CN111475746B - Point-of-interest mining method, device, computer equipment and storage medium - Google Patents

Point-of-interest mining method, device, computer equipment and storage medium Download PDF

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Publication number
CN111475746B
CN111475746B CN202010265690.9A CN202010265690A CN111475746B CN 111475746 B CN111475746 B CN 111475746B CN 202010265690 A CN202010265690 A CN 202010265690A CN 111475746 B CN111475746 B CN 111475746B
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extremum
area
target
position information
region
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CN111475746A (en
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赵琳琳
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Shenzhen Tencent Computer Systems Co Ltd
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Shenzhen Tencent Computer Systems 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
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The application relates to a method, a device, computer equipment and a storage medium for mining a point of interest position based on big data. The method comprises the following steps: acquiring position information reported when resource transfer processing is performed at the interest point, and obtaining a position information set; determining a reference area in which each piece of position information in the position information set is located in a map; performing longitude and latitude grid division on the reference region to obtain a candidate region; screening a target area corresponding to the interest point from the candidate areas; the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area; and positioning the position coordinates of the interest point in the target area. The method can improve the efficiency of acquiring the point of interest.

Description

Point-of-interest mining method, device, computer equipment and storage medium
Technical Field
The present application relates to the field of big data processing technology and the field of geographic information processing technology, and in particular, to a method, an apparatus, a computer device, and a storage medium for mining a point of interest.
Background
Along with the development of science and technology, big data analysis and processing are widely applied to various industries. The geographic information processing based on big data plays an increasingly important role in life and work of people. For a geographic information system, the information of interest points represents the value of the entire system to some extent. Therefore, it is important to obtain latitude and longitude information of the interest point.
In the traditional method, a map surveying staff is required to measure the longitude and latitude of a point of interest by adopting a precise surveying instrument, and then the point of interest is marked. It is clear that the conventional method requires manual measurement of the location of the point of interest, resulting in a very inefficient acquisition of the location of the point of interest.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a point of interest mining method, apparatus, computer device, and storage medium that can improve efficiency.
A point of interest mining method, the method comprising:
acquiring position information reported when resource transfer processing is performed at the interest point, and obtaining a position information set;
determining a reference area in which each piece of position information in the position information set is located in a map;
performing longitude and latitude grid division on the reference area to obtain a candidate area;
Screening a target area corresponding to the interest point from the candidate areas; the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area;
the location coordinates of the point of interest are located in the target area.
A point of interest mining apparatus, the apparatus comprising:
the acquisition module is used for acquiring the position information reported when the resource transfer processing is carried out at the interest point to obtain a position information set;
the area dividing module is used for determining a reference area where each piece of position information in the position information set is located in the map; performing longitude and latitude grid division on the reference area to obtain a candidate area;
the screening module is used for screening a target area corresponding to the interest point from the candidate areas; the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area;
and the position coordinate positioning module is used for positioning the position coordinates of the interest points in the target area.
In one embodiment, the reference area is a latitude and longitude grid pre-divided on the map according to a preset latitude and longitude side length; the regional division module is also used for positioning each position information in the position information set in the map; determining longitude and latitude grids in which each piece of position information is positioned after being positioned in a map, and obtaining a reference area; the reference area is at least one.
In one embodiment, the screening module is further configured to determine a reporting amount of the location information corresponding to each candidate region; filtering out candidate areas with the reporting amount of the position information being greater than or equal to the reporting amount threshold value; the reporting amount threshold is determined according to the total number of the reporting amounts of the position information;
and screening target areas corresponding to the interest points from the filtered candidate areas.
In one embodiment, the screening module is further configured to perform extremum detection processing on the reported amount of the position information of the filtered candidate area, so as to obtain an extremum area; and determining a target area of the interest point according to the extremum area.
In one embodiment, the points of interest are multiple points of interest that belong to the same object; the extremum area is a plurality of; the screening module is also used for iteratively selecting a target extremum area from the plurality of extremum areas and selecting a neighborhood of the target extremum area from the map according to a preset neighborhood selection condition; determining an extremum region in the neighborhood to obtain a reference extremum region; when the reporting amount approaching condition is met between the target extremum region and the reference extremum region, judging the target extremum region as a target region of the corresponding interest point; the report amount approach condition refers to a preset condition indicating that the report amount of the position information corresponding to the target extremum region is approaching to the report amount of the position information corresponding to the reference extremum region.
In one embodiment, the screening module is further configured to sort the target extremum region and the reference extremum region in descending order according to the reporting amount of the location information; determining the demarcation times according to the sorting result; the ratio of the position information reporting amount corresponding to the demarcation frequency to the position information reporting amount corresponding to the previous demarcation frequency is smaller than or equal to a preset threshold value; when the level of the target extremum area is before the demarcation level, the target extremum area is judged to be the target area; and when the level of the target extremum area is behind the demarcation level, judging the target extremum area as a non-target area.
In one embodiment, the screening module is further configured to sequentially select a current rank from a first rank in the ranking result; and when the ratio of the position information reporting amount corresponding to the next position of the current position is larger than a preset threshold value, the next position is used as the current position to be processed in an iterative way until the ratio is smaller than or equal to the preset threshold value, and the next position of the current position is judged to be the demarcation position.
In one embodiment, the screening module is further configured to obtain a preset radius value; and selecting a circular area on the map according to the radius value by taking the target extremum area as a circle center, and taking the circular area as a neighborhood of the target extremum area.
In one embodiment, the radius value is a plurality; the screening module is further used for continuously selecting the neighborhood according to the next radius value to carry out iterative processing when the reporting amount approaching condition is met between the reference extremum area in the neighborhood selected according to the last radius value and the target extremum area, and judging the target extremum area as the target area of the corresponding interest point when the reporting amount approaching condition is met between the reference extremum area in the neighborhood selected according to the last radius value and the target extremum area; the last radius value is smaller than the next radius value.
In one embodiment, the location coordinate positioning module is further configured to divide the target area to obtain a plurality of sub-areas; determining the reporting amount of the position information corresponding to each sub-area; and carrying out center point positioning on the subarea with the largest reporting amount of the position information, and acquiring the longitude and latitude of the positioned center point to obtain the position coordinates of the interest point.
In one embodiment, the obtaining module is further configured to obtain a set of resource transfer data; the resource transfer data is data generated when the interest point performs resource transfer processing; each piece of resource transfer data carries position information reported when the resource transfer processing is carried out; and respectively extracting the carried position information from each piece of resource transfer data in the resource transfer data set to obtain a position information set.
In one embodiment, the point of interest is an offline store, and the resource transfer process includes a payment process and location information, which is location information reported by the mobile terminal when performing the payment process in the offline store.
A computer device includes a memory storing a computer program and a processor implementing the steps of the point of interest mining method according to embodiments of the present application when the computer program is executed.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps in a point of interest mining method according to embodiments of the present application.
The method, the device, the computer equipment and the storage medium for mining the positions of the interest points acquire the position information reported when the resource transfer processing is carried out at the interest points, and a position information set is obtained; and determining a reference area in which each piece of position information in the position information set is located in a map. The reference area can embody an area range where the real position of the interest point is located. Further, longitude and latitude grid division processing is carried out on the reference area, and a candidate area is obtained; screening a target area corresponding to the interest point from the candidate areas; and the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area. Namely, the region range where the real position is located is subjected to region subdivision and filtering screening, so that the region range where the real position of the interest point possibly exists is further reduced, and the target region where the interest point is most likely to be located is screened out. Thus, the position coordinates of the point of interest can be accurately located in the target area. By utilizing the position information reported when the resource transfer processing is carried out at the interest point, the position coordinates of the interest point can be accurately mined and positioned, and compared with the traditional method which requires professional personnel to carry out manual measurement, the efficiency of acquiring the position of the interest point is greatly improved.
