CN106649450A - Method for identifying critical path in location service - Google Patents

Method for identifying critical path in location service Download PDF

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CN106649450A
CN106649450A CN201610842246.2A CN201610842246A CN106649450A CN 106649450 A CN106649450 A CN 106649450A CN 201610842246 A CN201610842246 A CN 201610842246A CN 106649450 A CN106649450 A CN 106649450A
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point
location
target
critical path
key
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CN106649450B (en
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王维龙
谢少军
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Xiamen Rong Extension Iot Technology Co Ltd
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Xiamen Rong Extension Iot Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/343—Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases

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Abstract

The invention provides a method for identifying a critical path in location services. The method comprises the steps that a target critical path is identified, and is maximally retained through the movement trajectory of the path, and the size of an output result set is decreased through attaching the number of non-critical location points to the attributes of the critical points, for this reason, path changes of the target movements are firstly required to be identified; after an average path is obtained, the movement process can be partitioned into zones, and the computation complexities are effectively reduced; after the partition, the key location points in the process of the target movements are extracted through a prediction algorithm. The critical path identification method adopts a distance reference associated with a target state and a deflection reference associated with a service scenario, and has strong applicability and maneuverability in location services, and on the whole the problem of distortion of position trajectory is solved effectively; on the basis of ensuring the quality of the data, information redundancy is reduced, and the efficiency and the economy of the platform in providing location services to mobile applications are effectively met.

Description

A kind of method that critical path is recognized in location-based service
Technical field
The present invention relates to recognize in location-based service (LBS) and data processing field, more particularly to a kind of location-based service crucial The method in path.
Background technology
With today of the continuous popularization of developing rapidly for satellite navigation positioning and mobile Internet, particularly location-based service, The position big data being made up of geodata, trace information and application record etc. has become government or enterprise for perceiving the mankind The grand strategy resource of Community rule, the geographical national conditions of analysis and structure smart city, is extremely weighed in big data practice The part wanted.Different from traditional sample statistics, there is significantly promiscuity, redundancy and sparse in large-scale position data Property, need to carry out it feature extraction and value is excavated, more accurate mobile behavior pattern could be obtained and region local is special Levy, so as to reduce and generate the Whole Data Model for meeting associated application analysis, and to carry out personalized, intelligentized position clothes Business provides critical data and supports.
Location Service Platform process service access on, due to platform and target be it is detached, it is impossible for platform The intention of target is understood completely, it does not often limit time, non-limiting distance, does not limit flow, by target period certainly Position data is reported mainly.Cloud computing increasingly it is perfect instantly, distributed big data platform can be effectively realized this High concurrent, the data storage of magnanimity and service.The service access way of this Target self-determination allows platform as much as possible The positional information of master goal.But, can not the fit completely action of target of the position for periodically frequently reporting is intended to, and this is just Reduce the total quality of data.On the other hand, due to the reliability and high cost of mobile network the features such as so that platform for The quality of data of service interface requires very high.
Goal of the invention
It is an object of the invention to provide a kind of method that critical path is recognized in location-based service, is inferred by routing information The motion intention of target, the information content of movement locus is reduced by anticipation algorithm and reduction number is recorded.
