CN113569978A - Travel track identification method and device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a travel track identification method and device, computer equipment and storage equipment. The method comprises the following steps: obtaining second measurement data including all position information through fingerprint positioning based on the obtained first measurement data; associating the second measurement data with the acquired signaling data to obtain first track data; performing data cleaning based on the first track data to obtain second track data; and performing cluster analysis based on the second track data to determine a residence point so as to determine a travel track of the user according to the residence point. The embodiment of the invention combines the first measurement data and the signaling data as an analysis basis, expands the position information in the first measurement data by means of fingerprint positioning, and obtains a more accurate identification result by taking richer data as a travel track identification basis.
Description
Technical Field
The invention relates to the technical field of traffic planning, in particular to a travel track identification method, a travel track identification device, computer equipment and a storage medium.
Background
In the process of traffic planning and solving traffic congestion, the root cause of congestion of each road section generally needs to be analyzed, and the crowd at the congested road section needs to be tracked and traced by OD (origin-destination) to know where the crowd at the congested road section comes from and goes to. Therefore, the road diversion optimization is carried out, and the traffic jam is fundamentally solved.
At present, most of data for people stream OD analysis are from traveling software of internet companies, such as riding codes, drip-and-shoot vehicles, and Goods navigation software. Compared with the prior art, the data comparison is simple, and each internet company forms a data island which is not communicated with each other.
In another way, signaling data of an operator is used, but the accuracy and reporting frequency of the signaling data are low, and even the traveling and residing states of a user cannot be distinguished, and the data has a limited value for analyzing the traffic OD.
Disclosure of Invention
In view of the above, the present invention provides a travel trajectory identification method, apparatus, device and storage medium, so as to obtain measurement data including rich location information by using fingerprint positioning data, and obtain a more accurate identification result by using the richer measurement data in combination with signaling data as a basis for travel trajectory identification.
In a first aspect, the present invention provides a travel track identification method, including:
obtaining second measurement data including all position information through fingerprint positioning based on the obtained first measurement data;
associating the second measurement data with the acquired signaling data to obtain first track data;
performing data cleaning based on the first track data to obtain second track data;
and performing cluster analysis based on the second track data to determine a residence point so as to determine a travel track of the user according to the residence point.
Optionally, in some embodiments, the first measurement data includes database-built measurement data with agps data and measurement data to be located without agps data, and the obtaining, by fingerprint location, second measurement data including all location information based on the obtained first measurement data includes:
establishing a grid fingerprint database based on the database establishing measurement data;
performing wireless index information matching on the to-be-positioned measurement data and the grid fingerprint database to determine positioning position information of the to-be-positioned measurement data;
and backfilling the positioning position information to the first measurement data to obtain second measurement data.
Optionally, in some embodiments, the associating the second measurement data with the acquired signaling data to obtain first trajectory data includes:
determining the incidence relation between the second measurement data and the signaling data according to a preset matching index;
and backfilling the user information in the signaling data to the second measurement data according to the incidence relation to obtain the first track data.
Optionally, in some embodiments, performing data cleansing based on the first trajectory data to obtain second trajectory data includes:
removing the Internet of things track data based on the first track data to obtain third track data;
performing data thinning based on the third track data to obtain fourth track data;
and rejecting abnormal track data based on the fourth track data to obtain second track data.
Optionally, in some embodiments, the removing abnormal trajectory data based on the fourth trajectory data to obtain second trajectory data includes:
determining continuous track points of the user according to time sequencing on the basis of the fourth track data;
and determining the moving speed between the adjacent continuous track points, if the moving speed is greater than or equal to a preset speed threshold value, judging abnormal track data, and rejecting the corresponding abnormal track data.
Optionally, in some embodiments, performing cluster analysis based on the second trajectory data to determine a residence point, so as to determine a user travel trajectory according to the residence point, includes:
performing space-time DBSCAN clustering on the second track data by using track points to determine clustering space, time and clustering parameters;
and determining a corresponding residence point based on the clustering space, and determining a user travel track by taking the time as residence time.
