CN106153031A - Movement locus method for expressing and device - Google Patents
Movement locus method for expressing and device Download PDFInfo
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- CN106153031A CN106153031A CN201510171053.4A CN201510171053A CN106153031A CN 106153031 A CN106153031 A CN 106153031A CN 201510171053 A CN201510171053 A CN 201510171053A CN 106153031 A CN106153031 A CN 106153031A
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Abstract
The disclosure is directed to a kind of movement locus method for expressing and device, belong to technical field of data processing.Described method includes: obtain k the position data corresponding to movement locus of destination object；Determining the movement locus access situation to n default hot spot region according to k position data, this hot spot region is the geographic area that the access temperature obtained according to several historical movement tracks statistics is higher than predetermined threshold value；Determining, according to access situation, the track characteristic vector that movement locus is corresponding, this track characteristic vector is for representing the feature of movement locus.The disclosure solves the larger data amount directly using position data to be brought to represent movement locus, and is unfavorable for the problem that follow-up calculating when being analyzed movement locus processes；Achieve employing less data volume movement locus is represented the most simplifiedly, reached the effect of Data Dimensionality Reduction, and be easy to the follow-up analytical calculation to movement locus.
Description
Technical field
It relates to technical field of data processing, particularly to a kind of movement locus method for expressing and device.
Background technology
Such as the movement locus of the object such as people, vehicle or other object generally positions in motor process with one group
The position data obtained is indicated.
As a example by user carries out riding motion, user can carry with the localizer with positioning function, such as GPS
(Global Positioning System, global positioning system) localizer.In ride, localizer is every
The position data of a user is gathered every predetermined time interval.The movement locus of the whole ride of user just may be used
The set using all position datas collected in this ride is indicated.Such as, localizer is every
Gathered a position data every 3 seconds, then user just can use this 1 hour at 1 hour interior movement locus
More than 1000 position data collected is indicated.
But, use aforesaid way to represent that movement locus will bring bigger data volume, and be unfavorable for follow-up
Calculating when being analyzed movement locus processes.
Summary of the invention
Disclosure embodiment provides a kind of movement locus method for expressing and device.Described technical scheme is as follows:
First aspect according to disclosure embodiment, it is provided that a kind of movement locus method for expressing, described method
Including:
Obtaining k the position data corresponding to movement locus of destination object, k is positive integer；
The described movement locus access feelings to n default hot spot region are determined according to described k position data
Condition, described hot spot region is that the access temperature obtained according to several historical movement tracks statistics is higher than default threshold
The geographic area of value, n is positive integer；
The track characteristic vector that described movement locus is corresponding, described track characteristic is determined according to described access situation
Vector is for representing the feature of described movement locus.
Alternatively, described determine that described movement locus is to n default focus according to described k position data
The access situation in region, including:
For each hot spot region, when described k position data exists the position belonging to described hot spot region
When putting data, determine that described movement locus is through described hot spot region；
When described k position data does not exist the position data belonging to described hot spot region, determine described
Movement locus is without described hot spot region.
Alternatively, described track characteristic vector a=(x1,…,xi,…,xn), i ∈ [1, n]；
Wherein, xiFor representing the described movement locus access situation to i-th hot spot region；Work as xiWhen=1,
Represent that described movement locus is through described i-th hot spot region；Work as xiWhen=0, represent described movement locus not
Through described i-th hot spot region.
Alternatively, described method also includes:
Target geographical area is divided into several area grids；
Obtain p sample, each sample comprises the historical movement track in a described target geographical area
At least one corresponding position data, p is positive integer；
Use density-based algorithms that the described position data comprised in described p sample is clustered
Obtain n class；
For each class, the position data comprised according to described apoplexy due to endogenous wind is in several area grids described
Distribution situation builds corresponding hot spot region, comprises at least one area grid in each hot spot region.
Alternatively, described method also includes:
Obtain another track characteristic that another movement locus of described destination object or other destination object is corresponding
Vector；
Calculate that described track characteristic is vectorial and the first similarity between described another track characteristic vector, described
First similarity is for representing the similarity between described movement locus and another movement locus described.
Alternatively, described method also includes:
M the track characteristic vector according to described destination object, calculates object corresponding to described destination object special
Levy vector, m >=1；
Wherein, described m track characteristic vector includes described track characteristic vector, described characteristics of objects vector
For representing the overall movement feature of described destination object.
Alternatively, described characteristics of objects vector b=(S1,…,Si,…,Sn), i ∈ [1, n]；
Wherein,J ∈ [1, m]；xi(j)For representing the jth motion rail of described destination object
The mark access situation to i-th hot spot region；Work as xi(j)When=1, represent that described jth movement locus is through institute
State i-th hot spot region；Work as xi(j)When=0, represent that described jth movement locus is without described i-th warm
Point region；SiRepresent the described destination object access times to i-th hot spot region.
Alternatively, described method also includes:
Obtain another characteristics of objects vector that another destination object is corresponding；
Calculate that described characteristics of objects is vectorial and the second similarity between described another characteristics of objects vector, described
Second similarity is for representing overall movement feature between described destination object and another destination object described
Similarity.
Second aspect according to disclosure embodiment, it is provided that a kind of movement locus represents device, described device
Including:
Data acquisition module, is configured to obtain k the position data corresponding to movement locus of destination object,
K is positive integer；
Access determines module, is configured to determine that described movement locus is to presetting according to described k position data
The access situation of n hot spot region, described hot spot region is to add up according to several historical movement tracks
The access temperature arrived is higher than the geographic area of predetermined threshold value, and n is positive integer；
Track characteristic vector determines module, is configured to determine described movement locus pair according to described access situation
The track characteristic vector answered, described track characteristic vector is for representing the feature of described movement locus.
Alternatively, described access determines module, including: first determines that submodule and second determines submodule；
Described first determines submodule, is configured to for each hot spot region, when described k positional number
When belonging to the position data of described hot spot region according to middle existence, determine that described movement locus is through described hot zone
Territory；
Described second determines submodule, is configured as in described k position data not existing belonging to described heat
When putting the position data in region, determine that described movement locus is without described hot spot region.
Alternatively, described track characteristic vector a=(x1,…,xi,…,xn), i ∈ [1, n]；
Wherein, xiFor representing the described movement locus access situation to i-th hot spot region；Work as xiWhen=1,
Represent that described movement locus is through described i-th hot spot region；Work as xiWhen=0, represent described movement locus not
Through described i-th hot spot region.
