CN107247761A - Track coding method based on bitmap - Google Patents
Track coding method based on bitmap Download PDFInfo
- Publication number
- CN107247761A CN107247761A CN201710402219.8A CN201710402219A CN107247761A CN 107247761 A CN107247761 A CN 107247761A CN 201710402219 A CN201710402219 A CN 201710402219A CN 107247761 A CN107247761 A CN 107247761A
- Authority
- CN
- China
- Prior art keywords
- track
- bitmap
- space
- orbit segment
- coding method
- 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.)
- Granted
Links
Classifications
-
- 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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2453—Query optimisation
-
- 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
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
- G06F16/2237—Vectors, bitmaps or matrices
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Compression, Expansion, Code Conversion, And Decoders (AREA)
Abstract
The invention discloses a kind of track coding method based on bitmap, it is included under default dividing precision, space is divided into the subspace of multiple mesh approximation shapes, each mesh space obtains the steps such as a unique mark：The present invention is based on bitmap technology, the use of the great advantage of bitmap data structure is to save memory space and Computationally efficient, database sets up index using bitmap index field smaller to codomain radix and fixed, and calculating speed is fast, memory space is small；Bitmap technology can also greatly lift computational efficiency using the optimization of cpu instruction collection；The locus that historical trajectory data is included is fixed, but the value ranges types of space coordinate are floating numbers, and the present invention is encoded by designing, and makes the inquiry of track data and efficient track data analysis can be realized using the advantage of bitmap technology.
Description
Technical field
The present invention relates to computer big data process field, specially a kind of track coding method based on bitmap.
Background technology
The applications such as traffic administration, meteorological monitoring, mobile computing need to manage substantial amounts of space-time data, with mobile device
Popularization, the development of public supervision and management and perfect, mobile computing and location Based service occur in that development upsurge, location data
Increase, the research location data that rises to of positioning precision provides data basis, also form the track data accumulation of magnanimity,
For example, the order of magnitude of the average daily data volume (sampled point) of vehicle GPS ten million to hundred million, the storage volume of track data reaches
The PB orders of magnitude, therefore the analysis inquiry pressure of track data is big, it is necessary to feasible efficient query scheme.
The management and inquiry of position data can be divided into real-time query and the class of the inquiry of historical data two by scene, in history
In the query scheme of track, it is to be based on spatial database scheme to have one kind.Existing relevant database can be based on space querying
Plug-in unit realizes the effect of management space data, still, existing expansion plug-in unit scheme, and for example PostGIS is mainly for space
The calculating and inquiry of feature, are good at the inquiry for being converted to plane geometry relation, and the data produced as mobile device not only with
Spatial information, also temporal information, track be not preferably can using simple conversion as with the data of geometric data type specification,
The spatial index realized in expansion scheme also lacks the optimization of the inquiry problem for space-time pattern, such as space-time condition combination
Range query, to the support also imperfection of space-time data.
By the GPS sensor of mobile device or special targeting scheme (such as ship automatic identification system AIS), it
The position sampled point that produces, it is different from independent location point, with sequential relationship and space characteristics, also be adapted in management
Data mining is carried out applied to locus model, the law characteristic in analysis communications and transportation proposes that route recommendation etc. is applied, and rail
Mark does not have corresponding data type, but inquiry and the special design that is stored with to track data in conventional Database Systems
Index technology and system schema, such as SETI and TrajStore, in addition, extraction and the trace analysis algorithm of track data
Lack unified Environmental Support, limit the analysis application of magnanimity track data.
Hadoop, Spark are that the large-scale data risen calculates the distributed computing approach of analysis, based on distribution meter
Calculation realizes that the analysis of extensive space-time data is current popular problem, and such as SpatialHadoop, GeoSpark are to be based on
Distributed Computing Platform realizes the scheme of spatial data analysis, they realize distributed index scheme can support point, it is polygon
The parallel computation and inquiry of the space type data such as shape, but the inquiry of track data, the spatial data for calculating demand and routine
Difference, the data structure that they are supported lacks the direct support for inquiring about track problem.And on the other hand, also there is research to be based on
The kNN query schemes for such as track that distributed platform is realized, but scheme is often limited to solve an other trajectory problem.
The content of the invention
The invention aims to overcome a kind of above-mentioned not enough track coding method based on bitmap of offer.