Optionally, in some embodiments, after determining the user travel trajectory according to the dwell point, the method further includes:
and visually displaying the user travel track according to a display rule.
In a second aspect, an embodiment of the present invention provides a travel track recognition apparatus, including:
the fingerprint positioning module is used for obtaining second measurement data comprising all position information through fingerprint positioning based on the acquired first measurement data;
the data association module is used for associating the second measurement data with the acquired signaling data to obtain first track data;
the data cleaning module is used for cleaning data based on the first track data to obtain second track data;
and the track analysis module is used for performing clustering analysis on the second track data to determine a residence point so as to determine a user travel track according to the residence point.
In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program which can be run on the processor, and the processor executes the computer program to realize the travel trajectory identification method.
In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program comprising program instructions that, when executed, implement the aforementioned travel trajectory identification method.
The travel track identification method comprises the steps of firstly obtaining first measurement data and signaling data reported by a terminal, completing position information on the first measurement data through fingerprint positioning to obtain second measurement data, then associating the second measurement data with the signaling data to obtain first track data, cleaning the first track data to obtain second track data, carrying out cluster analysis according to the second track data, and determining a residence point of a user, so that a travel track of the user is determined by the residence point.
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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 travel trajectory identification method according to an embodiment of the present invention;
fig. 2 is a sub-flowchart of a travel trajectory identification method according to a second embodiment of the present invention;
FIG. 3 is a multi-data source cooperative mobile network problem location association table provided in the second embodiment of the present invention;
fig. 4 is a sub-flowchart of a travel trajectory identification method according to a second embodiment of the present invention;
fig. 5 is a sub-flowchart of a travel trajectory identification method according to a second embodiment of the present invention;
fig. 6 is a sub-flowchart of a travel trajectory identification method according to a second embodiment of the present invention;
fig. 7 is a schematic structural diagram of a travel track recognition apparatus according to a third embodiment of the present invention;
fig. 8 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 present embodiment provides a travel trajectory identification method, which may be applied to a road traffic management system, where the system includes a terminal and a server, where the terminal and the server communicate with each other 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 travel track identification 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:
and S110, obtaining second measurement data comprising all position information through fingerprint positioning based on the acquired first measurement data.
The first measurement data refers to wireless measurement data (MR data) reported by the terminal, and currently, only part of the wireless measurement data will carry position information (agps data) when the terminal reports the wireless measurement data, and the part of the wireless measurement data usually only accounts for 2% -5% of the first measurement data.
In this embodiment, the first measurement data may directly obtain the wireless measurement data uploaded by the terminal, or download and collect data of the provincial side (for a nationwide management center) in an FTP/SFTP protocol manner of the data exchange platform, and then push the downloaded MR data and signaling data to the distributed data storage system for storage for subsequent calculation.
Specifically, in the fingerprint positioning process used in this embodiment, a grid fingerprint library is formed on the basis of the wireless measurement data carrying agps data as a library building base, the wireless measurement data not carrying agps data is matched with the grid fingerprint library to determine the position information of the wireless measurement data not carrying agps data, so that the complete position information of the first measurement data can be determined, and the first measurement data is backfilled to form second measurement data with complete position information.
And S120, associating the second measurement data with the acquired signaling data to obtain first track data.
The signaling data refers to second measurement data acquired through a plurality of data interfaces (such as structures S1, Uu, X2, S11, and the like), although the second measurement data includes rich location information, the main analysis focus of the second measurement data surrounds a serving cell and lacks user-related information, in the conventional technology, MR data is generally used for positioning calculation, and the signaling data includes important information such as detailed user events, but the signaling data has certain defects in location accuracy and data volume, so in the present embodiment, the signaling data is associated with the second measurement data, and the actual purpose thereof is to backfill user information in the signaling data into the second measurement data, thereby associating the user information with the location information to obtain the first trajectory data.
