CN111859059A

CN111859059A - Geographic information feature extraction method and system

Info

Publication number: CN111859059A
Application number: CN201910921793.3A
Authority: CN
Inventors: 王征; 罗卿
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2019-09-27
Filing date: 2019-09-27
Publication date: 2020-10-30

Abstract

The embodiment of the application discloses a method and a system for extracting geographic information features. The method comprises the following steps: acquiring historical order data; querying geographic information related to the historical order data based on the historical order data to obtain geographic information corresponding to the historical order data; and splicing the historical order data and the geographic information corresponding to the historical order data to obtain an order sample containing geographic information characteristics. According to the method and the device, the order data are associated with the heterogeneous data of different data sources and output in various coding forms, so that the problem of time delay during data query and calling is solved, and the portability of the data is improved; meanwhile, the road ID in the order data can be updated, and the road code dislocation is avoided.

Description

Geographic information feature extraction method and system

Technical Field

The present application relates to the field of geographic information, and in particular, to a method and a system for extracting geographic information features.

Background

The geographic data is data directly or indirectly related to a certain place with respect to the earth, and is a document of elements representing natural phenomena and social phenomena of geographic positions and distribution characteristics. The geographic information is information related to the spatial geographic distribution, and it represents a general term of numbers, characters, figures, images, etc. of data, quality, distribution characteristics, connection, and regularity inherent to the earth surface objects and the environment.

The application of geographic information is indispensable in the field of digital maps, and in the current GPS industry, a plurality of large amounts of geographic data exist, and geographic information from different information sources has different description information and different description granularities. Therefore, there is a need to provide a method and system for extracting geographic information features.

Disclosure of Invention

One embodiment of the application provides a geographic information feature extraction method. The method comprises the following steps: acquiring historical order data; querying geographic information related to the historical order data based on the historical order data to obtain geographic information corresponding to the historical order data; and splicing the historical order data and the geographic information corresponding to the historical order data to obtain an order sample containing geographic information characteristics.

One of the embodiments of the present application provides a geographic information feature extraction system. The system comprises: the system comprises a first acquisition module, a first query module and a first splicing module; wherein: the first acquisition module is used for acquiring historical order data; the first query module is used for querying geographic information related to the historical order data based on the historical order data to obtain geographic information corresponding to the historical order data; the first splicing module is used for splicing the historical order data and the geographic information corresponding to the historical order data to obtain an order sample containing geographic information characteristics.

One embodiment of the application provides a geographic information feature extraction method. The method comprises the following steps: acquiring current order data; inquiring geographic information related to the current order data based on the current order data to obtain geographic information corresponding to the current order data; and splicing the current order data and the geographic information corresponding to the current order data to obtain a current order sample containing geographic information characteristics.

One of the embodiments of the present application provides a geographic information feature extraction system. The system comprises: the system comprises a second acquisition module, a second query module and a second splicing module; wherein: the second obtaining module is used for obtaining the current order data; the second query module is configured to query geographic information associated with the current order data based on the current order data to obtain geographic information corresponding to the current order data; and the second splicing module is used for splicing the current order data and the geographic information corresponding to the current order data to obtain a current order sample containing geographic information characteristics.

One of the embodiments of the present application provides a geographic information feature extraction device. The apparatus comprises at least one processor and at least one storage device for storing instructions that, when executed by the at least one processor, perform a method as in any embodiment of the present application.

One of the embodiments of the present application provides a computer-readable storage medium. The storage medium stores computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes the method according to any embodiment of the application.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an application scenario of a geographic information feature extraction system according to some embodiments of the present application;

FIG. 2 is a schematic diagram of an exemplary computing device shown in accordance with some embodiments of the present application;

FIG. 3 is a block diagram of a geographic information feature extraction system shown in accordance with some embodiments of the present application;

FIG. 4 is a block diagram of a geographic information feature extraction system according to yet another embodiment of the present application;

FIG. 5 is an exemplary flow chart of a geographic information feature extraction method shown in accordance with some embodiments of the present application;

FIG. 6 is an exemplary flow chart of a geographic information feature extraction method according to yet another embodiment of the present application;

FIG. 7 is an exemplary schematic diagram of a road attribute shown in accordance with some embodiments of the present application;

FIG. 8 is an exemplary diagram of an intersection attribute shown in accordance with some embodiments of the present application; and

FIG. 9 is a diagram illustrating an exemplary format of an order sample containing geographic information features according to some embodiments of the present application.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Embodiments of the present application may be applied to different transportation systems, e.g., taxis, special cars, tailgating, buses, designated drives, etc. The terms "passenger", "passenger end", "user terminal", "customer", "demander", "service demander", "consumer", "user demander" and the like are used interchangeably and refer to a party that needs or orders a service, either a person or a tool. Similarly, "driver," "provider," "service provider," "server," and the like, as described herein, are interchangeable and refer to an individual, tool, or other entity that provides a service or assists in providing a service. In addition, a "user" as described herein may be a party that needs or subscribes to a service, or a party that provides or assists in providing a service.

