WO2018059315A1 - 基于地理位置数据的热点区域确定方法及装置 - Google Patents

基于地理位置数据的热点区域确定方法及装置 Download PDF

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Publication number
WO2018059315A1
WO2018059315A1 PCT/CN2017/102886 CN2017102886W WO2018059315A1 WO 2018059315 A1 WO2018059315 A1 WO 2018059315A1 CN 2017102886 W CN2017102886 W CN 2017102886W WO 2018059315 A1 WO2018059315 A1 WO 2018059315A1
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Prior art keywords
grid
point
area
curve
poi
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PCT/CN2017/102886
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English (en)
French (fr)
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张德品
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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Priority to BR112019006475A priority Critical patent/BR112019006475A2/pt
Priority to RU2019113130A priority patent/RU2728290C1/ru
Priority to AU2017335101A priority patent/AU2017335101B2/en
Priority to JP2019517065A priority patent/JP6875510B2/ja
Priority to EP17854765.9A priority patent/EP3522495B1/en
Priority to SG11201902737RA priority patent/SG11201902737RA/en
Priority to MX2019003650A priority patent/MX2019003650A/es
Priority to CA3038583A priority patent/CA3038583C/en
Priority to PL17854765T priority patent/PL3522495T3/pl
Priority to ES17854765T priority patent/ES2872963T3/es
Application filed by Alibaba Group Holding Ltd filed Critical Alibaba Group Holding Ltd
Priority to KR1020197011270A priority patent/KR102220963B1/ko
Publication of WO2018059315A1 publication Critical patent/WO2018059315A1/zh
Priority to US16/364,917 priority patent/US10943364B2/en
Priority to PH12019500683A priority patent/PH12019500683A1/en
Anticipated expiration legal-status Critical
Priority to ZA2019/02476A priority patent/ZA201902476B/en
Priority to AU2019101563A priority patent/AU2019101563A4/en
Priority to US17/176,037 priority patent/US11087490B2/en
Ceased legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/52—Network services specially adapted for the location of the user terminal
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
    • H04L65/40—Support for services or applications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences

Definitions

  • the present application relates to the field of Internet technologies, and in particular, to a hotspot area determining method and apparatus based on geographic location data.
  • the user may want to find a hot spot in the map, for example, the user wants to go to the hot spot for shopping, entertainment, dining, etc. Accordingly, the merchant also wants to open a store in the hot spot. Therefore, according to the needs of the user, a hot spot area is usually marked on the map.
  • hotspot areas are often manually determined.
  • the efficiency of manually determining the hot spot area is relatively low, and the cost is also large due to the use of labor.
  • the present application provides a hotspot area determining method and apparatus based on geographic location data, so as to solve the problem that the existing hot spot area is inefficient and costly.
  • a method for determining a hotspot region based on geographic location data includes:
  • the grid value is the number of users corresponding to the geographic location data reported in the grid;
  • the area within the curve is determined as a hot spot area.
  • the processing, where the map area to be processed is meshed specifically includes:
  • the map area to be processed is divided into at least one grid according to a preset side length.
  • the positioning point is a center point of the grid.
  • the method further includes:
  • the type with the largest number of POI points is determined as the hotspot type of the hotspot area.
  • the method further includes:
  • a curve including all the positioning points and the POI point is calculated based on a preset algorithm.
  • the preset algorithm is a convex hull algorithm
  • the curve is a convex hull curve.
  • the convex hull algorithm includes a graham algorithm, a jarvi s algorithm, a central method, a horizontal method, or a fast packet method.
  • a hotspot area determining apparatus based on geographic location data includes:
  • the processing unit performs meshing processing on the map area to be processed
  • a statistical unit that counts a grid value of each grid within a preset duration, the grid value being a number of users corresponding to the geographic location data reported in the grid;
  • the filtering unit filters out the grid value greater than the preset threshold from the statistically obtained grid values
  • a first determining unit determining an positioning point in a grid corresponding to the filtered grid value
  • a calculating unit according to the positioning point, calculating a curve including all the positioning points based on a preset algorithm
  • the second determining unit determines the area within the curve as the hot spot area.
  • the processing unit specifically includes:
  • the map area to be processed is divided into at least one grid according to a preset side length.
  • the positioning point is a center point of the grid.
  • the device further includes:
  • the subunit is determined, and the type with the largest number of POI points is determined as the hotspot type of the hotspot area.
  • the device further includes:
  • the calculating unit specifically includes:
  • a curve including all the positioning points and the POI point is calculated based on a preset algorithm.
  • the preset algorithm is a convex hull algorithm
  • the curve is a convex hull curve.
  • the convex hull algorithm includes a graham algorithm, a jarvis algorithm, a central method, a horizontal method, or a fast packet method.
  • the map area to be processed is meshed; the grid value of each grid in the preset duration is counted, and the grid value is the number of users corresponding to the geographic location data reported in the grid; Filtering a grid value greater than a preset threshold from the statistically obtained grid values; determining an anchor point in the grid corresponding to the filtered grid value; and calculating a map based on the preset algorithm based on the preset algorithm A curve containing all the anchor points; the area within the curve is determined as the hot spot area.
  • the server can automatically determine the hotspot area of the map area by using the geographical location data actually reported by the user, thereby avoiding the inefficiency and high cost caused by manually delimiting the hot spot area, thereby improving the efficiency of determining the hot spot area and reducing the efficiency. Determine the cost of the hotspot area.
  • FIG. 1 is a flowchart of a method for determining a hotspot area based on geographic location data according to an embodiment of the present application
  • FIG. 2 is a schematic diagram of a map area provided by the present application.
  • FIG. 3 is a schematic diagram of the map area gridding process provided by the present application.
  • FIG. 4 is a schematic diagram of a map area marked with a grid value provided by the present application.
  • FIG. 5 is a schematic diagram of filtered grid values provided by the present application.
  • Figure 6 is a schematic diagram of a coordinate system established by the present application.
  • FIG. 7 is a schematic diagram of a hot spot area determined according to an anchor point provided by the present application.
  • Figure 8 is a schematic diagram showing the addition of POI points on the map area shown in Figure 2;
  • FIG. 9 is a schematic diagram of a hot spot area determined according to a POI point and a positioning point provided by the present application.
  • FIG. 10 is a hardware structural diagram of a device where a hotspot area determining apparatus based on geographic location data provided by the present application is located;
  • FIG. 11 is a schematic block diagram of a hotspot area determining apparatus based on geographic location data according to an embodiment of the present application.
