CN106649450B - The method of critical path is identified in a kind of location-based service - Google Patents

The method of critical path is identified in a kind of location-based service Download PDF

Info

Publication number
CN106649450B
CN106649450B CN201610842246.2A CN201610842246A CN106649450B CN 106649450 B CN106649450 B CN 106649450B CN 201610842246 A CN201610842246 A CN 201610842246A CN 106649450 B CN106649450 B CN 106649450B
Authority
CN
China
Prior art keywords
point
location
target
critical path
key
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201610842246.2A
Other languages
Chinese (zh)
Other versions
CN106649450A (en
Inventor
王维龙
谢少军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xiamen Rong Extension Iot Technology Co Ltd
Original Assignee
Xiamen Rong Extension Iot Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xiamen Rong Extension Iot Technology Co Ltd filed Critical Xiamen Rong Extension Iot Technology Co Ltd
Priority to CN201610842246.2A priority Critical patent/CN106649450B/en
Publication of CN106649450A publication Critical patent/CN106649450A/en
Application granted granted Critical
Publication of CN106649450B publication Critical patent/CN106649450B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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

Abstract

The method of critical path is identified in a kind of location-based service of the present invention, critical path identification to target, it is retained by the maximization of its motion profile, and the number of non-key location point is attached in the attribute of key point to the size for reducing output result set, for this purpose, being required to the distance variation of identification target movement first；After having inequality distance, subregion could be carried out to motion process, the complexity of calculating is effectively reduced；After subregion, the key position point in target motion process is extracted by anticipation algorithm, it is relevant apart from benchmark and the relevant deflection benchmark of business scenario that critical path recognition methods of the invention uses dbjective state, in position, service field has very strong applicability and operability, the problem of dtmf distortion DTMF of location track is efficiently solved on the whole, on the basis of guaranteeing the quality of data, information redundancy is reduced, can effectively meet platform to mobile application and the efficiency and economy of location-based service are provided.

