CN114064834A - Target location determination method and device, storage medium and electronic equipment - Google Patents
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Abstract
The disclosure provides a target location determining method, a target location determining device, a computer readable storage medium and an electronic device, and relates to the technical field of computers. The target location determination method includes: acquiring track point data of a user, and clustering the track point data to obtain a plurality of clustering clusters; calculating the aggregation degree of the clustering cluster based on the reporting time interval between two track point data which are continuously reported in the clustering cluster; and screening the target cluster from the multiple clusters by combining the aggregation degree of the clusters and the attribute characteristics of the target location to determine the target location. The method and the device can accurately determine the place related to the user.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a target location determining method, a target location determining apparatus, a computer-readable storage medium, and an electronic device.
Background
With the development of terminal equipment and communication technology, the data mining capability is stronger. In order to better improve the convenience of work and life of the user, the place where the user is located can be determined based on the data mining result, and the service matched with the place is provided for the user.
It can be seen that the premise for providing proper service for users is to determine the location, however, there may be some inaccuracy and even mistakes in determining the location.
Disclosure of Invention
The present disclosure provides a target location determining method, a target location determining apparatus, a computer-readable storage medium, and an electronic device, thereby overcoming, at least to some extent, the problem of determining a location inaccurately.
According to a first aspect of the present disclosure, there is provided a target location determination method, including: acquiring track point data of a user, and clustering the track point data to obtain a plurality of clustering clusters; calculating the aggregation degree of the clustering cluster based on the reporting time interval between two track point data which are continuously reported in the clustering cluster; and screening the target cluster from the multiple clusters by combining the aggregation degree of the clusters and the attribute characteristics of the target location to determine the target location.
According to a second aspect of the present disclosure, there is provided a target location determination apparatus comprising: the clustering model is used for acquiring track point data of a user and clustering the track point data to obtain a plurality of clustering clusters; the aggregation degree calculation module is used for calculating the aggregation degree of the clustering cluster based on the reporting time interval between two continuously reported track point data in the clustering cluster; and the location determining module is used for screening the target cluster from the multiple clusters by combining the aggregation degree of the clusters and the attribute characteristics of the target location so as to determine the target location.
According to a third aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the target location determining method described above.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising a processor; a memory for storing one or more programs which, when executed by the processor, cause the processor to implement the target location determining method described above.
In the technical scheme provided by some embodiments of the present disclosure, a clustering means is adopted to obtain a plurality of places characterized by clustering clusters related to a user, and then an aggregation degree of each clustering cluster is obtained by combining a reporting time interval between two track point data continuously reported in the clustering clusters, and further a target clustering cluster is screened from the clustering clusters by combining the aggregation degree of the clustering clusters and attribute characteristics of a target place, so as to determine the target place. The scheme disclosed by the invention depends on the aggregation calculation result of the clustering cluster, so that the target location can be reasonably and accurately determined.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:
FIG. 1 illustrates a schematic diagram of an exemplary system architecture of a target location determination scheme of an embodiment;
FIG. 2 illustrates a schematic structural diagram of an electronic device suitable for use in implementing embodiments of the present disclosure;
FIG. 3 schematically illustrates a flow chart of a target location determination method according to an exemplary embodiment of the present disclosure;
FIG. 4 shows a schematic diagram of clustering results of an embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart of an overall process of determining a work site and a residential site of an embodiment of the disclosure;
fig. 6 schematically shows a block diagram of a target location determining apparatus according to an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the steps. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 shows a schematic diagram of an exemplary system architecture of a target location determination scheme of an embodiment.
As shown in fig. 1, the system architecture may include a terminal device 11 and a server 12. The terminal device 11 may be various electronic devices with a positioning function and a communication function, including but not limited to a smart phone, a smart wearable device, a tablet computer, and the like. The server 12 may be a single server or a server cluster composed of a plurality of servers.
