CN110751531A

CN110751531A - Track identification method and device and electronic equipment

Info

Publication number: CN110751531A
Application number: CN201811348837.XA
Authority: CN
Inventors: 蔡恒兴
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2018-11-13
Filing date: 2018-11-13
Publication date: 2020-02-04

Abstract

The application provides a track identification method, a track identification device and electronic equipment, wherein the method comprises the following steps: acquiring order information of a current order of a target service provider, wherein the order information comprises a first starting point and a first terminal point; selecting first historical order data; selecting second historical order data from the first historical order data; and forming a data set by the order route in the second historical order data and the driving track of the target service provider, and performing outlier detection on the driving track of the target service provider according to the data set to obtain a track identification result. The track identification method in the embodiment of the application can effectively identify the running route condition of the service provider when providing the service, so that some potential dangerous paths can be identified.

Description

Track identification method and device and electronic equipment

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a trajectory identification method, an apparatus, and an electronic device.

Background

At present, the background can acquire order data provided by the service provider, provide a navigation route for the service provider according to the order data, and obtain a driving route of the service provider in real time. This approach may enable the prompting of routes, but may not be able to identify some particular driving routes.

Disclosure of Invention

In view of this, an object of the embodiments of the present application is to provide a trajectory identification method, a device and an electronic device, which can solve the problem that whether a driving route of a service provider is normal cannot be determined by identifying a trajectory of the service provider in the prior art, so as to achieve the effects of identifying whether the driving trajectory of the service provider is abnormal and knowing the trajectory is abnormal in advance.

According to one aspect of the present application, an electronic device is provided that may include one or more storage media and one or more processors in communication with the storage media. One or more storage media store machine-readable instructions executable by a processor. When the electronic device is operated, the processor communicates with the storage medium through the bus, and the processor executes the machine readable instructions to perform one or more of the following operations:

acquiring order information of a current order of a target service provider, wherein the order information comprises a first starting point and a first terminal point;

selecting first historical order data, wherein a first endpoint of any order in the first historical order data is within a limited range of the first starting point, and the first endpoint is the starting point of the order or the end point of the order;

selecting second historical order data from the first historical order data; a second endpoint of any order in the second historical order data is within a limited range of the first endpoint, and the second endpoint is an endpoint of the order corresponding to the first endpoint or a starting point of the order;

and forming a data set by the order route in the second historical order data and the driving track of the target service provider, and performing outlier detection on the driving track of the target service provider according to the data set to obtain a track identification result.

In some embodiments, the step of selecting the first historical order data includes:

calculating a first distance between the first endpoint and a first endpoint of each order in historical data;

and screening the order corresponding to the first distance smaller than the first set value to obtain first historical order data.

In some embodiments, the step of calculating a first distance of the first endpoint from the first endpoint for each order in the historical data comprises:

obtaining first longitude and latitude data of the first starting point;

obtaining second longitude and latitude data of the first end point of each order in the historical data;

and calculating the spherical distance between the first endpoint and the first endpoint of each order in the historical data according to the first longitude and latitude data and the second longitude and latitude data.

In some embodiments, the step of calculating the spherical distance between the first endpoint and the first endpoint of each order in the historical data according to the first longitude and latitude data and the second longitude and latitude data includes:

and calculating the first longitude and latitude data and the second longitude and latitude data by using a hemiversine function to obtain the spherical distance between the first endpoint and the first endpoint of each order in the historical data.

In some embodiments, the calculating the first longitude and latitude data and the second longitude and latitude data using a hemiversine function is represented as:

where d represents the distance of the first endpoint from the first endpoint of each order in the historical data, r is the radius of the earth,

representing a latitude of the first origin;

a latitude representing a first endpoint of each order in the historical data; λ 1 represents the latitude of the first origin; λ 2 represents the latitude of the first endpoint of each order in the historical data; hav denotes the hemiversine function.

constructing a plane coordinate system of the first starting point and a plane where the first end point of each order in the historical data is located;

and calculating Euclidean distances between the first endpoint and the first endpoint of each order in the historical data in the plane coordinate system.

In some embodiments, the step of selecting the second historical order data from the first historical order data comprises:

calculating a second distance between the first end point and a second end point of each order in the first historical order data;

and screening the order corresponding to the second distance smaller than a second set value to obtain second historical order data.

In some embodiments, said step of calculating a second distance of said first endpoint from a second endpoint of each order in said first historical order data comprises:

calculating a spherical distance between the first end point and a second end point of each order in the first historical order data; or the like, or, alternatively,

calculating Euclidean distances between the first end point and a second end point of each order in the first historical order data.

In some embodiments, the step of forming a data set by the order route in the second historical order data and the driving track of the target service provider, and performing outlier detection on the driving track of the target service provider according to the data set to obtain a track identification result includes:

classifying according to attribute information carried by each order in the second historical order data to obtain at least one type of track data;

and performing outlier detection on the running track of the target service provider and the at least one type of track data to obtain a track identification result.