Description

The method of critical path is identified in a kind of location-based service
Technical field
It is crucial the present invention relates to being identified in location-based service (LBS) and data processing field more particularly to a kind of location-based service The method in path.
Background technique
With the rapid development of satellite navigation and positioning and mobile Internet, especially location-based service constantly universal today, Government is had become by the position big data that geodata, trace information and application record etc. are constituted or enterprise is used to perceive the mankind Community rule analyzes geographical national conditions and constructs the grand strategy resource of smart city, is extremely heavy in big data practice The a part wanted.Different from traditional sample statistics, there is apparent promiscuity, redundancy and sparse in large-scale position data Property, it needs to carry out it feature extraction and value is excavated, it is special that more accurate mobile behavior mode and region part could be obtained Sign so that reduction and generation meet the Whole Data Model of associated application analysis, and takes to carry out personalized, intelligentized position Business provides critical data support.
Location Service Platform, can not for platform since platform is separated with target in processing service access The intention for understanding target completely, does not limit the time often, non-limiting distance, does not limit flow, certainly by target period Position data is reported mainly.Improved day by day instantly in cloud computing, distributed big data platform can effectively realize this High concurrent, the storage of the data of magnanimity and service.The service access way of this Target self-determination allow platform as much as possible The location information of master goal.But the action that the position periodically frequently reported can not be bonded target completely is intended to, this is just Reduce the total quality of data.On the other hand, due to the reliability of mobile network and high cost the features such as so that platform for The quality of data of service interface requires very high.
Goal of the invention
The object of the present invention is to provide a kind of method for identifying critical path in location-based service, passage path information is inferred The motion intention of target to reduce the information content of motion profile and records reduction number by prejudging algorithm.
The method that critical path is identified in a kind of location-based service of the present invention, including providing being uniformly accessed into and taking for location-based service The cloud platform of business and the location library for being stored with position data, definition critical path are key position point of the target within certain period Set, the critical movements track of target during this period of time is described, the key position point be motion process in generate The location point of key feature, includes the following steps:
Step 1, data retrieval: the rail out of searched targets in location library whole trace informations either certain time period Mark information contains all location informations of target uplink 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 set number Shrinkage
Wherein, s is the size of the panel data collection S of target, and m is the size of expected output result set M, and m is and application The variable element of environmental correclation；Ceil (o) indicates the smallest positive integral for being more than or equal to o；WhenWhen, then leap to step Rapid 7 return to metadata；
Step 4, by way of across point sampling, calculate track apart from a reference value:
Step 5, grouping and the identification of transregional key point: being grouped trajectory location points apart from a reference value using above-mentioned, And identifying time most preceding location point in grouping is transregional key point；
Step 6, the calculating for organizing interior critical path: the motion state of calculating position point and this first two location point is expressed Formula determines whether the location point is motion state homogeneity with the first two location point, and then obtains the critical path of the grouping；
Step 7, result merge and analyze again: the critical path of all groupings are obtained according to the method for step 6, by each grouping Critical path result temporally front and back merge, and analyze output result set quantity and it is expected output result set ratio, Determine whether directly to return to processing result.
It is described calculated by above-mentioned Gauss-Ke Lvge map projection's algorithm after, obtain the panel data collection S of target, 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 as x1, Y axis coordinate value is set as y1；And so on, then SnX axis coordinate value be xn, Y axis coordinate yn, so it is found that other positions It sets a little in S1After time, referred to as S1Postposition point, wherein S2For S1Next point；Likewise, S1Referred to as S2Preposition point, It is tight preceding point；Define dynamic array M1Key position point information is stored, and as final output result set, wherein array member The structure of plain Data are as follows: { Lon longitude, Lat latitude, Date time ..., Stat location type, Drill: reduction node number }, Wherein, Lon longitude, Lat latitude, the raw information that the Date time is location point；Stat is the type of location point, and value includes 0 With 1, transregional point and inflexion point are respectively represented；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, include the following steps:
Step 4.1, panel data collection S and gained compression ratio r according to target, every r in the panel data collection S of target A point extracts a point and forms { S1, S1+r…S1+(m-1)rSet, and the distance of adjacent two o'clock is calculated, the method that distance calculates are as follows:
Wherein, (xi,yi) it is SiCoordinate, (xi+1,yi+1) it is Si+1Coordinate；
Step 4.2, the calculation method apart from a reference value are as follows:
Wherein, v indicates to calculate the number of compression ratio r operation, dirFor the resulting S of distance calculation formula in step 4.1iWith Si+rThe distance between, wherein [1, m] i ∈, j is preset parameter, and Δ d, which indicates to be equal to apart from a reference value, is accurate to hundred adjacent Sample point apart from mean value.
The grouping and the identification of transregional key point, specifically comprise the following steps:
The transverse and longitudinal coordinate of plane coordinate system is started from coordinate origin (0,0), using described apart from a reference value is square Shape side length carries out subregion to plane, and identifies the subregion with the maximum horizontal, ordinate in each smallest square, marks simultaneously Subregion coordinate where each position data then by one group of Time Continuous and the consistent location point merger of subregion, and identifies and divides Time most preceding location point is transregional key point in group.
Critical path calculates in the group, specifically comprises the following steps:
Location point in group is all considered as key position if number is less than threshold value by the number of location point in analysis group Point, and the compression for skipping this group is directly entered analysis in next group of group；
Successively differentiate all location points in grouping, the motion state expression of calculating position point and this first two location point Formula, determine the location point whether be with this first two location point motion state homogeneity, thereby determine that the next position point be filtering, Still inflection point point is identified as.
It advantages of the present invention and has the active effect that
(1) present invention is the critical path identification of target correlation, and the motion profile of target in a period of time is regarded It is the extension of tight preceding state, therefrom can simplify the motion state that target mixes, identifies the key position point in motion state, and Target is described it as in the critical movements track of the period；
(2) for the method for the present invention when determining a reference value, sampling policy is that target state is relevant, is to utilize target Inequality is sought in the change in displacement amplitude of certain period and is obtained, and the scene of different type target of effectively coincideing namely target exist It is larger apart from benchmark value during high-speed cruising, it is on the contrary then value is smaller, it in this way can be accurately locating for strategic point Region, so that the description of final critical path is consistent with the motion profile height of target itself；
(3) the method for the present invention not only allows for the mobile factor of position when position dotted state identifies, 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 method for the present invention is in the practical application of location-based service, not only from operational efficiency, accuracy rate and compression ratio all bodies Showed unprecedented advantage, enable distributed Location Service Platform it is accurate, it is convenient, provide be suitble to mobile network environment Under trace playback service.
Detailed description of the invention
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 reference 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 is that the data pick-up of the embodiment of the present invention handles logical flow chart.
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 the spatial position RT-CLBS, detailed description this The embodiment of invention.The spatial position RT-CLBS service cloud platform is an open public service platform, it is desirable to provide position Being uniformly accessed into and servicing for service, hereinafter referred to as cloud platform are set, wherein the storage assembly of 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 key position point in certain period, describes the critical movements of target during this period of time Track generates the location point of the key features such as transregional, orientation deflection in motion process, and we term it key points.
As shown in Figure 1, the method that critical path is identified in a kind of location-based service of the present invention, including providing the system of location-based service One access and the cloud platform serviced and the location library for being stored with position data, defining critical path is target within certain period The set of key position point describes the critical movements track of target during this period of time, which is movement The location point for generating key feature in the process, specifically comprises the following steps:
Step 1, data retrieval: the rail out of searched targets in location library whole trace informations either certain time period Mark information contains all location informations of target uplink 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 data of the metadata retrieved are converted to by Gauss Kru&4&ger projection Longitude and latitude data are converted using Gauss-Ke Lvge map projection's algorithm to plane right-angle coordinate, are generated by plane coordinates formula Dimensionality reduction source data, specific conversion process are as described below:
Step 2.1, according to Gauss-Ke Lvge map projection from 0 degree of meridian every 6 degree through difference, be divided into from West to East Reel number is respectively labeled as 1,2,3 ... 60 bands by 60 points of bands；
This 60 points of bands are projected to the Elliptic Cylinder being transverse on earth ellipsoid by step 2.2, the Elliptic Cylinder surface Tangent line with spheroid surface is a warp, and referred to as central meridian provides central meridian two sides then according to projection condition Point in range projects on Elliptic Cylinder surface, thus obtain extensible plane right-angle coordinate, i.e. Gauss-Ke Lvge Figure projection；
