CN107872765B - Big data hybrid positioning method and device - Google Patents
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Abstract
The invention discloses a big data hybrid positioning method and device, and relates to the field of big data application. The method comprises the following steps: acquiring a base station and the type of the base station in a target area; determining the coverage area of a base station in a target area; and positioning the crowd in the target area according to the crowd characteristics and the base station type of the target area and the coverage area. The invention can accurately position the target area crowd by analyzing the type and the coverage area of the target area base station and the characteristics of the area crowd.
Description
Technical Field
The invention relates to the field of big data application, in particular to a big data hybrid positioning method and device.
Background
The positioning of regional crowds is mainly to judge the positions of crowds according to the coverage condition of base stations in the region, and currently, AFLT (Advanced forward link three-dimensional measurement) triangulation technology is generally adopted. When positioning operation is carried out, the mobile phone monitors the pilot frequency information of a plurality of base stations at the same time, the distance from the mobile phone to the nearby base station is determined by utilizing the chip time delay, and finally the position of the user is calculated by using a triangulation method.
The existing regional population positioning method has the following problems: positioning errors are large, especially in complex areas with wide base station coverage and large crowd density, a triangular positioning method is adopted to obtain a large area range, and accurate positioning of crowd in small areas such as scenic spots, office buildings, residential quarters, shopping malls and the like is difficult to perform; the requirement of the client on the precision of the regional position big data product cannot be met.
Disclosure of Invention
The invention aims to provide a big data hybrid positioning method and device so as to improve the positioning accuracy of regional people.
According to an aspect of the present invention, a big data hybrid positioning method is provided, including: acquiring a base station and the type of the base station in a target area; determining the coverage area of a base station in a target area; and positioning the crowd in the target area according to the crowd characteristics and the base station type of the target area and the coverage area.
Further, the target area crowd characteristics comprise base stations for logging in by the target area crowd and time information for logging in the base stations.
Further, the method further comprises: and determining the target area as a mixed distribution area or an island-type area.
Further, the positioning the target area according to the target area crowd characteristics, the base station type and the coverage area comprises: if the target area is an island-type area, the target area population is a landing population of the island-type base station in the island-type area; if the target area is a mixed distribution area, judging whether the base station of the target area comprises an indoor distribution base station, and if the target area comprises the indoor distribution base station, determining that the target area population is a landing population of the indoor distribution base station; and if the indoor distribution base station is not included, determining an association relation base station according to the base station coverage range of the target area, and determining the target area crowd according to the association relation base station.
Further, determining an association relation base station according to the base station coverage of the target area, and determining the target area population according to the association relation base station includes: if the target area has the association relation base station, the target area group is a landing group of the association relation base station; and if the target area has no association relation base station, determining the target area crowd according to the time information of the target area crowd logging in the base station.
Further, the method further comprises: and determining the permanent population and the temporary population of the target area according to the time information of the target area population logging in the base station.
According to another aspect of the present invention, there is also provided a big data hybrid positioning apparatus, including: a target area base station obtaining unit, configured to obtain a base station and a type of the base station in a target area; a base station coverage area determining unit, configured to determine a base station coverage area of a target area; the crowd characteristic analysis unit is used for analyzing the crowd characteristics of the target area; and the area crowd positioning unit is used for positioning the crowd in the target area according to the crowd characteristics of the target area, the type of the base station and the coverage area.
Further, the target area crowd characteristics comprise base stations for logging in by the target area crowd and time information for logging in the base stations.
Further, the apparatus further comprises: and the target area characteristic determining unit is used for judging whether the target area is a mixed distribution area or an island type area.
Further, the regional population positioning unit is used for determining that the target region population is a landing population of the island-type base station in the island-type region if the target region is the island-type region; if the target area is a mixed distribution area and the base station of the target area comprises an indoor distribution base station, the target area population is a login population of the indoor distribution base station; and if the base station of the target area does not contain the indoor distributed base station, determining an association relation base station according to the coverage range of the base station of the target area, and determining the target area crowd according to the association relation base station.
Furthermore, the area crowd positioning unit is also used for determining that the target area crowd is a landing crowd of the association base station if the base station of the target area does not contain the indoor distribution base station but the target area has the association base station; and if the target area has no association relation base station, determining the target area crowd according to the time information of the target area crowd logging in the base station.
Furthermore, the area crowd positioning unit is also used for determining the permanent crowd and the temporary crowd in the target area according to the time information of the target area crowd logging in the base station.
