WO2019137230A1 - 位置信息的处理方法及装置 - Google Patents

位置信息的处理方法及装置 Download PDF

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Publication number
WO2019137230A1
WO2019137230A1 PCT/CN2018/124298 CN2018124298W WO2019137230A1 WO 2019137230 A1 WO2019137230 A1 WO 2019137230A1 CN 2018124298 W CN2018124298 W CN 2018124298W WO 2019137230 A1 WO2019137230 A1 WO 2019137230A1
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Prior art keywords
service
location information
transaction
region
information
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PCT/CN2018/124298
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English (en)
French (fr)
Inventor
杨维嘉
刘腾飞
夏威
陆青
陈弢
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阿里巴巴集团控股有限公司
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Priority to EP18900296.7A priority Critical patent/EP3719729A4/en
Priority to SG11202006203QA priority patent/SG11202006203QA/en
Publication of WO2019137230A1 publication Critical patent/WO2019137230A1/zh
Priority to US16/889,269 priority patent/US11176564B2/en

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    • 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
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4015—Transaction verification using location information
    • 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
    • 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
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • 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
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/32—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using wireless devices
    • G06Q20/322—Aspects of commerce using mobile devices [M-devices]
    • G06Q20/3224—Transactions dependent on location of M-devices
    • 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
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/32—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using wireless devices
    • G06Q20/327—Short range or proximity payments by means of M-devices
    • G06Q20/3274—Short range or proximity payments by means of M-devices using a pictured code, e.g. barcode or QR-code, being displayed on the M-device
    • 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
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/32—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using wireless devices
    • G06Q20/327—Short range or proximity payments by means of M-devices
    • G06Q20/3276—Short range or proximity payments by means of M-devices using a pictured code, e.g. barcode or QR-code, being read by the M-device
    • 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
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/405—Establishing or using transaction specific rules
    • 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
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/409—Device specific authentication in transaction processing
    • G06Q20/4093—Monitoring of device authentication
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0204—Market segmentation
    • G06Q30/0205—Location or geographical consideration

Definitions

  • the embodiments disclosed in the present specification relate to the field of Internet technologies, and in particular, to a method and an apparatus for processing location information.
  • Scanning code payment usually includes two types: one is that the merchant scans the user's QR code/barcode with a scan code gun. The other is that the user uses the terminal to scan the QR code of the merchant.
  • offline merchants are required to use the merchant QR code only in physical stores.
  • offline merchants there are still some offline merchants who use the merchant QR code in violation of regulations.
  • the merchant QR code is transferred to the online for use. Therefore, in order to urge offline merchants to regulate the use of merchant QR codes, it is necessary to provide a reliable method to identify the actual use of merchant QR codes by offline merchants.
  • This specification describes a method for processing location information, which clusters user location information in transaction information into at least one region, and determines a service provided by the offline service party according to the number of regions and transaction attributes corresponding to each region.
  • the degree of dispersion enables efficient and reliable identification of the actual use of the payment QR code by the offline service provider.
  • a method of processing location information includes:
  • a transaction attribute corresponding to each of the at least one area the transaction attribute including a number of users in the corresponding area and/or a number of transactions of the user in the corresponding area using the service;
  • the service provider Determining, according to the number of regions of the at least one region and the transaction attribute corresponding to each region, the service provider to provide service dispersion of the service.
  • the obtaining, by the multiple users, the transaction information of the service provided by the service party includes:
  • the method further includes: before the clustering the plurality of location information into the at least one region, the method further includes:
  • Position information exceeding a predetermined range of the plurality of pieces of position information is removed.
  • the clustering the multiple location information into the at least one region includes:
  • the plurality of location information is clustered into at least one region using a GEOHASH algorithm or a DBSCAN algorithm.
  • the determining, by the service provider, the service dispersion of the service includes:
  • the determining, by the service provider, the service dispersion of the service includes determining the service dispersion by using the following formula:
  • the determining, by the service provider, the service dispersion of the service includes determining the service dispersion by using the following formula:
  • the location information includes latitude and longitude information.
  • a processing apparatus for location information includes:
  • An obtaining unit configured to acquire transaction information of a plurality of users using a service provided by a service party, where the transaction information includes multiple location information of the multiple users;
  • a clustering unit configured to cluster the plurality of location information into at least one region
  • a determining unit configured to determine a transaction attribute corresponding to each of the at least one area, the transaction attribute including a number of users in the corresponding area and/or a number of transactions of the user in the corresponding area using the service;
  • a processing unit configured to determine, according to the number of regions of the at least one region and the transaction attribute corresponding to each region, the service provider to provide service dispersion of the service.
  • the obtaining unit is specifically configured to:
  • it also includes:
  • a removing unit configured to remove location information of the plurality of location information that exceeds a predetermined range.
  • the clustering unit is specifically used to:
  • the plurality of location information is clustered into at least one region using a GEOHASH algorithm or a DBSCAN algorithm.
  • the processing unit is specifically configured to:
  • the processing unit is operative to determine the service dispersion by the following formula:
  • the processing unit is operative to determine the service dispersion by the following formula:
  • the location information acquired by the acquiring unit includes latitude and longitude information.
  • a computer readable storage medium having a computer program stored thereon is provided.
  • the computer program is executed in a computer, the computer is caused to perform the method provided by any of the above-described first aspects.
  • a computing device comprising a memory and a processor.
  • An executable code is stored in the memory, and when the processor executes the executable code, the method provided by any one of the foregoing first aspects is implemented.
