TWM634464U - A system for estimating locations of advertisement slots based on telecommunication data - Google Patents

Abstract

A system for estimating the location of advertisement slots based on telecommunications data, which obtains multiple crowd description data and multiple interest point data from multiple telecommunications data; a processor uses a cellular hexagonal spatial index algorithm to redefine crowd description data and interests The accuracy of the point data, the crowd description data and the interest point data are respectively corresponding to the first precision and the second precision of the cellular hexagonal spatial index algorithm; then the second precision interest point data and the first precision crowd description The data are integrated; the processor searches the integrated data of the population description data and the point of interest data, so as to deduce at least one website location that matches both the population description and the point of interest. With the system of this creation, data of different precision can be quickly integrated together.

Description

A system for estimating the location of advertisement slots based on telecommunication data

This creation is related to an advertising delivery technology, especially a system for estimating the location of an advertising slot based on telecommunication data.

Advertising agencies or media agencies need to accurately place advertisements to target customers, thereby increasing the chances of products being sold. One of the keys to achieving precise marketing is to accurately target the attributes of the appropriate crowd. For example, near the park Set up a public broadcasting system. If you can know the description of the crowd near the park, for example, most of the young people like sports, or most of the middle-aged and elderly people like pets. Faced with different groups, the attributes of the advertisements broadcast are different. , in order to resonate with the people watching the advertisement and further generate commercial behavior. This is currently a major solution to increase the effectiveness of advertisements.

Crowd description data is generally obtained through telecommunications data, or in cooperation with telecommunications providers, to obtain the description of the number of people with gender, age, and interest preferences every day within a radius of 35*35 meters of the designated place. This part except In addition to the acquisition of telecom data, there are also technical components of data analysis and statistics, so that the correct crowd description information can be screened out under the massive amount of data. It is also difficult to obtain point of interest (POI) data for location descriptions. How to know how many banks, schools, parks, etc. facilities are around, in addition to spending manual collection, the most effective The identification is obtained using the Google Points of Interest Application Programming Interface (POI API). Although the current technology is organic The above two kinds of data can be obtained, but the integration and use of the telecommunications data of "describing the crowd" and the point-of-interest data of "describing the location" is a problem that cannot be solved technically. Because the accuracy of telecommunication data is based on a point-centered range of 35*35 square meters, if the same accuracy is used to collect information on points of interest, the range is too small, except for a few points of interest. In addition, it also needs to spend a lot of money to open the API. For example, the size of Taiwan is 36197000000 square meters. If the telecom data is 35*35=1225 square meters, 36197000000/1225=29533877, representative 35*35(m) accuracy to type the Google POI API, it takes nearly 30 million times to type one category; if you want to type 90 categories, you need to type the Google POI API to type 30 million times * 90 categories = 2.7 billion times to complete, which is a considerable astronomical figure and cost. Even so, it may not be possible to collect complete POIs, because the accuracy of 35*35(m) is too small, and the amount of information returned by the Google POI API every time is also incomplete.

In view of this, this creation aims at the deficiencies of the above-mentioned conventional technologies and future needs, and proposes a system for estimating the location of the advertisement space based on telecommunication data to solve the above-mentioned deficiencies. The specific structure and implementation methods will be described in detail below:

The main purpose of this creation is to provide a system for estimating the location of advertising slots based on telecommunication data. It uses the Uber H3 cellular hexagonal spatial index algorithm to integrate two kinds of data with different precisions, so as to solve the problem that it was impossible to describe the telecommunication data of the crowd in the past. The problem of integrating with the point-of-interest data that describes the crowd makes the location of the advertising slot more in line with the demand.

Another purpose of this creation is to provide a system for estimating the location of advertisement slots based on telecommunication data, which can expand the search range from one cell to six adjacent cells by using the Uber H3 cellular hexagonal spatial index algorithm.

Another purpose of this creation is to provide a system for estimating the position of an advertisement position based on telecommunication data, which can obtain the surrounding index value of a cell by using the Uber H3 cellular hexagonal spatial index algorithm, and quickly find the surrounding area and ensure that it is a neighboring point , to increase search speed.

In order to achieve the above purpose, this creation provides a system for estimating the location of the advertisement slot based on telecommunication data, which is installed in a server and is suitable for collecting crowd description data and points of interest data, and based on which the location of the advertisement slot is estimated , the system for estimating the position of the advertisement position based on the telecommunication data includes: a first collection module, which obtains multiple crowd description data from multiple telecommunication data; a second collection module, which obtains multiple interest point data; a first database, connected to the first collection module for storing crowd description data; a second database connected to the second collection module for storing point-of-interest data; and a processor connected to the first database and the second database, Use a cellular hexagonal spatial index algorithm to redefine the accuracy of crowd description data and interest point data, map the crowd description data to the first precision of the cellular hexagonal spatial index algorithm, and map the interest point data to the cellular hexagonal data A second precision of the shape space indexing algorithm, and then integrate the point of interest data of the second precision with the crowd description data of the first precision, so as to infer at least one website location that conforms to both the crowd description and the point of interest.

