WO2017054326A1 - 一种加油站poi自动发现的方法、装置、存储介质和设备 - Google Patents

一种加油站poi自动发现的方法、装置、存储介质和设备 Download PDF

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WO2017054326A1
WO2017054326A1 PCT/CN2015/097573 CN2015097573W WO2017054326A1 WO 2017054326 A1 WO2017054326 A1 WO 2017054326A1 CN 2015097573 W CN2015097573 W CN 2015097573W WO 2017054326 A1 WO2017054326 A1 WO 2017054326A1
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Prior art keywords
information
gas station
poi
oil
geo
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PCT/CN2015/097573
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English (en)
French (fr)
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高龙
焦尚伟
支钰
杨武
蔡观洋
张鑫
刘增刚
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百度在线网络技术（北京）有限公司
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Priority to US15/765,231 priority Critical patent/US11314830B2/en
Priority to EP15905237.2A priority patent/EP3349126B1/en
Priority to JP2018516833A priority patent/JP6651006B2/ja
Priority to KR1020187012263A priority patent/KR102054090B1/ko
Publication of WO2017054326A1 publication Critical patent/WO2017054326A1/zh

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    • G06Q50/40—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification
    • G06F16/287—Visualization; Browsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Definitions

  • Embodiments of the present invention relate to the field of search technologies, and in particular, to a method, an apparatus, a storage medium, and a device for automatically discovering a POI of a gas station.
  • the collection method of the point of interest (POI) of the gas station is mainly manual collection by means of step mining, vehicle mining and aerial photography.
  • the specific location of the gas station is not known in advance, and it is only necessary to find the person or the collecting vehicle to reach the corresponding position, the collecting cost is high, and the gas station may be missed;
  • the existing collection method cannot be quickly found, but it needs to be discovered in the next collection cycle, resulting in a long POI update time period at the gas station.
  • the object of the embodiments of the present invention is to provide a method, a device, a storage medium and a device for automatically discovering a POI of a gas station, so as to solve the problem that the POI collection cost of the gas station is high and the update time period is long.
  • an embodiment of the present invention provides a method for automatically discovering a POI of a gas station, including:
  • OBD On-Board Diagnostic
  • the clustering algorithm is used to cluster the oil mass burst points according to the location information
  • the location area where the gas station POI exists is determined based on the clustering result.
  • an embodiment of the present invention provides a device for automatically discovering a POI of a gas station, including:
  • the oil sudden increase point determining module is configured to collect information uploaded by the OBD device, and determine a fuel mass sudden increase point according to the information;
  • a clustering processing module configured to cluster the oil mass burst points according to location information by using a clustering algorithm
  • a location determining module configured to determine, according to the clustering result, a location area where the gas station POI exists.
  • an embodiment of the present invention provides a non-volatile computer storage medium, where the computer storage medium stores one or more modules, when the one or more modules are automatically discovered by a gas station POI.
  • the device of the method is executed, the device is caused to perform the following operations:
  • the clustering algorithm is used to cluster the oil mass burst points according to the location information
  • the location area where the gas station POI exists is determined based on the clustering result.
  • an embodiment of the present invention provides an apparatus, including:
  • One or more processors are One or more processors;
  • One or more programs the one or more programs being stored in the memory, and when executed by the one or more processors, performing the following operations:
  • the clustering algorithm is used to cluster the oil mass burst points according to the location information
  • the location area where the gas station POI exists is determined based on the clustering result.
  • the method, device, storage medium and device for automatically discovering a POI of a gas station provided by an embodiment of the present invention can determine a fuel mass burst point by processing related information according to relevant information uploaded by the OBD device, and then adopt a clustering algorithm. The relevant points of the processing result are clustered, and the location area of the POI of the gas station is automatically determined.
  • the POI of the gas station can be found only after the person or the collecting vehicle arrives at the corresponding position, thereby reducing the collection cost of the POI information of the gas station and shortening the POI of the gas station. Update time period.
  • FIG. 1 is a schematic flow chart of a method for automatically discovering a POI of a gas station according to a first embodiment of the present invention
  • FIG. 2 is a schematic flow chart of a method for automatically discovering a POI of a gas station according to a second embodiment of the present invention
  • FIG. 3 is a schematic diagram of a geo hash string identification location area according to Embodiment 3 of the present invention.
  • FIG. 4 is a schematic structural diagram of an apparatus for automatically discovering a POI of a gas station according to Embodiment 4 of the present invention
  • FIG. 5 is a schematic diagram showing the hardware structure of a device for performing a method for automatically discovering a POI of a gas station according to Embodiment 6 of the present invention.
  • FIG. 1 is a schematic flow chart of a method for automatically discovering a POI of a gas station according to a first embodiment of the present invention. This embodiment can be applied in the case of collecting a gas station POI.
  • the method can be performed by a device that is automatically discovered by a gas station POI, the device being implemented by software and/or hardware.
  • a method for automatically discovering a POI of a gas station according to an embodiment of the present invention specifically includes the following operations:
  • S110 Collect information uploaded by the OBD device, and determine a sudden increase point of the oil quantity according to the information;
  • OBD equipment is installed in the car and can be used to monitor the running status of the car and detect the car in real time. Multiple systems and components are used to determine if the car is in a fault condition.
  • the information uploaded by the OBD device includes information such as the position, speed, remaining fuel, instantaneous fuel consumption, tire pressure, and water temperature of the car.
  • the sudden increase in the amount of oil is the sudden increase in the amount of oil corresponding to the OBD equipment. It is the point of abstracting these cars with OBD equipment. If the information such as the speed and fuel quantity of the car uploaded by the OBD equipment meets certain conditions, Then the OBD device is a sudden increase in oil volume.
  • the location information is location information of the car uploaded by the OBD device corresponding to the oil sudden increase point, including latitude and longitude information of the car location.
  • the clustering algorithm is used to cluster the oil sudden increase points according to the positional confidence such as latitude and longitude, and then the geographical range can be divided into multiple regions, and the oil spurts with similar positions are clustered into one class.
