CN108536813A - Track querying method, electronic equipment and storage medium - Google Patents
Track querying method, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN108536813A CN108536813A CN201810303902.0A CN201810303902A CN108536813A CN 108536813 A CN108536813 A CN 108536813A CN 201810303902 A CN201810303902 A CN 201810303902A CN 108536813 A CN108536813 A CN 108536813A
- Authority
- CN
- China
- Prior art keywords
- point
- query
- track
- candidate tracks
- query point
- 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
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention provides a kind of track querying method, the method includes：Obtain the query set for the description for including each query point；The description of each query point is converted into the corresponding theme probability distribution with position and time tag of each query point；Each query point is scanned for matching, searches the candidate tracks collection of the query set by the corresponding theme probability distribution based on each query point with the semantic track data collection in database；Candidate tracks collection based on the query set, the candidate tracks for calculating the query set and the query set are concentrated at a distance from each candidate tracks, according to apart from size, concentrate each candidate tracks to be ranked up the candidate tracks of the query set；According to the candidate tracks collection after sequence, result is exported to user.The present invention also provides a kind of electronic equipment and storage mediums.The present invention can improve retrieval precision.
Description
Technical field
The present invention relates to a kind of data query field more particularly to track querying method, electronic equipment and storage mediums.
Background technology
It is different from traditional mobile object space-time track (Spatio-temporal Trajectory), semantic track data
Not only contain having time, spatial information, also contain abundant user behavior information：People do it is being thought, experience, pass through
Different media means such as by modes such as text, picture, videos, constitute a series of rail of behaviors in the expression of different location
Mark.Therefore, the relevant technology in semantic track not only facilitates solving road congestion problems with research, improves out line efficiency, guarantee
Traffic safety, and have important society and economic value, existing skill to saving the energy, optimization traffic quality and resource distribution
Semantic track in art indicates retrieval of the usual user based on text similarity, focuses on the difference of " shape " between text, such as logical
Semantic track inquiry in the prior art is crossed, " drinking coffee " is considered wide of the mark, Wu Fajian with tracing point description " Startbuck "
Rope arrives, and so relevant track of theme also can not just retrieve, and reduce query accuracy.
Invention content
In view of the foregoing, it is necessary to a kind of track querying method, electronic equipment and storage medium are provided, retrieval can be improved
Precision.
A kind of track querying method, the method includes：
Obtain the query set for the description for including each query point；
The description of each query point is converted into the corresponding theme probability with position and time tag of each query point
Distribution；
Corresponding theme probability distribution based on each query point, by the semantic track number in each query point and database
It scans for matching according to collection, searches the candidate tracks collection of the query set；
Candidate tracks collection based on the query set, the candidate tracks for calculating the query set and the query set are concentrated often
The distance of a candidate tracks concentrates each candidate tracks to be ranked up the candidate tracks of the query set according to apart from size；
According to the candidate tracks collection after sequence, result is exported to user.
According to the preferred embodiment of the present invention, it includes position coordinates and theme that the semanteme track data, which concentrates each tracing point,
Distributed intelligence, based on each tracing point include position coordinates and theme distribution information establish include space layer and subject layer layering
Index structure, wherein space layer establish index structure using quaternary tree, for each leafy node indicate multiple tracing points,
Subject layer establishes the corresponding LSH index structures of each leafy node based on position sensing Hash.
According to the preferred embodiment of the present invention, the corresponding theme probability distribution based on each query point is looked into each
It askes point to scan for matching with the semantic track data collection in database, the candidate tracks collection for searching the query set includes：
Corresponding theme probability distribution based on each query point, by the semantic track number in each query point and database
It scans for matching according to collection, searches the candidate tracks of each query point, the candidate tracks of each query point are determined as described look into
Ask the candidate tracks collection of collection；
The wherein corresponding theme probability distribution based on each query point, by the semantic rail in each query point and database
Mark data set scans for matching, and the candidate tracks of one query point q of inquiry include：
Based on the corresponding theme probability distribution of query point q, the leafy node of quaternary tree, obtains preferential team described in recursive traversal
Row, the Priority Queues are ranked up according to the ascending order of mdist (q, N), and the mdist (q, N) indicates query point q and leaf knot
The minimum range for multiple tracing points that point N is indicated；
Each leafy node in the Priority Queues is traversed successively, and each leaf is traversed using multiprobe LSH index technologies
The corresponding LSH index structures of child node obtain the candidate tracks point of the query point, will include the candidate tracks of the query point
Candidate tracks of the track of point as the query point, and using the candidate tracks of the query point as the candidate of the query set
A part for track.