  • first, second, third, etc. may be used to describe various information in this application, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other.
  • first information may also be referred to as the second information without departing from the scope of the present application.
  • second information may also be referred to as the first information.
  • word "if” as used herein may be interpreted as "when” or “when” or “in response to a determination.”
  • the hotspot regions in the map are usually manually divided. Since the manual partitioning is often susceptible to subjective mapping, there is a large error in the final hotspot boundary. For example, it is easy to classify a place that is not a hot spot area into a hot spot area, or not to place a place that belongs to a hot spot area into a hot spot area.
  • FIG. 1 is a flowchart of a method for determining a hotspot area based on geographic location data according to an embodiment of the present disclosure.
  • the embodiment is described from the server side, and includes the following steps:
  • Step 110 Perform meshing processing on the map area to be processed.
  • the map area to be processed may be selected by a staff member, for example, an area circled by a staff member in a map;
  • the server may also be determined according to a list of regions, which may be preset, and different regions are configured in the region list.
  • the area list is configured with the A area, the B area, and the C area; wherein the hotspot area of the A area has been determined; the server can determine the B area as the to-be-processed map area according to the area list. After the hotspot area of the B area is also determined, the server can determine that the C area is the map area to be processed.
  • the meshing process may include the server dividing the to-be-processed map area into at least one grid according to a preset side length.
  • the preset side length may be an empirical value preset by a person.
  • FIG. 2 is a schematic diagram of a map area provided by the present application, the map area having a length of 1,300 meters and a width of 1,050 meters.
  • the preset side length is 200 meters, that is, the map area is divided into several grids with a side length of 200 meters.
  • Figure 3 shows the grid area after the map area is processed. Schematic, as shown in Figure 3, each grid has a side length of 200 meters. Through gridding, the map area can be divided into grids of the same size.
  • the meshing process may not be performed for the edge region of the map area that is less than the preset side length.
  • the meshing process may further include: the server dividing the map area to be processed into a preset number of grids.
  • the preset number may be an empirical value preset by an artificial one. For example, divide the map area into 100 grids.
  • Step 120 Count the grid value of each grid in the preset duration, and the grid value is the number of users corresponding to the geographic location data reported in the grid.
  • the reported geographical location data specifically, the mobile terminal uploads the geographic location data of the user by using an application installed on the mobile terminal under the control of the user.
  • the mobile terminal may complete the payment process together with the interacting party at the same place.
  • the interaction party may be a user holding another mobile terminal, or may be a fixed terminal device.
  • the above process may involve a third party providing payment services in addition to two or more parties.
  • Such third-party payment service providers provide secure interactive services during the interaction process.
  • a third-party payment service provider can set up its own website or provide a server dedicated to payment, such as providing a payment platform. In this way, the payment party or parties can complete the payment through the service provided on the payment platform.
  • payment platforms such as eBay and Al ibaba.
  • the mobile terminal and/or the fixed terminal can access the payment platform provided by the third-party payment service provider framework through the Internet, and use the payment platform to complete the specific process involved in the payment.
  • the mobile terminal and/or the fixed terminal may install a dedicated client (one of which is an application), such as an application provided by the third party payment service provider, to efficiently complete the payment.
  • the geographic location data is, for example, a location where the mobile terminal where the client is located, and coordinate information representing the geographic location recorded by the positioning device that can record the geographic location data by the mobile terminal.
  • Common positioning devices can use the US GPS satellite navigation system, Europe "Galileo" Star navigation system, Russian GLONASS satellite navigation system, or China “Beidou” satellite navigation system, etc., or a similar combination.
  • the coordinate information of such positioning is also referred to as mobile positioning.
  • the time-raising data is also reported to be the time-stamped time, which may be the time when the positioning device determines the geographical location data; or may be the time when the client reports the geographical location data.
  • the geographical location data may be obtained by the network device based on the signal characteristics of the mobile terminal where the client is located, for example, by the network operator using the base station coverage principle, and the signal of the mobile terminal where the client is located is located through the base station.
  • Calculated location information In the latter positioning calculation, the downlink pilot signals of different base stations are generally measured by the mobile terminal, and the time of arrival (Time of Arrival, TOA) or Time Difference of Arrival (TDOA) of the downlink pilots of different base stations is obtained, according to The measurement result is combined with the coordinates of the base station, and a triangulation formula estimation algorithm is generally used to calculate the position of the mobile terminal.
  • TOA Time of Arrival
  • TDOA Time Difference of Arrival
  • the actual position estimation algorithm needs to consider the situation of multiple base stations (3 or more) positioning, and there are multiple algorithms in the prior art, which are complicated. In general, the more the number of base stations measured by the mobile station, the higher the measurement accuracy, and the more obvious the positioning performance improvement.
  • the geographical location data may also be a relatively accurate location obtained by the base station assisted positioning and being jointly positioned by the positioning device in the mobile terminal.
  • the uploaded geographical location data is represented by latitude and longitude, and according to the latitude and longitude, it can be determined in which grid of the map area the uploaded geographical location data is located. And according to the timestamp, the grid value in each grid within the preset duration can be counted.
  • the preset duration may be an empirical value preset by a person. For example, 1 day, that is, the grid value in each grid within 1 day, that is, the number of users reporting location information in each grid.
  • the heat of the area corresponding to each grid in the preset duration can be actually reflected.
  • Step 130 Filter out grid values greater than a preset threshold from the statistically obtained grid values.
  • the preset threshold may be an empirical value preset by a person.
  • FIG. 5 is a schematic diagram of the filtered grid values, and the grid numbers corresponding to the grid values greater than 200 are numbered p0 to p9.
  • Step 140 Determine an anchor point in a grid corresponding to the filtered grid value.
  • the positioning point may be a center point of the grid.
  • the positioning point may also be other points in the grid, such as a vertex of the grid, a point 1/2, 1/3 from the center point, etc., which is not limited in this application.
  • the anchor point can also be determined by the following methods:
  • the anchor point can be determined by the ratio of the two grid values.
  • the anchor point can be set at 1/3 of the distance between the two grids.
  • Step 150 Calculate a curve including all the positioning points according to the preset algorithm according to the positioning point.
  • the preset algorithm may be used to calculate a curve including all the positioning points.
  • a point set X ⁇ X1, X2, ..., Xn ⁇
  • the outermost points are connected to form a set of included points X.
  • the curve of all points may be used to calculate a curve including all the positioning points.