  • the increase point shares a location area, wherein the clustering algorithm includes a geo hash algorithm and the like.
  • the technical solution provided by the embodiment can process the related information according to the related information uploaded by the OBD device to determine the fuel mass burst point, and then use the clustering algorithm to cluster the processing result, and automatically determine the location area of the POI of the gas station. Reduce the POI collection cost of the gas station and shorten the POI update time period of the gas station.
  • FIG. 2 is a schematic flow chart of a method for automatically discovering a POI of a gas station according to a second embodiment of the present invention. This embodiment optimizes operation S110 on the basis of the first embodiment. As shown in Figure 2, the specific process of the method is as follows:
  • S210 Collect information uploaded by multiple OBD devices at a set time, where the information includes a vehicle speed and a remaining amount of oil;
  • W MAX ⁇ O n -O 1 ,O n-1 -O 1 ,...,O 3 -O 1 ,O 2 -O 1 ⁇ /(O 1 +1);
  • W is the value of the sudden increase in the amount of oil
  • n is the set time in seconds
  • O 1 to O n respectively represent the remaining amount of oil per second in the current n seconds.
  • the set time is 5s
  • the information such as the latitude and longitude, the vehicle speed, and the like uploaded by the OBD device is collected every 5s, and the information includes the information within the continuous 5 seconds
  • the oil volume increases W MAX ⁇ O 5 -O 1 , O 4 -O 1 , O 3 -O 1 , O 2 -O 1 ⁇ /(O 1 +1);
  • O 1 to O 5 are the remaining oil from the 1st second to the 5th second in 5 seconds. the amount.
  • the oil volume increase value can be accurately calculated.
  • the current OBD device is a fuel mass sudden increase point.
  • the degree of sudden increase of the oil amount is related to the time of refueling and the rate of adding the oil amount.
  • the speed of the vehicle is not necessarily zero, whether the ground in the area where the gas station is located is flat or the wind speed near the gas station may affect the current speed of the collected car. Therefore, the first set threshold and the second set threshold may be flexibly set according to actual needs, and the first set threshold may be set to other values greater than zero, and the second set threshold may be set to be greater than 0 and close to 0. .
  • the location information is location information of the car uploaded by the OBD device corresponding to the oil sudden increase point, including latitude and longitude information of the car location.
  • the technical solution provided by the embodiment adopts relevant algorithms and principles to accurately determine the sudden increase point of the oil quantity, and then uses the clustering algorithm to cluster the relevant points of the processing result, and automatically determines the location area of the POI of the gas station without any person. Or the collection vehicle can reach the corresponding position to find the POI of the gas station, thereby reducing the collection cost of the POI information of the gas station and shortening the update time period of the POI of the gas station.
  • the information uploaded by the OBD device includes latitude and longitude information, that is, the location information of the car uploaded by the OBD device is latitude and longitude information, the clustering algorithm is a geo hash algorithm, and the location information is latitude and longitude information;
  • Determining, according to the clustering result, a location area where a gas station POI exists including:
  • the number of points in the geo-hash grid reaches a third set threshold, it is determined that there is a gas station POI in the geo-hash grid, otherwise, it is determined that there is no gas station POI in the geo-hash grid.
  • the latitude and longitude range area indicated by the geo hash lattice is determined as the location area where the gas station POI exists.
  • the process of clustering the oil mass burst points according to the position information by using the geo hash algorithm is: according to the latitude and longitude information uploaded by the OBD device corresponding to each oil mass burst point, respectively, respectively, the oil mass burst point coding setting is respectively set.
  • a geo hash string the geo hash string identifies the location of the corresponding fuel spike point, and then uses the geo hash algorithm to perform each fuel mass burst point according to the geo hash string of each fuel spike point. Clustering.
  • the latitude and longitude of a fuel mass burst point is 116.389550 and 39.928167, respectively.
  • the process of geo-hash encoding the latitude and longitude is as follows:
  • the latitude interval of the earth is [-90, 90], and the latitude 39.928167 can be approximated by the following algorithm:
  • 39.928167 always belongs to a certain interval [a, b]. As each iteration interval [a, b] is always shrinking, and getting closer to 39.928167;
  • a sequence 1011100 is generated.
  • the length of the sequence is related to the given number of interval divisions.
  • the process of encoding according to the latitude is shown in the following table.
  • the code generated by latitude is 10111,00011.
  • the longitude interval of the earth is [-180, 180], and the code obtained by encoding the longitude 116.389550 is 11010 01011.
  • the even digits are placed in longitude, the odd number is the latitude, and the new code is 11100 11101 00100 01111.
  • convert the code to a decimal number. Since the 5-bit code corresponds to a decimal number, the converted decimal range is 0-31.
  • the decimal numbers 28, 29, 4, and 15 corresponding to the above encoding are finally converted into a string by the correspondence of the following table, that is, a hash string.
  • the hash character used is used.
  • the length of the string is 4, and the character string corresponding to latitude and longitude is WX4G.
  • the figure shows the geo hash strings corresponding to the nine regions, each of which corresponds to a rectangular region, that is, all the points (latitude and longitude coordinates) in the rectangular region share the same Geo hash string.
  • Each hash string corresponds to a hash grid.
  • the hash grid is determined to be corresponding.
  • the third set threshold is related to the actual number of cars refueling in the area where the gas station is located. It may be that in some remote areas, the number of cars refueling at some gas stations is small, and the calculated number is The number of points in the grid may be less. Therefore, different third setting thresholds may be set according to actual needs or for different location areas, and the third setting threshold may be any value greater than zero.
  • the number of points in the geo hash lattice reaches 21.5, it is considered that there is a gas station POI within the latitude and longitude coordinate range represented by the geo hash lattice.
  • the length of the geo hash string used is 7.
  • the longer the length of the geo hash string used the smaller the error of the gas station POI in the last determined region, the length of the geo hash string and the final clustering result.
  • the error correspondence is shown in the table below.
  • the length of the geo hash string is selected to be 7. Of course, other values can be used for the length of the string.