According to the preferred embodiment of the present invention, the calculation formula of the mdist (q, N) is as follows：
Mdist (q, N)=λ Ds(q,N)+(1-λ)·DT(q,N)；
Wherein DS(q, N) is the minimum boundary matrix N .rect based on leafy node N, from query point q to leafy node N
Minimum space length, DT(q, N) from q to the leafy node N the multiple tracing points indicated minimum theme distance.
According to the preferred embodiment of the present invention, one is concentrated in the candidate tracks for calculating the query set Q and query set Q
Candidate tracks apart from when, including：
Calculate each query point in the candidate tracks Tr at a distance from each tracing point；
According to each query point at a distance from each tracing point, calculated in the candidate tracks Tr each query point with it is described
The distance of candidate tracks Tr；
According to each query point at a distance from the candidate tracks Tr, the query set and the candidate tracks Tr's are calculated
Distance.
It is described to calculate each query point and each tracing point in the candidate tracks Tr according to the preferred embodiment of the present invention
Distance includes：
A query point q containing text entry q.W and geographical location q.l is given, from a tracing point p to query point q
Distance can according between tracing point p to query point q spatial proximity and topic relativity measure, calculation formula is as follows：
D (q, p)=λ DS(q,p)+(1-λ)·DT(q, p), wherein λ ∈ [0,1] are that user specifies parameter to be used to adjust
Save the weight of spatial proximity and topic similarity；DS(q, p) is space Euclidean distance；DT(q, p) is represented between q and p text entries
Theme distance.
According to the preferred embodiment of the present invention, each query point of basis in candidate tracks at a distance from each tracing point,
Calculate each query point includes at a distance from the candidate tracks Tr：
Given an a query point q and track Tr, one of tracing point p ∈ Tr, for other any in the track
For one tracing point p', there is d (q, p)≤d (q, p'), then tracing point p is expressed as in the track and the maximally related rails of query point q
Mark point Tr.MRP (q) is then indicated as the query point the distance between from maximally related tracing point Tr.MRP (q) to tracing point q
To the distance of track.
According to the preferred embodiment of the present invention, each query point of basis is at a distance from the candidate tracks, described in calculating
Query set includes at a distance from the candidate tracks Tr：
The given inquiry Q={ q for including m inquiry point set1,q2,Λ,qmAnd a track Tr, Q is to track Tr's for inquiry
Distance DQ(Tr) it is each query point qi(i ∈ [1, m]) arrives the sum of the distance of track Tr, calculates as follows：
The most related point set MRPs of each query point is formed the most related point set Tr.MRPs (Q) of the inquiry in inquiry.
A kind of electronic equipment, the electronic equipment include memory and processor, and the memory is for storing at least one
A instruction, the processor is for executing at least one instruction to realize that any one of any embodiment track is inquired
Method.
A kind of computer readable storage medium, the computer-readable recording medium storage has at least one instruction, described
Any one of any embodiment track querying method is realized at least one instruction when being executed by processor.
As can be seen from the above technical solutions, the present invention obtains the query set for the description for including each query point；Pass through base
In the similarity measurements flow function of theme distribution, by the description of each query point be converted to each query point it is corresponding with position and
The theme probability distribution of time tag；Corresponding theme probability distribution based on each query point, by each query point and data
Semantic track data collection in library scans for matching, and searches the candidate tracks collection of the query set；Based on the query set
Candidate tracks collection, the candidate tracks for calculating the query set and the query set are concentrated at a distance from each candidate tracks, according to away from
From size, the candidate tracks collection of the query set is ranked up；According to the candidate tracks collection after sequence, result is exported to use
Family.The present invention indicates that model indicates the tracing point in query point and database using semantic track, by tracing point and query point
Be the theme probability distribution, the i.e. a series of theme probability distribution with position and time tag are converted in text description so that energy
It enough more fully understands the intrinsic meaning of text description, and is closed by the similarity measurements based on theme distribution to characterize the semantic of them
Connection, to improve retrieval precision.Therefore, the present invention can be inquired based on the relevant track of theme, to improve retrieval precision.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is the flow chart of the preferred embodiment of track querying method of the present invention.
