CN109495848B - User space positioning method - Google Patents
User space positioning method Download PDFInfo
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- CN109495848B CN109495848B CN201811550251.1A CN201811550251A CN109495848B CN 109495848 B CN109495848 B CN 109495848B CN 201811550251 A CN201811550251 A CN 201811550251A CN 109495848 B CN109495848 B CN 109495848B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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Abstract
The invention relates to the technical field of data analysis, in particular to a user space positioning method, and particularly relates to an accurate positioning method for a mobile phone user. The invention accurately positions the position of the mobile phone user staying at each moment by utilizing mature and stable service signaling data provided by a communication operator and combining with map data actually surveyed and drawn by a map service provider. The invention has accurate positioning and high operation speed, overcomes the defect that the prior positioning system can not obtain the complete motion trail of the user for a long time, avoids the problem that the positioning position in the grid positioning method has no actual service meaning, simultaneously solves the problems that the MR signaling data of a communication operator has high processing difficulty and is difficult to be used for continuous production, and improves the practicability of the mobile signaling data.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a user space positioning method, and particularly relates to an accurate positioning method for a mobile phone user.
Background
With the improvement of the living standard of people, the mobile phone becomes a living necessity of everyone, and the mobile phone can be carried with everywhere. The mobile phone is required to realize the networking function and is inevitably communicated with the nearest base station, when the mobile phone moves from the coverage area of one base station to the coverage area of another base station, the mobile phone is automatically switched to be connected with the other base station, and therefore, the motion trail of a person carrying the mobile phone can be known by observing the motion trail of the mobile phone. However, since the coverage area of the base station is one surface, only the presence of the handset in the coverage area of the base station can be known, but the specific location of the presence is unknown. Meanwhile, in order to ensure the stability of the mobile phone networking, a plurality of base stations may exist in the same place, when the mobile phone enters the place from different positions, the connected base stations are different, and if the situation occurs, the motion track of the mobile phone cannot be accurately judged. In addition, because the coordinates of the same geographic entity building are a set of coordinate points, multiple base stations cover the geographic entity building at the same time, or one part of the geographic entity building is covered by the base station a, and the other part is covered by the base station B, when the mobile phone user is in the geographic entity building, different base stations may be connected, or when the mobile phone user moves in the geographic entity building, the base station switching may also occur, which affects accurate positioning.
Currently, there are several ways to perform positioning by using data of a communication operator:
1. MR signaling data fingerprinting: and training MR data containing the position information into a fingerprint library, and performing fingerprint matching on the MR without the position according to the characteristics to form the position information.
2. MR signaling multilateration: and calculating the distance according to the signal receiving field intensity and the path loss formula of the cell and at least the adjacent cell and the transceiving time difference.
The positioning results for the user in the above manner are typically presented in a 100 x 100 meter grid. The map is divided into grid-type squares of 100 × 100 meters according to the longitude and latitude, and the user is positioned in the grid mode, and only the user in which grid is located is known, but the specific position of the user is not known. Meanwhile, when the grid of 100 x 100 meters is positioned, the artificially divided grid has no representative meaning and has no reference value.
In the existing MR signaling data fingerprint positioning, MR data containing position information for training fingerprints needs to be scanned by a worker and actually measured by drive test, or provided by OTT data containing position information through analysis. The modes of frequency sweep, drive test and the like need a large amount of human resources, and generally only a small-range area is needed, so that large-scale drive test is difficult to perform. On the other hand, the OTT data has low resolution rate and high resolution difficulty, so that the method is difficult to be applied to large-scale production.
The MR multipoint positioning cannot comprehensively support the analysis and cleaning of MR historical large-scale data due to different IT systems of branch companies of provinces of communication operators, so that historical continuous track data of users are formed, generally only used for real-time one-time positioning requests, and cannot continuously track complete tracks of the users. Meanwhile, the positioning result of rasterization is difficult to match with a specific geographic position, and the commercial value is low.
Disclosure of Invention
The invention provides a user space positioning method, which solves the problem that a mobile phone user cannot be accurately positioned in the prior art.
