CN109522374B - Positioning method, positioning device, electronic equipment and readable storage medium - Google Patents
Positioning method, positioning device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The invention provides a positioning method and a positioning device, wherein the method comprises the following steps: acquiring multimedia data in behavior data of a user aiming at a business object; extracting the generation time and the positioning point of the multimedia data; clustering the positioning points to obtain one or more clustering clusters; determining an optimal clustering cluster according to the generation time and the number of positioning points in each clustering cluster; and determining the central positioning point in the optimal clustering cluster as a standard positioning point of the service object. The method and the device solve the problems of high error, poor timeliness and difficult execution in the prior positioning technology, and can determine the standard positioning point through the positioning point of the multimedia data in the user behavior data.
Description
Technical Field
The embodiment of the invention relates to the technical field of positioning, in particular to a positioning method and a positioning device.
Background
In the technical field of positioning, for example, in O2O and map-like applications, tens of millions or even hundreds of millions of POI (Point of Interest) data are recorded on line, that is, in a geographic information system, one POI may be a house, a shop, a mailbox, a bus station, etc., and the positioning accuracy of these POI data is particularly important, and has a great influence on user experience.
In the prior art, the scheme for calibrating the POI coordinates mainly includes the following three schemes: and (3) performing multi-source fusion algorithm through address inverse calculation or through field acquisition.
However, the address back calculation means that the longitude and latitude coordinates of the POI are calculated through the address of the POI, and a large error exists. The real mining means that a manual team is arranged to sweep a street, the POI address and the longitude and latitude coordinates under the line are collected on the spot, and due to errors caused by manual negligence and high labor cost, timeliness cannot be ensured. The longitude and latitude multi-source calibration algorithm is a sorting algorithm based on space density, needs to rely on too many data sources, requires more coverage merchants, and does not use positioning information contained in pictures.
Disclosure of Invention
The present invention provides a positioning method and apparatus to solve the above problems in the prior art.
According to a first aspect of the present invention, there is provided a positioning method, the method comprising:
acquiring multimedia data in behavior data of a user aiming at a business object;
extracting the generation time and the positioning point of the multimedia data;
clustering the positioning points to obtain one or more clustering clusters;
determining an optimal clustering cluster according to the generation time and the number of positioning points in each clustering cluster;
and determining the central positioning point in the optimal clustering cluster as a standard positioning point of the service object.
According to a second aspect of the present invention, there is provided a positioning device, the device comprising:
the multimedia data acquisition module is used for acquiring multimedia data in the behavior data of the user aiming at the service object;
the data information acquisition module is used for extracting the generation time and the positioning point of the multimedia data;
the clustering module is used for clustering the positioning points to obtain one or more clustering clusters;
the optimal clustering cluster determining module is used for determining an optimal clustering cluster according to the generation time and the positioning points in each clustering cluster;
and the standard positioning point determining module is used for determining the standard positioning point of the service object according to the central positioning point in the optimal clustering cluster.
According to a third aspect of the present invention, there is provided an electronic apparatus comprising:
a processor, a memory and a computer program stored on the memory and executable on the processor, the processor implementing the aforementioned method when executing the program.
According to a fourth aspect of the invention, there is provided a readable storage medium having instructions which, when executed by a processor of an electronic device, enable the electronic device to perform the aforementioned method.
The embodiment of the invention provides a positioning method and a positioning device, wherein the positioning method comprises the following steps: acquiring multimedia data in behavior data of a user aiming at a business object; extracting the generation time and the positioning point of the multimedia data; clustering the positioning points to obtain one or more clustering clusters; determining an optimal clustering cluster according to the generation time and the number of positioning points in each clustering cluster; and determining the central positioning point in the optimal clustering cluster as a standard positioning point of the service object. The method and the device solve the problems of high error, poor timeliness and difficult execution in the prior positioning technology, and can determine the standard positioning point through the positioning point of the multimedia data in the user behavior data. Therefore, the positioning point is efficiently and simply obtained.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flowchart illustrating specific steps of a positioning method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating specific steps of a positioning method according to a second embodiment of the present invention;
FIG. 2A is a flowchart illustrating an exemplary data processing method according to a second embodiment of the present invention;
fig. 3 is a structural diagram of a positioning apparatus according to a third embodiment of the present invention;
FIG. 4 is a block diagram of a positioning apparatus according to a fourth embodiment of the present invention
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, a flowchart illustrating specific steps of a positioning method according to an embodiment of the present invention is shown.
in the embodiment of the invention, data of various operations performed by a user aiming at a business object is behavior data, wherein the behavior data comprises user comment data, a scene description picture uploaded by a merchant, error reporting information added by the user, crowdsourcing tasks, user notes and the like.