In some embodiments, the attribute information includes at least one of time information, location information, and trajectory information.

In some embodiments, the step of classifying according to attribute information carried by each order in the second historical order data to obtain at least one type of trajectory data includes:

and classifying each order in the second historical order data by using a clustering algorithm to obtain at least one type of track data.

In some embodiments, the step of classifying each order in the second historical order data using a clustering algorithm to obtain at least one type of trajectory data includes:

classifying attribute information carried by each order in the second historical order data by using a density-based clustering algorithm of the DBSCAN to obtain at least one type of track data; or the like, or, alternatively,

and classifying the attribute information carried by each order in the second historical order data by using a k-means clustering algorithm to obtain at least one type of track data.

adding any one type of track data in the second historical order data into the current order to form a detection data set, using an anomaly detection algorithm to determine whether the current order in the detection data set is a data outlier sample, and if so, determining that the track identification result is track anomaly;

if not, adding other types of track data in the first historical order data into the current order to form a detection data set, and verifying whether the current order belongs to the order with the abnormal track until the verification is finished.

In some embodiments, after the step of forming the order route in the second historical order data and the driving track of the target service provider into a data set, performing outlier detection on the driving track of the target service provider according to the data set, and obtaining a track identification result, the method further includes:

and if the track identification result represents that the track is abnormal, generating alarm information about the target service provider.

In some embodiments, the case that the track identification result is characterized as track abnormality includes:

the target service provider's positioning signal disappears; or the like, or, alternatively,

and the running track corresponding to the target service provider is different from any track in the second historical order data.

According to another aspect of the present application, there is provided a trajectory recognition device including:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring order information of a current order of a target service provider, and the order information comprises a first starting point and a first terminal point;

the first selection module is used for selecting first historical order data, wherein a first endpoint of any order in the first historical order data is within a limited range of the first starting point, and the first endpoint is the starting point of the order or the end point of the order;

the second selection module is used for selecting second historical order data from the first historical order data; a second endpoint of any order in the second historical order data is within a limited range of the first endpoint, and the second endpoint is an endpoint of the order corresponding to the first endpoint or a starting point of the order;

and the matching module is used for forming a data set by the order route in the second historical order data and the driving track of the target service provider, and performing outlier detection on the driving track of the target service provider according to the data set to obtain a track identification result.

In some embodiments, the second selecting module includes:

the first calculation unit is used for calculating a first distance between the first endpoint and the first endpoint of each order in the historical data;

and the first screening unit is used for screening the orders corresponding to the first distance smaller than a first set value to obtain first historical order data.

In some embodiments, the first computing unit is further configured to:

obtaining first longitude and latitude data of the first starting point;

In some embodiments, the first computing unit is further configured to:

representing a latitude of the first origin;a latitude representing a first endpoint of each order in the historical data; λ 1 represents the latitude of the first origin; λ 2 represents the latitude of the first endpoint of each order in the historical data; hav denotes the hemiversine function.

In some embodiments, the first computing unit is further configured to:

In some embodiments, the second selecting module is further configured to:

a second calculating unit, configured to calculate a second distance between the first endpoint and a second endpoint of each order in the first historical order data;

and the second screening unit is used for screening the orders corresponding to the second distance smaller than a second set value to obtain second historical order data.

In some embodiments, the second computing unit is further configured to:

In some embodiments, the matching module comprises:

the classification unit is used for classifying according to attribute information carried by each order in the second historical order data to obtain at least one type of track data;

and the matching unit is used for carrying out outlier detection on the running track of the target service provider and the at least one type of track data to obtain a track identification result.

In some embodiments, the classification unit is further configured to:

In some embodiments, the matching module is further configured to:

In some embodiments, the apparatus further comprises:

and the generating module is used for generating alarm information about the target service provider if the track identification result is characterized as track abnormity.

According to another aspect of the present application, there is also provided a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the track identification method in the first aspect or any one of the possible implementation manners of the first aspect.

Based on any aspect, by comparing the track of the current order with the similar route in the historical order, the driving conditions of the routes in the current order and the historical order are identified, and compared with the prior art that only the driving route of the service provider can be obtained and whether the driving route of the service provider is normal or not cannot be known, the driving track provided by the target service provider and the driving track provided by the historical data are outlier or not, so that the driving track provided by the target service provider can be obtained, and some potential dangerous paths can be identified.

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

FIG. 1 is a schematic diagram illustrating an environment of a trajectory recognition system provided by an embodiment of the present application;

fig. 2 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;

FIG. 3 is a flow chart illustrating a trajectory recognition method provided by an embodiment of the present application;

FIG. 4 is a schematic diagram illustrating a two-position driving route provided by an embodiment of the present application;

FIG. 5 is a schematic diagram illustrating another two-location travel route provided by an embodiment of the present application;

fig. 6 is a detailed flowchart illustrating step S304 of a trajectory identification method according to an embodiment of the present application;

fig. 7 shows a schematic structural diagram of a trajectory recognition device provided in an embodiment of the present application.