Alternatively, described device also includes:
Region divides module, is configured to be divided into target geographical area several area grids；
Sample acquisition module, is configured to obtain p sample, comprises a described target ground in each sample
At least one position data corresponding to historical movement track in reason region, p is positive integer；
Data clusters module, is configured to use density-based algorithms to comprise in described p sample
Described position data carry out cluster and obtain n class；
Region builds module, is configured to, for each class, exist according to the position data that described apoplexy due to endogenous wind comprises
Distribution situation in several area grids described builds corresponding hot spot region, comprises in each hot spot region
At least one area grid.
Alternatively, described device also includes:
Track characteristic vector acquisition module, is configured to obtain described destination object or other destination object
Another track characteristic vector that another movement locus is corresponding；
Track similarity calculation module, is configured to calculate that described track characteristic is vectorial and described another track is special
Levy the first similarity between vector, described first similarity be used for representing described movement locus with described another
Similarity between movement locus.
Alternatively, described device also includes:
Characteristics of objects vector calculation module, is configured to m the track characteristic vector according to described destination object,
Calculate the characteristics of objects vector that described destination object is corresponding, m >=1；
Wherein, described m track characteristic vector includes described track characteristic vector, described characteristics of objects vector
For representing the overall movement feature of described destination object.
Alternatively, described characteristics of objects vector b=(S1,…,Si,…,Sn), i ∈ [1, n]；
Wherein,J ∈ [1, m]；xi(j)For representing the jth motion rail of described destination object
The mark access situation to i-th hot spot region；Work as xi(j)When=1, represent that described jth movement locus is through institute
State i-th hot spot region；Work as xi(j)When=0, represent that described jth movement locus is without described i-th warm
Point region；SiRepresent the described destination object access times to i-th hot spot region.
Alternatively, described device also includes:
Characteristics of objects vector acquisition module, is configured to obtain another characteristics of objects that another destination object is corresponding
Vector；
Object similarity calculation module, is configured to calculate that described characteristics of objects is vectorial and described another object is special
Levy the second similarity between vector, described second similarity be used for representing described destination object with described another
The similarity of the overall movement feature between destination object.
The third aspect according to disclosure embodiment, it is provided that a kind of movement locus represents device, described device
Including:
Processor；
For storing the memorizer of the executable instruction of described processor；
Wherein, described processor is configured to:
Obtaining k the position data corresponding to movement locus of destination object, k is positive integer；
The described movement locus access feelings to n default hot spot region are determined according to described k position data
Condition, described hot spot region is that the access temperature obtained according to several historical movement tracks statistics is higher than default threshold
The geographic area of value, n is positive integer；
The track characteristic vector that described movement locus is corresponding, described track characteristic is determined according to described access situation
Vector is for representing the feature of described movement locus.
The technical scheme that disclosure embodiment provides can include following beneficial effect:
Access the high geographic area of temperature as hot spot region by using, use hot spot region as motion rail
The fixed reference feature of mark, builds track characteristic vector according to movement locus to the access situation of each hot spot region,
And represent movement locus with this track characteristic vector；Solve and directly use position data to represent motion rail
The larger data amount that mark is brought, and it is unfavorable for that what follow-up calculating when being analyzed movement locus processed asks
Topic；Achieve employing less data volume movement locus is represented the most simplifiedly, reach data fall
The effect of dimension, and it is easy to the follow-up analytical calculation to movement locus.
It should be appreciated that it is only exemplary and explanatory that above general description and details hereinafter describe,
The disclosure can not be limited.
Accompanying drawing explanation
Accompanying drawing herein is merged in description and constitutes the part of this specification, it is shown that meet the disclosure
Embodiment, and for explaining the principle of the disclosure together with description.
Fig. 1 is the flow chart according to a kind of movement locus method for expressing shown in an exemplary embodiment；
Fig. 2 A is the flow chart according to a kind of movement locus method for expressing shown in another exemplary embodiment；
Fig. 2 B is the schematic diagram being distributed according to a kind of hot spot region shown in an exemplary embodiment；
Fig. 3 is the flow chart related to according to the structure hot spot region shown in an exemplary embodiment；
Fig. 4 is the flow chart according to a kind of movement locus method for expressing shown in another exemplary embodiment；
Fig. 5 is the block diagram representing device according to a kind of movement locus shown in an exemplary embodiment；
Fig. 6 is the block diagram representing device according to a kind of movement locus shown in another exemplary embodiment；
Fig. 7 is the block diagram according to a kind of device shown in an exemplary embodiment.
By above-mentioned accompanying drawing, it has been shown that the embodiment that the disclosure is clear and definite, hereinafter will be described in more detail.
These accompanying drawings and word describe the scope being not intended to be limited disclosure design by any mode, but logical
Crossing with reference to specific embodiment is the concept that those skilled in the art illustrate the disclosure.
Detailed description of the invention
Here will illustrate exemplary embodiment in detail, its example represents in the accompanying drawings.Following retouches
Stating when relating to accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawings represents same or analogous key element.
Embodiment described in following exemplary embodiment does not represent all embodiment party consistent with the disclosure
Formula.On the contrary, they only with describe in detail in appended claims, the disclosure some in terms of mutually one
The example of the apparatus and method caused.
Fig. 1 is the flow chart according to a kind of movement locus method for expressing shown in an exemplary embodiment.Such as Fig. 1
Shown in, this movement locus method for expressing can include following several step:
In a step 102, obtaining k the position data corresponding to movement locus of destination object, k is the most whole
Number.
At step 104, determine that movement locus is to n default hot spot region according to k position data
Access situation, this hot spot region is that the access temperature obtained according to several historical movement tracks statistics is higher than in advance
If the geographic area of threshold value, n is positive integer.
In step 106, the track characteristic vector that movement locus is corresponding, this track are determined according to access situation
Characteristic vector is for representing the feature of movement locus.