Fig. 1 is a schematic diagram of an application scenario of a geographic information feature extraction system 100 according to some embodiments of the present application. The geographic information feature extraction system 100 can splice order data and geographic information corresponding to the order data to obtain an order sample containing geographic information features and output the order sample in multiple coding forms, so that the problem of time delay caused by inquiring and calling multiple dimensional data together is solved, and the portability of the data is improved. The geographic information feature extraction system 100 may be a service platform for the internet or other networks. For example, the geographic information feature extraction system 100 may be an offline service platform that provides services for transportation. In some embodiments, the geographic information feature extraction system 100 may be applied to taxi appointment services, such as taxi calls, express calls, special calls, mini-bus calls, car pool, bus service, driver employment and pickup services, and the like. In some embodiments, the geographic information feature extraction system 100 may also be applied to designated driving, express delivery, take-away, and the like. In other embodiments, the geographic information feature extraction system 100 may also be applied to the fields of household services, travel (e.g., tourism) services, education (e.g., offline education) services, and the like. The geographic information feature extraction system 100 may include a server 110, one or more service requester terminals 120, a storage device 130, one or more service provider terminals 140, a network 150, and an information source 160. The server 110 may include a processing engine 112.

In some embodiments, the server 110 may be a single server or a group of servers. The server farm can be centralized or distributed (e.g., server 110 can be a distributed system). In some embodiments, the server 110 may be local or remote. For example, the server 110 may access information and/or data stored in the storage device 130, the service requester terminal 120 through the network 150. As another example, the server 110 may be directly connected to the storage device 130, the service requester terminal 120 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, a distributed cloud, between clouds, multiple clouds, the like, or any combination of the above. In some embodiments, server 110 may be implemented on a computing device similar to that shown in FIG. 2 of the present application. For example, server 110 may be implemented on one computing device 200 as shown in FIG. 2, including one or more components in computing device 200. In some embodiments, processing engine 112 may process data and/or information related to geographic information feature extraction to perform one or more of the functions described herein. For example, the processing engine 112 may query geographic information associated with the historical order data, and concatenate the historical order data with geographic information corresponding to the historical order data to obtain an order sample including geographic information characteristics. In some embodiments, the processing engine 112 may retrieve historical or current order data. In some embodiments, the processing engine 112 may query geographic information associated with historical or current order data based on the historical or current order data. In some embodiments, the processing engine 112 may concatenate the historical or current order data with geographic information corresponding to the historical or current order data to obtain an order sample containing geographic information characteristics. In some embodiments, the processing engine 112 may update the link ID in the historical or current order data if the link ID in the link information is not included in the historical or current order data.

In some embodiments, the user of the service requester terminal 120 may be the service requester himself. In some embodiments, the user of the service requester terminal 120 may be a person other than the service requester. For example, in the network car booking service, the user of the service requester terminal 120 may be the vehicle occupant himself or a person who places an order with the vehicle occupant, such as a relative or a friend of the vehicle occupant. For example, in the takeout service, the user of the service requester terminal 120 may be a target object for takeout delivery or a person who helps the target object to take out. For another example, in the home service, the user of the service requester terminal 120 may be an actual requester of the home service, or a person who helps the requester to purchase the home service.

In some embodiments, the user of the service provider terminal 140 may be the service provider himself. In some embodiments, the user of service provider terminal 140 may be a person other than the service provider. For example, in the network appointment service, the user of the service provider terminal 140 may be the driver himself or herself, or a person who helps the driver to take an order. For example, in the takeaway service, the user of the service provider terminal 140 may be the takeaway dispatcher himself or a person who helps the dispatcher take an order. For another example, in home services, the user of the service provider terminal 140 may be an actual service person (such as a maintenance person, a cleaner, etc.) of the home services, or a person who helps the service person to take an order.

In some embodiments, the service requester terminal 120 may include, but is not limited to, a desktop computer 120-1, a laptop computer 120-2, an in-vehicle built-in device 120-3, a mobile device 120-4, and the like or any combination thereof. In some embodiments, the in-vehicle built-in device 120-3 may include, but is not limited to, a personal computer, an in-vehicle heads-up display (HUD), an in-vehicle automatic diagnostic system (OBD), and the like, or any combination thereof. In some embodiments, mobile device 120-4 may include, but is not limited to, a smartphone, a Personal Digital Assistant (PDA), a tablet, a palmtop, smart glasses, a smart watch, a wearable device, a virtual display device, a display enhancement device, and the like, or any combination thereof. In some embodiments, the service requester terminal 120 may send the transportation service requirements to one or more devices in the geographic information feature extraction system 100. For example, the service requester terminal 120 may send the transport service requirements to the server 110 for processing.

