CN111046895B - Method and device for determining target area - Google Patents
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Abstract
The invention discloses a method and a device for determining a target area, and relates to the technical field of computers. One embodiment of the method comprises the following steps: clustering coordinates of sampling points to form a first-level sampling point cluster; classifying the first-level sampling point clusters according to the sampling time of the sampling points to form second-level sampling point clusters; determining the direction angle of the sampling points in the secondary sampling point cluster according to the coordinates and the sampling time of the sampling points; and determining whether the area corresponding to the secondary sampling point cluster is a target area according to the direction angle of the sampling points in the secondary sampling point cluster. According to the method, the target area can be determined according to the clustering result, the interference of human factors is small, the result is more accurate, the labor cost is low, and the method has practical application significance.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, an apparatus, an electronic device, and a computer readable medium for determining a target area.
Background
The collection and data processing of the movement track data are widely applied to a plurality of technical fields, such as logistics field, traffic field and the like, in the logistics field, more information, such as information of stay areas of the distribution personnel, the position of the goods and the like, can be obtained through the collection and analysis of the movement track of the distribution personnel.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
when track data is acquired, the acquisition point data comprises the spatial coordinates (such as longitude and latitude) of the sampling point and the sampling time of the sampling point. In the prior art, clustering is generally carried out according to coordinate values of sampling points, and data of each category is analyzed. In the prior art, sampling points of track data are clustered according to sampling time, sampling speed and the like, but in view of low accuracy of acquired data, an analysis result is inaccurate. In the traditional method, the region is marked by manpower, so that the workload is extremely high, the interference of human factors is large, and the practical application significance is very low.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method and a device for determining a target area, which can cluster sampling points according to sampled coordinates and sampling time, optimize a clustering result according to a direction angle of the sampling points, further optimize the clustering result according to a sampling time span, an average distance between the sampling points and a preset position, and an average distance between the sampling points and the preset position or a distance between the center points and the preset position, determine the target area according to the clustering result, and have the advantages of less interference of human factors, more accurate result, low labor cost and practical application significance.
To achieve the above object, according to one aspect of the embodiments of the present invention, there is provided a method of determining a target area, including: clustering coordinates of sampling points to form a first-level sampling point cluster; classifying the first-level sampling point clusters according to the sampling time of the sampling points to form second-level sampling point clusters; determining the direction angle of the sampling points in the secondary sampling point cluster according to the coordinates and the sampling time of the sampling points; and determining whether the area corresponding to the secondary sampling point cluster is a target area according to the direction angle of the sampling points in the secondary sampling point cluster.
Optionally, the method further comprises: determining the proportion of the number of sampling points with the direction angle of the sampling points in the secondary sampling point cluster not larger than a preset direction angle to the total number of the sampling points in the secondary sampling point cluster; and if the proportion is not greater than the preset proportion, determining the area corresponding to the secondary sampling point cluster as a target area.
Optionally, after determining that the region corresponding to the secondary sampling point cluster is the target region, further screening the target region by one or more of the following methods: determining a sampling time span of the secondary sampling point cluster, and determining an area corresponding to the secondary sampling point cluster with the sampling time span larger than a preset time threshold as a target area; determining the average distance between sampling points in the secondary sampling point cluster and the central point of the secondary sampling point cluster, and determining the area corresponding to the secondary sampling point cluster with the average distance larger than a preset distance threshold as a target area; determining the distance between the center point of the secondary sampling point cluster and a preset position or the average distance between the sampling points of the secondary sampling point cluster and the preset position, and determining the distance between the center point and the preset position or the area corresponding to the secondary sampling point cluster with the average distance smaller than a preset position threshold value as a target area.
Optionally, the preset direction angle is not greater than 90 degrees, the preset time threshold is not greater than 150 seconds, the preset distance threshold is not greater than 40 meters, and the preset position threshold is not greater than 5 kilometers.
Optionally, the method for classifying the primary sampling point clusters according to the sampling time of the sampling points to form secondary sampling point clusters includes: traversing the sampling points in the first-level sampling point cluster according to the sampling time sequence of the sampling points, and if the difference between the sampling time of the (i+1) th sampling point and the sampling time of the (i) th sampling point is greater than a preset time difference threshold, indicating that the sampling time of the (i+1) th sampling point and the sampling time of the (i) th sampling point are discontinuous; classifying the first-level sampling point clusters according to the (i+1) th sampling point and the (i) th sampling point with discontinuous sampling time, so that the difference of sampling time between two adjacent sampling points in the formed second-level sampling point clusters is not greater than the preset time difference threshold; where i=where i=1, 2,3, …, n-1, n is the total number of samples in the primary sample cluster.