In sum, the movement locus method for expressing that the present embodiment provides, access, by using, the ground that temperature is high
Reason region is as hot spot region, and use hot spot region is as the fixed reference feature of movement locus, according to movement locus
The access situation of each hot spot region is built track characteristic vector, and represents fortune with this track characteristic vector
Dynamic track；Solve the larger data amount directly using position data to be brought to represent movement locus, and not
It is beneficial to the problem that follow-up calculating when being analyzed movement locus processes；Achieve and use less data volume
Movement locus is represented the most simplifiedly, has reached the effect of Data Dimensionality Reduction, and be easy to follow-up to motion
The analytical calculation of track.
Fig. 2 A is the flow chart according to a kind of movement locus method for expressing shown in another exemplary embodiment.As
Shown in Fig. 2 A, this movement locus method for expressing can include following several step:
In step 201, obtaining k the position data corresponding to movement locus of destination object, k is the most whole
Number.
Wherein, destination object can be people, vehicle or other object, and this is not construed as limiting by the present embodiment.?
In the motor process of destination object, gather several position datas by location technology.Such as, above-mentioned k
Position data can be collected by GPS location technology.
In one example, as a example by user carries out riding motion.In ride, can be fixed by GPS
Position device is spaced the position data gathering a user, the movement locus pair of whole ride at predetermined time intervals
The set of all position datas that Ying Yu collects in this ride.
In the related, k position data corresponding to movement locus is directly used to represent movement locus；
And in the present embodiment, by following step 202 and step 203, k position data is carried out dimensionality reduction, with
The mode more simplified is to represent movement locus.
In step 202., determine that movement locus is to n default hot spot region according to k position data
Access situation, n is positive integer.
Wherein, hot spot region is to add up the access temperature obtained according to several historical movement tracks higher than presetting
The geographic area of threshold value.As it is shown on figure 3, in a kind of possible embodiment, can be by following several steps
The rapid hot spot region built in a certain target geographical area:
In step 31, target geographical area is divided into several area grids.
The requirement of computational accuracy is determined by the big I foundation of area grid.If the requirement to computational accuracy
Higher, target geographical area can be divided into the area grid that several sizes are less；Otherwise, if to calculating
The requirement of precision is relatively low, and target geographical area can be divided into several larger-size area grids.
In the step 32, obtain p sample, each sample comprises the history in a target geographical area
At least one position data corresponding to movement locus, p is positive integer.
This p sample is pre-recorded storage.
In step 33, the density-based algorithms position data to comprising in p sample is used to carry out
Cluster obtains n class.
Owing to position data is typically to be acquired in the way of the time is equidistant, position in a certain geographic area
The density of data is the highest, shows that the movement locus frequency through this geographic area is the highest, namely this geographic region
The access temperature in territory is the highest.In the present embodiment, density-based algorithms is used to wrap in p sample
The all position datas contained cluster, and the space clustering of available arbitrary shape, in each space clustering
Comprise distribution density several position datas higher than threshold value.
Alternatively, density-based algorithms can use DBSCAN (Density-Based Spatial
Clustering of Applications with Noise, has noisy density-based spatial clustering method) calculate
Method.DBSCAN algorithm has that cluster speed is fast, can effectively process noise spot and find arbitrary shape
The advantages such as space clustering.Certainly, in actual applications, it is possible to use other density-based algorithms,
This is not construed as limiting by the present embodiment.
In step 34, for each class, the position data comprised according to this apoplexy due to endogenous wind is in several districts above-mentioned
Distribution situation in the grid of territory builds corresponding hot spot region, comprises at least one region in each hot spot region
Grid.
After using density-based algorithms to obtain n space clustering, for each space clustering,
The position data it comprised distributed areas in several area grids above-mentioned are as a hot zone
Territory.Hot spot region in one target geographical area is that this target geographical area is interior accesses temperature higher than presetting
The geographic area of threshold value.
As shown in Figure 2 B, it illustrates the schematic diagram of a kind of hot spot region distribution.In target geographical area 21
Comprise 7 hot spot regions.Assume that these 7 hot spot regions are followed successively by the first hot spot region 22a, second hot area district
Territory 22b, the 3rd hot spot region 22c, the 4th hot spot region 22d, the 5th hot spot region 22e, the 6th hot zone
Territory 22f and the 7th hot spot region 22g.Each hot spot region is indicated with circle in fig. 2b.
It addition, be determined as follows the movement locus access situation to n default hot spot region: right
In each hot spot region, when k position data exists the position data belonging to hot spot region, determine
Movement locus is through hot spot region；Otherwise, when k position data does not exist the position belonging to hot spot region
During data, determine that movement locus is without hot spot region.
As shown in Figure 2 B, movement locus 23 (shown in solid in figure) through the first hot spot region 22a, second
Hot spot region 22b, the 3rd hot spot region 22c, the 4th hot spot region 22d and the 5th hot spot region 22e.
In step 203, the track characteristic vector that movement locus is corresponding, this track are determined according to access situation
Characteristic vector is for representing the feature of movement locus.
In the present embodiment, use hot spot region is as the fixed reference feature of movement locus, according to movement locus pair
The access situation of each hot spot region builds track characteristic vector, and represents motion with this track characteristic vector
Track.
Alternatively, track characteristic vector a=(x1,…,xi,…,xn), i ∈ [1, n].Wherein, xiFor representing
The movement locus access situation to i-th hot spot region；Work as xiWhen=1, represent that movement locus is through i-th warm
Point region；Work as xiWhen=0, represent that movement locus is without i-th hot spot region.
In conjunction with reference to Fig. 2 B, track characteristic vector a=(1,1,1,1,1,0,0) of movement locus 23.
Visible, compared to directly using k position data corresponding to movement locus to represent movement locus,
Use track characteristic vector to represent movement locus, reach the effect of Data Dimensionality Reduction, with the side more simplified
Formula represents movement locus.
Alternatively, the present embodiment may also include the steps of 204 and step 205:
In step 204, corresponding another of another movement locus of destination object or other destination object is obtained
One track characteristic vector.
This step is identical, herein with the mode obtaining track characteristic vector in above-mentioned steps 201 to step 203
Repeat no more.
In conjunction with reference to Fig. 2 B, the track characteristic vector of another movement locus 24 (in figure shown in dotted line)
A '=(1,1,1,1,0,0,0).
In step 205, calculate the first similarity between track characteristic vector and another track characteristic vector,
This first similarity is for representing the similarity between movement locus and another movement locus.
For two movement locus, by calculate two track characteristics corresponding to these two movement locus to
The first similarity between amount, can obtain the similarity between these two movement locus.