  • the preset algorithm may be a convex hull algorithm
  • the curve may be a convex hull curve
  • the convex hull algorithm is used in a real vector space, for a given set of points X ⁇ X1, X2, ..., Xn ⁇ , the intersection of all convex sets containing the set of points X S is called the convex hull of X.
  • the convex hull of X can be constructed with a linear combination of all points (X1, ... Xn) in X.
  • a convex hull is a convex polygonal type formed by joining the outermost points, and the convex polygonal type can contain all the points in the point set.
  • the convex hull algorithm may include a graham algorithm, a jarvis algorithm, a central method, a horizontal method, or a fast packet method.
  • the coordinates of each point in the actual midpoint set ⁇ p0, p1, p2, p3, p4, p5, p6, p7, p8, p9 ⁇ are composed of latitude and longitude, the longitude is the X axis, and the latitude is the Y axis.
  • the coordinates are simplified to integers, and in FIG.
  • the p0 coordinate is (1, -2); the p1 coordinate is (1, 2); the p2 coordinate is (1, -2); p3 coordinates (1,2); p4 coordinates are (1,-2); p5 coordinates are (1,2); p6 coordinates are (1,-2); p7 coordinates are (1,2); p8 coordinates are (1) , -2); p9 coordinates are (1, -2).
  • A1 Select one point among all points as the base point.
  • selecting a base point may be any one of the following ways:
  • the first type It can be the point that selects the smallest point of the Y coordinate among all the points. If there are multiple points with the smallest Y coordinate, the point with the smallest X coordinate is selected.
  • Second It can be the point of choosing the smallest point of the X coordinate among all the points. If there are multiple points with the smallest X coordinate, the point with the smallest Y coordinate is selected.
  • the third type It is possible to select the most basic point of the point where the Y coordinate is the largest among all the points. If there are multiple points with the largest Y coordinate, the point with the largest X coordinate is selected.
  • the fourth type It is possible to select the most basic point of the X coordinate at all points. If there are multiple points with the largest X coordinate, the point with the largest Y coordinate is selected.
  • the second method is adopted, that is, the point with the smallest X coordinate is selected. Since p0 and p1 exist, the point with the smallest y coordinate is selected, that is, p0 is selected as the base point.
  • A2 Sort the vector formed by the other points and the base point with the cosine of the angle of the X-axis.
  • the square of either side is equal to the sum of the squares of the other two sides. Subtract the two-fold product of the cosines of the two sides with their angles.
  • the range of the cosine value is [-1, 1].
  • the cosine value is positive in one quadrant, negative in the second and third quadrants, and zero on the X or Y axis.
  • the cosine of the angle formed by each point and the base point is calculated, and then sorted from large to small, and the scanning order is: p0, p1, p2, p4, p3, p5, p6, p7, P8, p9.
  • A3 Scan according to the sorted order, and retain the point where the vector product is greater than 0 to obtain a convex hull curve.
  • the vector product is a binary operation of a vector in a vector space
  • the calculation formula is as follows:
  • the vector product of the vector AB and the vector BC is less than 0, the vector AB is represented in the counterclockwise direction of the vector BC; the point B is deleted, and the AC is connected to form the vector AC;
  • the representation vector AB is collinear with the vector BC; the point B is deleted, and the connection AC constitutes the vector AC.
  • the final reserved points include p0, p2, p4, p6, p9.
  • the curve formed by the vector p0p2, the vector p2p4, the vector p4p6, the vector p6p9 and the vector p9p0 is a convex hull curve.
  • Step 160 Determine an area within the curve as a hot spot area.
  • FIG. 7 is a schematic diagram of a hot spot area determined according to an anchor point provided in the present application, and the curve (hot spot area boundary) in FIG. 7 includes a bit set (p0 to p9).
  • the server maps the map area to be processed by the server, and counts the grid values in each grid, and filters the grid values larger than the preset threshold from the statistical grid values to determine The selected grid value corresponds to the anchor point of the grid, and then a curve including all the anchor points is calculated based on the preset algorithm, and finally the area in the curve is determined as the hot spot area.
  • the server can automatically determine the hotspot area of the map area by using the geographical location data actually reported by the user, thereby avoiding the inefficiency and high cost caused by manually delimiting the hot spot area, thereby improving the efficiency of determining the hot spot area and reducing the efficiency. Determine the cost of the hotspot area.
  • the type of food court is usually a type of food and the type of a residential circle is usually a type of community.
  • the hotspot area obtained by the reported geographical location data in the above embodiment does not have a type.
  • the method may further include:
  • the type with the largest number of POI points is determined as the hotspot type of the hotspot area.
  • the POI point (information point) is an information point provided by the map.
  • the POI point can refer to an actual location. For example, large shopping malls, supermarkets, schools, residential areas, etc.
  • POI points have types that are used to distinguish between different functions, such as meal type, community type, campus type, hospital type, shopping type, and the like.
  • the POI point may be an information point provided by the map. It can also be an information point provided by other maps.
  • the server may collect the POI point data of the same type according to the type of the POI point in the hot spot area, and determine the type with the largest number of POI points as the hotspot type of the hot spot area. In this way, the hotspot type can be marked on the hotspot area, so that the user can quickly know the type of the hotspot area and improve the user experience.
  • the geographical location data reported by the user there may be a certain deviation due to the geographical location data reported by the user.
  • the positioning of the positioning device of the mobile terminal is biased, and the reported geographical location data is also biased.
  • the geographic location data may be last, but the uploaded geographic location data is a hotspot (such as wifi) connected to the mobile terminal or geographic location data of the base station, and the geographic location data may also have deviation.
  • the method may further include:
  • the POI point in the map area is acquired.
  • the step 150 includes:
  • a curve including all the positioning points and the POI point is calculated based on a preset algorithm.
  • the POI point is an information point provided by the map.
  • the POI point can refer to an actual location. For example, large shopping malls, supermarkets, schools, residential areas, etc.
  • the POI point itself represents a certain degree of heat, and the geographical location data of the POI point is generally accurate.
  • the POI point may be an information point provided by the map. It can also be an information point provided by other maps.
  • the server calculates a curve including all the positioning points and the POI points based on the positioning point and the acquired POI point based on a preset algorithm.
  • the calculation process of the preset algorithm is as shown in the foregoing embodiment, and details are not described in this embodiment.
  • FIG. 8 a schematic diagram of a POI point is added to the map area shown in FIG. 2.
  • FIG. 9 is a schematic diagram of a hot spot area determined according to a POI point and an anchor point.
  • the hotspot area shown in FIG. 9 has more popular places, so that the hotspot area is more accurate.