  • a method for processing location information provided by the present specification by acquiring transaction information of a plurality of users using a service provided by a service party, clustering a plurality of location information of a plurality of users included in the transaction information into at least one region, determining and Transaction attributes corresponding to respective ones of the at least one area. Then, according to the number of regions of the region and the transaction attributes corresponding to the respective regions, the service dispersion of the service provided by the service party is determined. Therefore, it is possible to efficiently and reliably identify the actual use of the payment QR code by the offline service party.
  • FIG. 1 is a schematic diagram of an application scenario of a method for processing location information according to an embodiment of the present disclosure
  • FIG. 2 is a flowchart of a method for processing location information according to an embodiment of the disclosure
  • FIG. 3 is a schematic diagram of clustering location information into regions according to an embodiment of the disclosure.
  • FIG. 4 is another schematic diagram of clustering location information into regions according to an embodiment of the disclosure.
  • FIG. 5 is a structural diagram of a device for processing location information according to an embodiment of the present disclosure.
  • FIG. 1 is a schematic diagram of an application scenario of a method for processing location information according to an embodiment of the present disclosure.
  • the execution body of the processing method may be a server.
  • a server eg, a server that can be an Alipay application
  • obtains services provided by multiple users using a service party eg, an offline party that the service party can provide a service provider QR code
  • the location information processing method provided by the multiple embodiments disclosed in the present specification may be used to obtain the multiple location information (
  • the location information may be a latitude and longitude information of the user terminal) clustering (eg, the clustering may be implemented by the GEOHASH algorithm) into at least one region (eg, at least one region may include multiple regions having different region numbers).
  • determining transaction attributes corresponding to the respective areas eg, the transaction attributes may include the number of users in the corresponding area
  • determining the service dispersion degree of the service provided by the service party according to the number of areas of the area and the transaction attributes corresponding to the respective areas ( For example, the lower the service dispersion, the higher the possibility that the service provider will use the QR code to collect the payment, and vice versa.
  • the method for processing location information provided by the multiple embodiments disclosed in the present specification is to cluster a plurality of location information of a plurality of users included in the transaction information into at least one by acquiring transaction information of a plurality of users using a service provided by the server. a region, determining a transaction attribute corresponding to each of the at least one region. Then, according to the number of regions of the region and the transaction attributes corresponding to the respective regions, the service dispersion of the service provided by the service party is determined. Therefore, it is possible to efficiently and reliably identify the actual use of the payment QR code by the offline service party.
  • FIG. 2 is a flowchart of a method for processing location information according to an embodiment of the disclosure.
  • the execution body of the method may be a device with processing capability: a server or a system or device or a software platform.
  • a server in Figure 1.
  • the method specifically includes:
  • Step S210 Acquire transaction information of a plurality of users using a service provided by a service party, where the transaction information includes multiple location information of multiple users.
  • the service party can provide the user with the payment two-dimensional code of the service party, and the user can use the terminal to scan the service party's payment two-dimensional code to complete the payment for the service used.
  • the server acquires transaction information of a plurality of users using the service provided by the servant, and the transaction information includes a plurality of location information of the plurality of users.
  • the location information may be location information collected from multiple terminals of multiple users by using a Location Based Service (LBS), and the location information may include latitude and longitude information.
  • LBS Location Based Service
  • acquiring transaction information of a plurality of users using the service provided by the service party may include: acquiring transaction information of a plurality of users using the service provided by the service party within a predetermined time.
  • the predetermined time may be any predetermined time period, such as, in the most recent week, or in the most recent month.
  • the transaction information may further include a service party identifier of the service party (eg, a service party name, a social credit code, a service party number in the server, etc.), and a user identifier of the user (eg, a phone number, an ID number). , the user number in the server, etc.) and the order number of the transaction.
  • a service party identifier of the service party eg, a service party name, a social credit code, a service party number in the server, etc.
  • a user identifier of the user eg, a phone number, an ID number.
  • the transaction information of a service provided by a user using the service provider includes: the latitude and longitude information of the user (eg, latitude 39°54'25.70′′, east longitude 116°23′28.49′′), the user's phone number (eg, , 12312345678), the social credit code of the service party (for example, 911101086615511250), the order number (for example, 123454321).
  • the latitude and longitude information of the user eg, latitude 39°54'25.70′′, east longitude 116°23′28.49′′
  • the user's phone number eg, , 12312345678
  • the social credit code of the service party for example, 911101086615511250
  • the order number for example, 123454321.
  • Step S220 clustering the plurality of location information into at least one region.
  • a plurality of location information is clustered into at least one region using a clustering algorithm.
  • the clustering algorithm may be a GEOSHASH algorithm or a DBSCAN algorithm, which is not limited herein.
  • the GEOSHASH algorithm is used to convert a plurality of location information into a GEOHASH grid and determine the grid number of each location information.
  • the transaction information acquired by the server in step S210 includes 100 latitude and longitude coordinates, and the 100 latitude and longitude coordinates are converted into a GEOHASH grid by the GEOHASH algorithm, as shown in FIG. 3, wherein the grid of 99 latitude and longitude coordinates The number is WX4DX, and the grid number of one latitude and longitude coordinate is WX4F2.
  • the above 100 pieces of position information are clustered into two areas.
  • each of the two latitude and longitude coordinates has the same
  • the grid number is 10 mesh numbers: WX4G01, WX4G02, WX4H03, WX4H04, WX4I05, WX4I06, WX4J07, WX4J08, WX4K09 and WX4K10.
  • multiple location information is clustered using the DBSCAN algorithm and the area number of each location information is determined.