In a kind of possible embodiment, Jaccard (Jie Kade) similarity calculating method can be used to count
Calculate the first similarity between two track characteristic vectors, to improve computational efficiency, and at the number of hot spot region
Amount has preferable autgmentability when increasing.The first similarity between two track characteristic vectors is equal to two rails
The element total quantity that between mark characteristic vector, the quantity of identical element is comprised divided by track characteristic vector.Such as,
The first similarity ε=6/7 ≈ 0.86 between above-mentioned track characteristic vector a and above-mentioned track characteristic vector a '.
The value of the first similarity is the biggest, shows that the similarity between two movement locus is the highest.When two motions
When track is movement locus produced by two different users, the first similarity reflects the geography of two users
The feature of interest dimension.First similarity is the highest, shows that the geographic interest of two users is the most similar.Alternatively,
Hot spot region can be realized according to the first similarity to recommend.Such as, when the first similarity is higher than predetermined threshold value,
The movement locus according to the first user access situation to each hot spot region, it is to be visited to be that the second user recommends
Hot spot region.In conjunction with reference to Fig. 2 B, it is assumed that movement locus 23 is the movement locus of first user, motion rail
Mark 24 is the movement locus of the second user, owing to both similarities are higher, and can be right in conjunction with movement locus 23
The access situation of each hot spot region, is that the second user recommends the 5th hot spot region 22e to wait to visit as the next one
The hot spot region asked.
In sum, the movement locus method for expressing that the present embodiment provides, access, by using, the ground that temperature is high
Reason region is as hot spot region, and use hot spot region is as the fixed reference feature of movement locus, according to movement locus
The access situation of each hot spot region is built track characteristic vector, and represents fortune with this track characteristic vector
Dynamic track；Solve the larger data amount directly using position data to be brought to represent movement locus, and not
It is beneficial to the problem that follow-up calculating when being analyzed movement locus processes；Achieve and use less data volume
Movement locus is represented the most simplifiedly, has reached the effect of Data Dimensionality Reduction, and be easy to follow-up to motion
The analytical calculation of track.
It addition, the movement locus method for expressing that the present embodiment provides, also by calculating two track characteristic vectors
Between the first similarity, reflect the similarity between two movement locus with this, and be follow-up path
Recommend to provide with reference to basis.
Fig. 4 is the flow chart according to a kind of movement locus method for expressing shown in another exemplary embodiment.As
Shown in Fig. 4, this movement locus method for expressing can include following several step:
In step 401, obtaining k the position data corresponding to movement locus of destination object, k is the most whole
Number.
In step 402, determine that movement locus is to n default hot spot region according to k position data
Access situation, n is positive integer.
Wherein, hot spot region is to add up the access temperature obtained according to several historical movement tracks higher than presetting
The geographic area of threshold value.
In step 403, the track characteristic vector that movement locus is corresponding, this track are determined according to access situation
Characteristic vector is for representing the feature of movement locus.
Alternatively, track characteristic vector a=(x1,…,xi,…,xn), i ∈ [1, n].Wherein, xiFor representing
The movement locus access situation to i-th hot spot region；Work as xiWhen=1, represent that movement locus is through i-th warm
Point region；Work as xiWhen=0, represent that movement locus is without i-th hot spot region.
Above-mentioned steps 401 to step 403 is identical to step 303 with step 301 in Fig. 2 A illustrated embodiment,
Seeing the introduction in Fig. 2 A illustrated embodiment and explanation, this is repeated no more by the present embodiment.
In step 404, according to m track characteristic vector of destination object, destination object is calculated corresponding
Characteristics of objects vector, m >=1.
Wherein, m track characteristic vector includes that the track characteristic determined in above-mentioned steps 403 is vectorial.This m
Individual track characteristic vector is corresponding to m movement locus of same destination object.In the present embodiment, according to same
M track characteristic vector of one film table object, calculates the characteristics of objects vector that this destination object is corresponding, and this is right
As characteristic vector is for representing the overall movement feature of destination object.
Alternatively, characteristics of objects vector b=(S1,…,Si,…,Sn), i ∈ [1, n].Wherein,
J ∈ [1, m]；xi(j)For representing the jth movement locus access feelings to i-th hot spot region of destination object
Condition；Work as xi(j)When=1, represent that jth movement locus is through i-th hot spot region；Work as xi(j)When=0, represent
Jth movement locus is without i-th hot spot region；SiRepresent the destination object visit to i-th hot spot region
Ask number of times.
For example, it is assumed that the 3 of destination object track characteristic vectors are respectively a1=(1,1,1,1,1,0,0),
a2=(1,0,0,1,1,1,0) and a3=(0,1,1,0,0,1,1), then the characteristics of objects vector that this destination object is corresponding
B=(2,2,2,2,2,2,1).
Alternatively, in data base, the form statistic record different target object that can use matrix is the most corresponding
Characteristics of objects vector.Such as, according to the characteristics of objects vector that l destination object is the most corresponding, target is built
Matrix Vl×n:
Wherein, Su,iRepresent the u destination object access times to i-th hot spot region, u ∈ [1, l],
I ∈ [1, n], l, n are positive integer.
The form statistic record different target object of the employing matrix access times to each hot spot region, at mesh
When the quantity of mark object and/or hot spot region increases, there is preferable autgmentability.
Alternatively, Su,iCan use any one expression way following:
1, Boolean type；When the u destination object is more than or equal to 1 to the access times of i-th hot spot region
Time, Su,i=1；When the u destination object is equal to 0 to the access times of i-th hot spot region, Su,i=0.
2, the successive value between 0～1；Each element in objective matrix is normalized to [0,1] interval.
3, weight represents；The scoring of integer weight is produced based on access times.
Alternatively, the present embodiment may also include the steps of 405 and step 406:
In step 405, another characteristics of objects vector that another destination object is corresponding is obtained.
This step is identical, herein with the mode obtaining characteristics of objects vector in above-mentioned steps 401 to step 404
Repeat no more.
In this example, it is assumed that another characteristics of objects vector that another destination object is corresponding
B '=(1,2,0,0,1,2,1).
In a step 406, calculate the second similarity between characteristics of objects vector and another characteristics of objects vector,
This second similarity is for representing the similar of overall movement feature between destination object to another destination object
Degree.