Icon: 100-a trajectory recognition system; 110-a server; 120-a network; 130-service request side; 140-service provider; 150-a database; 200-an electronic device; 210-a network port; 220-a processor; 230-a communication bus; 240-storage medium; 250-an interface; 401-an acquisition module; 402-a first selection module; 403-a second selection module; 404-matching module.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

In order to enable a person skilled in the art to use the present disclosure, the following embodiments are given in connection with a specific application scenario "identification of a driving route for a driver of a net appointment". It will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Although the present application is described primarily in the context of a net appointment, it should be understood that this is only one exemplary embodiment. The application can be applied to any other traffic type. For example, the present application may be applied to different transportation system environments, including terrestrial, marine, or airborne, among others, or any combination thereof. The vehicle of the transportation system may include a taxi, a private car, a windmill, a bus, a train, a bullet train, a high speed rail, a subway, a ship, an airplane, a spacecraft, a hot air balloon, or an unmanned vehicle, etc., or any combination thereof. The present application may also include any service system for other route-related services, for example, a service system for a system for sending and/or receiving couriers, takeoffs. Applications of the system or method of the present application may include web pages, plug-ins for browsers, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, among others, or any combination thereof.

It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.

The terms "passenger," "requestor," "service person," "service requestor," and "customer" are used interchangeably in this application to refer to an individual, entity, or tool that can request or order a service. The terms "driver," "provider," "service provider," and "provider" are used interchangeably in this application to refer to an individual, entity, or tool that can provide a service. The term "user" in this application may refer to an individual, entity or tool that requests a service, subscribes to a service, provides a service, or facilitates the provision of a service. For example, the user may be a passenger, a driver, an operator, etc., or any combination thereof. In the present application, "passenger" and "passenger terminal" may be used interchangeably, and "driver" and "driver terminal" may be used interchangeably.

The terms "service request" and "order" are used interchangeably herein to refer to a request initiated by a passenger, a service requester, a driver, a service provider, or a supplier, the like, or any combination thereof. Accepting the "service request" or "order" may be a passenger, a service requester, a driver, a service provider, a supplier, or the like, or any combination thereof. The service request may be charged or free.

The Positioning technology used in the present application may be based on a Global Positioning System (GPS), a Global Navigation Satellite System (GLONASS), a COMPASS Navigation System (COMPASS), a galileo Positioning System, a Quasi-Zenith Satellite System (QZSS), a Wireless Fidelity (WiFi) Positioning technology, or the like, or any combination thereof. One or more of the above-described positioning systems may be used interchangeably in this application.

One aspect of the present application relates to a trajectory recognition system. The system can obtain the running track of the service provider in real time through the background server, and form a data set by the running track of the service provider and specific historical data which are similar to the track of the order of the service currently provided by the service provider in the historical data, so that whether the running track of the service provider deviates from the specific historical data or not is detected, and the abnormity of the running track of the service provider is judged.

It is worth noting that before the application is provided, the background server monitors the driving route of the service provider, the driving route is matched with the navigation route provided by the background server, and if the driving route deviates from the navigation route, a prompt is sent to the service provider, and the route is planned again. However, the track identification method provided by the application can identify the route with larger difference from the historical data. Therefore, the order route in the specific historical order data and the running track of the target service provider form a data set, and the running track of the target service provider is subjected to outlier detection according to the data set, so that the track identification system can provide the service provider for the background server.

FIG. 1 is a block diagram of a trajectory recognition system 100 of some embodiments of the present application. For example, the trajectory recognition system 100 may be an online transportation service platform for transportation services such as taxis, designated driving services, express, carpooling, bus services, driver rentals, or regular bus services, or any combination thereof. The trajectory recognition system 100 may include one or more of a server 110, a network 120, a service requester 130, a service provider 140, and a database 150, and the server 110 may include a processor for executing instructions.

In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system). In some embodiments, the server 110 may be local or remote to the terminal. For example, the server 110 may access information and/or data stored in the service requester 130, the service provider 140, or the database 150, or any combination thereof, via the network 120. As another example, the server 110 may be directly connected to at least one of the service requester 130, the service provider 140, and the database 150 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (community cloud), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof. In some embodiments, the server 110 may be implemented on an electronic device 200 having one or more of the components shown in FIG. 2 in the present application.

In some embodiments, the server 110 may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described herein. For example, the processor may determine the target vehicle based on a service request obtained from the service requester 130. In some embodiments, a processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a reduced Instruction Set computer (reduced Instruction Set computer), a microprocessor, or the like, or any combination thereof.

Network 120 may be used for the exchange of information and/or data. In some embodiments, one or more components (e.g., the server 110, the service requester 130, the service provider 140, and the database 150) in the trajectory recognition system 100 may send information and/or data to other components. For example, the server 110 may obtain a service request from the service requester 130 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network, or combination thereof. Merely by way of example, Network 120 may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the trajectory recognition system 100 may connect to the network 120 to exchange data and/or information.