  • the technical solution provided by this embodiment can accurately determine whether there is a gas station POI in the area, reduce the cost of collecting the gas station POI, and reduce the POI update speed by performing geo hash clustering processing on the oil sudden increase point. .
  • FIG. 4 is a schematic structural diagram of an apparatus for automatically discovering a POI of a gas station according to Embodiment 4 of the present invention.
  • the specific structure of the device for automatic discovery of the POI of the gas station is as follows:
  • the oil quantity sudden increase point determining module 410 is configured to collect information uploaded by the OBD device, and determine a fuel quantity sudden increase point according to the information;
  • the clustering processing module 420 is configured to cluster the oil mass burst points according to the location information by using a clustering algorithm
  • the location determining module 430 is configured to determine, according to the clustering result, a location area where the gas station POI exists.
  • the oil quantity sudden increase point determining module 410 is specifically configured to:
  • each OBD device calculates the oil mass increase degree value corresponding to the current OBD device according to the remaining oil quantity uploaded by the current OBD device; if the calculated oil quantity sudden increase degree value is greater than or equal to the first set threshold value, and the current OBD If the vehicle speed uploaded by the device is less than or equal to the second set threshold, it is determined that the current OBD device is a fuel mass burst point.
  • the oil spurt degree value can be calculated by the following formula:
  • W MAX ⁇ O n -O 1 ,O n-1 -O 1 ,...,O 3 -O 1 ,O 2 -O 1 ⁇ /(O 1 +1);
  • W is the value of the sudden increase in the amount of oil
  • n is the set time in seconds
  • O 1 to O n respectively represent the remaining amount of oil per second in the current n seconds.
  • the information uploaded by the OBD device includes latitude and longitude information
  • the clustering algorithm is a geo hash algorithm
  • the location information is latitude and longitude information
  • the location determining module 430 includes:
  • the POI determining unit 431 is configured to determine, according to the number of points in the geo hash grid generated after the clustering, whether there is a gas station POI in the geo hash grid;
  • the POI determining unit 431 is specifically configured to:
  • the number of points in the geo-hash grid reaches a third set threshold, it is determined that there is a gas station POI in the geo-hash grid, otherwise, it is determined that there is no gas station POI in the geo-hash grid.
  • a POI determining unit 432 configured to determine, in the POI determining unit, that the geo hash lattice exists At the gas station POI, the latitude and longitude range region indicated by the geo hash lattice is determined as the location region where the gas station POI exists.
  • the clustering processing module 420 clusters the oil mass burst points according to the location information by using a geo hash algorithm
  • the length of the geo hash string used is 7.
  • the above device can perform the method for automatically discovering the POI of the gas station provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.
  • the embodiment provides a non-volatile computer storage medium storing one or more modules, when the one or more modules are executed by a device that performs a method of automatic discovery by a gas station POI, The device is caused to perform the following operations:
  • the clustering algorithm is used to cluster the oil mass burst points according to the location information
  • the location area where the gas station POI exists is determined based on the clustering result.
  • the information collected by the OBD device is determined according to the information, and may include:
  • each OBD device calculates the oil mass increase degree value corresponding to the current OBD device according to the remaining oil quantity uploaded by the current OBD device; if the calculated oil quantity sudden increase degree value is greater than or equal to the first set threshold value, and the current OBD If the vehicle speed uploaded by the device is less than or equal to the second set threshold, it is determined that the current OBD device is a fuel mass burst point.
  • W MAX ⁇ O n -O 1 ,O n-1 -O 1 ,...,O 3 -O 1 ,O 2 -O 1 ⁇ /(O 1 +1);
  • W is the value of the sudden increase in the amount of oil
  • n is the set time in seconds
  • O 1 to O n respectively represent the remaining amount of oil per second in the current n seconds.
  • the information uploaded by the OBD device may include latitude and longitude information, and the clustering algorithm may be a geo hash algorithm, and the location information may be Longitude and latitude information;
  • the determining, by the clustering result, the location area where the POI of the gas station is present may include:
  • the latitude and longitude range area indicated by the geo hash lattice is determined as the location area where the gas station POI exists.
  • determining whether there is a gas station POI in the geo hash grid according to the number of points in the geo hash grid generated after the clustering may include:
  • the number of points in the geo-hash grid reaches a third set threshold, it is determined that there is a gas station POI in the geo-hash grid, otherwise, it is determined that there is no gas station POI in the geo-hash grid.
  • the length of the geo hash string used may be 7 when the oil burst point is clustered according to the location information by using a geo hash algorithm.
  • the present invention can be implemented by software and necessary general hardware, and can also be implemented by hardware, but in many cases, the former is a better implementation. .
  • the technical solution of the present invention which is essential or contributes to the prior art, may be embodied in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk of a computer. , Read-Only Memory (ROM), Random Access Memory (RAM), Flash (FLASH), hard disk or optical disk, etc., including a number of instructions to make a computer device (can be a personal computer)
  • the server, or network device, etc. performs the methods described in various embodiments of the present invention.
  • FIG. 5 is a schematic structural diagram of hardware of a device for performing a method for automatically discovering a POI of a gas station according to Embodiment 6 of the present invention.
  • the device includes:
  • One or more processors 510, one processor 510 is taken as an example in FIG. 5;
  • Memory 520 and one or more modules.
  • the device may also include an input device 530 and an output device 540.
  • the processor 510, the memory 520, the input device 530, and the output device 540 in the device may be connected by a bus or other means, and the bus connection is taken as an example in FIG.
  • the memory 520 is used as a computer readable storage medium, and can be used for storing a software program, a computer executable program, and a module, such as a program instruction/module corresponding to a method for automatic discovery of a gas station POI in the embodiment of the present invention (for example, FIG. 4
  • the processor 510 executes various functional applications and data processing of the server by running software programs, instructions, and modules stored in the memory 520, that is, a method for realizing the automatic discovery of the gas station POI in the above method embodiments.
  • the memory 520 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to usage of the terminal device, and the like. Further, the memory 520 may include a high speed random access memory, and may also include a nonvolatile memory such as at least one magnetic disk storage device, flash memory device, or other nonvolatile solid state storage device. In some examples, memory 520 can further include memory remotely located relative to processor 510, which can be connected to the terminal device over a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • Input device 530 can be used to receive input digital or character information and to generate key signal inputs related to user settings and function control of the terminal.