Gauss Kru&4&ger projection has characteristics that
(1) the sub- warp in center and terrestrial equator are projected as straight line, and are the symmetry axis of projection；
(2) any angle on ellipsoid remains unchanged after projecting to plane；
(3) there is no length deformation after central meridian projection；
After step 2.3, above-mentioned point of band and Gauss-Ke Lvge map projection, the reel number range in China between 13~23, Plane right-angle coordinate, the coordinate of X-axis are all the natural value started by equator, and due to taking positive sign to the east of central meridian, to the west of Negative sign is taken, to avoid Y axis coordinate from negative value occur, so adding 500KM respectively in ordinate y value, and before ordinate Titled with reel number；
According to above-mentioned conversion, map upper warp and woof degree is the location point of { Lon:118 ° 10 ', Lat:24 ° 29 ' }, through Gauss-gram After Lv Ge map projection, plane coordinates is { X:2710562.99827803, Y:20619384.2129943 }, wherein two before Y value Position " 20 " represents this o'clock in the 20th projection zone；
Step 2.4 after calculating by above-mentioned Gauss-Ke Lvge map projection's algorithm, obtains the panel data collection of target S, panel data according to target integrate time number consecutively that location point in S reports as S1、S2、S3…Sn, wherein S1X axis coordinate Value is set as x1, Y axis coordinate value is set as y1；And so on, then SnX axis coordinate value be xn, Y axis coordinate yn, so it is found that other Location point is in S1After time, referred to as S1Postposition point, wherein S2For S1Next point；Likewise, S1Referred to as S2Preposition point, It is also tight preceding point；
Meanwhile defining dynamic array M1Key position point information is stored, and as final output result set, wherein number The structure of group element Data are as follows: { Lon longitude, Lat latitude, Date time ..., Stat location type, Drill: reduction node Number }
Wherein, Lon longitude, Lat latitude, the raw information that the Date time is location point；Stat is the type of location point, Value includes 0 and 1, respectively represents transregional point and inflexion point；Drill is the point and dynamic array M1In it is tight before the position that reduces between point Set a number；
Step 3, compression ratio r are calculated: metadata number is compared and can be pressed with expected output result set number Shrinkage
Wherein, s is the size of the panel data collection S of target, and m is the size of expected output result set M, and m is and application The variable element of environmental correclation；Ceil (o) indicates the smallest positive integral for being more than or equal to o；The cloud platform provides for mobile terminal Trace playback inquiry in period, according to the demand of actual scene, if m=20, can obtain r=n/20 according to above-mentioned formula, whenWhen, then it directly goes to step 7 and returns to metadata, do not do logical process；
Step 4, by way of across point sampling, calculate track apart from a reference value:
Step 4.1, panel data collection S and gained compression ratio r according to target, every r in the panel data collection S of target A point extracts a point and forms { S1, S1+r…S1+(m-1)rSet, and the distance of adjacent two o'clock is calculated, the method that distance calculates are as follows:
Wherein, (xi,yi) it is SiCoordinate, (xi+1,yi+1) it is Si+1Coordinate；
Step 4.2, the calculation method apart from a reference value are as follows:
Wherein, v indicates the number that step 3 is run, dirFor the resulting S of distance calculation formula in step 4iAnd Si+rBetween Distance, wherein [1, m] i ∈, for the high efficiency for taking into account data accuracy and calculating, j takes 2 in the present embodiment, and Δ d is indicated apart from base Quasi- value is equal to the adjacent samples point for being accurate to hundred apart from mean value；
Step 5, grouping and transregional key point identification: the data of above-mentioned dynamic array be it is variable, data source is in grouping 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 Data are grouped trajectory location points by apart from a reference value, then all data summations by domain grouping are initial data, use Above-mentioned is square side length to plane progress subregion apart from a reference value, and horizontal, the vertical seat with the maximum in each smallest square Mark while marking the subregion coordinate where each position data to identify the subregion, then by Time Continuous and subregion it is consistent One group of location point merger, and identifying time most preceding location point in grouping is transregional key point:
It is resulting apart from a reference value Δ d by step 4, in plane right-angle coordinate, with (0,0) for starting point, be with Δ d Side length carries out square subregion to plane, is (0,0), point (Δ d, subregion where Δ d) by the Labelling Regions where point (0,0) Labeled as (1,1), which is so that apart from benchmark, as side length, upper right boundary is solid line, the area that lower-left boundary is dotted line 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 sitting It falls in the subregion, and so on, location point (x is given belowd,yd) place subregion (Xd,Yd) calculation method:
According to above-mentioned formula, the location point in the panel data collection S of target can carry out to the conversion of subregion coordinate, and by the time The continuous and consistent location point of Labelling Regions is classified as one group, since target is that periodic location data report, so, the above-mentioned time Two location points of continuous representation are that tandem reports；