And S130, performing data cleaning based on the first track data to obtain second track data.
The second track data is obtained by data cleaning of the first track data, and the second data actually represents the movement condition of the user in the form of track points, and specifically comprises data such as the movement coordinates (track coordinates) and the movement time of the user. Specifically, the data cleaning aims to eliminate invalid data and data rarefaction, the invalid data comprise track data of unreal users and interfere with the analysis of travel tracks of the users to a certain extent, and the data rarefaction aims to improve the value density of the track data (the second track data is too huge in quantity compared with traditional road traffic management data).
And S140, performing clustering analysis based on the second track data to determine a residence point, so as to determine a user travel track according to the residence point.
The dwell point represents a place where the user stays for a period of time, and the dwell referred to herein does not mean that the user remains unmoved, but rather that the user's track point falls within a certain range, which can be considered to be the user's dwell within the range area. Specifically, in this embodiment, the track points in the second trajectory data are actually analyzed by using space-time DBSCAN clustering, and the track points are clustered and analyzed according to a preset clustering rule to discover different clusters, so as to identify the residence points of the user, and then the travel trajectory of the user is described according to the residence time, attributes and other information of the residence points.
The travel track identification method provided by the embodiment includes the steps of firstly obtaining first measurement data and signaling data reported by a terminal, completing position information on the first measurement data through fingerprint positioning to obtain second measurement data, then associating the second measurement data with the signaling data to obtain first track data, cleaning the first track data to obtain second track data, conducting cluster analysis according to the second track data, determining a residence point of a user, and accordingly determining a travel track of the user through the residence point.
Example two
The second embodiment provides a travel track identification 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, the process of generating the second measurement data in the travel trajectory identification method provided in this embodiment includes steps S111 to 113:
and S111, establishing a grid fingerprint database based on the database establishing measurement data.
S112, performing wireless index information matching on the to-be-positioned measurement data and the grid fingerprint database to determine the positioning position information of the to-be-positioned measurement data;
and S113, backfilling the positioning position information to the first measurement data to obtain second measurement data.
Specifically, the first measurement data comprises database-establishing measurement data with agps data and measurement data to be positioned without agps data, and the distance of fingerprint positioning is to divide a coverage area of the base station into grids with a certain size, generally a grid area of 10m multiplied by 10 m. And then, describing the wireless environment of the grids by using 2% -5% of agps data in the database establishing measurement data and wireless index information contained in the database establishing measurement data, wherein the wireless environment information of each grid comprises rsrp, rsrq, sinr, ta, AOA, enodebid and cellid.
Other to-be-positioned measurement data without agps are matched with the wireless environment information of grids in a fingerprint database by utilizing the wireless environment information to determine the similarity of the wireless environment information between the wireless environment information and the to-be-positioned measurement data, the grid with the highest similarity of the wireless environment information is taken as a candidate grid, the central longitude and latitude of a plurality of candidate grids (usually 3) with the highest similarity are weighted and averaged, the weight is the similarity, so that the position information of the to-be-positioned measurement data is determined, the longitude and latitude backfill of the to-be-positioned measurement data is realized, each measurement data in the backfilled first measurement data can determine corresponding position information, namely all position information is obtained, and the backfilled first measurement data is called as second measurement data.
More specifically, in some embodiments, as shown in FIG. 3, the process of generating the first trajectory data, i.e., step S120, includes steps S121-122:
s121, determining the incidence relation between the second measurement data and the signaling data according to a preset matching index.
And S122, backfilling the user information in the signaling data to the second measurement data according to the incidence relation to obtain the first track data.