For two destination objects, by calculate two characteristics of objects corresponding to these two destination objects to
The second similarity between amount, can obtain the similarity of overall movement feature between these two destination objects.
The value of the second similarity is the biggest, shows that the similarity of overall movement feature between two destination objects is the highest.
When two destination objects are two different users, the second similarity reflects the geographic interest dimension of two users
The feature of degree.Second similarity is the highest, shows that the geographic interest of two users is the most similar.
Alternatively, when the element in characteristics of objects vector uses the expression way of Boolean type, Jaccard can be used
Similarity calculating method calculates the second similarity between two characteristics of objects vectors, to improve computational efficiency,
And the quantity in hot spot region has preferable autgmentability when increasing.Between two characteristics of objects vectors second
The unit that similarity is comprised divided by characteristics of objects vector equal to the quantity of identical element between two characteristics of objects vectors
Element total quantity.Such as, Boolean type expression way corresponding for characteristics of objects vector b is b=(1,1,1,1,1,1,1), separately
Boolean type expression way corresponding for one characteristic vector b ' is b '=(1,1,0,0,1,1,1), and second between the two is similar
Degree η=5/7 ≈ 0.71.
Alternatively, when the element in characteristics of objects vector uses weight to represent, cosine angle can be used to measure
Mode calculate the second similarity between two characteristics of objects vectors.Now, the u destination object pair
The characteristics of objects vector bu answered is represented by:
bu=(wu,1tu,1,…,wu,itu,i,…,wu,ntu,n)；
Wherein, tu,iFor Boolean variable, represent whether the u destination object accessed i-th hot spot region, wu,i
Represent tu,iCorresponding weight, i ∈ [1, n].Alternatively, each weight can use learning algorithm to historical movement rail
Mark sample carries out study and is calculated.
In sum, the movement locus method for expressing that the present embodiment provides, access, by using, the ground that temperature is high
Reason region is as hot spot region, and use hot spot region is as the fixed reference feature of movement locus, according to movement locus
The access situation of each hot spot region is built track characteristic vector, and represents fortune with this track characteristic vector
Dynamic track；Solve the larger data amount directly using position data to be brought to represent movement locus, and not
It is beneficial to the problem that follow-up calculating when being analyzed movement locus processes；Achieve and use less data volume
Movement locus is represented the most simplifiedly, has reached the effect of Data Dimensionality Reduction, and be easy to follow-up to motion
The analytical calculation of track.
It addition, the movement locus method for expressing that the present embodiment provides, also by m the rail according to destination object
Mark characteristic vector, calculates the characteristics of objects vector that this destination object is corresponding, uses this characteristics of objects vector to carry out table
Show the overall movement feature of destination object, it is achieved that use the less data volume overall movement to destination object
Feature represents the most simplifiedly, has reached the effect of Data Dimensionality Reduction.
It addition, the movement locus method for expressing that the present embodiment provides, by track characteristic vector to movement locus
Carry out features localization, by characteristics of objects vector, one or more movement locus of same destination object is carried out
Features localization, it is achieved that use the mode more simplified to movement locus feature and the overall movement of destination object
Feature is demarcated.
Require supplementation with explanation a bit: the executive agent of above-mentioned movement locus method for expressing can be any tool
The standby electronic equipment calculating disposal ability, such as electronic equipments such as mobile phone, computer, servers, the disclosure is real
Execute example this is not construed as limiting.
Following for disclosure device embodiment, may be used for performing method of disclosure embodiment.For the disclosure
The details not disclosed in device embodiment, refer to method of disclosure embodiment.
Fig. 5 is the block diagram representing device according to a kind of movement locus shown in an exemplary embodiment.This motion
Track represents that device may include that data acquisition module 510, access determine module 520 and track characteristic vector
Determine module 530.
Data acquisition module 510, is configured to obtain k the positional number corresponding to movement locus of destination object
According to, k is positive integer.
Access determines module 520, is configured to determine that described movement locus is in advance according to described k position data
If the access situation of n hot spot region, described hot spot region is to add up according to several historical movement tracks
The access temperature obtained is higher than the geographic area of predetermined threshold value, and n is positive integer.
Track characteristic vector determines module 530, is configured to determine described movement locus according to described access situation
Corresponding track characteristic vector, described track characteristic vector is for representing the feature of described movement locus.
In sum, the movement locus that the present embodiment provides represents device, accesses, by using, the ground that temperature is high
Reason region is as hot spot region, and use hot spot region is as the fixed reference feature of movement locus, according to movement locus
The access situation of each hot spot region is built track characteristic vector, and represents fortune with this track characteristic vector
Dynamic track；Solve the larger data amount directly using position data to be brought to represent movement locus, and not
It is beneficial to the problem that follow-up calculating when being analyzed movement locus processes；Achieve and use less data volume
Movement locus is represented the most simplifiedly, has reached the effect of Data Dimensionality Reduction, and be easy to follow-up to motion
The analytical calculation of track.
Fig. 6 is the block diagram representing device according to a kind of movement locus shown in another exemplary embodiment.This fortune
Dynamic track represent device may include that data acquisition module 510, access determine module 520 and track characteristic to
Amount determines module 530.
Data acquisition module 510, is configured to obtain k the positional number corresponding to movement locus of destination object
According to, k is positive integer.
Access determines module 520, is configured to determine that described movement locus is in advance according to described k position data
If the access situation of n hot spot region, described hot spot region is to add up according to several historical movement tracks
The access temperature obtained is higher than the geographic area of predetermined threshold value, and n is positive integer.
Track characteristic vector determines module 530, is configured to determine described movement locus according to described access situation
Corresponding track characteristic vector, described track characteristic vector is for representing the feature of described movement locus.
Alternatively, described access determines module 520, including: first determines that submodule 520a and second determines
Submodule 520b.
Described first determines submodule 520a, is configured to for each hot spot region, when described k position
Put and data exist when belonging to the position data of described hot spot region, determine that described movement locus is through described warm
Point region.
Described second determines submodule 520b, is configured as in described k position data not existing belonging to institute
When stating the position data of hot spot region, determine that described movement locus is without described hot spot region.
Alternatively, described track characteristic vector a=(x1,…,xi,…,xn), i ∈ [1, n]；
Wherein, xiFor representing the described movement locus access situation to i-th hot spot region；Work as xiWhen=1,
Represent that described movement locus is through described i-th hot spot region；Work as xiWhen=0, represent described movement locus not
Through described i-th hot spot region.