Optionally, the method for determining the direction angle of the sampling point in the secondary sampling point cluster according to the coordinates and the sampling time of the sampling point comprises the following steps: traversing the j-th sampling point, the j+1-th sampling point and the j+2-th sampling point in the secondary sampling point cluster according to the sampling time sequence of the sampling points, determining an included angle between an extension line of a line from the coordinate of the j-th sampling point to the coordinate of the j+1-th sampling point to the line from the coordinate of the j+1-th sampling point to the coordinate of the j+2-th sampling point, and taking the included angle as a direction angle of the j+2-th sampling point, wherein j=1, 2,3, …, k-2 and k are the total number of the sampling points in the secondary sampling point cluster.
Optionally, the method for forming the secondary sampling point cluster further comprises: determining whether the total number of sampling points in the secondary sampling point cluster is not less than a preset number threshold; and reserving secondary sampling point clusters with the number of the sampling points not smaller than the preset number threshold.
To achieve the above object, according to another aspect of an embodiment of the present invention, there is provided an apparatus for determining a target area, including: the clustering module is used for clustering the coordinates of the sampling points to form a primary sampling point cluster; the classification module is used for classifying the primary sampling point clusters according to the sampling time of the sampling points to form secondary sampling point clusters; the direction module is used for determining the direction angle of the sampling points in the secondary sampling point cluster according to the coordinates and the sampling time of the sampling points; and the calculation module is used for determining whether the area corresponding to the secondary sampling point cluster is a target area according to the direction angle of the sampling points in the secondary sampling point cluster.
Optionally, the calculating module is further configured to determine a ratio of the number of sampling points with a direction angle of the sampling points in the secondary sampling point cluster not greater than a preset direction angle to the total number of sampling points in the secondary sampling point cluster; and if the proportion is not greater than the preset proportion, determining the area corresponding to the secondary sampling point cluster as a target area.
Optionally, the device further comprises a screening module for further screening the target region by one or more of the following methods: determining a sampling time span of the secondary sampling point cluster, and determining an area corresponding to the secondary sampling point cluster with the sampling time span larger than a preset time threshold as a target area; determining the average distance between sampling points in the secondary sampling point cluster and the central point of the secondary sampling point cluster, and determining the area corresponding to the secondary sampling point cluster with the average distance larger than a preset distance threshold as a target area; determining the distance between the center point of the secondary sampling point cluster and a preset position or the average distance between the sampling points of the secondary sampling point cluster and the preset position, and determining the distance between the center point and the preset position or the area corresponding to the secondary sampling point cluster with the average distance smaller than a preset position threshold value as a target area.
Optionally, the preset direction angle is not greater than 90 degrees, the preset time threshold is not greater than 150 seconds, the preset distance threshold is not greater than 40 meters, and the preset position threshold is not greater than 5 kilometers.
Optionally, the classification module is further configured to traverse the sampling points in the first-level sampling point cluster according to the sampling time sequence of the sampling points, and if the difference between the sampling time of the (i+1) th sampling point and the sampling time of the (i) th sampling point is greater than a preset time difference threshold, the sampling time of the (i+1) th sampling point and the sampling time of the (i) th sampling point are discontinuous; classifying the first-level sampling point clusters according to the (i+1) th sampling point and the (i) th sampling point with discontinuous sampling time, so that the difference of sampling time between two adjacent sampling points in the formed second-level sampling point clusters is not greater than the preset time difference threshold; where i=where i=1, 2,3, …, n-1, n is the total number of samples in the primary sample cluster.
Optionally, the direction module is further configured to traverse the jth sampling point, the jth+1th sampling point and the jth+2th sampling point in the secondary sampling point cluster according to the sampling time sequence of the sampling points, determine an included angle between an extension line of a line from the coordinate of the jth sampling point to the coordinate of the jth+1th sampling point to a line from the coordinate of the jth+1th sampling point to the coordinate of the jth+1th sampling point, and take the included angle as a direction angle of the jth+2th sampling point, where j=1, 2,3, …, k-2, k is a total number of sampling points in the secondary sampling point cluster.