Drawings
FIG. 1 is a diagram of an application environment for a point of interest location mining method in one embodiment;
FIG. 2 is a diagram of an application environment for a point of interest location mining method in another embodiment;
FIG. 3 is a flow diagram of a method of mining a point of interest location in one embodiment;
FIG. 4 is a diagram illustrating reporting of location information according to one embodiment;
FIG. 5 is a schematic diagram of partitioning candidate regions in one embodiment;
FIG. 6 is a schematic diagram of filtering candidate regions in one embodiment;
FIG. 7 is a schematic diagram of extremum regions in one embodiment;
FIG. 8 is a schematic diagram of cleaning extremum areas in one embodiment;
FIGS. 9-10 are schematic interface diagrams of cleaning extremum regions in one embodiment;
FIGS. 11-13 are schematic diagrams of determining point of interest coordinates in one embodiment;
FIG. 14 is a block diagram of an apparatus for mining a point of interest location in one embodiment;
fig. 15 is an internal structural view of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The method for mining the position of the interest point can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The server 104 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligence platforms, and the like. . The terminal 102 may be, but is not limited to, a smart phone, tablet, notebook, desktop computer, smart speaker, portable wearable device (e.g., smart watch or smart glasses), etc. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the present application is not limited herein.
The terminal 102 has a resource transfer processing function and a positioning function. The terminal 102 may perform resource transfer processing at the point of interest, locate the terminal 102, and report the location information of the located terminal 102 to the server 104. It will be appreciated that the location information is reported during the resource transfer process at the point of interest, i.e., the terminal 102 is located and reported at the point of interest.
The terminal 102 may be a mobile terminal (e.g., a handset with resource transfer and location capabilities). That is, a plurality of users perform resource transfer processing at points of interest using respective terminals 102, and each terminal 102 locates its own position at the time of the resource transfer processing and reports the positional information obtained by the location to the server 104. Then, the server 104 may obtain the location information reported when the resource transfer process is performed at the point of interest, and obtain the location information set.
The server 104 may determine a reference area in the map where each piece of location information in the set of location information is located; performing longitude and latitude grid division on the reference area to obtain a candidate area; screening a target area corresponding to the interest point from the candidate areas; the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area; the location coordinates of the point of interest are located in the target area.
It will be appreciated that the terminal 102 may also be a terminal that is fixedly located at a point of interest for performing a resource transfer process, such as a fixed cashier device in an off-line store.
It should be noted that fig. 1 illustrates a scene by taking position mining for one point of interest alone as an example. In practice, location mining may be performed on multiple points of interest in a batch. I.e. an application scenario diagram as shown in fig. 2. Each terminal 102 performing the resource transfer processing at the plurality of points of interest reports the location information to the server 104 during the resource transfer processing. The location information set obtained by the server 104 includes location information reported when the resource transfer processing is performed at a plurality of interest points, so that the location coordinate of each interest point is obtained by executing the interest point mining method in each embodiment of the present application based on the location information set.
In one embodiment, multiple points of interest may be attributed to the same object. Such as offline stores belonging to the same brand.
It should be noted that, in other embodiments, other computer devices may also obtain the reported location information set from the server 104 in a unified manner, and execute the method described in the embodiments of the present application, and the method described in the embodiments of the present application is not limited to be executed by the server 104 directly receiving the report from the terminal. It will be appreciated that other computer devices may be terminals or backend servers (where the backend server is a different backend server than server 104). In addition, the terminal 102 may directly report the location information to another terminal device that is provided with a method for executing the embodiments of the present application, and is not limited to uploading to the server 102.
In one embodiment, as shown in fig. 3, a method for mining a location of an interest point is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
step 302, obtaining position information reported when resource transfer processing is performed at the interest point, and obtaining a position information set.
Wherein a point of interest (POI, point of Interest), a term in a geographic information system, generally refers to everything that can be abstracted into a point. In a geographic information system, some geographic entities, such as a house, a shop, a mailbox, a bus station, etc., may be used as a point of interest.
The resource transfer process is a process for transferring resource data. The reported position information is the position information of the terminal reported when the terminal performs resource transfer processing at the interest point. It can be appreciated that the location information is reported once per resource transfer process. That is, the reported amount of the location information is the number of times of performing the resource transfer processing. The reported position information is position information with longitude and latitude data, namely belongs to longitude and latitude information.
In one embodiment, the resource transfer process may include at least one of a payment process, a resource donation process, and the like. It will be appreciated that the amount paid in the payment process pertains to the resource data, and that transferring the amount from one account to another pertains to transferring the resource data.
It can be understood that the user can use the terminal to perform the resource transfer operation at the interest point, and the terminal performs the resource transfer processing and locates itself to obtain the location information. The terminal can report the located position information to the server in the process of resource transfer processing. It can be appreciated that at one point of interest, multiple resource transfer processes may be performed by multiple users, so that the location information may be reported to the server multiple times, thereby forming a location information set.
In one embodiment, the terminal may detect (i.e., sniff) to a wireless network list (e.g., wi-Fi list) upon the resource transfer process, locate location information of the terminal based on the wireless network list, and report the location information to the server during the resource transfer process. That is, the reported location information is obtained by locating according to the detected wireless network list when the interest point performs the resource transfer processing.
Fig. 4 is a schematic diagram illustrating reporting of location information according to an embodiment. Referring to fig. 4, considering factors such as instability of the wireless network signal itself and external interference (e.g., building shielding, flowing personnel), the positional information is inaccurate (i.e., the reported positional information is not accurate enough) according to the wireless network list. For example, 10 mobile devices pay at the same convenience store, 10 different location information of a-J may be reported. As can be seen from fig. 4, some of these reported location information are far from the real location of the convenience store, and some are near to the real location of the convenience store, so these reported location information are not accurate enough, and by the method according to the embodiments of the present application, the accurate location coordinates (i.e. the accurate longitude and latitude) of the point of interest (even the convenience store) need to be mined based on these reported location information.
In one embodiment, the point of interest is an offline store, and the resource transfer process includes a payment process and location information, which is location information reported by the mobile terminal when performing the payment process in the offline store. That is, by the method in each embodiment of the present application, the position coordinates of the off-line store are mined by using the position information reported when the mobile terminal performs the payment processing on the off-line store.
In one embodiment, the server may directly obtain the location information that has been reported in the resource transfer processing stage, to obtain the location information set. That is, the location information may be reported in a separate form.
In other embodiments, the location information may also be reported in the form of resource transfer data along with other data generated during the resource transfer process. The server may extract the reported location information from the resource transfer data.
In one embodiment, step 302 includes: acquiring a set of resource transfer data; the resource transfer data is data generated when the interest point performs resource transfer processing; each piece of resource transfer data carries position information reported when the resource transfer processing is carried out; and respectively extracting the carried position information from each piece of resource transfer data in the resource transfer data set to obtain a position information set.
Specifically, when the terminal performs resource transfer processing at the interest point, resource transfer data is generated and reported to the server. The reported resource transfer data comprises position information of the terminal positioned when the terminal performs resource transfer processing. The server may acquire the reported set of resource transfer data, and then extract the location information carried in the set of resource transfer data from each piece of resource transfer data to obtain a location information set.
For ease of understanding, an example will now be described. For example, 10 mobile terminals pay (pay, i.e., resource transfer operation) at the same convenience store (convenience store point of interest), 10 reported location information can be obtained from mobile payment data generated by 10 payments.
In one embodiment, the server may perform data cleaning on the location information set, remove location information with incomplete latitude and longitude information (for example, missing fields/incorrect formats/messy codes, etc.) in the massive data of the location information set, and perform step 304 and subsequent steps on the location information set after data cleaning.
Step 304, determining a reference area where each piece of location information in the location information set is located in the map.
The reference area is an area used for generalizing the position of the point of interest in the map. It will be appreciated that the reference region serves as a reference for locating the position of the point of interest, i.e. the position coordinates of the point of interest may be further refined from the reference region. The reference area is at least one.
Specifically, the computer device may locate each piece of location information in the set of location information on the map, and determine, according to the location result, a reference area in which the set of location information is located.
In one embodiment, the reference area may be an area that is divided in advance according to a preset rule, and each piece of location information is located on the map, so as to obtain a reference area in which each piece of location information falls. In this case, the size of each reference area is not changed by the difference of the location information sets, and the specific reference areas and the number of reference areas corresponding to the different location information sets may be different, but the size of a single reference area is not affected, because the single reference area is already divided according to a preset rule.