  • the DBSCAN algorithm is a density-based clustering algorithm. Unlike the partitioning and hierarchical clustering methods, it defines the cluster as the largest set of points with density connections, can divide the area with sufficient high density into clusters, and can find clusters of arbitrary shapes in the spatial database of noise. Specifically, in the DBSCAN algorithm, all position points are first marked as core points, boundary points, or noise points, and noise points are deleted. Then assign an edge between all the core points within the preset parameters, each group of connected core points form a cluster, and assign each boundary point to a cluster of core points associated with it, thereby completing Clustering of location points.
  • the precision of the region obtained by the cluster can be controlled by adjusting the parameters of the clustering algorithm.
  • the accuracy of the region can be controlled by controlling the number of coded bits in the GEOSHASH algorithm that encodes the location information. More specifically, the more coded bits of position information, the more accurate the range of regions obtained.
  • the accuracy of the region can be controlled by controlling the magnitude of the neighborhood radius ⁇ in the DBSCAN algorithm. More specifically, the smaller the value of the input domain radius ⁇ , the more accurate the range of regions obtained.
  • step S230 transaction attributes corresponding to respective areas in the at least one area are determined.
  • the server determines a transaction attribute corresponding to each area, and the transaction attribute may include the number of transactions of the user in the corresponding area using the service. More specifically, the server can determine the number of transactions based on the amount of transaction information.
  • the transaction information acquired in step S210 may include an order number of the transaction, and accordingly, the server may determine the number of transactions corresponding to each area according to the number of the order number.
  • a plurality of pieces of position information are clustered into two areas in step S220, the number of orders corresponding to one of the areas is 99, and the number of orders corresponding to the other area is one. Accordingly, it can be determined that the number of transactions corresponding to the two regions is 99 and 1, respectively.
  • the transaction attribute may include the number of users in the corresponding area.
  • the transaction information acquired in step S210 may include the user identifier of the user, and the server may determine the number of users corresponding to each region according to the number of different user identifiers.
  • the plurality of location information is clustered into 10 regions in step S220, and the number of different user identifiers corresponding to each of the regions is 2. Accordingly, it can be determined that the number of users corresponding to each of the areas is 2.
  • step S220 After the location information is clustered into at least one region in step S220, and the transaction attributes corresponding to the respective regions are determined in step S230, next, in step S240, the number of regions of the at least one region that is clustered is corresponding to each region.
  • the transaction attribute determines the service dispersion of the service provided by the service provider.
  • the transaction attributes may include the number of transactions and/or the number of users.
  • the service dispersion degree of the service provided by the service party can be determined according to the number of areas and the number of transactions corresponding to each area.
  • the service dispersion degree of the service provided by the service party may be determined according to the number of areas and the number of users corresponding to each area.
  • the first service dispersion degree may be determined according to the number of areas and the number of transactions corresponding to each area, and the second service dispersion degree is determined according to the number of areas and the number of users corresponding to each area; and then, combining The first service dispersion degree and the second service dispersion degree comprehensively determine the service dispersion degree of the service provider providing the service (for example, the service dispersion degree may be equal to the average of the first service dispersion degree and the second service dispersion degree). Further, the actual usage of the two-dimensional code of the payment by the service party can be judged according to the service dispersion degree. If the determined service dispersion is lower, the likelihood that the servant specification uses its received two-dimensional code is higher. If the determined service dispersion is higher, the risk of the service party violating its use of the QR code is higher.
  • determining the service dispersal of the service provider to provide the service may include: determining a service dispersal of the service provided by the service party according to the information entropy value of the transaction attribute.
  • the calculation formula of the information entropy value can be:
  • the service dispersion is calculated by the following formula according to the manner in which the information entropy value is calculated:
  • Spread(b i ) represents the service dispersion
  • b i represents The transaction attribute corresponding to the i-th area
  • b j represents the transaction attribute corresponding to the j-th area.
  • the service dispersion degree calculated by the formula (2) can be used to determine the actual usage of the two-dimensional code of the payment by the service party. More specifically, if the determined service dispersity is closer to 1, and the average transaction attribute on each region is less than a predetermined value (eg, 3), indicating that the service user is more dispersed and more random, then there is a high probability that the service party will The QR code is used offline outside the store, so the higher the risk of the service party violating its use of the QR code. If the determined service dispersion becomes closer to zero, the more concentrated the service users are, the higher the possibility that the service provider will use the received two-dimensional code.
  • a predetermined value eg, 3
  • the transaction attribute includes the number of transactions, then b i and b j represent the number of transactions corresponding to the i-th region and the j-th region, respectively, and the predetermined value of the average number of transactions is three.
  • the transaction attribute includes the number of users, and b i and b j respectively represent the number of users corresponding to the i-th region and the j-th region, and the predetermined value of the average number of users is 3.
  • the service dispersion degree obtained according to formula (2) is 1, and the average transaction attribute is 2 ( ⁇ 3), and the risk of the service party violating its use of the two-dimensional code is higher.
  • determining the service dispersal of the service provider to provide the service may include: inputting the transaction attribute and the number of regions into the formula (3) based on the calculation principle of the Gini coefficient, and using the output result as Service dispersion.
  • Spread(b i ) represents the service dispersion
  • b i represents The transaction attribute corresponding to the i-th area
  • b j represents the transaction attribute corresponding to the j-th area.
  • the process of determining the degree of dispersion according to the formula (3) can be referred to the foregoing description of the process of determining the degree of dispersion according to the formula (2), and will not be described herein.
  • step S220 the method further includes: removing location information of the location information of the user that exceeds a predetermined range.
  • the remaining location information after the location information exceeding the predetermined range is removed from the location information acquired in step S210 is clustered into at least one region.