Specifically, multimedia data, i.e., image or video data, among a plurality of user behavior data is acquired.
It is understood that the merchant is also one of the users using the application platform.
It is to be understood that the multimedia data may include other multimedia data, such as audio, text, etc., which may provide user positioning information, besides the image or video data, and the embodiment of the present invention is not limited thereto.
specifically, when the user uploads the multimedia data, the mobile terminal can acquire the positioning information of the user in real time and mark the positioning information in the multimedia data, so that the positioning information can be extracted from the multimedia data.
Similarly, the user uploads the multimedia data with a timestamp, so the timestamp of the multimedia data can be extracted to obtain the specific time when the user shoots the multimedia data.
It is understood that the photographing time is a time when the user photographs multimedia data, which is marked in the mobile terminal.
And 103, clustering the positioning points to obtain one or more cluster clusters.
In the embodiment of the invention, the positioning points belonging to the same POI are clustered, wherein the POI is an abbreviation of 'Point of interest', and Chinese can be translated into 'interest points'. In the geographic information system, one POI may be one house, one shop, one mailbox, one bus station, and the like.
Therefore, after clustering, the positioning points under one POI are aggregated into different cluster clusters.
104, determining an optimal clustering cluster according to the generation time and the number of positioning points in each clustering cluster;
specifically, the point with the latest shooting time in the cluster or the point with the largest number of positioning points in the cluster is determined as the optimal cluster.
It can be understood that the provided positioning points can obtain more accurate positioning information for the clustering clusters with more time or the clustering clusters with more positioning points.
And 105, determining the central positioning point in the optimal clustering cluster as a standard positioning point of the service object.
Specifically, the longitude and latitude values of each positioning point in the optimal clustering cluster are subjected to average value calculation, and the obtained central positioning point corresponding to the final average value is the standard positioning point of the merchant.
It can be understood that the standard positioning points are obtained by performing center point calculation through an optimal clustering method, a density clustering method is used in the embodiment of the present invention, and in practical applications, the clustering method is not limited to the density clustering method, so that the final standard positioning point is not necessarily the center positioning point calculated through an average value, for example, a score of each point is calculated through a weight of each longitude and latitude, a positioning point with the highest score is selected as the center positioning point and determined as the standard positioning point, and since a weight value is set by a relevant technician, the obtained center positioning point is not necessarily the center positioning point obtained by the average value of each point, and therefore, the concept of the center positioning point in the embodiment of the present invention is not limited to the center positioning point corresponding to the described average value.
In summary, an embodiment of the present invention provides a positioning method, where the method includes: acquiring multimedia data in behavior data of a user aiming at a business object; extracting the generation time and the positioning point of the multimedia data; clustering the positioning points to obtain one or more clustering clusters; determining an optimal clustering cluster according to the generation time and the number of positioning points in each clustering cluster; and determining the central positioning point in the optimal clustering cluster as a standard positioning point of the service object. The method and the device solve the problems of high error, poor timeliness and difficult execution in the prior positioning technology, and can determine the standard positioning point through the positioning point of the multimedia data in the user behavior data.
Example two
Referring to fig. 2, a flowchart illustrating specific steps of a positioning method according to a second embodiment of the present invention is shown.
this step is the same as step 101 and will not be described in detail here.
this step is the same as step 102 and will not be described in detail here.
And step 203, clustering the positioning points through a noise-based density clustering algorithm to obtain one or more cluster clusters.
In the embodiment of the present invention, as shown in the multimedia data processing flow chart shown in fig. 2A, the longitude and latitude points belonging to the same POI adopt DBSCAN clustering, where POI is an abbreviation of "Point of Interest" and chinese can be translated into "Point of Interest". In the geographic information system, one POI may be one house, one shop, one mailbox, one bus station, and the like.
In particular, Density-Based Clustering of applications with Noise (DBSCAN) is a relatively representative Density-Based Clustering algorithm. Unlike the partitioning and hierarchical clustering method, which defines clusters as the largest set of density-connected points, it is possible to partition areas with sufficiently high density into clusters and find clusters of arbitrary shape in a spatial database of noise.