In some embodiments, the user of the service requestor 130 may be someone other than the actual demander of the service. For example, the user a of the service requester 130 may use the service requester 130 to initiate a service request for the actual service demander B (for example, the user a may call a car for his friend B), or receive service information or instructions from the server 110. In some embodiments, the user of the service provider 140 may be the actual provider of the service or may be another person other than the actual provider of the service. For example, user C of service provider 140 may use service provider 140 to receive a service request serviced by actual service provider D (e.g., user C may take an order for driver D employed by user C), and/or information or instructions from server 110. In some embodiments, "service requestor" and "service requestor" may be used interchangeably, and "service provider" may be used interchangeably.

In some embodiments, the service requester 130 may include a mobile device, a tablet computer, a laptop computer, or a built-in device in a motor vehicle, etc., or any combination thereof. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include smart lighting devices, control devices for smart electrical devices, smart monitoring devices, smart televisions, smart cameras, or walkie-talkies, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, and the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, or a point of sale (POS) device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like. In some embodiments, the built-in devices in the motor vehicle may include an on-board computer, an on-board television, and the like. In some embodiments, the service requester 130 may be a device having a location technology for locating the location of the service requester and/or the service requester.

In some embodiments, the service provider 140 may be a similar or the same device as the service requester 130. In some embodiments, the service provider 140 may be a device with location technology for locating the location of the service provider and/or the service provider. In some embodiments, the service requester 130 and/or the service provider 140 may communicate with other locating devices to determine the location of the service requester, the service requester 130, the service provider, or the service provider 140, or any combination thereof. In some embodiments, the service requester 130 and/or the service provider 140 may send the location information to the server 110.

Database 150 may store data and/or instructions. In some embodiments, the database 150 may store data obtained from the service requester 130 and/or the service provider 140. In some embodiments, database 150 may store data and/or instructions for the exemplary methods described herein. In some embodiments, database 150 may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, tapes, and the like; volatile read-write Memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double Data Rate Synchronous Dynamic RAM (DDRSDRAM); static RAM (SRAM), Thyristor-based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, database 150 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, across clouds, multiple clouds, or the like, or any combination thereof.

In some embodiments, a database 150 may be connected to the network 120 to communicate with one or more components of the trajectory recognition system 100 (e.g., the server 110, the service requester 130, the service provider 140, etc.). One or more components in the trajectory recognition system 100 may access data or instructions stored in the database 150 via the network 120. In some embodiments, the database 150 may be directly connected to one or more components in the trajectory recognition system 100 (e.g., the server 110, the service requester 130, the service provider 140, etc.); alternatively, in some embodiments, database 150 may also be part of server 110.

In some embodiments, one or more components (e.g., server 110, service requester 130, service provider 140, etc.) in the trajectory recognition system 100 may have access to a database 150. In some embodiments, one or more components in the trajectory recognition system 100 may read and/or modify information related to a service requester, a service provider, or the public, or any combination thereof, when certain conditions are met. For example, server 110 may read and/or modify information for one or more users after receiving a service request. As another example, the service provider 140 may access information related to the service requester when receiving the service request from the service requester 130, but the service provider 140 may not modify the related information of the service requester.

In some embodiments, the exchange of information by one or more components in the trajectory recognition system 100 may be accomplished by requesting a service. The object of the service request may be any product. In some embodiments, the product may be a tangible product or a non-physical product. Tangible products may include food, pharmaceuticals, commodities, chemical products, appliances, clothing, automobiles, homes, or luxury goods, and the like, or any combination thereof. The non-material product may include a service product, a financial product, a knowledge product, an internet product, or the like, or any combination thereof. The internet product may include a stand-alone host product, a network product, a mobile internet product, a commercial host product, an embedded product, or the like, or any combination thereof. The internet product may be used in software, programs, or systems of the mobile terminal, etc., or any combination thereof. The mobile terminal may include a tablet, a laptop, a mobile phone, a Personal Digital Assistant (PDA), a smart watch, a Point of sale (POS) device, a vehicle-mounted computer, a vehicle-mounted television, a wearable device, or the like, or any combination thereof. The internet product may be, for example, any software and/or application used in a computer or mobile phone. The software and/or applications may relate to social interaction, shopping, transportation, entertainment time, learning, or investment, or the like, or any combination thereof. In some embodiments, the transportation-related software and/or applications may include travel software and/or applications, vehicle dispatch software and/or applications, mapping software and/or applications, and the like. In the vehicle scheduling software and/or application, the vehicle may include a horse, a carriage, a human powered vehicle (e.g., unicycle, bicycle, tricycle, etc.), an automobile (e.g., taxi, bus, privatege, etc.), a train, a subway, a ship, an airplane (e.g., airplane, helicopter, space shuttle, rocket, hot air balloon, etc.), etc., or any combination thereof.

Fig. 2 illustrates a schematic diagram of exemplary hardware and software components of an electronic device 200 of a server 110, a service requester 130, a service provider 140, which may implement the concepts of the present application, according to some embodiments of the present application. For example, a processor may be used on the electronic device 200 and to perform the functions herein.