The method of critical path is recognized in a kind of location-based service of the present invention, including being uniformly accessed into and clothes for location-based service is provided The location library of the cloud platform of business and the position data that is stored with, definition critical path is key position point of the target within certain time period Set, it describes critical movements track of the target within the time period, the key position point be motion process in produce The location point of key feature, comprises the steps：
Step 1, data retrieval：Rail from location library in whole trace informations or certain time period of searched targets Mark information, contains the up all positional informations of target in the location library, wherein the position data collection for meeting search condition claims Be metadata；
Step 2, coordinate dimensionality reduction：The longitude and latitude of metadata is converted into plane coordinates formula by Gauss Kru＆4＆ger projection；
Step 3, compression ratio r are calculated：Metadata number is compared and can be pressed with expected output result collection number Shrinkage
Wherein, s is the size of the panel data collection S of target, and m is the size of expected output result collection M, and m is and application The variable element of environmental correclation；Ceil (o) represents the smallest positive integral more than or equal to 0；WhenWhen, then leap to step Rapid 7 return metadata；
Step 4, by way of across point sampling, calculate track apart from a reference value:
Step 5, packet and the identification of transregional key point：Using above-mentioned trajectory location points are grouped apart from a reference value, And it is transregional key point to identify time most front location point in packet；
The calculating of critical path in step 6, group：Calculate location point to express with the motion state of the first two location point Formula, determines that whether the location point is the motion state homogeneity with the first two location point, and then obtains the critical path of the packet；
Step 7, result merge and analyze again：The critical path of all packets is obtained according to the method for step 6, by each packet Critical path result temporally before and after merge, and analyze the quantity of output result collection and the ratio of expected output result collection, Determine whether directly to return result.
It is described to obtain the panel data collection S of target after above-mentioned Gauss-Ke Lvge map projections algorithm is calculated, Panel data according to target integrates time number consecutively that location point in S reports as S1、S2、S3…Sn, wherein, S1X-axis coordinate value It is set to x1, Y-axis coordinate value is set to y1；The like, then SnX-axis coordinate value be xn, Y-axis coordinate is yn, so understand, other positions Put a little in S1After time, referred to as S1Rearmounted point, wherein S2For S1Tight rear point；Likewise, S1Referred to as S2Preposition point, It is tight front point；Dynamic array M of definition1Storage key position point information, and as final output result collection, wherein, array unit The structure of plain Data is：{ Lon longitudes, Lat latitudes, Date times ..., Stat location types, Drill:Reduction node number }, Wherein, Lon longitudes, Lat latitudes, the raw information that the Date times are location point；Stat is the type of location point, and its value includes 0 With 1, transregional point and inflexion point are represented respectively；Drill is the point and dynamic array M1In it is tight before the location point that reduces between point Number；The step 4 by way of across point sampling, calculate track apart from a reference value, comprise the steps：
Step 4.1, according to the panel data collection S and gained compression ratio r of target, every r in the panel data collection S of target Individual point extracts a point and constitutes { S1, S1+r…S1+(m-1)rSet, and adjacent 2 points of distance is calculated, the method that distance is calculated is：
Wherein, (xi,yi) it is SiCoordinate, (xi+1,yi+1) it is Si+1Coordinate；
Step 4.2, the computational methods apart from a reference value are：
Wherein, v represents the number of times for calculating the operation of compression ratio r, dirFor in step 4.1 apart from the S obtained by computing formulaiWith Si+rThe distance between, wherein i ∈ [1, m], j are preset parameter, and Δ d is represented to be equal to apart from a reference value and is accurate to hundred adjacent Sample point apart from average.
Described packet and the identification of transregional key point, specifically include following steps：
By the transverse and longitudinal coordinate of plane coordinate system from the origin of coordinates (0,0) start, adopt it is described apart from a reference value for pros The shape length of side carries out subregion to plane, and identifies the subregion with the maximum horizontal stroke in each smallest square, ordinate, while mark The subregion coordinate that each position data is located, then by Time Continuous and one group consistent of location point merger of subregion, and identifies point Time most front location point is transregional key point in group.
Critical path is calculated in described group, specifically includes following steps：
The number of location point in analysis group, if number is less than threshold value, by the location point in group key position is all considered as Point, and skip the interior analysis of group that the compression of this group is directly entered next group；
The all location points in packet are differentiated successively, are calculated location point and are expressed with the motion state of the first two location point Formula, determines whether the location point is and the first two location point motion state homogeneity to thereby determine that the next position point is to filter, Still inflection point point is identified as.