According to the embodiment of the invention, the group description data is obtained from at least one telecommunications provider, and the group description data includes gender, age and interest preference.

According to the embodiment of the invention, the point-of-interest data is obtained by using a point-of-interest collection API, and the point-of-interest data includes public facilities in a specific area.

According to an embodiment of the present invention, a first comparison table corresponding to the first accuracy of the group description data is stored in the first database.

According to an embodiment of the present invention, a second comparison table corresponding to the point of interest data to the second precision is stored in the second database.

According to the embodiment of this invention, both the first precision and the second precision divide a map into a honeycomb grid composed of regular hexagonal cells, and the processor calculates the coordinate point in the first precision for any coordinate point. or an index value of second precision.

According to the embodiment of this invention, when the processor searches for crowd description data in one of the cells of the first precision, it can further calculate the precision of the upper level of the first precision, and merge the six adjacent cells around the cell into cell to expand the search.

According to the embodiment of this invention, when the processor searches for crowd description data or point-of-interest data in one of the cells of the first precision or the second precision, if the required data cannot be found, the cell can be used as a reference point to the surrounding relative Neighboring cells are searched.

10: A system for estimating the location of an advertisement slot based on telecommunication data

12:Server

13: The first collection module

14:Second Collection Module

15: First database

152: The first comparison table

16: Second database

162: The second comparison table

18: Processor

20: Telecommunications provider

22:Internet service provider

30: cell

32: First precision

34: second precision

36: Center cell

38: Upper cell

Figure 1 is a block diagram of the system for estimating the location of an advertisement position based on telecommunication data.

Figure 2 is a flow chart of a system for estimating the location of an advertisement position based on telecommunication data using the invention.

Figure 3 is a schematic diagram of the Uber H3 cellular hexagonal spatial indexing algorithm.

Figure 4 is a schematic diagram of this creation using the Uber H3 cellular hexagonal spatial index algorithm to integrate crowd description data and point-of-interest data.

Figure 5 This creation uses the Uber H3 cellular hexagonal spatial index algorithm to find the schematic diagram of adjacent cells.

The following will clearly and completely describe the technical solutions in the embodiments of the invention with reference to the accompanying drawings in the embodiments of the invention. Obviously, the described embodiments are part of the embodiments of the invention, not all of them. Based on the embodiments in this creation, all other embodiments obtained by those skilled in the art without making progressive efforts belong to the scope of protection of this creation.

It should be understood that when used in this specification and the appended claims, the terms "comprising" and "comprising" indicate the presence of described features, integers, steps, operations, elements and/or components, but do not exclude one or Presence or addition of multiple other features, integers, steps, operations, elements, components and/or collections thereof.

It should also be understood that the terminology used in this specification of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural unless the context clearly dictates otherwise.

It should also be further understood that the term "and/or" used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations .

This creation provides a system for estimating the location of an advertisement position based on telecommunication data. Please refer to Figure 1, which is a block diagram of the system for estimating the location of an advertisement position based on telecommunication data. The system 10 for estimating the position of an advertisement position based on telecommunication data of this creation includes a server 12, and the system 10 for estimating the position of an advertisement position based on telecommunication data includes a first collection module 13 and a second collection module 14. A first database 15, a second database 16 and a processor 18. The first collection module 13 is connected to the host signal of a telecom provider 20 to obtain the telecom data of the telecom provider 20, including the telecom provider 20 Descriptive data such as gender, age, interest preference (for example, which type of webpage is viewed on a mobile phone), mobile trajectory, etc. of the users. The first collection module 13 is connected to the first database 15 , and stores the collected crowd description data in the first database 15 . The second collection module 14 is connected to the host signal of an Internet service provider 22, and the Internet service provider 22, such as Google POI application program interface, obtains the information of the location description, including banks, schools, parks, etc. The location of the facility (i.e. point of interest). The second collection module 14 is connected to the second database 16 and stores the collected POI data in the second database 16 . The processor 18 is connected to the first database 15 and the second database 16 . The processor 18 uses a cellular hexagonal spatial indexing algorithm to redefine the precision of the crowd description data and the point of interest data, and combines the crowd description data and the point of interest data together.