Wherein, the preset cluster at least comprises 3 points, and the distance between the points is set to be 20 m.
Step 204, determining the cluster with the latest shooting time as a first cluster;
specifically, in all the obtained clusters, the number of days from the average shooting date to the present is calculated by combining the shooting time of the positioning point in each cluster, and the cluster closest to the present time is selected and marked as the first cluster.
specifically, the cluster with the largest number of anchor points is selected from the clustered clusters and marked as the second clustered cluster.
specifically, if the first cluster and the second cluster are the same, the cluster is selected as a final cluster, i.e., an optimal cluster.
Step 207, if the first cluster and the second cluster are different, acquiring a first positioning point number in the first cluster;
Specifically, if the first cluster and the second cluster are not the same, the number of positioning points of the cluster with the latest time is obtained, and if the number of the positioning points is greater than 1/3 (a second preset threshold) of the total number, the cluster with the latest time, namely the first cluster, is selected, otherwise, the cluster with the largest number, namely the second cluster, is selected and is the optimal cluster.
It can be understood that, in practical application, the above method is to cope with actions such as merchant relocation, and may combine better trade-offs of time and quantity, so that not only can the change be perceived, but also a certain confidence level can be ensured.
specifically, the number of localization points in the optimal cluster is discretized by combining a fibonacci sequence (golden section method), wherein the fibonacci sequence is as follows: 1123581321345589144 … … since a minimum of 3 points per cluster are defined, a corresponding scoring strategy is developed based on the number of discrete localization points, as described below.
specifically, the preset scoring strategy is that the number of discrete points is 3-5 to 10, 5-8 to 20, 8-13 to 30, 13-21 to 40, and so on, and 89-144 to 90 to … ….
The method has the advantages that continuous data are discretized in a golden section mode, and compared with geometric partitioning, the method is low-frequency sensitive and more suitable for current application scenarios.
Specifically, if a score curve composed of scores obtained by discretizing in the optimal cluster is too low and is lower than a first preset threshold set by a relevant technician, it indicates that the locating point in the optimal cluster is not accurate enough, and a prompt message is sent to the user to indicate that the locating point may not be accurate enough.
It can be understood that, after obtaining the prompt, the background technician may take corresponding measures to optimize the score, for example, pushing the address information corresponding to the cluster to the merchant for modification, performing manual review, or pushing the user for modification.
in the embodiment of the invention, each positioning point in the determined optimal clustering cluster has a longitude and latitude value, and the longitude and latitude value of each point is extracted for operation.
specifically, the ratio of the sum of the longitude values of the anchor points to the number of anchor points is the longitude average, and the latitude average is calculated in the same manner.
specifically, a positioning point is determined according to the longitude average and the latitude average, and the positioning point is the central positioning point in the optimal clustering cluster.
Step 216, determining the central positioning point as a standard positioning point of the service object.
Specifically, the central positioning point is determined as a standard positioning point of the business object, i.e. the merchant.
It will be appreciated that the canonical anchor point, derived in conjunction with clustering and time, does not depend on a description of the address.
The latitude and longitude of the latest positioning point can be obtained at any time, and the standard positioning point obtained by the mean value calculation mode has high accuracy and is simple to execute and independent of manual intervention.
In summary, an embodiment of the present invention provides a positioning method, where the method includes: acquiring multimedia data in behavior data of a user aiming at a business object; extracting the generation time and the positioning point of the multimedia data; clustering the positioning points to obtain one or more clustering clusters; determining an optimal clustering cluster according to the generation time and the number of positioning points in each clustering cluster; discretizing the positioning points in the optimal clustering cluster according to a golden section method to obtain discrete positioning points; scoring the discrete positioning points by using a preset scoring strategy to obtain positioning scores of the discrete positioning points; and if the positioning score is lower than a first preset threshold value, sending prompt information to the user. And determining the central positioning point in the optimal clustering cluster as a standard positioning point of the service object. The method and the device solve the problems of high error, poor timeliness and difficult execution in the prior positioning technology, and can determine the standard positioning point through the positioning point of the multimedia data in the user behavior data. In addition, the locating point discretization and scoring are carried out through the golden section method, and the accuracy of the locating point can be determined through the score value and is referred to the user.