Compared with the prior art, the method comprises the steps of acquiring the base station in the target area and the type of the base station; determining the coverage area of a base station in a target area; and positioning the crowd in the target area according to the crowd characteristics and the base station type of the target area and the coverage area. By analyzing the type and coverage area of the base station in the target area and the characteristics of the crowd in the area, the crowd in the target area can be accurately positioned.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
The invention will be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:
fig. 1 is a schematic flow chart of an embodiment of a big data hybrid positioning method according to the present invention.
Fig. 2 is a schematic flowchart of another embodiment of a big data hybrid location method according to the present invention.
Fig. 3 is a schematic flow chart of a target area crowd positioning algorithm.
Fig. 4 is a schematic structural diagram of an embodiment of a big data hybrid location device according to the present invention.
Fig. 5 is a schematic structural diagram of another embodiment of a big data hybrid location device according to the present invention.
FIG. 6 is a diagram illustrating a specific application of the regional population big data analysis of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
Fig. 1 is a schematic flow chart of an embodiment of a big data hybrid positioning method according to the present invention. The method comprises the following steps:
in step 110, base stations and types of base stations in the target area are obtained. For example, the target area and the possible base stations around the target area can be determined according to the latitude and longitude information of the base stations in the base station information table, and the types of the relevant base stations can be identified, such as determining whether the base stations are macro stations or indoor distributed base stations.
In step 120, the coverage area of each base station in the target area is determined. The coverage area of the base station can be obtained through two modes, the first mode is that the coverage radius of the base station is determined according to the base station transmitting power, the antenna height and the azimuth angle of the base station information table, and the coverage area of the base station is determined according to the coverage radius. The second mode is field test of a target area, usually, according to the main people flow direction and distribution of the target area, field multipoint test is carried out, the number of test points is not less than 10, the test points have representativeness of crowd flow direction and detention, such as a gate of the target area, an elevator, a main scenic spot, a must-pass intersection and the like, the information of a test point login base station can be recorded through a mobile phone engineering mode, and the ID of a crowd resident base station in the area is recorded. In addition, according to the pedestrian flow characteristics and the coverage condition of the target area, key stations in the area can be determined.
In step 130, the target area population is located according to the target area population characteristics and the base station type and coverage area. The target area crowd characteristics comprise base stations for logging in by the target area crowd and time information for logging in the base stations. For example, a base station where people log in can be accurately acquired in an area, and in addition, people in a target area are usually separated by time, for example, the area people are office workers, and have the characteristic of gathering the target area during office hours, tourists in a tourist area have the characteristic of gathering people in open time in the tourist area, and residences have the characteristic of gathering people at night.
If the target area is an office building, the crowd in the area has the feature of gathering the working time, and if the base station in the area is an indoor distribution base station and is a base station which the office building personnel must log in, the crowd in the area can be determined to be the crowd logging in the indoor distribution base station.
In the embodiment, the target area crowd can be accurately positioned by analyzing the type and the coverage area of the target area base station and the characteristics of the area crowd, in addition, the method does not need to carry out triangulation positioning operation, but directly applies the existing wireless order data and the position updating data, and can position the target crowd by combining with the area characteristic information, thereby improving the positioning accuracy and reducing the calculation workload.
Fig. 2 is a schematic flowchart of another embodiment of a big data hybrid location method according to the present invention. The method comprises the following steps:
in step 210, it is determined whether the target area is a mixed distribution area or an island-type area, and if the target area is a mixed distribution area and the base station is in cross coverage, association analysis of adjacent areas is required.
In step 220, the base station and the type of the base station in the target area are obtained, for example, the base station possibly included in the target area is determined, and the type of the base station is a macro base station or an indoor distributed base station.
In step 230, the coverage area of the base station in the target area is determined, and according to the people flow characteristics of the people in the target area and the coverage condition of the base station, association relationship sites in the area can be determined, for example, A, B, C base stations are located in the target area, a base station covers a ticket office, an amusement park and a lake center island, C base station covers the amusement park and the lake center island, B base station covers the lake center island, and then the a base station and the C base station are association relationship base stations.
In step 240, the characteristics of the target area crowd are determined, that is, the base station where the target area crowd logs in and the time information of logging in the base station are determined.
In step 250, the people in the target area are located according to the characteristics of the target area, the type and coverage area of the base station, the base station where the people in the area log in and the time information of logging in the base station. Specifically, as shown in fig. 3:
in step 310, it is determined whether the target region is an island region, if yes, step 320 is performed, otherwise, step 330 is performed.