In this embodiment, the preset matching index is selected from quintuple: the method comprises the steps of enabling the enode bid + eNBS1apid + mmecode + mmegorupid + mmes1apid, associating signaling data with wireless MR data (second measurement data) based on a quintuple, and backfilling user information contained in the signaling data into the wireless MR data (namely the second measurement data) with agps longitude and latitude position information according to the association relationship after the association relationship is determined, so that rich position information of users at each moment is obtained.
More specifically, in some embodiments, the data cleansing process in step S130 is as shown in fig. 4, and specifically includes steps S131 to 133:
s131, based on the first track data, removing the track data of the Internet of things to obtain third track data.
The user track obtained through the steps is not completely track data of a common user, and also comprises a large amount of longitude and latitude data reported by the internet of things card, the internet of things card is mainly embedded in wireless internet of things equipment for receiving and transmitting wireless information, such as a wireless camera and the like, the data is not a real user, and certain interference effect on data analysis should be removed. The removing method mainly uses the number segment as the distinguishing and removing basis, for example, a telecom operator distinguishes a real user and an internet of things service end by the number segment: the number of the telecommunication sections is 15, wherein the number 149 section is an LTE (including TD-LTE, LTE-FDD)/LTE-A (4G) data network number section, the number 141 section is an Internet of things service special number section, and the rest are a cdmaOne (2G), CDMA2000(3G), LTE/LTE-1(4G) and NR (5G) mixed number section, and the two satellite mobile phone special number sections can identify the Internet of things track data uploaded by the Internet of things service end and remove the Internet of things track data to obtain third track data.
And S132, performing data thinning based on the third track data to obtain fourth track data.
Because the wireless data reporting period is extremely short, the wireless data is reported once every ten seconds, the data volume is huge, and the high-frequency latitude and longitude data is a burden for OD (ORIGIN-degree) analysis, and the data needs to be further cleaned, so that the value density is improved. The method is mainly characterized in that time thinning is performed, for example, each user only keeps one point per minute, and thus data thinning operation is actually performed on third track data to obtain fourth track data.
And S133, removing abnormal track data based on the fourth track data to obtain second track data.
The sources of abnormal trajectory data are mainly caused by agps drift, fingerprint positioning error, user association error and the like. The method comprises the steps of judging whether longitude and latitude of a user are abnormal track data or not, wherein the main idea is to sort according to time of track points of the user, calculate speed according to distance and time of two adjacent track points in front and at back, and judge as an abnormal point when the speed exceeds a certain threshold. Namely: determining continuous track points of the user according to time sequencing on the basis of the fourth track data; and determining the moving speed between the adjacent continuous track points, if the moving speed is greater than or equal to a preset speed threshold value, judging abnormal track data, and rejecting the corresponding abnormal track data.
More specifically, in some embodiments, step S140, as shown in FIG. 5, includes S141-142:
and S141, performing space-time DBSCAN clustering on the second track data by using track points to determine clustering space, time and clustering parameters.
S142, determining a corresponding residence point based on the clustering space, and determining a user travel track by taking the time as residence time.
In the clustering algorithm adopted in this embodiment, the core samples satisfy the requirements: the sample object x satisfies the spatial radius < R and the number of objects in the time interval < t > MinPts (threshold value when defining the core point).
First, one point is arbitrarily selected, and then all points satisfying eps (neighborhood radius when defining density) are selected. If the number of data points is less than min samples, then this point is marked as noise. If the number of data points is greater than min _ samples, then this point is marked as a core sample and assigned a new cluster label, and the core sample point can reach the same cluster.
More specifically, in some embodiments, step S210 is further included after step S140 shown in fig. 6:
s210, visually displaying the user travel track according to a display rule.
In this embodiment, the display rule is used to determine a display manner of the travel track, for example, for different users to display the travel track in different colors, when the travel track of a user is displayed for a single user, the actual route may be displayed by a track line, and the user movement situation may also be briefly displayed by a distance-time diagram, which is not limited herein.