In one embodiment, the reference region may be generated from real-time localization of each location information in the set of location information. In this case, the size of the reference area is related to the distribution of the positions located on the map according to the respective position information. The size of the reference area corresponding to the different location information sets is different, for example, the more the location information distribution in the location information sets is scattered, the larger the corresponding reference area may be, whereas the more the location information distribution in the location information sets is concentrated, the smaller the corresponding reference area may be.
And 306, performing longitude and latitude grid division processing on the reference region to obtain a candidate region.
The candidate area is a longitude and latitude grid obtained by dividing longitude and latitude of the reference area.
Specifically, the server may further divide the longitude and latitude of the inside of each reference area, and take the longitude and latitude grid obtained by dividing as the candidate area.
In one embodiment, the server may obtain a preset candidate longitude and latitude side length, and divide the interior of the reference area according to the candidate longitude and latitude side length, so as to obtain a candidate area satisfying the candidate longitude and latitude side length. The candidate longitude and latitude side lengths may include a candidate longitude side length and a candidate latitude side length. The candidate longitude side length and the candidate latitude side length may be equal or unequal, and are not limited thereto.
FIG. 5 is a schematic diagram of partitioning candidate regions in one embodiment. Referring to fig. 5, a longitude and latitude grid with a side length r=100deg.m is a reference area, and the reference area is subjected to longitude and latitude division according to a candidate longitude and latitude side length e=20m, so as to obtain a candidate area with a longitude and latitude side length of 20m. That is, in fig. 5, the reference area is divided into 25 candidate areas, each of which is a longitude and latitude grid with a side length of 20 m. The location information is reported when the convenience store performs mobile payment processing, and the number in each candidate area refers to the reporting amount of the location information corresponding to the candidate area, namely the number of times of reporting the location information in the candidate area. It will be appreciated that in order to illustrate the relationship between the reported location information and the real location of the convenience store, the real location of the convenience store is marked with a location marker icon, and that in accordance with the method of embodiments of the present application, the location coordinates (i.e., latitude and longitude coordinates) of the real location are obtained. As can be seen from fig. 5, since the position information reported when the mobile payment process is performed at the convenience store is distributed around the real position of the convenience store, the reported position information is inaccurate latitude and longitude information distributed around the real position.
Step 308, screening a target area corresponding to the interest point from the candidate areas; the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area.
The reporting amount of the position information is the number of times of reporting the position information. It can be understood that the reporting amount of the location information corresponding to the area is the number of times of reporting the location information in the area.
The target region refers to a region for locating the position of the point of interest in the candidate region. I.e. the target area is the area where the real position of the point of interest is most likely located. The non-target region refers to a candidate region other than the target region.
In one embodiment, the server may determine the reporting amounts of the location information corresponding to the candidate areas, filter the candidate areas with a larger reporting amount of the location information, and then select the target area corresponding to the interest point from the filtered candidate areas.
In another embodiment, the server may also determine the reporting amounts of the location information corresponding to the candidate areas, directly compare the reporting amounts of the location information, and select the candidate area with the highest reporting amount of the location information from the comparison result to obtain the target area.
As shown in fig. 5, the number in each candidate area in fig. 5 refers to the reporting amount of the location information corresponding to the candidate area, that is, the number of times of reporting the location information in the candidate area. For example, the numeral 31 refers to the number of times of reporting the position information in the candidate area corresponding to the numeral 31, that is, 31 times of reporting the position information falls into the candidate area.
In step 310, the location coordinates of the point of interest are located in the target area.
It can be understood that the target area is an area which is obtained by dividing and screening the position information set in a large reference area positioned in the map step by step and is most likely to exist the true position of the interest point, and the target area is equivalent to the area which is accurately mined and is likely to exist the interest point, and the position coordinates of the interest point are positioned.
In one embodiment, the server may further subdivide the target area, screen the sub-area with the most reporting amount of the location information from the sub-areas obtained by subdivision, and then locate the location coordinates of the interest point from the screened sub-areas.
In other embodiments, the server may directly locate the center point of the target area, and use the latitude and longitude coordinates of the center point as the position coordinates of the interest point.
It should be noted that, when there are multiple points of interest, a target area corresponding to each point of interest may be obtained according to steps 302 to 308, and then the position coordinates of the corresponding point of interest are located in each target area. It will be appreciated that if the true locations of different points of interest are very close (e.g., the distance is less than a preset distance threshold), then the different points of interest may correspond to the same target area.
According to the interest point position mining method, the position information reported when the resource transfer processing is carried out at the interest point is obtained, and a position information set is obtained; and determining a reference area in which each piece of position information in the position information set is located in a map. The reference area can embody an area range where the real position of the interest point is located. Further, longitude and latitude grid division processing is carried out on the reference area, and a candidate area is obtained; screening a target area corresponding to the interest point from the candidate areas; and the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area. Namely, the region range where the real position is located is subjected to region subdivision and filtering screening, so that the region range where the real position of the interest point possibly exists is further reduced, and the target region where the interest point is most likely to be located is screened out. Thus, the position coordinates of the point of interest can be accurately located in the target area. By utilizing the position information reported when the resource transfer processing is carried out at the interest point, the position coordinates of the interest point can be accurately mined and positioned. Compared with the traditional method which requires professional personnel to carry out manual measurement, the method has the advantages that on the premise that the accuracy of the position coordinates of the dug interest points is guaranteed, the efficiency of obtaining the positions of the interest points is greatly improved. In addition, the labor cost is saved, and the cost is low.
In one embodiment, the reference area is a latitude and longitude grid pre-divided on the map according to a preset latitude and longitude side length. Step 304 includes: positioning each position information in the position information set in the map; determining longitude and latitude grids in which each piece of position information is positioned after being positioned in a map, and obtaining a reference area; the reference area is at least one.
It is understood that the preset longitude and latitude side length may include a longitude side length and a latitude side length. The longitudinal side length and the latitudinal side length may be equal or different, and are not limited thereto. For example, r1 = 0.01 latitude side length (corresponding to the ground about 1.1 km), and r2 = 0.01 longitude side length may be used to divide longitude and latitude, so as to obtain a longitude and latitude grid, i.e. a reference area. Alternatively, r1 and r2 may take different values to divide the longitude and latitude grid.
Specifically, the server may divide the map into a plurality of longitude and latitude grids in advance according to a preset longitude and latitude side length, and each longitude and latitude network may be used as a reference area. Thus, any positioning point for positioning on the map falls into a pre-divided longitude and latitude grid (i.e. a reference area). Then, the server can locate each position information in the position information set in the map to obtain locating points corresponding to each position information. The server can determine longitude and latitude grids where all positioning points are located, namely, a reference area where all the position information is located after being located in the map. It is understood that the reference area in which the set of location information is located in the map is at least one.
As shown in fig. 5, the reported position information falls into a reference area divided in advance. The longitude and latitude grid with the number is the area with the position information reported in the reference area.
In the embodiment, longitude and latitude grid division is performed on the map in advance to obtain the reference area, and the position coordinates of the interest points are further positioned and calculated according to the reference area in which the position information set falls, which is equivalent to calculating the longitude and latitude coordinates of the interest points by using the longitude and latitude grid, so that the efficiency of determining the positions of the interest points is improved.
In one embodiment, step 306 includes: determining the reporting amount of the position information corresponding to each candidate region; filtering out candidate areas with the reporting amount of the position information being greater than or equal to the reporting amount threshold value; the reporting amount threshold is determined according to the total number of the reporting amounts of the position information; and screening target areas corresponding to the interest points from the filtered candidate areas.
The total number of the reporting amounts of the location information refers to the sum of the reporting amounts of the location information corresponding to the candidate areas.
Specifically, the server may determine the reporting amounts of the location information corresponding to the candidate areas, and sum the reporting amounts of the location information to obtain the total number of reporting amounts of the location information. The server can determine the reporting amount threshold according to the total number, and compare the reporting amounts of the position information corresponding to the candidate areas with the reporting amount threshold respectively, and filter the candidate areas with the reporting amounts of the position information greater than or equal to the reporting amount threshold. The server may filter the target region corresponding to the point of interest from the filtered candidate regions.
In one embodiment, the server may obtain the adjustment factor and determine the reporting amount threshold based on the total number of reporting amounts of location information and the adjustment factor.