In step 320, the target area population is the landing population of the islanding base station in the islanding area.
In step 330, it is determined whether the target area includes an indoor distributed base station, if so, step 340 is performed, otherwise, step 350 is performed.
In step 340, the target area population is a landing population of the indoor distributed base station. If the people to live regularly and the temporary people are determined according to the time information of the target area people logging in the base station, for example, the people to live regularly are the people who log in the indoor distribution base station for more than 3 days in a week, and the temporary people are the people who log in the indoor distribution base station for less than 3 days in a week.
In step 350, it is determined whether the target area has an associated base station, if yes, step 360 is executed, otherwise, step 370 is executed.
In step 360, the target area population is the landing population of the association relationship base station, for example, when the sites where the population must pass are base stations a and C, the area population is the landing population of base stations a & B. Of course, the permanent population and the temporary population can be determined according to the time information of the target area population logging in the base station.
In step 370, the target area population is determined according to the time information of the target area population logging in the base station site, for example, when there is no indoor distribution system in the area and there is no correlation site, but the area target population and the non-target area population can be separated by the time characteristic, if the analysis of the office building staff population is the population logging in the base station in the specific time (from 8 am to 6 am).
In the embodiment, the geographical information of the base station is combined with the characteristics of the analyzed area, and the building and the area can be accurately associated according to the dimensional information such as the crowd characteristics, the time characteristics and the like, so that the problem that the people in the area are not accurately positioned based on the geographical information of the base station is solved.
Fig. 4 is a schematic structural diagram of an embodiment of a big data hybrid location device according to the present invention. The device comprises a target area base station acquisition unit 410, a base station coverage area determination unit 420, a crowd characteristic analysis unit 430 and an area crowd positioning unit 440, wherein:
the target area base station acquiring unit 410 is configured to acquire a base station in the target area and a type of the base station. For example, the target area and the possible base stations around the target area can be determined according to the latitude and longitude information of the base stations in the base station information table, and the types of the relevant base stations can be identified, such as determining whether the base stations are macro stations or indoor distributed base stations.
The base station coverage determining unit 420 is configured to determine coverage of each base station in the target area. The coverage area of the base station can be obtained through two modes, the first mode is that the coverage radius of the base station is determined according to the base station transmitting power, the antenna height and the azimuth angle of the base station information table, and the coverage area of the base station is determined according to the coverage radius. The second mode is field test of a target area, usually, according to the main people flow direction and distribution of the target area, field multipoint test is carried out, the number of test points is not less than 10, the test points have representativeness of crowd flow direction and detention, such as a gate of the target area, an elevator, a main scenic spot, a must-pass intersection and the like, the information of a test point login base station can be recorded through a mobile phone engineering mode, and the ID of a crowd resident base station in the area is recorded. In addition, according to the pedestrian flow characteristics and the coverage condition of the target area, key stations in the area can be determined.
The crowd characteristic analysis unit 430 is used for analyzing the crowd characteristics of the target area. The target area crowd characteristics comprise base stations for logging in by the target area crowd and time information for logging in the base stations. For example, a base station where people log in can be accurately acquired in an area, and in addition, people in a target area are usually separated by time, for example, the area people are office workers, and have the characteristic of gathering the target area during office hours, tourists in a tourist area have the characteristic of gathering people in open time in the tourist area, and residences have the characteristic of gathering people at night.
The regional population positioning unit 440 is configured to position the population in the target region according to the target region population characteristics and the base station type and coverage area. For example, if the target area is an office building, the crowd in the area has a feature of gathering time on duty, and if the base station in the area is an indoor distribution base station and is a base station that the office building personnel have to log in, the crowd in the area can be determined to be the crowd logging in the indoor distribution base station.
In this embodiment, by analyzing the type and coverage of the target area base station and the characteristics of the area population, the target area population can be more accurately located. In addition, the data acquisition in the embodiment is simple, namely, the existing wireless order data and the location updating data are utilized, so that the positioning cost is low.
Fig. 5 is a schematic structural diagram of another embodiment of a big data hybrid location device according to the present invention. The device comprises a target area characteristic determining unit 510, a target area base station acquiring unit 520, a base station coverage determining unit 530, a crowd characteristic analyzing unit 540 and an area crowd positioning unit 550, wherein:
the target area characteristic determining unit 510 is configured to determine whether the target area is a mixed distribution area or an island-type area, and if the target area is the mixed distribution area and the base station covers the target area in a cross manner, association analysis of adjacent areas is required.
