordinates are converted into plane coordinates by adopting methods such as mercator coordinate projection, Miller coordinate projection, transverse axis mercator projection, Gaussian-gram projection, Lambert equiangular secant cone projection and the like.

And acquiring a Chebyshev Distance between the two as an error Distance:

Distance＝max(|x₁-x₂|,|y₁-y₂|)

It should be understood that the above is only three ways to obtain the error distance of the position coordinate sample, and there may be other ways to calculate the error distance of the position coordinate sample, which are not listed here.

Further, the error distances obtained by the at least two methods may be averaged to obtain a value as a final error distance.

After obtaining the error distance of the position coordinate sample in step S2210, the process proceeds to:

step S2220 determines the position coordinate sample whose error distance is greater than the error distance threshold value as the position coordinate sample having the position error.

The error distance threshold is used for judging whether the position coordinate sample is the position coordinate sample with the position error. The error distance threshold may be set according to an application scenario and an application requirement, and for example, the error distance threshold may be set to 100 meters.

The position coordinate sample with the position error means that the corresponding vehicle position coordinate in the position coordinate sample has an error.

Step S2230 determines a spatial distribution of the position error according to the vehicle position coordinates of the position coordinate sample having the position error.

According to the step S2220, the position coordinate sample having the position error is determined according to the position coordinate sample having the error distance greater than the error distance threshold, the vehicle position coordinate of the position coordinate sample having the position error is marked on the corresponding map, and the distribution of the position coordinate sample having the position error in the geographic space is correspondingly obtained.

For example, in a map, vehicle position coordinates of a vehicle in a certain geographic space on a certain day are displayed, when an error distance corresponding to the vehicle is greater than an error distance threshold, the vehicle position coordinates corresponding to the vehicle are marked in the map by using a mark different from the vehicle position coordinates of a normal vehicle, and a map distribution map of the vehicle position coordinates marked with a position coordinate sample with a position error is a corresponding position error spatial distribution.

After determining the corresponding spatial distribution of errors in step S2200, the method proceeds to:

and step S2300, triggering and implementing corresponding vehicle management according to the position error spatial distribution.

Vehicle management may include vehicle position calibration, manual calibration by an operator, field trial calibration, and the like, and may use the calibration coordinates of the vehicle as the coordinates of the neighboring vehicle.

In one example, the vehicle management includes at least vehicle position calibration, and step S2300 may be as shown in fig. 6, including: steps S2310-S2330.

Step S2310, a vehicle calibration area where a vehicle positioning calibration requirement exists is determined in a geographic space corresponding to the spatial distribution of the position error.

The vehicle calibration area is an area for identifying that the vehicle position data needs to be calibrated.

Specifically, step S2310 may include, as shown in fig. 7: steps S2311-S2313.

Step S2311, a geographic space is divided into a plurality of geographic areas.

The geographic space may be divided according to a preset area size, for example, the geographic space may be divided into 100 × 100 grids, and the like, and may also be divided according to an administrative area of the region to which the geographic space belongs.

In step S2312, the number of samples of the position coordinate samples included in each geographic area, in which the position error exists, is obtained.

The number of samples of the position coordinate samples with the position errors is used for counting the number of the position coordinate samples with the position errors in each geographic area.

According to the position error spatial distribution corresponding to the vehicle position coordinates of the position coordinate samples with the position errors, the number of the position coordinate samples with the position errors in each geographic area can be counted.

Step S2313, a geographic area where the number of samples is greater than a preset number threshold is determined as a vehicle calibration area.

The number threshold is used to determine whether a geographic area is a vehicle calibration area. The number threshold may be set according to a specific application scenario or application requirements.

After step S2310, the process proceeds to:

in step S2320, a target vehicle is selected in the vehicle calibration area.

The target vehicle is used as a target for calibrating the position coordinates. The target vehicle may be a two-wheeled or three-wheeled bicycle, a power-assisted vehicle, an electric vehicle, or a motor vehicle with four or more wheels.

Specifically, the vehicle corresponding to the position coordinate sample with the largest error distance in the vehicle calibration area may be selected as the target vehicle, and any one of the vehicles may also be randomly selected as the target vehicle.

In step S2330, vehicle position calibration is performed on the target vehicle according to the vehicle position data of the target vehicle and the vehicles adjacent to the target vehicle to obtain a calibration position coordinate of the target vehicle.

Specifically, step S2330 may be as shown in fig. 8, including: steps S2331-S2333.

Step S2331, according to the vehicle position data of the target vehicle and the adjacent vehicle of the target vehicle, obtaining a position sample set comprising a plurality of position samples; wherein each position sample includes vehicle position coordinates obtained from one piece of vehicle position data.

The target vehicle may be a two-wheeled or three-wheeled bicycle, a power-assisted vehicle, an electric vehicle, or a motor vehicle with four or more wheels. There are a plurality of adjacent vehicles around the target vehicle.

In this example, the vehicle supports near field wireless communication, and the vehicle can receive information by receiving a near field communication signal.

Taking a target vehicle as an example of a shared bicycle, an intelligent lock arranged on the target vehicle can support short-range wireless communication, such as sending or receiving a short-range wireless communication signal, and the target vehicle can analyze the received short-range wireless communication signal, acquire a unique vehicle identifier included in the short-range wireless communication signal, determine the vehicle sending the short-range wireless communication, and determine that the vehicle sending the short-range wireless communication is an adjacent vehicle in a short-range communication range. In addition, in this example, a signal strength threshold value may be set in advance, and when the signal strength of the received short-range wireless communication signal is greater than the signal strength threshold value, it is determined that the vehicle that transmitted the short-range wireless communication is an adjacent vehicle within the short-range communication range. The signal strength threshold can be set according to application scenarios and application requirements.

In this embodiment, the vehicle position data is data related to the geographic position where the vehicle is located, which is reported by the vehicle through its own positioning module, and the vehicle position data at least includes a unique vehicle identifier of the vehicle, a vehicle position coordinate, and a time at which the vehicle position coordinate is obtained.

Step S2332, determine the location samples with the abnormality in the location sample set and filter them to obtain a calibration sample set.

The abnormal position samples are samples with abnormal distortion of corresponding position data, and the accuracy of vehicle positioning is affected. And obtaining a calibration sample set by determining the position samples with the abnormality in the position sample set and filtering the position samples with the abnormality in the position sample set.

The location samples for which an anomaly exists are determined by the sample distance between each location sample in the set of location samples and the sample mean of the set of location samples. The location samples with sample distances greater than the sample distance threshold are location samples with anomalies.

The sample distance is an index for measuring the similarity of two samples, the smaller the sample distance is, the higher the similarity between the two samples is, and conversely, the larger the sample distance is, the smaller the similarity between the two samples is.

The sample distance threshold may be set according to a specific application scenario or application requirements.

Step S2333, determine calibration location coordinates of the target vehicle according to the calibration sample set.

And acquiring a sample mean value of the calibration sample set as the calibration position coordinate of the target vehicle. The sample mean value of the calibration sample set can be obtained by an arithmetic mean value method, a median mean value method, a geometric mean value method and the like.

In addition, in the present embodiment, the calibration position coordinates of the target vehicle may also be taken as the calibration position coordinates of the adjacent vehicle.

In this example, the target vehicle's neighboring vehicles are typically within a distance of the order of ten meters from the target vehicle, e.g., the neighboring vehicles determined by the target vehicle from the received close-range wireless signals. The calibration position data of the target vehicle is used as the calibration position data of the adjacent vehicle of the target vehicle, the position error can be controlled within a ten-meter range, and the positioning accuracy of the vehicle is greatly improved compared with the position data of the adjacent vehicle, which is acquired by using a GPS (global positioning system) and has a position error of a hundred meter level. Meanwhile, the vehicle positioning method in the embodiment does not need to be implemented again for the adjacent vehicle of the target vehicle, so that the processing resource is saved, and the processing efficiency is improved.

< Server >

In this embodiment, there is also provided a server 200, as shown in fig. 9, including:

a memory 210 for storing executable instructions;

and the processor 220 is used for operating the server to execute any one of the vehicle positioning methods provided by the embodiment according to the control of the executable instructions.

In this embodiment, the server 200 may be embodied in various forms of entities. For example, the server 200 may be a cloud server. The server 200 may also be the server 1000 as shown in fig. 1.

Those skilled in the art will appreciate that server 200 may be implemented in a variety of ways. For example, server 200 may be implemented by an instruction configuration processor. For example, the server 200 may be implemented by storing instructions in ROM and reading the instructions from ROM into a programmable device when the device is started. For example, the server 200 may be consolidated into a dedicated device (e.g., ASIC). The server 200 may be divided into separate units or may be implemented by combining them together. The server 200 may be implemented in one of the various implementations described above, or may be implemented in a combination of two or more of the various implementations described above.

The vehicle management method and the server provided in the embodiment have been described above with reference to the drawings, according to the embodiment, a plurality of corresponding position coordinate samples are determined by a plurality of pieces of vehicle position data and reference position data, and according to the plurality of position coordinate samples, the position error spatial distribution is determined, and according to the position error spatial distribution, corresponding vehicle management can be triggered and implemented for a vehicle with a large position error, so that the vehicle management efficiency is correspondingly improved, and the vehicle use experience of a user is improved.

< second embodiment >

In the present embodiment, there is provided a vehicle management system 500, as shown in fig. 10, including:

a client 300;

a vehicle 400;

the first embodiment provides a server 200.

In the present embodiment, the vehicle management system 500 may be a shared bicycle management system, a shared motor vehicle management system, or the like. The vehicle management system 500 may also include other devices, such as a road locating device provided at a roadside for locating. The road positioning device is a device which is arranged on a road and used for positioning a specific geographic position of the road, for example, a device with a positioning function, which is realized by a short-distance wireless communication mode, such as a bluetooth spike and the like. In one example, the hardware configuration of the vehicle localization system 500 may be as shown in the vehicle system 100 of FIG. 1. The vehicle management method implemented by the vehicle management system 500 in the present embodiment will be described below, taking as an example that the vehicle management system 500 is a shared bicycle system. In this example, the vehicles 400 are shared bicycles, each shared bicycle is provided with an intelligent lock, the intelligent lock supports bluetooth communication, and a GPS module is arranged in the intelligent lock to obtain vehicle position data of the shared bicycles; the server 200 is a cloud server supporting management and use of the shared bicycle, and the client 300 is a mobile phone held by a user using the shared bicycle, and the mobile phone is provided with a GPS module for acquiring user position data of the user holding the mobile phone.

In this example, the vehicle 400 may acquire the vehicle location data through a GPS module provided in the smart car lock and transmit the vehicle location data to the server 200 when the user finishes locking the vehicle.

When the user scans and unlocks the two-dimensional code of the vehicle sharing the bicycle, the client 300 can acquire the position data of the user through the GPS of the client and send the position data to the server 200.

The server 200 may determine the spatial distribution of the position error according to the vehicle management method provided in the first embodiment, by using the vehicle position data acquired from the vehicle 400 and the user position data acquired from the client 300, and trigger and implement corresponding vehicle management for the vehicle with the larger position error according to the spatial distribution of the position error, so as to correspondingly improve the vehicle management efficiency and improve the vehicle use experience of the user.

In this example, the vehicle 400 may further receive bluetooth signals sent by bluetooth spikes set on nearby roads through the smart car lock, and when the user finishes using the smart car lock, the vehicle 400 may select a bluetooth signal with the maximum signal intensity from the bluetooth signals sent by the bluetooth spikes received around to perform analysis, obtain a device identifier of the corresponding bluetooth spike, and send the device identifier to the server, so that the server may obtain the device location data of the bluetooth spike as reference location data by querying from a database in which the device location data of the bluetooth spike is stored.

After the server 200 may obtain the vehicle position data from the vehicle 400 and obtain the device position data as the reference position data, according to the vehicle management method provided in the first embodiment, the position error spatial distribution is determined, and according to the position error spatial distribution, corresponding vehicle management is triggered and implemented for a vehicle with a large position error, so that the vehicle management efficiency is correspondingly improved, and the vehicle use experience of the user is improved.

The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.

Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A vehicle management method, implemented by a server, comprising:

acquiring a plurality of position coordinate samples according to the acquired plurality of pieces of vehicle position data and the plurality of pieces of reference position data;

the vehicle position data are data which are acquired through a positioning module of the vehicle and are related to the geographic position of the vehicle, the reference position data are data which are acquired through other positioning modules except the positioning module of the vehicle and are related to the geographic position of the vehicle, and each position coordinate sample comprises a vehicle position coordinate and a reference position coordinate corresponding to the vehicle position coordinate;

determining the spatial distribution of the position errors according to the plurality of position coordinate samples;

wherein the spatial distribution of the position errors is the distribution of the position coordinate samples with position errors in the geographic space;

and triggering to implement vehicle management according to the position error spatial distribution.

