CN104268243A - Position data processing method and device - Google Patents
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- CN104268243A CN104268243A CN201410513908.2A CN201410513908A CN104268243A CN 104268243 A CN104268243 A CN 104268243A CN 201410513908 A CN201410513908 A CN 201410513908A CN 104268243 A CN104268243 A CN 104268243A
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Abstract
The invention discloses a position data processing method and a position data processing device, which can analyze and process the position data quickly and efficiently under the condition of facing a large number of position data, so as to apply the large number of position data more efficiently and more accurately. In some feasible implementation ways of the method, the position data processing method comprises the steps of acquiring the user position data in a target area; gathering the user position data to obtain a user track; determining an optimal node of the user track on a region level tree in accordance with the position information of the user in a target area, wherein the region level tree takes the target area as a root node, and takes a subarea included in the target area as a tree structure of a subnode, and the optimal node includes a lowest layer node of the user of all position information in the target area; and building a mapping relation between the user track and the optimal node so obtain an area level index tree.
Description
Technical field
The present invention relates to data processing field, particularly relate to a kind of position data disposal route and device.
Background technology
Along with popularizing of smart mobile phone, the location data scale that communication network reports constantly expands, and is enough to be supported in the analysis business these data building multiple high grade.Current, location data is applied to " smart city ", and structure also more and more comes into one's own, and about location data in city planning, crowd's moving characteristic, in the carrying out that many-sided research such as commercial value evaluation is burning hot.
Position data analysis and excavate main user oriented position data, the MR data of such as communication network, data volume is large, average 100 general-purpose family 0.5T/ days, and these data are all with seasonal effect in time series feature.Current location data analysis and the distributed batch processing framework of the main employing of excavation are analyzed, if this type of Distributed Architecture of Spark:Spark is when answering positional data to analyze, the configuration of its cluster and internal memory is completely according to the size of data volume, data volume increases, individual task analysis input is in corresponding linear increase, and performance sharply declines.The current analysis for region needs first download whole network data and then filter, and this is when data volume increases, and increases system load and needs to filter out a large amount of invalid data, thus causing computing resource waste.Therefore, in the epoch that large data arrive, in the face of magnanimity position data, how these position datas for the treatment of and analysis rapidly and efficiently, thus make the application towards these magnanimity position datas more efficient more accurate, this becomes a problem demanding prompt solution.
Summary of the invention
Efficient treatment and analysis magnanimity position data how is read soon for large data age, thus make more efficient more accurately this technical matters of application towards these magnanimity position datas, the embodiment of the present invention provides a kind of position data disposal route and device, specifically comprises:
First aspect, a kind of position data disposal route, comprising:
Obtain the location data in target area;
Carry out polymerization according to described location data and obtain user trajectory, described user trajectory comprise described user in described target area by way of positional information;
According to described user in described target area by way of positional information, determine the optimum node of described user trajectory in the hierarchical tree of region, described region hierarchical tree is for root node with described target area, the tree structure that the subregion comprised with described target area is child node, described optimum node be comprise described user in described target area by way of the lowest level node of all positional informations;
Set up mapping relations between described user trajectory and described optimum node to obtain region level index tree, described region level index tree is the region hierarchical tree comprising described mapping relations.
In conjunction with first aspect, in the first possible embodiment of first aspect, described determine the optimum node of described user trajectory in the hierarchical tree of region before, described method also comprises:
In conjunction with described location data, according to data balancing principle and transregional minimum principle, division is carried out to described target area and obtains described region hierarchical tree.
In conjunction with the first possible embodiment of first aspect, in the embodiment that the second of first aspect is possible, location data described in described combination, according to data balancing principle and transregional minimum principle, division is carried out to described target area and obtains described region hierarchical tree and specifically comprise:
Prime area hierarchical tree is obtained according to data balancing principle and transregional minimum principle;
According to optimization principles described prime area hierarchical tree is optimized and obtains described region hierarchical tree.
In conjunction with embodiment possible in second of first aspect, in the third possible embodiment of first aspect, describedly obtain prime area hierarchical tree according to data balancing principle and transregional minimum principle; According to optimization principles described prime area hierarchical tree is optimized and obtains described region hierarchical tree, specifically comprise:
y
min＝a×M+b×N；
In the y that wherein a × M+b × N obtains, being worth minimum is y
min, wherein y represents described prime area hierarchical tree, wherein y
minrepresent described region hierarchical tree, wherein M embodies data balancing principle, and data more balanced M value is less, and in M=|0.5-child node position count/root node in always position count |; Wherein N embodies transregional minimum principle, and transregional fewer N value is less, and N=is across child node track number/total track number; Wherein a and b is respectively the weighted value of M and N, and a+b=1.
In conjunction with the first possible embodiment of first aspect and first aspect to any one embodiment of the third possible embodiment of first aspect, in the 4th kind of possible embodiment of first aspect, describedly carry out after polymerization obtains user trajectory according to described location data, described method also comprises: remove the abnormity point in described user trajectory, to the smoothing process of described user trajectory.
In conjunction with the first the possible embodiment in conjunction with first aspect and first aspect to the 4th kind of any one embodiment of possible embodiment of first aspect, in the 5th kind of possible embodiment of first aspect,
After the described mapping relations set up between described user trajectory and described optimum node, described method also comprises: described user trajectory is stored in memory location corresponding to described optimum node.