The target area base station obtaining unit 520 is configured to obtain a base station and a type of the base station in the target area, for example, determine the base station that may be included in the target area, where the type of the base station is a macro base station or an indoor distributed base station.
The base station coverage determining unit 530 is configured to determine a base station coverage of the target area, and according to the crowd flow characteristics and the base station coverage of the target area, may determine an association station in the area, for example, there are A, B, C base stations in the target area, where a base station covers a ticket office, an amusement park, and a lake center island, C base station covers the amusement park and the lake center island, and B base station covers the lake center island, and then the a base station and the C base station are association base stations.
The crowd characteristic analysis unit 540 determines the crowd characteristics of the target area, i.e. determines the base station where the crowd in the target area logs in and the time information of logging in the base station.
The regional crowd positioning unit 550 is configured to position the crowd in the target region according to the characteristics of the target region, the type and coverage of the base station, the base station where the regional crowd logs in, and the time information of logging in the base station. For example, if the target area is an island-type area, the target area population is a landing population of the island-type base station in the island-type area. If the target area is a mixed distribution area and the target area comprises the indoor distribution base station, the target area population is a landing population of the indoor distribution base station. If the target area is a mixed distribution area, but the target area does not contain indoor distribution base stations, and if the target area has an association relation base station, the target area population is a landing population of the association relation base station; if the target area has no association relation base station, determining the target area population according to the time information of the target area population logging in the base station site, for example, when no indoor distribution system exists in the area and no association relation site exists, but the area target population and the non-target area population can be separated by the time characteristic, if the office building staff population is analyzed, the target area population is the population logging in the base station in the specific time (from 8 am to 6 am).
In addition, the area group positioning unit 550 is further configured to determine a permanent group and a temporary group in the target area according to the time information of the target area group logging in the base station, for example, the permanent group is a group that logs in a certain base station for more than 3 days in a week, and the temporary group is a group that logs in the base station for less than 3 days in a week.
In the embodiment, the geographical information of the base station is combined with the characteristics of the analyzed area, and the building and the area can be accurately associated according to the dimensional information such as the crowd characteristics, the time characteristics and the like, so that the problem that the people in the area are not accurately positioned based on the geographical information of the base station is solved.
FIG. 6 is a diagram illustrating a specific application of the regional population big data analysis of the present invention. The area comprises residential areas, lake-center island artificial lakes, lake-center islands, office buildings, lake-center island scenic spots, scenic-area amusement parks, scenic-area ticket-selling halls and the like, and further comprises a macro base station A, an indoor distribution base station B, a macro base station C, a macro base station D and the like. The specific analysis process is as follows:
firstly, the target area is a mixed area, a non-island area and a base station cross coverage, so that adjacent area correlation analysis is required.
And secondly, the area comprises 4 base stations of the ABCD, wherein B is an indoor distribution station, and ACD is a macro base station.
Thirdly, analyzing the coverage condition of the base station as follows, and through field engineering mode testing, knowing that the base station A covers a ticket selling hall, an amusement park, an artificial lake, a lake center island and an office building; the indoor distribution base station B covers an office building; the base station C covers the lake center island, the amusement park and the office building; base station D covers the residential area, the lake-center island.
Analyzing the crowd characteristics of a target area, wherein the target area comprises a house, a scenic spot and an office area, and the crowd characteristics of the target area are obvious, namely crowd gathers when residents in the house area are 19 hours to 5 hours in the morning; office workers gather from 9 hours in the daytime to 17 hours in the afternoon; the scenic spot is the scenic spot of charging, and the visitor has open time to gather, except leading and scenic spot staff, few many days repeated visitor who gets into the scenic spot.
Fifthly, regional population location analysis is as follows:
for the positioning of the crowd in the office building, the coverage base station of the office building comprises three stations ABC, wherein the station B is an indoor distribution base station, the station does not cover the area outside the building, and the station is a station which is necessary for the staff in the office building to pass through, so that the crowd in the office building is the login crowd of the indoor station B. If the office building staff and the temporary visit staff are continuously distinguished, adding time characteristics, and if the office building resident crowd is the crowd logging in the indoor station B for more than 3 days in a week; the visit crowd of the office building on a certain day is the crowd logging in the indoor station B, namely the crowd logging in the indoor station B for more than 3 days in a week.