  • the output device 540 can include a display device such as a display screen.
  • the one or more modules are stored in the memory 520, and when executed by the one or more processors 510, perform the following operations:
  • the clustering algorithm is used to cluster the oil mass burst points according to the location information
  • the location area where the gas station POI exists is determined based on the clustering result.
  • the collecting the information uploaded by the OBD device, determining the fuel mass burst point according to the information may include:
  • each OBD device calculates the current OBD based on the remaining amount of oil uploaded by the current OBD device The oil spurt degree corresponding to the device; if the calculated oil spurt value is greater than or equal to the first set threshold, and the current OBD device upload speed is less than or equal to the second set threshold, the current OBD device is determined A sudden increase in the amount of oil.
  • oil spurt degree value can be calculated by the following formula:
  • W MAX ⁇ O n -O 1 ,O n-1 -O 1 ,...,O 3 -O 1 ,O 2 -O 1 ⁇ /(O 1 +1);
  • W is the value of the sudden increase in the amount of oil
  • n is the set time in seconds
  • O 1 to O n respectively represent the remaining amount of oil per second in the current n seconds.
  • the information uploaded by the OBD device may include latitude and longitude information
  • the clustering algorithm may be a geo hash algorithm
  • the location information may be latitude and longitude information
  • the determining, by the clustering result, the location area where the gas station POI exists may include:
  • the latitude and longitude range area indicated by the geo hash lattice is determined as the location area where the gas station POI exists.
  • determining whether there is a gas station POI in the geo hash grid according to the number of points in the geo hash lattice generated after the clustering may include:
  • the number of points in the geo-hash grid reaches a third set threshold, it is determined that there is a gas station POI in the geo-hash grid, otherwise, it is determined that there is no gas station POI in the geo-hash grid.
  • the length of the geo hash string used may be 7.
  • the units and modules included in the gas station are divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized.
  • the specific names of the respective functional units are only for convenience of distinguishing from each other, and are not intended to limit the scope of protection of the present invention.

Abstract

一种加油站POI自动发现的方法、装置、存储介质和设备，其中，所述方法包括：采集车载诊断系统OBD设备上传的信息，根据所述信息确定油量突增点（S110）；采用聚类算法将所述油量突增点按照位置信息进行聚类（S120）；根据聚类结果确定存在加油站POI的位置区域（S130）。所提供的技术方案能够自动确定加油站POI的位置区域，降低加油站POI信息采集成本，缩短加油站POI更新时间周期。

Description

一种加油站POI自动发现的方法、装置、存储介质和设备
本专利申请要求于2015年09月30日提交的、申请号为201510642160.0、申请人为百度在线网络技术(北京)有限公司、发明名称为“一种加油站POI自动发现的方法和装置”的中国专利申请的优先权，该申请的全文以引用的方式并入本申请中。
技术领域
本发明实施例涉及搜索技术领域，尤其涉及一种加油站POI自动发现的方法、装置、存储介质和设备。
