CN113420067B - Method and device for evaluating position credibility of target site - Google Patents

Method and device for evaluating position credibility of target site Download PDF

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Publication number
CN113420067B
CN113420067B CN202110690763.3A CN202110690763A CN113420067B CN 113420067 B CN113420067 B CN 113420067B CN 202110690763 A CN202110690763 A CN 202110690763A CN 113420067 B CN113420067 B CN 113420067B
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target
point
coordinates
cluster
cluster center
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CN113420067A (en
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祝夢卿
白冰
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Seashell Housing Beijing Technology Co Ltd
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Seashell Housing Beijing Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases

Abstract

The embodiment of the disclosure discloses a method and a device for evaluating the position credibility of a target place, a computer-readable storage medium and electronic equipment, wherein the method comprises the following steps: acquiring a position data set sent by a target user terminal cluster in a target area; clustering the point set represented by the position data set based on the coordinate data to obtain a target cluster; for a point included in the target cluster, determining the weight of the point based on attribute data corresponding to the point; determining a cluster center point of the target cluster based on the weights and coordinates corresponding to the points in the target cluster respectively; and generating matching degree information of the cluster center point and the target point based on the coordinates of the cluster center point and the coordinates of the pre-recorded target point. Compared with the prior art that the coordinate data is required to be manually uploaded to update the coordinates of the target site, the method and the device for analyzing the coordinate accuracy of the target site are higher in efficiency and lower in cost.

Description

Method and device for evaluating position credibility of target site
Technical Field
The disclosure relates to the technical field of computers, in particular to a method and a device for evaluating position credibility of a target place, a computer readable storage medium and electronic equipment.
Background
In LBS (location based services ) based user behavior analysis, the matching relationship between the user's spatiotemporal trajectory and the position of the POI (point of interest ) is the most basic one. When the POI coordinate data is deviated due to expiration, artificial interference, missing and the like, a reliable user behavior analysis conclusion cannot be supported.
Disclosure of Invention
The embodiment of the disclosure provides a position credibility evaluation method and device of a target place, a computer readable storage medium and electronic equipment.
The embodiment of the disclosure provides a position credibility evaluation method of a target place, which comprises the following steps: acquiring a position data set sent by a target user terminal cluster in a target area, wherein the position data in the position data set comprises coordinate data and attribute data; clustering the point set represented by the position data set based on the coordinate data to obtain a target cluster; for a point included in the target cluster, determining the weight of the point based on attribute data corresponding to the point; determining a cluster center point of the target cluster based on the weights and coordinates corresponding to the points in the target cluster respectively; and generating matching degree information of the cluster center point and the target point based on the coordinates of the cluster center point and the pre-recorded coordinates of the target point, wherein the matching degree information is used for representing the matching degree of taking the coordinates of the cluster center point as the coordinates of the target point.
In some embodiments, before acquiring the set of location data transmitted by the target user terminal cluster within the target area, the method further comprises: acquiring position data representing the position of a target user terminal received in a target history period; dividing longitude and latitude grids of a preset map based on preset longitude and latitude intervals to obtain a longitude and latitude grid set; determining target grids from the longitude and latitude grid set based on position data respectively corresponding to the longitude and latitude grids in the longitude and latitude grid set, wherein the number of target user terminals meeting preset conditions in the area represented by the target grids is greater than or equal to the preset number; and determining a target area corresponding to the target place based on the target grid.
In some embodiments, determining a target area corresponding to a target location based on a target grid includes: and determining the target grid and the area corresponding to the adjacent grid of the target grid as the target area corresponding to the target place.
In some embodiments, determining the weight of the point based on the attribute data corresponding to the point includes: determining state quantization values respectively corresponding to at least one attribute data corresponding to the point; and determining the weight of the point based on the preset sub-weight and the state quantization value corresponding to the at least one attribute data corresponding to the point.
In some embodiments, generating the matching degree information of the cluster center point and the target location based on the coordinates of the cluster center point and the coordinates of the pre-recorded target location includes: determining whether the coordinates of the cluster center point meet a preset stability condition or not to obtain a first determination result, wherein the stability condition is that the distance between the coordinates of the cluster center point determined last time and the coordinates of the cluster center point determined this time is smaller than or equal to a preset first distance threshold; determining whether the coordinates of the cluster center point meet a preset accurate condition or not to obtain a second determination result, wherein the accurate condition is that the distance between the coordinates of the cluster center point determined at the time and the coordinates of a pre-recorded target point is smaller than or equal to a preset second distance threshold; and generating matching degree information of the cluster center point and the target place based on the first determination result and the second determination result.
In some embodiments, generating the matching degree information of the cluster center point and the target location based on the first determination result and the second determination result includes: determining whether the first determination result and the second determination result meet a non-matching condition, wherein the non-matching condition is that the first determination result does not meet a stable condition, and the second determination result does not meet an accurate condition; if the mismatch condition is met, generating matching degree information for representing mismatch between the cluster center point and the target site; if the mismatch condition is not met, determining whether a position data subset which corresponds to the cluster center point and is contained in the position data set meets a preset threshold condition or not, and obtaining a third determination result; and generating matching degree information for representing the matching degree of the cluster center point and the target place based on the first determination result, the second determination result and the third determination result.
According to another aspect of the embodiments of the present disclosure, there is provided a position reliability evaluation device of a target site, the device including: the first acquisition module is used for acquiring a position data set sent by a target user terminal cluster in a target area, wherein the position data in the position data set comprises coordinate data and attribute data; the clustering module is used for clustering the point set represented by the position data set based on the coordinate data to obtain a target cluster; the first determining module is used for determining the weight of the point included in the target cluster based on the attribute data corresponding to the point; the second determining module is used for determining a cluster center point of the target cluster based on the weight and the coordinates corresponding to the points in the target cluster respectively; the generation module is used for generating matching degree information of the cluster center point and the target point based on the coordinates of the cluster center point and the coordinates of the pre-recorded target point, wherein the matching degree information is used for representing the matching degree of taking the coordinates of the cluster center point as the coordinates of the target point.