On the basis of the foregoing embodiment, the present embodiment further explains a fingerprint positioning process, a data association process, a data cleaning process, and a cluster analysis process, so that the measurement data and the signaling data used in the present embodiment are more suitable for travel trajectory identification, and the identification efficiency and the identification accuracy are improved.
EXAMPLE III
Fig. 7 is a schematic structural diagram of a travel trajectory recognition apparatus 300 according to a third embodiment of the present invention, and as shown in fig. 7, the apparatus 300 includes:
the fingerprint positioning module 310 is configured to obtain second measurement data including all position information through fingerprint positioning based on the obtained first measurement data;
a data association module 320, configured to associate the second measurement data with the obtained signaling data to obtain first trajectory data;
a data cleaning module 330, configured to perform data cleaning based on the first trajectory data to obtain second trajectory data;
and a trajectory analysis module 340, configured to perform cluster analysis based on the second trajectory data to determine a residence point, so as to determine a user travel trajectory according to the residence point.
Optionally, in some embodiments, the first measurement data includes database-built measurement data with agps data and measurement data to be located without agps data, and the fingerprint location module 310 is specifically configured to:
establishing a grid fingerprint database based on the database establishing measurement data;
performing wireless index information matching on the to-be-positioned measurement data and the grid fingerprint database to determine positioning position information of the to-be-positioned measurement data;
and backfilling the positioning position information to the first measurement data to obtain second measurement data.
Optionally, in some embodiments, the data association module 320 is specifically configured to:
determining the incidence relation between the second measurement data and the signaling data according to a preset matching index;
and backfilling the user information in the signaling data to the second measurement data according to the incidence relation to obtain the first track data.
Optionally, in some embodiments, the data cleansing module 330 is specifically configured to:
removing the Internet of things track data based on the first track data to obtain third track data;
performing data thinning based on the third track data to obtain fourth track data;
and rejecting abnormal track data based on the fourth track data to obtain second track data.
Optionally, in some embodiments, the rejecting abnormal trajectory data based on the fourth trajectory data to obtain second trajectory data includes:
determining continuous track points of the user according to time sequencing on the basis of the fourth track data;
and determining the moving speed between the adjacent continuous track points, if the moving speed is greater than or equal to a preset speed threshold value, judging abnormal track data, and rejecting the corresponding abnormal track data.
Optionally, in some embodiments, the trajectory analysis module 340 is specifically configured to:
performing space-time DBSCAN clustering on the second track data by using track points to determine clustering space, time and clustering parameters;
and determining a corresponding residence point based on the clustering space, and determining a user travel track by taking the time as residence time.
Optionally, in some embodiments, the method further includes:
and the visualization module is used for visually displaying the user travel track according to a display rule.
The embodiment provides a travel track recognition device, which includes the steps of firstly obtaining first measurement data and signaling data reported by a terminal, completing position information on the first measurement data through fingerprint positioning to obtain second measurement data, then associating the second measurement data with the signaling data to obtain first track data, cleaning the first track data to obtain second track data, performing cluster analysis according to the second track data to determine a residence point of a user, and determining a travel track of the user according to the residence point.
Example four
Fig. 8 is a schematic structural diagram of a computer device 400 according to a fourth embodiment of the present invention, as shown in fig. 8, 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. 8; the memory 410 and the processor 420 in the device may be connected by a bus or other means, and fig. 8 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 travel trajectory identification method in the embodiment of the present invention (for example, the fingerprint positioning module 310, the data association module 320, the data cleansing module 330, and the trajectory analysis module 340 in the travel trajectory identification system). The processor 420 executes various functional applications of the server and data processing by running software programs, instructions, and modules stored in the memory 410, that is, implements the travel trajectory recognition 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 second measurement data including all position information through fingerprint positioning based on the obtained first measurement data; step S120, associating the second measurement data with the acquired signaling data to obtain first track data; step S130, performing data cleaning based on the first track data to obtain second track data; and S140, performing clustering analysis based on the second track data to determine a residence point, so as to determine a travel track of the user according to the residence point.
