A smart city location recommendation system and method based on holographic map

Abstract

The present invention provides a smart city location recommendation system and method based on a holographic map, the recommendation system comprising a user classification module, a location storage processing module, a collection module and a recommendation module; the user classification module is used to classify users; the location storage processing module comprises a holographic map location storage database and a location storage processing unit, the collection module is used to collect user locations, real-time time periods and the length of distance between user locations and a number of map locations; the recommendation module is used to perform comprehensive processing based on information from the user classification module, the location storage processing module and the collection module, and derive a recommended location strategy. The present invention can combine the information of the holographic map with the location recommendation of the smart city, so as to solve the problems of the existing smart city location recommendation method being single, and the recommendation matching degree and utilization rate being low.

Description

Technical Field

[0001] The present invention relates to the technical field of holographic map applications, and in particular to a smart city location recommendation system and method based on holographic maps. Background Art

[0002] Holographic map is a kind of map based on computer holographic technology, information communication technology and laser technology, which is simulated and processed by optical holography in electronic computers. This kind of map is called holographic map. It has comprehensive and flexible functions, small size and realistic, and can be used for the production of thematic maps, and can also be used as ordinary maps.

[0003] Among the existing technologies, the existing location recommendation methods of smart cities are relatively simple. It is difficult for users to obtain highly matched recommended resources during travel or daily consumption. The existing recommendation methods are difficult to combine the comprehensive information of each location for recommendation. Therefore, the recommended location is poorly matched with the user's actual needs. Therefore, the utilization rate of the recommendation method is also low. When few people use the recommendation system, its maintenance will become increasingly slack, thus forming a vicious circle. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a smart city location recommendation system and method based on holographic maps, which can combine the information of holographic maps with the location recommendations of smart cities, so as to solve the problems of the existing smart city location recommendation method being single, and the recommendation matching degree and utilization rate being low.

[0005] In order to achieve the above-mentioned object, the present invention is implemented by the following technical solutions: a smart city location recommendation system based on holographic map, the recommendation system includes a user classification module, a location storage and processing module, a collection module and a recommendation module;

[0006] The user classification module is used to classify users; the user classification module is configured with a user classification strategy, which includes: classifying users into young male users, young female users, middle-aged users and elderly users, and setting a young male recommendation index, a young female recommendation index, a middle-aged user recommendation index and an elderly user recommendation index for the young male users, young female users, middle-aged users and elderly users in turn, wherein the recommendation index represents the frequency of recommendation by the recommendation system to the user, and the recommendation indexes are from high to low: the young male recommendation index, the young female recommendation index, the middle-aged user recommendation index and the elderly user recommendation index;

[0007] The location storage processing module includes a holographic map location storage database and a location storage processing unit. The holographic map location storage database stores map location information and location-related parameter information. The location storage processing unit is used to process the location-related parameter information to obtain a location score value.

[0008] The acquisition module is used to acquire the user's location, the real-time time period, and the distance between the user's location and several map locations;

[0009] The recommendation module is used to perform comprehensive processing based on the information of the user classification module, the location storage processing module and the acquisition module, and to derive a recommended location strategy.

[0010] Further, the location-related parameter information includes: average number of visitors to the map location in several time periods and a system score of the map location;

[0011] The location storage processing unit is configured with a location storage processing strategy, which includes: summing up the average number of people at the map location in several time periods within a day to obtain the daily average number of people, and then substituting the daily average number of people and the system score of the map location into the map location score formula to obtain the score value of the map location.

[0012] Furthermore, the map location scoring formula is: Fpd=Rrc×Fxt; wherein, Fpd is the scoring value of the map location, Rrc is the daily average number of people at the map location, and Fxt is the system score of the map location.

[0013] Further, the recommendation module includes a route recommendation unit, the route recommendation unit is configured with a route recommendation strategy, the route recommendation strategy includes: selecting a map position whose route length between the user location and a plurality of map locations is less than or equal to a first route selection length as a preliminary route recommendation position, substituting the route length between the preliminary route recommendation position and the user location and the score value of the preliminary route recommendation position into a route recommendation formula to obtain a route recommendation value;

[0014] The preliminary route recommended positions are arranged from high to low according to the recommended route values, and the preliminary route recommended positions with the first number in the arrangement order are selected for recommendation.