Alternatively, described device also includes: region division module 501, sample acquisition module 502, data are gathered
Generic module 503 and region build module 504.
Region divides module 501, is configured to be divided into target geographical area several area grids.
Sample acquisition module 502, is configured to obtain p sample, comprises a described target in each sample
At least one position data corresponding to historical movement track in geographic area, p is positive integer.
Data clusters module 503, is configured to use density-based algorithms to wrap in described p sample
The described position data contained carries out cluster and obtains n class.
Region builds module 504, is configured to for each class, the position data comprised according to described apoplexy due to endogenous wind
Distribution situation in several area grids described builds corresponding hot spot region, each hot spot region Zhong Bao
Containing at least one area grid.
Alternatively, described device also includes: track characteristic vector acquisition module 540 and track Similarity Measure
Module 550.
Track characteristic vector acquisition module 540, is configured to obtain described destination object or other destination object
Another track characteristic vector corresponding to another movement locus；
Track similarity calculation module 550, is configured to calculate described track characteristic vectorial with another track described
The first similarity between characteristic vector, described first similarity be used for representing described movement locus with described separately
Similarity between one movement locus.
Alternatively, described device also includes: characteristics of objects vector calculation module 560.
Characteristics of objects vector calculation module 560, is configured to m the track characteristic according to described destination object
Vector, calculates the characteristics of objects vector that described destination object is corresponding, m >=1；
Wherein, described m track characteristic vector includes described track characteristic vector, described characteristics of objects vector
For representing the overall movement feature of described destination object.
Alternatively, described characteristics of objects vector b=(S1,…,Si,…,Sn), i ∈ [1, n]；
Wherein,J ∈ [1, m]；xi(j)For representing the jth motion rail of described destination object
The mark access situation to i-th hot spot region；Work as xi(j)When=1, represent that described jth movement locus is through institute
State i-th hot spot region；Work as xi(j)When=0, represent that described jth movement locus is without described i-th warm
Point region；SiRepresent the described destination object access times to i-th hot spot region.
Alternatively, described device also includes: characteristics of objects vector acquisition module 570 and object Similarity Measure
Module 580.
Characteristics of objects vector acquisition module 570, another object being configured to obtain another destination object corresponding is special
Levy vector.
Object similarity calculation module 580, is configured to calculate described characteristics of objects vectorial with another object described
The second similarity between characteristic vector, described second similarity be used for representing described destination object with described separately
The similarity of the overall movement feature between one destination object.
In sum, the movement locus that the present embodiment provides represents device, accesses, by using, the ground that temperature is high
Reason region is as hot spot region, and use hot spot region is as the fixed reference feature of movement locus, according to movement locus
The access situation of each hot spot region is built track characteristic vector, and represents fortune with this track characteristic vector
Dynamic track；Solve the larger data amount directly using position data to be brought to represent movement locus, and not
It is beneficial to the problem that follow-up calculating when being analyzed movement locus processes；Achieve and use less data volume
Movement locus is represented the most simplifiedly, has reached the effect of Data Dimensionality Reduction, and be easy to follow-up to motion
The analytical calculation of track.
It addition, the movement locus that the present embodiment provides represents device, also by calculating two track characteristic vectors
Between the first similarity, reflect the similarity between two movement locus with this, and be follow-up path
Recommend to provide with reference to basis.
It addition, the movement locus that the present embodiment provides represents device, also by m the rail according to destination object
Mark characteristic vector, calculates the characteristics of objects vector that this destination object is corresponding, uses this characteristics of objects vector to carry out table
Show the overall movement feature of destination object, it is achieved that use the less data volume overall movement to destination object
Feature represents the most simplifiedly, has reached the effect of Data Dimensionality Reduction.
It addition, the movement locus that the present embodiment provides represents device, by track characteristic vector to movement locus
Carry out features localization, by characteristics of objects vector, one or more movement locus of same destination object is carried out
Features localization, it is achieved that use the mode more simplified to movement locus feature and the overall movement of destination object
Feature is demarcated.
Require supplementation with explanation a bit: above-mentioned movement locus represents that device can be applicable to any possess at calculating
In the electronic equipment of reason ability, such as the electronic equipments such as mobile phone, computer, server, disclosure embodiment pair
This is not construed as limiting.
It addition, about the device in above-described embodiment, wherein modules has performed the concrete mode of operation
It is described in detail in about the embodiment of the method, explanation will be not set forth in detail herein.
Fig. 7 is the block diagram according to a kind of device 700 shown in an exemplary embodiment.Such as, device 700
May be provided in a server.With reference to Fig. 7, device 700 includes processing assembly 722, and it farther includes
One or more processors, and by the memory resource representated by memorizer 732, can be by for storage
The instruction that reason parts 722 perform, such as application program.In memorizer 732, the application program of storage can wrap
Include one or more each corresponding to one group instruction module.Joined additionally, process assembly 722
It is set to perform instruction, to perform above-mentioned movement locus method for expressing.
Device 700 can also include that a power supply module 726 is configured to perform the power management of device 700,
One wired or wireless network interface 750 is configured to be connected to device 700 network, and an input is defeated
Go out (I/O) interface 758.Device 700 can operate based on the operating system being stored in memorizer 732, example
Such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or similar.
Those skilled in the art, after considering description and putting into practice invention disclosed herein, will readily occur to these public affairs
Other embodiment opened.The application is intended to any modification, purposes or the adaptations of the disclosure,
These modification, purposes or adaptations are followed the general principle of the disclosure and include that the disclosure is not disclosed
Common knowledge in the art or conventional techniques means.Description and embodiments is considered only as exemplary
, the true scope of the disclosure and spirit are pointed out by claim below.
It should be appreciated that the disclosure is not limited to accurate knot described above and illustrated in the accompanying drawings
Structure, and various modifications and changes can carried out without departing from the scope.The scope of the present disclosure is only by appended
Claim limits.
Claims (17)
1. a movement locus method for expressing, it is characterised in that described method includes:
Obtaining k the position data corresponding to movement locus of destination object, k is positive integer；
The described movement locus access feelings to n default hot spot region are determined according to described k position data
Condition, described hot spot region is that the access temperature obtained according to several historical movement tracks statistics is higher than default threshold
The geographic area of value, n is positive integer；
The track characteristic vector that described movement locus is corresponding, described track characteristic is determined according to described access situation
Vector is for representing the feature of described movement locus.