Every group of first location point is labeled as transregional location point, and the array element Data, Stat of construction location point 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 for 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 It is worth (such as 3), then the compression for the location point in group being all considered as key position point, and skipping this group is directly entered next group of group Interior analysis, the motion state expression formula of calculating position point and this 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 grouping, critical path in identification group, with gained first in step 5 For a grouping A, anticipation algorithm is further described, the location point being grouped in A is temporally successively labeled as from front to back A1、A2、A3..., and with (xi,yi) indicate AiCoordinate:
The initial reduction location point number drill of step 6.1, definition is 0, to describe dynamic array M1Middle adjacent position The location point number for actually including between point；
Step 6.2, setting subregion A (xa,ya) adjacent position point A1、A2, then A is connected1、A2VectorIt is sat in plane The motion state of system is marked as shown in Fig. 2, A1、A2Place straight line L expression formula are as follows: y=k (x-x1)+y1
Wherein, k is the slope of straight line L,As shown in figure 3, straight line L and the angle of X-axis are α, then tan α= k；
Step 6.3, along vectorDirection, with A2Make ray A` for origin, A` rotates counterclockwiseObtain ray L1, Middle ray L1Angle with X-axis is β, thenAzimuth reference value is set in the present embodiment
As shown in the angular relationship of Fig. 3 deflection reference line, according to trigonometric function induced formulas:
WhereinThen ray L1The expression formula of place straight line are as follows:
Similarly, ray L2The expression formula of place straight line are as follows:
Step 6.4 makees ray L1With the intersection point a of partition boundaries1With ray L2With the intersection point a of partition boundaries2, such as Fig. 4 plane Vector is 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 A2It is directed toward a1Vector,To connect A2It is directed toward a2 Vector；
Step 6.5 judges the mutual suitable counterclockwise relationship of two vectors, 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；
WhereinIt indicatesWithCross product；
Due to " A3?In anticipation region " be equivalent to "And", then A3With's Prejudge expression formula are as follows:AndAnd if only if A3WithAnticipation expression formula when being true, A3Symbol It closesAnticipation；
Step 6.6, A3WithAnticipation expression formula exist for true and be false two kinds of situations, the method specifically handled is such as Under:
If A3WithAnticipation expression formula when being true, filter A2Point, and add 1 for drill；If A3WithAnticipation table It is fictitious time up to formula, then regards A2Point is inflection point point, constructs A2The numerical value element Data of point, Stat are set to 1, Drill and are set to drill；By A2The numerical value element Data of point is as A1Next point be stored in dynamic array M1, finally resetting drill is 0；
After above-mentioned processing, continue to judge A4Whether meetAnticipation, and so on, until having determined all in grouping Location point；
Step 7, result merge and analyze again: obtaining the critical path of other groupings according to the method for step 6, temporally first The output result set M extracted for the first time is merged into afterwards1, remember M1Element number be m1；By the above-mentioned critical path result being respectively grouped Temporally front and back merges, and analyzes the quantity of output result set and the ratio of expected output result set, it is determined whether directly returns Processing result is returned, as shown in figure 5, carrying out processing analysis to result is extracted:
(1) when meeting exit criteriaWhen, terminate extraction process, and return to output result set M1；
(2) whenWhen, with dynamic array M1The middle maximum location point D of Drill value is end point, and location point D's is tight Preceding point is starting, and subset S is obtained from the panel data collection S of targettAnd both of the aforesaid location point is not included, 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, successively class It pushes away, when meeting exit criteriaWhen, terminate extraction process, and return to output result set 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 taken, until output result set M2When meeting exit criteria, terminate extraction process, and returns to output result set M2。
Of the invention focuses on: the critical path of target is identified, is retained by the maximization of its motion profile, and The number of non-key location point is attached in the attribute of key point to the size for reducing output result set, for this purpose, requiring first It can identify the distance variation of target movement；After having inequality distance, subregion could be carried out to motion process, calculating is effectively reduced Complexity；After subregion, the key position point in target motion process, critical path of the invention are extracted by anticipation algorithm Recognition methods is relevant apart from benchmark and the relevant deflection benchmark of business scenario using dbjective state, has in position service field There are very strong applicability and operability, efficiently solve the problem of dtmf distortion DTMF of location track on the whole, is guaranteeing the quality of data On the basis of, information redundancy is reduced, can effectively meet platform to mobile application and the efficiency and economy of location-based service are provided Property.
The above is only the preferable specific embodiments of the present invention, but scope of protection of the present invention is not limited thereto, any Those familiar with the art the variation that can readily occur in or replaces in the technical scope that the embodiment of the present invention discloses It changes, should all be included within the scope of the present invention.