Advantages of the present invention and good effect are：
(1) present invention is the critical path identification of target correlation, and target movement locus in a period of time is regarded For the extension of tight front state, can therefrom simplify the motion state that target mixes, recognize the key position point in motion state, and Target is described it as in the critical movements track of the time period；
(2) the inventive method is it is determined that during a reference value, its sampling policy is that target state is related, is to utilize target Inequality is sought in the change in displacement amplitude of certain time period and obtain, the scene of its different type target of effectively coincideing, namely target exists It is larger apart from benchmark value during high-speed cruising, on the contrary then value is less, so can accurately residing for strategic point Region so that the movement locus of the description and target itself of final critical path is highly consistent；
(3) the inventive method is when position dotted state is recognized, not only allows for the mobile factor of position, but also by position Orientation differences take into account, creative devises anticipation algorithm, realize simplified algorithm it is succinct, efficient, should be readily appreciated that；
(4) the inventive method is in the practical application of location-based service, not only from operational efficiency, accuracy rate and compression ratio all bodies Unprecedented advantage is showed so that distributed Location Service Platform being capable of the suitable mobile network environment of accurate, convenient, offer Under trace playback service.
Description of the drawings
Fig. 1 is the overall logic flow chart of the embodiment of the present invention；
Fig. 2 is the anticipation area schematic diagram of the embodiment of the present invention；
Fig. 3 is the deflection datum line angular relationship figure of the embodiment of the present invention；
Fig. 4 is the suitable counterclockwise relationship figure of two vectors in the plane coordinate system of the embodiment of the present invention；
Fig. 5 processes logical flow chart for the data pick-up of the embodiment of the present invention.
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
Specific embodiment
The present embodiment is identified as prototype with critical path in the service cloud platform of RT-CLBS locus, describes this in detail The embodiment of invention.RT-CLBS locus service cloud platform is the public service platform of an opening, it is desirable to provide position Being uniformly accessed into and service for service, hereinafter referred to as cloud platform are put, the storage assembly of wherein position data is referred to as location library.It is crucial Path feeling the pulse with the finger-tip is marked on the set of the point of the key position in certain time period, and it describes critical movements of the target within the time period Track, produces the location point of the key features such as transregional, orientation deflection, we term it key point in motion process.
As shown in figure 1, the method that critical path is recognized in a kind of location-based service of the invention, including the system that location-based service is provided One location library for accessing the cloud platform with service and the position data that is stored with, definition critical path is target within certain time period The set of key position point, it describes critical movements track of the target within the time period, and the key position point is motion During produce key feature location point, specifically include following steps：
Step 1, data retrieval：Rail from location library in whole trace informations or certain time period of searched targets Mark information, contains the up all positional informations of target in the location library, wherein the position data collection for meeting search condition claims Be metadata；
Step 2, coordinate dimensionality reduction：The longitude and latitude degrees of data of the metadata for retrieving is converted to by Gauss Kru＆4＆ger projection Plane coordinates formula, i.e., changed longitude and latitude degrees of data to plane right-angle coordinate using Gauss-Ke Lvge map projection's algorithms, is generated Dimensionality reduction source data, its concrete transfer process is as described below：
Step 2.1, foundation Gauss-Ke Lvge map projections are poor every 6 degree of Jing from 0 degree of meridian, are divided into from West to East 60 points of bands, by reel number 1,2,3 ... 60 bands are respectively labeled as；
Step 2.2, this 60 points of bands are projected to the Elliptic Cylinder being transverse on earth ellipsoid, the Elliptic Cylinder surface It is a warp with the tangent line on spheroid surface, referred to as central meridian, then according to projection condition, by central meridian both sides regulation In the range of spot projection on Elliptic Cylinder surface, so as to obtain extensible plane right-angle coordinate, i.e. Gauss-Ke Lvge ground Figure projection；
Gauss Kru＆4＆ger projection has following characteristic：
