People flow density determination method, device, equipment and storage medium
1. A people stream density determination method, comprising:
obtaining first measurement data comprising complete position information through fingerprint positioning based on first position information reported in operator measurement data;
performing association matching based on the first measurement data and the signaling data to obtain second measurement data comprising complete position information and user information;
determining first track information of a user based on the second measurement data, wherein the track information comprises residence information and first movement information;
performing movement correction based on the first movement information and the road network data to obtain second movement information;
and counting the people flow density according to the second mobile information and the residence information by regions.
2. The people flow density determination method according to claim 1, wherein obtaining the first measurement data including complete location information by fingerprint positioning based on the first location information reported in the operator measurement data comprises:
establishing a grid fingerprint database based on first measurement data including first location information in the operator measurement data;
and performing fingerprint positioning on second measurement data which does not include the position information in the operator measurement data based on the grid fingerprint database to obtain second position information, and backfilling the second position information to the operator measurement data to obtain first measurement data.
3. The people flow density determination method of claim 1, wherein the building a grid fingerprint database based on the first measurement data including the first location information in the operator measurement data comprises:
dividing grids according to the coverage area of the base station, screening the measurement data in each grid based on the first measurement data, and obtaining database building measurement data after screening;
and determining the wireless index information of each grid based on the database building measurement data to obtain standard wireless index information, and generating a fingerprint database by taking the standard wireless index information as data to be matched.
4. The people flow density determination method of claim 1, wherein determining first trajectory information of a user based on the second measurement data comprises:
grouping according to the user identity based on the second measurement data, sequencing according to time to obtain continuous motion information of the user, and filtering invalid data based on the continuous motion to obtain effective motion information;
and performing spatial clustering on the effective motion information to obtain clustering points, judging the clustering points according to a preset speed to obtain residence points and theoretical motion points, counting the residence points to obtain residence information, and counting the theoretical motion points to obtain first mobile data.
5. The method for determining the density of people streams according to claim 4, wherein the obtaining of the second movement information by performing movement correction based on the first movement information in combination with the road network data comprises:
performing deviation correction based on the theoretical moving points and the road network data to obtain corrected moving points;
determining a path road section of a user based on the corrected motion point, and continuously correcting the road section based on the path road section and the road network data to obtain a corrected path road section;
and reversely deducing the actual motion point of the user based on the corrected path road section to obtain second movement information.
6. The method of determining the density of people streams according to claim 5, wherein the step of determining a route segment of the user based on the modified motion point, and the step of continuously modifying the route segment based on the route segment and the road network data to obtain a modified route segment comprises the steps of:
determining a path road section of the user according to the corrected motion points in a time sequence;
and determining the shortest path between the path road sections to form a first code table, judging whether the track is continuous according to the path road sections and road network data, and if not, completing the road sections according to the first code table to obtain the corrected path road sections.
7. A people flow density determination apparatus, comprising:
the positioning module is used for obtaining first measurement data comprising complete position information through fingerprint positioning based on first position information reported in operator measurement data;
the correlation module is used for performing correlation matching on the basis of the first measurement data and the signaling data to obtain second measurement data comprising complete position information and user information;
the mobile screening module is used for determining first track information of the user based on the second measurement data, and the track information comprises resident information and first mobile information;
the movement correction module is used for carrying out movement correction on the basis of the first movement information and the road network data to obtain second movement information;
and the density counting module is used for counting the people flow density according to the second mobile information and the residence information according to regions.
8. The people flow density determination apparatus of claim 7, wherein the movement correction module comprises:
the motion point correction module is used for carrying out deviation correction on the basis of the theoretical motion point and the road network data to obtain a corrected motion point;
the road section correction module is used for determining a path road section of the user based on the corrected motion point and continuously correcting the road section based on the path road section and the road network data to obtain a corrected path road section;
and the backward-pushing module is used for backward-pushing the actual motion point of the user on the basis of the corrected path road section to obtain second movement information.
9. A computer device comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, the processor implementing the people stream density determination method according to any one of claims 1-6 when executing the computer program.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program comprising program instructions that, when executed, implement the people stream density determination method according to any one of claims 1-6.
Background
At present, in the aspects of urban planning, business district people flow analysis, large-scale gathering people flow monitoring and urban security and protection police deployment, accurate people flow density analysis needs to be mastered, and people flow thermodynamic diagram conditions at different time points are checked for various areas.
