CN113569978B - 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, a travel track identification device, computer equipment and storage equipment. The method comprises the following steps: obtaining second measurement data comprising all position information through fingerprint positioning based on the obtained first measurement data; correlating 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 carrying out cluster analysis based on the second track data to determine a resident point so as to determine a user travel track according to the resident point. The embodiment of the invention combines the first measurement data and the signaling data as analysis bases, expands the position information in the first measurement data by means of fingerprint positioning, and obtains more accurate identification results by taking richer data as travel track identification bases.
Description
Technical Field
The present invention relates to the field of traffic planning technologies, and in particular, to a travel track identification method, apparatus, computer device, and storage medium.
Background
In the process of traffic planning and solving traffic congestion, the root cause of congestion of each road section is usually required to be analyzed, and trace source OD analysis is required to be carried out on crowds of the congested road section, so that the crowds in the congested road section can be known from where to go. Therefore, road diversion optimization is carried out, and traffic jam is fundamentally solved.
The current data for carrying out the OD analysis of the people stream are mostly derived from travel software of Internet companies, such as riding codes, drip and get on a car, goldnavigation software and the like. The data comparison is on one side, and each internet company forms a data island which is not communicated with each other.
The other way is to use signaling data of operators, but the accuracy and reporting frequency of the signaling data are low, and even the traveling and resident states of users cannot be distinguished, so that the data have limited value for traffic OD analysis.
Disclosure of Invention
In view of the above, the present invention provides a travel track recognition method, apparatus, device and storage medium, so as to obtain measurement data including abundant location information through fingerprint positioning data, and obtain a more accurate recognition result by using the richer measurement data in combination with signaling data as a travel track recognition basis.
In a first aspect, the present invention provides a travel track recognition method, including:
obtaining second measurement data comprising all position information through fingerprint positioning based on the obtained first measurement data;
correlating 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 carrying out cluster analysis based on the second track data to determine a resident point so as to determine a user travel track according to the resident point.
Optionally, in some embodiments, the first measurement data includes database measurement data with agps data and measurement data to be located without agps data, and the obtaining, based on the obtained first measurement data, the second measurement data including all the location information by fingerprint positioning includes:
establishing a grid fingerprint library based on the library 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;
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 the first trajectory data includes:
determining the association 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 association relation to obtain the first track data.
Optionally, in some embodiments, performing data cleansing based on the first track data to obtain second track data includes:
removing the track data of the Internet of things based on the first track data to obtain third track data;
Performing data thinning on the basis of the third track data to obtain fourth track data;
and eliminating abnormal track data based on the fourth track data to obtain second track data.
Optionally, in some embodiments, the removing the abnormal track data based on the fourth track data to obtain the second track data includes:
Determining continuous track points of the user according to time sequence based on the fourth track data;
And determining the moving speed between the adjacent continuous track points, judging that abnormal track data appear if the moving speed is greater than or equal to a preset speed threshold, and eliminating the corresponding abnormal track data.
Optionally, in some embodiments, determining the stay point based on the second track data by performing cluster analysis to determine a user travel track according to the stay point includes:
performing space-time DBSCAN clustering on the track points based on the second track data to determine clustering space, time and clustering parameters;
and determining corresponding residence points 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 travel track of the user according to the residence point, the method further includes:
and visually displaying the user travel track according to the display rule.
In a second aspect, an embodiment of the present invention provides a travel track identifying device, including:
the fingerprint positioning module is used for obtaining second measurement data comprising all position information through fingerprint positioning based on the obtained 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 performing data cleaning based on the first track data to obtain second track data;
And the track analysis module is used for carrying out cluster analysis on the basis of the second track data to determine the stay points so as to determine the travel track of the user according to the stay points.
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 executable by the processor, the processor implementing a travel track identification 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 travel track identification method.