In one embodiment, the candidate region may be filtered out according to the following formula:
wherein c E Representing the reporting amount of the position information corresponding to each candidate region, wherein delta is an adjustment factor; delta sigma E∈R c E Indicating the total number of reporting amounts of the position information of each candidate area; delta sigma E∈R c E Indicating a reporting amount threshold. From the above formula, the candidate areas with the reporting amount of the position information greater than or equal to the reporting amount threshold are filtered out for reservation, and the candidate areas with the reporting amount less than the reporting amount threshold are discarded.
The filtering of candidate regions will now be explained in connection with fig. 5 and 6. Shown in fig. 5 is the candidate region before filtering. Fig. 6 shows the filtered candidate regions. Referring to fig. 6, candidate areas with a reporting amount of location information greater than or equal to the reporting amount threshold are reserved, that is, the longitude and latitude grids other than 0 in fig. 6 (for example, candidate areas corresponding to 235 and 821 are filtered and reserved candidate areas). The longitude and latitude grid marked with 0 in fig. 6 is the candidate area that is discarded (i.e., the candidate area whose reporting amount of the location information is smaller than the reporting amount threshold). And screening target areas corresponding to the interest points from all longitude and latitude grids (namely filtered candidate areas) which are not 0.
In the above embodiment, according to the reporting amount of the position information, the candidate area with larger reporting amount of the information is filtered out from the candidate area, and then the target area is determined based on the filtered candidate area, so that the accuracy of the position positioning is improved.
In one embodiment, filtering the target region corresponding to the point of interest from the filtered candidate regions includes: carrying out extremum detection processing on the filtered candidate regions, and detecting the candidate region with the largest reporting amount of the position information to obtain an extremum region; and determining a target area of the interest point according to the extremum area.
The extremum region is the candidate region with the largest information reporting amount in the local range among the filtered candidate regions.
Specifically, for any filtered candidate region, the extremum region is referred to as if the following condition is satisfied: the reporting amount of the position information corresponding to the candidate area is larger than that of the position information corresponding to other filtered candidate areas around the candidate area.
To facilitate understanding of the extremum regions, an explanation will now be given in connection with fig. 7. Referring to fig. 7, a [ i, j ] is a candidate region of the ith row and jth column. If the filtered candidate region a [ i, j ] is larger than the reporting amount of the position information corresponding to the surrounding candidate regions (namely, the candidate regions except a [ i, j ] in a [ i-1, j-1] to a [ i+1, j+1 ]), the reporting amount of the position information corresponding to a [ i, j ] is a local maximum value, and the candidate region a [ i, j ] is an extremum region. It can be understood that the maximum reporting amount of the location information corresponding to the extremum area corresponds to the maximum processing amount of the resource transfer (such as the maximum transaction amount), and then the real location of the interest point is likely to be in the extremum area.
It should be noted that the extremum area may be one or more. When there is only one extremum region, the extremum region can be directly used as the target region of the interest point. When there are multiple extremum regions, the multiple extremum regions can be further cleaned and screened to determine a final target region therefrom. It will be appreciated that when a location mining process is performed on a point of interest, an extremum region may be obtained. When the position is mined for a plurality of interest points in batches, a plurality of extremum regions can be obtained.
In the above embodiment, the extremum detection processing is performed on the filtered candidate region, which is equivalent to further screening (i.e., secondary screening) the candidate region where the interest point is more likely to be located, so that the target region of the interest point is determined based on the screened region, thereby improving the positioning accuracy.
In one embodiment, the points of interest are multiple points of interest that belong to the same object; the extremum area is a plurality of. In this embodiment, determining the target region of the interest point according to the extremum region includes: iteratively selecting a target extremum region from the plurality of extremum regions, and selecting a neighborhood of the target extremum region from the map according to a preset neighborhood selection condition; determining an extremum region in the neighborhood to obtain a reference extremum region; and when the reporting amount approaching condition is met between the target extremum region and the reference extremum region, judging the target extremum region as the target region of the corresponding interest point.
The plurality of interest points belonging to the same object means that the plurality of interest points belong to the same object. Such as offline stores belonging to the same brand. The objects may be organizations, communities, brands, and the like, having hierarchically structured properties.
It can be understood that the location information set includes location information reported when resource transfer processing is performed on a plurality of points of interest belonging to the same object, respectively. In the embodiment of the application, the position of the plurality of interest points belonging to the same object is mined uniformly by acquiring the position information reported when the resource transfer processing is respectively carried out on the plurality of interest points belonging to the same object. The method is equivalent to realizing batch processing of a plurality of interest points belonging to the same object, thereby obtaining a plurality of extremum regions. It can be understood that each interest point belonging to the same object has a corresponding extremum region, and the extremum region corresponding to each interest point is used for locating the final position coordinates of the interest point.
The preset neighborhood selection condition is a preset condition for selecting the neighborhood. The target extremum area is an extremum area to be judged whether to be the target area or not. The reference extremum region is an extremum region which is located in the neighborhood of the target extremum region and which is other than the target extremum region among the obtained extremum regions. It is understood that the reference extremum area is used for reference in determining whether or not the target extremum area is the target area.
The report amount approach condition is a preset condition indicating that the report amount of the position information corresponding to the target extremum region is approaching to the report amount of the position information corresponding to the reference extremum region.
Specifically, the server may iteratively select a target extremum region from a plurality of extremum regions corresponding to interest points belonging to the same object, and select a neighborhood around the target extremum region from the map according to a preset neighborhood selection condition. The server may determine an extremum region located in the neighborhood, resulting in a reference extremum region. And when the reporting amount approaching condition is met between the target extremum region and the reference extremum region, judging the target extremum region as the target region of the corresponding interest point. When the report amount approach condition is not satisfied between the target extremum region and the reference extremum region, the target extremum region is judged not to be the target region of the corresponding interest point, and the target extremum region can be taken as a noise region to be rejected.
It should be noted that, in the embodiment of the present application, each extremum region is sequentially used as a target extremum region, and is compared with a reference extremum region in its own neighboring region to determine whether the target extremum region is a target region. That is, it is necessary to perform processing of neighborhood selection and difference comparison with a reference extremum region in the own neighborhood for each extremum region, and each processing result is only used to determine whether the extremum region itself is a target region, but not used to determine whether other extremum regions are target regions.
For example, if there are 3 extremum regions, a1 to a10, then a1 to a10 are sequentially taken as the target extremum regions. Assuming that a1 is the target extremum region, the neighborhood of a1 includes 4 reference extremum regions, a2 to a5, and then, according to the reporting amount difference between a1 and a2 to a5, it is determined whether a1 is the target region (i.e., whether a1 is to be reserved or to be removed). Let a1 determine to cull. Then, when a2 is taken as the target extremum region, the neighborhood of a2 includes 5 reference extremum regions, namely a1 and a3 to a6, and then, according to the reporting amount difference between a2 and a1 and a3 to a6, whether a2 is the target region is judged (namely, whether a1 is to be reserved or to be removed is judged). It is apparent that the judgment result of whether a1 remains does not affect the judgment processing for a 2.
In the above embodiment, each extremum region is compared with other reference extremum regions in the neighborhood, so as to clean all extremum regions, and the accuracy of data cleaning is improved, so that the accuracy of determining the coordinates of the subsequent interest points is improved.
In one embodiment, when the report amount approach condition is satisfied between the target extremum region and the reference extremum region, determining that the target extremum region is the target region of the corresponding interest point includes: the target extremum area and the reference extremum area are sorted in descending order according to the reporting amount of the position information; determining the demarcation times according to the sorting result; the ratio of the position information reporting amount corresponding to the demarcation frequency to the position information reporting amount corresponding to the previous demarcation frequency is smaller than or equal to a preset threshold value; when the level of the target extremum area is before the demarcation level, the target extremum area is judged to be the target area; and when the level of the target extremum area is behind the demarcation level, judging the target extremum area as a non-target area.
It can be understood that the interest points corresponding to the target extremum region and the reference extremum region (i.e., the interest points corresponding to the extremum regions) belong to the same object. For example, when the point of interest is an offline store, the target extremum region and the reference extremum region may correspond to each offline store under the same brand. That is, the positions of multiple interest points belonging to the same object may be represented by each extremum region first, and then the extremum regions are cleaned by the method in the embodiment of the present application, and the extremum regions capable of more accurately representing each interest point are selected therefrom. It will be appreciated that the extremum region is not a specific location of the point of interest, but is merely a generalization of the region in which the point of interest may exist.
The boundary level is a boundary of the reporting amount of the position information and corresponds to a watershed. The difference between the amount of reporting of the positional information corresponding to the level before the level and the amount of reporting of the positional information corresponding to the level after the level is satisfied, and the condition of excessive difference is satisfied. That is, the number of positional information reports corresponding to the level before the boundary level is relatively large, and the number of positional information reports corresponding to the level after the boundary level is greatly reduced.
In one embodiment, the ratio between the amount of positional information reporting corresponding to the demarcation level and the amount of positional information reporting corresponding to the immediately preceding demarcation level is less than or equal to a predetermined threshold.
Specifically, the server may sort the target extremum region and the reference extremum region in order of the corresponding reporting amount of the position information from large to small (i.e., descending order). The server can determine the demarcation sequence according to the sequencing result; the ratio of the position information reporting amount corresponding to the demarcation frequency to the position information reporting amount corresponding to the previous demarcation frequency is smaller than or equal to a preset threshold value. It will be appreciated that in this case, the difference in the amount of reporting of the positional information between the preceding and following ranks is relatively large, that is, the condition of excessive difference is satisfied.
The server can determine the corresponding rank of the target extremum region in the sorting according to the sorting result, and when the rank of the target extremum region is before the demarcation rank, the report amount of the position information of the target extremum region is more, and the report amount approaching condition is satisfied between other reference extremum regions, so that the target extremum region can be determined as the target region. When the level of the target extremum area is after the demarcation level, the position information report amount of the target extremum area is little, and the report amount approaching condition is not satisfied between other reference extremum areas, the target extremum area can be judged to be a non-target area, and the target extremum area can be removed as a noise area.
In the above embodiment, for each target extremum region, the target extremum regions and other reference extremum regions corresponding to interest points in the neighboring regions and belonging to the same object are sorted according to the reporting amount of the position information, so as to determine the demarcation level. Therefore, the accuracy and efficiency of data cleaning are improved. And further, the accuracy and the efficiency of the position coordinates of the subsequent positioning interest points are improved.
In one embodiment, determining the demarcation order based on the ordering result includes: sequentially selecting the current bit from the first bit in the sequencing result; and when the ratio is smaller than or equal to a preset threshold value, determining that the next rank of the current rank is a demarcation rank.
It can be understood that the sorting result is obtained by sorting the target extremum region and the reference extremum region in the vicinity thereof according to the reporting amount of the position information in descending order. Therefore, the first extreme value region in the sequencing result is the extreme value region with the largest reporting amount of the position information in the extreme value regions participating in sequencing.
The position information reporting amount corresponding to the rank is the position information reporting amount corresponding to the extremum area of the rank.
Specifically, the server may sequentially select the current rank from the first rank in the ranking result. For each current rank, determining the ratio of the reporting amount of the position information corresponding to the next rank of the current rank to the reporting amount of the position information corresponding to the current rank.
It can be understood that when the ratio of the amount of reporting of the positional information corresponding to the next bit of the current bit and the amount of reporting of the positional information corresponding to the current bit is less than or equal to the preset threshold, the next bit of the current bit can be determined as the demarcation bit.
When the ratio is greater than a preset threshold, indicating that the position information reporting amount corresponding to the next position of the current position is not different from the position information reporting amount corresponding to the current position, taking the next position as a new current position to be processed in an iteration mode, namely continuously determining whether the position information reporting amount corresponding to the next position of the new current position is greater than the preset threshold or not, performing iteration processing until the ratio of the position information reporting amount corresponding to the next position of the current position to the position information reporting amount corresponding to the current position is less than or equal to the preset threshold, and determining that the next position of the current position is a demarcation position.
For example, from the first bit, when the ratio between the 2 nd bit and the position information reporting amount corresponding to the first bit is greater than a preset threshold (for example, 0.1), the ratio between the 3 rd bit and the position information reporting amount corresponding to the 2 nd bit is continuously compared with the preset threshold 0.1 until the ratio between the 5 th bit and the position information reporting amount corresponding to the 4 th bit obtained by iterative processing is less than the preset threshold 0.1, and then the 5 th bit is the demarcation bit. Then, the target extremum region is retained (i.e., the target region) assuming that the order of the target extremum region precedes the 5 th order, and the target extremum region is culled assuming that the order of the target extremum region follows the 5 th order. It is understood that the amount of reporting of the positional information of the extremum region of the 5 th order and subsequent order is much smaller than the amount of reporting of the positional information of the extremum region of the preceding order, so that it is highly possible to be a noise region, and it is possible to perform the removal.
In one embodiment, the extremum regions may be cleaned to determine the target region therefrom according to the following formula:
c 1 ＞c 2 ＞c 2 …＞c n
wherein c 1 ,…,c n Representing the reporting amount of the position information corresponding to each of the n extremum regions; c 1 ＞c 2 ＞c 3 …＞c n The n extreme value regions are shown to be sorted in descending order according to the reporting amount of the position information; t is the demarcation number; t represents a preset threshold;and the minimum bit number satisfying the condition that the ratio between the corresponding position information reporting amount and the position information reporting amount of the previous bit number is smaller than a preset threshold value T is expressed as the demarcation bit number. c t Reporting quantity of position information corresponding to extreme value region of boundary level, c i Reporting quantity for position information corresponding to the ith extremum area, c i ＞c t The reporting amount of the position information representing the ith extremum area is larger than the reporting amount of the position information corresponding to the demarcation level, and it can be understood that in this case, the level of the extremum area is arranged before the demarcation level; c i ≤c t The position information reporting amount representing the ith extremum area is smaller than or equal to the position information reporting amount corresponding to the demarcation level, in which case the level of the extremum area is arranged after the demarcation level; f (a) =1 indicates that the target extremum region is reserved, i.e., the target region, and the order indicating the target extremum region is reserved as the target region before the boundary order. f (a) =0 indicates that the target extremum region is eliminated, i.e., is a non-target region, and the order of the target extremum region is eliminated after the boundary order.
For ease of understanding, an example will now be described with reference to fig. 8. Referring to fig. 8, the preset threshold is 0.1, and the ratio between the reporting amounts of the position information corresponding to the 5 th order and the 4 th order is smaller than the preset threshold by 0.1 (i.e., the reporting amount of the position information corresponding to the extremum region of the 5 th order is much smaller than the reporting amount of the position information corresponding to the extremum region of the 4 th order, which means that the transaction amount occurring in the extremum region of the 5 th order is small), then the 5 th order can be found as the demarcation order. Assuming that the target extremum area is arranged before the 5 th order, for example, the target extremum area is arranged at the 3 rd order, that is, extremum area 3, then the target extremum area is reserved (that is, the target area). Assuming that the target extremum area is ranked at and after the 5 th order, for example, the target extremum area is ranked at the 6 th order, that is, extremum area 6, then the target extremum area is culled.
In the above embodiment, the iterative computation is started from the first bit in the sorting result, so that the minimum bit satisfying the condition that the ratio between the corresponding position information reporting amount and the position information reporting amount of the previous bit is smaller than the preset threshold value is used as the boundary bit, the boundary bit can be accurately determined, and the full computation of the data is not required, so that the operation resource is saved and the efficiency is improved.
In one embodiment, selecting the neighborhood of the target extremum area from the map according to the preset neighborhood selection condition includes: acquiring a preset radius value; and selecting a circular area on the map according to the radius value by taking the target extremum area as a circle center, and taking the circular area as a neighborhood of the target extremum area.
Specifically, the server may acquire a preset radius value, take the target extremum area as a center of a circle, and select a circular area on the map with the acquired radius value as a radius, where the circular area is a neighborhood of the target extremum area. The server can acquire the extremum region in the neighborhood to obtain the reference extremum region of the target extremum region.
Fig. 9 to 10 are schematic diagrams of interfaces for cleaning extremum regions in one embodiment. Referring to fig. 9, the extremum region a is a target extremum region, a circular region is selected as a neighborhood according to the radius r with a as a center, and then the extremum region b is located in the neighborhood. The information amount in fig. 9 and 10 is the position information report amount. Since the reporting amount of the position information corresponding to a is much worse than b, it is possible to determine the extremum region a as a noise region (i.e., a non-target region), and it is possible to wash out the extremum region a. Fig. 10 is a schematic diagram of cleaning out the extremum region a.