The technical scheme adopted by the invention is as follows:
a method of spatial positioning of a user, comprising the steps of:
s1, acquiring base station engineering parameters, mobile service signaling data and a set of spatial block actual position coordinate points provided by a map service provider, wherein the base station engineering parameters, the mobile service signaling data and the set of spatial block actual position coordinate points are provided by a communication operator;
s2, forming a geographic entity characteristic fingerprint through the base station engineering parameters and the spatial block actual position coordinate point set;
s3, aggregating the service signaling data according to the time and space relation, and determining the service signaling track data characteristics of the user; due to traffic signaling, there is only one base station at a time. However, when a user is at one location, base station switching may occur due to various factors, that is, multiple continuous service signaling of the user may all point to one location, and therefore, the service signaling of the user needs to be aggregated according to a time and space relationship;
s4, positioning each time interval of the mobile phone user according to the aggregated service signaling track data characteristics, and judging the specific geographic entity of the user in each time interval.
Preferably, in the step S2, the step of forming the geographic entity feature fingerprint includes:
s201, calculating the coverage area of the base station according to the base station engineering parameters;
s202, according to the coverage range of the geographic entity and the coverage surface of the base station, calculating to obtain a cross area S covered by the geographic entity and the base station through an gis space calculation engine; the coverage area of the geographic entity is as follows: connecting every two actual position coordinate points of the geographic entity provided by a map service provider to form a closed coverage area, namely a geographic entity coverage area;
s203: calculating the coverage area Sb of the base station according to the engineering parameters of the base station;
s204: calculating a spatial relationship coefficient alpha of the geographic entity and the base station through an equation according to the coverage area Sb and the cross area S of the base station, wherein the calculation equation is as follows: α ═ S ÷ Sb;
s205: outputting a relationship of a geographic entity and a base station covering the geographic entity:
{B,{Lc1,α},{Lc2,α}{Lc3,α}..{Lcn,α}} (1)
wherein, B is a geographic entity, and Lc is a base station number.
Preferably, in the step S3, the determining the service signaling trajectory data characteristic of the user includes the following steps:
s301, sequencing user service signaling records according to occurrence time, and combining two service signaling records if the continuous service signaling records are switched repeatedly;
for example, the base station A- > … - > base station A, if the time interval between the two occurrences of the base station A does not exceed 2 hours, and the distance between the other base station and the base station A which occur before the two occurrences of the base station A does not exceed 1km, then the records are merged;
s302, merging the service signaling data with the time interval of 1 minute;
because the service signaling acquisition sources are a plurality of data sources and the time of each data source may be slightly different, service signaling data with the time interval of 1 minute are merged;
s303, iteratively executing the step S301 and the step S302 until the combination can not be carried out;
s304, dividing the merged records into a plurality of time intervals according to 'start-end' time, wherein a plurality of records exist in each time interval, correcting error data, finding out the base station with the longest occurrence time in each time interval, and eliminating the records with the distance between the records and the base station being more than 1km in the time interval;
s305, learning historical data, storing the record processed in the step S304 into a database, performing similarity matching with the historical record, and merging the similar historical record into the time interval;
s306, calculating the occurrence frequency W of each base station occurring in the same time period in the last month;
s307, outputting the merged record:
{U,Ts,Te,{Lc1,W1},{Lc2,W2},{Lc3,W3}…{Lcn,Wn}} (2)
wherein, U is a user identifier, Ts is a time interval starting time, Te is a time interval ending time, Lcn is a base station cell identifier, and Wn is the occurrence frequency of the base station cell in the last month.
Preferably, in the step S305, if the historical records have a similarity greater than 80% with the time interval, and are all working days or all non-working days, and the longitude and latitude of the base station in the historical records are less than 1km from the longitude and latitude of all the base stations in the current time interval, the historical records are also merged into the time interval. Time interval similarity is the square of the same minutes over two time intervals divided by two minutes over one minute interval.
As a preferable aspect of the foregoing technical solution, in step S4, the determining the specific geographic entity where the user is located at each time interval includes:
performing correlation calculation on the formula (1) and the formula (2) according to an equation (3) to obtain a probability size P that the user may be located in the time period, wherein the equation (3) is as follows:
P{u,b}＝∑W*α (3)
forming a data set of likelihood sizes for each user within each geographic entity per time period,
{U,Ts,Te,{B1,P1},{B2,P2},{B3,P3}…{Bn,Pn}} (4)
wherein the geographic entity with the largest P is the stay position of the user in the time period.