Optionally, the classification module is further configured to determine whether the total number of sampling points in the secondary sampling point cluster is not less than a preset number threshold; and reserving secondary sampling point clusters with the number of the sampling points not smaller than the preset number threshold.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic device including: one or more processors; and a storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement any of the methods of determining a target area.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided a computer-readable medium having stored thereon a computer program which, when executed by one or more processors, implements any one of the methods of determining a target area.
One embodiment of the above invention has the following advantages or benefits: because the technical means of clustering according to the coordinates of the sampling points and classifying the sampling time of the sampling points and determining the target area according to the direction angle of the sampling points are adopted, the technical problem that the analysis attribute of the traditional method for the sampling points is single is solved, and the technical effects of optimizing the analysis processing result for the sampling points and improving the accuracy are achieved.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of a method of determining a target area according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a method of determining a direction angle of a sampling point according to an embodiment of the present invention;
FIG. 3-1 is a graph of an exemplary distribution of dispensing dwell areas versus non-dwell areas according to a time span of a sample point cluster in accordance with an embodiment of the present invention;
FIG. 3-2 is a graph illustrating an example of a distribution of average distances of a cluster of sampling points from a center point according to a time span of the cluster of sampling points according to an embodiment of the present invention;
3-3 are distribution example graphs of distances between center points of a cluster of sampling points and sites according to time spans of the cluster of sampling points according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of the main parts of an apparatus for determining a target area according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
fig. 6 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of main steps of a method for determining a target area according to an embodiment of the present invention, as shown in fig. 1:
step S101 represents clustering coordinates of sampling points to form a first-level sampling point cluster. The coordinates of the sampling points refer to the spatial coordinate values of the positions, such as longitude and latitude, of the acquired results, namely the positions, by sampling the positions. One embodiment of the present invention is to use a DBSCAN clustering method to cluster according to the coordinates of the sampling points, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a relatively representative Density-based clustering algorithm that defines clusters as the largest set of Density-connected points, is capable of dividing areas with a sufficiently high Density into clusters, and can find clusters of arbitrary shape in noisy spatial data. The primary sampling point clusters formed after clustering are a set containing a plurality of sampling points, and each primary sampling point cluster is one type.
Step S102 is to classify the primary sampling point clusters according to the sampling time of the sampling points to form secondary sampling point clusters. The sampling points have recorded, in addition to the spatial coordinates of the position, the time at which the coordinates were sampled, the set of sampling points being equivalent to the set of coordinate points having a time series. Because the primary sampling point clusters are clustered according to the coordinates of the sampling points and have no fusion time attribute, the clustered sampling point clusters are required to be classified according to the time attribute, and the secondary sampling point clusters obtained after classification have higher precision and are closer to the actual sampling scene.
Classifying the first-stage sampling point clusters according to the sampling time of the sampling points, and forming a second-stage sampling point cluster, wherein the method comprises the following steps: traversing the sampling points in the first-level sampling point cluster according to the sampling time sequence of the sampling points, and if the difference between the sampling time of the (i+1) th sampling point and the sampling time of the (i) th sampling point is greater than a preset time difference threshold, indicating that the sampling time of the (i+1) th sampling point and the sampling time of the (i) th sampling point are discontinuous; classifying the first-level sampling point clusters according to the (i+1) th sampling point and the (i) th sampling point with discontinuous sampling time, so that the difference of sampling time between two adjacent sampling points in the formed second-level sampling point clusters is not greater than the preset time difference threshold; where i=where i=1, 2,3, …, n-1, n is the total number of samples in the primary sample cluster. For example, the first-stage sampling point cluster has sampling points { a, b, c, d, e, f, g } according to the sequence of sampling times, the preset time difference threshold is Δt, the difference between the sampling times of two adjacent sampling points is calculated respectively, if the difference between the sampling time of the sampling point c and the sampling time of the sampling point b is greater than Δt, the difference between the sampling time of the sampling point g and the sampling point f is greater than Δt, and the differences between the sampling times of other adjacent sampling points are smaller than Δt, the first-stage sampling point cluster can be divided into three second-stage sampling point clusters { a, b }, { c, d, e, f } and { g }, respectively. Further, the preset time difference threshold value can be set by sorting the differences of sampling times of adjacent sampling points in each primary sampling point cluster from small to large, and counting the differences of the sampling times sorted in the first 25% as the threshold value.