背景技术
现有技术中，加油站信息点(Point of Interest，POI)的采集方法主要是利用步采，车采和航拍的方法人工进行采集。
上述方案中，无论是步采还是车采，事先都不知道加油站的具体位置，需要人或者采集车到达相应位置后才能发现，采集成本较高，并且会出现漏掉加油站的情况；另外，当某个地方新建一个加油站或者拆除一个加油站，现有的采集方法不能快速的发现，而是需要下一个采集周期才能够发现，导致加油站POI更新时间周期长。
发明内容
本发明实施例的目的在于提供一种加油站POI自动发现的方法、装置、存储介质和设备，以解决加油站POI采集成本较高，更新时间周期长的问题。
第一方面，本发明实施例提供了一种加油站POI自动发现的方法，包括：
采集车载诊断系统(On-Board Diagnostic，OBD)设备上传的信息，根据所述信息确定油量突增点；
采用聚类算法将所述油量突增点按照位置信息进行聚类；
根据聚类结果确定存在加油站POI的位置区域。
第二方面，本发明实施例提供了一种加油站POI自动发现的装置，包括：
油量突增点确定模块，用于采集OBD设备上传的信息，根据所述信息确定油量突增点；
聚类处理模块，用于采用聚类算法将所述油量突增点按照位置信息进行聚类；
位置确定模块，用于根据聚类结果确定存在加油站POI的位置区域。
第三方面，本发明实施例提供了一种非易失性计算机存储介质，所述计算机存储介质存储有一个或者多个模块，当所述一个或者多个模块被一个执行加油站POI自动发现的方法的设备执行时，使得所述设备执行如下操作：
采集OBD设备上传的信息，根据所述信息确定油量突增点；
采用聚类算法将所述油量突增点按照位置信息进行聚类；
根据聚类结果确定存在加油站POI的位置区域。
第四方面，本发明实施例提供了一种设备，包括：
一个或者多个处理器；
存储器；
一个或者多个程序，所述一个或者多个程序存储在所述存储器中，当被所述一个或者多个处理器执行时，进行如下操作：
采集OBD设备上传的信息，根据所述信息确定油量突增点；
采用聚类算法将所述油量突增点按照位置信息进行聚类；
根据聚类结果确定存在加油站POI的位置区域。
本发明实施例提供的加油站POI自动发现的方法、装置、存储介质和设备，能够根据OBD设备上传的相关信息，通过对相关的信息进行处理确定油量突增点，再采用聚类算法对处理结果的相关点进行聚类，自动确定加油站POI的位置区域，无需人或者采集车到达相应位置后才能发现加油站POI，从而降低了加油站POI信息的采集成本，缩短了加油站POI的更新时间周期。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需使用的附图作简单地介绍，当然，以下描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以对这些附图进行修改和替换。
图1是本发明实施例一提供的一种加油站POI自动发现的方法流程示意图；
图2是本发明实施例二提供的一种加油站POI自动发现的方法流程示意图；
图3是本发明实施例三提供的一种geo哈希字符串标识位置区域示意图；
图4是本发明实施例四提供的一种加油站POI自动发现的装置结构示意图；
图5是本发明实施例六提供的一种执行加油站POI自动发现的方法的设备的硬件结构示意图。
具体实施方式
下面将结合附图对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明的一部分实施例，而不是全部的实施例，是为了阐述本发明的原理，而不是要将本发明限制于这些具体的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
实施例一
图1是本发明实施例一提供的一种加油站POI自动发现的方法流程示意图。本实施例可在采集加油站POI的情况下应用。该方法可以由加油站POI自动发现的装置来执行，所述装置由软件和/或硬件实现。参见图1，本发明实施例提供的一种加油站POI自动发现的方法具体包括如下操作：
S110、采集OBD设备上传的信息，根据所述信息确定油量突增点；
OBD设备安装在汽车上，可以用来监测汽车的运行状态，能实时检测汽车 上多个系统和部件，判断汽车是否处于故障状态。OBD设备上传的信息包括汽车的位置、速度、剩余油量、瞬时油耗、轮胎气压、以及水温等信息。油量突增的点是与OBD设备对应的油量突增点，是将这些具有OBD设备的汽车抽象成为的一些点，如果OBD设备上传的汽车的速度和油量等信息满足一定的条件，则该OBD设备为油量突增点。
S120、采用聚类算法将所述油量突增点按照位置信息进行聚类；
位置信息为油量突增点对应的OBD设备上传的汽车的位置信息，包括汽车位置的经纬度信息。
采用聚类算法将油量突增点按照经纬度等位置信心进行聚类后，可以将地理范围分为多个区域，将位置相近的油量突增点聚为一类，这些同类的油量突增点就共享一个位置区域，其中，该聚类算法包括geo哈希算法等。
S130、根据聚类结果确定存在加油站POI的位置区域。
本实施例提供的技术方案，能够根据OBD设备上传的相关信息，对相关的信息进行处理确定油量突增点，再采用聚类算法对处理结果进行聚类，自动确定加油站POI的位置区域，降低加油站POI采集成本，缩短加油站POI更新时间周期。
实施例二
图2是本发明实施例二提供的一种加油站POI自动发现的方法流程示意图。本实施例是在实施例一的基础上对操作S110进行了优化。如图2所示，该方法的具体流程如下：
S210、每隔设定时间采集多个OBD设备上传的信息，所述信息包括车速以及剩余油量；
S220、针对每个OBD设备，根据当前OBD设备上传的剩余油量计算当前OBD设备对应的油量突增程度值；若计算出的油量突增程度值大于或等于第一设定阈值，并且当前OBD设备上传的车速小于或等于第二设定阈值，则确定当前OBD设备为油量突增点。
考虑到汽车在加油的时候，汽车的发动机都是熄灭的，汽车的车速一般为零或接近于零，所以可以根据这两个条件判断汽车是否在加油站附近加油。汽 车一般加油的时间为几秒到几分钟不等，所以可以每隔一定的设定时间采集多个OBD设备上传的车速和剩余油量等信息。然后针对每一个OBD设备，根据当前OBD上传的剩余油量信息计算当前OBD设备对应的油量突增程度值。可选的，油量突增程度值可采用以下公式进行计算：
W＝MAX{On-O1，On-1-O1，…，O3-O1，O2-O1}/(O1+1)；
其中，W表示油量突增程度值，n表示所述设定时间，单位为秒，O1至On分别表示当前n秒内每秒的剩余油量。
示例性的，当设定时间为5s时，即每隔5s采集一次OBD设备上传的经纬度、车速等信息，该信息包括这连续5秒内的信息，则油量突增值W＝MAX{O5-O1，O4-O1，O3-O1，O2-O1}/(O1+1)；O1至O5为连续5秒内第1秒至第5秒的剩余油量。通过该计算方法，可以精确的计算出油量突增值。
优选的，如果计算出的油量突增程度值大于或等于1.59，并且当前的车速为0，则确定当前OBD设备为油量突增点。