In some embodiments, the apparatus further comprises: a second acquisition module for acquiring position data representing the position of the target user terminal received in the target history period; the dividing module is used for dividing the longitude and latitude grid of the preset map based on the preset longitude and latitude interval to obtain a longitude and latitude grid set; the third determining module is used for determining target grids from the longitude and latitude grid set based on position data corresponding to the longitude and latitude grids in the longitude and latitude grid set respectively, wherein the number of target user terminals meeting preset conditions in the area represented by the target grids is larger than or equal to the preset number; and the fourth determining module is used for determining a target area corresponding to the target place based on the target grid.
In some embodiments, the fourth determination module is further to: and determining the target grid and the area corresponding to the adjacent grid of the target grid as the target area corresponding to the target place.
In some embodiments, the first determination module comprises: a first determining unit, configured to determine a state quantization value corresponding to at least one attribute data corresponding to the point; and the second determining unit is used for determining the weight of the point based on the preset sub-weight and the state quantization value which are respectively corresponding to the at least one attribute data corresponding to the point.
In some embodiments, the generating module comprises: a third determining unit, configured to determine whether the coordinates of the cluster center point meet a preset stability condition, to obtain a first determination result, where the stability condition is that a distance between the coordinates of the cluster center point determined last time and the coordinates of the cluster center point determined this time is less than or equal to a preset first distance threshold; a fourth determining unit, configured to determine whether the coordinates of the cluster center point meet a preset accurate condition, and obtain a second determination result, where the accurate condition is that a distance between the coordinates of the cluster center point determined this time and the coordinates of the pre-recorded target location is less than or equal to a preset second distance threshold; and the generating unit is used for generating the matching degree information of the cluster center point and the target place based on the first determination result and the second determination result.
In some embodiments, the generating unit comprises: the first determining subunit is used for determining whether the first determining result and the second determining result meet a mismatch condition, wherein the mismatch condition is that the first determining result does not meet a stable condition, and the second determining result does not meet an accurate condition; the first generation subunit is used for generating matching degree information for representing that the cluster center point is not matched with the target place if the mismatch condition is met; a second determining subunit, configured to determine whether a position data subset corresponding to the cluster center point and included in the position data set meets a preset threshold condition if the mismatch condition is not met, to obtain a third determination result; and the second generation subunit is used for generating matching degree information for representing the matching degree of the cluster center point and the target place based on the first determination result, the second determination result and the third determination result.
According to another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program for executing the above-described position reliability evaluation method of a target place.
According to another aspect of an embodiment of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; and the processor is used for reading the executable instructions from the memory and executing the instructions to realize the position credibility evaluation method of the target place.
According to another aspect of the disclosed embodiments, there is provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above-described method of estimating the position confidence of a target location.
According to the position reliability evaluation method, the device, the computer readable storage medium and the electronic equipment of the target location, the positions of the target user terminals in the target area are clustered, the weights of points included in the clustered target clusters are determined, the cluster center points are determined based on the weights, and finally matching degree information of the cluster center points and the target location is generated based on the coordinates of the cluster center points and the coordinates of the target location recorded in advance, so that whether the coordinates of the target location are matched with the coordinates of the cluster center points obtained through data analysis or not is automatically judged according to the uploaded position data, correction of the coordinates of the target location according to the coordinates of the cluster center points is facilitated, and compared with the situation that the coordinates of the target location are updated by manually uploading the coordinate data in the prior art, the accuracy analysis of the coordinates of the target location is higher in efficiency and lower in cost.
The technical scheme of the present disclosure is described in further detail below through the accompanying drawings and examples.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing embodiments thereof in more detail with reference to the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the disclosure, and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure, without limitation to the disclosure. In the drawings, like reference numerals generally refer to like parts or steps.
Fig. 1 is a system diagram to which the present disclosure is applicable.
Fig. 2 is a flow chart of a method for evaluating the position reliability of a target site according to an exemplary embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating a method for evaluating the position reliability of a target site according to another exemplary embodiment of the present disclosure.
Fig. 4 is a flowchart illustrating a method for evaluating the position reliability of a target site according to another exemplary embodiment of the present disclosure.
Fig. 5 is a flowchart illustrating a method for evaluating the position reliability of a target site according to another exemplary embodiment of the present disclosure.
Fig. 6 is a schematic structural diagram of a location reliability evaluation device for a target location according to an exemplary embodiment of the present disclosure.
Fig. 7 is a schematic structural view of a position reliability evaluation device for a target site according to another exemplary embodiment of the present disclosure.
Fig. 8 is a block diagram of an electronic device provided in an exemplary embodiment of the present disclosure.
Detailed Description
Hereinafter, example embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present disclosure and not all of the embodiments of the present disclosure, and that the present disclosure is not limited by the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless it is specifically stated otherwise.
It will be appreciated by those of skill in the art that the terms "first," "second," etc. in embodiments of the present disclosure are used merely to distinguish between different steps, devices or modules, etc., and do not represent any particular technical meaning nor necessarily logical order between them.
It should also be understood that in embodiments of the present disclosure, "plurality" may refer to two or more, and "at least one" may refer to one, two or more.
It should also be appreciated that any component, data, or structure referred to in the presently disclosed embodiments may be generally understood as one or more without explicit limitation or the contrary in the context.