In conjunction with the first the possible embodiment in conjunction with first aspect and first aspect to the 5th kind of any one embodiment of possible embodiment of first aspect, in the 6th kind of possible embodiment of first aspect, described method also comprises:
The user trajectory of described optimum node mapping is obtained according to described region level index tree;
User trajectory according to described optimum node mapping carries out adaptive region optimization with the node that is optimized to described optimum node;
According to the mapping relations between described user trajectory and described optimum node and described optimization node, obtain the mapping relations between described user trajectory and described optimization node;
According to described region level index tree and the mapping relations between described user trajectory and described optimization node, form the region level index tree after optimizing.
Second aspect, a kind of position data processing unit, comprising:
Data acquisition module, for obtaining the location data in target area;
Track acquisition module, obtains user trajectory for carrying out polymerization according to described location data, described user trajectory comprise described user in described target area by way of positional information;
Optimum node determination module, for according to described user in described target area by way of positional information, determine the optimum node of described user trajectory in the hierarchical tree of region, described region hierarchical tree is for root node with described target area, the tree structure that the subregion comprised with described target area is child node, described optimum node be comprise described user in described target area by way of the lowest level node of all positional informations;
Region level index tree acquisition module, for setting up mapping relations between described user trajectory and described optimum node to obtain region level index tree, described region level index tree is the region hierarchical tree comprising described mapping relations.
In conjunction with second aspect, in the first possible embodiment of second aspect, described device also comprises:
Region hierarchical tree divides module, in conjunction with described location data, according to data balancing principle and transregional minimum principle, carries out division obtain described region hierarchical tree to described target area.
In conjunction with the first possible embodiment of second aspect, in the embodiment that the second of second aspect is possible, described region hierarchical tree divides module, for:
Prime area hierarchical tree is obtained according to data balancing principle and transregional minimum principle;
According to optimization principles described prime area hierarchical tree is optimized and obtains described region hierarchical tree.
In conjunction with the embodiment that the second of second aspect is possible, in the third possible embodiment of second aspect, described region hierarchical tree divides module, specifically for:
y
min＝a×M+b×N；
In the y that wherein a × M+b × N obtains, being worth minimum is y
min, wherein y represents described prime area hierarchical tree, wherein y
minrepresent described region hierarchical tree, wherein M embodies data balancing principle, and data more balanced M value is less, and in M=|0.5-child node position count/root node in always position count |; Wherein N embodies transregional minimum principle, and transregional fewer N value is less, and N=is across child node track number/total track number; Wherein a and b is respectively the weighted value of M and N, and a+b=1.
In conjunction with the first possible embodiment of second aspect and second aspect to any one embodiment in the third possible embodiment of second aspect, in the 4th kind of possible embodiment of second aspect, described device also comprises smooth trajectory module, for removing the abnormity point in described user trajectory, to the smoothing process of described user trajectory.
In conjunction with the first possible embodiment of second aspect and second aspect to any one embodiment in the 4th kind of possible embodiment of second aspect, in the 5th kind of possible embodiment of second aspect, described device also comprises memory module, for described user trajectory being stored in memory location corresponding to described optimum node.
In conjunction with the first possible embodiment of second aspect and second aspect to any one embodiment in the 5th kind of possible embodiment of second aspect, in the 6th kind of possible embodiment of second aspect, described device also comprises adaptive region and optimizes module, and described adaptive region is optimized module and is used for:
The user trajectory of described optimum node mapping is obtained according to described region level index tree;
User trajectory according to described optimum node mapping carries out adaptive region optimization with the node that is optimized to described optimum node;
According to the mapping relations between described user trajectory and described optimum node and described optimization node, obtain the mapping relations between described user trajectory and described optimization node;
According to described region level index tree and the mapping relations between described user trajectory and described optimization node, form the region level index tree after optimizing.
In sum, by building region level index tree, location data is made to be that least unit carries out mapping and managing in the mode of region level index tree according to user trajectory, thus make towards these location data application and management is more efficient and accurately, especially towards the mass users position data of large data age, the method that the embodiment of the present invention provides can make towards the application of mass users position data and management more efficient quicker.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, below the accompanying drawing used required in describing embodiment is briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the process flow diagram of the position data disposal route that the embodiment of the present invention one provides;
Fig. 2 a is the schematic diagram of the position data disposal route that the embodiment of the present invention two provides;
Fig. 2 b is another schematic diagram of the position data disposal route that the embodiment of the present invention two provides;
Fig. 3 is the schematic diagram of the position data disposal route that the embodiment of the present invention three provides;
Fig. 4 is the position data process flow figure that yet another embodiment of the invention provides;
Fig. 5 a is the position data disposal route schematic diagram that yet another embodiment of the invention provides;
Fig. 5 b is another schematic diagram of the position data disposal route that yet another embodiment of the invention provides;
Fig. 6 is the schematic diagram of the position data processing unit that the embodiment of the present invention four provides;
Fig. 7 is the schematic diagram of a kind of computer equipment that the embodiment of the present invention provides.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly, below in conjunction with accompanying drawing, embodiment of the present invention is described further in detail.