由于采集OBD设备上传信息设定的时间不一样，而且加油的过程中，汽车的油量是逐渐增加的，油量突增的程度，与加油的时间，加入油量的速率有关。另外加油的过程中，车辆的速度不一定都为零，加油站所在的区域地面是否平坦或者加油站附近的风速等因素都可能影响到采集到的汽车的当前速度。所以可以根据实际需要灵活的设置第一设定阈值和第二设定阈值，可以设置第一设定阈值为其他大于零的数值，设置第二设定阈值为大于0且接近于0的数值等。
S230、采用聚类算法将所述油量突增点按照位置信息进行聚类；
位置信息为油量突增点对应的OBD设备上传的汽车的位置信息，包括汽车位置的经纬度信息。
S240、根据聚类结果确定存在加油站POI的位置区域。
本实施例提供的技术方案，采用相关的算法和原理，精确的确定油量突增点，再采用聚类算法对处理结果的相关点进行聚类，自动确定加油站POI的位置区域，无需人或者采集车到达相应位置后才能发现加油站POI，从而降低了加油站POI信息的采集成本，缩短了加油站POI的更新时间周期。
实施例三
本实施例是在上述各实施例的基础上进行的优化。在上述各实施例中，所述OBD设备上传的信息包括经纬度信息，即OBD设备上传的汽车的位置信息为经纬度信息，所述聚类算法为geo哈希算法，所述位置信息为经纬度信息；
所述根据聚类结果确定存在加油站POI的位置区域，包括：
根据聚类后生成的geo哈希格子中点的数量判断所述geo哈希格子内是否存在加油站POI；
进一步的，若所述geo哈希格子中点的数量达到第三设定阈值，则确定所述geo哈希格子内存在加油站POI，否则，确定所述geo哈希格子内不存在加油站POI。
若存在，则将所述geo哈希格子所表示的经纬度范围区域确定为存在加油站POI的位置区域。
采用geo哈希算法将所述油量突增点按照位置信息进行聚类的过程为：根据各油量突增点对应的OBD设备上传的经纬度信息，分别给每个油量突增点编码设置一个geo哈希字符串，geo哈希字符串标识对应油量突增点的位置，然后根据各油量突增点的geo哈希字符串，采用geo哈希算法将各油量突增点进行聚类。
示例性的，设一个油量突增点的经纬度分别为116.389550和39.928167，则对经纬度进行geo哈希编码的过程如下：
地球纬度区间是[-90，90]，可以通过下面算法对纬度39.928167进行逼近编码：
(1)将区间[-90，90]分为[-90，0)和[0，90]，称为左右区间，可以确定39.928167属于右区间[0，90]，给标记为1；
(2)接着将区间[0，90]进行二分为[0，45)，[45，90]，可以确定39.928167属于左区间[0，45)，给标记为0；
(3)递归上述过程，39.928167总是属于某个区间[a，b]。随着每次迭代区间[a，b]总在缩小，并越来越逼近39.928167；
(4)如果给定的纬度x(39.928167)属于左区间，则记录0，如果属于右区间则记录1。
这样随着算法的进行会产生一个序列1011100，序列的长度与给定的区间划分次数有关，则根据纬度得到编码的过程如下表所示。
区间左端点 区间中间值 区间右端点 编码位
-90.000 0.000 90.000 1
0.000 45.000 90.000 0
0.000 22.500 45.000 1
22.500 33.750 45.000 1
33.750 39.375 45.000 1
39.375 42.188 45.000 0
39.375 40.7815 42.188 0
39.375 40.07825 40.7815 0
39.375 39.726625 40.07825 1
39.726625 39.9024375 40.07825 1
纬度产生的编码为10111 00011，同理，地球的经度区间是[-180，180]，对经度116.389550进行编码得到的编码为11010 01011。将偶数位放经度，奇数为放纬度，组合成新编码为11100 11101 00100 01111。然后再将编码转换为十进制的数字，由于5位编码对应一个十进制的数字，转换的十进制范围为0-31。上面的编码对应的十进制数位28、29、4、15，最后按下述表格的对应关系，将十进制数转换成字符串，也即是哈希字符串，在本示例中，使用的哈希字符串的长度为4，经纬度对应的字符串为WX4G。
十进制 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
BASE32 0 1 2 3 4 5 6 7 8 9 B C D E F G
十进制 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
BASE32 H J K M N P Q R S T U V W X Y Z
如图3所示，该图展示了9个区域对应的geo哈希字符串，每一个字符串对应一个矩形的区域，也就是说，这个矩形区域内所有的点(经纬度坐标)都共享相同的geo哈希字符串。
每一个哈希字符串即对应一个哈希格子，经过聚类后，在同一个哈希格子，也即是同一矩形区域的点的数量达到第三设定阈值时，则判断该哈希格子对应的区域内存在加油站POI，该第三设定阈值与加油站所在区域加油的汽车的实际数量有关，可能在一些偏远地区，在某些加油站加油的汽车的数量较少，计算出的哈希格子里点的数量就可能较少，因此，可根据实际需要或者针对不同的位置区域设置不同的第三设定阈值，该第三设定阈值可以为大于零的任何数值。
优选的，当geo哈希格子里面的点的数量达到21.5则认为在此geo哈希格子所表示的经纬度坐标范围内，存在加油站POI。
优选的，当采用geo哈希算法将所述油量突增点按照位置信息进行聚类时，采用的geo哈希字符串的长度为7。
当采用geo哈希算法进行聚类时，采用的geo哈希字符串的长度越长，则最后确定的区域内存在加油站POI的误差越小，geo哈希字符串长度与最后聚类结果的误差对应关系如下表所示。为了兼顾精准度与采集的加油站POI的粒度，选择geo哈希字符串的长度为7。当然，字符串的长度也可选用其他值。
geo哈希字符串长度 经度误差 纬度误差 范围误差(Km)
1 23 23 2500
2 2.8 5.6 630
3 0.7 0.7 78
4 0.087 0.18 20
5 0.022 0.022 2.4
6 0.0027 0.0055 0.61
7 0.00068 0.00068 0.076
8 0.000085 0.00017 0.019
本实施例提供的技术方案，通过对油量突增点进行geo哈希聚类处理，可以精确的确定区域内是否存在加油站POI，降低采集加油站POI的成本，并且POI更新的速度较快。
实施例四
图4是本发明实施例四提供的一种加油站POI自动发现的装置结构示意图。参见图4，该加油站POI自动发现的装置的具体结构如下：
油量突增点确定模块410，用于采集OBD设备上传的信息，根据所述信息确定油量突增点；
聚类处理模块420，用于采用聚类算法将所述油量突增点按照位置信息进行聚类；
位置确定模块430，用于根据聚类结果确定存在加油站POI的位置区域。