Preferably, the base station engineering parameters include a regional area code, a base station identification code, a network type, an antenna azimuth angle, a base station coverage type, a base station antenna position longitude coordinate and a base station antenna position latitude coordinate; the mobile service signaling data comprises time, user numbers and base station numbers.
Preferably, the coverage type of the base station includes an indoor type and a non-indoor type; the antenna types include omni-directional antennas and directional antennas.
Preferably, in the above aspect, a coverage radius R of the indoor base station is a fixed value; the coverage radius R of the non-indoor base station is the product of the longitude and latitude coordinates of the base station antenna and the average distance of the nearest three non-indoor base stations and a specific coefficient. The specific coefficient is 1.6; the coverage radius R of the indoor base station is 400 meters by default.
Preferably, in the above technical solution, the method for calculating the coverage area of the omni-directional antenna base station includes: and taking the longitude and latitude of the antenna as a central point, extending the length of the coverage radius R of the base station outwards every 45 degrees to respectively obtain eight coordinate points, and connecting every two adjacent coordinate points by using straight lines to form a closed base station coverage area, namely obtaining the coverage surface of the omnidirectional antenna base station.
Preferably, in the above technical solution, the method for calculating the coverage area of the directional antenna base station includes: taking the longitude and latitude of the antenna as a central point, respectively extending the length of a coverage radius R of the base station outwards according to angles of A, A + H/6, A + H/3, A + H/2, A-H/6, A-H/3 and A + H/2 to obtain seven coordinate points, connecting every two adjacent coordinate points with straight lines, and respectively connecting the two coordinate points at the two ends with the longitude and latitude points of the antenna to form a closed base station coverage area, namely obtaining a coverage surface of the omnidirectional antenna base station; the angle A is the antenna azimuth angle, and the angle H is the horizontal lobe angle. The horizontal lobe angle calculation method is that if the number of the directional antennas of the base station is less than or equal to 2, the angle is 180 degrees, otherwise, the angle is 120 degrees.
The invention has the beneficial effects that:
the invention accurately positions the position of the mobile phone user staying at each moment by utilizing mature and stable service signaling data provided by a communication operator and combining with the geographical entity boundary actually surveyed and drawn by a map service provider by adopting a rapid positioning method. And forming a user space historical track by using the service signaling data of the user historical sediment, so that the data application value is fully released. The invention has accurate positioning and high operation speed, overcomes the defect that the prior positioning system can not obtain the complete motion trail of the user for a long time, avoids the problem that the positioning position in the grid positioning method has no actual service meaning, simultaneously solves the problems that the MR signaling data of a communication operator has high processing difficulty and is difficult to be used for continuous production, and improves the practicability of the mobile signaling data.
Detailed Description
The present invention will be described in detail below.
The invention/invention is further illustrated below with specific examples. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. The present invention/invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments of the present invention.
It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, B exists alone, and A and B exist at the same time, and the term "/and" is used herein to describe another association object relationship, which means that two relationships may exist, for example, A/and B, may mean: a alone, and both a and B alone, and further, the character "/" in this document generally means that the former and latter associated objects are in an "or" relationship.
It will be understood that when an element is referred to as being "connected," "connected," or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly adjacent" or "directly coupled" to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a similar manner (e.g., "between … …" versus "directly between … …", "adjacent" versus "directly adjacent", etc.).
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, elements, components, and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order. For example, depending on the functionality/acts involved, they may in fact be performed substantially concurrently, or may sometimes be performed in the reverse order.
In the following description, specific details are provided to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring example embodiments.
Example 1:
the embodiment provides a method for spatial positioning of a user.