Claims (3)

1. a kind of method for identifying critical path in location-based service, flat with the cloud of service including providing being uniformly accessed into for location-based service Platform and the location library for being stored with position data define the set that critical path is key position point of the target within certain period, It describes the critical movements track of target during this period of time, which is to generate key feature in motion process Location point, it is characterised in that include the following steps:
Step 1, data retrieval: believe from the track in whole trace informations either certain time period of searched targets in location library It ceases, all location informations of target uplink is contained in the location library, wherein the position data collection for meeting search condition is referred to as 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 set number can be obtained compression ratio
Wherein, s is the size of the panel data collection S of target, and m is the size of expected output result set M, and m is and application environment Relevant variable element；Ceil (o) indicates the smallest positive integral for being more than or equal to o；WhenWhen, then step 7 is leapt to, The metadata of return step 2；
Step 4, by way of across point sampling, calculate track apart from a reference value:
After calculating by above-mentioned Gauss-Ke Lvge map projection's algorithm, the panel data collection S of target is obtained, according to target flat Face data integrates time number consecutively that location point in S reports as S1、S2、S3…Sn, wherein S1X axis coordinate value be set as x1, Y-axis Coordinate value is set as y1；And so on, then SnX axis coordinate value be xn, Y axis coordinate yn, so it is found that other positions point is in S1When Between after, referred to as S1Postposition point, wherein S2For S1Next point；Likewise, S1Referred to as S2Preposition point, and it is tight before point； Define dynamic array M1Key position point information is stored, and as final output result set, the step 4 passes through across point sampling Mode, calculate track apart from a reference value, include the following steps:
Step 4.1, panel data collection S and gained compression ratio r according to target, every r point in the panel data collection S of target Extract a point composition { S1, S1+r…S1+(m-1)rSet, and the distance of adjacent two o'clock is calculated, the method that distance calculates are as follows:
Wherein, (xi,yi) it is SiCoordinate, (xi+1,yi+1) it is Si+1Coordinate；
Step 4.2, the calculation method apart from a reference value are as follows:
Wherein, v indicates to calculate the number of compression ratio r operation, dirFor the resulting S of distance calculation formula in step 4.1iAnd Si+rIt Between distance, wherein [1, m] i ∈, j is preset parameter, and Δ d indicates to be equal to the adjacent samples point for being accurate to hundred apart from a reference value Apart from mean value；
Step 5, grouping and the identification of transregional key point: trajectory location points are grouped apart from a reference value using above-mentioned, and are marked Knowing time most preceding location point in grouping is transregional key point；
Step 6, the calculating for organizing interior critical path: the motion state expression formula of calculating position point and this first two location point, really Whether the fixed location point is motion state homogeneity with the first two location point, and then obtains the critical path of the grouping；
Step 7, result merge and analyze again: the critical path of all groupings are obtained according to the method for step 6, by the pass of each grouping Temporally front and back merges key route result, and analyzes the quantity of output result set and the ratio of expected output result set, determines Whether processing result is directly returned.
2. identifying the method for critical path in a kind of location-based service according to claim 1, it is characterised in that point Group and the identification of transregional key point, specifically comprise the following steps:
The transverse and longitudinal coordinate of plane coordinate system is started from coordinate origin (0,0), is square side apart from a reference value using described It is long that subregion is carried out to plane, and the subregion is identified with the maximum horizontal, ordinate in each smallest square, while marking each Subregion coordinate where position data then by one group of Time Continuous and the consistent location point merger of subregion, and identifies in grouping Time most preceding location point is transregional key point.
3. identifying the method for critical path in a kind of location-based service according to claim 1, it is characterised in that the group Interior critical path calculates, and specifically comprises the following steps:
Location point in group is all considered as key position point if number is less than threshold value by the number of location point in analysis group, and The compression for skipping this group is directly entered next group of the interior analysis of group；
Successively all location points in differentiation grouping, the motion state expression formula of calculating position point and this first two location point, Determine the location point whether be with this first two location point motion state homogeneity, thereby determine that the next position point be filtering, also It is to identify as inflection point point.
CN201610842246.2A 2016-09-23 2016-09-23 The method of critical path is identified in a kind of location-based service Active CN106649450B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610842246.2A CN106649450B (en) 2016-09-23 2016-09-23 The method of critical path is identified in a kind of location-based service