2. The method of claim 1, wherein,

when a vehicle is finished being used, acquiring the vehicle position data of the vehicle;

when a user starts to use the vehicle, acquiring corresponding user position data;

and/or the presence of a gas in the gas,

the reference location data comprises device location data of a road locating device; the method further comprises the following steps:

when a vehicle is finished being used, the vehicle position data of the vehicle and the device position data of the road positioning device corresponding to the vehicle are acquired.

3. The method of claim 1, wherein the step of obtaining a plurality of position coordinate samples comprises:

determining the reference position data with the association for each piece of the vehicle position data;

and respectively obtaining the vehicle position coordinates according to each piece of vehicle position data, and obtaining the reference position coordinates corresponding to the vehicle position coordinates according to the reference position data associated with the vehicle position data to obtain the position coordinate samples, so as to obtain a plurality of position coordinate samples.

4. The method of claim 3, wherein,

the reference location data comprises user location data;

the step of determining that there is associated the reference position data for each of the pieces of the vehicle position data includes:

for each piece of vehicle position data, selecting the user position data corresponding to the acquisition time and meeting a preset time association condition from the user position data of the user of the vehicle corresponding to the vehicle position data as the reference position data associated with the vehicle position data;

and/or, the reference location data comprises device location data;

for each piece of the vehicle position data, the device position data of the road positioning device with the closest vehicle distance corresponding to the vehicle position data is selected as the reference position data associated with the vehicle position data.

5. The method of claim 4, wherein,

6. The method of claim 1, wherein the step of determining a spatial distribution of position errors from the plurality of position coordinate samples comprises:

for each position coordinate sample, obtaining an error distance of the position coordinate sample;

determining the position coordinate sample with the error distance larger than an error distance threshold value as the position coordinate sample with the position error;

7. The method of claim 6, wherein,

the step of obtaining the error distance of the position coordinate sample for each position coordinate sample comprises:

determining a position deviation parameter according to the vehicle position coordinates and the reference position coordinates of the position coordinate sample;

determining the error distance according to the position deviation parameters and the radius of the earth;

and/or the presence of a gas in the gas,

and acquiring a Manhattan distance or a Chebyshev distance between the vehicle position coordinate and the reference position coordinate of the position coordinate sample as the error distance.

8. The method of claim 6, wherein,

before the step of obtaining the error distance of the position coordinate sample, the method further comprises:

9. The method of any one of claims 1-8,

the vehicle management includes at least a vehicle position calibration;

the step of triggering implementation of vehicle management according to the spatial distribution of the position errors comprises:

determining a vehicle calibration area with vehicle positioning calibration requirements in a geographic space corresponding to the position error spatial distribution;

selecting a target vehicle in the vehicle calibration area;

and according to the target vehicle and the vehicle position data of the adjacent vehicles of the target vehicle, performing vehicle position calibration on the target vehicle to obtain a calibration position coordinate of the target vehicle.

10. The method of claim 9, wherein,

the step of determining a vehicle calibration area where a vehicle positioning calibration requirement exists in a geographic space corresponding to the position error spatial distribution comprises:

dividing the geographic space to obtain a plurality of geographic areas;

obtaining a sample number of the position coordinate samples with position errors included in each of the geographic areas;

determining the geographic area in which the number of samples is greater than a preset number threshold as the vehicle calibration area.

11. The method of claim 9, wherein the step of selecting a target vehicle in the vehicle calibration area comprises:

and selecting the vehicle corresponding to the position coordinate sample with the largest error distance in the vehicle calibration area as the target vehicle.

12. The method of claim 9, wherein the step of calibrating the vehicle position data of the target vehicle based on the vehicle position data of the target vehicle and vehicles adjacent to the target vehicle comprises:

acquiring a position sample set comprising a plurality of position samples according to the vehicle position data of the target vehicle and the adjacent vehicles of the target vehicle;

wherein each of the position samples comprises vehicle position coordinates obtained from one of the vehicle position data;

determining the position samples with abnormality in the position sample set and filtering to obtain a calibration sample set;

determining calibration position coordinates of the target vehicle from the set of calibration samples.

13. A server, comprising:

a memory for storing executable instructions;

a processor for operating the server to perform the vehicle management method of any one of claims 1-12, under control of the executable instructions.

14. A vehicle management system, comprising:

a vehicle;

a client;

and a server as claimed in claim 13.