A method of spatial positioning of a user, comprising the steps of:
s1, acquiring base station engineering parameters, mobile service signaling data and a set of spatial block actual position coordinate points provided by a map service provider, wherein the base station engineering parameters, the mobile service signaling data and the set of spatial block actual position coordinate points are provided by a communication operator; acquiring base station engineering parameters and mobile service signaling data of the previous day every day;
s2, forming a geographic entity characteristic fingerprint through the base station engineering parameters and the spatial block actual position coordinate point set;
s3, aggregating the service signaling data according to the time and space relation, and determining the service signaling track data characteristics of the user; due to traffic signaling, there is only one base station at a time. However, when a user is at one location, base station switching may occur due to various factors, that is, multiple continuous service signaling of the user may all point to one location, and therefore, the service signaling of the user needs to be aggregated according to a time and space relationship;
s4, positioning each time interval of the mobile phone user according to the aggregated service signaling track data characteristics, and judging the specific geographic entity of the user in each time interval.
In step S2, the step of forming the geographic entity feature fingerprint includes:
s201, calculating the coverage area of the base station according to the base station engineering parameters;
s202, according to the coverage range of the geographic entity and the coverage surface of the base station, calculating to obtain a cross area S covered by the geographic entity and the base station through an gis space calculation engine; the coverage area of the geographic entity is as follows: connecting every two actual position coordinate points of the geographic entity provided by a map service provider to form a closed coverage area, namely a geographic entity coverage area;
s203: calculating the coverage area Sb of the base station according to the engineering parameters of the base station;
s204: calculating a spatial relationship coefficient alpha of the geographic entity and the base station through an equation according to the coverage area Sb and the cross area S of the base station, wherein the calculation equation is as follows: α ═ S ÷ Sb;
s205: outputting a relationship of a geographic entity and a base station covering the geographic entity:
{B,{Lc1,α},{Lc2,α}{Lc3,α}..{Lcn,α}} (1)
wherein, B is a geographic entity, and Lc is a base station number.
In step S3, the step of determining the service signaling trajectory data feature of the user includes the following steps:
s301, sequencing user service signaling records according to occurrence time, and combining two service signaling records if the continuous service signaling records are switched repeatedly;
for example, the base station A- > … - > base station A, if the time interval between the two occurrences of the base station A does not exceed 2 hours, and the distance between the other base station and the base station A which occur before the two occurrences of the base station A does not exceed 1km, then the records are merged;
s302, merging the service signaling data with the time interval of 1 minute;
because the service signaling acquisition sources are a plurality of data sources and the time of each data source may be slightly different, service signaling data with the time interval of 1 minute are merged;
s303, iteratively executing the step S301 and the step S302 until the combination can not be carried out;
s304, dividing the merged records into a plurality of time intervals according to 'start-end' time, wherein a plurality of records exist in each time interval, correcting error data, finding out the base station with the longest occurrence time in each time interval, and eliminating the records with the distance between the records and the base station being more than 1km in the time interval;
s305, learning historical data, storing the record processed in the step S304 into a database, performing similarity matching with the historical record, and merging the similar historical record into the time interval;
s306, calculating the occurrence frequency W of each base station occurring in the same time period in the last month;
s307, outputting the merged record:
{U,Ts,Te,{Lc1,W1},{Lc2,W2},{Lc3,W3}…{Lcn,Wn}} (2)
wherein, U is a user identifier, Ts is a time interval starting time, Te is a time interval ending time, Lcn is a base station cell identifier, and Wn is the occurrence frequency of the base station cell in the last month.
In S305, if the historical records have a similarity greater than 80% with the time interval, and are both working days or both non-working days, and the longitude and latitude of the base station in the historical records are less than 1km from the longitude and latitude of all base stations in the current time interval, the historical records are also merged into the time interval. Time interval similarity is the square of the same minutes over two time intervals divided by two minutes over one minute interval.
In step S4, the step of determining the specific geographic entity where the user is located at each time interval includes:
performing correlation calculation on the formula (1) and the formula (2) according to an equation (3) to obtain a probability size P that the user may be located in the time period, wherein the equation (3) is as follows:
P{u,b}＝∑W*α (3)
forming a data set of likelihood sizes for each user within each geographic entity per time period,
{U,Ts,Te,{B1,P1},{B2,P2},{B3,P3}…{Bn,Pn}} (4)
wherein the geographic entity with the largest P is the stay position of the user in the time period.
The base station engineering parameters comprise a regional area code, a base station identification code, a network type, an antenna azimuth angle, a base station coverage type, a base station antenna position longitude coordinate and a base station antenna position latitude coordinate; the mobile service signaling data comprises time, user numbers and base station numbers.