The terminal device 11 may include at least a positioning module 111 and a communication module 112. In the embodiment of the present disclosure, the positioning module 111 may be configured to collect trajectory point data of a user; the communication module 112 may be configured to obtain the trace point data and report the trace point data to the server 12.
The server 12 may be configured to cluster the trace point data to obtain a plurality of cluster clusters, calculate an aggregation degree of the cluster clusters based on a reporting time interval between two trace point data continuously reported in the cluster clusters, and screen a target cluster from the plurality of cluster clusters by combining the aggregation degree of the cluster clusters and attribute characteristics of the target location to determine the target location.
For example, the attribute feature of the target location includes a time feature of the user at the target location, in this case, if the work location is to be determined, the cluster with the largest aggregation degree in the cluster of the reported trajectory data in the current period is selected as the target cluster in combination with the working time (which may be from 8 am to 6 pm), so as to determine the work location. Specifically, the center point of the target cluster may be determined as the work location of the user.
After determining the work place, the server 12 may transmit recommended meal place information, surrounding traffic information, and the like to the terminal device 11 when the user is at the work place.
The destination point determination method described in the present disclosure may be implemented by the server 12, that is, the server 12 may perform the respective steps of the destination point determination of the embodiment of the present disclosure, in which case the destination point determination means described below may be configured in the server 12.
Further, it should be understood that the target location determining method described in the present disclosure may be performed by the terminal device 11 itself, in addition to the above description.
Specifically, firstly, the track point data acquired by the positioning module 111 in the terminal device 11 may be reported to a processor in the terminal device 11, and the processor clusters the track point data to obtain a plurality of cluster clusters; next, the processor may calculate an aggregation level of the cluster based on a reporting time interval between two track point data that are continuously reported in the cluster; then, the processor can combine the aggregation degree of the cluster clusters and the attribute characteristics of the target location to screen the target cluster clusters from the plurality of cluster clusters so as to determine the target location.
After the terminal device 11 determines the target location, the information of the target location may be sent to the server 12, so that the server 12 recommends a service corresponding to the target location to the terminal device 11 based on the target location, so as to improve convenience of work and life of the user.
FIG. 2 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device used to implement the exemplary embodiments of this disclosure. The server of the disclosed embodiment may be configured as in fig. 2. It should be noted that the computer system 200 of the electronic device shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments of the present disclosure.
The electronic device of the present disclosure includes at least a processor and a memory for storing one or more programs, which when executed by the processor, cause the processor to implement the target location determining method of the exemplary embodiments of the present disclosure.
As shown in fig. 2, the computer system 200 includes a Central Processing Unit (CPU)201 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)202 or a program loaded from a storage section 208 into a Random Access Memory (RAM) 203. In the RAM 203, various programs and data necessary for system operation are also stored. The CPU 201, ROM 202, and RAM 203 are connected to each other via a bus 204. An input/output (I/O) interface 205 is also connected to bus 204.
The following components are connected to the I/O interface 205: an input portion 206 including a keyboard, a mouse, and the like; an output section 207 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 208 including a hard disk and the like; and a communication section 209 including a network interface card such as a LAN card, a modem, or the like. The communication section 209 performs communication processing via a network such as the internet. A drive 210 is also connected to the I/O interface 205 as needed. A removable medium 211 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 210 as necessary, so that a computer program read out therefrom is mounted into the storage section 208 as necessary.
The present disclosure also provides a computer-readable storage medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device.
A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may 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 suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be 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 storage medium may transmit, 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 storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The computer-readable storage medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method as described in the embodiments below.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also 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 flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
The following description will be given taking as an example a server executing the target location specifying method according to the embodiment of the present disclosure.
Fig. 3 schematically shows a flowchart of a target location determination method of an exemplary embodiment of the present disclosure. Referring to fig. 3, the target location determining method may include the steps of:
and S32, obtaining track point data of the user, and clustering the track point data to obtain a plurality of clustering clusters.