Embodiment one, please refer to Fig. 1, and it illustrates a kind of position data process flow figure that one embodiment of the invention provides, the method comprises the steps:
S103, the location data obtained in target area;
S105, carry out polymerization according to described location data and obtain user trajectory, described user trajectory comprise described user in described target area by way of positional information;
S107, according to described user in described target area by way of positional information, determine the optimum node of described user trajectory in the hierarchical tree of region, described region hierarchical tree is for root node with described target area, the tree structure that the subregion comprised with described target area is child node, described optimum node be comprise described user in described target area by way of the lowest level node of all positional informations;
S109, set up mapping relations between described user trajectory and described optimum node to obtain region level index tree, described region level index tree is the region hierarchical tree comprising described mapping relations.
In sum, the position data disposal route that the present embodiment provides, by obtaining the location data in target area, and carry out polymerization according to described location data and obtain user trajectory, and further according to described user in described target area by way of positional information determine the optimum node of described user trajectory in the hierarchical tree of region, set up mapping relations between described user trajectory and described optimum node to obtain region level index tree.Thus the mode of mass users position data according to region level index tree can be divided, and then can based on the user trajectory of this level index tree efficient retrieval appointed area, region.Even if when position data output increases severely, task analysis input also can region level index tree according to the embodiment of the present invention rapidly and efficiently find appointed area, thus without the need to downloading and filter analysis the data beyond appointed area, having saved resource consumption and having improved analytical performance.
What deserves to be explained is, user trajectory of the present invention can be understood as: user is at position consecutive variations and the curve formed in time in space, and namely this track comprises the information of time-space domain.This understanding is applicable to user trajectory in full, after repeat no more.
Embodiment two, the position data disposal route that another embodiment of the present invention provides on the basis of embodiment one is: before the step S107 of embodiment one, the present embodiment also comprises step S106: in conjunction with described location data, according to data balancing principle and transregional minimum principle, division is carried out to described target area and obtains described region hierarchical tree, specifically as described in Fig. 2 a, such as: the rectangle 1 on the left of Fig. 2 a represents target area, correspondingly, circle 1 on the right side of Fig. 2 a represents root node corresponding to target area, rectangle 2 and 3 on the left of Fig. 2 a represents the subregion 2 and 3 dividing target area and obtain, rectangle 4 and 5 represents the subregion 4 and 5 dividing subregion 2 and obtain, rectangle 6 and 7 represents the subregion 6 and 7 dividing subregion 3 and obtain.The rest may be inferred thus carried out the target area in left side dividing and finally obtain corresponding region hierarchical tree.What deserves to be explained is, figure herein is only used for helping to understand target area and divides the process obtaining region hierarchical tree, and wherein concrete dividing mode and numerical characteristics etc. do not cause any restriction to this programme.
Further, wherein data balancing principle represents: distribute as far as possible uniformly between each sub regions of location data in target area; Wherein transregional minimum principle represents: make the subregion number crossed between each sub regions node in the region hierarchical tree of user trajectory after division minimum as much as possible.Data balancing principle herein can be understood as horizontal data balancing, and transregional minimum principle can be understood as longitudinal data balancing.
For example: the region, core traffic main artery in a city, this urban population to flow and 80% all will pass through this region every day, if be now a sub regions by this Region dividing, then the user trajectory of 80% has all dropped on this region, all the other 20% are dispersed in other region, so just do not meet across community minimum principle, when carrying out task analysis to this 80% user trajectory region, due to granularity too coarse particle, the operation of a lot of redundancy can be caused equally.
Concrete, location data described in described combination, according to data balancing principle and transregional minimum principle, division is carried out to described target area and obtains described region hierarchical tree and specifically comprise:
Prime area hierarchical tree is obtained according to data balancing principle and transregional minimum principle;
According to optimization principles described prime area hierarchical tree is optimized and obtains described region hierarchical tree.
Further, described according to data balancing principle and transregional minimum principle acquisition prime area hierarchical tree; According to optimization principles described prime area hierarchical tree is optimized and obtains described region hierarchical tree and specifically comprise:
y
min＝a×M+b×N；
In the y that wherein a × M+b × N obtains, being worth minimum is y
min, wherein y represents described prime area hierarchical tree, wherein y
minrepresent described region hierarchical tree, wherein M embodies data balancing principle, and data more balanced M value is less, and in M=|0.5-child node position count/root node in always position count |; Wherein N embodies transregional minimum principle, and transregional fewer N value is less, and N=is across child node track number/total track number; Wherein a and b is respectively the weighted value of M and N, and a+b=1.It should be noted that, all the other correlation step of embodiment two are identical with the processing mode of the corresponding step of embodiment one, therefore repeat no more herein.
In sum, according to data balancing principle and transregional minimum principle, division is carried out to target area and obtain region hierarchical tree, from the horizontal and vertical equilibrium considering data, make to divide Data distribution8 between each sub regions that the region hierarchical tree that obtains divides target area more balanced, based on the region hierarchical tree of such data balancing as far as possible, according to user in target area by way of the optimum node of positional information determination user trajectory in the hierarchical tree of region, and the region level index tree finally got can be that a granularity is more suitable, also appointed area can be located more efficiently when oriented mission process application, avoid operating incoherent redundant data and wasting process resource.