(1) central sub- warp and terrestrial equator are projected as straight line, and the symmetry axis to project；
(2) arbitrarily angled on ellipsoid project to keep after plane it is constant；
(3) without length deformation after central meridian projection；
After step 2.3, above-mentioned point of band and Gauss-Ke Lvge map projections, the reel number scope of China between 13～23, Plane right-angle coordinate, the coordinate of X-axis is all the natural value started at by equator, and due to taking positive sign to the east of central meridian, to the west of Negative sign is taken, negative value occurs to avoid Y-axis coordinate, so add 500KM respectively in ordinate y values, and before ordinate Titled with reel number；
According to above-mentioned conversion, map upper warp and woof degree is { Lon:118°10′,Lat:24 ° 29 ' } location point, Jing Gausses-gram After Lv Ge map projections, plane coordinates is { X:2710562.99827803,Y:20619384.2129943 }, two wherein before Y value Position " 20 " represents this o'clock in the 20th projection zone；
Step 2.4, after above-mentioned Gauss-Ke Lvge map projections algorithm is calculated, obtain the panel data collection of target S, panel data according to target integrates time number consecutively that location point in S reports as S1、S2、S3…Sn, wherein, S1X-axis coordinate Value is set to x1, Y-axis coordinate value is set to y1；The like, then SnX-axis coordinate value be xn, Y-axis coordinate is yn, so understand, other Location point is in S1After time, referred to as S1Rearmounted point, wherein S2For S1Tight rear point；Likewise, S1Referred to as S2Preposition point, It is also tight front point；
Meanwhile, dynamic array M of definition1Storage key position point information, and as final output result collection, wherein, number The structure of constituent element element Data is：{ Lon longitudes, Lat latitudes, Date times ..., Stat location types, Drill:Reduction node Number }
Wherein, Lon longitudes, Lat latitudes, the raw information that the Date times are location point；Stat is the type of location point, its Value includes 0 and 1, and transregional point and inflexion point are represented respectively；Drill is the point and dynamic array M1In it is tight before the position that reduces between point Put a number；
Step 3, compression ratio r are calculated：Metadata number is compared and can be pressed with expected output result collection number Shrinkage
Wherein, s is the size of the panel data collection S of target, and m is the size of expected output result collection M, and m is and application The variable element of environmental correclation；Ceil (o) represents the smallest positive integral more than or equal to 0；The cloud platform is provided for mobile terminal Trace playback inquiry in time period, according to the demand of actual scene, if m=20, according to above-mentioned formula r=n/20 can be obtained, whenWhen, then directly go to step 7 and return metadata, do not do logical process；
Step 4, by way of across point sampling, calculate track apart from a reference value:
Step 4.1, according to the panel data collection S and gained compression ratio r of target, every r in the panel data collection S of target Individual point extracts a point and constitutes { S1, S1+r…S1+(m-1)rSet, and adjacent 2 points of distance is calculated, the method that distance is calculated is：
Wherein, (xi,yi) it is SiCoordinate, (xi+1,yi+1) it is Si+1Coordinate；
Step 4.2, the computational methods apart from a reference value are：
Wherein, v represents the number of times that step 3 is run, dirFor in step 4 apart from the S obtained by computing formulaiAnd Si+rBetween Distance, wherein i ∈ [1, m], to take into account the high efficiency of data accuracy and calculating, j takes 2 in the present embodiment, and Δ d is represented apart from base Quasi- value is equal to the adjacent samples point for being accurate to hundred apart from average；
Step 5, packet and the identification of transregional key point：The data of above-mentioned dynamic array are variable, and its data source is in packet Data, are its subsets, and final satisfactory Dynamic Packet is set of keypoints, and so-called to be grouped by domain, being will be original By being grouped to trajectory location points apart from a reference value, then all data summations being grouped by domain are initial data to data, are adopted It is above-mentioned to carry out subregion to plane for the square length of side apart from a reference value, and with the maximum in each smallest square it is horizontal, vertical sit Mark is identifying the subregion, while mark the subregion coordinate that each position data is located, then by Time Continuous and subregion is consistent One group of location point merger, and it is transregional key point to identify time most front location point in packet：
By step 4 gained apart from a reference value Δ d, in plane right-angle coordinate, with (0, it is 0) starting point, be with Δ d The length of side, to plane square subregion is carried out, by point (0,0) be located Labelling Regions for (0,0), point (Δ d, Δ d) place subregion Be labeled as (1,1), the square subregion be with apart from benchmark as the length of side, upper right border be solid line, lower-left border for dotted line area Domain.The coordinate points being distributed in square subregion, and the coordinate points being distributed on square top right-hand side can be described as sit Fall within the subregion, the like, location point (x is given belowd,yd) place subregion (Xd,Yd) computational methods：