The electronic device 200 may be a general purpose computer or a special purpose computer, both of which may be used to implement the trajectory recognition method of the present application. Although only a single computer is shown, for convenience, the functions described herein may be implemented in a distributed fashion across multiple similar platforms to balance processing loads.

For example, the electronic device 200 may include a network port 210 connected to a network, one or more processors 220 for executing program instructions, a communication bus 230, and a different form of storage medium 240, such as a disk, ROM, or RAM, or any combination thereof. Illustratively, the computer platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The electronic device 200 also includes an Input/Output (I/O) interface 250 between the computer and other Input/Output devices (e.g., keyboard, display screen).

For ease of illustration, only one processor is depicted in the electronic device 200. However, it should be noted that the electronic device 200 in the present application may also comprise a plurality of processors, and thus the steps performed by one processor described in the present application may also be performed by a plurality of processors in combination or individually. For example, if the processor of the electronic device 200 executes steps a and B, it should be understood that steps a and B may also be executed by two different processors together or separately in one processor. For example, a first processor performs step a and a second processor performs step B, or the first processor and the second processor perform steps a and B together.

FIG. 3 shows a flow chart of a trajectory identification method in one embodiment of the present application. The following describes the flow of the trajectory recognition method shown in fig. 3 in detail.

Step S301, obtaining order information of a current order of a target service provider.

The order information comprises a first starting point and a first ending point.

Further, the order information may further include travel track data of the target service provider. The driving track data can comprise position information of the target service party and time stamps corresponding to different positions. The mobile location information and timestamp may change with the location of the target service provider. The location information may include latitude and longitude information, altitude information, and the like.

Step S302, selecting first historical order data.

A first endpoint of any order in the first historical order data is within a defined range of the first starting point. The first endpoint may be a starting point of the order or an ending point of the order.

In one possible embodiment, orders with a terminal or starting point within a first starting point limit may be selected over a route segment that is bi-directional throughout. In one example, as shown in FIG. 4, location A and location B are road segments that can be traveled in both directions all the time, so if the current order is from location A to location B. The first endpoint may be the beginning of the order or the end of the order. In the road section which can be driven in two directions in the whole process, the routes from the position A to the position B or from the position B to the position A are the same, and only the directions are opposite, so that relatively more historical data can be used as reference in the remote road section.

In another possible implementation, an order having a starting point within a first starting point limit may be selected on a road segment where unidirectional travel exists. In one example, as shown in fig. 5, there are some road segments P1 to P2 in the position a 'and the position B', which are only capable of one-way driving, and since some road segments are only capable of one-way driving, the routes from the position a 'to the position B' and the routes from the position B 'to the position a' may be different, and if history data in which too many history orders with different routes are added as references may be capable of causing subsequent recognition errors, so that in a road segment in which one-way driving exists, an order with a starting point within the first starting point limit range is selected, and the accuracy of abnormality determination may be improved.

Step S302 is used to select a start point or an end point in the order in the history data to be within a limited range of the first start point. In one embodiment, it may be determined whether the start point or the end point in the order in the history data is within a defined range of the first start point by comparing whether the distance between the start point or the end point in the order in the history data and the first start point of the destination service provider is less than a set value. The process of selecting the first historical order data is described below in several embodiments.

In one embodiment, step 302, calculating a first distance between a first endpoint of the current order and a first endpoint of each order in the historical data; and screening the order corresponding to the first distance smaller than the first set value to obtain first historical order data.

The selection of the first set value can be set according to the area where the current order is located. For example, the first setting value may be selected to have a smaller value in an area where buildings are dense, and may be selected to have a larger value in an area where buildings are sparse.

The first distance may be a straight distance, a travel distance, a spherical distance, or the like. Wherein the travel distance may represent a distance required to travel from a first endpoint of a current order to a first endpoint of an order in the historical data.

In an implementation manner, the calculating a first distance between the first endpoint of the current order and the first endpoint of each order in the historical data includes: obtaining first longitude and latitude data of a first starting point of a current order; obtaining second longitude and latitude data of the first end point of each order in the historical data; and calculating the spherical distance between the first endpoint of the current order and the first endpoint of each order in the historical data according to the first longitude and latitude data of the current order and the second longitude and latitude data of each order in the historical data.

In one embodiment, calculating the spherical distance between the first endpoint of the current order and the first endpoint of each order in the historical data according to the first longitude and latitude data of the current order and the second longitude and latitude data of each order in the historical data may include: and calculating the first longitude and latitude data and the second longitude and latitude data by using a hemiversine function to obtain the spherical distance between the first endpoint and the first endpoint of each order in the historical data.

Further, the calculating the first longitude and latitude data and the second longitude and latitude data using a hemiversine function is represented as:

where d represents the distance of the first endpoint of the current order from the first endpoint of each order in the historical data, r is the radius of the earth,

a latitude representing a first starting point of the current order;

a latitude representing a first endpoint of each order in the historical data; λ 1 represents the latitude of the first starting point of the current order; λ 2 denotes the latitude of the first end point of each order in the historyDegree; hav denotes the hemiversine function.