其中，所述油量突增点确定模块410具体用于：
每隔设定时间采集多个OBD设备上传的信息，所述信息包括车速以及剩余油量；
针对每个OBD设备，根据当前OBD设备上传的剩余油量计算当前OBD设备对应的油量突增程度值；若计算出的油量突增程度值大于或等于第一设定阈值，并且当前OBD设备上传的车速小于或等于第二设定阈值，则确定当前OBD设备为油量突增点。
优选的，可采用如下公式计算油量突增程度值：
W＝MAX{On-O1，On-1-O1，…，O3-O1，O2-O1}/(O1+1)；
其中，W表示油量突增程度值，n表示所述设定时间，单位为秒，O1至On分别表示当前n秒内每秒的剩余油量。
进一步的，所述OBD设备上传的信息包括经纬度信息，所述聚类算法为geo哈希算法，所述位置信息为经纬度信息；
所述位置确定模块430包括：
POI判断单元431，用于根据聚类后生成的geo哈希格子中点的数量判断所述geo哈希格子内是否存在加油站POI；
进一步的，所述POI判断单元431具体用于：
若所述geo哈希格子中点的数量达到第三设定阈值，则确定所述geo哈希格子内存在加油站POI，否则，确定所述geo哈希格子内不存在加油站POI。
POI确定单元432，用于在所述POI判断单元判断所述geo哈希格子内存在 加油站POI时，将所述geo哈希格子所表示的经纬度范围区域确定为存在加油站POI的位置区域。
优选的，所述聚类处理模块420采用geo哈希算法将所述油量突增点按照位置信息进行聚类时，采用的geo哈希字符串的长度为7。
上述装置可执行本发明任意实施例所提供的加油站POI自动发现的方法，具备执行方法相应的功能模块和有益效果。
实施例五
本实施例提供一种非易失性计算机存储介质，所述计算机存储介质存储有一个或者多个模块，当所述一个或者多个模块被一个执行加油站POI自动发现的方法的设备执行时，使得所述设备执行如下操作：
采集OBD设备上传的信息，根据所述信息确定油量突增点；
采用聚类算法将所述油量突增点按照位置信息进行聚类；
根据聚类结果确定存在加油站POI的位置区域。
上述存储介质中存储的模块被所述设备执行时，所述采集OBD设备上传的信息，根据所述信息确定油量突增点，可包括：
每隔设定时间采集多个OBD设备上传的信息，所述信息包括车速以及剩余油量；
针对每个OBD设备，根据当前OBD设备上传的剩余油量计算当前OBD设备对应的油量突增程度值；若计算出的油量突增程度值大于或等于第一设定阈值，并且当前OBD设备上传的车速小于或等于第二设定阈值，则确定当前OBD设备为油量突增点。
上述存储介质中存储的模块被所述设备执行时，可采用如下公式计算油量突增程度值：
W＝MAX{On-O1，On-1-O1，…，O3-O1，O2-O1}/(O1+1)；
其中，W表示油量突增程度值，n表示所述设定时间，单位为秒，O1至On分别表示当前n秒内每秒的剩余油量。
上述存储介质中存储的模块被所述设备执行时，所述OBD设备上传的信息可包括经纬度信息，所述聚类算法可以为geo哈希算法，所述位置信息可以为 经纬度信息；
上述存储介质中存储的模块被所述设备执行时，所述根据聚类结果确定存在加油站POI的位置区域，可包括：
根据聚类后生成的geo哈希格子中点的数量判断所述geo哈希格子内是否存在加油站POI；
若存在，则将所述geo哈希格子所表示的经纬度范围区域确定为存在加油站POI的位置区域。
上述存储介质中存储的模块被所述设备执行时，所述根据聚类后生成的geo哈希格子中点的数量判断所述geo哈希格子内是否存在加油站POI，可包括：
若所述geo哈希格子中点的数量达到第三设定阈值，则确定所述geo哈希格子内存在加油站POI，否则，确定所述geo哈希格子内不存在加油站POI。
上述存储介质中存储的模块被所述设备执行时，采用geo哈希算法将所述油量突增点按照位置信息进行聚类时，采用的geo哈希字符串的长度优选可为7。
通过以上关于实施方式的描述，所属领域的技术人员可以清楚地了解到，本发明可借助软件及必需的通用硬件来实现，当然也可以通过硬件实现，但很多情况下前者是更佳的实施方式。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如计算机的软盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、闪存(FLASH)、硬盘或光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述的方法。
实施例六
图5为本发明实施例六提供的一种执行加油站POI自动发现的方法的设备的硬件结构示意图。参见图5，所述设备包括：
一个或者多个处理器510，图5中以一个处理器510为例；
存储器520；以及一个或者多个模块。
所述设备还可以包括：输入装置530和输出装置540。所述设备中的处理器510、存储器520、输入装置530和输出装置540可以通过总线或其他方式连接，图5中以通过总线连接为例。
存储器520作为一种计算机可读存储介质，可用于存储软件程序、计算机可执行程序以及模块，如本发明实施例中的加油站POI自动发现的方法对应的程序指令/模块(例如，附图4所示的加油站POI自动发现的装置中的油量突增点确定模块410、聚类处理模块420和位置确定模块430)。处理器510通过运行存储在存储器520中的软件程序、指令以及模块，从而执行服务器的各种功能应用以及数据处理，即实现上述方法实施例中的加油站POI自动发现的方法。
存储器520可包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据终端设备的使用所创建的数据等。此外，存储器520可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储器520可进一步包括相对于处理器510远程设置的存储器，这些远程存储器可以通过网络连接至终端设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
输入装置530可用于接收输入的数字或字符信息，以及产生与终端的用户设置以及功能控制有关的键信号输入。输出装置540可包括显示屏等显示设备。
所述一个或者多个模块存储在所述存储器520中，当被所述一个或者多个处理器510执行时，执行如下操作：
采集OBD设备上传的信息，根据所述信息确定油量突增点；
采用聚类算法将所述油量突增点按照位置信息进行聚类；
根据聚类结果确定存在加油站POI的位置区域。
进一步的，所述采集OBD设备上传的信息，根据所述信息确定油量突增点，可包括：
每隔设定时间采集多个OBD设备上传的信息，所述信息包括车速以及剩余油量；
针对每个OBD设备，根据当前OBD设备上传的剩余油量计算当前OBD 设备对应的油量突增程度值；若计算出的油量突增程度值大于或等于第一设定阈值，并且当前OBD设备上传的车速小于或等于第二设定阈值，则确定当前OBD设备为油量突增点。
进一步的，可采用如下公式计算油量突增程度值：