Method the most according to claim 1, it is characterised in that described according to described k position data
Determine the described movement locus access situation to n default hot spot region, including:
For each hot spot region, when described k position data exists the position belonging to described hot spot region
When putting data, determine that described movement locus is through described hot spot region；
When described k position data does not exist the position data belonging to described hot spot region, determine described
Movement locus is without described hot spot region.
Method the most according to claim 2, it is characterised in that
Described track characteristic vector a=(x1,…,xi,…,xn), i ∈ [1, n]；
Wherein, xiFor representing the described movement locus access situation to i-th hot spot region；Work as xiWhen=1,
Represent that described movement locus is through described i-th hot spot region；Work as xiWhen=0, represent described movement locus not
Through described i-th hot spot region.
Method the most according to claim 1, it is characterised in that described method also includes:
Target geographical area is divided into several area grids；
Obtain p sample, each sample comprises the historical movement track in a described target geographical area
At least one corresponding position data, p is positive integer；
Use density-based algorithms that the described position data comprised in described p sample is clustered
Obtain n class；
For each class, the position data comprised according to described apoplexy due to endogenous wind is in several area grids described
Distribution situation builds corresponding hot spot region, comprises at least one area grid in each hot spot region.
Method the most according to claim 1, it is characterised in that described method also includes:
Obtain another track characteristic that another movement locus of described destination object or other destination object is corresponding
Vector；
Calculate that described track characteristic is vectorial and the first similarity between described another track characteristic vector, described
First similarity is for representing the similarity between described movement locus and another movement locus described.
Method the most according to claim 1, it is characterised in that described method also includes:
M the track characteristic vector according to described destination object, calculates object corresponding to described destination object special
Levy vector, m >=1；
Wherein, described m track characteristic vector includes described track characteristic vector, described characteristics of objects vector
For representing the overall movement feature of described destination object.
Method the most according to claim 6, it is characterised in that
Described characteristics of objects vector b=(S1,…,Si,…,Sn), i ∈ [1, n]；
Wherein,J ∈ [1, m]；xi(j)For representing the jth motion rail of described destination object
The mark access situation to i-th hot spot region；Work as xi(j)When=1, represent that described jth movement locus is through institute
State i-th hot spot region；Work as xi(j)When=0, represent that described jth movement locus is without described i-th warm
Point region；SiRepresent the described destination object access times to i-th hot spot region.
Method the most according to claim 6, it is characterised in that described method also includes:
Obtain another characteristics of objects vector that another destination object is corresponding；
Calculate that described characteristics of objects is vectorial and the second similarity between described another characteristics of objects vector, described
Second similarity is for representing overall movement feature between described destination object and another destination object described
Similarity.
9. a movement locus represents device, it is characterised in that described device includes:
Data acquisition module, is configured to obtain k the position data corresponding to movement locus of destination object,
K is positive integer；
Access determines module, is configured to determine that described movement locus is to presetting according to described k position data
The access situation of n hot spot region, described hot spot region is to add up according to several historical movement tracks
The access temperature arrived is higher than the geographic area of predetermined threshold value, and n is positive integer；
Track characteristic vector determines module, is configured to determine described movement locus pair according to described access situation
The track characteristic vector answered, described track characteristic vector is for representing the feature of described movement locus.
Device the most according to claim 9, it is characterised in that described access determines module, including:
First determines that submodule and second determines submodule；
Described first determines submodule, is configured to for each hot spot region, when described k positional number
When belonging to the position data of described hot spot region according to middle existence, determine that described movement locus is through described hot zone
Territory；
Described second determines submodule, is configured as in described k position data not existing belonging to described heat
When putting the position data in region, determine that described movement locus is without described hot spot region.
11. devices according to claim 10, it is characterised in that
Described track characteristic vector a=(x1,…,xi,…,xn), i ∈ [1, n]；
Wherein, xiFor representing the described movement locus access situation to i-th hot spot region；Work as xiWhen=1,
Represent that described movement locus is through described i-th hot spot region；Work as xiWhen=0, represent described movement locus not
Through described i-th hot spot region.
12. devices according to claim 9, it is characterised in that described device also includes:
Region divides module, is configured to be divided into target geographical area several area grids；
Sample acquisition module, is configured to obtain p sample, comprises a described target ground in each sample
At least one position data corresponding to historical movement track in reason region, p is positive integer；
Data clusters module, is configured to use density-based algorithms to comprise in described p sample
Described position data carry out cluster and obtain n class；
Region builds module, is configured to, for each class, exist according to the position data that described apoplexy due to endogenous wind comprises
Distribution situation in several area grids described builds corresponding hot spot region, comprises in each hot spot region
At least one area grid.
13. devices according to claim 9, it is characterised in that described device also includes:
Track characteristic vector acquisition module, is configured to obtain described destination object or other destination object
Another track characteristic vector that another movement locus is corresponding；
Track similarity calculation module, is configured to calculate that described track characteristic is vectorial and described another track is special
Levy the first similarity between vector, described first similarity be used for representing described movement locus with described another
Similarity between movement locus.
14. devices according to claim 9, it is characterised in that described device also includes:
Characteristics of objects vector calculation module, is configured to m the track characteristic vector according to described destination object,
Calculate the characteristics of objects vector that described destination object is corresponding, m >=1；
Wherein, described m track characteristic vector includes described track characteristic vector, described characteristics of objects vector
For representing the overall movement feature of described destination object.
15. devices according to claim 14, it is characterised in that
Described characteristics of objects vector b=(S1,…,Si,…,Sn), i ∈ [1, n]；
Wherein,J ∈ [1, m]；xi(j)For representing the jth motion rail of described destination object
The mark access situation to i-th hot spot region；Work as xi(j)When=1, represent that described jth movement locus is through institute
State i-th hot spot region；Work as xi(j)When=0, represent that described jth movement locus is without described i-th warm
Point region；SiRepresent the described destination object access times to i-th hot spot region.