Track coding method of the invention based on bitmap, comprises the following steps：
1st step：Under default dividing precision, space is divided into the subspace of multiple mesh approximation shapes, each net
Grid space obtains a unique mark；
2nd step：One track is split into continuous orbit segment, the orbit segment is traveled through one by one, calculated respectively
Go out with having the mesh space of common location relation in the mesh space that is obtained in the 1st step, so as to obtain the track corresponding one
Group Marking the cell sequence；
3rd step：By the one group of Marking the cell sequence obtained in the 2nd step, duplicate keys processing is removed；
4th step：The networking trellis coding sequence being removed in 3rd step after duplicate keys processing is converted into a bitmap
Formatted data.
2nd step specifically includes following steps：
21st step：For an orbit segment, all tracing points for belonging to the orbit segment are found out, if between tracing point
Interval exceeds the ultimate range set during grid division space, then inserting auxiliary point enables new orbit segment to be surrounded by region；
22nd step：According to each tracing point obtained in the 21st step, the tracing point is obtained by GeoHash algorithms and existed
Hash coding in space；
23rd step：Collect orbit segment and obtained all Hash coding is calculated in the 22nd step, be converted to globally unique, no
The integer mark repeated.
The track is the sequence of one continuous (x, y, t), and (x, y) is the point under space coordinate, and t is the sampling time,
Represent (xi, yi, ti) in tiThe position of moment mobile object is in (xi, yi), the track can be expressed as Trajectory=
[(x1, y1, t1) ..., (xi, yi, ti) ... (xn, yn, tn)](t1＜ ti＜ tn)；In some time range [ti, tj] in, rail
The relation of a certain componental movement process and mass motion process of mark can be represented with sub-trajectory.
The orbit segment be track in two groups of samples of arbitrary neighborhood into track paragraph, the sampled point quantity of track
N, then the orbit segment TS=Trajecotry (i, i+1) (1≤i ＜ n).
The sub-trajectory is the time range [t in definitioni, tj] in, by groups of samples into belong to track part transport
Dynamic process, the sampled point quantity of track is n, and sub-trajectory can be expressed as
Trajectory (i, j)=[(xi, yi, ti), (xi+1, yi+1, ti+1) ..., (xj, yj, tj)],1≤i≤j≤
n。
The step of carrying out relation judgement according to the bitmap formatted data of track is as follows：
61st step：Two spaces object, it is assumed that two tracks A, B, corresponding bitmap formatted data is respectively GEA、GEB；
62nd step：By corresponding bitmap formatted data GEA、GEBStep-by-step and computing；
63rd step：Calculate the quantity length of nonzero digit in bitmap structure after bit arithmetic；The overlapping detection of coding is to be based on track
The computing of coding, calculates GEBWith GEALength after bit arithmetic is exactly the quantity of the corresponding overlapping region in two tracks；When it
For 0 when, then completely misaligned, when it is not 0, then two codings are intersecting.
64th step：The corresponding codings of A and B find out overlapping region quantity in overlapping detection calculating, and its result is less than B sky
Between encode corresponding region quantity size, then two coding intersect；A and B overlapping detected value is corresponding with B space encoding
Region quantity is equal, then two encode the judgement included.
The subspace of the mesh approximation shape is under space coordinates, based on GeoHash algorithms by longitude and latitude
It is divided into the consistent numerical intervals of interval.
The preset model is either linear interpolation method or the knowledge to be moved according to the characteristics of motion and mobile object
Other non-directional paths such as storehouse, the knot for more meeting actual motion path is obtained using Map-Match algorithms in road network
Really.
The present invention is based on bitmap technology, the use of the great advantage of bitmap data structure is to save memory space to calculate with high
Efficiency, database sets up index using bitmap index field smaller to codomain radix and fixed, and calculating speed is fast, storage is empty
Between it is small；Bitmap technology can also greatly lift computational efficiency using the optimization of cpu instruction collection；The sky that historical trajectory data is included
Between position be fixed, but the value ranges types of space coordinate are floating numbers, and the present invention is encoded by designing, and makes track data
Inquiry the analysis of efficient track data can be realized using the advantage of bitmap technology.
Brief description of the drawings
Fig. 1 is schematic flow sheet of the present invention.
Embodiment
Below in conjunction with the accompanying drawings and embodiment further illustrates the present invention.
Embodiment：One track describes the historical movement information of mobile target, and track is continuous motion process, but
It is with event to be described and store by sampled point, the sampled point of track at least includes time and spatial information.