The method for forming the secondary sampling point cluster further comprises the following steps: determining whether the total number of sampling points in the secondary sampling point cluster is not less than a preset number threshold; and reserving secondary sampling point clusters with the number of the sampling points not smaller than the preset number threshold. The purpose of this step is to ensure a sufficient number of samples, too small a number of samples may affect the final analysis result. For example, in the above embodiment, the first-stage sampling point clusters are divided into three second-stage sampling point clusters, which are { a, b }, { c, d, e, f } and { g }, where { g } contains only one sampling point, the number is too small, and the meaning of actual analysis is not high, so that the second-stage sampling point clusters with too small number of sampling points (less than the preset number threshold) are not reserved, and the second-stage sampling point clusters with a sufficiently large number of sampling points (not less than the preset number threshold) are further analyzed.
Step S103 represents determining a direction angle of the sampling point in the secondary sampling point cluster according to the coordinates of the sampling point and the sampling time. Since the sampling points are a group of coordinate points with time sequence, the coordinates of the sampling points can be connected in time sequence to form a track, the track has an extending direction, and the direction angle of the sampling points can be determined according to the extending direction of the track. The embodiment of the invention divides the extending direction of the track (namely the direction angle of the sampling point) into two types, namely one along one direction and one with a plurality of directions. For example, a linear track has only one extending direction, and the direction angles of sampling points on the linear track are all 0; the staggered curve track has a plurality of extending directions, and the direction angles of the sampling points on the track are not 0.
The method for determining the direction angle of the sampling point in the secondary sampling point cluster according to the coordinates of the sampling point and the sampling time comprises the following steps: traversing the j-th sampling point, the j+1-th sampling point and the j+2-th sampling point in the secondary sampling point cluster according to the sampling time sequence of the sampling points, determining an included angle between an extension line of a line from the coordinate of the j-th sampling point to the coordinate of the j+1-th sampling point to the line from the coordinate of the j+1-th sampling point to the coordinate of the j+2-th sampling point, and taking the included angle as a direction angle of the j+2-th sampling point, wherein j=1, 2,3, …, k-2 and k are the total number of the sampling points in the secondary sampling point cluster. As shown in FIG. 2, the jth sampling point is traversed in accordance with the sampling time sequence (arrow direction) of the sampling points,A j+1th sampling point and a j+2th sampling point, coordinates of the j th sampling point (x j ,y j ) Coordinates (x) to the j+1th sampling point j+1 ,y j+1 ) An extension line (shown by a dotted line in the figure) of the line of the (j+1) th sampling point to the coordinate of the (j+2) th sampling point j+2 ,y j+2 ) The included angle between the lines of (c) is beta, so the direction angle of the j+2th sampling point is beta. For example, for a linear track, the direction angles of the sampling points on the linear track are all 0, i.e. β=0; for a disordered curve track, the direction angle of the sampling point is not 0, i.e. β+.0.
Step S104 represents determining whether the region corresponding to the secondary sampling point cluster is a target region according to the direction angle of the sampling points in the secondary sampling point cluster. After the direction angles of the sampling points are determined, the sampling points in the secondary sampling point cluster can be classified according to requirements, so that the clustering result is further optimized. The region formed by the coordinates of the sampling points in the sampling point cluster is the target region corresponding to the sampling point cluster.
Determining the proportion of the number of sampling points with the direction angle of the sampling points in the secondary sampling point cluster not larger than a preset direction angle to the total number of the sampling points in the secondary sampling point cluster; and if the proportion is not greater than the preset proportion, determining the area corresponding to the secondary sampling point cluster as a target area. In the embodiment of the invention, the preset direction angle is an angle close to 0 degree (the preset direction angle is not more than 90 degrees), namely, the aim of the step is to determine the proportion of sampling points with small track direction change to the number of sampling points in the secondary sampling point cluster, wherein the larger the proportion is, the more the track formed by the sampling points in the secondary sampling point cluster has certain directivity, the condition indicates that the track extends along a certain direction, and the sampled object possibly moves; conversely, a smaller ratio indicates that the trajectory formed by the sampling points in the secondary cluster of points is disordered, which indicates that the trajectory is disordered and that the sampled object may reside in a certain area. This step may be used to determine the area where the sampled object may reside (i.e., the target area).