The method which is relatively common in the market is to acquire personnel spatio-temporal data (position information with time) through internet software, count the personnel quantity of each area and render the data on a map. Or the number of people density in the nearby area is counted based on the position of the base station connected with the mobile phone by using the operator signaling data.
The method is simple and feasible, and has accurate data for counting the number of people in a large area. However, when the time range and the region range are reduced, the statistical accuracy of the data is low. The reason is that the longitude and latitude information reported by the internet software is not continuously reported, and not all users are provided with the software of related internet companies, nor are all users capable of turning on the GPS function all the time. Similarly, the base station data is not reported every moment, and the method cannot be used when people density data in a more accurate statistical area is pursued.
Disclosure of Invention
In view of this, the invention provides a method, an apparatus, a device and a storage medium for determining people stream density, which are used for correcting a user track by combining road network data on the basis of determining the user track by using operator measurement data and signaling data and fingerprint positioning, thereby realizing higher-precision people stream density statistics.
In a first aspect, the present invention provides a people stream density determining method, including:
obtaining first measurement data comprising complete position information through fingerprint positioning based on first position information reported in operator measurement data;
performing association matching based on the first measurement data and the signaling data to obtain second measurement data comprising complete position information and user information;
determining first track information of a user based on the second measurement data, wherein the track information comprises residence information and first movement information;
performing movement correction based on the first movement information and the road network data to obtain second movement information;
and counting the people flow density according to the second mobile information and the residence information by regions.
Optionally, in some embodiments, obtaining the first measurement data including the complete location information by fingerprint positioning based on the first location information reported in the operator measurement data includes:
establishing a grid fingerprint database based on first measurement data including first location information in the operator measurement data;
and performing fingerprint positioning on second measurement data which does not include the position information in the operator measurement data based on the grid fingerprint database to obtain second position information, and backfilling the second position information to the operator measurement data to obtain first measurement data.
Optionally, in some embodiments, obtaining the first measurement data including the complete location information by fingerprint positioning based on the first location information reported in the operator measurement data includes:
establishing a grid fingerprint database based on first measurement data including first location information in the operator measurement data;
and performing fingerprint positioning on second measurement data which does not include the position information in the operator measurement data based on the grid fingerprint database to obtain second position information, and backfilling the second position information to the operator measurement data to obtain first measurement data.
Optionally, in some embodiments, the establishing a grid fingerprint database based on the first measurement data including the first location information in the operator measurement data includes:
dividing grids according to the coverage area of the base station, screening the measurement data in each grid based on the first measurement data, and obtaining database building measurement data after screening;
and determining the wireless index information of each grid based on the database building measurement data to obtain standard wireless index information, and generating a fingerprint database by taking the standard wireless index information as data to be matched.
Optionally, in some embodiments, determining the first trajectory information of the user based on the second measurement data includes:
grouping according to the user identity based on the second measurement data, sequencing according to time to obtain continuous motion information of the user, and filtering invalid data based on the continuous motion to obtain effective motion information;
and performing spatial clustering on the effective motion information to obtain clustering points, judging the clustering points according to a preset speed to obtain residence points and theoretical motion points, counting the residence points to obtain residence information, and counting the theoretical motion points to obtain first mobile data.
Optionally, in some embodiments, performing a movement correction based on the first movement information and the road network data to obtain second movement information includes:
performing deviation correction based on the theoretical moving points and the road network data to obtain corrected moving points;
determining a path road section of a user based on the corrected motion point, and continuously correcting the road section based on the path road section and the road network data to obtain a corrected path road section;
and reversely deducing the actual motion point of the user based on the corrected path road section to obtain second movement information.
In a second aspect, the present invention provides a people stream density determination apparatus, comprising:
the positioning module is used for obtaining first measurement data comprising complete position information through fingerprint positioning based on first position information reported in operator measurement data;
the correlation module is used for performing correlation matching on the basis of the first measurement data and the signaling data to obtain second measurement data comprising complete position information and user information;
the mobile screening module is used for determining first track information of the user based on the second measurement data, and the track information comprises resident information and first mobile information;
the movement correction module is used for carrying out movement correction on the basis of the first movement information and the road network data to obtain second movement information;
and the density counting module is used for counting the people flow density according to the second mobile information and the residence information according to regions.
In a third aspect, the present invention provides a computer device comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, the processor implementing the people stream density determination method as described above when executing the computer program.