The sequence of one continuous (x, y, t), (x, y) is the point under space coordinate, and t is the sampling time, represents (xi, yi,
ti) in tiThe position of moment mobile object is in (xi, yi), it can be expressed as
Trajectory=[(x1, y1, t1) ..., (xi, yi, ti) ... (xn, yn, tn)](t1＜ ti＜ tn)。
The longer motion process in one section of track, it is in some time range [ti, tj] in, a certain componental movement mistake of track
The relation of journey and mass motion process can be represented with sub-trajectory.
Time range [ts of the track Trajectory in definitioni, tj] in, by groups of samples into the part for belonging to track
Motion process.The sampled point quantity of track is n, and sub-trajectory can be expressed as
Trajectory (i, j)=[(xi, yi, ti), (xi+1, yi+1, ti+1) ..., (xj, yj, tj)],1≤i≤j≤
n。
Existing system is generally using R- tree types index.Wherein, with minimum outsourcing rectangle (MBR) to any spatial data
Encirclement expression is carried out, the object of Anomalistic space shape is stored.But track is an approximate broken line, long and narrow shape of movement
Area relative to MBR regions can almost be ignored, therefore detect that the track found is unsatisfactory for inquiry by MBR space overlaps
The probability of condition is higher, reduces the efficiency of inquiry.Therefore, the present invention divides space into the region compared with small area first, draws
The method in point space has compared with more options, but divides space with data distribution correlation greatly, and division result is lack of consistency, example
Grid index is such as carried out based on preset parameter, or has being divided based on data rule for dynamic characteristic by Quad-tree
The method in region, but above method causes zoning inconsistent with granularity such as the change of spatial point, so as to same
The expression of one track can not unify.The present invention is based on
Division of the GeoHash algorithms to space, GeoHash be can to longitude [- 90,90] latitude [- 180,180] it
Between space coordinate carry out unified processing, its parameter is simple, the uniformity for easily keeping track to represent.
In track two groups of samples of arbitrary neighborhood into track paragraph (TS), the sampled point quantity of track is n, then TS=
Trajecotry (i, i+1) (1≤i ＜ n).
The sample frequency of track sampled point and sampling interval are inconsistent, when track two neighboring sampled point distance and
Sampling time is more than critical condition, in identification Trajectory Arithmetic, and the point sequence partition that be able to will be sampled according to this condition sets up independent
Track；When the conditions such as sampling interval are all under critical condition, the present invention will handle the distance and space zoning model of sampled point
The relation enclosed, it is adjacent continuous to make the zone number sequence that track is passed through.
The process that all regional sequences that track is passed through are calculated is referred to as track encryption algorithm.
A track Trajectory is given, its all sampled point is converted to the bit stored with bitmap data structure
Set, element therein is the numbering for the area of space that track is passed through, GE (trajectory)=[GIDi, GIDi+1,
......GIDj]。
It is as follows that track encodes corresponding algorithm：
1. under default dividing precision, space is divided into the subspace of multiple mesh approximation shapes, each grid is empty
Between obtain a unique mark.
2. a pair track, by traveling through the orbit segment that it is included, calculates the mesh space with being obtained in 1 respectively
In have the mesh space of common location relation, so as to obtain the corresponding one group of Marking the cell sequence in track.
3. by obtained in 2 one group of Marking the cell sequence, remove duplicate keys.
4. the networking trellis coding sequence handled in 3 is converted into a bitmap formatted data.
By above step, finally data are represented in bitmap format for track, the second step of journey processed above be calculate and
The committed step of processing, the problem of solving how to find correspondence zoning according to each sampled point, this process presses following step
Rapid processing, it returns in the region belonging to tracing point with integer coding.
1. for an orbit segment, finding out all tracing points for belonging to the orbit segment, drawn if being spaced to exceed between tracing point
The ultimate range set during subnetting grid space, then insert auxiliary point.
2. according to each tracing point obtained in 1, the Kazakhstan of the tracing point in space is obtained by GeoHash algorithms
Uncommon coding.
3. collect orbit segment calculates obtained all Hash coding in 2, globally unique, unduplicated integer is converted to
Mark.