In addition, the term "and/or" in this disclosure is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" in the present disclosure generally indicates that the front and rear association objects are an or relationship.
It should also be understood that the description of the various embodiments of the present disclosure emphasizes the differences between the various embodiments, and that the same or similar features may be referred to each other, and for brevity, will not be described in detail.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Embodiments of the present disclosure may be applicable to electronic devices such as terminal devices, computer systems, servers, etc., which may operate with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with the terminal device, computer system, server, or other electronic device include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the above systems, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer system/server may be implemented in a distributed cloud computing environment in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Summary of the application
At present, the updating of the coordinate information of the POI mainly depends on manual data uploading, and the cost and the efficiency are lower. Therefore, an inexpensive and efficient coordinate estimation and correction scheme is needed.
Exemplary System
Fig. 1 illustrates an exemplary system architecture 100 of a target site location reliability assessment method or a target site location reliability assessment apparatus to which embodiments of the present disclosure may be applied.
As shown in fig. 1, a system architecture 100 may include a cluster of terminal devices 101, a network 102, and a server 103. The network 102 is used as a medium for providing communication links between the cluster of terminal devices 101 and the server 103. Network 102 may include various connection types such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 103 via the network 102 using terminal devices in the cluster of terminal devices 101 to receive or send messages or the like. The terminal device may have various communication client applications installed thereon, such as a map-type application, a search-type application, a web browser application, a shopping-type application, an instant messaging tool, and the like.
The terminal device may be various electronic devices having a positioning function, including, but not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like.
The server 103 may be a server providing various services, such as a background server analyzing the location data uploaded by the cluster of terminal devices 101. The background server may perform clustering and other processing on the received position data set to obtain a processing result (for example, coordinates of a cluster center point, matching degree information, and the like).
It should be noted that, the method for evaluating the position reliability of the target location provided by the embodiments of the present disclosure may be performed by the server 103, or may be performed by a certain terminal device in the terminal device cluster 101, and accordingly, the device for evaluating the position reliability of the target location may be set in the server 103, or may be set in a certain terminal device in the terminal device cluster 101.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Exemplary method
Fig. 2 is a flow chart of a method for evaluating the position reliability of a target site according to an exemplary embodiment of the present disclosure. The present embodiment may be applied to an electronic device (such as the terminal device or the server 103 included in the terminal device cluster 101 shown in fig. 1), and as shown in fig. 2, the method includes the following steps:
Step 201, acquiring a position data set sent by a target user terminal cluster in a target area.
In this embodiment, the electronic device may acquire the location data set sent by the target user terminal cluster in the target area. Wherein the location data in the location data set comprises coordinate data and attribute data.
The target area may be an area defined in advance on the map, or may be an area selected from the map based on the distribution of the position data set (for example, a grid having the largest number of points represented by position data included in a plurality of grids defined in advance is defined as the target area). As an example, the target area may be an area where a certain store is located.
The target user terminal cluster may be a cluster of terminal devices in the terminal device cluster 101 as shown in fig. 1. The target user terminal may be a terminal used by a user of the target type, for example, a terminal used by a staff member of a certain store.
The location data in the set of location data may be uploaded to the electronic device by the target user terminal, and one target user terminal may correspond to at least one location data. For example, a target user terminal may send location data to an electronic device at intervals. The location data may include coordinate data generated by the positioning system for positioning the target user terminal in real time, typically longitude and latitude coordinates. The attribute data is used to characterize the attributes of the target user terminal at the current location, and may include, by way of example, but not be limited to, at least one of the following: positioning accuracy, current moving speed and height of a target user terminal, identification of WiFi (wireless fidelity) connected with the target user terminal, IP (Internet protocol) address, IP address of an external network outlet, equipment code of the target user terminal, timestamp of current time, user identification, address where the target user terminal is currently located and the like.
Step 202, clustering the point set represented by the position data set based on the coordinate data to obtain a target cluster.
In this embodiment, the electronic device may cluster the point set represented by the location data set based on the coordinate data, to obtain the target cluster.
The clustering of the point sets can be achieved by adopting an existing clustering algorithm, for example, a DBScan algorithm can be adopted to cluster the point sets represented by the position data sets. In general, at least one cluster can be obtained after clustering the point set, and one cluster can be selected as a target cluster, for example, a cluster having the largest number of points included therein can be used as a target cluster, or a cluster having the largest density of points included therein can be used as a target cluster.
In step 203, for a point included in the target cluster, a weight of the point is determined based on attribute data corresponding to the point.
In this embodiment, for a point included in the target cluster, the electronic device may determine the weight of the point based on attribute data corresponding to the point. The electronic device may determine the weight of each point in the target cluster, and may also determine the weight of certain points in the target cluster (e.g., randomly selecting a preset number of points and determining the weight of each point selected).
Wherein the weights are used to characterize the extent to which attribute data generated at a location contributes to determining coordinates of the target location. As an example, if it is determined from the attribute data that the coordinates of a certain point are located indoors, a high weight is set for the point, and if it is located outdoors, a low weight is set for the point.
And 204, determining a cluster center point of the target cluster based on the weights and coordinates corresponding to the points in the target cluster.
In this embodiment, the electronic device may determine the cluster center point of the target cluster based on the weights and coordinates corresponding to the points in the target cluster, respectively. Specifically, the coordinates (i.e., longitude and latitude) of each point in the target cluster may be weighted and averaged by using the weight corresponding to each point, and the obtained coordinates are the coordinates of the cluster center point.
In step 205, matching degree information between the cluster center point and the target point is generated based on the coordinates of the cluster center point and the coordinates of the pre-recorded target point.