In some embodiments, the service provider terminal 140 may be a similar or identical device as the service requestor terminal 120. In some embodiments, the service provider terminal 140 may be a device with location technology to determine the location of the service provider and/or the service provider terminal 140. In some embodiments, the service requester terminal 120 and/or the service provider terminal 140 may communicate with other locating devices to determine the location of the service requester, service requester terminal 120, service provider, or service provider terminal 140. In some embodiments, the service requester terminal 120 and/or the service provider terminal 140 may send the location information to the server 110.

Storage device 130 may store data and/or instructions. In some embodiments, the storage device 130 may store data obtained from the service requester terminal 120. In some embodiments, storage device 130 may store data and/or instructions for execution or use by server 110, which may be executed or used by server 110 to implement the example methods described herein. In some embodiments, the storage device 130 may be connected to a network 150 to enable communication with one or more components (e.g., the server 110, the service requester terminal 120, etc.) in the geographic information feature extraction system 100. One or more components of the geographic information feature extraction system 100 may access data or instructions stored in the storage device 130 via the network 150. In some embodiments, the storage device 130 may be directly connected to or in communication with one or more components of the geographic information feature extraction system 100 (e.g., the server 110, the service requester terminal 120, etc.). In some embodiments, storage device 130 may be part of server 110.

The network 150 may facilitate the exchange of information and/or data. In some embodiments, one or more components of the geographic information feature extraction system 100 (e.g., the server 110, the storage device 130, and the service requester terminal 120, etc.) may send information and/or data to other components of the geographic information feature extraction system 100 via the network 150. For example, the server 110 may obtain/obtain data information from the service requester terminal 120 through the network 150. In some embodiments, the network 150 may be any one of, or a combination of, a wired network or a wireless network. For example, network 150 may include a cable network, a wired 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 Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, the like, or any combination of the above. In some embodiments, network 150 may include one or more network access points. For example, the network 150 may include wired or wireless network access points, such as base stations and/or Internet switching points 150-1, 150-2, and so forth. Through the access point, one or more components of the geographic information feature extraction system 100 may be connected to a network 150 to exchange data and/or information.

The information source 160 is a source that provides other information to the geographic information feature extraction system 100. Information sources 160 may be used to provide the system with information related to order information, such as service times, service locations, legal information, news information, life guide information, and the like. The information source 160 may be in the form of a single central server, or may be in the form of a plurality of servers connected via a network, or may be in the form of a large number of personal devices. When the information source 160 is in the form of a plurality of personal devices, the devices may upload text, voice, images, videos, etc. to the cloud server in a user-generated content manner, so that the cloud server communicates with the plurality of personal devices connected thereto to form the information source 160.

FIG. 2 is a schematic diagram of an exemplary computing device 200 shown in accordance with some embodiments of the present application. Server 110 and storage device 130 may be implemented on computing device 200. For example, the processing engine 112 may be implemented on the computing device 200 and configured to implement the functionality disclosed herein.

Computing device 200 may include any components used to implement the systems described herein. For example, the processing engine 112 may be implemented on the computing device 200 by its hardware, software programs, firmware, or a combination thereof. For convenience, only one computer is depicted in the figures, but the computing functions described herein in connection with the geographic information feature extraction system 100 may be implemented in a distributed manner by a set of similar platforms to distribute the processing load of the system.

Computing device 200 may include a communication port 250 for connecting to a network for enabling data communication. Computing device 200 may include a processor (e.g., CPU)220 that may execute program instructions in the form of one or more processors. Exemplary computer platforms may include an internal bus 210, various forms of program storage and data storage including, for example, a hard disk 270, Read Only Memory (ROM)230 or Random Access Memory (RAM)240 for storing various data files that are processed and/or transmitted by the computer. An exemplary computing device may include program instructions stored in read-only memory 230, random access memory 240, and/or other types of non-transitory storage media that are executed by processor 220. The methods and/or processes of the present application may be embodied in the form of program instructions. Computing device 200 also includes input/output component 260 for supporting input/output between the computer and other components. Computing device 200 may also receive programs and data in the present disclosure via network communication.

For ease of understanding, only one processor is exemplarily depicted in fig. 2. However, it should be noted that the computing device 200 in the present application may include multiple processors, and thus the operations and/or methods described in the present application that are implemented by one processor may also be implemented by multiple processors, collectively or independently. For example, if in the present application the processors of computing device 200 perform

steps

1 and 2, it should be understood that

steps

1 and 2 may also be performed by two different processors of computing device 200, either collectively or independently (e.g., a first processor performing step 1, a second processor performing step 2, or a first and second

processor performing steps

1 and 2 collectively).