Generally, the spatial dimension of each net region of division result is more than the distance between track sampled point,
Due to track the tracing point included spacing distance may beyond net region scope, in order that the sequence being converted to
It is spatially continuous to be listed in, and is determined by the model pre-set, by inserting auxiliary point, supplies the interval of an orbit segment,
After the conversion process to track is completed, by designing the basic operation based on this coded data, height can be progressively extended to
In level application, the bitmap formatted data encoded based on track, its basic calculating, which is operated, is：It is overlapping, intersect and comprising.
The step of carrying out relation judgement according to the bitmap formatted data of track is as follows：
1. two spaces object, it is assumed that two tracks A, B, corresponding bitmap formatted data is respectively GEA、 GEB。
2. by corresponding bitmap formatted data GEA、GEBStep-by-step and computing.
3. calculate the quantity length of nonzero digit in bitmap structure after bit arithmetic.
The overlapping detection of coding is the computing encoded based on track, calculates GEBWith GEALength after bit arithmetic is exactly two
The quantity of the corresponding overlapping region in track.It is when it is 0, then completely misaligned, when it is not 0, it is possible to judge two volumes
Code-phase is handed over.Similar, judgement can be extended to and intersected and inclusion relation.Defined according to spatial relationship, intersection refers to two spaces
Object A and B have common factor, but some region of B is not belonging to the region of A processes, for example：The corresponding codings of A and B are overlapping
Overlapping region quantity is found out in detection calculating, and its result is less than the B corresponding region quantity size of space encoding；And inclusion relation
Refer to that two spaces object has common factor, and one is completely included by another, for example：The sky of A and B overlapping detected value and B
Between to encode corresponding region quantity equal, it is therefore overlapping plus on the basis of surveying encoding, add once relation and judge i.e. achievable
Intersect and comprising judgement.
Above-described embodiment is preferably embodiment, but embodiments of the present invention are not by above-described embodiment of the invention
Limitation, other any Spirit Essences without departing from the present invention and the change made under principle, modification, replacement, combine, it is simple
Change, should be equivalent substitute mode, be included within protection scope of the present invention.
Claims (6)
1. a kind of track coding method based on bitmap, it is characterised in that comprise the following steps：
1st step：Under default dividing precision, space is divided into the subspace of multiple mesh approximation shapes, and each grid is empty
Between obtain a unique mark；
2nd step：One track is split into continuous orbit segment, the orbit segment traveled through one by one, calculate respectively with
There is the mesh space of common location relation in the mesh space obtained in 1st step, so as to obtain the corresponding networking in the track
Case marker knows sequence；
3rd step：By the one group of Marking the cell sequence obtained in the 2nd step, duplicate keys processing is removed；
4th step：The networking trellis coding sequence being removed in 3rd step after duplicate keys processing is converted into a bitmap format number
According to.
2. the track coding method according to claim 1 based on bitmap, it is characterised in that the 2nd step specifically include with
Lower step：
21st step：For an orbit segment, all tracing points for belonging to the orbit segment are found out, if being spaced between tracing point super
Go out the ultimate range set during grid division space, then inserting auxiliary point enables new orbit segment to be surrounded by region；
22nd step：According to each tracing point obtained in the 21st step, the tracing point is obtained in space by GeoHash algorithms
Hash coding；
23rd step：Collect orbit segment and obtained all Hash coding is calculated in the 22nd step, be converted to globally unique, unduplicated
Integer is identified.
3. the track coding method according to claim 2 based on bitmap, it is characterised in that the track is one continuous
(x, y, t) sequence, (x, y) is the point under space coordinate, and t is the sampling time, represent (xi, yi, ti) in tiMoment motive objects
The position of body is in (xi, yi), the track can be expressed as Trajectory=[(x1, y1, t1) ..., (xi, yi, ti) ...
(xn, yn, tn)](t1＜ ti＜ tn)；In some time range [ti, tj] in, a certain componental movement process of track and mass motion
The relation of process can be represented with sub-trajectory.
4. the track coding method according to claim 3 based on bitmap, it is characterised in that the orbit segment is in track
Two groups of samples of arbitrary neighborhood into track paragraph, the sampled point quantity of track is n, then the orbit segment TS=
Trajecotry (i, i+1) (1≤i ＜ n).
5. the track coding method according to claim 4 based on bitmap, it is characterised in that the sub-trajectory is in definition
Time range [ti, tj] in, by groups of samples into the componental movement process for belonging to track, the sampled point quantity of track is n,
Sub-trajectory can be expressed as Trajectory (i, j)=[(xi, yi, ti), (xi+1, yi+1, ti+1) ..., (xj, yj, tj)],1
≤ i ＜ j≤n.