In this embodiment, the electronic device may generate the matching degree information of the cluster center point and the target location based on the coordinates of the cluster center point and the coordinates of the pre-recorded target location. The matching degree information is used for representing the matching degree of taking the coordinates of the cluster center point as the coordinates of the target point.
The matching degree information may be various types of information, for example, may be scores, and the higher the score, the higher the matching degree; the level information may be the level information, and the higher the level, the higher the matching degree.
The target location may be a preset location, for example, an address or an identification of a store, which has preset coordinates. As an example, the electronic device may determine a distance between coordinates of the cluster center point and coordinates of the target location, and determine matching degree information of the cluster center point and the target location according to the distance. For example, the matching degree information may be grade information, where the grade information has a preset correspondence with the distance, and the larger the distance is, the smaller the matching degree between the cluster center point and the target location is, and the lower the corresponding grade is. If the distance is greater than or equal to the preset distance, matching degree information indicating that the cluster center point is not matched with the target place can be generated.
According to the method provided by the embodiment of the disclosure, the positions of the target user terminals in the target area are clustered, the weights of points included in the clustered target clusters are determined, the cluster center points are determined based on the weights, and finally the matching degree information of the cluster center points and the target points is generated based on the coordinates of the cluster center points and the coordinates of the pre-recorded target points, so that whether the coordinates of the target points are matched with the coordinates of the cluster center points obtained through data analysis or not is automatically judged according to the uploaded position data, the coordinates of the target points are corrected according to the coordinates of the cluster center points, and compared with the prior art that the coordinates of the target points are updated by manually uploading the coordinate data, the method is higher in efficiency and lower in cost in analyzing the coordinates of the target points.
In some alternative implementations, in step 203, for a point included in the target cluster, the weight of the point may be determined as follows:
first, a state quantization value corresponding to at least one attribute data corresponding to the point is determined.
The state quantization value is a value obtained by quantizing the current state of a certain attribute of the point. As an example, the at least one attribute data includes: attribute data indicating whether the point is indoors, attribute data indicating whether the target user terminal corresponding to the point scans to store WiFi or surrounding WiFi, attribute data indicating whether the target user terminal corresponding to the point is connected to store WiFi, and the like, and if the certain attribute data indicates yes, the corresponding quantization state value is 1, and if no, the corresponding quantization state value is-1.
For attribute data representing GPS accuracy: if the GPS precision is greater than 200 meters, the corresponding quantized state value is-1; if less than or equal to 200 meters, the corresponding quantization status value is 1- (precision/200 meters).
For attribute data representing height: if the height of the store is 3 meters or less (one floor height), the corresponding quantized state value is 1, if the height of the store is more than 3 meters, the corresponding quantized state value is-1, and if the attribute data representing the height is invalid or does not represent the attribute data of the height, the corresponding quantized state value is 0.
For attribute data representing speed: if the moving speed of the point is less than 1.5m/s (the walking speed of the person), the corresponding quantized state value is 1, otherwise, the quantized state value is-1.
Then, the weight of the point is determined based on the preset sub-weight and the state quantization value corresponding to at least one attribute data corresponding to the point.
The preset sub-weights corresponding to the attribute data represent the importance degrees of the corresponding quantization status values. As an example, the preset sub-weight corresponding to the attribute data indicating whether the point is indoors is 0.15; whether the target user terminal corresponding to the point scans the attribute data of the store WiFi or surrounding WiFi or not is indicated that the preset sub-weight corresponding to the attribute data of the store WiFi or surrounding WiFi is 0.15; whether the target user terminal corresponding to the point is connected to the store WiFi or not is indicated, and the preset sub-weight corresponding to the attribute data of the store WiFi is 0.15; the preset sub-weight corresponding to the attribute data representing the GPS precision is 0.4; the preset sub-weight corresponding to the height attribute data is 0.05; the preset sub-weight corresponding to the attribute data representing the GPS speed is 0.1. The sum of the above-described respective preset sub-weights is 1, and therefore, the weights of the points in the target cluster can be generally determined using the following weighted sum formula:
W＝w i ×χ i
wherein W is the weight of the point, W i For the preset sub-weight corresponding to the ith attribute data, χ i And the state quantization value corresponding to the ith attribute data.
According to the method, the state quantization values respectively corresponding to at least one attribute data of the points in the target cluster are determined, the weight of the points is determined based on the preset sub-weights corresponding to the state quantization values, the contribution degree of each attribute data to the weight of the determined points can be fully reflected, so that more important attribute data are reflected in the weight of the points, and the pertinence and the accuracy of the weight of the determined points are improved.
In some alternative implementations, as shown in fig. 3, step 205 may be performed as follows:
step 2051, determining whether the coordinates of the cluster center point meet a preset stable condition, and obtaining a first determination result.
The stability condition is that a distance between the coordinates of the cluster center point determined last time and the coordinates of the cluster center point determined this time is smaller than or equal to a preset first distance threshold (for example, a radius of activity of a worker in a store around the store is taken). The method for determining the coordinates of the cluster center point last time is the same as the method for determining the cluster center point this time, and therefore, if the distance between the cluster centers determined twice is equal to or less than the first distance threshold, the coordinates of the cluster center point can be considered to be stable.
Step 2052, determining whether the coordinates of the cluster center point meet a preset accurate condition, and obtaining a second determination result.
The accuracy condition is that the distance between the coordinates of the cluster center point determined at the time and the coordinates of the pre-recorded target point is smaller than or equal to a preset second distance threshold. The second distance threshold may be the same as or different from the first distance threshold. The coordinates of the target point recorded in advance may be manually set coordinates or coordinates of a cluster center point once determined recorded as coordinates of the target point.
Step 2053, generating matching degree information of the cluster center point and the target location based on the first determination result and the second determination result.
As an example, if the first determination result indicates that the stable condition is not satisfied and the second determination result indicates that the accurate condition is not satisfied, matching degree information (for example, number 0) indicating that the cluster center point does not match the target place is generated. If the first determination result indicates that the stable condition is met and the second determination result indicates that the accurate condition is not met, or the first determination result indicates that the stable condition is not met and the second determination result indicates that the accurate condition is met, matching degree information (for example, numeral 1) indicating that the cluster center point matches the target point to a medium degree is generated. If the first determination result indicates that the stable condition is met and the second determination result indicates that the accurate condition is met, matching degree information (for example, numeral 2) indicating a complete match of the cluster center point and the target place is generated.
The implementation method determines the matching degree information by determining whether the coordinates of the cluster center points meet the stability condition and the accuracy condition, so that the matching degree information can accurately reflect whether the coordinates of the cluster center points are stable and accurate, and the accuracy of evaluating the coordinates of the target site is improved.
In some alternative implementations, as shown in fig. 4, step 2053 may be performed as follows:
step 20531, it is determined whether the first determination result and the second determination result satisfy the mismatch condition.
If so, step 20532 is performed, and if not, step 20533 is performed.
The unmatched condition is that the first determination result indicates that the stable condition is not met, and the second determination result indicates that the accurate condition is not met.
Step 20532, if the mismatch condition is satisfied, matching degree information for characterizing the mismatch between the cluster center point and the target site is generated.
As an example, the matching degree information indicating the mismatch may be digital 0, or a type of information such as a text prompt.
And 20533, if the mismatch condition is not satisfied, determining whether the position data subset corresponding to the cluster center point and included in the position data set meets a preset threshold condition, and obtaining a third determination result.
The subset of position data included in the set of position data may be set arbitrarily, and may be, for example, a set of position data corresponding to points included in the target cluster, or a set of position data corresponding to points within a pre-divided region (e.g., a pre-divided longitude and latitude grid including a cluster center point of the target cluster). In general, statistics of the number of the target users, the number of the position points of the target users, the number of average person uploading points of the target users and the like can be performed by using the position data included in the position data subset, if the number of the statistics is greater than or equal to a preset number threshold, the situation that the threshold condition is met is determined, and otherwise, the threshold condition is not met.
As an example, the number of people of the target user, the number of location points of the target user, and the number of people upload points of the target user respectively correspond to a preset number threshold, and if all three numbers are greater than or equal to the corresponding number threshold, it is determined that the threshold condition is met, or at least one of the three numbers is greater than or equal to the corresponding number threshold, and it is determined that the threshold condition is met. The threshold condition may be set arbitrarily, and embodiments of the present disclosure are not limited.
Step 20534, generating matching degree information for characterizing a matching degree of the cluster center point and the target location based on the first determination result, the second determination result, and the third determination result.
By the third determination result, each case that does not satisfy the above-described mismatch condition can be divided into two levels, respectively. As an example, the matching degree information may be divided into seven levels, wherein level 7 may indicate that the coordinates of the cluster center point are neither stable nor accurate. Class 1-class 6 respectively represent:
class 1: the coordinates of the cluster center points are stable and accurate and meet the threshold condition;
class 2: the coordinates of the cluster center points are stable and accurate and do not meet the threshold condition;
grade 3: the coordinates of the cluster center points are stable but inaccurate and meet the threshold condition;
grade 4: the coordinates of the cluster center points are stable but inaccurate and do not meet the threshold condition;
grade 5: the coordinates of the cluster center points are unstable and accurate and meet the threshold condition;
grade 6: the coordinates of the cluster center point are unstable but accurate and do not meet the threshold condition.
In general, the above-described level may be applied to an evaluation of the position of a target site, and a user may perform various evaluations of the position of the target site with reference to the output level. For example, if the coordinates of the cluster center point satisfy the level 1-level 6, the coordinates of the cluster center point may be used as the coordinates of the target point, and the targets such as attendance management, positioning and tracking for the target user may be achieved by using the coordinates. If the coordinates of the cluster center point meet the level 7, the coordinates of the target location are determined to be unreliable, and the coordinates of the target location need to be further positioned.
According to the implementation method, the conditions that the first determination result and the second determination result do not meet the mismatch condition are further divided by setting the mismatch condition and the threshold condition, and the grade of the matching degree information is refined, so that the accuracy of the matching degree information is higher, and the position of the target site can be further evaluated more pertinently.
With further reference to fig. 5, a flow diagram of yet another embodiment of a method of location confidence assessment for a target place is shown. As shown in fig. 5, on the basis of the embodiment shown in fig. 2, before step 201, the following steps may be included:
step 501, location data representing the location of a target user terminal received within a target history period is obtained.
The target historical time period may be a time period of any preset duration, for example, the last 30 days. The target user terminal in this step may be a terminal of a user within a certain range. For example, the terminal may be used by staff of a company in a city.
Alternatively, the location data obtained in this step may be filtered location data, for example, location data uploaded outside the working time of each day (e.g., 8:00-18:00) and location data with larger errors (e.g., errors greater than 100 meters, errors may be included in the uploaded location data) may be filtered.
Step 502, dividing longitude and latitude grids of a preset map based on preset longitude and latitude intervals to obtain a longitude and latitude grid set.
Wherein, longitude and latitude interval can be set arbitrarily. As an example, the longitude and latitude interval may be set to 0.001, i.e., the degree interval between two warps and two wefts constituting each longitude and latitude grid is 0.001.
In step 503, a target grid is determined from the longitude and latitude grid set based on the position data corresponding to the longitude and latitude grids in the longitude and latitude grid set, respectively.