FIG. 3 is a block diagram of a geographic information feature extraction system shown in accordance with some embodiments of the present application. As shown in fig. 3, the geographic information feature extraction system 300 may include a first obtaining module 310, a first querying module 320, a first stitching module 330, and an updating module 340. In some embodiments, the first obtaining module 310, the first querying module 320, the first stitching module 330, and the updating module 340 may be included in the processing engine 112 shown in fig. 1.

The first obtaining module 310 may be used to obtain historical order data.

In some embodiments, the historical orders may include network appointment service orders (e.g., taxi orders, express orders, carpool orders, tailwind orders, bus orders, and the like). In some embodiments, the historical order data may include data related to historical orders. Specifically, the historical order data may include a road ID and one or more of the following: driver information, passenger information, weather, order time, regional information, route information, and intersection information.

In some embodiments, historical order data may be stored in storage 130. In some embodiments, the first obtaining module 31O may obtain historical order data from the storage device 130.

The first query module 320 may be configured to query geographic information associated with the historical order data based on the historical order data to obtain geographic information corresponding to the historical order data. In some embodiments, the geographic information corresponding to historical order data may include road information and traffic information.

As shown in fig. 3, the first query module 320 may further include a road information query unit 321 and a traffic information query unit 322. The road information query unit 321 may be configured to query the road network database for road information based on the road ID in the historical order data, so as to obtain road information corresponding to the historical order data. In some embodiments, the road network database may store road information such as road ID, road width, and road length. In some embodiments, the road information may include road attributes and/or intersection attributes, which may be from mapping data.

The traffic information query unit 322 may be configured to query the traffic information in the traffic database based on the road ID in the historical order data, and obtain traffic information corresponding to the historical order data. In some embodiments, information such as road ID, road traffic time, and traffic light waiting time may be stored in the traffic database. In some embodiments, the traffic information includes one or more of road speed, road travel time, traffic light wait time. In some embodiments, the traffic information in the traffic database may be obtained through other historical order statistics or through model training, which is not limited in this application.

In some embodiments, the historical order data and the geographic information may be heterogeneous data. The heterogeneous data refers to the data sources with different encoding forms, for example, the historical order data may be in a binary encoding form, and the geographic information may be in a text encoding form.

In some embodiments, the road information and the traffic information may be stored in the storage device 130, and the first query module 320 may obtain the data for feature extraction.

The first splicing module 330 may be configured to splice the historical order data and geographic information corresponding to the historical order data to obtain an order sample including a geographic information feature.

In some embodiments, the order sample containing the geographic information characteristic may be a geographic information characteristic code. In some embodiments, the encoded form of the order sample containing the geographic information characteristic may include one or more of binary encoding, text encoding, HTML5, and ASCII encoding. Through splicing of historical order data and corresponding geographic information, different encoding forms are output and can be used as feature samples by different models or platforms.

The update module 340 may be configured to update the road ID in the historical order data if the road ID in the road information is not included in the historical order data.

It should be understood that the system and its modules shown in FIG. 3 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It should be noted that the above description of the geographic information feature extraction system and the modules thereof is only for convenience of description, and does not limit the present application within the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, the update module 340 and the first query module 320 may be the same module, and the first query module 320 may query geographic information associated with the historical order data and update the road ID in the historical order data at the same time. Such variations are within the scope of the present application.

Fig. 4 is a block diagram of a geographic information feature extraction system according to yet another embodiment of the present application. As shown in fig. 4, the geographic information feature extraction system 400 may include a second obtaining module 410, a second querying module 420, and a second stitching module 430. In some embodiments, the second obtaining module 410, the second querying module 420, and the second splicing module 430 may be included in the processing engine 112 shown in fig. 1.

The second obtaining module 410 may be used to obtain current order data.

In some embodiments, the current order may include a network appointment service order (e.g., a taxi order, a express order, a carpool order, a tailwind order, a bus order, etc.). In some embodiments, the current order data may include data related to the current order. In particular, the current order data may include a road ID and one or more of the following: driver information, passenger information, weather, order time, regional information, route information, and intersection information.

In some embodiments, the current order data may be stored in the storage device 130. In some embodiments, the second obtaining module 410 may obtain the current order data from the storage device 130.

The second query module 420 may be configured to query geographic information associated with the current order data based on the current order data to obtain geographic information corresponding to the current order data.

In some embodiments, the geographic information corresponding to the current order data may include road information and traffic information. In some embodiments, querying geographic information associated with the current order data based on the current order data may include querying a road network database for road information based on a road ID in the current order data, resulting in road information corresponding to the current order data. In some embodiments, the road network database may store road information such as road ID, road width, and road length. Further description of the road information and traffic information may be found elsewhere in this application (e.g., in flowchart 5 and its associated description), and will not be repeated herein.