  • the location information includes latitude and longitude information
  • the predetermined range of location information includes: the longitude is within the interval [-180°, 180°], and the latitude is within the interval [-90°, 90°].
  • the position information acquired in step S210 includes latitude and longitude (200°, 50°)
  • the latitude and longitude is removed from the acquired position information.
  • step S240 the service dispersion degree of the service provided by the service party can be directly calculated using the formula (1).
  • the upper limit of the service dispersion for determining whether the service party uses the two-dimensional code is correspondingly increased.
  • the method for processing location information provided by the multiple embodiments disclosed in the present specification collects multiple location information of multiple users included in the transaction information by acquiring transaction information of multiple users using the service provided by the service party. Classifying into at least one region, determining transaction attributes corresponding to respective ones of the at least one region. Then, according to the number of regions of the region and the transaction attributes corresponding to the respective regions, the service dispersion of the service provided by the service party is determined. Therefore, it is possible to efficiently and reliably identify the actual use of the payment QR code by the offline service party.
  • the multiple embodiments disclosed in the present specification further provide a location information processing apparatus.
  • the apparatus includes:
  • the obtaining unit 510 is configured to acquire transaction information of a service provided by multiple users using a service party, where the transaction information includes multiple location information of multiple users;
  • a clustering unit 520 configured to cluster a plurality of location information into at least one region
  • a determining unit 530 configured to determine a transaction attribute corresponding to each area in the at least one area, where the transaction attribute includes the number of users in the corresponding area and/or the number of transactions of the user using the service in the corresponding area;
  • the processing unit 540 is configured to determine, according to the number of regions of the at least one area and the transaction attributes corresponding to the respective areas, the service dispersity of the service provided by the service party.
  • the obtaining unit 510 is specifically configured to:
  • it also includes:
  • the removing unit 550 is configured to remove location information that exceeds a predetermined range in the location information of the user.
  • the clustering unit 520 is specifically configured to:
  • a plurality of location information is clustered into at least one region using a GEOHASH algorithm or a DBSCAN algorithm.
  • the processing unit 540 is specifically configured to:
  • the service dispersion of the service provided by the service provider is determined according to the information entropy value of the transaction attribute.