W＝MAX{On-O1，On-1-O1，…，O3-O1，O2-O1}/(O1+1)；
其中，W表示油量突增程度值，n表示所述设定时间，单位为秒，O1至On分别表示当前n秒内每秒的剩余油量。
进一步的，所述OBD设备上传的信息可包括经纬度信息，所述聚类算法可以为geo哈希算法，所述位置信息可为经纬度信息；
所述根据聚类结果确定存在加油站POI的位置区域，可包括：
根据聚类后生成的geo哈希格子中点的数量判断所述geo哈希格子内是否存在加油站POI；
若存在，则将所述geo哈希格子所表示的经纬度范围区域确定为存在加油站POI的位置区域。
进一步的，所述根据聚类后生成的geo哈希格子中点的数量判断所述geo哈希格子内是否存在加油站POI，可包括：
若所述geo哈希格子中点的数量达到第三设定阈值，则确定所述geo哈希格子内存在加油站POI，否则，确定所述geo哈希格子内不存在加油站POI。
进一步的，采用geo哈希算法将所述油量突增点按照位置信息进行聚类时，采用的geo哈希字符串的长度可以为7。
值得注意的是，上述加油站POI自动发现的装置的实施例中，所包括的各个单元和模块只是按照功能逻辑进行划分的，但并不局限于上述的划分，只要能够实现相应的功能即可；另外，各功能单元的具体名称也只是为了便于相互区分，并不用于限制本发明的保护范围。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明披露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以所述权利要求的保护范围为准。

Claims (14)

  1. 一种加油站信息点POI自动发现的方法，其特征在于，包括：
    采集车载诊断系统OBD设备上传的信息，根据所述信息确定油量突增点；
    采用聚类算法将所述油量突增点按照位置信息进行聚类；
    根据聚类结果确定存在加油站POI的位置区域。
  2. 根据权利要求1所述的方法，其特征在于，所述采集OBD设备上传的信息，根据所述信息确定油量突增点，包括：
    每隔设定时间采集多个OBD设备上传的信息，所述信息包括车速以及剩余油量；
    针对每个OBD设备，根据当前OBD设备上传的剩余油量计算当前OBD设备对应的油量突增程度值；若计算出的油量突增程度值大于或等于第一设定阈值，并且当前OBD设备上传的车速小于或等于第二设定阈值，则确定当前OBD设备为油量突增点。
  3. 根据权利要求2所述的方法，其特征在于，采用如下公式计算油量突增程度值：
    W＝MAX{On-O1，On-1-O1，…，O3-O1，O2-O1}/(O1+1)；
    其中，W表示油量突增程度值，n表示所述设定时间，单位为秒，O1至On分别表示当前n秒内每秒的剩余油量。
  4. 根据权利要求1所述的方法，其特征在于，所述OBD设备上传的信息包括经纬度信息，所述聚类算法为geo哈希算法，所述位置信息为经纬度信息；
    所述根据聚类结果确定存在加油站POI的位置区域，包括：
    根据聚类后生成的geo哈希格子中点的数量判断所述geo哈希格子内是否存在加油站POI；
    若存在，则将所述geo哈希格子所表示的经纬度范围区域确定为存在加油站POI的位置区域。
  5. 根据权利要求4所述的方法，其特征在于，所述根据聚类后生成的geo哈希格子中点的数量判断所述geo哈希格子内是否存在加油站POI，包括：
    若所述geo哈希格子中点的数量达到第三设定阈值，则确定所述geo哈希格子内存在加油站POI，否则，确定所述geo哈希格子内不存在加油站POI。
  6. 根据权利要求4或5所述的方法，其特征在于，采用geo哈希算法将所述油量突增点按照位置信息进行聚类时，采用的geo哈希字符串的长度为7。
  7. 一种加油站POI自动发现的装置，其特征在于，包括：
    油量突增点确定模块，用于采集车载诊断系统OBD设备上传的信息，根据所述信息确定油量突增点；
    聚类处理模块，用于采用聚类算法将所述油量突增点按照位置信息进行聚类；
    位置确定模块，用于根据聚类结果确定存在加油站POI的位置区域。
  8. 根据权利要求7所述的装置，其特征在于，所述油量突增点确定模块具体用于：
    每隔设定时间采集多个OBD设备上传的信息，所述信息包括车速以及剩余油量；
    针对每个OBD设备，根据当前OBD设备上传的剩余油量计算当前OBD设备对应的油量突增程度值；若计算出的油量突增程度值大于或等于第一设定阈值，并且当前OBD设备上传的车速小于或等于第二设定阈值，则确定当前OBD设备为油量突增点。
  9. 根据权利要求8所述的装置，其特征在于，所述油量突增点确定模块具体用于：采用如下公式计算油量突增程度值：
    W＝MAX{On-O1，On-1-O1，…，O3-O1，O2-O1}/(O1+1)；
    其中，W表示油量突增程度值，n表示所述设定时间，单位为秒，O1至On分别表示当前n秒内每秒的剩余油量。
  10. 根据权利要求7所述的装置，其特征在于，所述OBD设备上传的信息包括经纬度信息，所述聚类算法为geo哈希算法，所述位置信息为经纬度信息；
    所述位置确定模块包括：
    POI判断单元，用于根据聚类后生成的geo哈希格子中点的数量判断所述geo哈希格子内是否存在加油站POI；
    POI确定单元，用于在所述POI判断单元判断所述geo哈希格子内存在加油站POI时，将所述geo哈希格子所表示的经纬度范围区域确定为存在加油站 POI的位置区域。
  11. 根据权利要求10所述的装置，其特征在于，所述POI判断单元具体用于：
    若所述geo哈希格子中点的数量达到第三设定阈值，则确定所述geo哈希格子内存在加油站POI，否则，确定所述geo哈希格子内不存在加油站POI。
  12. 根据权利要求10或11所述的装置，其特征在于，所述聚类处理模块采用geo哈希算法将所述油量突增点按照位置信息进行聚类时，采用的geo哈希字符串的长度为7。
  13. 一种非易失性计算机存储介质，所述计算机存储介质存储有一个或者多个模块，其特征在于，当所述一个或者多个模块被一个执行加油站POI自动发现的方法的设备执行时，使得所述设备执行如下操作：
    采集OBD设备上传的信息，根据所述信息确定油量突增点；
    采用聚类算法将所述油量突增点按照位置信息进行聚类；
    根据聚类结果确定存在加油站POI的位置区域。
  14. 一种设备，其特征在于，包括：
    一个或者多个处理器；
    存储器；
    一个或者多个程序，所述一个或者多个程序存储在所述存储器中，当被所述一个或者多个处理器执行时，进行如下操作：
    采集OBD设备上传的信息，根据所述信息确定油量突增点；
    采用聚类算法将所述油量突增点按照位置信息进行聚类；
    根据聚类结果确定存在加油站POI的位置区域。
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