In some embodiments, the current order data and the geographic information may be heterogeneous data. The heterogeneous data refers to the data sources with different encoding forms, for example, the current order data may be in a binary encoding form, and the geographic information may be in a text encoding form.

In some embodiments, the road information and traffic information may be stored in the storage device 130, and the second query module 420 may retrieve the data for feature extraction.

The second splicing module 430 may be configured to splice the current order data and geographic information corresponding to the current order data to obtain an order sample including a geographic information feature.

In some embodiments, the order sample containing the geographic information characteristic may be a geographic information characteristic code. In some embodiments, the encoded form of the order sample containing the geographic information characteristic may include one or more of binary encoding, text encoding, HTML5, and ASCII encoding. .

It should be understood that the system and its modules shown in FIG. 4 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It should be noted that the above description of the geographic information feature real-time extraction system and the modules thereof is only for convenience of description, and does not limit the present application within the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, the second query module 420 and the second concatenation module 430 may be the same module, and the second query module 420 may concatenate the geographic information associated with the current order data immediately after querying the geographic information. Such variations are within the scope of the present application.

Fig. 5 is an exemplary flow chart of a geographic information feature extraction method shown in accordance with some embodiments of the present application. In some embodiments, flow 500 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions run on a processing device to perform hardware simulation), etc., or any combination thereof. One or more operations in the process 500 for extracting geographic information features shown in fig. 5 may be implemented by the geographic information feature extraction system 100 shown in fig. 1. For example, the flow 500 may be stored in the storage device 130 in the form of instructions and executed and/or invoked by the processing engine 112 (e.g., the processor 220 of the computing device 200 shown in fig. 2).

In step 510, historical order data may be obtained. In particular, step 510 may be performed by the first obtaining module 310.

In some embodiments, the historical orders may include network appointment service orders (e.g., taxi orders, express orders, carpool orders, tailwind orders, bus orders, and the like). In some embodiments, the historical order data may include data related to historical orders. Specifically, the historical order data may include a road ID and one or more of the following: driver information, passenger information, weather, order time, regional information, route information, and intersection information. In some embodiments, the link ID may be a unique code given to each link in order to distinguish each link. With the road ID, the corresponding road can be quickly found on the digital map.

In some embodiments, the driver information or passenger information may include a driver or passenger history, driver or passenger personal information, and driver or passenger subscription requirements for services. In some embodiments, the driver or passenger history may be the driver or passenger's operational behavior on the platform. In some embodiments, the operation behavior of the driver or passenger on the platform may include the order cancellation frequency of the driver or passenger, the order cancellation timing, the order modification operation, and the like. For example, the frequency of a passenger or driver taking an order within a day, or the frequency of a driver taking an order again after a continuous order snatching within 10 minutes. In some embodiments, the driver or passenger history may also be a driver or passenger's or total number of orders over a time frame. For example, the passenger or driver completes the number of orders within a month.

In some embodiments, the personal information of the driver or the passenger may include a sex of the driver or the passenger, a registration time of the driver or the passenger, an address confidence of the driver or the passenger, a work place confidence of the driver or the passenger, a loan condition of the driver or the passenger, an education level of the driver or the passenger, a number of complaints made by the driver or the passenger in a certain time range, and an evaluated condition of the driver or the passenger in a certain time range. In some embodiments, the driver or passenger address confidence, the driver or passenger work location confidence, may be determined based on the user frequented location confidence.

In some embodiments, the loan condition may include, but is not limited to, the number of loans, the amount of borrowed, the term of borrowed, and the repayment condition. In some embodiments, the first obtaining module 310 may obtain the driver's or passenger's loan status from a third party database. The third party database includes but is not limited to bank database, social security agency database, credit assessment agency database, p2p network lending platform database.

In some embodiments, the level of education may be represented by discrete values. For example, the primary school culture is represented by 0, the junior middle school culture is represented by 1, the high school culture is represented by 2, and the subject and above are represented by 3. In some embodiments, the educational level may be represented by two values, where the educational level is represented by 1 if the academic story is above the subject and 0 if below the subject.

In some embodiments, the personal information of the driver or passenger may include the number of complaints that the driver or passenger was complained about within a certain time frame and/or the evaluated condition of the driver or passenger within a certain time frame. For example, the number of complaints made by the driver or passenger in a month may be counted. In some embodiments, drivers or passengers may be rated against each other, with a full score of five stars, and at worst there may be no or one star; or directly give good or bad comments for mutual evaluation. For example, the number of bad comments received by the driver or passenger, the number of good comments received by the driver or passenger within one month may be counted; or the order quantity of one-star evaluation received in one month and the order quantity of five-star evaluation received in one month.