According to the travel track identification method, first measurement data and signaling data reported by a terminal are firstly obtained, second measurement data are obtained through fingerprint positioning and position supplementing of the first measurement data, then the second measurement data are associated with the signaling data to obtain first track data, the first track data are cleaned to obtain second track data, clustering analysis is carried out according to the second track data, and residence points of a user are determined, so that travel tracks of the user are determined by the residence points.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly explain the drawings required to be used in the embodiments or the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a travel track recognition method according to an embodiment of the present invention;
fig. 2 is a sub-flowchart of a travel track recognition method according to a second embodiment of the present invention;
FIG. 3 is a table of problem location associations for a multi-data source collaborative mobile network provided in accordance with a second embodiment of the present invention;
fig. 4 is a sub-flowchart of a travel track recognition method according to a second embodiment of the present invention;
fig. 5 is a sub-flowchart of a travel track recognition method according to a second embodiment of the present invention;
Fig. 6 is a sub-flowchart of a travel track recognition method according to a second embodiment of the present invention;
fig. 7 is a schematic structural diagram of a travel track recognition device 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 scheme in the implementation of the present application is clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It is to be understood that the specific embodiments described herein are merely illustrative of, and not restrictive on, some, but not all embodiments of the application. It should be further noted that, based on the embodiments of the present application, all other embodiments obtained by a person having ordinary skill in the art without making any inventive effort are within the 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 herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein 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 directions, acts, steps, or elements, etc., but these directions, acts, 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, a first instance may be referred to as a second instance, and similarly, a second instance may be referred to as a first instance, without departing from the scope of the present invention. Both the first case and the second case are cases, but they are not the same case. The terms "first," "second," and the like, are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include a combination of one or more features. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. It should be noted that when one portion is referred to as being "fixed to" another portion, it may be directly on the other portion or there may be a portion in the middle. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only and do not represent the only embodiment.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example 1
Referring to fig. 1, the present embodiment provides a travel track recognition 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 through a network, the terminal may be, but is not limited to, various smartphones, tablet computers and portable wearable devices, and the server may be implemented by an independent server or a server cluster formed by a plurality of servers. Based on the system, the travel track identification method can be executed by a terminal or a server, and can also be realized through interaction between the terminal and the server. As shown in fig. 1, the method specifically includes:
s110, obtaining second measurement data comprising all position information through fingerprint positioning based on the obtained first measurement data.
The first measurement data refers to wireless measurement data (MR data) reported by the terminal, only part of the wireless measurement data will carry position information (agps data) when the terminal reports the wireless measurement data, the part of the wireless measurement data usually only accounts for 2% -5% of the first measurement data, and for the wireless measurement data without the position information, the position of the wireless measurement data is backfilled by fingerprint positioning in the embodiment so as to obtain wireless measurement data including complete position information, namely second measurement data.
In this embodiment, the first measurement data may directly obtain the wireless measurement data uploaded by the terminal, or may download and collect the data of the provincial end (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 a 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 a database construction by taking wireless measurement data carrying agps data, and wireless measurement data not carrying agps data is matched with the grid fingerprint library to determine position information of the wireless measurement data not carrying agps data, so that complete position information of first measurement data can be determined, and backfilling is performed to the first measurement data 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 the fact that although the second measurement data acquired through a plurality of data interfaces (such as structures of S1, uu, X2, S11, etc.) includes abundant location information, but the main analysis emphasis surrounds the serving cell and lacks user related information, in the conventional technology, MR data is generally used for positioning calculation, while the signaling data includes important information such as detailed user events, etc., but the signaling data has certain defects in location accuracy and data volume, so that in the embodiment, the signaling data is associated with the second measurement data, and the real purpose is to backfill the user information in the signaling data to the second measurement data, thereby associating the user information with the location information, and obtaining the first track data.
S130, data cleaning is carried out based on the first track data to obtain second track data.
The second track data is obtained by cleaning 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 movement coordinates (track coordinates) and movement time of the user. Specifically, the data cleaning aims at removing invalid data and data thinning, wherein the invalid data comprises track data of non-real users, a certain interference exists on travel tracks of analysis users, and the data thinning aims at improving the value density of the track data (the second track data is excessively huge in quantity compared with the traditional road traffic management data).
And S140, carrying out cluster analysis based on the second track data to determine a resident point so as to determine a user travel track according to the resident point.
A dwell point refers to a place where a user stays for a period of time, where the reference to dwell does not mean that the user remains stationary, but that the user's trajectory point falls within a range, which can be considered to be where the user resides. Specifically, in this embodiment, space-time DBSCAN clustering is actually used to analyze the track points in the second track data, and cluster analysis is performed on the track points according to a preset clustering rule to discover different clusters, so as to identify the residence points of the user, and then the travel track of the user is described according to the residence time, attribute and other information of the residence points.
According to the travel track identification method, first measurement data and signaling data reported by a terminal are acquired, second measurement data are obtained through fingerprint positioning and position supplementing of the first measurement data, then the second measurement data are associated with the signaling data to obtain first track data, the first track data are cleaned to obtain second track data, clustering analysis is conducted according to the second track data, resident points of a user are determined, and accordingly travel tracks of the user are determined through the resident points.