For the positioning of tourists in the lake-center island scenic area, the coverage base station of the lake-center island scenic area comprises ACD3 macro stations, wherein ticket-selling halls and amusement park areas are covered by the stations A and C, and the ACD stations in the lake-center island are covered, and when the characteristic ticket-selling halls and the lake-center island in the area are analyzed to be the places where the tourists enter the area to travel, the scenic area people are 9 to 16 (login A | login C). If the scenic spot crew is distinguished, the scenic spot visitors are the 9 to 16 time (login a | login C) crowd-one week for more than 3 days (login a | login C).
For the positioning of the population in the residential area, the residential area is covered by two base stations of the CD, the population characteristic of the coverage area is obvious, the visitor population usually enters the coverage area from 9 to 16 hours, the population in the residential area usually enters the coverage area after 0 hour at night, and the population in the residential area logs in the base station D when the population in the residential area is 18-5 hours. And (4) subdividing the residential area frequent crowd, and logging in the base station D crowd when the residential area frequent crowd exceeds 10 days (times) 18-5 in a month.
By the embodiment, the big data hybrid positioning method combines the geographic information of the base station with the characteristics of the analyzed area, finds out the association relation base station according to the crowd characteristics, can associate people with buildings and areas more accurately through dimensions such as time characteristics and the like, and improves the accuracy of positioning the crowd in the areas.
Thus far, the present invention has been described in detail. Some details well known in the art have not been described in order to avoid obscuring the concepts of the present invention. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
The method and apparatus of the present invention may be implemented in a number of ways. For example, the methods and apparatus of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
Although some specific embodiments of the present invention have been described in detail by way of illustration, it should be understood by those skilled in the art that the above illustration is only for the purpose of illustration and is not intended to limit the scope of the invention. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (4)
1. A big data hybrid positioning method is characterized by comprising the following steps:
acquiring a base station in a target area and the type of the base station;
determining the base station coverage of the target area, wherein the target area is a mixed distribution area or an island area;
determining a base station for logging in by people in a target area and time information for logging in the base station;
positioning the crowd of the target area according to the crowd characteristics of the target area, the type of the base station and the coverage area, wherein if the target area is a mixed distribution area, whether the base station of the target area comprises an indoor distribution base station is judged, and if the target area comprises the indoor distribution base station, the crowd of the target area is a landing crowd of the indoor distribution base station; if the indoor distributed base stations are not included, determining an association relation base station according to the coverage area of the base station of the target area, and if the target area has the association relation base station, determining the target area group as a login group of the association relation base station; if the target area has no association relation base station, determining the target area crowd according to the time information of the target area crowd logging in the base station; and if the target area is an island type area, the target area group is a landing group of the island type base station in the island type area.
2. The method of claim 1, further comprising:
and determining the permanent population and the temporary population of the target area according to the time information of the target area population logging in the base station.
3. A big data hybrid location device, comprising:
a target area base station obtaining unit, configured to obtain a base station in a target area and a type of the base station;
a base station coverage area determining unit, configured to determine a base station coverage area of the target area;
the target area characteristic determining unit is used for judging whether the target area is a mixed distribution area or an island area;
the crowd characteristic analysis unit is used for analyzing the crowd characteristics of the target area, and the crowd characteristics of the target area comprise a base station for logging in the target area and time information for logging in the base station;
the area crowd positioning unit is used for positioning the crowd in the target area according to the crowd characteristics of the target area, the base station type and the coverage area, wherein the crowd in the target area is positioned according to the crowd characteristics of the target area, the base station type and the coverage area, and if the target area is a mixed distribution area and the base station of the target area comprises an indoor distribution base station, the crowd in the target area is a landing crowd of the indoor distribution base station; if the base station of the target area does not comprise the indoor distribution base station but the target area has an association relation base station, the target area group is a landing group of the association relation base station; if the target area has no association relation base station, determining the target area crowd according to the time information of the target area crowd logging in the base station; and if the target area is an island type area, the target area group is a landing group of the island type base station in the island type area.
4. The apparatus of claim 3, wherein the regional population positioning unit is further configured to determine the permanent population and the temporary population of the target region according to the time information of the target region population logging on the base station.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610852539.9A CN107872765B (en) | 2016-09-26 | 2016-09-26 | Big data hybrid positioning method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610852539.9A CN107872765B (en) | 2016-09-26 | 2016-09-26 | Big data hybrid positioning method and device |
Publications (2)
| Publication Number | Publication Date |
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| CN107872765A CN107872765A (en) | 2018-04-03 |
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