Of course, the server provided in the embodiment of the present invention is not limited to the above method operations, and may also perform related operations in the travel trajectory identification 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 system, 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.
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 travel trajectory identification method, where the travel trajectory identification method includes:
obtaining second measurement data including all position information through fingerprint positioning based on the obtained first measurement data;
associating the second measurement data with the acquired signaling data to obtain first track data;
performing data cleaning based on the first track data to obtain second track data;
and performing cluster analysis based on the second track data to determine a residence point so as to determine a travel track of the user according to the residence point.
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 server (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.
Claims (10)
1. A travel track identification method is characterized by comprising the following steps:
obtaining second measurement data including all position information through fingerprint positioning based on the obtained first measurement data;
associating the second measurement data with the acquired signaling data to obtain first track data;
performing data cleaning based on the first track data to obtain second track data;
and performing cluster analysis based on the second track data to determine a residence point so as to determine a travel track of the user according to the residence point.
2. A travel trajectory identification method according to claim 1, wherein the first measurement data includes database-building measurement data with agps data and measurement data to be positioned without agps data, and the second measurement data including all position information is obtained by fingerprint positioning based on the acquired first measurement data, and the method includes:
establishing a grid fingerprint database based on the database establishing measurement data;
performing wireless index information matching on the to-be-positioned measurement data and the grid fingerprint database to determine positioning position information of the to-be-positioned measurement data;
and backfilling the positioning position information to the first measurement data to obtain second measurement data.
3. A travel trajectory identification method according to claim 1, wherein the step of associating the second measurement data with the obtained signaling data to obtain first trajectory data comprises:
determining the incidence relation between the second measurement data and the signaling data according to a preset matching index;
and backfilling the user information in the signaling data to the second measurement data according to the incidence relation to obtain the first track data.
4. A travel trajectory recognition method according to claim 1, wherein the step of performing data cleansing based on the first trajectory data to obtain second trajectory data comprises:
removing the Internet of things track data based on the first track data to obtain third track data;
performing data thinning based on the third track data to obtain fourth track data;
and rejecting abnormal track data based on the fourth track data to obtain second track data.
5. A travel trajectory identification method according to claim 4, wherein the removing abnormal trajectory data based on the fourth trajectory data to obtain second trajectory data comprises:
determining continuous track points of the user according to time sequencing on the basis of the fourth track data;
and determining the moving speed between the adjacent continuous track points, if the moving speed is greater than or equal to a preset speed threshold value, judging abnormal track data, and rejecting the corresponding abnormal track data.
6. The travel trajectory identification method according to claim 1, wherein clustering analysis is performed based on the second trajectory data to determine a dwell point, so as to determine a travel trajectory of the user according to the dwell point, and the method comprises:
performing space-time DBSCAN clustering on the second track data by using track points to determine clustering space, time and clustering parameters;
and determining a corresponding residence point based on the clustering space, and determining a user travel track by taking the time as residence time.
7. A travel trajectory recognition method according to claim 1, after determining a travel trajectory of the user according to the dwell point, further comprising:
and visually displaying the user travel track according to a display rule.
8. A travel trajectory recognition device, comprising:
the fingerprint positioning module is used for obtaining second measurement data comprising all position information through fingerprint positioning based on the acquired first measurement data;
the data association module is used for associating the second measurement data with the acquired signaling data to obtain first track data;
the data cleaning module is used for cleaning data based on the first track data to obtain second track data;
and the track analysis module is used for performing clustering analysis on the second track data to determine a residence point so as to determine a user travel track according to the residence point.
9. A computer device, characterized by comprising a memory and a processor, the memory having stored thereon a computer program executable on the processor, the processor implementing the travel trajectory recognition method according to any one of claims 1 to 7 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 travel trajectory identification method according to any one of claims 1 to 7.
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