According to above-mentioned formula, the location point in the panel data collection S of target can be carried out subregion Coordinate Conversion, and by the time It is continuous and the consistent location point of Labelling Regions is classified as one group, because target is that periodic location data are reported, so, the above-mentioned time Two location points of continuous representation are that tandem is reported；
Per group of first location point is labeled as into transregional location point, and the array element Data of construction location point, its Stat Attribute is all set to 0, and transregional location point is the starting point for being grouped critical path, is one kind of key position point, and a subregion One and only one transregional location point, is then sequentially stored into dynamic array M by array element Data1In；
Step 6, by the calculating of critical path in the group in domain point：The number of location point in analysis group, if number is less than threshold Value (such as 3), then be all considered as key position point by the location point in group, and skips the group that the compression of this group is directly entered next group Interior analysis, calculates the motion state expression formula of location point and the first two location point, can determine that the location point is by analysis No is motion state homogeneity, and then obtains the critical path of the packet, critical path in identification group, with gained first in step 5 As a example by individual packet A, anticipation algorithm is further described, the location point being grouped in A is temporally labeled as successively from front to back A1、A2、A3..., and with (xi,yi) represent AiCoordinate：
Initial reduction location point number drill of step 6.1, definition is 0, to describe dynamic array M1Middle adjacent position The location point number actually included between point；
Step 6.2, setting subregion A (xa,ya) adjacent position point A1、A2, then A is connected1、A2VectorSit in plane The motion state of mark system is as shown in Fig. 2 A1、A2Place straight line L expression formulas are：Y=k (x-x1)+y1
Wherein, k is the slope of straight line L,As shown in figure 3, the angle of straight line L and X-axis is α, then tan α= k；
Step 6.3, along vectorDirection, with A2Make ray A`, A` rotate counterclockwises for originObtain ray L1, its Middle ray L1It is β with the angle of X-axis, thenAzimuth reference value is set in the present embodiment
As shown in the angular relationship that Fig. 3 deflects datum line, it can be seen from trigonometric function induced formulas：
WhereinThen ray L1The expression formula of place straight line is：
In the same manner, ray L2The expression formula of place straight line is：
Step 6.4, make ray L1With the intersection point a of partition boundaries1With ray L2With the intersection point a of partition boundaries2, such as Fig. 4 planes Vector along counterclockwise relationship, due to A3Must be in partition boundaries, then " A3 In anticipation region " be equivalent to " 's Counterclockwise and Clockwise direction ", whereinTo connect A2Point to a1Vector,To connect A2Point to a2 Vector；
Step 6.5, two vectors suitable counterclockwise relationship each other is judged, according to vector cross-products theorem, above-mentioned vectorWithSuitable counterclockwise relationship, can be described as：
(1) ifThen Clockwise direction；
(2) ifThen Counter clockwise direction；
(3) ifThenWithCollinearly；
WhereinRepresentWithCross product；
Due to " A3 In anticipation region " be equivalent to "And", then A3WithIt is pre- Sentencing expression formula is：AndAnd if only if A3WithAnticipation expression formula be true time, A3MeetAnticipation；
Step 6.6, A3WithAnticipation expression formula exist for true and be false two kinds of situations, its concrete method for processing is such as Under：
If A3WithAnticipation expression formula be true time, filter A2Point, and add 1 for drill；If A3WithAnticipation table It is fictitious time up to formula, then regarding A2Point is inflection point point, constructs A2The numerical value element Data of point, its Stat are set to 1, Drill and are set to drill；By A2The numerical value element Data of point is used as A1It is tight after point be stored in dynamic array M1, it is 0 finally to reset drill；
After above-mentioned process, continuation judges A4Whether meetAnticipation, the like, until having judged all in packet Location point；
Step 7, result merge and analyze again：The critical path of other packets is obtained according to the method for step 6, temporally first The output result collection M for extracting for the first time is merged into afterwards1, remember M1Element number be m1；By the critical path result of above-mentioned each packet Merge before and after temporally, and analyze the quantity of output result collection and the ratio of expected output result collection, it is determined whether directly return Result is returned, as shown in figure 5, carrying out Treatment Analysis to extracting result：
