CN113742607B - Stay position recommending method based on geographical track of principal - Google Patents
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Abstract
The invention provides a stay position recommending method based on a geographical track of a principal, and relates to the field of track analysis. The stay position recommending method based on the geographical track of the principal comprises a smooth track dividing algorithm and a stay domain function. The method is applied to the track person searching model by using the track stay domain function method to primarily identify the approximate area of the frequent stay position, overcomes the defects of overlarge data quantity and low accuracy of the traditional track analysis, solves the problem of too many dependent parameters of the track searching of the model in the prior art, divides the track of the principal into a plurality of smooth tracks by the smooth track dividing algorithm so as to reduce the later workload, improve the efficiency of the model, and has very fast algorithm and effectively improve the identification efficiency of the stay points by using the stay domain function and the calculation involved in the mark grading and the link.
Description
Technical Field
The invention relates to the technical field of track analysis, in particular to a stay position recommending method based on a geographical track of a principal.
Background
When a court executive bureau performs a court officer to find out a belief-losing executive person, the situation that the executive person frequently occurs to hide the trace occurs, and the current executive officer can only pass through the originally registered identity card address or the original contact information by searching for the executive person; because the complex location data cannot be effectively analyzed, once each executed person does not work or live at the original address, searching for the executed person becomes very troublesome.
The current traffic travel track analysis method is mainly focused on the DBSCAN proposed by Ester and the like for a density-based clustering algorithm, two parameters of an Eps neighborhood and a MinPts are set, and the connected search clusters are continuously expanded outwards in a classification mode with the density being reachable based on sample points. Birandom et al define spatial distance by using Eps1, and define non-spatial distance by using Eps2, and propose ST-DBSCAN algorithm of space-time clustering. Tran et al change Eps in DBSCAN into time linear neighborhood, minPts into minimum time length, and propose TrigDBSCASN algorithm. Gong et al introduces a direction change coefficient and an abnormal point proportion in the track, and proposes a C-DBSCAN algorithm aiming at time sequence limitation and direction change limitation of parking points. G.Agamennoni, J.Nieto, E.Nebot proposes an algorithm to find the salient locations in an automatic way, using two types of places, namely a low speed area and a loading station. The dwell index is proposed to Long Gang and the like, and the space-time aggregation degree of the track is reflected in the number of the neighborhood inner points, the space distance and the dwell time. He Yuanhao and the like combine DBSCAN with geographic semantics, extract clusters through density clustering, and then combine the semantics according to the reverse geographic coding of the cluster center. Zhou Yang, yang Chao propose a new space-time clustering algorithm AT-DBSCAN to identify parking points in a track, and can provide a certain basis for identifying travel modes and travel purposes. Yuan Hua, qian Yu, yang Rui have studied the problem of efficiently mining the location of a user's point of interest in a given spatial area and frequent travel sequences, i.e., frequent paths, from a large amount of GPS trajectory data. Wearing dew passes through trip test design, has gathered the complete trip chain GPS data of individual to utilize three kind trip endpoint identification algorithm respectively: the method has the advantages that the travel endpoints can be identified and evaluated based on a rule identification algorithm, a density-based spatial clustering algorithm DBSCAN and a density-based spatial-temporal clustering algorithm ST-DBScan, and the travel endpoints of most travel types can be identified, but the technology has the defects of overlarge analysis data volume and low accuracy, and meanwhile has the problem that model track searching dependent parameters are too many, so that the method is urgent for a court executive office to find out a believed executive.
Disclosure of Invention
(one) solving the technical problems
The invention aims to overcome the defects and the shortcomings of the prior art, and provides a stay position recommending method based on a geographical track of a principal, which is applied to a track person searching model by using a track stay domain function method to primarily identify the approximate area of frequent stay positions, overcomes the shortcomings of overlarge data quantity and low accuracy of the traditional track analysis, and solves the problem of too many dependent parameters of track searching of the model in the prior art.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the stay position recommending method based on the geographical track of the principal comprises a smooth track dividing algorithm and a stay domain function, and the method comprises the following steps:
s1, preprocessing the original data, including data cleaning and filling missing data by a linear interpolation method;
s2, dividing the track into a plurality of smooth tracks through a smooth track dividing algorithm and processing the smooth tracks by using a stay domain function, wherein the result of the smooth track dividing can ensure that characteristic points in the track, namely fixed active points and frequent paths of a principal can be effectively dug under the condition of less information loss;
s3, processing each smooth track by using a stay domain function, marking important places as geographical areas which are significant to users through scoring of the tracks and calculation related to links, deducing activities through data, and associating the activities with different places.