The coverage type of the base station comprises an indoor type and a non-indoor type; the antenna types include omni-directional antennas and directional antennas.
The coverage radius R of the indoor base station is a fixed value; the coverage radius R of the non-indoor base station is the product of the longitude and latitude coordinates of the base station antenna and the average distance of the nearest three non-indoor base stations and a specific coefficient. The specific coefficient is 1.6; the coverage radius R of the indoor base station is 400 meters by default.
The method for calculating the coverage area of the base station of the omnidirectional antenna comprises the following steps: and taking the longitude and latitude of the antenna as a central point, extending the length of the coverage radius R of the base station outwards every 45 degrees to respectively obtain eight coordinate points, and connecting every two adjacent coordinate points by using straight lines to form a closed base station coverage area, namely obtaining the coverage surface of the omnidirectional antenna base station.
The method for calculating the coverage area of the directional antenna base station comprises the following steps: taking the longitude and latitude of the antenna as a central point, respectively extending the length of a coverage radius R of the base station outwards according to angles of A, A + H/6, A + H/3, A + H/2, A-H/6, A-H/3 and A + H/2 to obtain seven coordinate points, connecting every two adjacent coordinate points with straight lines, and respectively connecting the two coordinate points at the two ends with the longitude and latitude points of the antenna to form a closed base station coverage area, namely obtaining a coverage surface of the omnidirectional antenna base station; the angle A is the antenna azimuth angle, and the angle H is the horizontal lobe angle. The horizontal lobe angle calculation method is that if the number of the directional antennas of the base station is less than or equal to 2, the angle is 180 degrees, otherwise, the angle is 120 degrees.
The invention accurately positions the position of the mobile phone user staying at each moment by utilizing mature and stable service signaling data provided by a communication operator and combining with map data actually surveyed and drawn by a map service provider. The invention has accurate positioning and high operation speed, overcomes the defect that the prior positioning system can not obtain the complete motion trail of the user for a long time, avoids the problem that the positioning position in the grid positioning method has no actual service meaning, simultaneously solves the problems that the MR signaling data of a communication operator has high processing difficulty and is difficult to be used for continuous production, and improves the practicability of the mobile signaling data.
Example 2:
the embodiment provides a space-time big data analysis system supporting the invention, which comprises a calculation layer and a service layer, wherein:
the calculation layer is used for calculating a track chain of each mobile phone user every day according to base station engineering parameters, mobile service signaling data and a coordinate point set of an actual position of a space block, wherein the base station engineering parameters, the mobile service signaling data and the coordinate point set are provided by a map service provider, and labeling is carried out on each mobile phone user;
and the service layer extracts different data in the calculation layer according to different business requirements, and obtains corresponding business model data after counting the extracted data.
The tag content includes occupation, work and residence attributes of the mobile phone user.
The base station engineering parameters comprise a regional area code, a base station identification code, a network type, an antenna azimuth angle, a base station coverage type, a base station antenna position longitude coordinate and a base station antenna position latitude coordinate; the mobile service signaling data comprises time, user numbers and base station numbers.
The coverage type of the base station comprises an indoor type and a non-indoor type; the antenna types comprise an omnidirectional antenna and a directional antenna; the coverage radius R of the indoor base station is a fixed value; the coverage radius R of the non-indoor base station is the product of the longitude and latitude coordinates of the base station antenna and the average distance of the nearest three non-indoor base stations and a specific coefficient. The specific coefficient is 1.6; the coverage radius R of the indoor base station is 400 meters by default.
The service layer converts the obtained business model data into one or more of API, SDK and visual components for the third-party software to call.
And the computing layer and the service layer are both provided with system detection modules, the system detection modules are used for detecting whether the operation of each module in the system is normal or not, and if the operation state of the system is abnormal, alarm information is sent out.
The computation layer includes:
the track library is used for storing a track chain of each mobile phone user every day;
the population library is used for storing each mobile phone user label;
the basic database is used for storing the acquired base station engineering parameters, mobile service signaling data and a set of spatial block actual position coordinate points provided by a map service provider;
and the model library is used for storing an algorithm module, and the algorithm module is used for obtaining a track library and a population library according to the content of the basic database.