Fig. 2 is the functional block diagram of the preferred embodiment of track inquiry unit of the present invention.
Fig. 3 is the structural schematic diagram of the preferred embodiment of electronic equipment at least one example of the present invention.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, below in conjunction with the accompanying drawings and specific real
Applying mode, the present invention is described in further detail.
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
The every other embodiment that member is obtained without making creative work should all belong to the model that the present invention protects
It encloses.
Term " first ", " second " and " third " in description and claims of this specification and above-mentioned attached drawing etc. is
For distinguishing different objects, not for description particular order.In addition, term " comprising " and their any deformations, it is intended that
Non-exclusive include in covering.Such as process, method, system, product or the equipment for containing series of steps or unit do not have
It is defined in the step of having listed or unit, but further includes the steps that optionally not listing or unit, or further include optionally
For the intrinsic other steps of these processes, method, product or equipment or unit.
As shown in Figure 1, being the flow chart of the first preferred embodiment of track querying method of the present invention.According to different need
It asks, the sequence of step can change in the flow chart, and certain steps can be omitted.
S10, acquisition include the query set Q of the description of each query point.
In the alternative embodiment of the present invention, the query set Q includes at least one query point.For example, user is in user
The description with multiple positions inputted on interface, needs the data for inquiring the profile matching of the multiple position, one of them
Position is described as a query point, for example, the B companies under safety scientific ＆ technical corporation financial product, then can in the query set Q
To include two query points, query point one：Description, query point two to safety scientific ＆ technical corporation：Description to B companies.
S11, the description of each query point is converted into the corresponding theme with position and time tag of each query point
Probability distribution.
Preferably, each query point is made of text description.The text description includes position and time tag.It utilizes
Semantic track indicates that model indicates the corresponding theme probability distribution with position and time tag of each query point.
Specifically, the semantic track indicates that model is as follows：Each query point is one and is made of n spatial key
Text W, Z={ z1,z2,…,zmBe the theme collection, the theme collection is used to indicate theme belonging to n spatial key, then W pairs
The probability topic distribution TD answeredWThe middle each theme TD of correspondenceW[zi] calculation formula it is as follows：
Wherein Nw∈(WIWz)Indicate that the number of keyword in W, α indicate symmetrical border, be usually arranged as 0.1, | W | it indicates in W
The number of keyword, | Z | indicate theme number in total.
S12, the corresponding theme probability distribution based on each query point, by the semantic rail in each query point and database
Mark data set scans for matching, and searches the candidate tracks collection of the query set Q.
Preferably, it includes position coordinates and theme distribution information that the semantic track data, which concentrates each tracing point, is based on
Each tracing point includes position coordinates and the foundation of theme distribution information includes space layer and the hierarchical index structure of subject layer.Wherein
Space layer establishes index structure using quaternary tree, to achieve the purpose that in space layer Fast Convergent, and in four forks of space layer
Each leafy node of tree indicates multiple tracing points, for multiple tracing points that each leafy node indicates, is established in subject layer
The corresponding LSH index structures of each leafy node based on position sensing Hash, in this way by using hash function by each leaf
Similar tracing point is mapped to same Hash bucket in multiple tracing points that child node indicates.
Specifically, quaternary tree index is the tree construction that location information recurrence is divided into different levels.By known range
Space is divided into four equal subspaces, and such recurrence is gone down, until the level of tree reaches certain depth or meets certain
Stop segmentation after it is required that.Quaternary tree it is relatively simple for structure, and when spatial data object distribution it is relatively uniform when, have compare
High spatial data is inserted into and search efficiency.Tracing point is stored on leafy node, and intermediate node and root node do not store
Tracing point.