  • processing unit 540 is operative to determine service dispersion by the following formula:
  • processing unit 540 processing unit is operative to determine service dispersion by the following formula:
  • the location information acquired by the acquisition unit 510 includes latitude and longitude information.
  • the processing device for location information provided by the multiple embodiments disclosed in the present specification
  • the obtaining unit 510 acquires transaction information of a plurality of users using the service provided by the service party
  • the clustering unit 520 includes a plurality of users included in the transaction information.
  • the plurality of location information is clustered into at least one region, and the determining unit 530 determines a transaction attribute corresponding to each of the at least one region.
  • the processing unit 540 determines the service dispersion degree of the service provided by the service party according to the number of regions of the region and the transaction attributes corresponding to the respective regions. Therefore, it is possible to efficiently and reliably identify the actual use of the payment QR code by the offline service party.

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Abstract

一种位置信息的处理方法，该方法包括：获取多个用户使用服务方提供的服务的交易信息，该交易信息包括多个用户的多个位置信息（S210）；将多个位置信息聚类成至少一个区域（S220）；确定与至少一个区域中的各个区域对应的交易属性（S230）；根据聚类成的至少一个区域的区域个数和与各个区域对应的交易属性，确定服务方提供服务的服务分散度（S240）。

Description

位置信息的处理方法及装置 技术领域
本说明书披露的多个实施例涉及互联网技术领域，尤其涉及一种位置信息的处理方法及装置。
背景技术
随着互联网技术的发展，人们在使用商户提供的服务的过程中，越来越多的使用扫码支付的方式完成付款。扫码支付的方式通常包括两种：一种是商户用扫码枪扫描用户的二维码/条形码。另一种是用户使用终端扫描商户的二维码。
通常情况下，对于线下商户，要求其只能在实体门店使用商户二维码。但是，仍有一些线下商户违规使用商户二维码，例如，将商户二维码转移至线上进行使用。因此，为了督促线下商户规范使用商户二维码，需要提供一种可靠的方法来识别线下商户对商户二维码的真实使用情况。
发明内容
本说明书描述了一种位置信息的处理方法，将交易信息中的用户位置信息聚类成至少一个区域，并根据区域的个数和与各个区域对应的交易属性确定线下服务方提供服务的服务分散度，从而实现高效可靠地识别线下服务方对其付款二维码的真实使用情况。
第一方面，提供了一种位置信息的处理方法。该方法包括：
获取多个用户使用服务方提供的服务的交易信息，所述交易信息包括所述多个用户的多个位置信息；
将所述多个位置信息聚类成至少一个区域；
确定与所述至少一个区域中的各个区域对应的交易属性，所述交易属性包括对应区域中的用户数量和/或对应区域中用户使用所述服务的交易次数；
根据所述至少一个区域的区域个数和所述与各个区域对应的交易属性，确定所述服务方提供所述服务的服务分散度。
在一种可能的实施方式中，所述获取多个用户使用服务方提供的服务的交易信息， 包括：
获取预定时间内所述多个用户使用服务方提供的服务的交易信息。
在一种可能的实施方式中，其特征在于，所述将所述多个位置信息聚类成至少一个区域之前，还包括：
去除所述多个位置信息中超过预定范围的位置信息。
在一种可能的实施方式中，所述将所述多个位置信息聚类成至少一个区域，包括：
使用GEOHASH算法或DBSCAN算法，将所述多个位置信息聚类成至少一个区域。
在一种可能的实施方式中，所述确定所述服务方提供所述服务的服务分散度，包括：
根据所述交易属性的信息熵值，确定所述服务方提供所述服务的服务分散度。
在一种可能的实施方式中，所述确定所述服务方提供所述服务的服务分散度，包括通过以下公式确定所述服务分散度：
Figure PCTCN2018124298-appb-000001
其中，Speead(b i)表示所述服务分散度，i，j＝1，...，m,m表示所述区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
在一种可能的实施方式中，所述确定所述服务方提供所述服务的服务分散度，包括通过以下公式确定所述服务分散度：
Figure PCTCN2018124298-appb-000002
其中，Spread(b i)表示所述服务分散度，i，j＝1，...，m,m表示所述区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
在一种可能的实施方式中，所述位置信息包括经纬度信息。
第二方面，提供了一种位置信息的处理装置。该装置包括：
获取单元，用于获取多个用户使用服务方提供的服务的交易信息，所述交易信息包括所述多个用户的多个位置信息；
聚类单元，用于将所述多个位置信息聚类成至少一个区域；
确定单元，用于确定与所述至少一个区域中的各个区域对应的交易属性，所述交易属性包括对应区域中的用户数量和/或对应区域中用户使用所述服务的交易次数；
处理单元，用于根据所述至少一个区域的区域个数和所述与各个区域对应的交易属性，确定所述服务方提供所述服务的服务分散度。
在一种可能的设计中，所述获取单元具体用于：
获取预定时间内所述多个用户使用服务方提供的服务的交易信息。
在一种可能的设计中，还包括：
去除单元，用于去除所述多个位置信息中超过预定范围的位置信息。
在一种可能的设计中，所述聚类单元具体用于：
使用GEOHASH算法或DBSCAN算法，将所述多个位置信息聚类成至少一个区域。
在一种可能的设计中，所述处理单元具体用于：
根据所述交易属性的信息熵值，确定所述服务方提供所述服务的服务分散度。
在一种可能的设计中，所述处理单元用于通过以下公式确定所述服务分散度：
Figure PCTCN2018124298-appb-000003
其中，Spread(b i)表示所述服务分散度，i，j＝1，...，m,m表示所述区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
在一种可能的设计中，所述处理单元用于通过以下公式确定所述服务分散度：
Figure PCTCN2018124298-appb-000004
其中，Spread(b i)表示所述服务分散度，i，j＝1，...，m,m表示所述区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
在一种可能的设计中，所述获取单元获取的位置信息包括经纬度信息。
第三方面，提供了一种计算机可读存储介质，其上存储有计算机程序。当所述计算 机程序在计算机中执行时，令计算机执行上述第一方面中任一种实施方式提供的方法。
第四方面，提供了一种计算设备，包括存储器和处理器。所述存储器中存储有可执行代码，所述处理器执行所述可执行代码时，实现上述第一方面中任一种实施方式提供的方法。
本说明书提供的一种位置信息的处理方法，通过获取多个用户使用服务方提供的服务的交易信息，将交易信息中包括的多个用户的多个位置信息聚类成至少一个区域，确定与所述至少一个区域中的各个区域对应的交易属性。然后，根据区域的区域个数和与各个区域对应的交易属性，确定服务方提供服务的服务分散度。从而实现高效可靠地识别线下服务方对其付款二维码的真实使用情况。
附图说明
为了更清楚地说明本说明书披露的多个实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本说明书披露的多个实施例，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，还可以根据这些附图获得其它的附图。
图1为本说明书披露的一个实施例提供的一种位置信息的处理方法的应用场景示意图；
图2为本说明书披露的一个实施例提供的一种位置信息的处理方法的流程图；
图3为本说明书披露的一个实施例提供的一种将位置信息聚类成区域的示意图；
图4为本说明书披露的一个实施例提供的另一种将位置信息聚类成区域的示意图；
图5为本说明书披露的一个实施例提供的一种位置信息的处理装置的结构图。
具体实施方式
下面结合附图，对本说明书披露的多个实施例进行描述。
图1为本说明书披露的一个实施例提供的一种位置信息的处理方法的应用场景示意图。所述处理方法的执行主体可以为服务器。图1中，服务器(如，服务器可以为支付宝应用的服务器)在获取多个用户使用服务方(如，服务方可以为向用户提供服务方二维码的线下服务方)提供的服务(如，服务可以为商品服务)的交易信息(如，交易信 息可以包括用户的位置信息)后，可以采用本说明书披露的多个实施例提供的位置信息的处理方法，将获取的多个位置信息(如，位置信息可以为用户终端的经纬度信息)聚类(如，聚类可以通过GEOHASH算法来实现)成至少一个区域(如，至少一个区域可以包括具有不同区域编号的多个区域)。然后，确定与各个区域对应的交易属性(如，交易属性可以包括对应区域的用户数量)，并根据区域的区域个数和与各个区域对应的交易属性，确定服务方提供服务的服务分散度(如，服务分散度越低，服务方规范使用其收款二维码的可能性越高，反之越低)。
本说明书披露的多个实施例提供的位置信息的处理方法，通过获取多个用户使用服务方提供的服务的交易信息，将交易信息中包括的多个用户的多个位置信息聚类成至少一个区域，确定与所述至少一个区域中的各个区域对应的交易属性。然后，根据区域的区域个数和与各个区域对应的交易属性，确定服务方提供服务的服务分散度。从而实现高效可靠地识别线下服务方对其付款二维码的真实使用情况。
图2为本说明书披露的一个实施例提供的一种位置信息的处理方法的流程图。所述方法的执行主体可以为具有处理能力的设备：服务器或者系统或者装置或者软件平台。如，图1中的服务器。如图2所示，所述方法具体包括：
步骤S210，获取多个用户使用服务方提供的服务的交易信息，该交易信息中包括多个用户的多个位置信息。
具体地，服务方可以向用户提供该服务方的收款二维码，用户可以使用终端扫描服务方的收款二维码，完成对其所使用的服务的支付。相应地，服务器获取多个用户使用此服务方提供的服务的交易信息，该交易信息中包括多个用户的多个位置信息。其中，多个位置信息可以为通过基于位置的服务(Location Based Service，LBS)从多个用户的多个终端采集的位置信息，且位置信息中可以包括经纬度信息。
在一个实施例中，获取多个用户使用服务方提供的服务的交易信息，可以包括：获取预定时间内多个用户使用服务方提供的服务的交易信息。其中预定时间可以为预先设定的任意时间段，如，最近一周内，或者最近一个月内等。
需要说明的是，交易信息中还可以包括服务方的服务方标识(如，服务方名称、社会信用代码、服务器中的服务方编号等)、用户的用户标识(如，电话号码、身份证号、服务器中的用户编号等)和交易的订单号等。
在一个例子中，某用户使用服务方提供的服务的交易信息中包括：该用户的经纬度 信息(如，北纬39°54′25.70″，东经116°23′28.49″)、用户的电话号码(如，12312345678)、该服务方的社会信用代码(如，911101086615511250)、订单号(如，123454321)。
步骤S220，将多个位置信息聚类成至少一个区域。
具体地，采用聚类算法将多个位置信息聚类成至少一个区域。其中，聚类算法可以为GEOHASH算法或DBSCAN算法，在此不作限定。
在一个实施例中，采用GEOHASH算法将多个位置信息转换成GEOHASH网格，并确定各个位置信息的网格编号。
在一个例子中，在步骤S210中服务器获取的交易信息中包括100个经纬度坐标，采用GEOHASH算法将这100经纬度坐标转换成GEOHASH网格后，如图3所示，其中99个经纬度坐标的网格编号为WX4DX，还有1个经纬度坐标的网格编号为WX4F2。如此，以上100个位置信息被聚类成2个区域。
在另一个例子中，在步骤S210中服务器获取的交易信息中包括20个经纬度坐标，采用GEOHASH算法将这100经纬度坐标转换成GEOHASH网格后，如图4所示，每两个经纬度坐标具有相同的网格编号，得到的10个网格编号分别为：WX4G01、WX4G02、WX4H03、WX4H04、WX4I05、WX4I06、WX4J07、WX4J08、WX4K09和WX4K10。如此，以上20个位置信息被聚类为10个区域。
在另一个实施例中，采用DBSCAN算法对多个位置信息进行聚类，并确定各个位置信息的区域编号。DBSCAN算法是一种基于密度的聚类算法。与划分和层次聚类方法不同，它将簇定义为密度相连的点的最大集合，能够把具有足够高密度的区域划分为簇，并可在噪声的空间数据库中发现任意形状的聚类。具体而言，在DBSCAN算法中，首先将所有位置点标记为核心点、边界点或噪声点，删除其中的噪声点。然后为距离在预设参数之内的所有核心点之间赋予一条边，每组连通的核心点形成一个簇，将每个边界点指派到一个与之关联的核心点的簇中，由此完成位置点的聚类。