The number of target user terminals meeting preset conditions in the region represented by the target grid is greater than or equal to the preset number. The preset condition may be a condition set by a user screening the grid. As an example, the target user terminal meeting the preset condition may be: the number of location data reported during the target history period reaches a certain number (e.g., 125) of target user terminals. The number of the preset numbers and the number contained in the preset conditions can be obtained by counting the position data uploaded by a large number of target user terminals in advance. For example, it is found through statistics in advance that more than 85% of stores contain more than 5 staff reporting position data, and data points reported by people are not less than 125, so that the preset number can be set to 5, and the reporting number of position data included in the preset condition can be set to 125.
Step 504, determining a target area corresponding to the target location based on the target grid.
As an example, an area corresponding to the target grid may be taken as the target area.
In some alternative implementations, step 504 may be performed as follows:
and determining the target grid and the area corresponding to the adjacent grid of the target grid as the target area corresponding to the target place. Wherein adjacent grids of the target grid may include at least one of 8 grids located around the target grid, up, down, left, right, up left, down left, up right, down right. In general, a region corresponding to a nine grid cell composed of the 8 grids and the target grid may be used as the target region. According to the implementation mode, the target grids and the adjacent grids are used as target areas, the range of a position data set for subsequent clustering can be enlarged, and the accuracy of the cluster center point determined after clustering is higher.
According to the method provided by the corresponding embodiment of fig. 5, through dividing the longitude and latitude of the preset map, extracting the target grids from the divided grids and determining the target area according to the target grids, a large number of position data uploaded by the target user terminals can be screened more pertinently, the influence of outlier data on clustering is removed, and further accuracy of reliability assessment on coordinates of the target sites is improved.
Exemplary apparatus
Fig. 6 is a schematic structural diagram of a location reliability evaluation device for a target location according to an exemplary embodiment of the present disclosure. The present embodiment may be applied to an electronic device, as shown in fig. 6, where the location reliability evaluation device of the target location includes: a first obtaining module 601, configured to obtain a location data set sent by a target user terminal cluster in a target area, where location data in the location data set includes coordinate data and attribute data; the clustering module 602 is configured to cluster the point set represented by the location data set based on the coordinate data, to obtain a target cluster; a first determining module 603, configured to determine, for a point included in the target cluster, a weight of the point based on attribute data corresponding to the point; a second determining module 604, configured to determine a cluster center point of the target cluster based on the weights and coordinates corresponding to the points in the target cluster, respectively; the generating module 605 is configured to generate, based on the coordinates of the cluster center point and the coordinates of the pre-recorded target location, matching degree information of the cluster center point and the target location, where the matching degree information is used to characterize a matching degree of the coordinates of the cluster center point as the coordinates of the target location.
In this embodiment, the first obtaining module 601 may obtain a location data set sent by a target user terminal cluster in a target area. Wherein the location data in the location data set comprises coordinate data and attribute data.
The target area may be an area defined in advance on the map, or may be an area selected from the map based on the distribution of the position data set (for example, a grid having the largest number of points represented by position data included in a plurality of grids defined in advance is defined as the target area). As an example, the target area may be an area where a certain store is located.
The target user terminal cluster may be a cluster of terminal devices in the terminal device cluster 101 as shown in fig. 1. The target user terminal may be a terminal used by a user of the target type, for example, a terminal used by a staff member of a certain store.
The location data in the set of location data may be uploaded to the electronic device by the target user terminal, and one target user terminal may correspond to at least one location data. For example, a target user terminal may send location data to an electronic device at intervals. The location data may include coordinate data generated by the positioning system for positioning the target user terminal in real time, typically longitude and latitude coordinates. The attribute data is used to characterize the attributes of the target user terminal at the current location, and may include, by way of example, but not be limited to, at least one of the following: positioning accuracy, current moving speed and height of a target user terminal, identification of WiFi (wireless fidelity) connected with the target user terminal, IP (Internet protocol) address, IP address of an external network outlet, equipment code of the target user terminal, timestamp of current time, user identification, address where the target user terminal is currently located and the like.
In this embodiment, the clustering module 602 may cluster the point set represented by the location data set based on the coordinate data, to obtain the target cluster.
The clustering of the point sets can be achieved by adopting an existing clustering algorithm, for example, a DBScan algorithm can be adopted to cluster the point sets represented by the position data sets. In general, at least one cluster can be obtained after clustering the point set, and one cluster can be selected as a target cluster, for example, a cluster having the largest number of points included therein can be used as a target cluster, or a cluster having the largest density of points included therein can be used as a target cluster.
In this embodiment, the first determining module 603 may determine the weight of the point based on the attribute data corresponding to the point. The first determining module 603 may determine the weight of each point in the target cluster, and may also determine the weight of certain points in the target cluster (e.g., randomly selecting a preset number of points and determining the weight of each point selected).
Wherein the weights are used to characterize the extent to which attribute data generated at a location contributes to determining coordinates of the target location. As an example, if it is determined from the attribute data that the coordinates of a certain point are located indoors, a high weight is set for the point, and if it is located outdoors, a low weight is set for the point.
In this embodiment, the second determining module 604 may determine the cluster center point of the target cluster based on the weights and coordinates corresponding to the points in the target cluster, respectively. Specifically, the coordinates (i.e., longitude and latitude) of each point in the target cluster may be weighted and averaged by using the weight corresponding to each point, and the obtained coordinates are the coordinates of the cluster center point.
In this embodiment, the generating module 605 may generate the matching degree information of the cluster center point and the target location based on the coordinates of the cluster center point and the coordinates of the pre-recorded target location. The matching degree information is used for representing the matching degree of taking the coordinates of the cluster center point as the coordinates of the target point.
The matching degree information may be various types of information, for example, may be scores, and the higher the score, the higher the matching degree; the level information may be the level information, and the higher the level, the higher the matching degree.