The corresponding theme probability distribution of each query point is preferably based on to scan for matching with semantic track data collection,
The candidate tracks point of each query point is obtained, using the track of the candidate tracks point comprising each query point as each query point
Candidate tracks, using the candidate tracks of each query point as the candidate tracks of the query set Q.It is based on each query point pair in this way
The theme probability distribution answered calculates the similarity that each query point concentrates tracing point with semantic track data, is based on theme distribution
Similarity characterize query point and semantic track data concentrates semantic association between tracing point, enabling more fully understand text
The intrinsic meaning of this description.
Preferably, it includes following to search the candidate tracks of a query point q (the query point q is any one query point)
Step：
(1), it is based on the corresponding theme probability distribution of query point q, the leafy node of quaternary tree described in recursive traversal obtains excellent
First queue, the Priority Queues are ranked up according to the ascending order of mdist (q, N), and the mdist (q, N) indicates query point q and leaf
The minimum range for multiple tracing points that child node N is indicated, mdist (q, N) is smaller, and query point q and leafy node N indicates multiple
The similarity of tracing point is higher.
Preferably, the calculation formula of the mdist (q, N) is as follows：
Mdist (q, N)=λ Ds(q,N)+(1-λ)·DT(q, N),
Wherein λ ∈ [0,1] are weight parameter, DTThe specific formula for calculation of (q, N) is：
Wherein DS(q, N) is the minimum boundary matrix N .rect based on leafy node N, from query point q to leafy node N
Minimum space length；DT(q, N) is the minimum theme distance of the multiple tracing points indicated from q to the leafy node N.
||TDq, N.o | | it is the theme in space and inquires
Euclidean distance between the theme distribution of point q to anchor point N.o.
(2), each leafy node in the Priority Queues is traversed successively, utilizes multiprobe LSH indexes (Multi-
Probe LSH Indexing) the corresponding LSH index structures of each leafy node of technology traversal, obtain the candidate of the query point
Tracing point looks into the track of the candidate tracks point comprising the query point as the candidate tracks of the query point, and by described
A part of the candidate tracks of inquiry point as the candidate tracks of the query set Q.
For example, there are two leafy node, the first node and the second node, the sequence in Priority Queues is：Second node,
First node.The tracing point under second node is traversed first with multiprobe LSH index technologies, then is searched under the first node
Tracing point.
Specifically, the multiprobe LSH index technologies utilize detection sequence (carefully derived probing
Sequence), obtain and the approximate multiple Hash buckets of query point.According to the property of LSH, if we understand and query point q phases
Close data are no and query point q is mapped in the same bucket, it is probably mapped in the bucket of surrounding (i.e. two buckets
Cryptographic Hash there was only little difference), target in this way be to position these buckets closed on, search neighbour's data to increase
Chance.
Each query point is inquired according to above-mentioned steps (1), (2), obtains each query point in the query set Q
Candidate tracks.
By above-mentioned implementation, it is based on the corresponding theme probability distribution of each query point, calculates each query point and semantic rail
The similarity of tracing point in mark data set concentrates rail to characterize query point based on the similarity of theme distribution with semantic track data
Semantic association between mark point, enabling more fully understand the intrinsic meaning of text description.For example query specification " drinks coffee in this way
Coffee " and tracing point description " Startbuck " will be considered related because of its similar theme distribution.Inquiry can be made more acurrate in this way.Example
As query specification " drinking coffee " and tracing point description " Startbuck " will be considered related because of its similar theme distribution.Such energy
Keep inquiry more acurrate.
S13, the candidate tracks collection based on the query set Q, calculate the candidate rail of the query set Q and the query set Q
Mark concentrates the distance of each track, according to apart from size, concentrates each candidate tracks to carry out the candidate tracks of the query set Q
Sequence.
Specifically, semantic track data collection a τ, an a series of limited theme collection Z, including query points are looked into are given
Ask collection Q, shaping variable k that a user specifies (k ＜ | τ |), the then similar inquiry in semantic track that a user oriented is intended to
(User-oriented Trajectory Similarity Query, UTSQ), returns to independent k track from τ, and this k
Trajectory distance query set Q has the distance D of top-k minimumsQ(Tr)。
Preferably, the candidate tracks for calculating the query set Q and query set Q concentrate any one candidate tracks
The distance of Tr includes：
(1) calculate each query point in the candidate tracks Tr at a distance from each tracing point.