Wherein, hav calculation of hemiversine function:

where θ represents a calculation parameter of the hemidyadic function.

In another practical manner, the calculating the first distance between the first endpoint of the current order and the first endpoint of each order in the history data may further include: constructing a plane coordinate system of a plane where a first starting point of a current order and a first end point of each order in historical data are located; and calculating Euclidean distances between the first endpoint and the first endpoint of each order in the historical data in the plane coordinate system.

In one example, the abscissa may be taken as the line on which the first starting point of the current order and the first end point of each order in the history data are located, and the ordinate may be taken as any line perpendicular to the abscissa, for example, any line perpendicular to the abscissa that passes through the midpoint between the first starting point of the current order and the first end point of each order in the history data may be taken as the ordinate.

In another practical manner, the calculating the first distance between the first endpoint of the current order and the first endpoint of each order in the history data may further include: and calculating Euclidean distances between the first starting point of the current order and the first end point of each order in the historical data in the world coordinate system.

The historical order close to the first starting point of the current order is selected as the first historical order data in multiple modes, so that the server can select the calculation mode more flexibly, and different calculation modes can be selected according to different use scenes.

Step S303, selecting second historical order data from the first historical order data.

A second endpoint of any of the above second historical order data is within a defined range of the first endpoint. The second endpoint is an end point of the order or a start point of the order corresponding to the first endpoint. Specifically, if the first endpoint is the beginning of an order in the order, the second endpoint represents the end of the order. Specifically, if a first endpoint is the end point of an order in the order, a second endpoint represents the beginning of the order.

Step S303 may include: calculating a second distance between the first end point of the current order and the second end point of each order in the first historical order data selected in the step S302; and screening the order corresponding to the second distance smaller than the second set value to obtain second historical order data.

The second setting may be selected in a similar manner to the first setting. For example, the selection of the second setting value may be set according to the area where the current order is located. For example, the second setting value may take a smaller value in an area where buildings are dense, and may take a larger value in an area where buildings are sparse.

The second distance may be a straight distance, a travel distance, a spherical distance, or the like. Wherein the travel distance may represent a distance required to travel from a first endpoint of a current order to a second endpoint of an order in the historical data.

The calculating the second distance between the first endpoint of the current order and the second endpoint of each order in the first historical order data selected in step S302 includes: calculating the spherical distance between the first end point of the current order and the second end point of each order in the first historical order data; or, calculating the Euclidean distance between the first end point of the current order and the second end point of each order in the first historical order data.

In one possible embodiment, calculating the spherical distance between the first end point and the second end point of each order in the first historical order data comprises: obtaining third longitude and latitude data of a first terminal of the current order; obtaining fourth longitude and latitude data of a second endpoint of each order in the historical data; and calculating the spherical distance between the first end point of the current order and the second end point of each order in the historical data according to the third longitude and latitude data of the current order and the fourth longitude and latitude data of each order in the historical data.

Specifically, the step of calculating the spherical distance between the first end point of the current order and the second end point of each order in the history data may refer to the process of calculating the spherical distance in step S302, and is not repeated here.

In another practical manner, the calculating the second distance between the first endpoint of the current order and the second endpoint of each order in the first historical order data selected in step S302 may further include: constructing a plane coordinate system of a plane where a first end point of the current order and a second end point of each order in the first historical order data are located; and calculating the Euclidean distance between the first end point and the second end point of each order in the first historical order data in the plane coordinate system.

In one example, any line perpendicular to the abscissa on a line where the first end point of the current order and the second end point of each order in the first historical order data selected in step S302 are located may be taken as the abscissa, and any line perpendicular to the abscissa may be taken as the ordinate, for example, any line perpendicular to the abscissa that passes through a midpoint between the first end point of the current order and the second end point of each order in the first historical order data selected in step S302 may be taken as the ordinate.

In another practical manner, the calculating the second distance between the first endpoint of the current order and the second endpoint of each order in the first historical order data selected in step S302 may further include: and calculating Euclidean distances between the first end point of the current order and the second end point of each order in the first historical order data in the world coordinate system.

Step S304, forming a data set by the order route in the second historical order data and the driving track of the target service provider, and performing outlier detection on the driving track of the target service provider according to the data set to obtain a track identification result.

Since the travel routes provided by the service providers may be different under different attributes, for example, during limited number times, the travel routes provided by the service providers may bypass the limited travel area; as another example, during rush hour periods, travel routes provided by service providers may bypass congested road segments, and the like. Therefore, based on the above consideration, the selected second historical order data can be subjected to clustering analysis, so that the trajectory identification method of the application can be adapted to various scenes. As shown in fig. 6, step S304 may include the following steps.

Step S3041, classifying according to the attribute information carried by each order in the second historical order data, to obtain at least one type of track data.

Step S3042, performing outlier detection on the driving trajectory of the target service provider and the at least one type of trajectory data to obtain a trajectory identification result.