What deserves to be explained is, dividing target area according to the division of administrative region is a kind of common Region dividing mode, but in embodiments of the present invention, concrete dividing mode is not limited, but divide according to data balancing principle and transregional minimum principle, if administrative division just can meet data balancing principle and transregional minimum principle, then also can become one of the present invention and divide embodiment.Specifically can be as shown in Figure 2 b, such as, be illustrate in target area with Hangzhou, suppose that the administrative division on the left of figure just meets data balancing principle and transregional minimum principle, then the right side dendrogram of corresponding left side administrative division is carry out dividing the region hierarchical tree obtained according to Hangzhou, target area.This figure should only for meeting a kind of possible dividing mode of data balancing principle and transregional minimum principle, not as the limited features of this programme.
Embodiment three, on the basis of embodiment one and embodiment two, please refer to Fig. 3, it illustrates the position data disposal route schematic diagram that yet another embodiment of the invention provides.The method comprises:
Concrete, the step S105 in embodiment one, carry out polymerization according to described location data and obtain user trajectory, described user trajectory comprise described user in described target area by way of positional information.Specifically can carry out polymerization according to mode as described in Figure 3 according to location data and obtain user trajectory.Be location data on the left of Fig. 3, wherein P1 correspondence position point 1, P2 correspondence position point 2, until Pn ..., each location point can include but not limited to longitude and latitude and the temporal information of its correspondence.The track on right side namely according to leftward position data carry out being polymerized obtain comprise P1, P2 ... the user trajectory of the positional information of Pn.
Optionally, as shown in Figure 4, it illustrates the position data process flow figure that yet another embodiment of the invention provides, embodiment one S105 carry out according to described location data can also comprise after polymerization obtains user trajectory: S105a, remove the abnormity point in described user trajectory, to the smoothing process of described user trajectory.
Particularly, the smooth trajectory process that abnormity point is removed, mainly considers in conjunction with track rationality.Abnormity point is removed and the method for smooth trajectory has multiple, does not limit in the embodiment of the present invention to this.For the ease of understanding, the embodiment of the present invention provides a kind of optional cleaning rule, and is explained with concrete example, specific as follows:
1, abnormity point is removed:
Here abnormity point refers to the location point obviously not meeting general knowledge, such as: for the people moved in city, even if (comprise subway by means of the vehicles, do not comprise aircraft), the speed of its movement can not more than 120km/h, consider the measuring error of former and later two location points of user again, so for adjacent two now and pre, the distance between 2 should more than T1 value:
T1＝(now.time-pre.time)*120km/h+now.error+pre.error (1.1)
Due to current we obtain less than the measuring error of each point, so can be set to a definite value, because the measuring error of overwhelming majority's point is within 300 meters, therefore (1.1) formula can replace with T2:
T2＝(now.time-pre.time)*120km/h+2*300m (1.2)
Decision logic: first determine a normal point in adjacent 2, when the distance between 2 o'clock is greater than (1.2) formula, by another point deletion.
What deserves to be explained is: above-mentioned 120km/h and 300m rule of thumb or can need to carry out the parameter that adjusts.
2, removing repeats a little:
Remove multiple longitude and latitude points (location point) of the same user of synchronization, namely as same user, when multiple location point appears in synchronization, we think necessarily there is abnormity point, this abnormity point we be called and repeat a little, removal repeats a little to allow same user at an a moment only corresponding location point, and this is that we will do.
The method that concrete removing repeats a little can be:
2.1, when the spacing of the plurality of location point is near, to mutually in the same time multiple somes positions of different longitude and latitude average.
2.2, between the plurality of location point during distance, the relatively degree of accuracy of the location point that this point is corresponding with the immediate historical juncture, specifically can with T1 or T2 judge degree of accuracy, such as: get location point A corresponding to immediate 3 historical junctures, B, C, corresponding two of current time repeats to be some D, E, then judge D and A respectively according to above-mentioned T1 or T2, B, degree of accuracy between C, if D and A, B, two in C tri-location points meet the permissible error with T1 or T2, then think that the degree of accuracy of D is 2, E and A is judged respectively according to above-mentioned T1 or T2, B, degree of accuracy between C, if E and A, B, 1 in C tri-location points meets the permissible error with T1 or T2, then think that the degree of accuracy of E is 1, at this time we think that degree of accuracy at current time D point is higher than E, further, judge that whether the distance of this D point and penultimate moment location point is normal by the method in 1 again, if normal, retain.Otherwise do not do any operation.
In sum, to the smoothing process of user trajectory, know the abnormity point fallen wherein, make the user trajectory that obtains more accurate, thus it is also just more accurate to make to find the optimum node of user trajectory in the hierarchical tree of region, the confidence level of the region level index tree exported further is also higher.
In addition, on the basis of embodiment one, two, three, based on such scheme, stores processor can also be carried out further, concrete: at S109: after setting up the mapping relations between described user trajectory and described optimum node, described method also comprises: described user trajectory to be stored in memory location corresponding to described optimum node.
Based on such operation, for user trajectory finds corresponding area stores position, thus make calling of the management of user trajectory more succinct.