Specifically, give a query point q containing text entry q.W and geographical location q.l, from a tracing point p to
Its distance can according between them spatial proximity and topic relativity measure, specific formula for calculation is as follows：
D (q, p)=λ DS(q,p)+(1-λ)·DT(q, p),
Wherein, λ ∈ [0,1] are that user specifies parameter to be used to adjust the weight of spatial proximity and topic similarity；DS
(q, p) is space Euclidean distance, herein equally using the sigmoid function specifications distance between section [0,1]；DT(q, p),
It is DTThe simplification of (q.W, p.W) represents the theme distance between q and p text entries.
(2) according to each query point at a distance from each tracing point, calculated in the candidate tracks Tr each query point with
The distance of the candidate tracks.
Specifically, an a query point q and track Tr is given, if one of tracing point p ∈ Tr, for the track
In for other any one tracing point p', we have d (q, p)≤d (q, p'), then tracing point p can be expressed as in the track
With the maximally related tracing points of query point q (Most Relevant Point, MRP), it is defined as Tr.MRP (q).Then from most reference point
The distance between Tr.MRP (q) to tracing point q is indicated as the query point to the distance of track, under specifically can define：
Dmrp(q, Tr)=minp∈TrD (q, p),
(3) query set and the candidate tracks are calculated at a distance from the candidate tracks Tr according to each query point
The distance of Tr.
Specifically, the query set Q={ q for including m query point are given1,q2,…qmAnd a track Tr, we define and look into
Ask the distance D of collection Q to track TrQ(Tr) it is each query point qi(i ∈ [1, m]) arrives the sum of the distance of track Tr, calculates as follows：
It can be seen that the most related point set MRPs of each query point is formed the most related point set of the inquiry in inquiry,
Tr.MRPs (Q), therefore the most related point set MRPs for finding a query set Q can be broken down into search and each be inquired in the inquiry
The most reference point MRP of point.
S14 exports result to user according to the candidate tracks collection after sequence.
Preferably, show on a user interface sequence after candidate tracks collection, the candidate tracks collection after the sequence be by
Range is from being from small to large ranked up.Maximally related result can thus be shown up front so that user is intuitive to see
Maximally related query result.
The present invention is converted to each look by the similarity measurements flow function based on theme distribution, by the description of each query point
Ask the corresponding theme probability distribution with position and time tag of point, the corresponding theme probability based on each query point point
Each query point is scanned for matching, searches the candidate rail of the query set Q by cloth with the semantic track data collection in database
Mark collection, based on the candidate tracks collection of the query set Q, the candidate tracks for calculating the query set Q and query set Q are concentrated often
The distance of a track is ranked up the candidate tracks collection of the query set Q, according to apart from size according to the candidate after sequence
Track collection exports result to user.The present invention indicates that model indicates the tracing point in query point and database using semantic track,
Tracing point and the description of the text of query point are converted into be the theme probability distribution, the i.e. a series of master with position and time tag
Inscribe probability distribution, enabling more fully understand text description intrinsic meaning, and by the similarity based on theme distribution come
The semantic association for characterizing them, to improve retrieval precision.
As shown in Fig. 2, the functional block diagram of the first preferred embodiment of track inquiry unit of the present invention.The track inquiry
Device 2 includes, but are not limited to one or more following module：Acquisition module 20, conversion module 21, searching module 22, sequence
Module 23 and output module 24.The so-called unit of the present invention refer to it is a kind of can be performed by the processor of track inquiry unit 2
And the series of computation machine program segment of fixed function can be completed, storage is in memory.Function about each unit will
It is described in detail in subsequent embodiment.
The acquisition module 20 obtains the query set Q for the description for including each query point.
In the alternative embodiment of the present invention, the query set Q includes at least one query point.For example, user is in user
The description with multiple positions inputted on interface, needs the data for inquiring the profile matching of the multiple position, one of them
Position is described as a query point, for example, the B companies under safety scientific ＆ technical corporation financial product, then can in the query set Q
To include two query points, query point one：Description, query point two to safety scientific ＆ technical corporation：Description to B companies.
The conversion module 21 by the description of each query point be converted to each query point it is corresponding have position and time
The theme probability distribution of label.