The service layer comprises:
the service DB is used for storing data read in a track library and a population library of the computing layer according to different service requirements;
the third-party data access/acquisition module is used for receiving the service data input by a third party or actively acquiring the third-party service data;
and the business service module is used for counting the data stored in the business DB according to business needs to obtain corresponding business model data.
The mode of actively collecting the third-party service data is to read the required information in the search engine through a web crawler.
The service layer also comprises a user management module, and the user management module is used for user registration and user authority management; the user management module is respectively connected with a user library and an operation and maintenance library in a data mode, the user library is used for storing registered user information, and the operation and maintenance library is used for storing system operation data and operation logs.
The service layer also comprises a charging module, and the charging module is used for charging the user and managing the balance according to the consumption condition of the user. After the user recharges, the charging module records the balance after the user recharges, when the user accesses the data in the calculation layer, the charging is carried out according to the population number, the geographical area range, the geographical precision, the service use duration, the label use type and the use depth of the tracking data included in the user access data, the fee is deducted from the balance in real time, and the deducted balance is displayed.
Example 3:
the embodiment provides a method for spatial positioning of a user.
A method of spatial positioning of a user, comprising the steps of:
s1, acquiring base station engineering parameters, mobile service signaling data and a set of spatial block actual position coordinate points provided by a map service provider, wherein the base station engineering parameters, the mobile service signaling data and the set of spatial block actual position coordinate points are provided by a communication operator; acquiring base station engineering parameters and mobile service signaling data of the previous day every day;
s2, forming a geographic entity characteristic fingerprint through the base station engineering parameters and the spatial block actual position coordinate point set;
s3, aggregating the service signaling data according to the time and space relation, and determining the service signaling track data characteristics of the user; due to traffic signaling, there is only one base station at a time. However, when a user is at one location, base station switching may occur due to various factors, that is, multiple continuous service signaling of the user may all point to one location, and therefore, the service signaling of the user needs to be aggregated according to a time and space relationship;
s4, positioning each time interval of the mobile phone user according to the aggregated service signaling track data characteristics, and judging the specific geographic entity of the user in each time interval.
The invention accurately positions the position of the mobile phone user staying at each moment by utilizing mature and stable service signaling data provided by a communication operator and combining with the geographical entity boundary actually surveyed and drawn by a map service provider by adopting a rapid positioning method. And forming a user space historical track by using the service signaling data of the user historical sediment, so that the data application value is fully released. The invention has accurate positioning and high operation speed, overcomes the defect that the prior positioning system can not obtain the complete motion trail of the user for a long time, avoids the problem that the positioning position in the grid positioning method has no actual service meaning, simultaneously solves the problems that the MR signaling data of a communication operator has high processing difficulty and is difficult to be used for continuous production, and improves the practicability of the mobile signaling data.
The present invention is not limited to the above-described alternative embodiments, and various other forms of products can be obtained by anyone in light of the present invention. The above detailed description should not be taken as limiting the scope of the invention, which is defined in the claims, and which the description is intended to be interpreted accordingly.