In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program comprising program instructions which, when executed, implement the aforementioned people stream density determination method.
The invention provides a people flow density determining method, which obtains complete position information by fingerprint positioning based on self-contained first position information in operator measured data, thereby backfilling to obtain first measured data, then performs correlation matching based on the first measured data and signaling data to obtain second measured data comprising the complete position information and user information, analyzes the second measured data to determine first track information of a user, then performs movement correction on the first moving information in combination with road network data in the first track information to obtain second moving information, and finally counts people flow density according to regions based on the second moving information and resident information in the first track information, the method combines the operator measured data and the signaling data to obtain more comprehensive user track, and then performs multiple data repairing and backfilling measures such as road section matching and binding based on the road data, path matching and backfilling, track point backfilling in the road section and the like, more comprehensive user track data are obtained, and the accuracy of regional people flow statistics data is finally guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only part of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a people stream density determining method according to an embodiment of the present invention;
fig. 2 is a sub-flowchart of a people stream density determining method according to an embodiment of the present invention;
fig. 3 is a flowchart of a people stream density determining method according to a second embodiment of the present invention;
fig. 4 is a sub-flowchart of a people stream density determining method according to a second embodiment of the present invention;
fig. 5 is a schematic diagram of a first code table according to a second embodiment of the present invention;
fig. 6 is a schematic structural diagram of a people stream density determining apparatus according to a third embodiment of the present invention;
fig. 7 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The technical solution in the implementation of the present application is described clearly and completely below with reference to the drawings in the embodiments of the present application. It is to be understood that the specific embodiments described herein are merely illustrative of some, and not restrictive, of the current application. It should be further noted that, based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without any creative effort belong to the protection scope of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first example may be referred to as a second use case, and similarly, the second example may be referred to as the first use case, without departing from the scope of the present invention. Both the first and second use cases are use cases, but they are not the same use case. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include a combination of one or more features. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. It should be noted that when one portion is referred to as being "secured to" another portion, it may be directly on the other portion or there may be an intervening portion. When a portion is said to be "connected" to another portion, it may be directly connected to the other portion or intervening portions may be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only and do not denote a unique embodiment.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Example one
Referring to fig. 1, the embodiment provides a people stream density determining method, which may be applied to a system with a positioning function, where the system includes a terminal and a server, where the terminal and the server communicate through a network, the terminal may be, but is not limited to, various smart phones, tablet computers, and portable wearable devices, and the server may be implemented by an independent server or a server cluster formed by multiple servers. Based on the system, the people flow density determination method can be executed by the terminal or the server, and can also be realized through the interaction between the terminal and the server. As shown in fig. 1, the method specifically includes:
s110, obtaining first measurement data including complete position information through fingerprint positioning based on first position information reported in operator measurement data.
The operator Measurement data refers to Measurement data, that is, MR (Measurement Report) data, which is acquired by an operator when providing a terminal communication service. Usually, each of the operator measurement data does not include location information, only a small portion of the data carries location information, and for convenience of distinguishing, the location information carried by the portion of the operator measurement data is called first location information.
And S120, performing correlation matching based on the first measurement data and the signaling data to obtain second measurement data comprising complete position information and user information.
In this embodiment, the second measurement data actually still exists in the format of the MR data, and when the correlation matching is performed, the MR data is used as a master table, and the correlation is performed according to the endbid, mmecode, mmes1apid, mmegorroupid quadruple information and the signaling data, and the user information of the signaling data is refilled into the MR data, because the MR data already contains the location information, the location information of the user is obtained, and the MR data obtained in this step is collectively referred to as the second measurement data.
S130, determining first track information of the user based on the second measurement data, wherein the track information comprises residence information and first movement information.
The first track information is obtained by grouping users based on the second measurement data, and is used for describing a general name of relevant data for the movement track, corresponding position information, time information and the like are included in the same user, and generally, sequencing is performed according to a certain rule, for example, time sequence, so as to form a complete track, and in order to further analyze information represented by points in the track, each point in the track is classified into a stay point and a movement point, so that stay information and first movement information are obtained.
And S140, performing movement correction based on the first movement information and the road network data to obtain second movement information.
Since many pieces of position information in the second measurement data are obtained by fingerprint positioning and there is a certain error in conventional data acquisition, many pieces of error information may exist in the first trajectory information, and in order to reduce the influence of these pieces of error information on the final people flow density statistics, the step corrects the first movement information in combination with the road network data, and mainly aims to correct the part of the first movement information that does not conform to the road network data into the part that conforms to the road network data, thereby obtaining the second movement information.
And S150, counting the people flow density according to the second mobile information and the residence information by regions.
The second movement information and the residence information already describe the complete track from residence to movement of the user in detail, and corresponding time information is correspondingly recorded, so that the number of the users in any area at any time can be counted through zoning.
The embodiment provides a people flow density determination method, which includes obtaining complete position information through fingerprint positioning based on self-contained first position information in operator measured data, backfilling to obtain first measured data, performing correlation matching based on the first measured data and signaling data to obtain second measured data comprising the complete position information and user information, analyzing the second measured data to determine first track information of a user, performing movement correction on the first moving information in combination with road network data in the first track information to obtain second moving information, and finally counting people flow density according to regions based on the second moving information and resident information in the first track information, wherein the method combines the operator measured data and the signaling data to obtain more comprehensive user tracks, and then obtains multiple data backfilling measures such as road segment matching and binding based on road data, path matching and backfilling, track point backfilling in road segments and the like, more comprehensive user track data are obtained, and the accuracy of regional people flow statistics data is finally guaranteed.
Example two
The second embodiment provides a people stream density determining method, which can be implemented on the basis of the first embodiment, and further supplements the content in the first embodiment, specifically including:
as shown in FIG. 2, step S110 includes steps S111-112:
s111, establishing a grid fingerprint database based on first measurement data including first position information in the operator measurement data.
Specifically, step S111 includes:
and dividing grids according to the coverage area of the base station, screening the measurement data in each grid based on the first measurement data, and obtaining database building measurement data after screening. Reporting agps data by 2% -5% of the MR data, calculating a grid (the grid of 10m X10 m) where the MR data are located according to longitude and latitude information of the data, calculating the grid where the MR data with known longitude and latitude are located, wherein a plurality of pieces of MR data are arranged in the same grid, if the number of the MR data is more than or equal to three, screening three pieces of MR data closest to the center point of the grid to serve as fingerprint library calculation input data, if the number of the MR data is less than three, screening and filtering are not carried out, and the rest filtered MR data are library building measurement data.
And determining the wireless index information of each grid based on the database building measurement data to obtain standard wireless index information, and generating a fingerprint database by taking the standard wireless index information as data to be matched. And calculating the weighted average of the infinite indexes of Ta, rsrp, rsrq, sinr and aoa of the MR data under each base station according to the screened MR data, wherein the weighted value is the reciprocal of the distance of the MR from the central point of the grid, and the closer to the central point, the more the weighted value can represent the wireless index value of the grid. According to the method, wireless index values of grids covered by all base stations are calculated and used as data to be matched.
And S112, performing fingerprint positioning on second measurement data which does not include the position information in the operator measurement data based on the grid fingerprint database to obtain second position information, and backfilling the second position information to the operator measurement data to obtain first measurement data.
And matching the MR data (namely the second measurement data) without the agps data with the wireless indexes of the grids covered by the base stations, wherein the first three grids most accord with the MR wireless indexes are matched, and carrying out weighted average according to the longitude and latitude of the center of the grids, wherein the weighted value is the reciprocal of the matching difference value of the wireless indexes, and the closer the MR wireless indexes are to the grid wireless indexes, the more probable the positions corresponding to the second measurement data are in the grids. And calculating the longitude and latitude information of the second measurement data according to the method.
As shown in fig. 3, the process of acquiring the first track information, namely step S130, includes steps S131-132:
s131, grouping according to user identities based on the second measurement data, sequencing according to time to obtain continuous motion information of the user, and filtering invalid data based on the continuous motion to obtain effective motion information.
The second measurement data represents the position information of a plurality of users at a plurality of times, the grouping by user identities is used for counting the position information of a single user, and the sequencing by time is used for obtaining continuous position change information of the single user. The invalid data filtering is that after grouping and sorting, for example, grouping is carried out according to dates and users, after grouping, sorting is carried out according to time, the records in continuous time periods are removed from user tracks with the movement speed more than v being 120km/h, and the track point is considered as a serious exceeding speed judgment and is a deviation point. In addition, the same user retains only the median point of time per minute for thinning.
S132, carrying out spatial clustering based on the effective motion information to obtain clustering points, judging the clustering points according to a preset speed to obtain residence points and theoretical motion points, counting the residence points to obtain residence information, and counting the theoretical motion points to obtain first mobile data.