Please also refer to Figure 2, which is a flow chart of a system for estimating the location of an advertisement position based on telecommunication data using this invention. In step S10 , obtain a plurality of population description data and obtain a plurality of POI data from the plurality of telecommunication data. In step S12, the processor 18 uses a cellular hexagonal spatial indexing algorithm to redefine the precision of the crowd description data and the POI data, and maps the crowd description data to a first precision of the cellular hexagonal spatial indexing algorithm. Next, as described in step S14 , the processor 18 maps the point-of-interest data to a second precision of the cellular hexagonal spatial index algorithm, and then integrates the point-of-interest data of the second precision and the crowd description data of the first precision. Finally, as described in step S16, after the group description data and the point-of-interest data are integrated, the processor 18 searches the integrated data by keywords, and then at least one site that matches both the group description and the point-of-interest can be deduced.

The cellular hexagonal spatial index algorithm used in this creation is the Uber H3 algorithm, which divides the space of the earth into recognizable cells, and encodes the latitude and longitude H3 into a hexagonal network Cell index, as shown in Figure 3, plural cells 30 form a honeycomb grid, and each cell 30 is surrounded by 6 adjacent cells 30, and these seven cells 30 can form another Larger hexagons, as shown in Figure 4. The H3 index value of every location in the world is uniform and divided into 16 stages (Level), each stage represents a different accuracy, with stage 0 as the largest diameter (side length 1107712 meters), and stage 15 as the smallest Diameter (side length: 0.5 meters), the smaller the diameter, the higher the precision, so stage 15 has the highest precision. The index value can be deduced upwards and downwards, for example, the area covered by the hexagon of stage 10 is 7 times that of stage 11 (1884*7=13188). The index value (cell id) of the cell 30 can be derived from each other. Assuming that the index value in stage 11 is 8b4ba010e0a1 fff, then through the H3 algorithm, the index value in stage 10 can be deduced to be 8a4ba010e0a7fff. Therefore, with the Uber H3 algorithm, two kinds of data with different precision can be effectively deduced from each other into data with the same precision and mixed.

Based on the above-mentioned principle of Uber H3, as shown in FIG. 4, the processor 18 maps the crowd description data to the first precision 32 of the cellular hexagonal spatial index algorithm, and maps the interest point data to the cellular hexagonal spatial index A second precision 34 of the algorithm, and then integrate the point-of-interest data of the second precision 34 with the crowd description data of the first precision 32 . Since the accuracy of the telecommunications data is 35 square meters, it is most appropriate to correspond to the appropriate H3 index value of stage 11 (radius of about 25 meters). The first precision 32 is thus stage 11 . In addition, the accuracy of the most important POI data is stage 8 (radius about 460 meters) as the second precision 34, so that the number of calls to the Google POI API will be greatly reduced. The whole Taiwan is 36197000000 square meters, and the hexagonal area of each H3 stage 8 is 646367 square meters, 36197000000/646367=56000 times. In other words, it only takes 56,000*90=5,040,000 times to collect all 90 categories. The number is reduced from 2.7 billion times mentioned in the previous technology to 5 million times after using the H3 algorithm, but the collected interest The quality of the point data will not be degraded by this. In this way, the problem of mixing data with two different precisions can be solved For any coordinate point, the processor 18 calculates the index value of the coordinate point in the first precision 32 or the second precision 34, so as to infer at least one site that conforms to the description of the crowd and the point of interest. In addition, the application of the H3 algorithm in this creation can also save the number of calls to the POI API.

In this invention, in addition to storing crowd description data in the first database 15 , the first comparison table 152 corresponding to the first accuracy 32 of the telecommunications data is also stored in the first database 15 . Similarly, in addition to storing the POI data in the second database 16 , the second comparison table 162 corresponding to the second accuracy 34 is also stored in the second database 16 .

In addition, since the index value of the H3 algorithm can be calculated from the index value of the small stage to the index value of the large stage, it can achieve the function of index superposition. When the processor 18 searches for crowd description data in one of the cells 30 of the first precision 32, it can further calculate the upper level of precision of the first precision 32, and merge the six adjacent cells around the cell 30 into the cell 30 to expand your search. For example, assuming that stage 8 is currently used to define this amount of telecom data, of course, when going up to stage 7, the diameter is larger, and when a larger range is obtained, the surrounding six cell data of stage 8 can be integrated. , as a larger range of data index. Take Figure 4 as an example, assuming that only one convenience store is found in a central cell 36, if the number is insufficient, go up to the next level, and frame the surrounding six adjacent cells to become a larger upper cell 38 to You can find more convenience stores that are close to the target point (lat, lon) by using the big basket.