In an exemplary embodiment of the present disclosure, the track point data may be acquired by a Positioning module of the terminal device, for example, a GPS (Global Positioning System) module configured in the terminal device. In some embodiments, the positioning module may also be a WiFi module, a bluetooth module, etc. of the terminal device. That is, the trace point data of the present disclosure may be determined by one or more radio frequency positioning methods, which is not limited by the present disclosure.
The terminal equipment can send the acquired trace point data to the server.
According to some embodiments of the present disclosure, the terminal device may send the trace point data collected in one day to the server. In this case, the server may determine the target location using the location data for the day.
According to other embodiments of the disclosure, the terminal device may send all trace point data collected over a historical period of time to the server. In this case, the server can determine the target location using the track point data. Wherein the historical period of time may be preconfigured, e.g., within one week, one month, etc., and the disclosure is not limited thereto.
In addition, the track point data acquired by the server in step S32 may be data obtained after denoising processing. The denoising process may be implemented by a terminal device, or the denoising process may be implemented by a server. The server executes the denoising process.
First, the server may obtain raw data characterizing the user trajectory sent by the terminal device. That is, the terminal device sends raw data collected by the positioning module and unprocessed to the server.
Next, the server may perform denoising processing on the raw data to obtain trajectory data of the user as described in step S32.
Specifically, the server may determine two data representing the user trajectory, which are continuously reported in the raw data. On one hand, the server calculates the spatial distance corresponding to the two data representing the user track; on the other hand, the server may calculate the reporting time interval between the two data characterizing the user trajectory.
And the server carries out denoising processing on the original data by utilizing the space distance corresponding to the two data representing the user track and the reporting time interval between the two data representing the user track. For example, if the quotient of the spatial distance and the reporting interval is greater than a predetermined threshold, it is indicative of a problem with at least one of the two data characterizing the user trajectory. At this time, another data uploaded adjacently (continuously uploaded) can be combined to make a judgment to determine which data has an error.
For example, if the quotient of the spatial distance corresponding to b and c and the reporting time interval is greater than a predetermined threshold, it is necessary to determine which group of data has a problem by combining the determination results of a and b and c and d.
The denoising methods described in the embodiments of the present disclosure include, but are not limited to, deleting, correcting, and the like, and the present disclosure does not limit this.
After the server obtains the track point data of the user, the track point data can be clustered to obtain a plurality of clustering clusters. And the spatial distance corresponding to each track point data in the same cluster is within a preset distance range.
It will be appreciated that clustering is an unsupervised way of classification, which is the automatic grouping of similar data objects into a category, a cluster of clusters being a collection of a group of data objects.
For example, the server may convert each trace point data into a feature vector, partition similar or consistent feature vectors into the same cluster, partition inconsistent feature vectors into different clusters, and enable the similarity of the feature vectors partitioned into the same cluster to meet a predetermined similarity requirement. The predetermined similarity requirement of the vector can be judged by using a threshold, and the specific value of the threshold is not limited by the disclosure.
In some embodiments of the present disclosure, a K-means clustering (K-means) algorithm may be employed to achieve clustering of the trajectory point data. The K mean algorithm is a clustering analysis algorithm for iterative solution, and the method comprises the steps of dividing data into K groups in advance, randomly selecting K objects as initial clustering centers, calculating the distance between each object and each seed clustering center, and allocating each object to the nearest clustering center. The cluster centers and the objects assigned to them represent a cluster. The cluster center of a cluster is recalculated for each sample assigned, based on the objects existing in the cluster. This process will be repeated until a predetermined termination condition is met. The predetermined termination condition may be that no (or a minimum number) objects are reassigned to different cluster clusters, no (or a minimum number) cluster centers are changed again, and the sum of squared errors is locally minimal.
It should be understood that the K-means clustering algorithm is only one example for implementing the present disclosure to cluster the trajectory point data, and in other embodiments of the present disclosure, other clustering algorithms may also be employed, including but not limited to mean shift clustering algorithms, density-based clustering algorithms, and the like.
Fig. 4 shows clustering results of an embodiment of the present disclosure. Referring to fig. 4, after clustering, at least a cluster 41 and a cluster 42 may be obtained based on the obtained trajectory point data of the user.