16. devices according to claim 14, it is characterised in that described device also includes:
Characteristics of objects vector acquisition module, is configured to obtain another characteristics of objects that another destination object is corresponding
Vector；
Object similarity calculation module, is configured to calculate that described characteristics of objects is vectorial and described another object is special
Levy the second similarity between vector, described second similarity be used for representing described destination object with described another
The similarity of the overall movement feature between destination object.
17. 1 kinds of movement locus represent device, it is characterised in that described device includes:
Processor；
For storing the memorizer of the executable instruction of described processor；
Wherein, described processor is configured to:
Obtaining k the position data corresponding to movement locus of destination object, k is positive integer；
The described movement locus access feelings to n default hot spot region are determined according to described k position data
Condition, described hot spot region is that the access temperature obtained according to several historical movement tracks statistics is higher than default threshold
The geographic area of value, n is positive integer；
The track characteristic vector that described movement locus is corresponding, described track characteristic is determined according to described access situation
Vector is for representing the feature of described movement locus.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107133269A (en) * | 2017-04-01 | 2017-09-05 | 中国人民解放军国防科学技术大学 | Frequent location track generation method and device based on mobile target |
| CN108256060A (en) * | 2018-01-16 | 2018-07-06 | 广州视源电子科技股份有限公司 | A kind of closed loop detection method, device, terminal and storage medium |
| WO2019100505A1 (en) * | 2017-11-24 | 2019-05-31 | 江俊逢 | Computer numerical control method and system |
| CN110688435A (en) * | 2018-07-04 | 2020-01-14 | 北京嘀嘀无限科技发展有限公司 | Similar track searching method and system |
| CN111291278A (en) * | 2020-01-16 | 2020-06-16 | 深圳市前海随手数据服务有限公司 | Method and device for calculating track similarity, storage medium and terminal |
| WO2020191876A1 (en) * | 2019-03-26 | 2020-10-01 | 中国电子科技集团公司第二十八研究所 | Hotspot path analysis method based on density clustering |
| CN111991069A (en) * | 2020-09-03 | 2020-11-27 | 河北春立航诺新材料科技有限公司 | PFNA intramedullary nail and system with short proximal end |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050280657A1 (en) * | 2000-05-11 | 2005-12-22 | Osamu Hori | Object region data describing method and object region data creating apparatus |
| CN102722541A (en) * | 2012-05-23 | 2012-10-10 | 中国科学院计算技术研究所 | Method and system for calculating space-time locus similarity |
| CN102880719A (en) * | 2012-10-16 | 2013-01-16 | 四川大学 | User trajectory similarity mining method for location-based social network |
| CN103942310A (en) * | 2014-04-18 | 2014-07-23 | 厦门雅迅网络股份有限公司 | User behavior similarity mining method based on space-time mode |
| CN104239453A (en) * | 2014-09-02 | 2014-12-24 | 百度在线网络技术（北京）有限公司 | Data processing method and device |
| CN104252527A (en) * | 2014-09-02 | 2014-12-31 | 百度在线网络技术（北京）有限公司 | Method and device for determining resident point information of mobile subscriber |
-
2015
- 2015-04-13 CN CN201510171053.4A patent/CN106153031B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050280657A1 (en) * | 2000-05-11 | 2005-12-22 | Osamu Hori | Object region data describing method and object region data creating apparatus |
| CN102722541A (en) * | 2012-05-23 | 2012-10-10 | 中国科学院计算技术研究所 | Method and system for calculating space-time locus similarity |
| CN102880719A (en) * | 2012-10-16 | 2013-01-16 | 四川大学 | User trajectory similarity mining method for location-based social network |
| CN103942310A (en) * | 2014-04-18 | 2014-07-23 | 厦门雅迅网络股份有限公司 | User behavior similarity mining method based on space-time mode |
| CN104239453A (en) * | 2014-09-02 | 2014-12-24 | 百度在线网络技术（北京）有限公司 | Data processing method and device |
| CN104252527A (en) * | 2014-09-02 | 2014-12-31 | 百度在线网络技术（北京）有限公司 | Method and device for determining resident point information of mobile subscriber |
Non-Patent Citations (3)
| Title |
|---|
| 张莹，等: ""基于位置的社交网络用户轨迹相似性算法"", 《四川大学学报(工程科学版)》 * |
| 陈康，等: ""基于位置信息的用户行为轨迹分析与应用综述"", 《电信科学》 * |
| 龚玺，等: ""时空轨迹聚类方法研究进展"", 《地理科学进展》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107133269A (en) * | 2017-04-01 | 2017-09-05 | 中国人民解放军国防科学技术大学 | Frequent location track generation method and device based on mobile target |
| CN107133269B (en) * | 2017-04-01 | 2021-06-18 | 中国人民解放军国防科技大学 | Frequent position track generation method and device based on moving target |
| WO2019100505A1 (en) * | 2017-11-24 | 2019-05-31 | 江俊逢 | Computer numerical control method and system |
| CN108256060A (en) * | 2018-01-16 | 2018-07-06 | 广州视源电子科技股份有限公司 | A kind of closed loop detection method, device, terminal and storage medium |
| CN108256060B (en) * | 2018-01-16 | 2021-02-09 | 广州视源电子科技股份有限公司 | Closed loop detection method, device, terminal and storage medium |
| CN110688435A (en) * | 2018-07-04 | 2020-01-14 | 北京嘀嘀无限科技发展有限公司 | Similar track searching method and system |
| CN110688435B (en) * | 2018-07-04 | 2022-04-05 | 北京嘀嘀无限科技发展有限公司 | Similar track searching method and system |
| WO2020191876A1 (en) * | 2019-03-26 | 2020-10-01 | 中国电子科技集团公司第二十八研究所 | Hotspot path analysis method based on density clustering |
| CN111291278A (en) * | 2020-01-16 | 2020-06-16 | 深圳市前海随手数据服务有限公司 | Method and device for calculating track similarity, storage medium and terminal |
| CN111291278B (en) * | 2020-01-16 | 2024-01-12 | 深圳市卡牛科技有限公司 | Track similarity calculation method and device, storage medium and terminal |
| CN111991069A (en) * | 2020-09-03 | 2020-11-27 | 河北春立航诺新材料科技有限公司 | PFNA intramedullary nail and system with short proximal end |
| CN111991069B (en) * | 2020-09-03 | 2022-04-12 | 河北春立航诺新材料科技有限公司 | PFNA intramedullary nail and system with short proximal end |
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