In one embodiment, the predetermined radius value is a plurality of. When the report amount approach condition is satisfied between the target extremum region and the reference extremum region, determining that the target extremum region is the target region of the corresponding interest point includes: when the reference extremum area in the neighborhood selected according to the last radius value meets the reporting amount approaching condition with the target extremum area, continuing to select the neighborhood according to the next radius value for iterative processing until the reporting amount approaching condition is met between the reference extremum area in the neighborhood selected according to the last radius value and the target extremum area, and judging the target extremum area as the target area of the corresponding interest point; the last radius value is smaller than the next radius value.
It will be appreciated that when the preset radius value is plural, the server may select one radius value according to the order of the radius values from small to large, to perform the steps of selecting a circular area on the map according to the radius value with the target extremum area as the center, to serve as the neighborhood of the target extremum area, and to determine the extremum area located in the neighborhood, to obtain the reference extremum area. When the report amount approach condition is satisfied between the reference extremum region in the neighborhood selected according to the upper extremum value and the target extremum region, the next extremum value is selected continuously according to the order of the radius values from small to large, the round region is selected on the map according to the radius values by taking the target extremum region as the circle center, the neighborhood of the target extremum region is used as the neighborhood, and the step of determining the extremum region in the neighborhood to obtain the reference extremum region is executed. And judging the target extremum region as the target region of the corresponding interest point until the report amount approaching condition is met between the reference extremum region and the target extremum region in the neighborhood selected according to the last radius value.
For example, the preset radius values r are respectively 50 meters, 100 meters and 500 meters. If it is to be determined whether the extremum area a is to be reserved, then the neighborhood 1 of the extremum area a may be selected by taking the extremum area a as the center of a circle and taking 50 meters as the radius. When the report amount approach condition (for example, the order of the extremum region a is behind the boundary order of the order result of the whole neighborhood 1) is not satisfied between the reference extremum region and the extremum region a in the neighborhood 1, the extremum region a is judged to be a noise region, the neighborhood is not selected according to the next radius value after the extremum region a is removed and cleaned. When the reporting amount approaching condition is satisfied between the reference extremum region and the extremum region a in the neighborhood 1 (for example, the ranking order of the extremum region a is before the demarcation order of the ranking result of the whole neighborhood 1), then the neighborhood 2 of the extremum region a can be selected with the extremum region a as the center of a circle and with a radius of 100 meters, whether the reporting amount approaching condition is satisfied between the reference extremum region and the extremum region a in the neighborhood 2 is continuously judged, if so, the neighborhood 3 of the extremum region a is selected with the extremum region a as the center of a circle and with a radius of 500 meters, and when the reporting amount approaching condition is satisfied between the reference extremum region and the extremum region a in the neighborhood 3, then the extremum region a can be finally judged as the target region, and needs to be reserved.
In the above embodiment, the data cleaning accuracy can be improved by sequentially selecting the plurality of radius values from the small to the large to perform the layered cleaning.
In one embodiment, locating the location coordinates of the point of interest in the target area includes: dividing a target area to obtain a plurality of subareas; determining the reporting amount of the position information corresponding to each sub-area; and carrying out center point positioning on the subarea with the largest reporting amount of the position information, and acquiring the longitude and latitude of the positioned center point to obtain the position coordinates of the interest point.
Specifically, the server may further divide the interior of the target area according to a preset side length, to obtain a plurality of sub-areas. It can be understood that, since the target area is a longitude and latitude grid, each sub-area divided by the target area is also equivalent to the longitude and latitude grid, and has longitude and latitude information.
The server may determine the amount of reporting of the location information corresponding to each sub-area, i.e., determine the amount of location information reported in each sub-area. The server can compare the reporting amount of the position information corresponding to each sub-area, determine the sub-area with the largest reporting amount of the position information, and position the center point of the determined sub-area to obtain the center point of the sub-area. It can be understood that, because the subareas are longitude and latitude grids, the longitude and latitude corresponding to the central point of the subareas can be obtained according to the longitude and latitude information corresponding to the determined subareas.
Fig. 11-13 are schematic diagrams of determining point of interest coordinates in one embodiment. Referring to fig. 11, the sub-area divided into the inside of the target area is 25 sub-areas. The number in each sub-area shown in fig. 12 is the reporting amount of the position information corresponding to the sub-area. The sub-area with the largest reporting amount of the position information needs to be screened out, namely, the sub-area with the number of 107 is displayed (the number of the position information reported in the sub-area is 107). As shown in fig. 13, the server may perform center point positioning on the sub-area with the display number of 107 to obtain a center point (the black dot in fig. 13 is the center point), and calculate the longitude and latitude (i.e., longitude: x, latitude: y) of the center point as the position coordinates of the convenience store. As is clear from fig. 13, the sub-area with the number 107 is displayed, that is, the sub-area with the largest amount of reported position information, and the closer to the sub-area, the larger the amount of reported position information is, which means that the resource transfer processing occurring in the sub-area and the vicinity thereof is the largest, and the more likely the real position of the convenience store is in the sub-area. As shown in fig. 13, the center point obtained by final positioning is very close to the actual position of the convenience store, so that the position coordinates of the interest points excavated by the embodiment of the application are very accurate.
It can be appreciated that since the target region is obtained through multiple rounds of region division and region screening, the interest points are located in the corresponding target region to a large extent, i.e., the target region can already reflect the positions of the interest points more accurately. And subdividing the target area again, and determining the subarea with the largest reporting amount of the position information from the target area, wherein the determined subarea with the largest reporting amount of the position information can more accurately reflect the position of the interest point. Therefore, the longitude and latitude of the determined center point are used as the position coordinates of the interest point corresponding to the target area, so that the true position of the interest point can be accurately reflected.
It can be understood that the position coordinates of the points of interest mined by the method according to the embodiments of the present application may be stored in the point of interest information base of the map, so as to update the point of interest information base timely and accurately. Then, the accurate position coordinates of the interest points can be obtained from the interest point information base, so that the accuracy of subsequent processing is improved. In addition, the position coordinates of the interest points mined by the method according to the embodiments of the present application may also be applied to a recommendation algorithm based on LBS (Location Based Services, location-based services). For example, when the interest point is pushed to the user terminal, the appropriate interest point in the current position can be accurately recommended to the user terminal according to the position coordinates of the mined interest point. For example, nearby offline stores can be more accurately recommended to the user terminal. Furthermore, by using the method in this embodiment, the position coordinates of the interest points are determined by using the position information reported during the resource transfer processing, and the changes of the interest points such as adding, closing and transferring can be quickly found according to the changes of the position coordinates of the same interest point, so that the positions of the interest points can be updated or supplemented in time.
It can be understood that the method for mining the interesting points in the embodiments of the present application is equivalent to using artificial intelligence technology, and mining the positions of the interesting points by analyzing big data of the position information set.
Artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use the knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision.
The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
It can be appreciated that the point of interest mining method in the embodiments of the present application is equivalent to using big data processing techniques in artificial intelligence techniques.
Big data (Big data) refers to a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is a massive, high-growth-rate and diversified information asset which needs a new processing mode to have stronger decision-making ability, insight discovery ability and flow optimization ability. With the advent of the cloud age, big data has attracted more and more attention, and special techniques are required for big data to effectively process a large amount of data within a tolerant elapsed time. Technologies applicable to big data include massively parallel processing databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the internet, and scalable storage systems. For example, according to the method for mining the interest points in the embodiments of the present application, big data analysis processing can be implemented based on the cloud computing platform, so that the positions of the interest points are mined.
It should be understood that, although the steps in the flowcharts are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps in each flowchart may include a plurality of steps or stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily sequential, and may be performed in rotation or alternatively with at least a portion of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 14, there is provided a point of interest location mining apparatus, which may employ a software module or a hardware module, or a combination of both, as a part of a computer device, and specifically includes: an acquisition module 1402, a region partitioning module 1404, a screening module 1406, and a location coordinate positioning module 1408, wherein:
the acquiring module 1402 is configured to acquire location information reported when performing resource transfer processing at the point of interest, and obtain a location information set.