And S34, calculating the aggregation degree of the clustering cluster based on the reporting time interval between two track point data which are continuously reported in the clustering cluster.
It should be understood that due to network instability, user power on/off, and the like, the reporting time intervals between the continuously reported track point data are not consistent, that is, the reporting time intervals are not fixed values.
In an exemplary embodiment of the present disclosure, the server may calculate the aggregation degree of the cluster based on a reporting time interval between two track point data that are continuously reported in the cluster. It should be understood that this step is to calculate the aggregation of all cluster clusters separately.
First, the server may calculate the aggregation level of two track point data based on the reporting time interval between two track points that are continuously reported in the cluster.
The reporting time interval between two track points which are continuously reported is in positive correlation with the aggregation degree, namely, the larger the reporting time interval between the two track points which are continuously reported is, the larger the corresponding aggregation degree is. In the disclosed embodiment, the time decay coefficient in newton's law of cooling is used to define the relationship between the two.
y＝C*eaT
Wherein, T is the reporting time interval between two track points which are continuously reported, y is the aggregation degree between the two track points, and C and a are constants.
Next, the server may calculate the aggregation level of the cluster by using the aggregation levels of all the two trace point data continuously reported in the cluster. Specifically, the aggregation degrees of all the two continuously reported trajectory point data in the cluster may be added, and the added result is used as the aggregation degree of the cluster.
Based on the above process, the aggregation degree of each cluster can be determined.
And S36, screening the target cluster from the multiple clusters according to the aggregation degree of the clusters and the attribute characteristics of the target location to determine the target location.
It should be noted that the embodiment of the present disclosure is to determine which of the respective locations that the user passes through is a designated target location. Such as which site is a work site, which site is a residential site, etc. That is, the target location is a location of a designated scene, and the embodiment of the present disclosure can determine which location is the target location by analyzing the trajectory point data.
Thus, in determining the target location, the server may obtain the attribute characteristics of the target location. The attribute characteristics of the target location may include a temporal characteristic of the target location at which the user is located. Taking the target location as an example of the work location, the time characteristic is, for example, 8 am to 6 pm.
After determining the aggregation degree of the cluster clusters and the attribute characteristics of the target location, the information can be combined to screen out the target cluster clusters from the plurality of cluster clusters so as to determine the target location.
First, the server may order clusters by the size of the aggregation. Specifically, the cluster clusters may be sorted in the order from the large aggregation degree to the small aggregation degree, or may be sorted in the order from the small aggregation degree to the large aggregation degree.
Next, the server may select a second cluster from the plurality of clusters by combining the sorting result and the attribute feature of the target location to determine the target location.
In an embodiment where the attribute characteristic of the target location includes a time characteristic that the user is at the target location, the server may determine the reporting time of each cluster, and may specifically be characterized by an average reporting time of each track point data in the cluster.
The server may screen out a cluster matched with the time characteristic of the target location from the plurality of clusters obtained in step S32 based on the reporting time of each cluster, to obtain a cluster subset. It is understood that the term match is used herein to mean that two times are the same or similar, and that similarity is used to mean that the error between the two times is less than a predetermined error threshold.
The server can determine the cluster with the maximum aggregation degree from the cluster subset as a target cluster by using the sequencing result so as to determine the target location.
Specifically, the central point of the target cluster may be determined as the target location, or the regions corresponding to the entire target cluster may be used as the target locations.
In addition, the attribute feature of the target location may also be scene attribute information determined by a big data manner, for example, the attribute feature of the target location may include information that all the surroundings of the target location are residential areas or all the surroundings of an office building.
Specifically, after the cluster clusters are sorted according to the degree of aggregation, on one hand, the cluster with the largest degree of aggregation in the cluster clusters around the office building can be determined and used as the first target cluster to determine the working place. On the other hand, the cluster with the largest aggregation degree in the cluster clusters around all the residential areas can be determined to serve as the second target cluster, so that the residential site can be determined.