  • the POI point can be used to correct the geographical location data reported by the user, so that the final hot spot area is more accurate.
  • the present application further provides an embodiment of the hotspot region determining device based on the geographic location data.
  • the embodiments of the hotspot region determining apparatus based on the geographic location data of the present application may be separately applied to the server device.
  • the device embodiment may be implemented by software, or may be implemented by hardware or a combination of hardware and software. Taking the software implementation as an example, as a logical means, the processor of the device in which it is located reads the corresponding computer program instructions in the non-volatile memory into the memory. From a hardware level, as shown in FIG. 10, a hardware structure diagram of a device where the hotspot area determining device is based on the geographic location data of the present application, except for the processor, the network interface, the memory, and the nonvolatile memory shown in FIG. In addition to the physical memory, the device in which the device is located in the embodiment is generally based on the actual function determined by the hotspot area based on the geographic location data, and may also include other hardware, and details are not described herein again.
  • FIG. 11 is a schematic diagram of a module for determining a hotspot area based on geographic location data according to an embodiment of the present disclosure. The embodiment is described from a server side, where the apparatus includes: a processing unit 610, a statistics unit 620, and a screening unit. 630. The first determining unit 640, the calculating unit 650, and the second determining unit 660.
  • the processing unit 610 performs a meshing process on the map area to be processed
  • the statistic unit 620 is configured to count the grid value of each grid in the preset duration, and the grid value is the number of users corresponding to the geographic location data reported in the grid;
  • the filtering unit 630 filters out the grid value greater than the preset threshold from the statistically obtained grid values
  • the first determining unit 640 determines an anchor point in the grid corresponding to the filtered grid value
  • the calculating unit 650 calculates, according to the positioning point, a curve including all the positioning points according to the positioning point;
  • the second determining unit 660 determines the area within the curve as the hot spot area.
  • the processing unit 610 specifically includes:
  • the map area to be processed is divided into at least one grid according to a preset side length.
  • the anchor point is the center point of the grid.
  • the device also includes:
  • the subunit is determined, and the type with the largest number of POI points is determined as the hotspot type of the hotspot area.
  • the device also includes:
  • the calculating unit 650 specifically includes:
  • a convex hull curve including all the positioning points and the POI point is calculated based on a preset algorithm.
  • the convex hull algorithm includes a graham algorithm, a jarvis algorithm, a central method, a horizontal method, or a fast packet method.
  • the server maps the map area to be processed by the server, and counts the grid values in each grid, and filters out the grid values from the statistics to be greater than the preset threshold.
  • the grid value determines that the selected grid value corresponds to the anchor point of the grid, and then calculates a curve including all the anchor points based on the preset algorithm, and finally determines the area in the curve as the hot spot area.
  • the server can automatically determine the hotspot area of the map area by using the geographical location data actually reported by the user, thereby avoiding the inefficiency and high cost caused by manually delimiting the hot spot area, thereby improving the efficiency of determining the hot spot area and reducing the efficiency. Determine the cost of the hotspot area.
  • the device embodiment since it basically corresponds to the method embodiment, reference may be made to the partial description of the method embodiment.
  • the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located A place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the objectives of the present application. Those of ordinary skill in the art can understand and implement without any creative effort.

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Abstract

本申请提供基于地理位置数据的热点区域确定方法及装置，所述方法包括：对待处理的地图区域进行网格化处理；统计预设时长内每一个网格的网格值，所述网格值为对应网格内上报地理位置数据的用户数量；从统计得到的网格值中筛选出大于预设阈值的网格值；在所述筛选出的网格值对应的网格中确定定位点；根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线；将所述曲线内的区域确定为热点区域。应用本申请实施例，利用用户真实上报的地理位置数据自动地确定热点区域，避免了因为人工划定热点区域所造成的低效和高成本，从而提高了确定热点区域的效率，降低了确定热点区域的成本。

Description

基于地理位置数据的热点区域确定方法及装置 技术领域
本申请涉及互联网技术领域，尤其涉及一种基于地理位置数据的热点区域确定方法及装置。
背景技术
现如今，基于地理位置数据的应用越来越广泛，特别是基于地理位置数据的地图。
在实际的用户需求中，用户可能会想要找到地图中的热点区域，例如用户想要到热点区域进行购物、娱乐、就餐等；相应地，商家也会想要到热点区域开设门店。由此，根据用户需要，通常在地图中会标注有热点区域。
现有技术中，热点区域往往是人工确定的。然而，人工确定热点区域的效率比较低，而且由于使用了人工，成本也较大。
发明内容
本申请提供基于地理位置数据的热点区域确定方法及装置，以解决现有存在的确定热点区域效率较低、成本较大的问题。
根据本申请实施例提供的一种基于地理位置数据的热点区域确定方法，所述方法包括：
对待处理的地图区域进行网格化处理；
统计预设时长内每一个网格的网格值，所述网格值为对应网格内上报地理位置数据的用户数量；
从统计得到的网格值中筛选出大于预设阈值的网格值；
在所述筛选出的网格值对应的网格中确定定位点；
根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线；
将所述曲线内的区域确定为热点区域。
可选的，所述对待处理的地图区域进行网格化处理，具体包括：
根据预设边长将待处理的地图区域划分为至少一个的网格。
可选的，所述定位点为该网格的中心点。
可选的，所述方法还包括：
获取所述曲线范围内POI点的类型；
统计相同类型的POI点数量；
将POI点数量最多的类型确定为该热点区域的热点类型。
可选的，所述方法还包括：
在所述筛选出的网格值对应的网格中确定定位点之后，获取所述地图区域内的POI点；
所述根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线，具体包括：
根据所述POI点和所述定位点，基于预设算法计算出一条包含所有定位点和POI点的曲线。
可选的，所述预设算法为凸包算法；
所述曲线为凸包曲线。
可选的，所述凸包算法包括graham算法、jarvi s算法、中心法、水平法或快包法。
根据本申请实施例提供的一种基于地理位置数据的热点区域确定装置，所述装置包括：
处理单元，对待处理的地图区域进行网格化处理；
统计单元，统计预设时长内每一个网格的网格值，所述网格值为对应网格内上报地理位置数据的用户数量；
筛选单元，从统计得到的网格值中筛选出大于预设阈值的网格值；
第一确定单元，在所述筛选出的网格值对应的网格中确定定位点；
计算单元，根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线；
第二确定单元，将所述曲线内的区域确定为热点区域。
可选的，所述处理单元，具体包括：
根据预设边长将待处理的地图区域划分为至少一个的网格。
可选的，所述定位点为该网格的中心点。
可选的，所述装置还包括：
获取子单元，获取所述曲线范围内POI点的类型；
统计子单元，统计相同类型的POI点数量；
确定子单元，将POI点数量最多的类型确定为该热点区域的热点类型。
可选的，所述装置还包括：
获取子单元，在所述筛选出的网格值对应的网格中确定定位点之后，获取所述地图区域内的POI点；
相应地，所述计算单元，具体包括：
根据所述POI点和所述定位点，基于预设算法计算出一条包含所有定位点和POI点的曲线。
可选的，所述预设算法为凸包算法；
所述曲线为凸包曲线。
可选的，所述凸包算法包括graham算法、jarvis算法、中心法、水平法或快包法。
本申请实施例中，通过对待处理的地图区域进行网格化处理；统计预设时长内每一个网格的网格值，所述网格值为对应网格内上报地理位置数据的用户数量；从统计得到的网格值中筛选出大于预设阈值的网格值；在所述筛选出的网格值对应的网格中确定定位点；根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线；将所述曲线内的区域确定为热点区域。如此，服务器可以利用用户真实上报的地理位置数据自动地确定所述地图区域的热点区域，避免了因为人工划定热点区域所造成的低效和高成本，从而提高了确定热点区域的效率，降低了确定热点区域的成本。
附图说明
图1是本申请一实施例提供的基于地理位置数据的热点区域确定方法的流程图；
图2是本申请提供的地图区域的示意图；
图3是本申请提供的对地图区域网格化处理后的示意图；
图4是本申请提供的标记有网格值的地图区域的示意图；
图5是本申请提供的筛选出的网格值的示意图；
图6是本申请建立的坐标系的示意图；
图7是本申请提供的根据定位点确定的热点区域的示意图；
图8是图2所示地图区域上添加了POI点的示意图；
图9是本申请提供的根据POI点与定位点合确定的热点区域的示意图；
图10是本申请提供的基于地理位置数据的热点区域确定装置所在设备的一种硬件结构图；
图11是本申请一实施例提供的基于地理位置数据的热点区域确定装置的模块示意图。
具体实施方式
这里将详细地对示例性实施例进行说明，其示例表示在附图中。下面的描述涉及附图时，除非另有表示，不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本申请相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的、本申请的一些方面相一致的装置和方法的例子。
在本申请使用的术语是仅仅出于描述特定实施例的目的，而非旨在限制本申请。在本申请和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。还应当理解，本文中使用的术语“和/或”是指并包含一个或多个相关联的列出项目的任何或 所有可能组合。
应当理解，尽管在本申请可能采用术语第一、第二、第三等来描述各种信息，但这些信息不应限于这些术语。这些术语仅用来将同一类型的信息彼此区分开。例如，在不脱离本申请范围的情况下，第一信息也可以被称为第二信息，类似地，第二信息也可以被称为第一信息。取决于语境，如在此所使用的词语“如果”可以被解释成为“在……时”或“当……时”或“响应于确定”。
现有技术中，地图中的热点区域通常是人工进行划分的，由于人工划分往往容易收到主观映射的影响，导致最终划分出的热点区域边界存在较大的误差。例如，容易把不是热点区域的地方划归到热点区域中，或者没有把属于热点区域的地方划归到热点区域中。
为了解决上述问题，请参见图1，为本申请一实施例提供的基于地理位置数据的热点区域确定方法的流程图，该实施例从服务器侧进行描述，包括以下步骤：
步骤110：对待处理的地图区域进行网格化处理。
本实施例中，所述待处理的地图区域可以是工作人员选定的，例如工作人员在地图中圈出的一块区域；
也可以是服务器根据地区列表确定的，所述地区列表可以是预先设置的，在该地区列表中配置有不同的地区。例如，地区列表中配置有A区、B区和C区；其中，A区的热点区域已确定；服务器根据该地区列表可以确定B区为待处理的地图区域。当B区的热点区域也确定后，该服务器又可以确定C区为待处理的地图区域。
所述网格化处理可以包括：服务器根据预设边长将待处理的地图区域划分为至少一个的网格。
所述预设边长可以是人为预先设置的一个经验值。
例如，图2所示为本申请提供的地图区域的示意图，该地图区域的长为1300米，宽为1050米。假设预设边长为200米，也就是说以边长为200米将该地图区域划分为若干网格。图3所示为对地图区域进行网格化处理后的 示意图，如图3中每个网格的边长都是200米，通过网格化处理，可以将地图区域划分为相同大小的网格。如图3中所示，对于地图区域中不足预设边长的边缘区域，可以不进行网格化处理。当然，也可以将不足预设边长的区域网格化，这里不加以限定。
在另一个实施例中，所述网格化处理还可以包括：服务器将待处理的地图区域划分为预设数量的网格。
所述预设数量可以是人为预设的一个经验值。例如将地图区域划分为100个网格。
步骤120：统计预设时长内每一个网格的网格值，所述网格值为对应网格内上报地理位置数据的用户数量。
本实施例中，所述上报的地理位置数据，具体地是移动终端在用户的控制下，利用安装于所述移动终端上的应用程序来上传用户的地理位置数据。
例如，移动支付场景中，移动终端在用户的控制下，可能与交互方在相同地点共同完成支付过程。所述交互方可以是持另一移动终端的用户，也可以是固定的终端设备。上述过程中可能涉及除交互双方或多方以外的提供支付服务的第三方。这类第三方支付服务提供商在交互过程中提供安全交互服务。
第三方支付服务提供商可以架设自己的网站或者提供服务器以专门用于支付，例如提供支付平台。这样，支付双方或多方可以通过该支付平台上提供的服务完成支付。例如eBay、Al ibaba这样的支付平台。移动终端和/或固定终端可以通过互联网接入第三方支付服务提供商架提供的支付平台，并利用该支付平台完成支付涉及的特定流程。此外，为了便捷和一些安全的考虑，移动终端和/或固定终端可以安装专门的客户端(其中一种形式为应用)，例如由该第三方支付服务提供商提供的应用，高效的完成支付。
所述地理位置数据例如是客户端所位于的移动终端所处的位置，经该移动终端可以记录地理位置数据的定位装置记录下的，代表地理位置的坐标信息。常见的定位装置可以是采用美国GPS卫星导航系统，欧洲“伽利略”卫 星导航系统，俄罗斯GLONASS卫星导航系统，或者中国“北斗”卫星导航系统等，或者类似的组合。这类定位的坐标信息也称为移动定位。并且，通常情况下上报地理位置数据还携带有上报的时间戳，所述时间戳可以是上述定位装置确定地理位置数据时的时间；或者，可以是客户端上报地理位置数据时的时间。
所述地理位置数据，可以是网络设备基于客户端所位于的移动终端的信号特点转换得到的，例如由网络运营商利用基站覆盖原理，通过所述客户端所位于的移动终端的信号通过基站定位计算得到的位置信息。在后者的定位计算中，一般由移动终端测量不同基站的下行导频信号，得到不同基站下行导频的到达时刻(Time of Arrival，TOA)或到达时间差(Time Difference of Arrival，TDOA)，根据该测量结果并结合基站的坐标，一般采用三角公式估计算法，从而计算出移动终端的位置。实际的位置估计算法需要考虑多基站(3个或3个以上)定位的情况，现有技术中有多种算法，较为复杂。一般而言，移动台测量的基站数目越多，测量精度越高，定位性能改善越明显。
此外，所述地理位置数据，还可以是通过基站辅助定位并结合移动终端中的定位装置共同定位得到的较为精确的位置。
一般的，上传的地理位置数据是以经纬度来表示的，根据该经纬度就可以确定上传的地理位置数据位于该地图区域的哪个网格中。并且根据时间戳可以统计预设时长内每一个网格内的网格值。
所述预设时长可以是人为预先设置的一个经验值。例如，1天，即统计1天内每一个网格内的网格值，即每一个网格内上报位置信息的用户数量。
通过统计用户数量，可以实际反映出所述预设时长内每个网格对应的区域的热度。所述网格值越大，说明该网格内的用户越多，则该网格对应的区域越趋向于热点区域；反之，所述网格值越小，所述该网格内的用户越少，则该网格对应的区域越趋向于非热点区域。
步骤130：从统计得到的网格值中筛选出大于预设阈值的网格值。
本实施例中，所述预设阈值可以是人为预先设置的一个经验值。
在上述图3的例子中，通过统计每个网格内上报地理位置数据的用户数量后，可以得到每个网格内的网格值，如图4所示为标记有网格值的地图区域的示意图。图4中，为了示意图的清楚，已将图3中具体的地图略去，仅保留了网格。
假设预设阈值为200，即筛选出大于200的网格值对应的网格。如图5所示为筛选出的网格值的示意图，将大于200的网格值对应的的网格编号，序号为p0至p9。
步骤140：在所述筛选出的网格值对应的网格中确定定位点。
本实施例中，所述定位点可以是该网格的中心点。
在其它实施例中，所述定位点还可以是网格内的其它点，例如网格的顶点、距离中心点1/2、1/3的点等，本申请对此并不其加以限定。
值得一提的是，定位点还可以是通过如下的方式确定：
在两个相邻网格的网格值，一个大于预设阈值，一个小于预设阈值的情况下，可以通过这两个网格值的比值来确定定位点。
例如，一个网格值为100，另一个网格值为300的情况下，可以将定位点设置在该两个网格距离的1/3处。
步骤150：根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线。
本实施例中，所述预设算法可以用于计算包含所有定位点的曲线。一般的，针对一个点集X{X1，X2,...,Xn}，将该点集X标注在二维平面上时，将最外层的点连接后就可以构成一条包含点集X中所有点的曲线。
在一个具体实施例中，所述预设算法可以是凸包算法，所述曲线可以是凸包曲线。
本实施例中，所述凸包算法用于在一个实数向量空间中，对于给定的一个点集X{X1，X2,...,Xn}，所有包含该点集X的凸集的交集S被称为X的凸包。X的凸包可以用X内所有点(X1，...Xn)的线性组合来构造。
简单的说，在一个二维平面上的点集中，凸包就是将最外层的点连接起来所构成的凸多边型，该凸多边型可以包含所述点集中所有的点。
所述凸包算法可以包括graham算法、jarvis算法、中心法、水平法或快包法。
以下以graham算法为例加以说明。在图5所示的网格基础上，如图6所示建立坐标系。
实际中点集{p0，p1，p2，p3，p4，p5，p6，p7，p8，p9}中每个点的坐标是由经纬度构成，经度为X轴，纬度为Y轴。本实施例中，为了便于说明，坐标简化为整数，在图6中假设p0坐标为(1，-2)；p1坐标为(1，2)；p2坐标为(1，-2)；p3坐标为(1，2)；p4坐标为(1，-2)；p5坐标为(1，2)；p6坐标为(1，-2)；p7坐标为(1，2)；p8坐标为(1，-2)；p9坐标为(1，-2)。
如下详细介绍整个凸包算法的运算过程：
A1：在所有点中选取一个点作为基点。
具体地所述选取一个基点可以是如下方式中的任意一种：
第一种：可以是在所有点中选取Y坐标最小的一点最为基点。如果存在多个Y坐标最小的点，则选取X坐标最小的点。
第二种：可以是在所有点中选取X坐标最小的一点最为基点。如果存在多个X坐标最小的点，则选取Y坐标最小的点。
第三种：可以是在所有点中选取Y坐标最大的一点最为基点。如果存在多个Y坐标最大的点，则选取X坐标最大的点。
第四种：可以是在所有点中选取X坐标最大的一点最为基点。如果存在多个X坐标最大的点，则选取Y坐标最大的点。
本实施例中，采取第二种方式，即选取X坐标最小的点，由于存在p0和p1，所以选取y坐标最小的点即选取p0作为基点。
A2：根据其它各点和所述基点构成的向量与X轴夹角的余弦值进行排序。
本实施例中，对于任意三角形，任何一边的平方等于其他两边平方的和 减去这两边与它们夹角的余弦的两倍积。
假设三边为a，b，c,三角为A，B，C，余弦值的计算公式如下公式所示：
Figure PCTCN2017102886-appb-000001
Figure PCTCN2017102886-appb-000002
Figure PCTCN2017102886-appb-000003
其中，余弦值的范围为[-1,1]。在直角坐标系中，余弦值在一四象限是正值，在二三象限是负值，在X轴或Y轴上值为0。
根据上述公式计算得出每一个点与基点构成的向量夹角的余弦值后，从大到小进行排序，得出扫描顺序为：p0，p1，p2，p4，p3，p5，p6，p7，p8，p9。
A3：根据排序后的顺序进行扫描，将向量积大于0的点保留，得到凸包曲线。
本实施例中，所述向量积是一种在向量空间中向量的二元运算，计算公式如下所示：
其中，sinθ为向量AB和向量BC的正弦值，向量积的结果可以是正值，也可以是负值，也可以是0。
在向量AB与向量BC的向量积大于0的情况下，表示向量AB在向量BC的顺时针方向；保留点B；
在向量AB与向量BC的向量积小于0的情况下，表示向量AB在向量BC的逆时针方向；删除点B，连接AC构成向量AC；
在向量AB与向量BC的向量积等于0的情况下，表示向量AB与向量BC共线；删除点B，连接AC构成向量AC。
如图6所示的扫描步骤(1-14)：
1：首先，连接p0与p1，构成向量p0p1。
2：连接p1与p2，构成向量p1p2；
由于向量p0p1和向量p1p2的向量积为0；所以删除p1，连接p0和p2构成向量p0p2。
3：连接p2与p4，构成向量p2p4；
由于向量p0p2和向量p2p4的向量积大于0，所以保留p2。
4：连接p4与p3，构成向量p4p3；
由于向量p2p4和向量p4p3的向量积大于0，所以保留p4。
5：连接p3与p5，构成向量p3p5；
由于向量p4p3和向量p3p5的向量积小于0，所以删除p3。
6：连接p4和p5，构成向量p4p5；
由于向量p2p4和向量p4p5的向量积大于0，所以继续保留p4。
7：连接p5和p6，构成向量p5p6；
由于向量p4p5和向量p5p6的向量积小于0，所以删除p5。
8：连接p4和p6，构成向量p4p6；
由于向量p2p4和向量p4p6的向量积大于0，所以继续保留p4。
9：连接p6和p7，构成向量p6p7；
由于向量p4p6和向量p6p7的向量积大于0，所以保留p6。
10：连接p7和p8，构成向量p7p8；
由于向量p6p7和向量p7p8的向量积小于0，所以删除p7。
11：连接p6和p8，构成向量p6p8；
由于向量p4p6和向量p6p8的向量积大于0，所以继续保留p6。
12：连接p8和p9，构成向量p8p9；
由于向量p6p8和向量p8p9的向量积小于0，所以删除p8。
13：连接p6和p9，构成向量p6p9；
由于向量p4p6和向量p6p9的向量积大于0，所以继续保留p6。
14：连接p9和p0，构成向量p9p0；
由于向量p6p9和向量p9p0的向量积大于0，所以保留p9。
综上所述，最终保留的点包括p0，p2，p4，p6，p9。并且由向量p0p2，向量p2p4，向量p4p6，向量p6p9和向量p9p0，构成的曲线即为凸包曲线。
步骤160：将所述曲线内的区域确定为热点区域。
如图7所示为本申请提供的根据定位点确定的热点区域的示意图，图7中所述曲线(热点区域边界)内包含有所有点集(p0至p9)。
通过本实施例，首先服务器对待处理的地图区域进行网格化处理，并统计每个网格内的网格值，从统计得到的网格值中筛选出大于预设阈值的网格值，确定筛选出的网格值对应网格的定位点，然后基于预设算法计算一条包含所有定位点的曲线，最后将所述曲线内的区域确定为热点区域。如此，服务器可以利用用户真实上报的地理位置数据自动地确定所述地图区域的热点区域，避免了因为人工划定热点区域所造成的低效和高成本，从而提高了确定热点区域的效率，降低了确定热点区域的成本。
在实际应用中，不同的热点区域具有不同的类型，例如美食广场的类型通常为餐饮类型，住宅圈的类型通常是社区类型等。而上述实施例中通过上报的地理位置数据得到的热点区域并不具有类型。
为了解决上述问题，在本申请的一个具体地实施例中，在所述步骤160之后，所述方法还可以包括：
获取所述曲线范围内POI点的类型；
统计相同类型的POI点数量；
将POI点数量最多的类型确定为该热点区域的热点类型。
本实施例中，所述POI点(Point of Information，信息点)是地图提供的信息点。所述POI点可以指实际的地点。例如大型商场、超市、学校、住宅区等。
通常，POI点具有用于区分不同功能的类型，例如餐饮类型、社区类型、校园类型、医院类型、购物类型等。
值得一提的是，所述POI点可以是本地图提供的信息点。也可以是由其它地图提供的信息点。
通过本实施例，服务器可以根据热点区域内POI点的类型，统计相同类型的POI点数据，将POI点数量最多的类型确定为热点区域的热点类型。如此，可以对得出的热点区域标注热点类型，方便用户快速得知热点区域的类型，提高了用户体验。
在实际应用中，由于用户上报的地理位置数据可以存在一定的偏差。例如，移动终端的定位装置定位有偏差，导致上报的地理位置数据也有偏差。再例如，移动终端关闭定位装置时，也可以上次地理位置数据，但是上传的地理位置数据是该移动终端连接的热点(如wifi)或者基站所在的地理位置数据，该地理位置数据也会有偏差。
为了解决上述问题，在本申请的一个具体地实施例中，所述方法还可以包括：
在所述筛选出的网格值对应的网格中确定定位点(上述步骤140)之后，获取所述地图区域内的POI点。
相应地，所述步骤150，具体包括：
根据所述POI点和所述定位点，基于预设算法计算出一条包含所有定位点和POI点的曲线。
本实施例中，所述POI点是地图提供的信息点。所述POI点可以指实际的地点。例如大型商场、超市、学校、住宅区等。
由于POI点本身就代表了一定的热度，并且POI点的地理位置数据一般都是准确的。
值得一提的是，所述POI点可以是本地图提供的信息点。也可以是由其它地图提供的信息点。
本实施例中，服务器根据定位点和获取的POI点，基于预设算法计算出一条包含所有定位点和POI点的曲线。所述预设算法的计算过程如上述实施例所示，本实施例中不再赘述。
如图8所示，在图2所示地图区域上添加了POI点的示意图。
相应地，如图9所示为根据POI点与定位点确定的热点区域的示意图。 与图7所示的热点区域相比，图9所示的热点区域多了一些热门地方，使得热点区域更加准确，