Example two
The second embodiment provides a travel track recognition method, which can be implemented on the basis of the first embodiment, and further supplements the content in the first embodiment, and specifically includes:
As shown in fig. 2, the generation process of the second measurement data in the travel track recognition method provided in this embodiment includes steps S111-113:
S111, establishing a grid fingerprint database based on the database establishing measurement data.
S112, carrying out 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;
S113, backfilling the positioning position information to the first measurement data to obtain second measurement data.
Specifically, the first measurement data includes database measurement data with agps data and measurement data to be positioned without agps data, and the fingerprint is located far away from the database measurement data by dividing the coverage area of the base station into grids with a certain size, typically 10m by 10m grid areas. And describing the wireless environment of the grids by using agps% of data in the database building measurement data and wireless index information contained in the database building measurement data, wherein the wireless environment information of each grid comprises rsrp, rsrq, sinr, ta, AOA, enodebid, cellid.
Other measurement data to be positioned without agps are matched with the wireless environment information of the grids in the fingerprint library by utilizing the wireless environment information to determine the similarity of the wireless environment information between the wireless environment information and the fingerprint library, the highest similarity of the wireless environment information is taken as a candidate grid, the central longitudes and latitudes of a plurality of candidate grids (usually 3) with the highest similarity are weighted and averaged, the weight is the similarity, and therefore the position information of the measurement data to be positioned is determined, the longitude and latitude backfill of the measurement data to be positioned is realized, and therefore, the corresponding position information can be determined for each measurement data in the first measurement data after backfill, namely all the position information is obtained, and the backfilled first measurement data is called 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 association relation between the second measurement data and the signaling data according to a preset matching index.
S122, backfilling the user information in the signaling data to the second measurement data according to the association relation to obtain the first track data.
In this embodiment, the preset matching index is five-tuple: enodbid + eNBS1apid + mmecode + mmegroupid + mmes1apid, associating the signaling data with the wireless MR data (second measurement data) based on the five-tuple, and backfilling user information contained in the signaling data into the wireless MR data (second measurement data) with agps longitude and latitude position information according to the association relationship after determining the association relationship, thereby obtaining the position information rich in users at all times.
More specifically, in some embodiments, the data cleansing process in step S130 is as shown in fig. 4, and specifically includes steps S131-133:
s131, removing the track data of the Internet of things based on the first track data to obtain third track data.
The user track obtained through the steps is not completely track data of a common user, and also contains a large amount of longitude and latitude data reported by an Internet of things card, wherein the Internet of things card is mainly embedded in wireless Internet of things equipment for wireless information receiving and transmitting, such as a wireless camera and the like, and the data are not real users and have certain interference effect on data analysis and should be removed. The removal method mainly uses the number segments as the distinguishing and removal basis, for example, a telecom operator distinguishes real users from the service end of the Internet of things by the number segments: telecommunication is 15 segments in total, wherein the 149 segments are LTE (including TD-LTE, LTE-FDD)/LTE-A (4G) data Internet surfing segments, the 141 segments are Internet of things service special segments, the rest are cdmaOne (2G), CDMA2000 (3G), LTE/LTE-1 (4G) and NR (5G) mixed segments, and the two satellite mobile phone special segments can identify Internet of things track data uploaded by an Internet of things service end according to the Internet of things track data, and third track data is obtained after the Internet of things track data are removed.
And S132, performing data thinning on the basis of the third track data to obtain fourth track data.
Because the wireless data reporting period is extremely short, reporting is carried out every ten seconds, the data volume is extremely huge, and the high-frequency longitude and latitude data is a burden on the OD (ORIGIN-DESTINATION) analysis, so that the data needs to be further cleaned, and the value density is improved. The main idea is to perform data thinning operation on the third track data to obtain fourth track data according to time thinning, for example, each user only keeps one point per minute.
S133, eliminating abnormal track data based on the fourth track data to obtain second track data.
The source of abnormal trajectory data is mainly caused by agps drift, fingerprint positioning errors, user association errors and the like. Judging whether the longitude and latitude of a user are abnormal track data or not, wherein the main idea is to sort according to the time of the track points of the user, calculate the speed according to the distance and the time of the front and rear adjacent track points, and judge the speed as an abnormal point when the speed exceeds a certain threshold value. Namely: determining continuous track points of the user according to time sequence based on the fourth track data; and determining the moving speed between the adjacent continuous track points, judging that abnormal track data appear if the moving speed is greater than or equal to a preset speed threshold, and eliminating the corresponding abnormal track data.