Still taking the clustering result shown in fig. 4 as an example, the clustering cluster 41 may be a first target clustering cluster, and determines a working location; cluster 42 may be a second target cluster to determine a residence location.
Fig. 5 schematically illustrates a flow chart of the overall process of determining the work site and the residential site of an embodiment of the present disclosure.
In step S502, the server may obtain raw data characterizing the user trajectory.
In step S504, the server may perform denoising processing on the original data to obtain track point data of the user.
In step S506, the server may cluster the trace point data to obtain a plurality of cluster clusters.
In step S508, the server may calculate the aggregation degree of the cluster clusters.
In step S510, the server may sort the cluster clusters by aggregation.
In step S512, the server may determine the working period, for example, the server may determine the working period by means of big data or user input.
In step S514, the server may determine, based on the sorting result in step S510, a cluster with the reporting time consistent with the working time period and the highest aggregation degree as a target cluster.
In step S516, the server may determine the work site of the user based on the target cluster.
In step S518, the server may determine the home time period, for example, the server may determine the home time period by means of big data or user input.
In step S520, the server may determine, based on the sorting result in step S510, a cluster whose reporting time is consistent with the home time period and whose aggregation degree is the greatest, as a target cluster.
In step S522, the server may determine the residence of the user based on the target cluster.
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Further, the present exemplary embodiment also provides a target location determination apparatus.
Fig. 6 schematically shows a block diagram of a target location determining apparatus of an exemplary embodiment of the present disclosure. Referring to fig. 6, the target location determining apparatus 6 according to an exemplary embodiment of the present disclosure may include a clustering module 61, an aggregation degree calculating module 63, and a location determining module 65.
Specifically, the clustering module 61 may be configured to obtain track point data of a user, and cluster the track point data to obtain a plurality of cluster clusters; the aggregation calculation module 63 may be configured to calculate the aggregation of the cluster based on a reporting time interval between two track point data that are continuously reported in the cluster; the location determining module 65 may be configured to combine the aggregation of the cluster clusters and the attribute characteristics of the target location to screen a target cluster from the plurality of cluster clusters to determine the target location.
According to an exemplary embodiment of the present disclosure, the aggregation calculation module 63 may be configured to perform: calculating the aggregation degree of two track point data based on the reporting time interval between the two track point data which are continuously reported in the clustering cluster; and calculating the aggregation degree of the clustering cluster by using the aggregation degrees of the two continuously reported track point data in the clustering cluster.
According to an exemplary embodiment of the present disclosure, the aggregation calculation module 63 may be configured to perform: and adding the aggregation degrees of the two continuously reported track point data in the cluster to obtain the aggregation degree of the cluster.
According to an exemplary embodiment of the present disclosure, the clustering module 61 may be configured to perform: acquiring original data representing a user track; and denoising the original data to obtain the track point data of the user.
According to an exemplary embodiment of the present disclosure, the process of the clustering module 61 performing the denoising process may be configured to perform: determining two data representing user tracks which are continuously reported in the original data, and calculating a space distance corresponding to the two data representing the user tracks and a reporting time interval between the two data representing the user tracks; and denoising the original data by utilizing the corresponding spatial distance of the two data representing the user track and the reporting time interval between the two data representing the user track.
According to an exemplary embodiment of the present disclosure, the location determination module 65 may be configured to perform: and sequencing the clustering clusters according to the aggregation degree, and screening the target clustering clusters from the multiple clustering clusters by combining the sequencing result and the attribute characteristics of the target site to determine the target site.
According to an exemplary embodiment of the present disclosure, the attribute characteristic of the target location includes a time characteristic of the user being at the target location. In this case, the location determination module 65 may be configured to perform: determining the reporting time of each cluster in a plurality of clusters; screening cluster clusters matched with the time characteristics of the target site from the plurality of cluster clusters based on the reporting time of each cluster, and constructing a cluster subset; and determining the cluster with the maximum aggregation degree from the cluster subset as a target cluster by using the sequencing result so as to determine the target location.