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610842246.2A CN106649450B (en) 2016-09-23 2016-09-23 The method of critical path is identified in a kind of location-based service

Publications (2)

Publication Number Publication Date
CN106649450A CN106649450A (en) 2017-05-10
CN106649450B true CN106649450B (en) 2019-07-23

Family

ID=58852609

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610842246.2A Active CN106649450B (en) 2016-09-23 2016-09-23 The method of critical path is identified in a kind of location-based service

Country Status (1)

Country Link
CN (1) CN106649450B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112699979A (en) * 2021-01-04 2021-04-23 北京国腾联信科技有限公司 Cargo movement detection method and device and electronic equipment

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101900565A (en) * 2009-05-26 2010-12-01 南京敏思科技有限公司 Path determining method and device
CN103996292A (en) * 2014-05-29 2014-08-20 南京新奕天科技有限公司 Moving vehicle tracking method based on corner matching
CN104142156A (en) * 2014-08-26 2014-11-12 天津市普迅电力信息技术有限公司 Path navigation method
CN105556243A (en) * 2013-08-06 2016-05-04 高通股份有限公司 Method and apparatus for position estimation using trajectory

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8565783B2 (en) * 2010-11-24 2013-10-22 Microsoft Corporation Path progression matching for indoor positioning systems

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101900565A (en) * 2009-05-26 2010-12-01 南京敏思科技有限公司 Path determining method and device
CN105556243A (en) * 2013-08-06 2016-05-04 高通股份有限公司 Method and apparatus for position estimation using trajectory
CN103996292A (en) * 2014-05-29 2014-08-20 南京新奕天科技有限公司 Moving vehicle tracking method based on corner matching
CN104142156A (en) * 2014-08-26 2014-11-12 天津市普迅电力信息技术有限公司 Path navigation method

Also Published As

Publication number Publication date
CN106649450A (en) 2017-05-10

Similar Documents

Publication Publication Date Title
Liu et al. Adaptive douglas-peucker algorithm with automatic thresholding for AIS-based vessel trajectory compression
EP3443482B1 (en) Classifying entities in digital maps using discrete non-trace positioning data
CN105976376A (en) High resolution SAR image target detection method based on part model
CN106951455A (en) A kind of similar track analysis system and its analysis method
CN113239753A (en) Improved traffic sign detection and identification method based on YOLOv4
CN114707616A (en) Method, device and equipment for identifying incidental relationship between tracks
Lin et al. Noise filtering, trajectory compression and trajectory segmentation on GPS data
Jung et al. Real-time car tracking system based on surveillance videos
Eum et al. Vehicle detection from airborne LiDAR point clouds based on a decision tree algorithm with horizontal and vertical features
CN106649450B (en) The method of critical path is identified in a kind of location-based service
Bashir et al. An intelligent linear time trajectory data compression framework for smart planning of sustainable metropolitan cities
Pfoser et al. Trajectory indexing using movement constraints
Sharma et al. Nearest neighbour classification for trajectory data
Zygouras et al. Corridor learning using individual trajectories
CN109561384B (en) Wireless sensor network node positioning method under composite noise condition
CN114117260B (en) Spatiotemporal trajectory indexing and query processing method, device, equipment and medium
Delpriori et al. Efficient algorithms for accuracy improvement in mobile crowdsensing vehicular applications
Zhong et al. A trajectory data compression algorithm based on spatio-temporal characteristics
CN101986242B (en) Method for tracking target track in video compression coding process
CN113873423B (en) Terminal positioning method, device and system
Han et al. A parallel online trajectory compression approach for supporting big data workflow
US10989786B2 (en) Outdoor localization using hotspot labels
Luft et al. Content-based Image Retrieval for Map Georeferencing
Safra et al. Integrating data from maps on the world-wide web
Zhang et al. Online vehicle trajectory compression algorithm based on motion pattern recognition

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant