WO2015067119A1 - 一种位置兴趣点聚类方法和相关装置 - Google Patents
一种位置兴趣点聚类方法和相关装置 Download PDFInfo
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
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Definitions
- the present invention relates to the field of geographic information processing technologies, and in particular, to a location interest point clustering method and related apparatus.
- Point Of Interest refers to a location area where users frequently stay for a long time, such as a home, an office, a frequent supermarket, and the like, which are important to the user.
- the trajectory information of the daily activities of the user can be obtained by using a Wi-Fi network such as a mobile phone, a Global Positioning System (GPS), and a positioning function of an ID (Identity), and the trajectory information is composed of a large number of locating information. Deviation of the positioning coordinate points, study how to extract the user's POI from these trajectory information, which is of great value for context-aware and location-based service (LBS) applications and services. Research hotspots.
- the above method extracts the user's resident point through the space-time dimension, and the resident point can only represent the user's single visit, cannot represent the POI location important to the user, and only refers to the user's historical location data when mining the POI. The reliability and reference value of the excavated POI are low.
- Embodiments of the present invention provide a location interest point clustering method and related apparatus for improving reliability and reference value of a POI.
- a first aspect of the present invention provides a method for clustering location points of interest, including:
- each of the resident points in the set of the resident points represents a hot zone, and the hot zone satisfies the following condition: a geographical position of any two of the hot spots The distance is smaller than the positioning accuracy of the two positioning points; the maximum value of the time interval between the positioning points in the hot zone is greater than the preset time threshold;
- the density-connected trusted resident points are clustered into a location interest point, wherein the density connection refers to the direct or indirect connection of the hot zone represented by the two trusted resident points.
- the foregoing generates a set of resident points according to the foregoing set of positioning points, including:
- the geometric center point of the above hot zone is a dwell point representing the above hot zone.
- the calculating the reliability of each of the resident points in the set of the resident points includes: :
- the obtaining, by the foregoing, the motion states of the positioning points in the set of the positioning points specifically:
- the motion state of each positioning point in the set of positioning points is obtained according to the sensor data on the user's terminal or the strength and number of the Wi-Fi network.
- the above-mentioned density-connected trusted station Clustering points into a location of interest points including:
- the closed area formed by sequentially connecting all the leaf dwell points in all the trusted dwell points connected by the density is determined as an area of the position interest point, wherein all the preset radius coverage centered on the leaf dwell point The sum of the trustworthiness of the dwelling points is not greater than the preset threshold.
- a second aspect of the present invention provides a location point of interest clustering apparatus, including:
- An obtaining unit configured to acquire a set of positioning points of a user in a predetermined time period
- a residing point generating unit configured to generate a residing point set according to the set of positioning points acquired by the acquiring unit, where each of the residing point sets represents a hot zone, and the hot zone satisfies the following condition: The geographical distance of any two positioning points in the hot zone is smaller than the positioning accuracy of the positioning accuracy of the two positioning points; the maximum value of the time interval between the positioning points in the hot zone is greater than the preset time threshold;
- a computing unit configured to calculate a credibility of each of the residing points in the set of the residing points, wherein the average speed corresponding to the motion state of all the positioning points in the hot zone represented by the dwelling point is smaller, the dwell point The higher the credibility;
- a filtering unit configured to filter a trusted resident point from the set of the resident points according to the credibility of each of the resident points in the set of the resident points calculated by the calculating unit, where the trusted resident point The credibility is greater than the preset credibility threshold;
- a clustering unit is configured to cluster the density-connected trusted resident points into a location interest point, wherein the density connection refers to directly or indirectly connecting the ranges of the hot zones represented by the two trusted resident points. Pick up.
- the above resident point generating unit includes:
- a first determining unit configured to determine a hot zone that satisfies the above conditions
- a second determining unit configured to determine a geometric center point of the hot zone to represent a dwell point of the hot zone.
- the above calculation unit includes:
- a sub-acquisition unit configured to acquire motion states of respective positioning points included in the hot zone represented by each of the foregoing resident points
- Sub-computing unit for formulating And the motion state of each anchor point included in the hot zone represented by each of the foregoing resident points obtained by the foregoing sub-acquisition unit, and calculating the reliability of each of the resident points in the set of the resident points,
- Conf i represents a dwell point
- n represents n possible motion states
- W k represents the credibility weight of the kth motion state
- n k represents the motion state in the hot zone represented by the dwell point i is the kth motion state
- the foregoing sub-acquisition unit is specifically configured to: according to the sensor data on the terminal of the user or the strength of the Wi-Fi network The number of changes changes to obtain the motion state of each of the positioning points in the set of positioning points.
- the clustering unit is specifically configured to: cluster all trusted residing points connected by density into one location interest point; and all trusted residing points connected to the density
- the closed area formed by connecting all the dwelling points of the leaves in turn is determined as the area of a positional interest point, wherein the sum of the credibility of all the dwelling points within the preset radius coverage centered on the leaf dwelling point is not Greater than the preset threshold.
- a plurality of positioning points form a dwell point according to the positioning accuracy and the time threshold, and most of the short stay points, the positioning jump points, and the way points can be determined by the positioning accuracy and the time threshold constraint. Filtering, at the same time, combining the motion state of the anchor point in the dwell point to calculate the credibility of the dwell point and filtering out the less reliable dwell point according to the credibility of the dwell point, further filtering out part The noise dwelling point on the way (such as traffic jams, red lights, slow dwellings, etc.), so that the reliability and reference value of the location interest points finally clustered by the density-connected trusted dwelling points higher.
- FIG. 1 is a schematic flow chart of an embodiment of a location point of interest clustering method according to the present invention
- FIG. 2 is a schematic diagram of a trajectory of a set of positioning points in an application scenario according to the present invention
- FIG. 2 is a schematic diagram of a set of resident points formed in an application scenario according to the present invention
- Figure 2-c is a schematic diagram of a collection of credibility residing points selected in an application scenario provided by the present invention.
- FIG. 2-d is a schematic diagram of a POI clustered in an application scenario provided by the present invention.
- FIG. 3 is a schematic structural diagram of an embodiment of a location point of interest clustering apparatus according to the present invention.
- FIG. 4 is a schematic structural diagram of another embodiment of a location point of interest clustering apparatus according to the present invention.
- FIG. 5 is a schematic structural diagram of still another embodiment of a location point of interest clustering apparatus according to the present invention.
- Embodiments of the present invention provide a location interest point clustering method and related apparatus.
- a method for clustering a location point of interest according to an embodiment of the present invention includes:
- the set of positioning points includes one or more positioning points, and the positioning points are used to indicate location information of the user.
- the positioning point is a GPS position point represented by a longitude value and a latitude value
- the location point of interest clustering device acquires the set of positioning points of the user in the predetermined time period from the GPS location data of the user.
- Each of the residing point sets represents a hot zone, and the hot zone satisfies the following condition: a geographical distance of any two of the hot zones is less than a positioning accuracy of the two positioning points. Large positioning accuracy; the maximum value of the time interval between the positioning points in the above hot zone is greater than the preset time threshold.
- the set of positioning points ⁇ P j , P j+1 , . . . , P j+L ⁇ constitutes a hot zone that can form a dwelling point
- the set of positioning points ⁇ P j , P j+1 ,. .., P j+L ⁇ must meet the following two conditions:
- the geographical distance of any two positioning points in ⁇ P j , P j+1 ,..., P j+L ⁇ is smaller than the spatial threshold Dth, where Dth is not a fixed value, which is based on the positioning points involved.
- Dth is not a fixed value, which is based on the positioning points involved.
- D th (P 1 , P 2 ) is equal to the positioning point.
- the larger positioning accuracy in P 1 and the positioning point P 2 is equal to 10 meters, and if the positioning accuracy of the positioning point P 3 is 15 meters, for the positioning point P 1 and the positioning point P 3 , D th (P 1 , P 3 ) is equal to a larger positioning accuracy in the positioning point P 1 and the positioning point P 3 , that is, D th (P 1 , P 3 ) is equal to 15 meters.
- ⁇ P j , P j+1 , . . . , P j+L ⁇ also needs to satisfy: ⁇ P j , P j+1 , . . .
- T th The preset time threshold T th (for example, T th may take 5 minutes, 7 minutes, or 10 minutes, etc.), that is, ⁇ P j , P j+1 , . . . , P j+L ⁇
- T th can be specifically set according to actual needs, and is not limited herein.
- the geometric center point of the hot zone is a dwell point representing the hot zone
- the dwell point may represent all the anchor points in the hot zone.
- other methods may be used to determine the dwell point corresponding to a hot zone.
- the center of gravity of the hot zone is used as a dwelling point representing the hot zone, wherein the center of gravity of the hot zone is related to the distribution of the anchor points in the hot zone. .
- the location point of interest clustering device acquires motion states of the respective anchor points included in the hot zone represented by each of the resident points in the set of the resident points, and represents each of the resident points in the set of the resident points.
- Motion state and formula of each anchor point included in the hot zone The credibility weight of the dynamic state, n k represents the number of anchor points in the hot zone represented by the dwelling point i, which is the kth motion state, wherein each motion state corresponds to a credibility weight, and The smaller the motion speed corresponding to the motion state, the greater the weight of the confidence of the motion state.
- the motion state is stationary, walking, or riding, for the three motion states, since the motion speed corresponding to the motion state is from small to large, it is: stationary, walking, or riding, therefore, the motion state
- the weight of credibility is from small to small: stationary, walking or riding.
- the location interest point clustering device changes according to sensor data (such as acceleration, gyroscope, etc.) on the user's terminal (such as a mobile phone, a tablet computer, a vehicle terminal, etc.) or the strength and number of the Wi-Fi network. Acquiring the motion state of each of the locating points in the set of locating points; or the locating point clustering device may obtain the motion state of each locating point in the set of locating points from other locating devices (such as a server), which is not limited herein. .
- the credibility of the trusted residing point is greater than the preset credibility threshold.
- the location interest point clustering device filters out the trusted resident point from the set of the resident points (that is, the resident point whose reliability is greater than the preset reliability threshold), and the rejection reliability is not greater than The dwell point of the preset credibility threshold.
- the above density connection means that the ranges of the hot zones represented by the two trusted resident points are directly or indirectly connected.
- the point P resides trusted coverage area coverage trusted resident 1 P 2 intersects the point, called the hot zone residence trusted dwell point P 1 and point P 2 trusted represented by the The range is directly connected, in which case the trusted resident point P 1 and the trusted resident point P 2 are connected in density; or, assuming the coverage of the trusted resident point P 1 and the trusted resident point P 3
- the coverage of the intersection intersects, the coverage of the trusted resident point P 2 intersects the coverage of the trusted resident point P 4 , and the coverage of the trusted resident point P 3 intersects the coverage of the trusted resident point P 4
- the range of the hot zone represented by the density of the trusted resident point P 1 and the trusted resident point P 2 is indirectly connected, in this case also referred to as the trusted resident point P 1 and the trusted resident.
- the point P 2 density is connected.
- the trusted resident point P 1 (which may be a leaf resident point or an internal resident point) is reachable from the internal resident point o
- the trusted resident point P 2 (may be a leaf resident
- the residual point which can also be an internal resident point, is connected to the density of the internal resident point o, and the density of the trusted resident point P 1 and the trusted point P 2 is said to be connected.
- Leaf Retention Point The sum of the credibility of all dwell points within the preset radius coverage centered on the dwell point is not greater than the preset threshold.
- Direct density up to: If a trusted resident point P (which can be a leaf resident point or an internal resident point) is within the coverage radius of a trusted resident point Q, and the trusted resident point Q For the internal resident point, then the trusted resident point P is said to be directly reachable from the trusted resident point Q.
- a trusted resident point P which can be a leaf resident point or an internal resident point
- all the trusted residing points connected by the density are clustered into one POI, and the closed areas formed by sequentially connecting all the leaf residing points in all the trusted residing points connected by the density are determined as one POI.
- the area, or the area of the POI may also be a minimum circular area or a square area including all trusted resident points to which the density is connected, which is not limited herein.
- a plurality of positioning points form a dwell point according to the positioning accuracy and the time threshold, and most of the short stay points, the positioning jump points, and the way points can be determined by the positioning accuracy and the time threshold constraint. Filtering, at the same time, combining the motion state of the anchor point in the dwell point to calculate the credibility of the dwell point and filtering out the less reliable dwell point according to the credibility of the dwell point, further filtering out part The noise dwelling point on the way (such as traffic jams, red lights, slow dwellings, etc.), so that the reliability and reference value of the location interest points finally clustered by the density-connected trusted dwelling points higher.
- Step 1 The location point of interest clustering device uses the dynamic space threshold and the time threshold to determine a hot zone that satisfies the following two conditions from the set of anchor points shown in FIG. 2-a: 1. any two anchor points in the hot zone The geographical distance is smaller than the larger positioning accuracy of the two positioning points; 2. The maximum value of the time interval between the positioning points in the hot zone is greater than the preset time threshold. After determining the hot zone that satisfies the condition, determining the geometric center point in the hot zone as a dwelling point representing the hot zone, forming a plurality of dwelling points, and obtaining a dwell point trajectory diagram as shown in FIG. 2-b.
- each small black dot in Figure 2-b represents a formed dwell point
- each dwelling point can represent a large number of anchor points generated when the user stays at the same place
- Figure 2-b and Figure 2 After comparison, it can be seen that most of the short stop points, positioning jump points and route points are filtered out in Figure 2-b.
- Step 2 The location interest point clustering device calculates the credibility of each resident point according to the motion state of the user (such as stationary, walking, riding, etc.) at all the positioning points forming a dwell point, wherein if The smaller the average moving speed corresponding to the motion state of all the anchor points of a dwelling point, the greater the credibility of the dwelling point. For example, if there is a dwelling point, the motion state is stationary (ie, motion).
- Step 3 performing credibility-based clustering on the above-mentioned residing points, and finding a set of the largest residing points connected to all the densities (that is, any two trusted residing points in the set are connected by density), and the maximum is
- the residing point set is aggregated into a POI, and the POI area can be represented by a closed polygon formed by sequentially connecting all the leaf dwell points in the largest dwell point set, as shown in FIG. 2-d. .
- Step 3 Consider the credibility of each dwelling point in the clustering process, which can improve the reliability of POI clustering, and make the range of the POI region closer to the real situation.
- the embodiment of the present invention further provides a location point of interest clustering apparatus.
- the location point of interest clustering apparatus 300 in the embodiment of the present invention includes: an obtaining unit 301, a resident point generating unit 302, and a calculating unit. 303. Filter unit 304 and clustering unit 305.
- the obtaining unit 301 is configured to acquire a set of positioning points of the user in a predetermined time period
- the set of positioning points includes one or more positioning points, and the positioning points are used to indicate location information of the user.
- the positioning point is a GPS position point represented by a longitude value and a latitude value
- the location point of interest clustering device acquires the set of positioning points of the user in the predetermined time period from the GPS location data of the user.
- the residing point generating unit 302 is configured to generate a residing point set according to the set of positioning points acquired by the obtaining unit 301, where each of the residing point sets represents a hot zone, and the hot zone satisfies the following conditions: The geographical distance of any two positioning points in the hot zone is smaller than the positioning accuracy of the positioning accuracy of the two positioning points; the maximum time interval between the positioning points in the hot zone is greater than the preset time threshold;
- the resident point generating unit 302 includes: a first determining unit 3021, configured to determine that the above a conditional hot zone; a second determining unit 3022, configured to determine a geometric center point of the hot zone as a dwell point representing the hot zone.
- the calculating unit 303 is configured to calculate the reliability of each of the resident points in the set of the resident points, wherein the average speed corresponding to the motion state of all the positioning points in the hot zone represented by the resident point is smaller, the resident The higher the credibility of the point;
- the calculating unit 303 includes:
- a sub-acquisition unit configured to acquire motion states of respective positioning points included in the hot zone represented by each of the foregoing resident points
- the foregoing sub-acquisition unit is configured to: obtain, according to the sensor data (such as acceleration, gyroscope, etc.) on the terminal of the user, or the strength and the number of the Wi-Fi network, acquire each positioning in the set of positioning points. The state of motion of the point.
- the sensor data such as acceleration, gyroscope, etc.
- the filtering unit 304 is configured to: according to the credibility of each of the residing points in the set of the residing points calculated by the calculating unit 303, select a trusted residing point from the set of the residing points, wherein the trusted residing The credibility of the point is greater than the preset credibility threshold;
- the clustering unit 305 is configured to cluster the density-connected trusted resident points into a location interest point, wherein the density connection refers to directly or indirectly connecting the ranges of the hot areas represented by the two trusted resident points. Docked.
- the clustering unit 305 is further configured to: cluster all the trusted residing points connected by the density into one location interest point; and sequentially connect all the leaf residing points in all the trusted residing points connected by the density.
- the closed area is determined as an area of the location interest point, wherein the sum of the reliability of all the resident points within the preset radius coverage centered on the leaf resident point is not greater than the preset threshold.
- the location interest point clustering device in the embodiment of the present invention may be a terminal (such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, or other terminal having a positioning function, etc.), or the location interest point clustering device may be A device that is independent of the terminal and can communicate with the terminal by wire or wirelessly is not limited herein.
- location interest point clustering apparatus in the embodiment of the present invention may be implemented as described above.
- the location point of interest clustering device in the embodiment may be used to implement all the technical solutions in the foregoing method embodiments, and the functions of the respective function modules may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the foregoing Related descriptions in the embodiments are not described herein again.
- a plurality of positioning points form a dwell point according to the positioning accuracy and the time threshold, and most of the short stay points, the positioning jump points, and the way points can be determined by the positioning accuracy and the time threshold constraint. Filtering, at the same time, combining the motion state of the anchor point in the dwell point to calculate the credibility of the dwell point and filtering out the less reliable dwell point according to the credibility of the dwell point, further filtering out part The noise dwelling point on the way (such as traffic jams, red lights, slow dwellings, etc.), so that the reliability and reference value of the location interest points finally clustered by the density-connected trusted dwelling points higher.
- the embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores a program, and the program execution includes some or all of the arrangements described in the foregoing method embodiments.
- the location interest point clustering apparatus 500 in the embodiment of the present invention includes:
- the input device 501, the output device 502, the memory 503, and the processor 504 (the number of processors 504 of the location point of interest clustering device 500 may be one or more, and FIG. 5 takes a processor as an example).
- the input device 501, the output device 502, the memory 503, and the processor 504 may be connected by a bus or other means, as shown in FIG.
- the memory 503 is used to store data input from the input device 501, and may also store information such as necessary files processed by the processor 504; the input device 501 and the output device 502 may include the location interest point clustering device 500 and other devices.
- the communication port may also include an output device externally connected to the location point of interest clustering device 500, such as a display, a keyboard, a mouse, a printer, etc.
- the input device 501 may include a mouse and a keyboard, etc.
- the output device 502 may include a display or the like.