After determining that the region corresponding to the secondary sampling point cluster is the target region, further screening the target region by one or more of the following methods:
determining a sampling time span of the secondary sampling point cluster, and determining an area corresponding to the secondary sampling point cluster with the sampling time span larger than a preset time threshold as a target area; the sampling time span refers to the difference between the sampling time of the sampling point with the earliest sampling time and the sampling time of the sampling point with the latest sampling time in the sampling point cluster, and is used for representing the stay time in the target area corresponding to the sampling point cluster.
Determining the average distance between the sampling points in the secondary sampling point cluster and the central point of the secondary sampling point cluster, and determining the area corresponding to the secondary sampling point cluster with the average distance larger than a preset distance threshold as a target area.
Determining the distance between the center point of the secondary sampling point cluster and a preset position or the average distance between the sampling points of the secondary sampling point cluster and the preset position, and determining the distance between the center point and the preset position or the area corresponding to the secondary sampling point cluster with the average distance smaller than a preset position threshold value as a target area.
Wherein the preset time threshold is not greater than 150 seconds, the preset distance threshold is not greater than 40 meters, and the preset position threshold is not greater than 5 kilometers.
The purpose of this step is to further optimize the secondary sample point clusters.
For example, in the field of express delivery, determining whether the area where the delivery person stays is a stay area where delivery is performed, and based on data statistics, as shown in fig. 3-1, the time span (stay time in the figure) of most of the sampling point clusters belonging to the area where delivery stays exceeds 310 seconds, and taking 110 seconds as a threshold, almost 93% of the stay area can be distinguished; as shown in fig. 3-2, the average sample point-to-center point distance in almost all non-dispensing dwell areas is less than 30 meters; as shown in fig. 3-3, the center point of most of the distribution stay areas (the current position in the figure, i.e., the center point of the cluster of sampling points) is within 4 km from the station (i.e., the preset position), whereas the center point of the non-distribution stay areas is more distributed over distance from the station.
Therefore, a window filter can be manufactured to filter all secondary sampling point clusters, and the secondary sampling point clusters with the time span smaller than a preset time threshold and the average distance between each sampling point and the central point of the secondary sampling point cluster smaller than a preset distance threshold or the secondary sampling point clusters with the time span smaller than the preset time threshold and the distance between the central point and the preset position or the average distance between each sampling point and the preset position larger than the preset position threshold are filtered. The target area determined by the secondary sampling point clusters remaining after filtering is more accurate.
Furthermore, the steps of the embodiment of the invention can also train one or more of the parameters of the preset direction angle, the preset proportion, the preset time threshold, the preset distance threshold, the preset position threshold, the preset time difference threshold, the preset quantity threshold and the like by means of a machine learning method by using sampling point data of the manually marked target area, so that a machine learning model is manufactured, and the processing process is more accurate.
Fig. 4 is a schematic diagram of main parts of an apparatus 400 for determining a target area according to an embodiment of the present invention, as shown in fig. 4:
the clustering module 401 is configured to cluster coordinates of the sampling points to form a first-level sampling point cluster.
The classification module 402 is configured to classify the primary sampling point cluster according to the sampling time of the sampling point to form a secondary sampling point cluster. Because the primary sampling point clusters are clustered according to the coordinates of the sampling points and have no fusion time attribute, the clustered sampling point clusters are required to be classified according to the time attribute, and the secondary sampling point clusters obtained after classification have higher precision and are closer to the actual sampling scene.
The classification module 402 is further configured to traverse sampling points in the first-level sampling point cluster according to a sampling time sequence of the sampling points, and if a difference between sampling times of the (i+1) th sampling point and the (i) th sampling point is greater than a preset time difference threshold, indicate that sampling times of the (i+1) th sampling point and the (i) th sampling point are discontinuous; classifying the first-level sampling point clusters according to the (i+1) th sampling point and the (i) th sampling point with discontinuous sampling time, so that the difference of sampling time between two adjacent sampling points in the formed second-level sampling point clusters is not greater than the preset time difference threshold; where i=where i=1, 2,3, …, n-1, n is the total number of samples in the primary sample cluster.