通过本实施例，利用POI点可以对用户上报的地理位置数据加以修正，使得最后得出的热点区域更加准确。
与前述基于地理位置数据的热点区域确定方法实施例相对应，本申请还提供了基于地理位置数据的热点区域确定装置的实施例。
本申请基于地理位置数据的热点区域确定装置的实施例可以分别应用在服务器设备上。装置实施例可以通过软件实现，也可以通过硬件或者软硬件结合的方式实现。以软件实现为例，作为一个逻辑意义上的装置，是通过其所在设备的处理器将非易失性存储器中对应的计算机程序指令读取到内存中运行形成的。从硬件层面而言，如图10所示，为本申请基于地理位置数据的热点区域确定装置所在设备的一种硬件结构图，除了图10所示的处理器、网络接口、内存以及非易失性存储器之外，实施例中装置所在的设备通常根据该基于地理位置数据的热点区域确定的实际功能，还可以包括其他硬件，对此不再赘述。
请参见图11，为本申请一实施例提供的基于地理位置数据的热点区域确定装置的模块示意图，该实施例从服务器侧进行描述，所述装置包括：处理单元610、统计单元620、筛选单元630、第一确定单元640、计算单元650和第二确定单元660。
其中，处理单元610，对待处理的地图区域进行网格化处理；
统计单元620，统计预设时长内每一个网格的网格值，所述网格值为对应网格内上报地理位置数据的用户数量；
筛选单元630，从统计得到的网格值中筛选出大于预设阈值的网格值；
第一确定单元640，在所述筛选出的网格值对应的网格中确定定位点；
计算单元650，根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线；
第二确定单元660，将所述曲线内的区域确定为热点区域。
在一个可选的实现方式中：
所述处理单元610，具体包括：
根据预设边长将待处理的地图区域划分为至少一个的网格。
在一个可选的实现方式中：
所述定位点为该网格的中心点。
在一个可选的实现方式中：
所述装置还包括：
获取子单元，获取所述曲线范围内POI点的类型；
统计子单元，统计相同类型的POI点数量；
确定子单元，将POI点数量最多的类型确定为该热点区域的热点类型。
在一个可选的实现方式中：
所述装置还包括：
获取子单元，在所述筛选出的网格值对应的网格中确定定位点之后，获取所述地图区域内的POI点；
相应地，所述计算单元650，具体包括：
根据所述POI点和所述定位点，基于预设算法计算出一条包含所有定位点和POI点的凸包曲线。
在一个可选的实现方式中：
所述凸包算法包括graham算法、jarvis算法、中心法、水平法或快包法。
综上所述，通过申请本实施例，首先服务器对待处理的地图区域进行网格化处理，并统计每个网格内的网格值，从统计得到的网格值中筛选出大于预设阈值的网格值，确定筛选出的网格值对应网格的定位点，然后基于预设算法计算一条包含所有定位点的曲线，最后将所述曲线内的区域确定为热点区域。如此，服务器可以利用用户真实上报的地理位置数据自动地确定所述地图区域的热点区域，避免了因为人工划定热点区域所造成的低效和高成本，从而提高了确定热点区域的效率，降低了确定热点区域的成本。
上述装置中各个单元的功能和作用的实现过程具体详见上述方法中对应步骤的实现过程，在此不再赘述。
对于装置实施例而言，由于其基本对应于方法实施例，所以相关之处参见方法实施例的部分说明即可。以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本申请方案的目的。本领域普通技术人员在不付出创造性劳动的情况下，即可以理解并实施。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本申请的其它实施方案。本申请旨在涵盖本申请的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本申请的一般性原理并包括本申请未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本申请的真正范围和精神由下面的权利要求指出。
应当理解的是，本申请并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本申请的范围仅由所附的权利要求来限制。

Claims (14)

  1. 一种基于地理位置数据的热点区域确定方法，其特征在于，所述方法包括：
    对待处理的地图区域进行网格化处理；
    统计预设时长内每一个网格的网格值，所述网格值为对应网格内上报地理位置数据的用户数量；
    从统计得到的网格值中筛选出大于预设阈值的网格值；
    在所述筛选出的网格值对应的网格中确定定位点；
    根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线；
    将所述曲线内的区域确定为热点区域。
  2. 根据权利要求1所述的方法，其特征在于，所述对待处理的地图区域进行网格化处理，具体包括：
    根据预设边长将待处理的地图区域划分为至少一个的网格。
  3. 根据权利要求1所述的方法，其特征在于，所述定位点为该网格的中心点。
  4. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    获取所述曲线范围内POI点的类型；
    统计相同类型的POI点数量；
    将POI点数量最多的类型确定为该热点区域的热点类型。
  5. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    在所述筛选出的网格值对应的网格中确定定位点之后，获取所述地图区域内的POI点；
    所述根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线，具体包括：
    根据所述POI点和所述定位点，基于预设算法计算出一条包含所有定位点和POI点的曲线。
  6. 根据权利要求1所述的方法，其特征在于，所述预设算法为凸包算法；
    所述曲线为凸包曲线。
  7. 根据权利要求6所述的方法，其特征在于，所述凸包算法包括graham算法、jarvis算法、中心法、水平法或快包法。
  8. 一种基于地理位置数据的热点区域确定装置，其特征在于，所述装置包括：
    处理单元，对待处理的地图区域进行网格化处理；
    统计单元，统计预设时长内每一个网格的网格值，所述网格值为对应网格内上报地理位置数据的用户数量；
    筛选单元，从统计得到的网格值中筛选出大于预设阈值的网格值；
    第一确定单元，在所述筛选出的网格值对应的网格中确定定位点；
    计算单元，根据所述定位点，基于预设算法计算出一条包含所有定位点的曲线；
    第二确定单元，将所述曲线内的区域确定为热点区域。
  9. 根据权利要求8所述的装置，其特征在于，所述处理单元，具体包括：
    根据预设边长将待处理的地图区域划分为至少一个的网格。
  10. 根据权利要求8所述的装置，其特征在于，所述定位点为该网格的中心点。
  11. 根据权利要求8所述的装置，其特征在于，所述装置还包括：
    获取子单元，获取所述曲线范围内POI点的类型；
    统计子单元，统计相同类型的POI点数量；
    确定子单元，将POI点数量最多的类型确定为该热点区域的热点类型。
  12. 根据权利要求8所述的装置，其特征在于，所述装置还包括：
    获取子单元，在所述筛选出的网格值对应的网格中确定定位点之后，获取所述地图区域内的POI点；
    相应地，所述计算单元，具体包括：
    根据所述POI点和所述定位点，基于预设算法计算出一条包含所有定位 点和POI点的曲线。
  13. 根据权利要求8所述的装置，其特征在于，所述预设算法为凸包算法；
    所述曲线为凸包曲线。
  14. 根据权利要求13所述的装置，其特征在于，所述凸包算法包括graham算法、jarvis算法、中心法、水平法或快包法。
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