EXAMPLE III
Referring to fig. 3, a structural diagram of a positioning apparatus according to a third embodiment of the present invention is shown, which is as follows.
A multimedia data obtaining module 301, configured to obtain multimedia data in behavior data of a user for a service object;
a data information obtaining module 302, configured to extract a generation time and a location point of the multimedia data;
a clustering module 303, configured to cluster the anchor points to obtain one or more cluster clusters;
an optimal cluster determining module 304, configured to determine an optimal cluster according to the generation time and the number of positioning points in each cluster;
a standard positioning point determining module 305, configured to determine, according to the central positioning point in the optimal clustering cluster, a standard positioning point of the service object.
In summary, an embodiment of the present invention provides a positioning apparatus, including: the multimedia data acquisition module is used for acquiring multimedia data in the behavior data of the user aiming at the service object; the data information acquisition module is used for extracting the generation time and the positioning point of the multimedia data; the clustering module is used for clustering the positioning points to obtain one or more clustering clusters; the optimal clustering cluster determining module is used for determining an optimal clustering cluster according to the generation time and the positioning points in each clustering cluster; and the standard positioning point determining module is used for determining the standard positioning point of the service object according to the central positioning point in the optimal clustering cluster. The method and the device solve the problems of high error, poor timeliness and difficult execution in the prior positioning technology, and can determine the standard positioning point through the positioning point of the multimedia data in the user behavior data.
The third embodiment is a corresponding apparatus embodiment to the first embodiment, and the detailed information may refer to the detailed description of the first embodiment, which is not described herein again.
Example four
Referring to fig. 4, a structural diagram of a positioning apparatus according to a fourth embodiment of the present invention is shown, which is as follows.
A multimedia data obtaining module 401, configured to obtain multimedia data in behavior data of a user for a service object;
a data information obtaining module 402, configured to extract a generation time and a location point of the multimedia data;
a clustering module 403, configured to cluster the anchor points to obtain one or more cluster clusters;
preferably, the clustering module 403 includes:
and the clustering submodule is used for clustering the positioning points through a noise-based density clustering algorithm to obtain one or more clustering clusters.
An optimal cluster determining module 404, configured to determine an optimal cluster according to the generation time and the number of positioning points in each cluster;
preferably, the optimal cluster determining module 404 includes:
the first clustering sub-module is used for determining the clustering cluster with the latest shooting time as a first clustering cluster;
the second cluster sub-module is used for determining the cluster with the largest number of positioning points in the clusters as a second cluster;
the optimal clustering cluster determining sub-module is used for determining the clustering cluster as the optimal clustering cluster if the first clustering cluster is the same as the second clustering cluster;
a first positioning point number obtaining sub-module, configured to obtain a first positioning point number in the first cluster if the first cluster is different from the second cluster;
a first optimal clustering cluster determining submodule, configured to determine the first clustering cluster as an optimal clustering cluster if the number of the first certain location points exceeds a second preset threshold;
and the second optimal clustering cluster determining submodule is used for determining the second clustering cluster as the optimal clustering cluster if the number of the first positioning points does not exceed a second preset threshold value.
A discretization module 405, configured to discretize the number of positioning points in the optimal clustering cluster according to a golden section method to obtain a discretization positioning point;
a positioning score obtaining module 406, configured to score the discrete positioning points by using a preset scoring policy to obtain a positioning score of the discrete positioning points;
and the prompt module 407 is configured to send a prompt message to the user if the positioning score is lower than a first preset threshold.
And a standard positioning point determining module 407, configured to determine, according to the central positioning point in the optimal clustering cluster, a standard positioning point of the service object.
Preferably, the standard positioning point determining module 407 includes:
the longitude and latitude value acquisition submodule is used for acquiring the longitude and latitude values of all positioning points in the optimal clustering cluster;
the average longitude and latitude value operator module is used for calculating the average longitude and latitude value of the longitude and latitude values;
the central positioning point obtaining submodule is used for obtaining the central positioning point in the optimal clustering cluster according to the average longitude and latitude value;
and the standard positioning point determining submodule is used for determining the central positioning point as the standard positioning point of the service object.