The attribute information includes at least one of time information, position information, and track information. The time information may include information on a month, a week, an hour, a weekday or a weekend, whether to be holiday or not, and the like; the position information comprises position longitude and latitude, distance from the city center, direction to the city center (direction to the city center or direction far away from the city center) and the like; the track information comprises the speed and the driving direction of each intermediate point of the vehicle running.

Step S3042 may include: and classifying each order in the second historical order data by using a clustering algorithm to obtain at least one type of track data.

Further, in some optional embodiments, classifying each order in the second historical order data by using a clustering algorithm, and obtaining at least one type of trajectory data includes: classifying attribute information carried by each order in the second historical order data by using a Density-Based clustering algorithm (DBSCAN) to obtain at least one type of track data; or classifying the attribute information carried by each order in the second historical order data by using a k-means clustering algorithm to obtain at least one type of track data.

In this embodiment, an anomaly detection algorithm Isolation Forest may be used to detect whether the current order belongs to an abnormal track in a detection data set formed by the current order and various orders in the second historical data. Step S304 may include: step S3043, adding any one kind of track data in the second historical order data into the current order to form a detection data set, and using an anomaly detection algorithm to determine whether the current order in the detection data set is a data outlier sample.

If so, the track identification result is track abnormity; if not, step S3043 is executed until the current order is added to each type of track formation detection data set in the second historical order data for verification.

In addition to using the anomaly detection algorithm Isolation Forest to detect whether the current order belongs to an abnormal track or not in a detection data set formed by the current order and various orders in the second historical data, other anomaly value detection algorithms can be used, for example, a distribution-based anomaly detection algorithm can be used to perform anomaly detection on the current order. Specifically, the distribution-based anomaly detection algorithm can be briefly described as: assuming that data points formed by the current order and various orders in the second historical data are in Gaussian distribution, when the data points of the current order deviate from the average value of the overall data by three times of standard deviation, the current order is considered to be an abnormal point, and then the track identification result of the current order can be judged to be track abnormality.

Further, if the track recognition result of the current order is track abnormity, some management measures can be further taken, and the occurrence of events which are not beneficial to the service requester can be reduced. The track identification method may further include, on the basis of fig. 3: and if the track identification result is characterized as track abnormity, generating alarm information about a target service provider with the track abnormity.

Further, the alarm information may be sent to a service requester of the current order, may also be sent to a management maintenance end corresponding to the order management background, and may also be sent to a service provider of the current order. In one example, the current order may be a network car booking order, and the alarm information is sent to a passenger of the current order, or may be sent to a management device of a network car booking platform, that is, a terminal or an account corresponding to a manager of the network car booking, or may be sent to a driver of the current order.

Wherein, the condition that the track recognition result is characterized as track abnormity comprises the following steps: the positioning signal of the target service provider disappears; or, the driving track corresponding to the target service provider is different from any track in the second historical order data.

In other embodiments, if the route of the current order is long, the current order may be divided into a plurality of segment routes, each segment route is processed according to the flow shown in fig. 3, and whether the travel track of each segment route of the target server is abnormal or not is determined.

Based on the same application concept, the embodiment of the present application further provides a trajectory recognition device corresponding to the trajectory recognition method, and as the principle of solving the problem of the device in the embodiment of the present application is similar to the trajectory recognition method in the embodiment of the present application, the implementation of the device may refer to the implementation of the method, and repeated details are not repeated.

Fig. 7 is a block diagram illustrating a trajectory recognition device according to some embodiments of the present application, which implements functions corresponding to the steps performed by the above-described method. The device may be understood as the server or a processor of the server, or may be understood as a component that is independent of the server or the processor and implements the functions of the present application under the control of the server, as shown in fig. 7, the trajectory recognition device may include an obtaining module 401, a first selecting module 402, a second selecting module 403, and a matching module 404.

The obtaining module 401 may be configured to obtain order information of a current order of a target service provider, where the order information includes a first starting point and a first ending point;

the first selecting module 402 may be configured to select first historical order data, where a first endpoint of any order in the first historical order data is within a limited range of the first starting point;

the second selecting module 403 may be configured to select second historical order data from the first historical order data; a second endpoint of any order in the second historical order data is within a limited range of the first endpoint, and the second endpoint is an endpoint of the order corresponding to the first endpoint or a starting point of the order;

the matching module 404 may be configured to form a data set from the order route in the second historical order data and the driving track of the target service provider, and perform outlier detection on the driving track of the target service provider according to the data set to obtain a track identification result.

The first selecting module 402 includes:

In an optional implementation manner, the first computing unit may be further configured to:

obtaining first longitude and latitude data of the first starting point;

Optionally, the calculating the first longitude and latitude data and the second longitude and latitude data by using a hemiversine function is represented as:

representing a latitude of the first origin;

The second selecting module 403 is further configured to:

In an optional implementation manner, the second calculating unit may be further configured to:

The matching module 404 includes:

The attribute information includes at least one of time information, position information and track information.