Similarly, based on the H3 algorithm, the characteristics of the surrounding indexes can be known. When the index value of the central cell 36 is known, the index values of the six surrounding cells can be known. Therefore, when the processor 18 searches for crowd description data or point-of-interest data in one of the cells 30 of the first precision 32 or the second precision 34, if the required data cannot be found, the cell 30 can be used as a reference point to the surrounding Neighboring cells are searched. This method can ensure that the found data are adjacent points, and there is no need to calculate the distance between points of interest. The straight-line distance between them can also avoid the shortcoming that each point of interest in the prior art has to be scanned once. This method speeds up the indexing of data, which can be a huge improvement in search speed.

In this creation, the placement device can be an Android box, an electronic billboard, a display screen at a bus station or a convenience store, a screen in an elevator, etc., and can play a designated advertisement at a designated time. This creation uses the Uber H3 algorithm to integrate the accuracy of crowd description data and point-of-interest data, so that you can quickly search for the desired advertising location. For example, if you want a location where most young men like to watch movies, and there is a commercial area nearby, there are department stores. This condition is applicable to the speculation based on telecommunications data provided by this creation The system of ad slot location.

To sum up, this creation provides a system for estimating the location of advertisement slots based on telecommunication data, which can integrate crowd description data and point-of-interest data with different precisions through the index results of the Uber H3 algorithm, effectively saving data purchase and data The number of collections. In addition, as long as a geographical coordinate (lat, lon) is confirmed at a point on the earth, all the index values of this coordinate falling into stage 1~stage 15 can be known, so it is also possible to achieve integrated query among various data, Take out the materials of various stages and mix them for use.

The above-mentioned ones are only preferred embodiments of this creation, and are not intended to limit the scope of this creation. Therefore, all equal changes or modifications based on the characteristics and spirit described in the scope of application for this creation shall be included in the scope of patent application for this creation.

10: A system for estimating the location of an advertisement slot based on telecommunication data

12:Server

13: The first collection module

14:Second Collection Module

15: First database

152: The first comparison table

16: Second database

162: The second comparison table

18: Processor

20: Telecommunications provider

22:Internet service provider

Claims (10)

A system for estimating the location of an advertisement slot based on telecommunication data, which is installed in a server, and is suitable for collecting crowd description data and points of interest data, and infers the setting location of the advertisement slot based on this. The location system includes: A first collection module, which obtains the description data of the plurality of groups of people from the plurality of telecommunication data; A second collection module for obtaining data on multiple points of interest; A first database, connected to the first collection module, for storing the group description data; A second database, connected to the second collection module, for storing the data of the points of interest; and A processor, connected to the first database and the second database, using a cellular hexagonal spatial index algorithm to redefine the precision of the group description data and the interest point data, and map the group description data to A first precision of the cellular hexagonal spatial indexing algorithm, and corresponding the interest point data to a second precision of the cellular hexagonal spatial indexing algorithm, and then combining the interest point data of the second precision with the first precision of the cellular hexagonal spatial indexing algorithm The crowd description data of the first precision are integrated together to infer at least one website location that conforms to both the crowd description and the point of interest. The system for estimating the location of an advertisement position based on telecommunication data as described in Claim 1, wherein the group description data is obtained from at least one telecommunication provider. The system for estimating the location of an advertisement position based on telecommunication data as described in Claim 1, wherein the group description data includes gender, age and interest preference. The system for estimating the location of an advertisement position based on telecommunication data as described in claim 1, wherein the data of the points of interest is obtained by using a point of interest collection API. The system for estimating the location of an advertisement position based on telecommunication data as described in claim 1, wherein the point-of-interest data includes public facilities in a specific area. The system for estimating the location of an advertisement position based on telecommunication data as described in Claim 1, wherein a first comparison table corresponding to the first accuracy of the group description data is stored in the first database. The system for estimating the location of the advertisement position based on the telecommunication data as described in Claim 1, wherein a second comparison table corresponding to the point of interest data with the second precision is stored in the second database. The system for estimating the position of an advertisement position based on telecommunication data as described in Claim 1, wherein both the first precision and the second precision divide a map into a honeycomb grid composed of regular hexagonal plural cells, and The processor calculates the index value of the coordinate point in the first precision or the second precision for any coordinate point. The system for estimating the location of an advertisement position based on telecommunication data as described in claim 8, wherein when the processor searches for the group description data in one of the cells of the first precision, it further calculates to the upper layer of the first precision Accuracy, merge the six adjacent cells around this cell to this cell to expand the search range. The system for estimating the location of an advertisement position based on telecommunication data as described in Claim 9, wherein when the processor searches for the group description data or the point-of-interest data in one of the cells of the first precision or the second precision, If you can't find the required data, use the cell as the reference point to search the surrounding adjacent cells.

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