In summary, an embodiment of the present invention provides a positioning apparatus, where the apparatus includes a multimedia data obtaining module, configured to obtain multimedia data in behavior data of a user for a service object; the data information acquisition module is used for extracting the generation time and the positioning point of the multimedia data; the clustering module is used for clustering the positioning points to obtain one or more clustering clusters; the optimal clustering cluster determining module is used for determining an optimal clustering cluster according to the generation time and the positioning points in each clustering cluster; the discretization module is used for discretizing the positioning points in the optimal clustering cluster according to a golden section method to obtain discrete positioning points; the positioning score acquisition module is used for scoring the discrete positioning points by using a preset scoring strategy to obtain the positioning scores of the discrete positioning points; and the prompt module is used for sending prompt information to the user if the positioning score is lower than a first preset threshold value. And the standard positioning point determining module is used for determining the standard positioning point of the service object according to the central positioning point in the optimal clustering cluster. And determining the central positioning point in the optimal clustering cluster as a standard positioning point of the service object. The method and the device solve the problems of high error, poor timeliness and difficult execution in the prior positioning technology, and can determine the standard positioning point through the positioning point of the multimedia data in the user behavior data. Besides, the locating point discretization and scoring are carried out by the golden section method, the accuracy of the locating point can be determined through the score, and the user is given reference
The fourth embodiment is a device embodiment corresponding to the second embodiment, and the detailed information may refer to the detailed description of the second embodiment, which is not repeated herein.
An embodiment of the present invention further provides an electronic device, including: a processor, a memory and a computer program stored on the memory and executable on the processor, the processor implementing the aforementioned method when executing the program.
Embodiments of the present invention also provide a readable storage medium, and when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the foregoing method.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a positioning apparatus according to embodiments of the present invention. The present invention may also be embodied as an apparatus or device program for carrying out a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method of positioning, the method comprising:
acquiring multimedia data in behavior data of a user aiming at a business object, wherein the multimedia data is multimedia data providing the user positioning information;
extracting the generation time and the positioning point of the multimedia data;
clustering the positioning points according to the same POI to obtain one or more clustering clusters;
determining an optimal clustering cluster according to the generation time and the number of positioning points in each clustering cluster;
determining a standard positioning point of the service object according to the center in the optimal clustering cluster;
the method further comprises the following steps:
discretizing the positioning points in the optimal clustering cluster according to a golden section method to obtain discrete positioning points;
scoring the discrete positioning points by using a preset scoring strategy to obtain positioning scores of the discrete positioning points;
and if the positioning score is lower than a first preset threshold value, sending prompt information to the user.
2. The method according to claim 1, wherein the step of clustering the anchor points to obtain one or more cluster clusters comprises:
and clustering the positioning points according to the same POI (point of interest) by using a noise density-based clustering algorithm to obtain one or more clustering clusters.
3. The method according to claim 1, wherein the step of determining an optimal cluster according to the generation time and the number of the positioning points in each cluster comprises:
determining the cluster with the latest generation time as a first cluster;
determining the cluster with the largest number of positioning points in the clusters as a second cluster;
if the first cluster is the same as the second cluster, determining the cluster as an optimal cluster;
if the first cluster is different from the second cluster, acquiring the number of first positioning points in the first cluster;
if the number of the first fixed positions exceeds a second preset threshold value, determining the first clustering cluster as an optimal clustering cluster;
and if the number of the first positioning points does not exceed a second preset threshold value, determining the second cluster as an optimal cluster.
4. The method according to claim 3, wherein the step of determining a central positioning point in the optimal cluster as a standard positioning point of the business object comprises:
acquiring warp and weft values of positioning points in the optimal clustering cluster;
calculating an average warp and weft value of the warp and weft values;
acquiring a central positioning point in the optimal clustering cluster according to the average warp and weft values;
and determining the central positioning point as a standard positioning point of the service object.
5. A positioning device, the device comprising:
the multimedia data acquisition module is used for acquiring multimedia data in behavior data of a user aiming at a service object, wherein the multimedia data is the multimedia data for providing the user positioning information;
the data information acquisition module is used for extracting the generation time and the positioning point of the multimedia data;
the clustering module is used for clustering the positioning points according to the same POI to obtain one or more clustering clusters;
the optimal clustering cluster determining module is used for determining an optimal clustering cluster according to the generation time and the positioning points in each clustering cluster;
a standard positioning point determining module, configured to determine a standard positioning point of the service object according to a central positioning point in the optimal clustering cluster;
the device further comprises:
the discretization module is used for discretizing the positioning points in the optimal clustering cluster according to a golden section method to obtain discrete positioning points;
the positioning score acquisition module is used for scoring the discrete positioning points by using a preset scoring strategy to obtain the positioning scores of the discrete positioning points;
and the prompt module is used for sending prompt information to the user if the positioning score is lower than a first preset threshold value.