[0015] Furthermore, the route recommendation formula is configured as: Among them, Tlc is the recommended distance value, and Sty is the distance length between the initial recommended distance location and the user location.

[0016] Furthermore, the recommendation module further includes a time period recommendation unit, the time period recommendation unit is configured with a time period recommendation strategy, the time period recommendation strategy includes: firstly obtaining the real-time time period where the user is located at this time, selecting a map position in which the average number of people in the time period is greater than or equal to the first time period person threshold value as a preliminary time period recommendation position, substituting the time period average number of people in the preliminary time period recommendation position and the score value of the preliminary time period recommendation position into the time period recommendation formula to obtain the time period recommendation value;

[0017] The preliminary time period recommended positions are sorted from high to low according to the time period recommendation values, and the second-highest number of preliminary time period recommended positions in the sorting order are selected for recommendation.

[0018] Furthermore, the time period recommendation formula is configured as: Tsjd=Rsc×Fpd; wherein Tsjd is the time period recommendation value, and Rsc is the time period average number of people at the location recommended in the preliminary time period.

[0019] Further, the recommendation module includes a comprehensive recommendation unit, the comprehensive recommendation unit is configured with a comprehensive recommendation strategy, the comprehensive recommendation strategy includes: obtaining a map location whose distance between the user location and a plurality of map locations is less than or equal to a second distance selection length and whose average number of people in the time period is greater than or equal to a second time period threshold as a preliminary comprehensive recommendation location;

[0020] Then, the score of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user's location, and the young male recommendation index are substituted into the young male comprehensive recommendation formula to obtain the young male comprehensive recommendation value, and then the young male comprehensive recommendation values ​​are sorted from high to low for recommendation;

[0021] Then, the score of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user's location, and the young women recommendation index are substituted into the young women comprehensive recommendation formula to obtain the young women comprehensive recommendation value, and then the young women comprehensive recommendation values ​​are sorted from high to low for recommendation;

[0022] Then, the score of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user location, and the middle-aged user recommendation index are substituted into the middle-aged user comprehensive recommendation formula to obtain the middle-aged user comprehensive recommendation value, and then the middle-aged user comprehensive recommendation values ​​are sorted from high to low for recommendation;

[0023] Then, the score of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user location, and the elderly user recommendation index are substituted into the elderly user comprehensive recommendation formula to obtain the elderly user comprehensive recommendation value, and then the elderly users are sorted from high to low according to the comprehensive recommendation value and recommended.

[0024] Furthermore, the comprehensive recommendation formula for young men is configured as follows: The comprehensive recommended formula for young women is: The comprehensive recommendation formula for middle-aged users is: The comprehensive recommendation formula for elderly users is: Among them, Tnaz is the comprehensive recommendation value for young men, Fpz is the score value of the preliminary comprehensive recommendation location, Styz is the distance between the preliminary comprehensive recommendation location and the user location, Zna is the recommendation index for young men, Tnvz is the comprehensive recommendation value for young women, Znv is the recommendation index for young women, Tznz is the comprehensive recommendation value for middle-aged users, Zzn is the recommendation index for middle-aged users, Tlnz is the comprehensive recommendation value for elderly users, and Zln is the recommendation index for elderly users.

[0025] A recommendation method for a smart city location recommendation system based on a holographic map, the recommendation method comprising the following steps:

[0026] Step S10, first classify the users;

[0027] Step S20, then obtaining the map location information and location-related parameter information stored in the holographic map location storage database, and then processing the stored location-related parameter information to obtain a location score value;

[0028] Step S30, obtaining the user location, the real-time time period, and the distance between the user location and several map locations through real-time collection;

[0029] Step S40, performing comprehensive processing based on user classification information, map location storage processing information and user information collected and acquired in real time, and deriving a recommended location strategy.

[0030] Beneficial effects of the present invention: The present invention first classifies users; then obtains map location information and location-related parameter information stored in a holographic map location storage database, and then processes the stored location-related parameter information to obtain a location score value; then obtains the user location, real-time time period, and the length of the distance between the user location and several map locations through real-time collection; finally, based on the user classification information, map location storage processing information, and user information obtained through real-time collection, comprehensive processing is performed, and a recommended location strategy is derived. This recommendation method can make comprehensive recommendations based on the score, distance, and user type of each map location, thereby improving the matching degree of the location recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0032] Figure 1 is a principle block diagram of the recommendation system of the present invention;

[0033] Figure 2 A flow chart of a recommended method of the present invention. DETAILED DESCRIPTION

[0034] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0035] See also Figure 1 The present invention provides a smart city location recommendation system based on holographic map, which can combine the information of holographic map with the location recommendation of smart city to solve the problems of single smart city location recommendation method, low recommendation matching degree and utilization rate.

[0036] The recommendation system includes a user classification module, a location storage processing module, a collection module and a recommendation module.

[0037] The user classification module is used to classify users; the user classification module is configured with a user classification strategy, which includes: classifying users into young male users, young female users, middle-aged users and elderly users, and setting a young male recommendation index, a young female recommendation index, a middle-aged user recommendation index and an elderly user recommendation index for the young male users, young female users, middle-aged users and elderly users respectively, wherein the recommendation index represents the frequency of recommendation by the recommendation system to the user, and the recommendation indexes are from high to low, namely, the young male recommendation index, the young female recommendation index, the middle-aged user recommendation index and the elderly user recommendation index.

[0038] The location storage processing module includes a holographic map location storage database and a location storage processing unit, wherein the holographic map location storage database stores map location information and location-related parameter information; the location storage processing unit is used to process the location-related parameter information to obtain a location score value; the location-related parameter information includes: the average number of people in several time periods of the map location and the system score of the map location; the location storage processing unit is configured with a location storage processing strategy, and the location storage processing strategy includes: summing the average number of people in several time periods of the map location within a day to obtain the daily average number of people, and then substituting the daily average number of people and the system score of the map location into the map location scoring formula to obtain the score value of the map location, the map location scoring formula is: Fpd=Rrc×Fxt; wherein Fpd is the score value of the map location, Rrc is the daily average number of people of the map location, and Fxt is the system score of the map location. The system score of the map location is obtained by combining the information of various existing scoring software, and the daily average number of people is obtained based on the existing database.

[0039] The collection module is used to collect the user's location, the real-time time period, and the distance length between the user's location and several map locations.

[0040] The recommendation module is used to perform comprehensive processing based on the information of the user classification module, the location storage processing module and the collection module, and to derive a recommended location strategy. The recommendation module includes a route recommendation unit, and the route recommendation unit is configured with a route recommendation strategy, which includes: selecting a map position whose route length between the user location and a plurality of map locations is less than or equal to a first route selection length as a preliminary route recommendation position, substituting the route length between the preliminary route recommendation position and the user location and the score value of the preliminary route recommendation position into a route recommendation formula to obtain a route recommendation value; the route recommendation formula is configured as: Among them, Tlc is the recommended distance value, Sty is the distance length between the preliminary recommended distance position and the user's location; the preliminary recommended distance positions are arranged from high to low according to the recommended distance values, and the first number of preliminary recommended distance positions in the arrangement order are selected for recommendation. Some users may care about the length of the distance, so setting a distance recommendation unit can make recommendations based on the user's perception of the length of the distance.

[0041] The recommendation module also includes a time period recommendation unit, which is configured with a time period recommendation strategy. The time period recommendation strategy includes: first obtaining the real-time time period of the user at this time, selecting a map location with an average number of people in the time period greater than or equal to a first time period threshold as a preliminary time period recommended location, substituting the average number of people in the time period of the preliminary time period recommended location and the score value of the preliminary time period recommended location into the time period recommendation formula to obtain the time period recommendation value; the time period recommendation formula is configured as: Tsjd=Rsc×Fpd; wherein Tsjd is the time period recommendation value, and Rsc is the average number of people in the time period of the preliminary time period recommended location; sorting the preliminary time period recommended locations from high to low according to the time period recommendation values, selecting the second largest number of preliminary time period recommended locations in the arrangement order for recommendation, and users have different needs in different time periods of a day. During meal time periods, restaurants are given more priority, and during rest time periods, leisure and entertainment locations are given more priority.