More specifically, in some embodiments, step S140, as shown in FIG. 5, includes S141-142:
s141, performing space-time DBSCAN clustering on the track points based on the second track data to determine clustering space, time and clustering parameters.
S142, determining corresponding residence points 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 thereof satisfy the requirements: sample object x, satisfies a spatial radius < R and a time interval < number of objects in the t field > =minpts (threshold when defining the core point).
One point is selected arbitrarily, 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 is reachable as the same cluster.
More specifically, in some embodiments, step S210 is further included after step S140 shown in fig. 6:
And S210, visually displaying the travel track of the user according to the display rule.
In this embodiment, the display rule is used to determine a display manner of the travel track, for example, for displaying the travel track with different colors for different users, and when the travel track of the user is displayed for a single user, the actual route can be displayed with a track line, and the movement condition of the user can also be displayed briefly with a distance-time diagram, which is not limited herein.
The embodiment further explains the fingerprint positioning process, the data association process, the data cleaning process and the cluster analysis process on the basis of the embodiment, so that the measurement data and the signaling data used in the embodiment are more suitable for travel track recognition, and the recognition efficiency and the recognition accuracy are improved.
Example III
Fig. 7 is a schematic structural diagram of a travel track recognition device 300 according to a third embodiment of the present invention, where, as shown in fig. 7, the device 300 includes:
A fingerprint positioning module 310, configured to obtain second measurement data including all position information by fingerprint positioning based on the obtained first measurement data;
a data association module 320, configured to associate the second measurement data with the acquired signaling data to obtain first track data;
The data cleaning module 330 is configured to perform data cleaning based on the first track data to obtain second track data;
And the track analysis module 340 is configured to perform cluster analysis based on the second track data to determine a resident point, so as to determine a travel track of the user according to the resident point.
Optionally, in some embodiments, the first measurement data includes database 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 library based on the library 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;
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 association 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 association relation to obtain the first track data.
Optionally, in some embodiments, the data cleansing module 330 is specifically configured to:
removing the track data of the Internet of things based on the first track data to obtain third track data;
Performing data thinning on the basis of the third track data to obtain fourth track data;
and eliminating abnormal track data based on the fourth track data to obtain second track data.
Optionally, in some embodiments, the removing the abnormal track data based on the fourth track data to obtain the second track data includes:
Determining continuous track points of the user according to time sequence based on the fourth track data;
And determining the moving speed between the adjacent continuous track points, judging that abnormal track data appear if the moving speed is greater than or equal to a preset speed threshold, and eliminating the corresponding abnormal track data.
Optionally, in some embodiments, the track analysis module 340 is specifically configured to:
performing space-time DBSCAN clustering on the track points based on the second track data to determine clustering space, time and clustering parameters;
and determining corresponding residence points 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 comprises:
And the visualization module is used for visually displaying the travel track of the user according to the display rule.
The embodiment provides a travel track recognition device, which comprises the steps of firstly acquiring first measurement data and signaling data reported by a terminal, obtaining second measurement data through fingerprint positioning and complementing position information on the first 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, and determining a residence point of a user, thereby determining a travel track of the user by the residence point.
Example IV
Fig. 8 is a schematic structural diagram of a computer device 400 according to a fourth embodiment of the present invention, where, as shown in fig. 8, the device includes a memory 410 and a processor 420, and the number of the processors 420 in the device may be one or more, and in fig. 8, one processor 420 is taken as an example. The memory 410, processor 420 in the device may be connected by a bus or other means, for example in fig. 8.
The memory 410 is used as a computer readable storage medium for storing software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the travel track recognition method in the embodiment of the present invention (e.g., the fingerprint positioning module 310, the data association module 320, the data cleansing module 330, and the track analysis module 340 in the travel track recognition 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, i.e., implements the travel track recognition method described above.
Wherein the processor 420 is configured to execute a computer executable program stored in the memory 410 to implement the following steps: step S110, obtaining second measurement data comprising all position information through fingerprint positioning based on the obtained first measurement data; step S120, the second measurement data and the acquired signaling data are correlated to obtain first track data; step S130, performing data cleaning based on the first track data to obtain second track data; and step 140, performing cluster analysis based on the second track data to determine a resident point so as to determine a user travel track according to the resident point.
Of course, the server provided by the embodiment of the present invention is not limited to the above-mentioned method operations, and may also perform the related operations in the travel track identification method provided by any embodiment of the present invention.