The classification module 402 is further configured to determine whether the total number of sampling points in the secondary sampling point cluster is not less than a preset number threshold; and reserving secondary sampling point clusters with the number of the sampling points not smaller than the preset number threshold. The aim is to ensure a sufficient number of samples, too small a number of samples may affect the final analysis result.
A direction module 403, configured to determine a direction angle of a sampling point in the secondary sampling point cluster according to the coordinates of the sampling point and the sampling time; since the sampling points are a group of coordinate points with time sequence, the coordinates of the sampling points can be connected in time sequence to form a track, the track has an extending direction, and the direction angle of the sampling points can be determined according to the extending direction of the track.
The direction module 403 is further configured to traverse the jth sampling point, the jth+1th sampling point, and the jth+2th sampling point in the secondary sampling point cluster according to the sampling time sequence of the sampling points, determine an included angle between an extension line of a line from coordinates of the jth sampling point to coordinates of the jth+1th sampling point to a direction from coordinates of the jth+1th sampling point and a line from coordinates of the jth+1th sampling point to coordinates of the jth+2th sampling point, and use the included angle as a direction angle of the jth+2th sampling point, where j=1, 2,3, …, k-2, k is a total number of sampling points in the secondary sampling point cluster.
And a calculating module 404, configured to determine whether the region corresponding to the secondary sampling point cluster is a target region according to the direction angle of the sampling points in the secondary sampling point cluster.
The calculation module 404 is further configured to determine a ratio of the number of sampling points with a direction angle of the sampling points in the secondary sampling point cluster not greater than a preset direction angle to the total number of sampling points in the secondary sampling point cluster; and if the proportion is not greater than the preset proportion, determining the area corresponding to the secondary sampling point cluster as a target area.
The apparatus 400 may further comprise a screening module for further screening the target region by one or more of the following methods:
determining a sampling time span of the secondary sampling point cluster, and determining an area corresponding to the secondary sampling point cluster with the sampling time span larger than a preset time threshold as a target area;
determining the average distance between sampling points in the secondary sampling point cluster and the central point of the secondary sampling point cluster, and determining the area corresponding to the secondary sampling point cluster with the average distance larger than a preset distance threshold as a target area;
determining the distance between the center point of the secondary sampling point cluster and a preset position or the average distance between the sampling points of the secondary sampling point cluster and the preset position, and determining the distance between the center point and the preset position or the area corresponding to the secondary sampling point cluster with the average distance smaller than a preset position threshold value as a target area.
Wherein the preset direction angle is not greater than 90 degrees, the preset time threshold is not greater than 150 seconds, the preset distance threshold is not greater than 40 meters, and the preset position threshold is not greater than 5 kilometers.
Fig. 5 illustrates an exemplary system architecture 500 of a method of determining a target area or an apparatus of determining a target area to which embodiments of the present invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 is used as a medium to provide communication links between the terminal devices 501, 502, 503 and the server 505. The network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 505 via the network 504 using the terminal devices 501, 502, 503 to receive or send messages or the like. Various communication client applications can be installed on the terminal devices 501, 502, 503.
The terminal devices 501, 502, 503 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server providing support for users with the terminal devices 501, 502, 503.
It should be noted that, in the embodiment of the present invention, a method for determining a target area is generally performed by the server 505, and accordingly, a device for determining a target area is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 6 is a schematic diagram of a computer system 600 suitable for use in implementing the terminal device of the embodiment of the present invention. The terminal device shown in fig. 6 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
In particular, the processes described in the above step diagrams may be implemented as computer software programs according to the disclosed embodiments of the invention. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the step diagrams. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 601.
The computer readable medium shown in the present invention includes a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium includes, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, semiconductor system, apparatus, or device, or any combination of the preceding. Computer-readable storage media include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any combination of the foregoing. In the context of this disclosure, a computer-readable storage medium includes any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device; a computer readable signal medium includes a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave, and the propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF (radio frequency), or the like, or any combination of the foregoing.
The steps of the figures or block diagrams, which illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention, may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or step diagrams, and combinations of blocks in the block diagrams or step diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.
The modules or units involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules or units may also be provided in a processor, for example, as: a processor includes a clustering module, a classification module, a direction module, and a calculation module. The names of these modules or units do not in some cases limit the module or unit itself, and for example, the clustering module may also be described as "a module for clustering coordinates of sampling points to form a primary sampling point cluster".
In another aspect, the embodiment of the present invention further provides a computer readable medium, which may be included in the apparatus described in the above embodiment; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: clustering coordinates of sampling points to form a first-level sampling point cluster; classifying the first-level sampling point clusters according to the sampling time of the sampling points to form second-level sampling point clusters; determining the direction angle of the sampling points in the secondary sampling point cluster according to the coordinates and the sampling time of the sampling points; and determining whether the area corresponding to the secondary sampling point cluster is a target area according to the direction angle of the sampling points in the secondary sampling point cluster.
According to the technical scheme provided by the embodiment of the invention, the sampling points can be clustered according to the sampled coordinates and the sampling time, the clustering result is optimized according to the direction angle of the sampling points, and the clustering result is further optimized according to the sampling time span, the average distance between the sampling points and the central point and the average distance between the sampling points and the preset positions or the distance between the central point and the preset positions, and the target area is determined according to the clustering result.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (14)
1. A method of determining a target area, comprising:
clustering coordinates of sampling points to form a first-level sampling point cluster;
classifying the first-level sampling point clusters according to the sampling time of the sampling points to form second-level sampling point clusters;
determining the direction angle of the sampling points in the secondary sampling point cluster according to the coordinates and the sampling time of the sampling points;
determining whether the area corresponding to the secondary sampling point cluster is a target area according to the direction angle of the sampling points in the secondary sampling point cluster;
the method further comprises the steps of: determining the proportion of the number of sampling points with the direction angle of the sampling points in the secondary sampling point cluster not larger than a preset direction angle to the total number of the sampling points in the secondary sampling point cluster; if the proportion is not greater than a preset proportion, determining the area corresponding to the secondary sampling point cluster as a target area; the target area is a stay area.
2. The method of claim 1, further comprising, after determining the region corresponding to the secondary sampling point cluster as a target region, further screening the target region by one or more of:
determining a sampling time span of the secondary sampling point cluster, and determining an area corresponding to the secondary sampling point cluster with the sampling time span larger than a preset time threshold as a target area;
determining the average distance between sampling points in the secondary sampling point cluster and the central point of the secondary sampling point cluster, and determining the area corresponding to the secondary sampling point cluster with the average distance larger than a preset distance threshold as a target area;
determining the distance between the center point of the secondary sampling point cluster and a preset position or the average distance between the sampling points of the secondary sampling point cluster and the preset position, and determining the distance between the center point and the preset position or the area corresponding to the secondary sampling point cluster with the average distance smaller than a preset position threshold value as a target area.
3. The method of claim 2, wherein the predetermined direction angle is no greater than 90 degrees, the predetermined time threshold is no greater than 150 seconds, the predetermined distance threshold is no greater than 40 meters, and the predetermined location threshold is no greater than 5 kilometers.
4. The method of claim 1, wherein the classifying the primary clusters of sample points according to the sample times of the sample points, the forming the secondary clusters of sample points comprises:
traversing the sampling points in the first-level sampling point cluster according to the sampling time sequence of the sampling points, and if the difference between the sampling time of the (i+1) th sampling point and the sampling time of the (i) th sampling point is greater than a preset time difference threshold, indicating that the sampling time of the (i+1) th sampling point and the sampling time of the (i) th sampling point are discontinuous;
classifying the first-level sampling point clusters according to the (i+1) th sampling point and the (i) th sampling point with discontinuous sampling time, so that the difference of sampling time between two adjacent sampling points in the formed second-level sampling point clusters is not greater than the preset time difference threshold;
where i=1, 2,3, …, n-1, n is the total number of sampling points in the primary sampling point cluster.
5. The method of claim 1, wherein determining the direction angle of the sampling points in the secondary sampling point cluster from the coordinates of the sampling points and the sampling time comprises:
traversing the j-th sampling point, the j+1-th sampling point and the j+2-th sampling point in the secondary sampling point cluster according to the sampling time sequence of the sampling points, determining an included angle between an extension line of a line from the coordinate of the j-th sampling point to the coordinate of the j+1-th sampling point to the line from the coordinate of the j+1-th sampling point to the coordinate of the j+2-th sampling point, and taking the included angle as a direction angle of the j+2-th sampling point, wherein j=1, 2,3, …, k-2 and k are the total number of the sampling points in the secondary sampling point cluster.