6. the track coding method based on bitmap according to any one in claim 1-5, it is characterised in that according to rail
The step of bitmap formatted data of mark carries out relation judgement is as follows：
61st step：Two spaces object, it is assumed that two tracks A, B, corresponding bitmap formatted data is respectively GEA、GEB；
62nd step：By corresponding bitmap formatted data GEA、GEBStep-by-step and computing；
63rd step：Calculate the quantity length of nonzero digit in bitmap structure after bit arithmetic；The overlapping detection of coding is based on track coding
Computing, calculate GEBWith GEALength after bit arithmetic is exactly the quantity of the corresponding overlapping region in two tracks；When it is 0,
Then completely misaligned, when it is not 0, then two codings are intersecting.
64th step：The corresponding codings of A and B find out overlapping region quantity in overlapping detection calculating, and its result is less than B space encoding
Corresponding region quantity size, then two encode intersection；A and B overlapping detected value region quantity corresponding with B space encoding
Equal, then two encode the judgement included.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710402219.8A CN107247761B (en) | 2017-06-01 | 2017-06-01 | Track coding method based on bitmap |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710402219.8A CN107247761B (en) | 2017-06-01 | 2017-06-01 | Track coding method based on bitmap |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107247761A true CN107247761A (en) | 2017-10-13 |
| CN107247761B CN107247761B (en) | 2021-10-15 |
Family
ID=60017796
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710402219.8A Active CN107247761B (en) | 2017-06-01 | 2017-06-01 | Track coding method based on bitmap |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107247761B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109815993A (en) * | 2019-01-03 | 2019-05-28 | 西北大学 | Region Feature Extraction, Database and crossing recognition methods based on GPS track |
| CN110704388A (en) * | 2018-07-10 | 2020-01-17 | 南京航空航天大学 | SETI index implementation technology based on Berkeley DB storage management |
| CN112041210A (en) * | 2019-10-23 | 2020-12-04 | 北京航迹科技有限公司 | System and method for autonomous driving |
| CN113298954A (en) * | 2021-04-13 | 2021-08-24 | 中国人民解放军战略支援部队信息工程大学 | Method and device for determining and navigating movement track of object in multi-dimensional variable-granularity grid |
| CN113688193A (en) * | 2020-05-19 | 2021-11-23 | 北京京东振世信息技术有限公司 | Track data storage and indexing method and device, electronic equipment and readable medium |
| CN114238384A (en) * | 2022-02-24 | 2022-03-25 | 阿里云计算有限公司 | Area positioning method, device, equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102646070A (en) * | 2012-02-29 | 2012-08-22 | 武汉大学 | Space-time trajectory data storage method based on area |
| RU2013139845A (en) * | 2013-08-28 | 2015-03-10 | Андрей Витальевич Фрейдман | METHOD FOR TRANSFORMING AN IMAGE IN A SOUND IMAGE |
| CN104520732A (en) * | 2012-02-10 | 2015-04-15 | Isis创新有限公司 | Method of locating sensor and related apparatus |
| US9128973B1 (en) * | 2011-09-29 | 2015-09-08 | Emc Corporation | Method and system for tracking re-sizing and re-creation of volumes using modification time |
| US20150363508A1 (en) * | 2014-06-17 | 2015-12-17 | Naveen NANDAN | Grid-based analysis of geospatial trajectories |
| CN105258704A (en) * | 2014-06-16 | 2016-01-20 | 中国科学院沈阳自动化研究所 | Multi-scale space-time hot point path detection method based on rapid road network modeling |
-
2017
- 2017-06-01 CN CN201710402219.8A patent/CN107247761B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9128973B1 (en) * | 2011-09-29 | 2015-09-08 | Emc Corporation | Method and system for tracking re-sizing and re-creation of volumes using modification time |
| CN104520732A (en) * | 2012-02-10 | 2015-04-15 | Isis创新有限公司 | Method of locating sensor and related apparatus |
| CN102646070A (en) * | 2012-02-29 | 2012-08-22 | 武汉大学 | Space-time trajectory data storage method based on area |
| RU2013139845A (en) * | 2013-08-28 | 2015-03-10 | Андрей Витальевич Фрейдман | METHOD FOR TRANSFORMING AN IMAGE IN A SOUND IMAGE |
| CN105258704A (en) * | 2014-06-16 | 2016-01-20 | 中国科学院沈阳自动化研究所 | Multi-scale space-time hot point path detection method based on rapid road network modeling |