Also it should be noted is that: on the basis of embodiment one, two, three, based on such scheme, process can also be optimized further, concrete: at S109: set up mapping relations between described user trajectory and described optimum node with after obtaining region level index tree, the method also comprises:
The user trajectory of described optimum node mapping is obtained according to described region level index tree;
User trajectory according to described optimum node mapping carries out adaptive region optimization with the node that is optimized to described optimum node;
According to the mapping relations between described user trajectory and described optimum node and described optimization node, obtain the mapping relations between described user trajectory and described optimization node;
According to described region level index tree and the mapping relations between described user trajectory and described optimization node, form the region level index tree after optimizing.
Concrete, the adaptive region optimization in the program can operate according to following step:
Judge in optimum node, whether to comprise tracking clustering center, many places, many places are herein more than Liang Chu and two places, if for there is tracking clustering center, many places in judged result, then this optimum node is carried out multidomain treat-ment and obtain multiple node, and minimum enclosed rectangle process is carried out respectively in region corresponding to the plurality of node further, be optimized node, specifically as shown in Figure 5 a, large rectangle wherein in the figure of the left side represents optimum node, comprise two tracking clustering centers, place in this optimum node, wherein the little dashed rectangle in right side is for optimizing node.Figure herein is only used to example, and wherein data characteristics does not form any restriction to this programme.It should be noted that, the number of optimization node herein can be that obtain the node number of multiple node with corresponding multidomain treat-ment consistent, can certainly be less than the node number of the plurality of node, not limit herein.
If judged result is not for exist tracking clustering center, many places, then minimum enclosed rectangle process is carried out in region corresponding for this optimum node, be optimized node.Concrete large rectangle wherein in the figure of the left side represents optimum node, comprises a tracking clustering center, place in this optimum node as shown in Figure 5 b, and wherein the large dashed rectangle in right side is for optimizing node.Figure herein is only used to example, and wherein feature does not form any restriction to this programme.
What deserves to be explained is, minimum enclosed rectangle is generic noun in this area, being interpreted as of its correspondence: minimum enclosed rectangle (minimum bounding rectangle, MBR), also be translated into minimum boundary rectangle, minimumly comprise rectangle, or minimum outsourcing rectangle.Minimum enclosed rectangle refers to the maximum magnitude of the some two-dimensional shapes (such as point, straight line, polygon) represented with two-dimensional coordinate, namely fixs the rectangle on border with the maximum horizontal ordinate in each summit of given two-dimensional shapes, minimum horizontal ordinate, maximum ordinate, minimum ordinate.
By adaptive region optimization process, region level index tree after the optimization of final formation is compared to the region level index tree (being called in the embodiment of the present invention " region level index tree ") before optimization, there is meticulousr node division, thus also can be more accurately more efficient for the task operating of appointed area, thus the operation further avoided incoherent redundant data, save resource consumption and improved task operating performance.
In order to contribute to clearerly understanding such scheme, the embodiment of the present invention still with the Hangzhou shown in Fig. 2 b for target area illustrates.Still suppose administrative division as shown in Figure 2 b just result meet data balancing principle and transregional minimum principle, then corresponding right side dendrogram is divide according to this target area the region hierarchical tree obtained, the user trajectory of corresponding region is mapped in this region hierarchical tree according to the mapping relations with optimum node, then obtains region level index tree.So, when needing to carry out task operating to the location data in such as north, the West Lake, this task operating can be any associative operation, be assumed to be mobility status statistics in region herein, then directly find corresponding node (being assumed to be node 6) according to region level index tree, all user trajectory obtaining this node 6 correspondence carry out analytic statistics.Such method makes no longer to need the data first downloading whole large regions to filter when carrying out task operating towards appointed area again, such as first download the location data of whole Hangzhou, again by the data one by one filtering irrelevant with north, the West Lake, finally the position data in remaining north, the West Lake carries out task operating.Such processing mode not only wastes resource and analytical performance is very low.As can be seen here, in feasible embodiments more of the present invention, the mode of region hierarchical tree is adopted user trajectory to be mapped on the optimum node in this region hierarchical tree one by one, this optimum node be comprise user in target area by way of the lowest level node of all positional informations, thus obtain region level index tree according to the mapping relations set up between user trajectory and optimum node, it is more efficient more accurate with application to the task process towards position data to make based on such region level index tree.
In order to better implement the such scheme of the embodiment of the present invention, be also provided for below coordinating the relevant apparatus implementing such scheme.
Embodiment four, please refer to Fig. 6, and the embodiment of the present invention provides a kind of position data processing unit 600, can comprise:
Data acquisition module 630, for obtaining the location data in target area;
Track acquisition module 650, obtains user trajectory for carrying out polymerization according to described location data, described user trajectory comprise described user in described target area by way of positional information;
Optimum node determination module 670, for according to described user in described target area by way of positional information, determine the optimum node of described user trajectory in the hierarchical tree of region, described region hierarchical tree is for root node with described target area, the tree structure that the subregion comprised with described target area is child node, described optimum node be comprise described user in described target area by way of the lowest level node of all positional informations;
Region level index tree acquisition module 690, for setting up mapping relations between described user trajectory and described optimum node to obtain region level index tree, described region level index tree is the region hierarchical tree comprising described mapping relations.