6. The method of claim 1, wherein the method of forming the secondary sampling point cluster further comprises:
determining whether the total number of sampling points in the secondary sampling point cluster is not less than a preset number threshold;
and reserving secondary sampling point clusters with the number of the sampling points not smaller than the preset number threshold.
7. An apparatus for determining a target area, comprising:
the clustering module is used for clustering the coordinates of the sampling points to form a primary sampling point cluster;
the classification module is used for classifying the primary sampling point clusters according to the sampling time of the sampling points to form secondary sampling point clusters;
the direction module is used for determining the direction angle of the sampling points in the secondary sampling point cluster according to the coordinates and the sampling time of the sampling points;
the computing module is used for determining whether the area corresponding to the secondary sampling point cluster is a target area according to the direction angle of the sampling points in the secondary sampling point cluster;
the calculation module is further used for determining the proportion of the number of sampling points with the direction angle of the sampling points in the secondary sampling point cluster not larger than a preset direction angle to the total number of the sampling points in the secondary sampling point cluster; if the proportion is not greater than a preset proportion, determining the area corresponding to the secondary sampling point cluster as a target area; the target area is a stay area.
8. The apparatus of claim 7, further comprising a screening module for further screening the target region by one or more of the following methods:
determining a sampling time span of the secondary sampling point cluster, and determining an area corresponding to the secondary sampling point cluster with the sampling time span larger than a preset time threshold as a target area;
determining the average distance between sampling points in the secondary sampling point cluster and the central point of the secondary sampling point cluster, and determining the area corresponding to the secondary sampling point cluster with the average distance larger than a preset distance threshold as a target area;
determining the distance between the center point of the secondary sampling point cluster and a preset position or the average distance between the sampling points of the secondary sampling point cluster and the preset position, and determining the distance between the center point and the preset position or the area corresponding to the secondary sampling point cluster with the average distance smaller than a preset position threshold value as a target area.
9. The apparatus of claim 8, wherein the predetermined direction angle is no greater than 90 degrees, the predetermined time threshold is no greater than 150 seconds, the predetermined distance threshold is no greater than 40 meters, and the predetermined location threshold is no greater than 5 kilometers.
10. The apparatus of claim 7, wherein the classification module is further configured to traverse the sampling points in the first-level sampling point cluster according to a sampling time sequence of the sampling points, and if a difference between sampling times of the i+1th sampling point and the i-th sampling point is greater than a preset time difference threshold, the sampling times of the i+1th sampling point and the i-th sampling point are discontinuous;
classifying the first-level sampling point clusters according to the (i+1) th sampling point and the (i) th sampling point with discontinuous sampling time, so that the difference of sampling time between two adjacent sampling points in the formed second-level sampling point clusters is not greater than the preset time difference threshold;
where i=1, 2,3, …, n-1, n is the total number of sampling points in the primary sampling point cluster.
11. The apparatus of claim 7, wherein the direction module is further configured to traverse a j-th sampling point, a j+1-th sampling point, and a j+2-th sampling point in the secondary sampling point cluster according to a sampling time sequence of the sampling points, determine an included angle between an extension line of a coordinate of the j-th sampling point to a coordinate of the j+1-th sampling point and a line of the coordinate of the j+1-th sampling point to a coordinate of the j+2-th sampling point, and take the included angle as a direction angle of the j+2-th sampling point, where j=1, 2,3, …, k-2, k is a total number of sampling points in the secondary sampling point cluster.
12. The apparatus of claim 7, wherein the classification module is further configured to determine whether a total number of sampling points in the secondary sampling point cluster is not less than a preset number threshold; and reserving secondary sampling point clusters with the number of the sampling points not smaller than the preset number threshold.
13. An electronic device, comprising:
one or more processors; a storage means for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.
14. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by one or more processors, implements the method according to any of claims 1-6.
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| CN112735253B (en) * | 2020-10-22 | 2023-03-10 | 深圳安途智行科技有限公司 | Traffic light automatic labeling method and computer equipment |
| CN114386500A (en) * | 2021-12-31 | 2022-04-22 | 苏州市公安局 | Infectious disease sampling point selection method, device, equipment and storage medium |
| CN114511044B (en) * | 2022-04-18 | 2022-06-28 | 新石器慧通（北京）科技有限公司 | Unmanned vehicle passing control method and device |
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