Memory 410 may include primarily a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal, etc. In addition, 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 the device via 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
A fifth embodiment of the present invention also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing a travel track identification method comprising:
obtaining second measurement data comprising all position information through fingerprint positioning based on the obtained first measurement data;
correlating 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 carrying out cluster analysis based on the second track data to determine a resident point so as to determine a user travel track according to the resident point.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk, or an optical disk of a computer, etc., including several instructions for causing a server (which may be a personal computer, a device, or a network device, etc.) to perform the method according to the embodiments of the present invention.
It should be noted that, in the embodiment of the authorization device, each unit and module included are only divided according to the functional logic, but not limited to the above division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. 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, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (7)
1. The travel track recognition method is characterized by comprising the following steps of:
obtaining second measurement data comprising all position information through fingerprint positioning based on the obtained first measurement data;
correlating the second measurement data with the acquired signaling data to obtain first track data; wherein the signaling data comprises detailed user events;
Performing data cleaning based on the first track data to obtain second track data;
performing cluster analysis based on the second track data to determine a resident point so as to determine a user travel track according to the resident point;
the step of performing data cleaning based on the first track data to obtain second track data includes:
removing the track data of the Internet of things based on the first track data to obtain third track data;
Performing data thinning on the basis of the third track data to obtain fourth track data;
Removing abnormal track data based on the fourth track data to obtain second track data;
the step of removing the abnormal track data based on the fourth track data to obtain second track data includes:
Determining continuous track points of the user according to time sequence based on the fourth track data;
determining the moving speed between adjacent continuous track points, if the moving speed is greater than or equal to a preset speed threshold, judging that abnormal track data appear, and eliminating the corresponding abnormal track data;
the step of associating the second measurement data with the acquired signaling data to obtain first track data includes:
associating the signaling data with the wireless MR data based on a preset matching index, and determining an association relationship between the second measurement data and the signaling data; wherein, the preset matching index is five-tuple: enodbid + eNBS1apid + mmecode + mmegroupid + mmes1apid, the wireless MR data being second measurement data;
And backfilling the user information in the signaling data to the second measurement data according to the association relation to obtain the first track data.
2. The travel track identification method according to claim 1, wherein the first measurement data includes database-built measurement data with agps data and measurement data to be positioned without agps data, the obtaining second measurement data including all position information based on the obtained first measurement data by fingerprint positioning includes:
establishing a grid fingerprint library based on the library 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;
backfilling the positioning position information to the first measurement data to obtain second measurement data.
3. The travel track recognition method according to claim 1, wherein determining a stay point based on the second track data by cluster analysis to determine a user travel track from the stay point comprises:
performing space-time DBSCAN clustering on the track points based on the second track data to determine clustering space, time and clustering parameters;
and determining corresponding residence points based on the clustering space, and determining a user travel track by taking the time as residence time.
4. The travel track recognition method according to claim 1, further comprising, after determining a travel track of the user according to the resident point:
and visually displaying the user travel track according to the display rule.
5. A travel track recognition device, comprising:
the fingerprint positioning module is used for obtaining second measurement data comprising all position information through fingerprint positioning based on the obtained 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; wherein the signaling data comprises detailed user events;
the data cleaning module is used for performing data cleaning based on the first track data to obtain second track data;
The track analysis module is used for carrying out cluster analysis on the basis of the second track data to determine a resident point so as to determine a user travel track according to the resident point;
the data cleaning module is specifically configured to:
removing the track data of the Internet of things based on the first track data to obtain third track data;
Performing data thinning on the basis of the third track data to obtain fourth track data;
Removing abnormal track data based on the fourth track data to obtain second track data;
the step of removing the abnormal track data based on the fourth track data to obtain second track data includes:
Determining continuous track points of the user according to time sequence based on the fourth track data;
determining the moving speed between adjacent continuous track points, if the moving speed is greater than or equal to a preset speed threshold, judging that abnormal track data appear, and eliminating the corresponding abnormal track data;
The data association module is specifically configured to:
associating the signaling data with the wireless MR data based on a preset matching index, and determining an association relationship between the second measurement data and the signaling data; wherein, the preset matching index is five-tuple: enodbid + eNBS1apid + mmecode + mmegroupid + mmes1apid, the wireless MR data being second measurement data;
And backfilling the user information in the signaling data to the second measurement data according to the association relation to obtain the first track data.
6. A computer device comprising a memory and a processor, the memory having stored thereon a computer program executable by the processor, the processor implementing the trip trajectory identification method of any one of claims 1-4 when the computer program is executed.
7. A computer readable storage medium, characterized in that the storage medium stores a computer program comprising program instructions which, when executed, implement the travel track identification method according to any one of claims 1-4.
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