可以理解的是，可以通过调整聚类算法的参数，控制聚类得到的区域的精度(如，区域的大小)。例如，可以通过控制GEOHASH算法中对位置信息进行编码的编码位数，控制区域的精度。更具体地，位置信息的编码位数越多，得到的区域范围越精确。又例如，可以通过控制DBSCAN算法中邻域半径ε的大小，控制区域的精度。更具体地，输入的领域半径ε的值越小，得到的区域范围越精确。
接着，在步骤S230，确定与至少一个区域中的各个区域对应的交易属性。
具体地，服务器确定与各个区域对应的交易属性，该交易属性可以包括对应区域中用户使用服务的交易次数。更具体地，服务器可以根据交易信息的数量确定交易次数。或者，在步骤S210获取的交易信息中可以包括交易的订单号，相应地，服务器可以根据订单号的数量确定与各个区域对应的交易次数。
在一个例子中，在步骤S220将多个位置信息聚类成了2个区域，与其中一个区域对应的订单数量为99个，与另一个区域对应的订单数量为1个。相应地，可以确定与这两个区域对应的交易次数分别为99和1。
在一个实施例中，交易属性可以包括对应区域中的用户数量。例如，在步骤S210获取的交易信息中可以包括用户的用户标识，服务器可以根据不同的用户标识的数量确定与各个区域对应的用户数量。
在一个例子中，在步骤S220将多个位置信息聚类成了10个区域，与其中每个区域对应的不同用户标识的数量均为2。相应地，可以确定与其中各个区域对应的用户数量均为2。
在步骤S220将位置信息聚类成至少一个区域，以及在步骤S230确定与各个区域对应的交易属性后，接着，在步骤S240，根据聚类成的至少一个区域的区域个数和与各个区域对应的交易属性，确定服务方提供服务的服务分散度。
具体地，交易属性可以包括交易次数和/或用户数量。相应地，可以根据区域个数和与各个区域对应的交易次数，确定服务方提供服务的服务分散度。或者，可以根据区域个数和与各个区域对应的用户数量，确定服务方提供服务的服务分散度。又或者，可以根据区域个数和与各个区域对应的交易次数，确定出第一服务分散度，以及根据区域个数和与各个区域对应的用户数量，确定出第二服务分散度；然后，结合第一服务分散度和第二服务分散度，综合确定出服务方提供服务的服务分散度(例如，该服务分散度可以等于第一服务分散度和第二服务分散度的平均值)。更进一步地，可以根据服务分散度判断服务方对其收款二维码的真实使用情况。若确定的服务分散度越低，则服务方规范使用其收款二维码的可能性越高。若确定的服务分散度越高，则服务方违规使用其收款二维码的风险越高。
在一个实施例中，确定服务方提供服务的服务分散度，可以包括：根据交易属性的信息熵值，确定服务方提供服务的服务分散度。
其中，信息熵值的计算公式可以为：
Figure PCTCN2018124298-appb-000005
上式中，H(b i)表示交易属性的信息熵值，i，j＝1，...，m,m表示所述区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
在一个实施例中，根据信息熵值的计算方式，通过以下公式计算服务分散度：
Figure PCTCN2018124298-appb-000006
上式中，Spread(b i)表示服务分散度，Spread(b i)∈[0，1]；i，j＝1，...，m,m表示所述区域个数；b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
进一步地，可以根据公式(2)计算出的服务分散度判断服务方对其收款二维码的真实使用情况。更具体地，若确定的服务分散度越趋近于1，且各区域上的平均交易属性小于预定值(如，3)，说明服务用户越分散越随机，那么有很大概率服务方将其二维码在线下门店之外使用，因此服务方违规使用其收款二维码的风险越高。若确定的服务分散度越趋近于0，说明服务用户越集中，则服务方规范使用其收款二维码的可能性越高。
在一个例子中，交易属性包括交易次数，则b i和b j分别表示与第i个区域和与第j个区域对应的交易次数，且平均交易次数的预定值为3。例如，步骤S220中将位置信息聚类成2个区域，步骤S230中确定出这2个区域的交易次数分别为99和1，即m＝2，且b 1＝99，b 2＝1。相应地，根据公式(2)得到的服务分散度为0.08，因此服务方规范使用其收款二维码的可能性较高。
在另一个例子中，交易属性包括用户数量，则b i和b j分别表示与第i个区域和与第j个区域对应的用户数量，且平均用户数量的预定值为3。例如，步骤S220中将位置信息聚类成10个区域，步骤S230中确定出这10个区域的用户数量均为2，即m＝10，且b i＝2，i＝1，...，10。相应地，根据公式(2)得到的服务分散度为1，且平均交易属性为2(＜3)，服务方违规使用其收款二维码的风险较高。
在另一个实施例中，确定服务方提供服务的服务分散度，可以包括：将交易属性和区域个数输入基于基尼(Gini)系数的计算原理的公式(3)中，并将输出的结果作为服务分散度。
Figure PCTCN2018124298-appb-000007
上式中，Spread(b i)表示服务分散度，Spread(b i)∈[0，1]；i，j＝1，...，m,m表示所述区域个数；b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
根据公式(3)确定分散度的过程，可以参考前述对根据公式(2)确定分散度的过程的描述，在此不作赘述。
需要说明的是，在步骤S220之前，还可以包括：去除所述用户的位置信息中超过预定范围的位置信息。相应地，在步骤S220中，包括：将步骤S210中获取的位置信息中、去除超过预定范围的位置信息后的剩余位置信息，聚类成至少一个区域。
在一个例子中，位置信息包括经纬度信息，位置信息的预定范围包括：经度在区间[-180°，180°]内，且纬度在区间[-90°，90°]内。例如，步骤S210中获取的位置信息中包括经纬度(200°，50°)，则将此经纬度从获取的位置信息中去除。
此外，在步骤S240中，可以直接采用公式(1)计算服务方提供服务的服务分散度。相应地，对于区域个数较多的服务方，用于判定该服务方是否规范使用其二维码的服务分散度上限相应提高。
由上可知，本说明书披露的多个实施例提供的位置信息的处理方法，通过获取多个用户使用服务方提供的服务的交易信息，将交易信息中包括的多个用户的多个位置信息聚类成至少一个区域，确定与所述至少一个区域中的各个区域对应的交易属性。然后，根据区域的区域个数和与各个区域对应的交易属性，确定服务方提供服务的服务分散度。从而实现高效可靠地识别线下服务方对其付款二维码的真实使用情况。
与上述位置信息的处理方法对应地，本说明书披露的多个实施例还提供一种位置信息的处理装置，如图5所示，该装置包括：
获取单元510，用于获取多个用户使用服务方提供的服务的交易信息，交易信息包括多个用户的多个位置信息；
聚类单元520，用于将多个位置信息聚类成至少一个区域；
确定单元530，用于确定与至少一个区域中的各个区域对应的交易属性，交易属性包括对应区域中的用户数量和/或对应区域中用户使用服务的交易次数；
处理单元540，用于根据至少一个区域的区域个数和与各个区域对应的交易属性，确定服务方提供服务的服务分散度。