The target location may be a preset location, for example, an address or an identification of a store, which has preset coordinates. As an example, the generation module 605 may determine a distance between coordinates of the cluster center point and coordinates of the target location, and determine the matching degree information of the cluster center point and the target location according to the distance. For example, the matching degree information may be grade information, where the grade information has a preset correspondence with the distance, and the larger the distance is, the smaller the matching degree between the cluster center point and the target location is, and the lower the corresponding grade is. If the distance is greater than or equal to the preset distance, matching degree information indicating that the cluster center point is not matched with the target place can be generated.
Referring to fig. 7, fig. 7 is a schematic structural view of a position reliability evaluation device of a target site according to another exemplary embodiment of the present disclosure.
In some alternative implementations, the apparatus may further include: a second obtaining module 606, configured to obtain location data representing a location of the target user terminal received in the target history period; the dividing module 607 is configured to divide a longitude and latitude grid of a preset map based on a preset longitude and latitude interval, so as to obtain a longitude and latitude grid set; a third determining module 608, configured to determine, from the longitude and latitude grid set, a target grid based on position data corresponding to the longitude and latitude grids in the longitude and latitude grid set, where the number of target user terminals meeting the preset condition in the area represented by the target grid is greater than or equal to the preset number; a fourth determining module 609 is configured to determine, based on the target grid, a target area corresponding to the target location.
In some alternative implementations, the fourth determination module 609 may further be configured to: and determining the target grid and the area corresponding to the adjacent grid of the target grid as the target area corresponding to the target place.
In some alternative implementations, the first determining module 603 may include: a first determining unit 6031 for determining a state quantization value respectively corresponding to at least one attribute data corresponding to the point; a second determining unit 6032 for determining the weight of the point based on the preset sub-weight and the state quantization value corresponding to the at least one attribute data corresponding to the point.
In some alternative implementations, the generation module 605 may include: a third determining unit 6051, configured to determine whether the coordinates of the cluster center point meet a preset stability condition, and obtain a first determination result, where the stability condition is that a distance between the coordinates of the cluster center point determined last time and the coordinates of the cluster center point determined this time is less than or equal to a preset first distance threshold; a fourth determining unit 6052, configured to determine whether the coordinates of the cluster center point meet a preset accuracy condition, and obtain a second determination result, where the accuracy condition is that a distance between the coordinates of the cluster center point determined this time and the coordinates of the pre-recorded target location is less than or equal to a preset second distance threshold; a generating unit 6053 for generating the matching degree information of the cluster center point and the target place based on the first determination result and the second determination result.
In some alternative implementations, the generating unit 6053 may include: a first determining subunit 60531, configured to determine whether the first determining result and the second determining result meet a mismatch condition, where the mismatch condition is that the first determining result indicates that the stability condition is not met, and the second determining result indicates that the accuracy condition is not met; a first generating subunit 60532, configured to generate matching degree information for characterizing that the cluster center point does not match the target location if the mismatch condition is satisfied; a second determining subunit 60533, configured to determine whether a subset of the location data included in the location data set, corresponding to the cluster center point, meets a preset threshold condition if the mismatch condition is not met, to obtain a third determination result; the second generating subunit 60534 is configured to generate, based on the first determining result, the second determining result, and the third determining result, matching degree information for characterizing a matching degree of the cluster center point and the target location.
According to the position reliability evaluation device for the target location, provided by the embodiment of the disclosure, the positions of the target user terminals in the target area are clustered, then the weights of points included in the clustered target cluster are determined, then the cluster center point is determined based on the weights, and finally the matching degree information of the cluster center point and the target location is generated based on the coordinates of the cluster center point and the coordinates of the pre-recorded target location, so that whether the coordinates of the target location are matched with the coordinates of the cluster center point obtained through data analysis or not is automatically judged according to the uploaded position data, the correction of the coordinates of the target location according to the coordinates of the cluster center point is facilitated, and compared with the situation that the coordinates of the target location need to be updated by manually uploading the coordinate data in the prior art, the accuracy analysis of the coordinates of the target location is higher in efficiency and lower in cost.
Exemplary electronic device
Next, an electronic device according to an embodiment of the present disclosure is described with reference to fig. 8. The electronic device may be either or both of the terminal device cluster 101 and the server 103 as shown in fig. 1, or a stand-alone device independent thereof, which may communicate with the terminal device cluster 101 and the server 103 to receive the acquired input signals therefrom.
Fig. 8 illustrates a block diagram of an electronic device according to an embodiment of the disclosure.
As shown in fig. 8, the electronic device 800 includes one or more processors 801 and memory 802.
The processor 801 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities and may control other components in the electronic device 800 to perform desired functions.
Memory 802 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM) and/or cache memory (cache) and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer readable storage medium and the processor 801 may execute the program instructions to implement the location reliability assessment method of a target site and/or other desired functions of the various embodiments of the present disclosure above. Various contents such as a set of location data, matching degree information, and the like may also be stored in the computer-readable storage medium.
In one example, the electronic device 800 may further include: an input device 803 and an output device 804, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
For example, when the electronic device is the terminal device cluster 101 or the server 103, the input means 803 may be a mouse, a keyboard, a touch screen or the like for inputting a set of position data, instructions required for executing the method or the like. When the electronic device is a stand-alone device, the input means 803 may be a communication network connector for receiving the set of location data from the cluster of terminal devices 101 and the server 103, instructions required for performing the method, etc.
The output device 804 may output various information to the outside, including the determined matching degree information. The output devices 804 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 800 that are relevant to the present disclosure are shown in fig. 8, with components such as buses, input/output interfaces, etc. omitted for simplicity. In addition, the electronic device 800 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer readable storage Medium
In addition to the methods and apparatus described above, embodiments of the present disclosure may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in a method of estimating the position reliability of a target site according to various embodiments of the present disclosure described in the above "exemplary method" section of the present description.