[0042] The recommendation module includes a comprehensive recommendation unit, and the comprehensive recommendation unit is configured with a comprehensive recommendation strategy, which includes: obtaining a map location whose distance between the user location and a plurality of map locations is less than or equal to a second distance selection length and whose average number of people in the time period is greater than or equal to a second time period threshold as a preliminary comprehensive recommendation location;

[0043] Then, the score of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user location, and the young male recommendation index are substituted into the young male comprehensive recommendation formula to obtain the young male comprehensive recommendation value, and then the young male comprehensive recommendation values ​​are sorted from high to low and recommended; the young male comprehensive recommendation formula is configured as: Among them, Tnaz is the comprehensive recommendation value of young men, Fpz is the score value of the preliminary comprehensive recommendation location, Styz is the distance between the preliminary comprehensive recommendation location and the user location, and Zna is the recommendation index for young men. Then, the score value of the preliminary comprehensive recommendation location, the distance between the preliminary comprehensive recommendation location and the user location, and the recommendation index for young women are substituted into the comprehensive recommendation formula for young women to obtain the comprehensive recommendation value for young women. Then, the young women are ranked from high to low according to the comprehensive recommendation value for young women and recommended. The comprehensive recommendation formula for young women is: Tnvz is the comprehensive recommendation value for young women, and Znv is the recommendation index for young women. Young men and women care more about the score than the distance. As long as the score is high, they can accept a longer distance, and locations with high scores are recommended first.

[0044] Then, the score of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user location, and the middle-aged user recommendation index are substituted into the middle-aged user comprehensive recommendation formula to obtain the middle-aged user comprehensive recommendation value, and then the middle-aged user comprehensive recommendation values ​​are sorted from high to low for recommendation; the middle-aged user comprehensive recommendation formula is: Among them, Tznz is the comprehensive recommendation value for middle-aged users, Zzn is the recommendation index for middle-aged users, and then the score value of the preliminary comprehensive recommendation location, the distance between the preliminary comprehensive recommendation location and the user location, and the recommendation index for elderly users are substituted into the comprehensive recommendation formula for elderly users to obtain the comprehensive recommendation value for elderly users, and then the elderly users are ranked from high to low according to the comprehensive recommendation value for elderly users and recommended. The comprehensive recommendation formula for elderly users is: Among them, Tlnz is the comprehensive recommendation value for elderly users, and Zln is the recommendation index for elderly users. Middle-aged and elderly users care more about the length of the journey, so locations that are short in distance are recommended first.

[0045] See also Figure 2 The present invention also provides a recommendation method for a smart city location recommendation system based on a holographic map, the recommendation method comprising the following steps:

[0046] Step S10, first classify the users;

[0047] The users are divided into young male users, young female users, middle-aged users and elderly users, and the young male users, young female users, middle-aged users and elderly users are set with the young male users, young female users, middle-aged users and elderly users as the young male recommendation index, young female recommendation index, middle-aged user recommendation index and elderly user recommendation index, respectively. The recommendation index indicates the frequency of recommendation to the user by the recommendation system, and the recommendation indexes are from high to low as follows: the young male recommendation index, the young female recommendation index, the middle-aged user recommendation index and the elderly user recommendation index;

[0048] Step S20, then obtaining the map location information and location-related parameter information stored in the holographic map location storage database, and then processing the stored location-related parameter information to obtain a location score value;

[0049] The location-related parameter information includes the average number of visitors in several time periods of the map location and the system score of the map location. The average number of visitors in several time periods of the map location within one day is summed up to obtain the daily average number of visitors. The daily average number of visitors and the system score of the map location are then substituted into the map location score formula to obtain the score value of the map location.

[0050] Step S30, obtaining the user location, the real-time time period, and the distance between the user location and several map locations through real-time collection;

[0051] Step S40, performing comprehensive processing based on user classification information, map location storage processing information and user information collected and acquired in real time, and deriving a recommended location strategy;

[0052] The step S40 further includes the following sub-steps:

[0053] Step S401, select map positions whose distance length between the user location and several map locations is less than or equal to the first distance selection length as preliminary route recommended positions, substitute the distance length between the preliminary route recommended position and the user location and the score value of the preliminary route recommended position into the route recommendation formula to obtain the route recommendation value; arrange the preliminary route recommended positions from high to low according to the route recommendation value, and select the first number of preliminary route recommended positions in the arrangement order for recommendation.