In some embodiments, the driver or passenger subscription requirements for the service may include the passenger subscription requirements for the service tool or the driver subscription requirements, and may also include the driver or passenger subscription requirements for the other party. For example, the passenger's requirement for the gender of the driver, the driver's claim for the amount of goodness, the brand of the vehicle, the model of the vehicle, or the price of the vehicle, etc. As another example, a driver or passenger's requirement for a party's personal habits (e.g., no smoking).

In some embodiments, the weather information may include temperature, humidity, weather conditions (e.g., sunny, rainy, or cloudy), and air quality. In some embodiments, the order time may include an order placement time and an order execution time. In some embodiments, the order time may be the time at which the service request order is submitted to the server and the order execution time may be the time at which the passenger gets on the bus. For example, the passenger's order time is 7:00 am and the order execution time is 9:00 am.

In some embodiments, the regional information may include provinces, cities, districts, street names, and road names. In some embodiments, the route information and the intersection information may include route information of a history order corresponding to the route information and the intersection information, for example, a Link and a Node of a road through which the route passes. In some embodiments, the topological structure of the roads in the electronic map is formed by line segments, Link is a basic unit of a road model in the electronic map and can represent a section of a road; node represents the intersection point of the road and the road, and the Node and Link can represent the topological structure of the whole road; roads of a road model in the electronic map are directional, and the road model is similar to a mesh structure. The number corresponding to the Link is the Link ID.

In some embodiments, historical order data may be stored in storage 130. In some embodiments, the first retrieval module 310 may retrieve historical order data from the storage device 130.

In step 520, geographic information associated with the historical order data may be queried based on the historical order data, resulting in geographic information corresponding to the historical order data. In particular, step 520 may be performed by the first query module 320.

In some embodiments, the geographic information corresponding to historical order data may include road information and traffic information. In some embodiments, querying geographic information associated with the historical order data based on the historical order data may include querying a road network database for road information based on a road ID in the historical order data, resulting in road information corresponding to the historical order data. In some embodiments, the road network database may store road information such as road ID, road width, and road length. In some embodiments, the road information may include road attributes and/or intersection attributes, which may be from mapping data. In some specific embodiments, as shown in fig. 7, the road attributes may include one or more of a map view ID, a road width, a road length, a road class number, a traffic direction, and a toll setting. In some specific embodiments, as shown in fig. 8, the intersection attributes may include one or more of a map panel ID, a road ID, an intersection level number, an intersection identification, traffic light information, an intersection connection Link number, an intersection main point number, an intersection sub point number, a connection adjacent point panel ID, a connection adjacent point number, and a connection Link number.

In some embodiments, querying the geographic information associated with the historical order data based on the historical order data may further include querying traffic information in a traffic database based on a road ID in the historical order data to obtain traffic information corresponding to the historical order data. In some embodiments, information such as road ID, road traffic time, and traffic light waiting time may be stored in the traffic database. In some embodiments, the traffic information includes one or more of road speed, road travel time, traffic light wait time. In some embodiments, the traffic information in the traffic database may be obtained through other historical order statistics or through model training, which is not limited in this application.

In step 530, the historical order data and the geographic information corresponding to the historical order data may be spliced to obtain an order sample including a geographic information feature. In particular, step 530 may be performed by the first stitching module 330.

In some embodiments, the order sample containing the geographic information characteristic may be a geographic information characteristic code. As shown in fig. 9, the geographic information feature code may include three portions. Specifically, a first portion (also referred to as portion 1) of the geographic information characteristic encoding format may encode a data header beginning with a road ID in the historical order data; a second part of the geographic information feature encoding format (also referred to as part 2) may encode road information corresponding to historical order data, such as road attributes and intersection attributes; a third portion of the geographic information characteristic encoding format (also referred to as portion 3) may encode traffic information corresponding to historical order data, such as road travel speed, road travel time, traffic light wait time, and the like. It should be noted that the three portions of the geographic information characteristic code are provided for illustration only, and the format of the geographic information characteristic code may include any number of portions. For example, the second portion may be divided into two separate portions.

In some embodiments, the portions of the geographic information characteristic code may be placed in a predetermined order. The predetermined order may be an order from part 1 to part 3 (e.g., part 1, part 2, part 3). Alternatively, in addition to section 1, the other two sections of the geographic information feature code may be randomly arranged (e.g., section 1, section 3, section 2).

In some embodiments, the first stitching module 330 may stitch the data of the above sections 1 to 3 to obtain an order sample containing geographic information features. In some embodiments, the encoded form of the order sample containing the geographic information characteristic may include one or more of binary encoding, text encoding, HTML5, and ASCII encoding. The historical order data and the corresponding geographic information are spliced (wherein the historical order data and the geographic information data can be heterogeneous data), so that different encoding forms are output and can be used as feature samples by different models or platforms.