The above classification unit is further configured to:

The matching module 404 may further be configured to:

In other embodiments, the trajectory recognition device further includes:

The case that the track identification result is characterized as track abnormity can include: the target service provider's positioning signal disappears; or, the driving track corresponding to the target service provider is different from any track in the second historical order data.

The modules may be connected or in communication with each other via a wired or wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, etc., or any combination thereof. The wireless connection may comprise a connection over a LAN, WAN, bluetooth, ZigBee, NFC, or the like, or any combination thereof. Two or more modules may be combined into a single module, and any one module may be divided into two or more units.

The description of the processing flow of each module in the device and the interaction flow between the modules may refer to the related description in the above method embodiments, and will not be described in detail here.

Based on the track identification method, the track identification device and the track identification system, the running conditions of the routes in the current order and the historical order are identified by comparing the track of the current order with the similar routes in the historical order, and compared with the prior art that the running route of the service provider can only be obtained and whether the running route of the service provider is normal or not can not be obtained, the running track provided by the target service provider and the running route provided by the historical data are outlier or not, so that the running track provided by the target service provider can be obtained, and some potential dangerous paths can be identified.

In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the trajectory identification method in the above-mentioned method embodiment.

The computer program product of the trajectory identification method provided in the embodiment of the present application includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the steps of the trajectory identification method in the above method embodiment, which may be referred to specifically in the above method embodiment, and are not described herein again.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to corresponding processes in the method embodiments, and are not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A trajectory recognition method, comprising:

2. The method of claim 1, wherein said step of extracting first historical order data comprises:

3. The method of claim 2, wherein the step of calculating a first distance of the first endpoint from the first endpoint for each order in the historical data comprises:

obtaining first longitude and latitude data of the first starting point;

4. The method of claim 3, wherein the step of calculating the spherical distance between the first endpoint and the first endpoint of each order in the history data based on the first longitude and latitude data and the second longitude and latitude data comprises:

5. The method of claim 4, wherein the calculating the first longitude and latitude data and the second longitude and latitude data using a hemiversine function is represented as:

representing a latitude of the first origin;

representing historical dataThe latitude of the first endpoint of each order; λ 1 represents the latitude of the first origin; λ 2 represents the latitude of the first endpoint of each order in the historical data; hav denotes the hemiversine function.

6. The method of claim 2, wherein the step of calculating a first distance of the first endpoint from the first endpoint for each order in the historical data comprises:

7. The method of claim 1, wherein the step of selecting second historical order data from the first historical order data comprises:

8. The method of claim 7, wherein said step of calculating a second distance of said first endpoint from a second endpoint of each order in said first historical order data comprises:

9. The method of claim 1, wherein the step of forming the order route in the second historical order data and the travel track of the target service provider into a data set, and performing outlier detection on the travel track of the target service provider according to the data set to obtain a track identification result comprises:

10. The method of claim 9, wherein the attribute information includes at least one of time information, location information, and trajectory information.

11. The method according to claim 9, wherein the step of classifying according to attribute information carried by each order in the second historical order data to obtain at least one type of track data comprises:

12. The method of claim 11, wherein said step of using a clustering algorithm to classify each order in said second historical order data to obtain at least one type of trajectory data comprises:

13. The method of claim 1, wherein the step of forming the order route in the second historical order data and the travel track of the target service provider into a data set, and performing outlier detection on the travel track of the target service provider according to the data set to obtain a track identification result comprises:

14. The method of claim 1, wherein after the step of forming the order route in the second historical order data into a data set with the travel trajectory of the destination service provider, performing outlier detection on the travel trajectory of the destination service provider based on the data set, and obtaining a trajectory identification result, the method further comprises:

15. The method of claim 1, wherein the condition that the track identification result is characterized as a track anomaly comprises:

16. A trajectory recognition device, comprising:

17. The apparatus of claim 16, wherein the first selection module comprises:

18. The apparatus as recited in claim 17, said first computing unit to further:

obtaining first longitude and latitude data of the first starting point;

19. The apparatus as recited in claim 18, said first computing unit to further:

20. The apparatus of claim 19, wherein the first longitude and latitude data and the second longitude and latitude data are computed using a hemiversine function as:

representing a latitude of the first origin;

21. The apparatus as recited in claim 17, said first computing unit to further:

22. The apparatus of claim 16, wherein the second selection module is further configured to:

23. The apparatus of claim 22, wherein the second computing unit is further configured to:

24. The apparatus of claim 16, wherein the matching module comprises:

25. The apparatus of claim 24, wherein the attribute information comprises at least one of time information, location information, and trajectory information.

26. The apparatus of claim 24, wherein the classification unit is further configured to:

27. The apparatus of claim 26, wherein the classification unit is further configured to:

28. The apparatus of claim 16, wherein the matching module is further configured to:

29. The apparatus of claim 16, wherein the apparatus further comprises:

30. The apparatus of claim 16, wherein the condition that the track identification result is characterized as a track anomaly comprises:

31. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of the method of any of claims 1 to 15.

32. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 15.