在一种可能的设计中，获取单元510具体用于：
获取预定时间内用户使用服务方提供的服务的交易信息。
在一种可能的设计中，还包括：
去除单元550，用于去除用户的位置信息中超过预定范围的位置信息。
在一种可能的设计中，聚类单元520具体用于：
使用GEOHASH算法或DBSCAN算法，将多个位置信息聚类成至少一个区域。
在一种可能的设计中，处理单元540具体用于：
根据交易属性的信息熵值，确定服务方提供服务的服务分散度。
在一种可能的设计中，处理单元540用于通过以下公式确定服务分散度：
Figure PCTCN2018124298-appb-000008
其中，Spread(b i)表示服务分散度，i，j＝1，...，m,m表示区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
在一种可能的设计中，处理单元540处理单元用于通过以下公式确定服务分散度：
Figure PCTCN2018124298-appb-000009
其中，Spread(b i)表示服务分散度，i，j＝1，...，m,m表示区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
在一种可能的设计中，获取单元510获取的位置信息包括经纬度信息。
由上可知，本说明书披露的多个实施例提供的位置信息的处理装置，获取单元510获取多个用户使用服务方提供的服务的交易信息，聚类单元520将交易信息中包括的多个用户的多个位置信息聚类成至少一个区域，确定单元530确定与所述至少一个区 域中的各个区域对应的交易属性。然后，处理单元540根据区域的区域个数和与各个区域对应的交易属性，确定服务方提供服务的服务分散度。从而实现高效可靠地识别线下服务方对其付款二维码的真实使用情况。
本领域技术人员应该可以意识到，在上述一个或多个示例中，本说明书披露的多个实施例所描述的功能可以用硬件、软件、固件或它们的任意组合来实现。当使用软件实现时，可以将这些功能存储在计算机可读介质中或者作为计算机可读介质上的一个或多个指令或代码进行传输。
以上所述的具体实施方式，对本说明书披露的多个实施例的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本说明书披露的多个实施例的具体实施方式而已，并不用于限定本说明书披露的多个实施例的保护范围，凡在本说明书披露的多个实施例的技术方案的基础之上，所做的任何修改、等同替换、改进等，均应包括在本说明书披露的多个实施例的保护范围之内。

Claims (16)

  1. 一种位置信息的处理方法，其特征在于，包括：
    获取多个用户使用服务方提供的服务的交易信息，所述交易信息包括所述多个用户的多个位置信息；
    将所述多个位置信息聚类成至少一个区域；
    确定与所述至少一个区域中的各个区域对应的交易属性，所述交易属性包括对应区域中的用户数量和/或对应区域中用户使用所述服务的交易次数；
    根据所述至少一个区域的区域个数和所述与各个区域对应的交易属性，确定所述服务方提供所述服务的服务分散度。
  2. 根据权利要求1所述的方法，其特征在于，所述获取多个用户使用服务方提供的服务的交易信息，包括：
    获取预定时间内所述多个用户使用服务方提供的服务的交易信息。
  3. 根据权利要求1所述的方法，其特征在于，所述将所述多个位置信息聚类成至少一个区域之前，还包括：
    去除所述多个位置信息中超过预定范围的位置信息。
  4. 根据权利要求1所述的方法，其特征在于，所述将所述多个位置信息聚类成至少一个区域，包括：
    使用GEOHASH算法或DBSCAN算法，将所述多个位置信息聚类成至少一个区域。
  5. 根据权利要求1所述的方法，其特征在于，所述确定所述服务方提供所述服务的服务分散度，包括：
    根据所述交易属性的信息熵值，确定所述服务方提供所述服务的服务分散度。
  6. 根据权利要求5所述的方法，其特征在于，所述确定所述服务方提供所述服务的服务分散度，包括通过以下公式确定所述服务分散度：
    Figure PCTCN2018124298-appb-100001
    其中，Spread(b i)表示所述服务分散度，i，j＝1，...，m,m表示所述区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
  7. 根据权利要求1所述的方法，其特征在于，所述确定所述服务方提供所述服务的服务分散度，包括通过以下公式确定所述服务分散度：
    Figure PCTCN2018124298-appb-100002
    其中，Spread(b i)表示所述服务分散度，i，j＝1，...，m,m表示所述区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
  8. 根据权利要求1-7中任一项所述的方法，其特征在于，所述位置信息包括经纬度信息。
  9. 一种位置信息的处理装置，其特征在于，包括：
    获取单元，用于获取多个用户使用服务方提供的服务的交易信息，所述交易信息包括所述多个用户的多个位置信息；
    聚类单元，用于将所述多个位置信息聚类成至少一个区域；
    确定单元，用于确定与所述至少一个区域中的各个区域对应的交易属性，所述交易属性包括对应区域中的用户数量和/或对应区域中用户使用所述服务的交易次数；
    处理单元，用于根据所述至少一个区域的区域个数和所述与各个区域对应的交易属性，确定所述服务方提供所述服务的服务分散度。
  10. 根据权利要求9所述的装置，其特征在于，所述获取单元具体用于：
    获取预定时间内所述多个用户使用服务方提供的服务的交易信息。
  11. 根据权利要求9所述的装置，其特征在于，还包括：
    去除单元，用于去除所述多个位置信息中超过预定范围的位置信息。
  12. 根据权利要求9所述的装置，其特征在于，所述聚类单元具体用于：
    使用GEOHASH算法或DBSCAN算法，将所述多个位置信息聚类成至少一个区域。
  13. 根据权利要求9所述的装置，其特征在于，所述处理单元具体用于：
    根据所述交易属性的信息熵值，确定所述服务方提供所述服务的服务分散度。
  14. 根据权利要求13所述的装置，其特征在于，所述处理单元用于通过以下公式确定所述服务分散度：
    Figure PCTCN2018124298-appb-100003
    其中，Spread(b i)表示所述服务分散度，i，j＝1，...，m,m表示所述区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
  15. 根据权利要求9所述的装置，其特征在于，所述处理单元用于通过以下公式确定所述服务分散度：
    Figure PCTCN2018124298-appb-100004
    其中，Spread(b i)表示所述服务分散度，i，j＝1，...，m,m表示所述区域个数，b i表示与第i个区域对应的交易属性，b j表示与第j个区域对应的交易属性。
  16. 根据权利要求9-15中任一项所述的装置，其特征在于，所述获取单元获取的位置信息包括经纬度信息。
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