Claims (7)
1. A method of spatial positioning of a user, comprising the steps of:
s1, acquiring base station engineering parameters, mobile service signaling data and a set of spatial block actual position coordinate points provided by a map service provider, wherein the base station engineering parameters, the mobile service signaling data and the set of spatial block actual position coordinate points are provided by a communication operator;
s2, forming a geographic entity characteristic fingerprint through the base station engineering parameters and the spatial block actual position coordinate point set;
s3, aggregating the service signaling data according to the time and space relation, and determining the service signaling track data characteristics of the user;
s4, positioning each time interval of the mobile phone user according to the aggregated service signaling track data characteristics, and judging the specific geographic entity of the user in each time interval;
in step S2, the step of forming the geographic entity feature fingerprint includes:
s201, calculating the coverage area of the base station according to the base station engineering parameters;
s202, according to the coverage range of the geographic entity and the coverage surface of the base station, calculating to obtain a cross area S covered by the geographic entity and the base station through an gis space calculation engine;
s203: calculating the coverage area Sb of the base station according to the engineering parameters of the base station;
s204: calculating a spatial relationship coefficient alpha of the geographic entity and the base station through an equation according to the coverage area Sb and the cross area S of the base station, wherein the calculation equation is as follows: α ═ S ÷ Sb;
s205: outputting a relationship of a geographic entity and a base station covering the geographic entity:
{B,{Lc1,α},{Lc2,α}{Lc3,α}..{Lcn,α}} (1)
b is a geographic entity, and Lc1, Lc2, Lc3 and Lcn are base station numbers;
in step S3, the step of determining the service signaling trajectory data feature of the user includes the following steps:
s301, sequencing user service signaling records according to occurrence time, and combining two service signaling records if the continuous service signaling records are switched repeatedly;
s302, merging the service signaling data with the time interval of 1 minute;
s303, iteratively executing the step S301 and the step S302 until the combination can not be carried out;
s304, correcting error data, finding out the base station with the longest occurrence time in each time interval, and eliminating records with the distance between the base station and the base station being more than 1km in the time interval;
s305, learning historical data, storing the record processed in the step S304 into a database, performing similarity matching with the historical record, and merging the similar historical record into the time interval;
s306, calculating the occurrence frequency W of each base station occurring in the same time period in the last month;
s307, outputting the merged record:
{U,Ts,Te,{Lc1,W1},{Lc2,W2},{Lc3,W3}…{Lcn,Wn}} (2)
wherein, U is a user identifier, Ts is a time interval starting time, Te is a time interval ending time, and Wn is the occurrence frequency of a base station cell in a month;
in step S4, the step of determining the specific geographic entity where the user is located at each time interval includes:
performing correlation calculation on the formula (1) and the formula (2) according to an equation (3) to obtain a probability size P of a specific geographic entity where the user may be located in the time period, wherein the equation (3) is as follows:
P＝∑W*α (3)
forming a data set of likelihood sizes for each user within each geographic entity per time period,
{U,Ts,Te,{B1,P1},{B2,P2},{B3,P3}…{Bn,Pn}} (4)
wherein the geographic entity with the largest P is the stay position of the user in the time period.
2. The method according to claim 1, wherein in S305, if there is a similarity greater than 80% with the time interval in the history record, and both are working days or non-working days, and the longitude and latitude of the base station in the history record are less than 1km from the longitude and latitude of all base stations in the current time interval, then the history record is also incorporated into the time interval.
3. The method of user spatial localization according to claim 1, characterized by: the base station engineering parameters comprise an antenna type, an antenna azimuth angle, a base station coverage type, a base station antenna position longitude coordinate and a base station antenna position latitude coordinate; the mobile service signaling data comprises time, user numbers and base station numbers.
4. A method of user spatial localization according to claim 3, characterized by: the coverage type of the base station comprises an indoor type and a non-indoor type; the antenna types include omni-directional antennas and directional antennas.
5. The method of user spatial localization according to claim 4, characterized by: the coverage radius R of the indoor base station is a fixed value; the coverage radius R of the non-indoor base station is the product of the longitude and latitude coordinates of the base station antenna and the average distance of the nearest three non-indoor base stations and a specific coefficient.
6. The method of user spatial localization according to claim 5, characterized by: the method for calculating the coverage area of the base station of the omnidirectional antenna comprises the following steps: and taking the longitude and latitude of the antenna as a central point, extending the length of the coverage radius R of the base station outwards every 45 degrees to respectively obtain eight coordinate points, and connecting every two adjacent coordinate points by using straight lines to form a closed base station coverage area, namely obtaining the coverage surface of the omnidirectional antenna base station.
7. The method of user spatial localization according to claim 5, characterized by: the method for calculating the coverage area of the directional antenna base station comprises the following steps: taking the longitude and latitude of the antenna as a central point, respectively extending the length of a coverage radius R of the base station outwards according to angles of A, A + H/6, A + H/3, A + H/2, A-H/6, A-H/3 and A + H/2 to obtain seven coordinate points, connecting every two adjacent coordinate points with straight lines, and respectively connecting the two coordinate points at the two ends with the longitude and latitude points of the antenna to form a closed base station coverage area, namely obtaining a coverage surface of the omnidirectional antenna base station; the angle A is the antenna azimuth angle, and the angle H is the horizontal lobe angle.
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