(1) when meeting exit criteriaWhen, terminate extraction process, and return output result collection M1；
(2) whenWhen, with dynamic array M1The maximum location point D of middle Drill values is end point, and location point D's is tight Front point is starting, and subset S is obtained from the panel data collection S of targettAnd not comprising both of the aforesaid location point, and arrive by step 3 The way of step 7, obtains subset StCritical path Mt, by MtIt is stored in dynamic array M1After middle immediate vicinity point D, class successively Push away, when meeting exit criteriaWhen, terminate extraction process, and return output result collection M1；
(3) whenWhen, by M1It is considered as the data set S of second decimation1, and start the cycle over processing data from step 3 and take out The process for taking, until output result collection M2When meeting exit criteria, terminate extraction process, and return output result collection M2。
The present invention's focuses on：The critical path of target is recognized, is retained by the maximization of its movement locus, and The number of non-key location point is attached in the attribute of key point to reduce the size of output result collection, for this purpose, requiring first It is capable of identify that the distance change that target is moved；After having inequality distance, subregion could be carried out to motion process, effectively reduce calculating Complexity；After subregion, the key position point in target motion process, the critical path of the present invention are extracted by anticipation algorithm Recognition methods employs the related deflection benchmark related to business scenario apart from benchmark of dbjective state, the service field tool in position There is very strong applicability and operability, the problem of dtmf distortion DTMF of location track is efficiently solved on the whole, ensureing the quality of data On the basis of, information redundancy is reduced, can effectively meet efficiency from location-based service to Mobile solution and economy that platform provides Property.
The present invention preferably specific embodiment is these are only, but protection scope of the present invention is not limited thereto, it is any Those familiar with the art the change that can readily occur in or replaces in the technical scope that the embodiment of the present invention is disclosed Change, all should be included within the scope of the present invention.

Claims (4)

1. the method for critical path is recognized in a kind of location-based service, including being uniformly accessed into for location-based service is provided and put down with the cloud of service The location library of platform and the position data that is stored with, defines set of the critical path for key position point of the target within certain time period, It describes critical movements track of the target within the time period, and the key position point is to produce key feature in motion process Location point, it is characterised in that comprise the steps：
Step 1, data retrieval：Track letter from location library in whole trace informations or certain time period of searched targets Breath, contains the up all positional informations of target, wherein the position data collection for meeting search condition is referred to as in the location library Metadata；
Step 2, coordinate dimensionality reduction：The longitude and latitude of metadata is converted into plane coordinates formula by Gauss Kru＆4＆ger projection；
Step 3, compression ratio r are calculated：Metadata number is compared with expected output result collection number compression ratio is obtained
Wherein, s is the size of the panel data collection S of target, and m is the size of expected output result collection M, and m is and applied environment Related variable element；Ceil (o) represents the smallest positive integral more than or equal to 0；WhenWhen, then leap to step 7 and return Return metadata；
Step 4, by way of across point sampling, calculate track apart from a reference value:
Step 5, packet and the identification of transregional key point：Using above-mentioned trajectory location points are grouped apart from a reference value, and are marked It is transregional key point to know time most front location point in packet；
The calculating of critical path in step 6, group：The motion state expression formula of location point and the first two location point is calculated, really Whether the fixed location point is the motion state homogeneity with the first two location point, and then obtains the critical path of the packet；
Step 7, result merge and analyze again：The critical path of all packets is obtained according to the method for step 6, by the pass of each packet Key route result temporally merges in front and back, and analyzes the quantity of output result collection and the ratio of expected output result collection, it is determined that Whether result is directly returned.