6. The apparatus of claim 5, wherein the clustering module comprises:
and the clustering submodule is used for clustering the positioning points according to the same POI (point of interest) by a noise density-based clustering algorithm to obtain one or more clustering clusters.
7. The apparatus of claim 5, wherein the optimal cluster determining module comprises:
the first clustering sub-module is used for determining the clustering cluster with the latest generation time as a first clustering cluster;
the second cluster sub-module is used for determining the cluster with the largest number of positioning points in the clusters as a second cluster;
the optimal clustering cluster determining sub-module is used for determining the clustering cluster as the optimal clustering cluster if the first clustering cluster is the same as the second clustering cluster;
a first positioning point number obtaining sub-module, configured to obtain a first positioning point number in the first cluster if the first cluster is different from the second cluster;
a first optimal clustering cluster determining submodule, configured to determine the first clustering cluster as an optimal clustering cluster if the number of the first certain location points exceeds a second preset threshold;
and the second optimal clustering cluster determining submodule is used for determining the second clustering cluster as the optimal clustering cluster if the number of the first positioning points does not exceed a second preset threshold value.
8. The apparatus according to claim 7, wherein said normative positioning point determining module comprises:
the longitude and latitude value acquisition submodule is used for acquiring the longitude and latitude values of all positioning points in the optimal clustering cluster;
the average longitude and latitude value operator module is used for calculating the average longitude and latitude value of the longitude and latitude values;
the central positioning point obtaining submodule is used for obtaining the central positioning point in the optimal clustering cluster according to the average longitude and latitude value;
and the standard positioning point determining submodule is used for determining the central positioning point as the standard positioning point of the service object.
9. An electronic device, comprising:
processor, memory and computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to one or more of claims 1-4 when executing the program.
10. A readable storage medium, characterized in that instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method according to one or more of method claims 1-4.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811075583.9A CN109522374B (en) | 2018-09-14 | 2018-09-14 | Positioning method, positioning device, electronic equipment and readable storage medium |
| PCT/CN2019/092354 WO2020052312A1 (en) | 2018-09-14 | 2019-06-21 | Positioning method and apparatus, electronic device, and readable storage medium |
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| CN109522374B (en) * | 2018-09-14 | 2020-04-17 | 北京三快在线科技有限公司 | Positioning method, positioning device, electronic equipment and readable storage medium |
| CN111737374B (en) * | 2019-06-21 | 2023-05-09 | 腾讯科技（深圳）有限公司 | Position coordinate determination method, device, electronic equipment and storage medium |
| CN110888866B (en) * | 2019-12-05 | 2023-01-17 | 秒针信息技术有限公司 | Data expansion method and device, data processing equipment and storage medium |
| CN111079859B (en) * | 2019-12-31 | 2020-12-04 | 哈尔滨工程大学 | Passive multi-station multi-target direction finding cross positioning and false point removing method |
| CN111898624B (en) * | 2020-01-21 | 2024-04-02 | 北京畅行信息技术有限公司 | Method, device, equipment and storage medium for processing positioning information |
| CN111667026B (en) * | 2020-06-30 | 2022-11-25 | 成都新潮传媒集团有限公司 | Debugging method and device for geographic position of multimedia equipment |
| CN113342915A (en) * | 2021-06-22 | 2021-09-03 | 深圳壹账通智能科技有限公司 | Address desensitization method, device, electronic equipment and storage medium |
| CN117457114B (en) * | 2023-12-25 | 2024-03-12 | 大连锦辉盛世科技有限公司 | Intelligent storage management method for western medicine pharmaceutical composition data |
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| CN104584094A (en) * | 2012-06-29 | 2015-04-29 | 通腾发展德国公司 | Location estimation method and system |
| CN104133845A (en) * | 2014-06-30 | 2014-11-05 | 百度在线网络技术（北京）有限公司 | Multimedia information display method and multimedia information display processing equipment |
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| WO2020052312A1 (en) | 2020-03-19 |
| CN109522374A (en) | 2019-03-26 |
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