| US20150363508A1 (en) * | 2014-06-17 | 2015-12-17 | Naveen NANDAN | Grid-based analysis of geospatial trajectories |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110704388A (en) * | 2018-07-10 | 2020-01-17 | 南京航空航天大学 | SETI index implementation technology based on Berkeley DB storage management |
| CN109815993A (en) * | 2019-01-03 | 2019-05-28 | 西北大学 | Region Feature Extraction, Database and crossing recognition methods based on GPS track |
| CN109815993B (en) * | 2019-01-03 | 2023-05-23 | 西北大学 | GPS track-based regional feature extraction, database establishment and intersection identification method |
| CN112041210A (en) * | 2019-10-23 | 2020-12-04 | 北京航迹科技有限公司 | System and method for autonomous driving |
| WO2021077313A1 (en) * | 2019-10-23 | 2021-04-29 | Beijing Voyager Technology Co., Ltd. | Systems and methods for autonomous driving |
| CN112041210B (en) * | 2019-10-23 | 2023-10-31 | 北京航迹科技有限公司 | System and method for autopilot |
| CN113688193A (en) * | 2020-05-19 | 2021-11-23 | 北京京东振世信息技术有限公司 | Track data storage and indexing method and device, electronic equipment and readable medium |
| CN113688193B (en) * | 2020-05-19 | 2024-05-17 | 北京京东振世信息技术有限公司 | Track data storage and indexing method and device, electronic equipment and readable medium |
| CN113298954A (en) * | 2021-04-13 | 2021-08-24 | 中国人民解放军战略支援部队信息工程大学 | Method and device for determining and navigating movement track of object in multi-dimensional variable-granularity grid |
| CN113298954B (en) * | 2021-04-13 | 2022-11-22 | 中国人民解放军战略支援部队信息工程大学 | Method and device for determining and navigating movement track of object in multi-dimensional variable-granularity grid |
| CN114238384A (en) * | 2022-02-24 | 2022-03-25 | 阿里云计算有限公司 | Area positioning method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107247761B (en) | 2021-10-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107247761A (en) | Track coding method based on bitmap | |
| CN105243128B (en) | A kind of user behavior method of trajectory clustering based on data of registering | |
| CN106931974B (en) | Method for calculating personal commuting distance based on mobile terminal GPS positioning data record | |
| CN110427360B (en) | Processing method, processing device, processing system and computer program product of trajectory data | |
| CN104050817B (en) | Speed limiting information base generation and speed limiting information detection method and system | |
| Li et al. | T-DesP: Destination prediction based on big trajectory data | |
| CN107291842A (en) | The track querying method encoded based on track | |
| CN106202335B (en) | A kind of traffic big data cleaning method based on cloud computing framework | |
| Huang et al. | Survey on vehicle map matching techniques | |
| CN103383682A (en) | Geographic coding method, and position inquiring system and method | |
| CN107741982B (en) | Coordinate and administrative region matching system and method | |
| CN108595608B (en) | Road network communication scene oriented neighboring object index query method | |
| CN106408124A (en) | Moving path hybrid forecasting method oriented to data sparse environment | |
| CN110473251B (en) | Self-defined range spatial data area statistical method based on grid spatial index | |
| CN108510008B (en) | Road network extraction method based on floating car track point spatial relationship and distribution | |
| CN106991149B (en) | Massive space object storage method fusing coding and multi-version data | |
| CN102567497A (en) | Inquiring method of best matching with fuzzy trajectory problems | |
| CN111291776A (en) | Channel information extraction method based on crowd-sourced trajectory data | |
| CN111353008A (en) | Beidou position stream data real-time space aggregation method | |
| CN116860905A (en) | Space unit coding generation method of city information model | |
| Cho et al. | A GPS trajectory map-matching mechanism with DTG big data on the HBase system | |
| Ai et al. | A map generalization model based on algebra mapping transformation | |
| Yin et al. | Efficient trajectory compression and range query processing | |
| CN105096589A (en) | Method of selecting representative node in traffic road, system and client | |
| CN110909260B (en) | Vehicle display method, device, computer readable storage medium and computer equipment |
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 |