Since each functional module of the target location determining apparatus in the embodiments of the present disclosure is the same as that in the above-described method embodiments, it is not described herein again.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (10)
1. A method of target location determination, comprising:
acquiring track point data of a user, and clustering the track point data to obtain a plurality of clustering clusters;
calculating the aggregation degree of the clustering cluster based on the reporting time interval between two track point data which are continuously reported in the clustering cluster;
and screening the target cluster from the plurality of clusters by combining the aggregation degree of the clusters and the attribute characteristics of the target location to determine the target location.
2. The method of claim 1, wherein calculating the clustering degree of the cluster based on the reporting time interval between two track point data reported continuously in the cluster comprises:
calculating the aggregation degree of the two track point data based on the reporting time interval between the two track point data which are continuously reported in the clustering cluster;
and calculating the aggregation degree of the clustering cluster by using the aggregation degrees of the two continuously reported track point data in the clustering cluster.
3. The method for determining a target location according to claim 2, wherein calculating the aggregation of the cluster by using the aggregation of two track point data reported continuously in the cluster comprises:
and adding the aggregation degrees of the two continuously reported track point data in the cluster to obtain the aggregation degree of the cluster.
4. The method of claim 1, wherein obtaining track point data for a user comprises:
acquiring original data representing a user track;
and denoising the original data to obtain the track point data of the user.
5. The method of claim 4, wherein de-noising the raw data comprises:
determining two data representing user tracks which are continuously reported in original data, and calculating a space distance corresponding to the two data representing the user tracks and a reporting time interval between the two data representing the user tracks;
and denoising the original data by utilizing the spatial distance corresponding to the two data representing the user tracks and the reporting time interval between the two data representing the user tracks.
6. The method for determining a target location according to claim 1, wherein the step of selecting a target cluster from the plurality of clusters to determine the target location according to the aggregation of the clusters and the attribute characteristics of the target location comprises:
and sequencing all the clustering clusters according to the aggregation degree, and screening a target clustering cluster from the clustering clusters by combining a sequencing result and the attribute characteristics of the target location to determine the target location.
7. The method of claim 6, wherein the attribute characteristic of the target location comprises a time characteristic of a user being at the target location; the method for screening the target cluster from the plurality of clusters by combining the sequencing result and the attribute characteristics of the target location to determine the target location comprises the following steps:
determining the reporting time of each cluster in the plurality of clusters;
screening cluster clusters matched with the time characteristics of the target place from the plurality of cluster clusters based on the reporting time of each cluster to obtain a cluster subset;
and determining the cluster with the maximum aggregation degree from the cluster subset as a target cluster by using the sequencing result so as to determine the target location.
8. A target location determination apparatus, comprising:
the system comprises a clustering module, a tracking module and a tracking module, wherein the clustering module is used for acquiring track point data of a user and clustering the track point data to obtain a plurality of clustering clusters;
the aggregation degree calculation module is used for calculating the aggregation degree of the clustering cluster based on the reporting time interval between two track point data which are continuously reported in the clustering cluster;
and the place determining module is used for screening the target cluster from the plurality of clusters by combining the aggregation degree of the clusters and the attribute characteristics of the target place so as to determine the target place.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the target location determination method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor;
a memory for storing one or more programs which, when executed by the processor, cause the processor to implement the target location determination method of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114663691A (en) * | 2022-05-24 | 2022-06-24 | 浙江大华技术股份有限公司 | Method and device for positioning foothold and electronic equipment |
| CN115952364A (en) * | 2023-03-07 | 2023-04-11 | 之江实验室 | Route recommendation method and device, storage medium and electronic equipment |
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2021
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114663691A (en) * | 2022-05-24 | 2022-06-24 | 浙江大华技术股份有限公司 | Method and device for positioning foothold and electronic equipment |
| CN115952364A (en) * | 2023-03-07 | 2023-04-11 | 之江实验室 | Route recommendation method and device, storage medium and electronic equipment |
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