In sum, the position data processing unit 600 that the present embodiment provides, the location data in target area is obtained by data acquisition module 630, track acquisition module 650 carries out polymerization according to described location data and obtains user trajectory, and further optimum node determination module 670 according to described user in described target area by way of positional information determine the optimum node of described user trajectory in the hierarchical tree of region, region level index tree acquisition module 690 sets up mapping relations between described user trajectory and described optimum node to obtain region level index tree.Thus the mode of mass users position data according to region level index tree can be divided, and then can based on the user trajectory of this level index tree efficient retrieval appointed area, region.Even if when position data output increases severely, task analysis input also can region level index tree according to the embodiment of the present invention rapidly and efficiently find appointed area, thus without the need to downloading and filter analysis the data beyond appointed area, having saved resource consumption and having improved analytical performance.
What deserves to be explained is, user trajectory of the present invention can be understood as: user is at position consecutive variations and the curve formed in time in space, and namely this track comprises the information of time-space domain.This understanding is applicable to user trajectory in full, after repeat no more.
In some embodiments of the present invention, described device 600 can also comprise:
Region hierarchical tree divides module, in conjunction with described location data, according to data balancing principle and transregional minimum principle, carries out division obtain described region hierarchical tree to described target area.
Further alternative, this region hierarchical tree divides module, for:
Prime area hierarchical tree is obtained according to data balancing principle and transregional minimum principle;
According to optimization principles described prime area hierarchical tree is optimized and obtains described region hierarchical tree.
Further optional, this region hierarchical tree divides module, specifically for:
y
min＝a×M+b×N；
In the y that wherein a × M+b × N obtains, being worth minimum is y
min, wherein y represents described prime area hierarchical tree, wherein y
minrepresent described region hierarchical tree, wherein M embodies data balancing principle, and data more balanced M value is less, and in M=|0.5-child node position count/root node in always position count |; Wherein N embodies transregional minimum principle, and transregional fewer N value is less, and N=is across child node track number/total track number; Wherein a and b is respectively the weighted value of M and N, and a+b=1.
In sum, region hierarchical tree division module is carried out division according to data balancing principle and transregional minimum principle to target area and is obtained region hierarchical tree, from the horizontal and vertical equilibrium considering data, make to divide Data distribution8 between each sub regions that the region hierarchical tree that obtains divides target area more balanced, based on the region hierarchical tree of such data balancing as far as possible, according to user in target area by way of the optimum node of positional information determination user trajectory in the hierarchical tree of region, and the region level index tree finally got can be that a granularity is more suitable, also appointed area can be located more efficiently when oriented mission process application, avoid operating incoherent redundant data and wasting process resource.
What deserves to be explained is, dividing target area according to the division of administrative region is a kind of common Region dividing mode, but in embodiments of the present invention, concrete dividing mode is not limited, but divide according to data balancing principle and transregional minimum principle, if administrative division just can meet data balancing principle and transregional minimum principle, then also can become one of the present invention and divide embodiment.Specifically can be as shown in Figure 2 b, such as, be illustrate in target area with Hangzhou, suppose that the administrative division on the left of figure just meets data balancing principle and transregional minimum principle, then the right side dendrogram of corresponding left side administrative division is carry out dividing the region hierarchical tree obtained according to Hangzhou, target area.This figure should only for meeting a kind of possible dividing mode of data balancing principle and transregional minimum principle, not as the limited features of this programme.
In other examples of implementation of the present invention, described device 600 can also comprise: smooth trajectory module, for removing the abnormity point in described user trajectory, to the smoothing process of described user trajectory.
In concrete operating process, the smooth trajectory process that abnormity point is removed, mainly considers in conjunction with track rationality.Abnormity point is removed and the method for smooth trajectory has multiple, does not limit in the embodiment of the present invention to this.For the ease of understanding, the smooth trajectory module in the embodiment of the present invention provides a kind of optional cleaning rule, and is explained with concrete example, specific as follows:
1, abnormity point is removed:
Here abnormity point refers to the location point obviously not meeting general knowledge, such as: for the people moved in city, even if (comprise subway by means of the vehicles, do not comprise aircraft), the speed of its movement can not more than 120km/h, consider the measuring error of former and later two location points of user again, so for adjacent two now and pre, the distance between 2 should more than T1 value:
T1＝(now.time-pre.time)*120km/h+now.error+pre.error (1.1)
Due to current we obtain less than the measuring error of each point, so can be set to a definite value, because the measuring error of overwhelming majority's point is within 300 meters, therefore (1.1) formula can replace with T2:
T2＝(now.time-pre.time)*120km/h+2*300m (1.2)
Decision logic: first determine a normal point in adjacent 2, when the distance between 2 o'clock is greater than (1.2) formula, by another point deletion.
What deserves to be explained is: above-mentioned 120km/h and 300m rule of thumb or can need to carry out the parameter that adjusts.
2, removing repeats a little:
Remove multiple longitude and latitude points (location point) of the same user of synchronization, namely as same user, when multiple location point appears in synchronization, we think necessarily there is abnormity point, this abnormity point we be called and repeat a little, removal repeats a little to allow same user at an a moment only corresponding location point, and this is that we will do.