In some embodiments, step 540 may be further included, if the road ID in the road information is not included in the historical order data, updating the road ID in the historical order data. In particular, this step 540 may be performed by the update module 340.

In some embodiments, the road information is updated for mapping or the like, and the road ID in the road information may not be included in the historical order data. Specifically, if the historical order data does not include the road ID of the road M (e.g., the road ID is "null"), the method in steps 510 to 530 cannot query the geographic information associated with the historical order data, at this time, the GPS point of the route or intersection of the road M in the historical order data may be used to query the geographic information associated with the historical order data, and the queried geographic information includes the road ID of the road M (e.g., 0018), and then the road ID of the road M in the historical order data is updated to 0018. If another road N with a road ID of 0018 is already included in the history order data, the road M is numbered 0251 if the last road in the history order data is numbered in sequence, for example, the current last road in the history order data is numbered 0250. By updating the road ID in the historical order data, calculation errors caused by code misalignment can be avoided.

It should be noted that the above description related to the flow 500 is only for illustration and explanation, and does not limit the applicable scope of the present application. Various modifications and changes to flow 500 may occur to those skilled in the art upon review of the present application. However, such modifications and variations are intended to be within the scope of the present application. For example, step 520 may be split into two steps, and the first query module 320 may first query the road information in the road network database, and then query the traffic information in the traffic database; the first query module 320 may also query the traffic information in the traffic database and then query the road information in the road network database.

Fig. 6 is an exemplary flowchart illustrating a geographic information feature extraction method according to still another embodiment of the present application. In some embodiments, flow 600 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions run on a processing device to perform hardware simulation), etc., or any combination thereof. One or more operations in the flow 600 for extracting geographic information features shown in fig. 6 may be implemented by the geographic information feature extraction system 100 shown in fig. 1. For example, the flow 600 may be stored in the storage device 150 in the form of instructions and executed and/or invoked by the processing engine 112 (e.g., the processor 220 of the computing device 200 shown in fig. 2).

In step 610, current order data is obtained. In particular, this step 610 may be performed by the second obtaining module 410.

In some embodiments, the real-time order may include a network appointment service order (e.g., a taxi order, a express order, a carpool order, a tailwind order, a bus order, etc.). In some embodiments, the current order data may include data related to a real-time order. In particular, the current order data may include a road ID and one or more of the following: driver information, passenger information, weather, order time, regional information, route information, and intersection information. Further description of the order data may be found elsewhere in this application (e.g., in flowchart 5 and related description), and will not be repeated herein.

In some embodiments, the current order data may be stored in the storage device 130, and the second obtaining module 410 may obtain the current order data from the storage device 130.

In step 620, geographic information associated with the current order data may be queried based on the current order data to obtain geographic information corresponding to the current order data. In particular, this step 610 may be performed by the second query module 420.

In some embodiments, the geographic information corresponding to the current order data may include road information and traffic information. In some embodiments, querying geographic information associated with the current order data based on the current order data may include querying a road network database for road information based on a road ID in the current order data, resulting in road information corresponding to the current order data. In some embodiments, the road network database may store road information such as road ID, road width, and road length. In some embodiments, querying the geographic information associated with the current order data based on the current order data may further include querying traffic information in a traffic database based on a road ID in the current order data to obtain traffic information corresponding to the current order data. In some embodiments, information such as road ID, road traffic time, and traffic light waiting time may be stored in the traffic database.

More details about querying the geographic information can be found elsewhere in the present application (e.g., in flowchart 5 and related description), and are not repeated herein. In some embodiments, the road information and traffic information may be stored in the storage device 130, and the second query module 420 may retrieve the data for feature extraction.

In step 630, the current order data and the geographic information corresponding to the current order data may be spliced to obtain a real-time order sample including a geographic information feature. In particular, this step 630 may be performed by the second stitching module 430.

In some embodiments, the real-time order sample containing the geographic information characteristic may be a geographic information characteristic code. Specifically, the geographic information feature code may include three portions. Further description of the geographic information feature code can be found elsewhere in this application (e.g., in flowchart 5 and related description), and will not be repeated herein.

In some embodiments, the second stitching module 430 may stitch the geographic information with the current order data to obtain a real-time order sample containing geographic information characteristics. In some embodiments, the encoded form of the real-time order sample containing the geographic information features may include one or more of binary encoding, text encoding, HTML5, and ASCII encoding. By splicing the current order data and the corresponding geographic information (wherein the current order data and the geographic information data can be heterogeneous data), different encoding forms are output and can be used as feature samples by different models or platforms.