S4, gathering and classifying similar points according to a time threshold value of a certain position, constructing a place hierarchy through a time scale and a position scale, and calculating and identifying travel endpoints and frequent stay positions
S5, overlapping the position trace on the map, wherein the frequently accessed place is regarded as an important place. Furthermore an off-the-shelf GPS unit is connected to a person and processes the collected data to infer him or his activity, as well as an important series of places.
Preferably, the application step of the dwell domain function is as follows:
s1, firstly, processing data into two ordered setsAnd->
S2, the first group comprises position vectorsAnd is referred to as tracking, and the second group containsFeature vector
S3, summarizing any additional information about the agent at the same time point, wherein the agent only comprises the instant speed V when extracting the low-speed area n As a feature;
s4, selecting a corresponding position set with the score of 1Wherein, segmentation:the set contains all the locations of the current agent belonging to the low speed region.
Preferably, the residence domain function formula (V is velocity) is:
preferably, the feature vector may consist of raw data obtained directly from GPS readings, such as velocity and head values, as well as other values derived from the data.
Preferably, the smooth track dividing algorithm is as follows: given xi i For the ith GPS point of a certain individual, a series of GPS points with time sequence represent the individual's slave xi i Move to xi n Is a track TP of (2); tp= { ζ 1 ξ 2 …ξ iξi+1 …ξ n -a }; suppose that xi i (i=k+1, … n) inThe mapping on is ζ' i Then xi i And xi n The Euclidean distance between the two points is point xi i Is provided with a disturbance of the position of (a),by studying the sub-track STP= { ζ k ξ k+1 …ξ n Dots in } surrounding ∈>Position disturbance in directionDetecting whether a sub-track is a smooth track or not; giving a sub-trackAnd a predetermined small disturbance threshold τ 0 > 0, if satisfied->We consider that the position perturbation variation is not very large for all points within the STP (at the same time τ k+1 The position disturbance of (a) varies greatly), the sub-track being a smooth track, denoted SST (ζ) i ，ξ j ). Wherein xi j Is a turning point between two smooth tracks, namely a characteristic point; at this time, the track division problem can be embodied as finding a set of feature points ζ j So as to satisfy the two formulas.
(III) beneficial effects
The invention provides a stay position recommending method based on a geographical track of a principal. The device comprises the following
The beneficial effects are that:
1. the method starts with the analysis of the traffic travel track of the executed person, provides the potential geographic position information of the executed person for the executive officer, and improves the case execution efficiency.
2. The invention can divide the principal track into a plurality of smooth sub-tracks by using a smooth track dividing algorithm so as to reduce the workload in the later period and improve the efficiency of the model.
3. The invention uses the stay domain function, the algorithm is very fast through the mark scoring and the calculation related in the link, and the efficiency of identifying the stay point is effectively improved.
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FIG. 1 is a schematic flow chart of the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
as shown in fig. 1, an embodiment of the present invention provides a stay location recommendation method based on a geographical track of a principal, including a smooth track dividing algorithm and a stay domain function, which is characterized in that: the method comprises the following steps:
s1, preprocessing the original data, including data cleaning and filling missing data by a linear interpolation method;
s2, dividing the track into a plurality of smooth tracks through a smooth track dividing algorithm and processing the smooth tracks by using a stay domain function so as to reduce the workload of the later stage, improve the efficiency of the model, and ensure that the characteristic points in the track, namely the fixed active points and the frequent paths of the principal, can be effectively dug under the condition of less information loss by the aid of the result of smooth track dividing;
s3, processing each smooth track by using a stay domain function, marking important places in the smooth tracks as geographical areas which are significant to users, such as residence addresses, workplaces, houses of friends and shopping centers, deducing activities by data and associating the activities with different places through scoring the tracks, wherein the related calculation is very simple, the algorithm is very fast, and the efficiency of identifying the stay points can be improved.
S4, gathering and classifying similar points according to a time threshold value of a certain position, constructing a place hierarchy through a time scale and a position scale, and calculating and identifying travel endpoints and frequent stay positions
S5, overlapping the position trace on the map, wherein the frequently accessed place is regarded as an important place. Furthermore an off-the-shelf GPS unit is connected to a person and processes the collected data to infer him or his activity, as well as an important series of places.
The dwell domain function formula (V is velocity) is:
the feature vector may consist of raw data obtained directly from GPS readings, such as velocity and head values, and other values derived from the data, the smooth trajectory partitioning algorithm being: given xi i For the ith GPS point of a certain individual, a series of GPS points with time sequence represent the individual's slave xi i Move to xi n Is a track TP of (2); tp= { ζ 1 ξ 2 …ξ iξi+1 …ξ n -a }; suppose that xi i (i=k+1, … n) inThe mapping on is ζ' i Then xi i And xi n The Euclidean distance between the two points is point xi i Is provided with a disturbance of the position of (a),by studying the sub-track STP= { ζ k ξ k+1 …ξ n Dots in } surrounding ∈>Position disturbance in direction Detecting whether a sub-track is a smooth track or not; giving a sub-track->And a predetermined small disturbance threshold τ 0 > 0, if satisfied->We consider that the position perturbation variation is not very large for all points within the STP (at the same time τ k+1 The position disturbance of (a) varies greatly), the sub-track being a smooth track, denoted SST (ζ) i ，ξ j ). Wherein xi j Is a turning point between two smooth tracks, namely a characteristic point; at this time, the track division problem can be embodied as finding a set of feature points ζ j In order to satisfy the above two formulas, through dividing the orbit into sub-trajectories first to carry out preliminary screening to the orbit, reduce the work load of follow-up model, improved the operating efficiency of model, stay domain function can be used in the orbit and seek people model to discern the approximate region of frequent stay position, its application step is:
s1, firstly, processing data into two ordered setsAnd->
S2, the first group comprises position vectorsAnd is referred to as tracking, and the second group contains feature vectors
S3, summarizing any additional agents at the same time pointInformation including only the instantaneous velocity V when extracting the low velocity region n As a feature;
s4, selecting a corresponding position set with the score of 1The set contains all the locations of the current agent belonging to the low speed region.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (2)
1. The stay position recommending method based on the geographical track of the principal comprises a smooth track dividing algorithm and a stay domain function, and is characterized in that: the method comprises the following steps:
s1, preprocessing the original data, including data cleaning and filling missing data by a linear interpolation method;
s2, dividing the track into a plurality of smooth tracks through a smooth track dividing algorithm and processing the smooth tracks by using a dwell domain function;
s3, processing each smooth track by using a stay domain function, marking important places as geographical areas significant to users through scoring of the tracks and calculation related to links, deducing activities through data, and associating the activities with different places;
s4, gathering and classifying similar points according to a time threshold value of a certain position, constructing a place hierarchy through a time scale and a position scale, and calculating and identifying travel endpoints and frequent stay positions
S5, superposing the position trace on the map, wherein the frequently accessed place is regarded as an important place; furthermore, connecting an off-the-shelf GPS unit to a person and processing the collected data to infer his or her activity, as well as a significant series of places;
the application steps of the residence domain function are as follows:
s1, firstly, processing data into two ordered setsAnd->
S2, the first group comprises position vectorsAnd is referred to as tracking, and the second group contains feature vectors
S3, summarizing any additional information about the agent at the same time point, wherein the agent only comprises the instant speed V when extracting the low-speed area n As a feature;
s4, selecting a corresponding position set with the score of 1The set contains all the locations of the current agent belonging to the low speed region;
the dwell domain function formula is:
wherein V is the speed;
the smooth track dividing algorithm is as follows: given xi i For the ith GPS point of a certain individual, a series of GPS points with time sequence represent the individual's slave xi i Move to xi n Is a track TP of (2); tp= { ζ 1 ξ 2 …ξ i ξ i+1 …ξ n -a }; suppose that xi i (i＝k+1, … n) inThe mapping on is ζ' i Then xi i And xi n The Euclidean distance between the two points is point xi i Is disturbed by the position of->By studying the sub-track STP= { ζ k ξ k+1 …ξ n Dots in } surrounding ∈>Position disturbance in direction ∈>Detecting whether a sub-track is a smooth track or not; giving a sub-track->And a predetermined disturbance threshold tau 0 > 0, if satisfied->Then we consider that the position perturbation variation is not very large for all points within the STP, at the same time +.>The sub-track is a smooth track, denoted SST (ζ) i ，ξ j ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein xi j Is a turning point between two smooth tracks, namely a characteristic point; at this time, the track division problem can be embodied as finding a set of feature points ζ j So as to satisfy the two formulas.
2. The stay location recommendation method based on a geographical trajectory of a party according to claim 1, wherein: the feature vector may consist of raw data obtained directly from the GPS readings, as well as other values derived from the data.
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