The method that concrete removing repeats a little can be:
2.1, when the spacing of the plurality of location point is near, to mutually in the same time multiple somes positions of different longitude and latitude average.
2.2, between the plurality of location point during distance, the relatively degree of accuracy of the location point that this point is corresponding with the immediate historical juncture, specifically can with T1 or T2 judge degree of accuracy, such as: get location point A corresponding to immediate 3 historical junctures, B, C, corresponding two of current time repeats to be some D, E, then judge D and A respectively according to above-mentioned T1 or T2, B, degree of accuracy between C, if D and A, B, two in C tri-location points meet the permissible error with T1 or T2, then think that the degree of accuracy of D is 2, E and A is judged respectively according to above-mentioned T1 or T2, B, degree of accuracy between C, if E and A, B, 1 in C tri-location points meets the permissible error with T1 or T2, then think that the degree of accuracy of E is 1, at this time we think that degree of accuracy at current time D point is higher than E, further, judge that whether the distance of this D point and penultimate moment location point is normal by the method in 1 again, if normal, retain.Otherwise do not do any operation.
In sum, smooth trajectory module is to the smoothing process of user trajectory, know the abnormity point fallen wherein, make the user trajectory that obtains more accurate, thus it is also just more accurate to make to find the optimum node of user trajectory in the hierarchical tree of region, the confidence level of the region level index tree exported further is also higher.
On the basis of above-mentioned all device embodiments, described device 600 can further include: memory module, for described user trajectory being stored in memory location corresponding to described optimum node.
Be that user trajectory finds corresponding area stores position by memory module, thus make calling of the management of user trajectory more succinct.
On the basis of above-mentioned all device embodiments, described device 600 can further include: adaptive region optimizes module, and described adaptive region is optimized module and is used for:
The user trajectory of described optimum node mapping is obtained according to described region level index tree;
User trajectory according to described optimum node mapping carries out adaptive region optimization with the node that is optimized to described optimum node;
According to the mapping relations between described user trajectory and described optimum node and described optimization node, obtain the mapping relations between described user trajectory and described optimization node;
According to described region level index tree and the mapping relations between described user trajectory and described optimization node, form the region level index tree after optimizing.
The concrete operation method that described adaptive region optimizes module see the description of embodiment of the method relevant position, can repeat no more herein.
By adaptive region optimization process, region level index tree after the optimization of final formation is compared to the region level index tree (being called in the embodiment of the present invention " region level index tree ") before optimization, there is meticulousr node division, thus also can be more accurately more efficient for the task operating of appointed area, thus the operation further avoided incoherent redundant data, save resource consumption and improved task operating performance.
The embodiment of the present invention also provides a kind of computer-readable medium, it is characterized in that, comprises computer executed instructions, and when the processor for computing machine performs described computer executed instructions, described computing machine performs position data disposal route disclosed in Fig. 1 embodiment.
Please refer to Fig. 7, the embodiment of the present invention also provides a kind of computer equipment 700, can comprise: processor 710, storer 720, communication interface 730, bus 740; Described processor 710, storer 720, communication interface 730 is connected and mutual communication by described bus 740; Described communication interface 730, for receiving and sending data; Described storer 720 is for storing computer executed instructions; When described computer equipment runs, described processor 710, for performing the described computer executed instructions in described storer, performs customer location disposal route disclosed in Fig. 1 embodiment with described computer equipment.
Above, the embodiment of the invention discloses a kind of computer equipment, this equipment adopts from network or the local location data obtained, polymerization obtains user trajectory, determine the optimum node of user trajectory in the hierarchical tree of region, and then the mapping relations set up between user trajectory and optimum node are to obtain the technical scheme of region level index tree, the region level index tree utilizing the method to obtain, can towards magnanimity position data, solve these position datas for the treatment of and analysis rapidly and efficiently, thus make the more efficient technical matters more accurately of application towards these magnanimity position datas.
In the above-described embodiments, the description of each embodiment is all emphasized particularly on different fields, in certain embodiment, there is no the part described in detail, can see the associated description of other embodiment.
It should be noted that, for aforesaid each embodiment of the method, in order to simple description, therefore it is all expressed as a series of combination of actions, but those skilled in the art should know, the present invention is not by the restriction of described sequence of movement, because according to the present invention, some step can adopt other order or carry out simultaneously.Secondly, those skilled in the art also should know, the embodiment described in instructions all belongs to preferred embodiment, and involved action and module might not be that the present invention is necessary.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment is that the hardware that can carry out instruction relevant by program has come, this program can be stored in a computer-readable recording medium, and storage medium can comprise: ROM, RAM, disk or CD etc.
The location data disposal route provided the embodiment of the present invention above and device are described in detail, apply specific case herein to set forth principle of the present invention and embodiment, the explanation of above embodiment just understands method of the present invention and core concept thereof for helping; Meanwhile, for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (14)
1. a position data disposal route, is characterized in that, comprising:
Obtain the location data in target area;
Carry out polymerization according to described location data and obtain user trajectory, described user trajectory comprise described user in described target area by way of positional information;
According to described user in described target area by way of positional information, determine the optimum node of described user trajectory in the hierarchical tree of region, described region hierarchical tree is for root node with described target area, the tree structure that the subregion comprised with described target area is child node, described optimum node be comprise described user in described target area by way of the lowest level node of all positional informations;
Set up mapping relations between described user trajectory and described optimum node to obtain region level index tree, described region level index tree is the region hierarchical tree comprising described mapping relations.