The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) the order data is associated with heterogeneous data of different data sources, so that geographic information can be conveniently and quickly extracted, and the problem of time delay caused by the inquiry and calling of a plurality of dimensional data is solved; (2) by outputting various coding forms, the method can be suitable for different models or platforms, and improves the portability of data; (3) the road ID in the order data can be updated, and calculation errors caused by code dislocation are avoided. It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code 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 latter scenario, the remote computer may be connected to the user's computer through any network format, such as 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), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A geographic information feature extraction method, the method comprising:

acquiring historical order data;

querying geographic information related to the historical order data based on the historical order data to obtain geographic information corresponding to the historical order data;

and splicing the historical order data and the geographic information corresponding to the historical order data to obtain an order sample containing geographic information characteristics.

2. The method of claim 1, wherein the historical order data comprises a road ID and at least one of:

driver information, passenger information, weather information, order time, regional information, route information, and intersection information.

3. The method of claim 2, wherein the querying the geographic information associated with the historical order data based on the historical order data to obtain the geographic information corresponding to the historical order data comprises:

Inquiring road information in a road network database based on the road ID in the historical order data to obtain road information corresponding to the historical order data;

inquiring traffic information in a traffic database based on the road ID in the historical order data to obtain traffic information corresponding to the historical order data;

the road network database at least stores road width and road length information, and the traffic database at least stores road passing time and traffic light waiting time information.

4. The method of claim 3, wherein the road information comprises road attributes and/or intersection attributes, the road information being from mapping data.

5. The method of claim 4, wherein the road attributes include at least one of a road ID, a road width, a road length, a road grade, a traffic direction, and a toll setting.

6. The method of claim 4, wherein the intersection attributes comprise at least one of intersection identification and traffic light information.

7. The method of claim 3, wherein the traffic information includes at least one of road speed, road travel time, traffic light wait time.

8. The method of claim 1, further comprising:

and if the road ID in the road information is not contained in the historical order data, updating the road ID in the historical order data.

9. The method of claim 1, wherein the encoded form of the order sample containing the geographic information characteristic comprises at least one of binary encoding, text encoding, HTML5, and ASCII encoding.

10. The method of claim 1, wherein the historical order data and the geographic information are heterogeneous data.

11. A geographic information feature extraction system is characterized by comprising a first acquisition module, a first query module and a first splicing module; wherein:

the first acquisition module is used for acquiring historical order data;

the first query module is used for querying geographic information related to the historical order data based on the historical order data to obtain geographic information corresponding to the historical order data;

the first splicing module is used for splicing the historical order data and the geographic information corresponding to the historical order data to obtain an order sample containing geographic information characteristics.

12. The system of claim 11, wherein the historical order data includes a road ID and at least one of:

13. The system of claim 12, wherein the first query module includes a road information query unit and a traffic information query unit; wherein:

the road information query unit is used for querying road information in a road network database based on the road ID in the historical order data to obtain the road information corresponding to the historical order data;

the traffic information inquiry unit is used for inquiring traffic information in a traffic database based on the road ID in the historical order data to obtain traffic information corresponding to the historical order data;

the road network database at least stores road width and road length information, and the traffic database at least stores road traffic time and traffic light waiting time information.

14. The system of claim 13, wherein the road information comprises road attributes and/or intersection attributes, the road information being from mapping data.

15. The system of claim 14, wherein the road attributes include at least one of a road ID, a road width, a road length, a road grade, a traffic direction, and a toll setting.

16. The system of claim 14, wherein the intersection attributes comprise at least one of intersection identification and traffic light information.

17. The system of claim 13, wherein the traffic information includes at least one of road speed, road travel time, traffic light wait time.

18. The system of claim 1, further comprising an update module, wherein:

and the updating module is used for updating the road ID in the historical order data if the road ID in the road information is not contained in the historical order data.

19. The system of claim 11, wherein the coded form of the order sample containing the geographic information characteristic comprises at least one of binary code, text code, HTML5, and ASCII code.

20. The system of claim 11, wherein the historical order data and the geographic information are heterogeneous data.

21. A geographic information feature extraction method, the method comprising:

Acquiring current order data;

inquiring geographic information related to the current order data based on the current order data to obtain geographic information corresponding to the current order data;

and splicing the current order data and the geographic information corresponding to the current order data to obtain a current order sample containing geographic information characteristics.

22. A geographic information feature extraction system is characterized by comprising a second acquisition module, a second query module and a second splicing module; wherein:

the second obtaining module is used for obtaining the current order data;

the second query module is configured to query geographic information associated with the current order data based on the current order data to obtain geographic information corresponding to the current order data;

and the second splicing module is used for splicing the current order data and the geographic information corresponding to the current order data to obtain a current order sample containing geographic information characteristics.

23. An apparatus for geographic information feature extraction, the apparatus comprising at least one processor and at least one storage device, the storage device configured to store instructions that, when executed by the at least one processor, implement the method of any one of claims 1-10 or 21.

24. A computer readable storage medium storing computer instructions which, when read by a computer, cause the computer to perform the method of any one of claims 1 to 10 or 21.