A region dividing module 1404, configured to determine a reference region in the map where each piece of location information in the set of location information is located; and carrying out longitude and latitude grid division processing on the reference region to obtain a candidate region.
A screening module 1406, configured to screen, from the candidate areas, a target area corresponding to the interest point; and the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area.
A location coordinate positioning module 1408 is configured to position location coordinates of the point of interest in the target area.
In one embodiment, the reference area is a latitude and longitude grid pre-divided on the map according to a preset latitude and longitude side length. The region division module 1404 is further configured to locate each location information in the set of location information in the map; determining longitude and latitude grids in which each piece of position information is positioned after being positioned in a map, and obtaining a reference area; the reference area is at least one.
In one embodiment, the screening module 1406 is further configured to determine a reporting amount of the location information corresponding to each candidate area; filtering out candidate areas with the reporting amount of the position information being greater than or equal to the reporting amount threshold value; the reporting amount threshold is determined according to the total number of the reporting amounts of the position information; and screening target areas corresponding to the interest points from the filtered candidate areas.
In one embodiment, the screening module 1406 is further configured to perform extremum detection processing on the reported amount of the position information of the filtered candidate area, so as to obtain an extremum area; and determining a target area of the interest point according to the extremum area.
In one embodiment, the points of interest are multiple points of interest that belong to the same object; the extremum area is a plurality of; the screening module 1406 is further configured to iteratively select a target extremum area from the plurality of extremum areas, and select a neighborhood of the target extremum area from the map according to a preset neighborhood selection condition; determining an extremum region in the neighborhood to obtain a reference extremum region; when the reporting amount approaching condition is met between the target extremum region and the reference extremum region, judging the target extremum region as a target region of the corresponding interest point; the report amount approach condition refers to a preset condition indicating that the report amount of the position information corresponding to the target extremum region is approaching to the report amount of the position information corresponding to the reference extremum region.
In one embodiment, the screening module 1406 is further configured to sort the target extremum region and the reference extremum region in descending order according to the reported amount of the location information; determining the demarcation times according to the sorting result; the ratio of the position information reporting amount corresponding to the demarcation frequency to the position information reporting amount corresponding to the previous demarcation frequency is smaller than or equal to a preset threshold value; when the level of the target extremum area is before the demarcation level, the target extremum area is judged to be the target area; and when the level of the target extremum area is behind the demarcation level, judging the target extremum area as a non-target area.
In one embodiment, the screening module 1406 is further configured to sequentially select a current rank from a first rank in the ranking result; and when the ratio of the position information reporting amount corresponding to the next position of the current position is larger than a preset threshold value, the next position is used as the current position to be processed in an iterative way until the ratio is smaller than or equal to the preset threshold value, and the next position of the current position is judged to be the demarcation position.
In one embodiment, the screening module 1406 is further configured to obtain a preset radius value; and selecting a circular area on the map according to the radius value by taking the target extremum area as a circle center, and taking the circular area as a neighborhood of the target extremum area.
In one embodiment, the radius value is a plurality. The screening module 1406 is further configured to, when the reference extremum area in the neighborhood selected according to the previous radius value and the target extremum area meet the reporting amount approaching condition, continue selecting the neighborhood according to the next radius value for iterative processing until the reference extremum area in the neighborhood selected according to the last radius value and the target extremum area meet the reporting amount approaching condition, and determine the target extremum area as the target area of the corresponding interest point; the last radius value is smaller than the next radius value.
In one embodiment, the location coordinate positioning module 1408 is further configured to divide the target area into a plurality of sub-areas; determining the reporting amount of the position information corresponding to each sub-area; and carrying out center point positioning on the subarea with the largest reporting amount of the position information, and acquiring the longitude and latitude of the positioned center point to obtain the position coordinates of the interest point.
In one embodiment, the acquiring module 1402 is further configured to acquire a set of resource transfer data; the resource transfer data is data generated when the interest point performs resource transfer processing; each piece of resource transfer data carries position information reported when the resource transfer processing is carried out; and respectively extracting the carried position information from each piece of resource transfer data in the resource transfer data set to obtain a position information set.
In one embodiment, the point of interest is an offline store, and the resource transfer process includes a payment process and location information, which is location information reported by the mobile terminal when performing the payment process in the offline store.
For specific limitations on the point of interest location mining apparatus, reference may be made to the above limitations on the point of interest location mining method, and no further description is given here. The various modules in the point of interest location mining apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server or a terminal, and the internal structure of which may be as shown in fig. 15. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a point of interest mining method.
It will be appreciated by those skilled in the art that the structure shown in fig. 15 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements are applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it is possible for a person skilled in the art to make several variants and modifications without departing from the inventive concept, which fall within the scope of protection of the present application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims (26)

1. A method of point of interest mining, the method comprising:
acquiring position information reported by the same object when resource transfer processing is performed at a plurality of interest points, and obtaining a position information set;
determining a reference area in which each piece of position information in the position information set is located in a map;
performing longitude and latitude grid division on the reference region to obtain a candidate region;
Performing extremum detection processing on the reported quantity of the position information of the candidate region so as to determine a plurality of extremum regions from the candidate region; each of the interest points corresponds to one of the extremum regions; iteratively selecting a target extremum region from the extremum regions, and selecting a neighborhood of the target extremum region from the map according to a preset neighborhood selection condition;
determining an extremum region in the neighborhood to obtain a reference extremum region;
when the report amount approach condition is met between the target extremum region and the reference extremum region, judging the target extremum region as a target region of the corresponding interest point; the report amount approach condition is a preset condition indicating that the report amount of the position information corresponding to the target extremum region is close to the report amount of the position information corresponding to the reference extremum region; the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area;
and positioning the position coordinates of the interest point in the target area.
2. The method of claim 1, wherein the reference area is a latitude and longitude grid pre-divided on a map according to a preset latitude and longitude side length;
The determining the reference area where each piece of the location information in the location information set is located in the map includes:
locating each location information in the set of location information in a map;
determining longitude and latitude grids in which each piece of position information is positioned after being positioned in a map, and obtaining a reference area; the reference area is at least one.
3. The method according to claim 1, wherein the method further comprises:
determining reporting amounts of the position information corresponding to the candidate areas respectively;
filtering out candidate areas with the reporting amount of the position information being greater than or equal to a reporting amount threshold; the reporting amount threshold is determined according to the total number of the reporting amounts of the position information; and executing the extremum detection processing on the reporting amount of the position information of the candidate region based on the filtered candidate region so as to determine a plurality of extremum regions from the candidate region and the subsequent steps.
4. A method according to claim 3, wherein the reported location information is location information having latitude and longitude data.
5. The method of claim 4, wherein the reference region is a region of a map used to generalize a location of a point of interest.
6. The method according to claim 1, wherein when the report amount approach condition is satisfied between the target extremum region and the reference extremum region, determining that the target extremum region is a target region of the corresponding interest point comprises:
the target extremum area and the reference extremum area are sorted in descending order according to the reporting amount of the position information;
determining the demarcation times according to the sorting result; the ratio of the position information reporting amount corresponding to the demarcation frequency to the position information reporting amount corresponding to the previous demarcation frequency is smaller than or equal to a preset threshold value;
when the level of the target extremum area is before the demarcation level, judging the target extremum area as a target area;
and when the level of the target extremum area is behind the demarcation level, judging the target extremum area as a non-target area.
7. The method of claim 6, wherein determining the demarcation order based on the ordering result comprises:
sequentially selecting the current bit from the first bit in the sequencing result;
and when the ratio of the position information reporting amount corresponding to the next position of the current position is larger than a preset threshold, the next position is used as the current position to be processed in an iterative way until the ratio is smaller than or equal to the preset threshold, and the next position of the current position is judged to be the demarcation position.
8. The method of claim 1, wherein selecting the neighborhood of the target extremum area from the map according to a preset neighborhood selection condition comprises:
acquiring a preset radius value;
and selecting a circular area on the map according to the radius value by taking the target extremum area as a circle center, and taking the circular area as a neighborhood of the target extremum area.