The computer program product may write program code for performing the operations of embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium, having stored thereon computer program instructions, which when executed by a processor, cause the processor to perform the steps in a method of estimating the position credibility of a target site according to various embodiments of the present disclosure described in the above-mentioned "exemplary method" section of the present description.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Exemplary computer program
The disclosed embodiments also provide a computer program product comprising a computer program/instruction which, when executed by a processor, can implement a method for estimating the position reliability of a target site in any of the possible implementations described above.
The computer program product may be realized in particular by means of hardware, software or a combination thereof. In one alternative example, the computer program product is embodied as a computer storage medium, and in another alternative example, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), or the like.
The basic principles of the present disclosure have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present disclosure. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, since the disclosure is not necessarily limited to practice with the specific details described.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, apparatuses, devices, systems referred to in this disclosure are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
It is also noted that in the apparatus, devices and methods of the present disclosure, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered equivalent to the present disclosure.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the disclosure to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.

Claims (9)

1. A method of evaluating position reliability of a target site, comprising:
acquiring a position data set sent by a target user terminal cluster in a target area, wherein the position data in the position data set comprises coordinate data and attribute data, the target area is an area which is defined in advance on a map or is an area which is selected on the map according to the distribution of the position data set, and the attribute data is used for representing the attribute of a target user terminal at the current position;
clustering the point set represented by the position data set based on the coordinate data to obtain a target cluster;
for a point included in the target cluster, determining the weight of the point based on attribute data corresponding to the point;
determining a cluster center point of the target cluster based on the weights and coordinates corresponding to the points in the target cluster respectively;
And generating matching degree information of the cluster center point and the target point based on the coordinates of the cluster center point and the coordinates of the pre-recorded target point, wherein the matching degree information is used for representing the matching degree of taking the coordinates of the cluster center point as the coordinates of the target point.
2. The method of claim 1, wherein prior to the acquiring the set of location data transmitted by the target user terminal cluster within the target area, the method further comprises:
acquiring position data representing the position of a target user terminal received in a target history period;
dividing longitude and latitude grids of a preset map based on preset longitude and latitude intervals to obtain a longitude and latitude grid set;
determining target grids from the longitude and latitude grid set based on position data respectively corresponding to the longitude and latitude grids in the longitude and latitude grid set, wherein the number of target user terminals meeting preset conditions in an area represented by the target grids is greater than or equal to the preset number;
and determining a target area corresponding to the target place based on the target grid.
3. The method of claim 2, wherein the determining, based on the target grid, a target area corresponding to the target location comprises:
And determining the target grid and the area corresponding to the adjacent grid of the target grid as the target area corresponding to the target place.
4. The method of claim 1, wherein the determining the weight of the point based on the attribute data corresponding to the point comprises:
determining state quantization values respectively corresponding to at least one attribute data corresponding to the point;
and determining the weight of the point based on the preset sub-weight and the state quantization value corresponding to the at least one attribute data corresponding to the point.
5. The method of claim 1, wherein the generating the matching degree information of the cluster center point and the target location based on the coordinates of the cluster center point and the coordinates of the pre-recorded target location comprises:
determining whether the coordinates of the cluster center point meet a preset stability condition or not to obtain a first determination result, wherein the stability condition is that the distance between the coordinates of the cluster center point determined last time and the coordinates of the cluster center point determined this time is smaller than or equal to a preset first distance threshold;
determining whether the coordinates of the cluster center point meet a preset accurate condition or not to obtain a second determination result, wherein the accurate condition is that the distance between the coordinates of the cluster center point determined this time and the coordinates of the pre-recorded target point is smaller than or equal to a preset second distance threshold;
And generating matching degree information of the cluster center point and the target place based on the first determination result and the second determination result.
6. The method of claim 5, wherein the generating the matching degree information of the cluster center point and the target location based on the first determination result and the second determination result comprises:
determining whether the first determination result and the second determination result meet a mismatch condition, wherein the mismatch condition is that the first determination result indicates that the stable condition is not met and the second determination result indicates that the accurate condition is not met;
if the mismatch condition is met, generating matching degree information for representing that the cluster center point is not matched with the target place;
if the mismatch condition is not met, determining whether a position data subset which corresponds to the cluster center point and is contained in the position data set meets a preset threshold condition or not, and obtaining a third determination result;
and generating matching degree information for representing the matching degree of the cluster center point and the target place based on the first determination result, the second determination result and the third determination result.
7. A position reliability evaluation device of a target site, comprising:
the first acquisition module is used for acquiring a position data set sent by a target user terminal cluster in a target area, wherein the position data in the position data set comprises coordinate data and attribute data, the target area is an area which is defined in advance on a map or is an area which is selected on the map according to the distribution of the position data set, and the attribute data is used for representing the attribute of the target user terminal at the current position;
the clustering module is used for clustering the point set represented by the position data set based on the coordinate data to obtain a target cluster;
the first determining module is used for determining the weight of the point included in the target cluster based on the attribute data corresponding to the point;
the second determining module is used for determining cluster center points of the target clusters based on weights respectively corresponding to the points in the target clusters;
the generation module is used for generating matching degree information of the cluster center point and the target point based on the coordinates of the cluster center point and the coordinates of the pre-recorded target point, wherein the matching degree information is used for representing the matching degree of taking the coordinates of the cluster center point as the coordinates of the target point.
8. A computer readable storage medium storing a computer program for performing the method of any one of the preceding claims 1-6.
9. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any of the preceding claims 1-6.
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