[0054] Step S402, first obtain the real-time time period in which the user is currently located, select the map locations within the time period whose average number of people is greater than or equal to the first time period person threshold as the preliminary time period recommended locations, substitute the time period average number of people of the preliminary time period recommended locations and the score value of the preliminary time period recommended locations into the time period recommendation formula to obtain the time period recommendation value; sort the preliminary time period recommended locations from high to low according to the time period recommendation values, and select the second-largest number of preliminary time period recommended locations in the arrangement order for recommendation.

[0055] Step S403, obtaining map locations whose distance between the user location and a plurality of map locations is less than or equal to a second selected distance and whose average number of people in the time period is greater than or equal to a second time period threshold as preliminary comprehensive recommended locations;

[0056] Then, the score value of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user location, and the recommendation index of young men are substituted into the comprehensive recommendation formula of young men to obtain the comprehensive recommendation value of young men, and then the comprehensive recommendation values ​​of young men are sorted from high to low for recommendation; then, the score value of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user location, and the recommendation index of young women are substituted into the comprehensive recommendation formula of young women to obtain the comprehensive recommendation value of young women, and then the comprehensive recommendation values ​​of young women are sorted from high to low for recommendation;

[0057] Then, the score of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user location, and the middle-aged user recommendation index are substituted into the middle-aged user comprehensive recommendation formula to obtain the middle-aged user comprehensive recommendation value, and then the comprehensive recommendation values ​​of middle-aged users are sorted from high to low and recommended; then, the score of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user location, and the elderly user recommendation index are substituted into the elderly user comprehensive recommendation formula to obtain the elderly user comprehensive recommendation value, and then the comprehensive recommendation values ​​of elderly users are sorted from high to low and recommended.

[0058] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A smart city location recommendation system based on holographic maps, It is characterized in that The recommendation system includes a user classification module, a location storage processing module, a collection module and a recommendation module; The user classification module is used to classify users; the user classification module is configured with a user classification strategy, which includes: classifying users into young male users, young female users, middle-aged users and elderly users, and setting corresponding recommendation indexes for each type of user in turn, wherein the recommendation index represents the frequency of recommendation by the recommendation system to the user, and the recommendation indexes are from high to low, namely, the young male recommendation index, the young female recommendation index, the middle-aged user recommendation index and the elderly user recommendation index; The location storage processing module includes a holographic map location storage database and a location storage processing unit. The holographic map location storage database stores map location information and location-related parameter information. The location storage processing unit is used to process the location-related parameter information to obtain a location score value. The acquisition module is used to acquire the user's location, the real-time time period, and the distance between the user's location and several map locations; The recommendation module is used to perform comprehensive processing based on the information of the user classification module, the location storage processing module and the acquisition module, and derive a recommended location strategy; The recommendation module includes a comprehensive recommendation unit, which obtains map locations whose distance between the user location and a plurality of map locations is less than or equal to a second distance selection length and whose average number of people in the time period is greater than or equal to a second time period threshold as preliminary comprehensive recommendation locations; The score of the preliminary comprehensive recommended location, the distance between the preliminary comprehensive recommended location and the user's location, and the above-mentioned user recommendation indexes are substituted into the comprehensive recommendation formula of the user to obtain a comprehensive recommendation value, and then the locations are sorted from high to low according to the comprehensive recommendation value and recommended.

2. According to claim 1, a smart city location recommendation system based on holographic map, It is characterized in that The location-related parameter information includes: average number of visitors to the map location in several time periods and the system score of the map location; The location storage processing unit is configured with a location storage processing strategy, which includes: summing up the average number of people at the map location in several time periods within a day to obtain the daily average number of people, and then substituting the daily average number of people and the system score of the map location into the map location score formula to obtain the score value of the map location.

3. A smart city location recommendation system based on holographic map according to claim 2, It is characterized in that The map location scoring formula: ; Among them, Fpd is the score value of the map location, Rrc is the daily average number of visitors to the map location, and Fxt is the system score of the map location.

4. A recommendation method for a smart city location recommendation system based on a holographic map according to any one of claims 1 to 3, It is characterized in that The recommended method comprises the following steps: Step S10, first classify the users; Step S20, then obtaining the map location information and location-related parameter information stored in the holographic map location storage database, and then processing the stored location-related parameter information to obtain a location score value; Step S30, obtaining the user location, the real-time time period, and the distance between the user location and several map locations through real-time collection; Step S40, performing comprehensive processing based on user classification information, map location storage processing information and user information collected and acquired in real time, and deriving a recommended location strategy.

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