9. The method of claim 8, wherein the radius value is a plurality;
when the report amount approach condition is satisfied between the target extremum region and the reference extremum region, determining that the target extremum region is the target region of the corresponding interest point includes:
when a reference extremum area in the neighborhood selected according to the last radius value meets the reporting amount approaching condition with the target extremum area, continuing to select the neighborhood according to the next radius value for iterative processing until the reporting amount approaching condition is met between the reference extremum area in the neighborhood selected according to the last radius value and the target extremum area, and judging the target extremum area as a target area of the corresponding interest point; the last radius value is smaller than the next radius value.
10. The method of claim 1, wherein the locating the location coordinates of the point of interest in the target area comprises:
dividing the target area to obtain a plurality of subareas;
determining the reporting amount of the position information corresponding to each sub-region;
and carrying out center point positioning on the subarea with the largest reporting amount of the position information, and acquiring the longitude and latitude of the positioned center point to obtain the position coordinates of the interest point.
11. The method of claim 1, wherein the obtaining location information reported by the same object when performing resource transfer processing at a plurality of points of interest, and obtaining a location information set comprise:
acquiring a set of resource transfer data; the resource transfer data is data generated when the same object performs resource transfer processing on a plurality of interest points; each piece of resource transfer data carries position information reported when the interest point carries out resource transfer processing;
and respectively extracting the carried position information from each piece of resource transfer data in the resource transfer data set to obtain a position information set.
12. The method according to any one of claims 1 to 11, wherein the point of interest is an offline store, the resource transfer process includes a payment process, and the location information is location information reported by a mobile terminal when the mobile terminal performs the payment process in the offline store.
13. A point of interest mining apparatus, the apparatus comprising:
the acquisition module is used for acquiring position information reported when the same object performs resource transfer processing at a plurality of interest points to obtain a position information set;
the area dividing module is used for determining a reference area where each piece of position information in the position information set is located in a map; performing longitude and latitude grid division on the reference region to obtain a candidate region;
the screening module is used for carrying out extremum detection processing on the reported quantity of the position information of the candidate region so as to determine a plurality of extremum regions from the candidate region; each of the interest points corresponds to one of the extremum regions; iteratively selecting a target extremum region from the extremum regions, and selecting a neighborhood of the target extremum region from the map according to a preset neighborhood selection condition; determining an extremum region in the neighborhood to obtain a reference extremum region; when the report amount approach condition is met between the target extremum region and the reference extremum region, judging the target extremum region as a target region of the corresponding interest point; the report amount approach condition is a preset condition indicating that the report amount of the position information corresponding to the target extremum region is close to the report amount of the position information corresponding to the reference extremum region; the reporting amount of the position information corresponding to the target area is higher than that of the position information corresponding to the non-target area;
And the position coordinate positioning module is used for positioning the position coordinates of the interest points in the target area.
14. The apparatus of claim 13, wherein the reference area is a latitude and longitude grid pre-divided on a map according to a preset latitude and longitude side length; the regional division module is also used for positioning each position information in the position information set in the map; determining longitude and latitude grids in which each piece of position information is positioned after being positioned in a map, and obtaining a reference area; the reference area is at least one.
15. The apparatus of claim 13, wherein the screening module is further configured to determine a reporting amount of location information corresponding to each of the candidate regions; filtering out candidate areas with the reporting amount of the position information being greater than or equal to a reporting amount threshold, and executing the extremum detection processing on the reporting amount of the position information of the candidate areas based on the filtered candidate areas so as to determine a plurality of extremum areas from the candidate areas and subsequent steps; and the reporting amount threshold is determined according to the total number of the reporting amounts of the position information.
16. The apparatus of claim 15, wherein the reported location information is location information having latitude and longitude data.
17. The apparatus of claim 16, wherein the reference region is a region of a map used to generalize a representation of a location of a point of interest.
18. The apparatus of claim 17, wherein the screening module is further configured to sort the target extremum region and the reference extremum region in descending order according to a reporting amount of the location information; determining the demarcation times according to the sorting result; the ratio of the position information reporting amount corresponding to the demarcation frequency to the position information reporting amount corresponding to the previous demarcation frequency is smaller than or equal to a preset threshold value; when the level of the target extremum area is before the demarcation level, judging the target extremum area as a target area; and when the level of the target extremum area is behind the demarcation level, judging the target extremum area as a non-target area.
19. The apparatus of claim 18, wherein the screening module is further configured to sequentially select a current rank from a first rank in the ranking result; and when the ratio of the position information reporting amount corresponding to the next position of the current position is larger than a preset threshold, the next position is used as the current position to be processed in an iterative way until the ratio is smaller than or equal to the preset threshold, and the next position of the current position is judged to be the demarcation position.
20. The apparatus of claim 17, wherein the screening module is further configured to obtain a preset radius value; and selecting a circular area on the map according to the radius value by taking the target extremum area as a circle center, and taking the circular area as a neighborhood of the target extremum area.
21. The apparatus of claim 20, wherein the radius value is a plurality; the screening module is further configured to, when a reference extremum area in the neighborhood selected according to the previous radius value and the target extremum area meet a reporting amount approaching condition, continue selecting the neighborhood according to the next radius value for iterative processing until the reference extremum area in the neighborhood selected according to the last radius value and the target extremum area meet the reporting amount approaching condition, and determine that the target extremum area is a target area of the corresponding interest point; the last radius value is smaller than the next radius value.
22. The apparatus of claim 13, wherein the position coordinate positioning module is further configured to divide the target area into a plurality of sub-areas; determining the reporting amount of the position information corresponding to each sub-region; and carrying out center point positioning on the subarea with the largest reporting amount of the position information, and acquiring the longitude and latitude of the positioned center point to obtain the position coordinates of the interest point.
23. The apparatus of claim 13, wherein the means for obtaining is further configured to obtain a set of resource transfer data; the resource transfer data is data generated when the same object performs resource transfer processing on a plurality of interest points; each piece of resource transfer data carries position information reported when the interest point carries out resource transfer processing; and respectively extracting the carried position information from each piece of resource transfer data in the resource transfer data set to obtain a position information set.
24. The apparatus according to any one of claims 13 to 23, wherein the point of interest is an offline store, the resource transfer process includes a payment process, and the location information is location information reported by a mobile terminal when the mobile terminal performs the payment process in the offline store.
25. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 12 when the computer program is executed.
26. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 12.
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CN112084429A (en) * 2020-08-05 2020-12-15 汉海信息技术(上海)有限公司 Data processing method and device, electronic equipment and storage medium
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102853840A (en) * 2012-09-10 2013-01-02 无锡坦程物联网科技有限公司 Method for discovering routine vehicle parking region based on grids
CN105206057A (en) * 2015-09-30 2015-12-30 哈尔滨工业大学深圳研究生院 Detection method and system based on floating car resident trip hot spot regions
CN107070961A (en) * 2016-09-30 2017-08-18 阿里巴巴集团控股有限公司 Hot spot region based on geographic position data determines method and device
CN109615260A (en) * 2018-12-19 2019-04-12 国网北京市电力公司 The method for determining the installation addresses of charging pile
CN110795642A (en) * 2019-09-27 2020-02-14 腾讯科技（深圳）有限公司 Position name generating method and position name display method

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7707214B2 (en) * 2007-02-21 2010-04-27 Donald Martin Monro Hierarchical update scheme for extremum location with indirect addressing
CN104991924B (en) * 2015-06-26 2018-10-09 百度在线网络技术（北京）有限公司 Method and apparatus for the address for determining new supply centre

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102853840A (en) * 2012-09-10 2013-01-02 无锡坦程物联网科技有限公司 Method for discovering routine vehicle parking region based on grids
CN105206057A (en) * 2015-09-30 2015-12-30 哈尔滨工业大学深圳研究生院 Detection method and system based on floating car resident trip hot spot regions
CN107070961A (en) * 2016-09-30 2017-08-18 阿里巴巴集团控股有限公司 Hot spot region based on geographic position data determines method and device
CN109615260A (en) * 2018-12-19 2019-04-12 国网北京市电力公司 The method for determining the installation addresses of charging pile
CN110795642A (en) * 2019-09-27 2020-02-14 腾讯科技（深圳）有限公司 Position name generating method and position name display method

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