2. method according to claim 1, is characterized in that, described determine the optimum node of described user trajectory in the hierarchical tree of region before, described method also comprises:
In conjunction with described location data, according to data balancing principle and transregional minimum principle, division is carried out to described target area and obtains described region hierarchical tree.
3. method according to claim 2, is characterized in that, location data described in described combination, according to data balancing principle and transregional minimum principle, carries out division obtain described region hierarchical tree and specifically comprise described target area:
Prime area hierarchical tree is obtained according to data balancing principle and transregional minimum principle;
According to optimization principles described prime area hierarchical tree is optimized and obtains described region hierarchical tree.
4. method according to claim 3, is characterized in that, described according to data balancing principle and transregional minimum principle acquisition prime area hierarchical tree; According to optimization principles described prime area hierarchical tree is optimized and obtains described region hierarchical tree, specifically comprise:
y
min＝a×M+b×N；
In the y that wherein a × M+b × N obtains, being worth minimum is y
min, wherein y represents described prime area hierarchical tree, wherein y
minrepresent described region hierarchical tree, wherein M embodies data balancing principle, and data more balanced M value is less, and in M=|0.5-child node position count/root node in always position count |; Wherein N embodies transregional minimum principle, and transregional fewer N value is less, and N=is across child node track number/total track number; Wherein a and b is respectively the weighted value of M and N, and a+b=1.
5. the method according to any one of Claims 1-4, it is characterized in that, describedly carry out after polymerization obtains user trajectory according to described location data, described method also comprises: remove the abnormity point in described user trajectory, to the smoothing process of described user trajectory.
6. the method according to any one of claim 1 to 5, it is characterized in that, after the described mapping relations set up between described user trajectory and described optimum node, described method also comprises: described user trajectory is stored in memory location corresponding to described optimum node.
7. the method according to any one of claim 1 to 6, is characterized in that, described method also comprises:
The user trajectory of described optimum node mapping is obtained according to described region level index tree;
User trajectory according to described optimum node mapping carries out adaptive region optimization with the node that is optimized to described optimum node;
According to the mapping relations between described user trajectory and described optimum node and described optimization node, obtain the mapping relations between described user trajectory and described optimization node;
According to described region level index tree and the mapping relations between described user trajectory and described optimization node, form the region level index tree after optimizing.
8. a position data processing unit, is characterized in that, comprising:
Data acquisition module, for obtaining the location data in target area;
Track acquisition module, obtains user trajectory for carrying out polymerization according to described location data, described user trajectory comprise described user in described target area by way of positional information;
Optimum node determination module, for according to described user in described target area by way of positional information, determine the optimum node of described user trajectory in the hierarchical tree of region, described region hierarchical tree is for root node with described target area, the tree structure that the subregion comprised with described target area is child node, described optimum node be comprise described user in described target area by way of the lowest level node of all positional informations;
Region level index tree acquisition module, for setting up mapping relations between described user trajectory and described optimum node to obtain region level index tree, described region level index tree is the region hierarchical tree comprising described mapping relations.
9. device according to claim 8, is characterized in that, described device also comprises:
Region hierarchical tree divides module, in conjunction with described location data, according to data balancing principle and transregional minimum principle, carries out division obtain described region hierarchical tree to described target area.
10. device according to claim 9, is characterized in that, described region hierarchical tree divides module, for:
Prime area hierarchical tree is obtained according to data balancing principle and transregional minimum principle;
According to optimization principles described prime area hierarchical tree is optimized and obtains described region hierarchical tree.
11. devices according to claim 10, is characterized in that, described region hierarchical tree divides module, specifically for:
y
min＝a×M+b×N；
In the y that wherein a × M+b × N obtains, being worth minimum is y
min, wherein y represents described prime area hierarchical tree, wherein y
minrepresent described region hierarchical tree, wherein M embodies data balancing principle, and data more balanced M value is less, and in M=|0.5-child node position count/root node in always position count |; Wherein N embodies transregional minimum principle, and transregional fewer N value is less, and N=is across child node track number/total track number; Wherein a and b is respectively the weighted value of M and N, and a+b=1.
Device described in 12. any one of according to Claim 8 to 11, it is characterized in that, described device also comprises smooth trajectory module, for removing the abnormity point in described user trajectory, to the smoothing process of described user trajectory.
Device described in 13. any one of according to Claim 8 to 12, it is characterized in that, described device also comprises memory module, for described user trajectory being stored in memory location corresponding to described optimum node.
Device described in 14. any one of according to Claim 8 to 13, is characterized in that, described device also comprises adaptive region and optimizes module, and described adaptive region is optimized module and is used for:
The user trajectory of described optimum node mapping is obtained according to described region level index tree;
User trajectory according to described optimum node mapping carries out adaptive region optimization with the node that is optimized to described optimum node;
According to the mapping relations between described user trajectory and described optimum node and described optimization node, obtain the mapping relations between described user trajectory and described optimization node;
According to described region level index tree and the mapping relations between described user trajectory and described optimization node, form the region level index tree after optimizing.
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