The method comprises the steps of carrying out spatial clustering on users, calculating clustering points of the users, carrying out average speed calculation on each clustering point, judging the clustering point to be a resident point when the speed meets a certain lower limit threshold value, such as less than 5km/h, and judging other points to be motion points.
Correspondingly, the process of generating the second movement information, that is, step S140, as shown in fig. 4, includes steps S141 to 143:
and S141, performing deviation correction based on the theoretical moving point and the road network data to obtain a corrected moving point.
And matching the theoretical moving point with road network data, wherein the matching algorithm is that the minimum distance between the track point and the road network is smaller than a threshold value, and then, the theoretical moving point is pulled back to the vertical intersection point of the road network to be matched with the road where the user is located, so as to obtain the corrected moving point.
And S142, determining a path road section of the user based on the corrected motion point, and continuously correcting the road section based on the path road section and the road network data to obtain a corrected path road section.
More specifically, in some embodiments, step S142 specifically includes steps S1421-1422 (not shown):
and S1421, determining a path road section of the user according to the corrected motion points in a time sequence.
S1422, determining the shortest path between the path road sections to form a first code table, judging whether the track is continuous according to the path road sections and the road network data, and if not, performing road section completion according to the first code table to obtain the corrected path road sections.
The situation of each time of the user on the road is calculated, and the table structure is as follows:
notes on field types
imsi String imsi
starting time of starttime String
end time String end time
mapId String MAPID
ID String road section ID
name of main road to which name String belongs.
This table records the start time and end time of the user on each link, as well as link information.
In a specific example, the first code table is shown in fig. 5, a, b, c, d in the matrix represent road segment junction points, 1234 represents four road segments, shortest path information from the horizontal coordinate point to the vertical coordinate point is recorded in the matrix, it is checked whether each recorded road segment is adjacent, if not, the middle road segment record information is supplemented by inquiring the matrix information provided in step 3, the supplemented road segment start time and end time are refilled according to the average speed of the user according to the length of the road segment,
and S143, reversely deducing the actual motion point of the user based on the corrected path road section to obtain second movement information.
And after the user road information is backfilled, obtaining continuous road section information of the user, then further backfilling the backfilled road section information, uniformly backfilling the user track points to the road section according to the distance of 100m, and calculating the track point time at a constant speed according to the distance.
On the basis of the foregoing embodiment, the present embodiment further provides a process of acquiring complete position information based on fingerprint positioning, and a process of performing track point correction and road segment correction according to road network data, which further ensures that the track information of the user better conforms to the actual situation, and improves the accuracy of people stream density statistics.
EXAMPLE III
Fig. 6 is a schematic structural diagram of a people stream density determining apparatus 300 according to a third embodiment of the present invention, and as shown in fig. 6, the apparatus 300 includes:
a positioning module 310, configured to obtain first measurement data including complete location information through fingerprint positioning based on first location information reported in operator measurement data;
an association module 320, configured to perform association matching based on the first measurement data and the signaling data to obtain second measurement data including complete location information and user information;
a movement screening module 330, configured to determine first trajectory information of the user based on the second measurement data, where the trajectory information includes residence information and first movement information;
a movement correction module 340, configured to perform movement correction based on the first movement information in combination with the road network data to obtain second movement information;
and a density statistics module 350, configured to perform statistics on the people flow density by regions according to the second movement information and the residence information.
Optionally, in some embodiments, obtaining the first measurement data including the complete location information by fingerprint positioning based on the first location information reported in the operator measurement data includes:
establishing a grid fingerprint database based on first measurement data including first location information in the operator measurement data;
and performing fingerprint positioning on second measurement data which does not include the position information in the operator measurement data based on the grid fingerprint database to obtain second position information, and backfilling the second position information to the operator measurement data to obtain first measurement data.
Optionally, in some embodiments, the establishing a grid fingerprint database based on the first measurement data including the first location information in the operator measurement data includes:
dividing grids according to the coverage area of the base station, screening the measurement data in each grid based on the first measurement data, and obtaining database building measurement data after screening;
and determining the wireless index information of each grid based on the database building measurement data to obtain standard wireless index information, and generating a fingerprint database by taking the standard wireless index information as data to be matched.