2. the method that critical path is recognized in a kind of location-based service according to claim 1, it is characterised in that：By above-mentioned Gauss-Ke Lvge map projections algorithms calculate after, obtain the panel data collection S of target, panel data collection S middle positions according to target It is S to put the time number consecutively for a little reporting1、S2、S3…Sn, wherein, S1X-axis coordinate value be set to x1, Y-axis coordinate value is set to y1； The like, then SnX-axis coordinate value be xn, Y-axis coordinate is yn, so understanding, other positions point is in S1After time, referred to as S1 Rearmounted point, wherein S2For S1Tight rear point；Likewise, S1Referred to as S2Preposition point, be also it is tight before point；Dynamic array M of definition1 Storage key position point information, and as final output result collection, wherein, the structure of array element Data is：Lon longitudes, Lat latitudes, Date times ..., Stat location types, Drill:Reduction node number, wherein, Lon longitudes, Lat latitudes, The Date times are the raw information of location point；Stat for location point type, its value includes 0 and 1, and transregional point and partially is represented respectively Turning point；Drill is the point and dynamic array M1In it is tight before the location point number reduced between point；The step 4 across point by taking out The mode of sample, calculate track apart from a reference value, comprise the steps：
Step 4.1, according to the panel data collection S and gained compression ratio r of target, every r point in the panel data collection S of target Extract a point and constitute { S1, S1+r…S1+(m-1)rSet, and adjacent 2 points of distance is calculated, the method that distance is calculated is：
d = ( x i + 1 - x i ) 2 + ( y i + 1 - y i ) 2
Wherein, (xi,yi) it is SiCoordinate, (xi+1,yi+1) it is Si+1Coordinate；
Step 4.2, the computational methods apart from a reference value are：
Δ d = 10 j · v · c e i l ( Σ i = 1 m d i r 10 j · m )
Wherein, v represents the number of times for calculating the operation of compression ratio r, dirFor in step 4.1 apart from the S obtained by computing formulaiAnd Si+rIt Between distance, wherein i ∈ [1, m], j are preset parameter, and Δ d is represented and the adjacent samples point for being accurate to hundred is equal to apart from a reference value Apart from average.
3. the method that critical path is recognized in a kind of location-based service according to claim 1, it is characterised in that described divides Group and the identification of transregional key point, specifically include following steps：
By the transverse and longitudinal coordinate of plane coordinate system from the origin of coordinates (0,0) start, adopt it is described apart from a reference value for square side Length carries out subregion to plane, and identifies the subregion with the maximum horizontal stroke in each smallest square, ordinate, while marking each The subregion coordinate that position data is located, then by Time Continuous and one group consistent of location point merger of subregion, and identifies in packet Time most front location point is transregional key point.
4. the method that critical path is recognized in a kind of location-based service according to claim 1, it is characterised in that described group Interior critical path is calculated, and specifically includes following steps：
The number of location point in analysis group, if number is less than threshold value, by the location point in group key position point is all considered as, and The compression for skipping this group is directly entered analysis in next group of group；
The all location points in packet are differentiated successively, calculate the motion state expression formula of location point and the first two location point, Determine whether the location point is and the first two location point motion state homogeneity to thereby determine that the next position point is to filter, also It is to identify as inflection point point.
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