- the ports in the input device 501 and the output device 502 that communicate with other devices in this embodiment are antennas and/or communication cables.
- the processor 504 performs the following steps:
- each of the resident points in the set of the resident points represents a hot zone, and the hot zone satisfies the following condition: a geographical position of any two of the hot spots The distance is smaller than the positioning accuracy of the two positioning points; the maximum value of the time interval between the positioning points in the hot zone is greater than the preset time threshold;
- the density-connected trusted resident points are clustered into a location interest point, wherein the density connection refers to the direct or indirect connection of the hot zone represented by the two trusted resident points.
- the positioning point is a GPS location point represented by a longitude value and a latitude value
- the processor 504 is configured to obtain, from the GPS location data of the user, the set of positioning points of the user within a predetermined time period.
- the processor 504 determines that the geometric center point of the hot zone is a dwell point representing the hot zone, and the dwell point may represent all the anchor points in the hot zone.
- the center of gravity of the hot zone is used as a dwelling point representing the hot zone, wherein the center of gravity of the hot zone is related to the distribution of the anchor points in the hot zone. .
- the processor 504 acquires a motion state of each of the anchor points included in the hot zone represented by each of the resident points in the foregoing camping point set, according to the hot zone represented by each of the camping point sets.
- Showing n possible motion states W k represents the credibility weight of the kth motion state, and n k represents the number of anchor points in the hot zone represented by the dwell point i as the kth motion state,
- Each of the motion states corresponds to a credibility weight, and the smaller the motion speed corresponding to the motion state, the greater the credibility weight of the motion state.
- the motion state is stationary, walking, or riding, for the three motion states, since the motion speed corresponding to the motion state is from small to large, it is: stationary, walking, or riding, therefore, the motion state
- the weight of credibility is from small to small: stationary, walking or riding.
- the processor 504 acquires the motion state of each positioning point in the set of positioning points according to the sensor data (such as acceleration, gyroscope, etc.) on the terminal of the user or the strength and number of the Wi-Fi network.
- the location point of interest clustering device may also obtain the motion state of each of the positioning points in the set of positioning points from other positioning devices (such as a server), which is not limited herein.
- the processor 504 clusters all trusted residing points connected by the density into one POI, and determines a closed area formed by sequentially connecting all the leaf residing points of all trusted residing points connected to the density as A region of a POI, or a region of a POI, may also be a site containing density
- the smallest circular area or square area with a trusted resident point is not limited here.
- the location interest point clustering device in the embodiment of the present invention may be a terminal (such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, or other terminal having a positioning function, etc.), or the location interest point clustering device may be A device that is independent of the terminal and can communicate with the terminal by wire or wirelessly is not limited herein.
- location point of interest clustering apparatus in the embodiment of the present invention may be used as the location point of interest clustering apparatus in the foregoing method embodiments, and may be used to implement all the technical solutions in the foregoing method embodiments, and the respective functional modules.
- specific implementation process reference may be made to the related description in the foregoing embodiments, and details are not described herein again.
- a plurality of positioning points form a dwell point according to the positioning accuracy and the time threshold, and most of the short stay points, the positioning jump points, and the way points can be determined by the positioning accuracy and the time threshold constraint. Filtering, at the same time, combining the motion state of the anchor point in the dwell point to calculate the credibility of the dwell point and filtering out the less reliable dwell point according to the credibility of the dwell point, further filtering out part The noise dwelling point on the way (such as traffic jams, red lights, slow dwellings, etc.), so that the reliability and reference value of the location interest points finally clustered by the density-connected trusted dwelling points higher.
- the disclosed apparatus and method may be implemented in other manners.
- the device embodiments described above are merely illustrative.
- the division of the unit is only a logical function division.
- there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
- the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
- the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. You can choose some of them according to actual needs or All units are used to achieve the objectives of the solution of this embodiment.
- each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
- the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
- the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
- the technical solution of the present invention which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
- a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
- the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .
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Abstract
Description
Claims (10)
- 一种位置兴趣点聚类方法,其特征在于,包括:获取预定时间段内用户的定位点集合;根据所述定位点集合生成驻留点集合,其中,所述驻留点集合中的每个驻留点代表一个热区,所述热区满足如下条件:所述热区中的任意两个定位点的地理位置距离小于所述两个定位点的定位精度中的较大定位精度;所述热区中定位点间的时间间隔的最大值大于预设时间阈值;计算所述驻留点集合中各个驻留点的可信度,其中,驻留点代表的热区中的所有定位点的运动状态所对应的平均速度越小,该驻留点的可信度越高;根据所述驻留点集合中各个驻留点的可信度,从所述驻留点集合中筛选出可信驻留点,其中,所述可信驻留点的可信度大于预设可信度阈值;将密度相连的可信驻留点聚类成一个位置兴趣点,其中,所述密度相连是指两个可信驻留点所代表的热区的范围直接相接或间接相接。
- 根据权利要求1所述的方法,其特征在于,所述根据所述定位点集合生成驻留点集合,包括:确定满足所述条件的热区;确定所述热区的几何中心点为代表所述热区的驻留点。
- 根据权利要求3所述的方法,其特征在于,所述获取所述定位点集合中的各个定位点的运动状态,具体为:根据用户的终端上的传感器数据或者Wi-Fi网络的强度和个数变化,获 取所述定位点集合中的各个定位点的运动状态。
- 根据权利要求1至4任一项所述的方法,其特征在于,所述将密度相连的可信驻留点聚类成一个位置兴趣点,包括:将密度相连的所有可信驻留点中的所有叶子驻留点依次连接形成的封闭区域确定为一个位置兴趣点的区域,其中,以叶子驻留点为中心的预设半径覆盖范围内的所有驻留点的可信度之和均不大于预设门限值。
- 一种位置兴趣点聚类装置,其特征在于,包括:获取单元,用于获取预定时间段内用户的定位点集合;驻留点生成单元,用于根据所述获取单元获取的定位点集合生成驻留点集合,其中,所述驻留点集合中的每个驻留点代表一个热区,所述热区满足如下条件:所述热区中的任意两个定位点的地理位置距离小于所述两个定位点的定位精度中的较大定位精度;所述热区中定位点间的时间间隔的最大值大于预设时间阈值;计算单元,用于计算所述驻留点集合中各个驻留点的可信度,其中,驻留点代表的热区中的所有定位点的运动状态所对应的平均速度越小,该驻留点的可信度越高;过滤单元,用于根据所述计算单元计算出的所述驻留点集合中各个驻留点的可信度,从所述驻留点集合中筛选出可信驻留点,其中,所述可信驻留点的可信度大于预设可信度阈值;聚类单元,用于将密度相连的可信驻留点聚类成一个位置兴趣点,其中,所述密度相连是指两个可信驻留点所代表的热区的范围直接相接或间接相接。
- 根据权利要求6所述的位置兴趣点聚类装置,其特征在于,所述驻留点生成单元,包括:第一确定单元,用于确定满足所述条件的热区;第二确定单元,用于确定所述热区的几何中心点为代表所述热区的驻留点。
- 根据权利要求8所述的位置兴趣点聚类装置,其特征在于,所述子获取单元具体用于:根据所述用户的终端上的传感器数据或者Wi-Fi网络的强度和个数变化,获取所述定位点集合中的各个定位点的运动状态。
- 根据权利要求6至9任一项所述的位置兴趣点聚类装置,其特征在于,所示聚类单元具体用于:将密度相连的所有可信驻留点聚类成一个位置兴趣点;将密度相连的所有可信驻留点中的所有叶子驻留点依次连接形成的封闭区域确定为一个位置兴趣点的区域,其中,以叶子驻留点为中心的预设半径覆盖范围内的所有驻留点的可信度之和均不大于预设门限值。
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110990455A (zh) * | 2019-11-29 | 2020-04-10 | 杭州数梦工场科技有限公司 | 大数据识别房屋性质的方法与系统 |
CN113075648A (zh) * | 2021-03-19 | 2021-07-06 | 中国舰船研究设计中心 | 一种无人集群目标定位信息的聚类与滤波方法 |
Families Citing this family (37)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106294485B (zh) * | 2015-06-05 | 2019-11-01 | 华为技术有限公司 | 确定显著地点的方法及装置 |
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BR112018072934A2 (pt) * | 2016-05-09 | 2019-02-19 | Tata Consultancy Services Limited | método e sistema para alcançar agrupamento auto-adaptativo em cluster em uma rede sensorial |
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CN108287841A (zh) * | 2017-01-09 | 2018-07-17 | 北京四维图新科技股份有限公司 | 景点数据采集和更新方法及系统、游客终端设备及助导游设备 |
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WO2018227373A1 (zh) * | 2017-06-13 | 2018-12-20 | 深圳市伊特利网络科技有限公司 | 基于定位的报警方法及系统 |
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CN108303100A (zh) * | 2017-12-25 | 2018-07-20 | 厦门市美亚柏科信息股份有限公司 | 聚焦点分析方法及计算机可读存储介质 |
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CN109189876B (zh) * | 2018-08-31 | 2021-09-10 | 深圳市元征科技股份有限公司 | 一种数据处理方法及装置 |
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CN109168195B (zh) * | 2018-10-25 | 2020-08-28 | 北京搜狐新媒体信息技术有限公司 | 一种定位信息提取方法及服务平台 |
CN111126653B (zh) * | 2018-11-01 | 2022-06-17 | 百度在线网络技术(北京)有限公司 | 用户职住地预测方法、装置及存储介质 |
KR102213276B1 (ko) * | 2018-11-27 | 2021-02-04 | 공주대학교 산학협력단 | 이동 경로 정보를 이용한 주소 인증 장치 및 방법 |
CN111380541B (zh) * | 2018-12-29 | 2022-09-13 | 沈阳美行科技股份有限公司 | 兴趣点确定方法、装置、计算机设备和存储介质 |
CN109813318A (zh) * | 2019-02-12 | 2019-05-28 | 北京百度网讯科技有限公司 | 坐标修正方法及装置、设备及存储介质 |
CN110213714B (zh) * | 2019-05-10 | 2020-08-14 | 中国联合网络通信集团有限公司 | 终端定位的方法及装置 |
US11860911B2 (en) * | 2019-08-20 | 2024-01-02 | International Business Machines Corporation | Method and apparatus of data classification for routes in a digitized map |
CN110544132B (zh) * | 2019-09-06 | 2023-04-07 | 上海喜马拉雅科技有限公司 | 用户常活动位置的确定方法、装置、设备和存储介质 |
CN110705480B (zh) * | 2019-09-30 | 2022-12-02 | 重庆紫光华山智安科技有限公司 | 目标对象的停留点定位方法及相关装置 |
CN110850955B (zh) * | 2019-10-30 | 2023-06-02 | 腾讯科技(深圳)有限公司 | 终端的位置信息处理方法、装置及计算设备、存储介质 |
CN110781855B (zh) * | 2019-11-04 | 2022-12-06 | 浙江大华技术股份有限公司 | 目标停留点的确定方法、装置、设备及存储装置 |
CN111159583B (zh) * | 2019-12-31 | 2023-08-04 | 中国联合网络通信集团有限公司 | 用户行为分析方法、装置、设备及存储介质 |
CN111898624B (zh) * | 2020-01-21 | 2024-04-02 | 北京畅行信息技术有限公司 | 定位信息的处理方法、装置、设备及存储介质 |
CN112765226A (zh) * | 2020-12-06 | 2021-05-07 | 复旦大学 | 基于轨迹数据挖掘的城市语义图谱构建方法 |
CN112749349A (zh) * | 2020-12-31 | 2021-05-04 | 北京搜狗科技发展有限公司 | 一种交互方法和耳机设备 |
CN115083161B (zh) * | 2022-06-15 | 2023-08-04 | 平安国际融资租赁有限公司 | 车辆停留点的评估方法、装置、电子设备及可读存储介质 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102595323A (zh) * | 2012-03-20 | 2012-07-18 | 北京交通发展研究中心 | 基于手机定位数据的居民出行特征参数的获取方法 |
CN102682041A (zh) * | 2011-03-18 | 2012-09-19 | 日电(中国)有限公司 | 用户行为识别设备及方法 |
WO2013060925A1 (en) * | 2011-10-28 | 2013-05-02 | Nokia Corporation | Method and apparatus for constructing a road network based on point-of-interest (poi) information |
CN103218442A (zh) * | 2013-04-22 | 2013-07-24 | 中山大学 | 一种基于移动设备传感器数据的生活模式分析方法及系统 |
Family Cites Families (24)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5844522A (en) * | 1995-10-13 | 1998-12-01 | Trackmobile, Inc. | Mobile telephone location system and method |
CA2265875C (en) * | 1996-09-09 | 2007-01-16 | Dennis Jay Dupray | Location of a mobile station |
US7082365B2 (en) | 2001-08-16 | 2006-07-25 | Networks In Motion, Inc. | Point of interest spatial rating search method and system |
US7289105B2 (en) * | 2003-06-04 | 2007-10-30 | Vrbia, Inc. | Real motion detection sampling and recording for tracking and writing instruments using electrically-active viscous material and thin films |
JP5362544B2 (ja) | 2006-03-15 | 2013-12-11 | クゥアルコム・インコーポレイテッド | ユーザのルートに基づいて関連性のある対象地点の情報を決定する方法および装置 |
US20080036758A1 (en) | 2006-03-31 | 2008-02-14 | Intelisum Inc. | Systems and methods for determining a global or local position of a point of interest within a scene using a three-dimensional model of the scene |
WO2008097694A1 (en) * | 2007-02-05 | 2008-08-14 | Andrew Corporation | System and method for optimizing location estimate of mobile unit |
EP2145265A4 (en) | 2007-03-30 | 2011-09-14 | Amazon Tech Inc | CLUSTER-BASED ASSESSMENT OF USER INTERESTS |
US8396470B2 (en) * | 2007-04-26 | 2013-03-12 | Research In Motion Limited | Predicting user availability from aggregated signal strength data |
US8339399B2 (en) | 2007-10-31 | 2012-12-25 | Microsoft Corporation | Declustering point-of-interest icons |
JP5049745B2 (ja) * | 2007-11-05 | 2012-10-17 | 株式会社エヌ・ティ・ティ・ドコモ | 位置情報解析装置、情報配信システムおよび位置情報解析方法 |
US8634796B2 (en) * | 2008-03-14 | 2014-01-21 | William J. Johnson | System and method for location based exchanges of data facilitating distributed location applications |
US9078095B2 (en) * | 2008-03-14 | 2015-07-07 | William J. Johnson | System and method for location based inventory management |
US8527308B2 (en) * | 2008-10-02 | 2013-09-03 | Certusview Technologies, Llc | Methods and apparatus for overlaying electronic locate information on facilities map information and/or other image information displayed on a locate device |
US8358224B2 (en) | 2009-04-02 | 2013-01-22 | GM Global Technology Operations LLC | Point of interest location marking on full windshield head-up display |
US20120046995A1 (en) * | 2009-04-29 | 2012-02-23 | Waldeck Technology, Llc | Anonymous crowd comparison |
US8335990B2 (en) | 2009-08-18 | 2012-12-18 | Nokia Corporation | Method and apparatus for grouping points-of-interest on a map |
WO2011046113A1 (ja) | 2009-10-14 | 2011-04-21 | 日本電気株式会社 | 行動類型抽出システム、装置、方法、プログラムを記憶した記録媒体 |
CN101742545B (zh) * | 2009-12-15 | 2012-11-14 | 中国科学院计算技术研究所 | WiFi环境中的定位方法及其系统 |
US8543143B2 (en) | 2009-12-23 | 2013-09-24 | Nokia Corporation | Method and apparatus for grouping points-of-interest according to area names |
JP5536485B2 (ja) * | 2010-02-17 | 2014-07-02 | Kddi株式会社 | ユーザの移動に伴って住所/居所を推定する携帯端末、サーバ、プログラム及び方法 |
WO2012080787A1 (en) * | 2010-12-17 | 2012-06-21 | Nokia Corporation | Identification of points of interest and positioning based on points of interest |
US9497584B2 (en) | 2011-01-14 | 2016-11-15 | Nec Corporation | Action pattern analysis device, action pattern analysis method, and action pattern analysis program |
US20130262479A1 (en) * | 2011-10-08 | 2013-10-03 | Alohar Mobile Inc. | Points of interest (poi) ranking based on mobile user related data |
-
2013
- 2013-11-07 CN CN201310552636.2A patent/CN104636354B/zh active Active
-
2014
- 2014-10-13 JP JP2016528174A patent/JP6225257B2/ja active Active
- 2014-10-13 EP EP14860583.5A patent/EP3056999B1/en active Active
- 2014-10-13 KR KR1020167013679A patent/KR101806948B1/ko active IP Right Grant
- 2014-10-13 WO PCT/CN2014/088443 patent/WO2015067119A1/zh active Application Filing
-
2016
- 2016-05-06 US US15/148,365 patent/US10423728B2/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102682041A (zh) * | 2011-03-18 | 2012-09-19 | 日电(中国)有限公司 | 用户行为识别设备及方法 |
WO2013060925A1 (en) * | 2011-10-28 | 2013-05-02 | Nokia Corporation | Method and apparatus for constructing a road network based on point-of-interest (poi) information |
CN102595323A (zh) * | 2012-03-20 | 2012-07-18 | 北京交通发展研究中心 | 基于手机定位数据的居民出行特征参数的获取方法 |
CN103218442A (zh) * | 2013-04-22 | 2013-07-24 | 中山大学 | 一种基于移动设备传感器数据的生活模式分析方法及系统 |
Non-Patent Citations (1)
Title |
---|
See also references of EP3056999A4 * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110990455A (zh) * | 2019-11-29 | 2020-04-10 | 杭州数梦工场科技有限公司 | 大数据识别房屋性质的方法与系统 |
CN110990455B (zh) * | 2019-11-29 | 2023-10-17 | 杭州数梦工场科技有限公司 | 大数据识别房屋性质的方法与系统 |
CN113075648A (zh) * | 2021-03-19 | 2021-07-06 | 中国舰船研究设计中心 | 一种无人集群目标定位信息的聚类与滤波方法 |
CN113075648B (zh) * | 2021-03-19 | 2024-05-17 | 中国舰船研究设计中心 | 一种无人集群目标定位信息的聚类与滤波方法 |
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EP3056999A1 (en) | 2016-08-17 |
JP2016537718A (ja) | 2016-12-01 |
JP6225257B2 (ja) | 2017-11-01 |
CN104636354A (zh) | 2015-05-20 |
KR101806948B1 (ko) | 2017-12-08 |
CN104636354B (zh) | 2018-02-06 |
KR20160075686A (ko) | 2016-06-29 |
US10423728B2 (en) | 2019-09-24 |
EP3056999A4 (en) | 2016-11-23 |
EP3056999B1 (en) | 2019-02-06 |
US20160253407A1 (en) | 2016-09-01 |
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