Optionally, in some embodiments, determining the first trajectory information of the user based on the second measurement data includes:
grouping according to the user identity based on the second measurement data, sequencing according to time to obtain continuous motion information of the user, and filtering invalid data based on the continuous motion to obtain effective motion information;
and performing spatial clustering on the effective motion information to obtain clustering points, judging the clustering points according to a preset speed to obtain residence points and theoretical motion points, counting the residence points to obtain residence information, and counting the theoretical motion points to obtain first mobile data.
Optionally, in some embodiments, the movement correction module 340 includes:
the motion point correction module is used for carrying out deviation correction on the basis of the theoretical motion point and the road network data to obtain a corrected motion point;
the road section correction module is used for determining a path road section of the user based on the corrected motion point and continuously correcting the road section based on the path road section and the road network data to obtain a corrected path road section;
and the backward-pushing module is used for backward-pushing the actual motion point of the user on the basis of the corrected path road section to obtain second movement information.
Optionally, in some embodiments, the road segment correcting module is specifically configured to:
determining a path road section of the user according to the corrected motion points in a time sequence;
and determining the shortest path between the path road sections to form a first code table, judging whether the track is continuous according to the path road sections and road network data, and if not, completing the road sections according to the first code table to obtain the corrected path road sections.
The embodiment provides a people flow density determination device, which obtains complete position information through fingerprint positioning based on self-contained first position information in operator measured data, backfills the complete position information to obtain first measured data, performs correlation matching based on the first measured data and signaling data to obtain second measured data comprising the complete position information and user information, analyzes the second measured data to determine first track information of a user, performs movement correction on the first moving information in combination with road network data in the first track information to obtain second moving information, and finally counts people flow density according to regions based on the second moving information and resident information in the first track information, the method combines the operator measured data and the signaling data to obtain more comprehensive user tracks, and then performs multiple data backfilling measures such as road section matching and binding based on road data, path matching and backfilling, track point backfilling in road sections and the like, more comprehensive user track data are obtained, and the accuracy of regional people flow statistics data is finally guaranteed.
Example four
Fig. 7 is a schematic structural diagram of a computer device 400 according to a fourth embodiment of the present invention, as shown in fig. 7, the device includes a memory 410 and a processor 420, the number of the processors 420 in the device may be one or more, and one processor 420 is taken as an example in fig. 7; the memory 410 and the processor 420 in the device may be connected by a bus or other means, and fig. 7 illustrates the connection by a bus as an example.
The memory 410 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the people stream density determination method in the embodiment of the present invention (for example, the positioning module 310, the association module 320, the number movement filtering module 330, the movement modification module 340, and the density statistics module 350 in the people stream density determination device). The processor 420 executes various functional applications of the computer device and data processing by executing software programs, instructions and modules stored in the memory 410, namely, implements the people stream density determination method described above.
Wherein the processor 420 is configured to run the computer executable program stored in the memory 410 to implement the following steps: step S110, obtaining 4G measurement data comprising position information and user information and 5G measurement data comprising user information; step S120, filtering the 4G measurement data comprising the position information and the user information according to a speed threshold value to obtain first measurement data; step S130, correlating the first measurement data with the 5G measurement data including the user information, and backfilling the position information to the 5G measurement data including the user information to obtain second measurement data; step S140, generating a grid fingerprint database based on wireless index information according to the second measurement data; and S150, acquiring 5G measurement data to be positioned, and matching the 5G measurement data to be positioned with the grid fingerprint database according to wireless index information to determine an actual position.
Of course, the computer device provided in the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the people stream density determination method provided in any embodiment of the present invention.
The memory 410 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating device, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 410 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 410 may further include memory located remotely from processor 420, which may be connected to devices through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiment provides a computer device, which corrects a user track by combining road network data on the basis of determining the user track by using operator measurement data and signaling data through fingerprint positioning, and realizes higher-precision counting of the number of people.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, where the computer-executable instructions are executed by a computer processor to perform a people stream density determination method, where the people stream density determination method includes:
obtaining first measurement data comprising complete position information through fingerprint positioning based on first position information reported in operator measurement data;
performing association matching based on the first measurement data and the signaling data to obtain second measurement data comprising complete position information and user information;
determining first track information of a user based on the second measurement data, wherein the track information comprises residence information and first movement information;
performing movement correction based on the first movement information and the road network data to obtain second movement information;
and counting the people flow density according to the second mobile information and the residence information by regions.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a device, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the authorization apparatus, the included units and modules are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
