## 2 The Current Study
Most visitors will not leave any personal data at the time of ticket sales. Therefore, if there are similar events in the future and want to reach the past visitors, they can only be implemented through extensive advertising again but lack more effective media exposure. Case 3: Delivering according to specific audience types at the current location  
For example, passersby in front of specific outdoor media, or men and women who want to marry in front of Yuelao Temple. Large brand companies often have the budget to purchase outdoor media and can advertise to offline audiences. However, if they want to interact across screens with audiences, the only way to do so is to let audiences scan the QR code on outdoor media screens. Then they can participate through their phone, and there is no way for the brand owner to directly contact the audience in front of the outdoor media. Another example is the dating platform services, which can only be matched online and cannot match real-time and on-site through the data of a specific time and location (as shown in Figure 3). Although some apps have done similar services before (such as finding nearby people), this kind of matching is still only suitable for people who have appeared in the place now (or not long ago). It is also through the server to do non-immediate matching, and it is still a gap compare to real-time and on-site matching. Figure 2: Audience feature in Case 2. Case 4: Real-time delivery based on movement trajectory  
For example, tourists from nearby business districts or drivers start from a particular place and will pass by a specific location. Many famous shopping districts or night markets will have some foot massage shops. Their most desired advertising targets are passersby who have just been shopping nearby for a while (as shown in Figure 4), but such advertising audiences are difficult to receive under the traditional advertising system. Since the traditional advertising system cannot collect and analyze all the object positions in a specific location in real-time, this kind of advertising audience screening needs eventually becomes a situation. That is, there is only market demand but no solution. Similarly, suppose a specific business wants to capture the employees of a few specific commercial buildings nearby and hopes to place advertisements when these employees pass near their own stores after get off work. It is also impossible to implement these kinds of needs in the current advertising system on the market. Figure 4: Audience feature in Case 4. Figure 3: Audience feature in Case 3. Case 5: Serving according to mobile location habits  
For example, office workers who commute for a long time or couriers who move frequently. Real estate brokers or landlords often want to promote to potential housing demand users. The common feature of these users is that they will have a long commuting distance or commuting time. Because the location information collected by the current advertising system is usually not continuous, it is difficult to ensure that all objects on a specific commuting route can be reached. It is impossible to find an advertising delivery service that can provide this kind of advertising delivery service in the current market. Another example is an insurance company or a nutritious food company that may be very interested in long-distance moving business or courier personnel. Still, in terms of advertising systems that can be found on the market, no system can achieve this type of advertising (see Figure 5). The main reason lies in the difficulty of collecting location information, and the audience that can be reached based on the location is not broad enough [14; 15; 16]. These advertisement requirements, as mentioned above, are all business problems that cannot be implemented or are costly to implement in the current advertising market. This research aims to find out a general data processing mode based on the real-time user geographic data returned by a base station through the development of positioning technology brought by 5G.