Preferably, each query point is made of text description.The text description includes position and time tag.It utilizes
Semantic track indicates that model indicates the corresponding theme probability distribution with position and time tag of each query point.
Specifically, the semantic track indicates that model is as follows：Each query point is one and is made of n spatial key
Text W, Z={ z1,z2,…,zmBe the theme collection, the theme collection is used to indicate theme belonging to n spatial key, then W pairs
The probability topic distribution TD answeredWThe middle each theme TD of correspondenceW[zi] calculation formula it is as follows：
Wherein Nw∈(WIWz)Indicate that the number of keyword in W, α indicate symmetrical border, be usually arranged as 0.1, | W | it indicates in W
The number of keyword, | Z | indicate theme number in total.
Corresponding theme probability distribution of the searching module 22 based on each query point, by each query point and database
In semantic track data collection scan for matching, search the candidate tracks collection of the query set Q.
Preferably, it includes position coordinates and theme distribution information that the semantic track data, which concentrates each tracing point, is based on
Each tracing point includes position coordinates and the foundation of theme distribution information includes space layer and the hierarchical index structure of subject layer.Wherein
Space layer establishes index structure using quaternary tree, to achieve the purpose that in space layer Fast Convergent, and in four forks of space layer
Each leafy node of tree indicates multiple tracing points, for multiple tracing points that each leafy node indicates, is established in subject layer
The corresponding LSH index structures of each leafy node based on position sensing Hash, in this way by using hash function by each leaf
Similar tracing point is mapped to same Hash bucket in multiple tracing points that child node indicates.
Specifically, quaternary tree index is the tree construction that location information recurrence is divided into different levels.By known range
Space is divided into four equal subspaces, and such recurrence is gone down, until the level of tree reaches certain depth or meets certain
Stop segmentation after it is required that.Quaternary tree it is relatively simple for structure, and when spatial data object distribution it is relatively uniform when, have compare
High spatial data is inserted into and search efficiency.Tracing point is stored on leafy node, and intermediate node and root node do not store
Tracing point.
The corresponding theme probability distribution of each query point is preferably based on to scan for matching with semantic track data collection,
The candidate tracks point of each query point is obtained, using the track of the candidate tracks point comprising each query point as each query point
Candidate tracks, using the candidate tracks of each query point as the candidate tracks of the query set Q.It is based on each query point pair in this way
The theme probability distribution answered calculates the similarity that each query point concentrates tracing point with semantic track data, is based on theme distribution
Similarity characterize query point and semantic track data concentrates semantic association between tracing point, enabling more fully understand text
The intrinsic meaning of this description.
Preferably, the searching module 22 searches the time of a query point q (the inquiry q points are any one query point)
Track is selected to include the following steps：
(1), it is based on the corresponding theme probability distribution of query point q, the leafy node of quaternary tree described in recursive traversal obtains excellent
First queue, the Priority Queues are ranked up according to the ascending order of mdist (q, N), and the mdist (q, N) indicates query point q and leaf
The minimum range for multiple tracing points that child node N is indicated, mdist (q, N) is smaller, and query point q and leafy node N indicates multiple
The similarity of tracing point is higher.
Preferably, the calculation formula of the mdist (q, N) is as follows：
Mdist (q, N)=λ Ds(q,N)+(1-λ)·DT(q, N),
Wherein DS(q, N) is the minimum boundary matrix N .rect based on leafy node N, from query point q to leafy node N
Minimum space length；DT(q, N) is the minimum theme distance of the multiple tracing points indicated from q to the leafy node N.
Wherein λ ∈ [0,1] are weight parameter, DTThe specific formula for calculation of (q, N) is：
||TDq, N.o | | it is the theme in space and inquires
Euclidean distance between the theme distribution of point q to anchor point N.o.
(2), each leafy node in the Priority Queues is traversed successively, utilizes multiprobe LSH indexes (Multi-
Probe LSH Indexing) the corresponding LSH index structures of each leafy node of technology traversal, obtain the candidate of the query point
Tracing point looks into the track of the candidate tracks point comprising the query point as the candidate tracks of the query point, and by described
A part of the candidate tracks of inquiry point as the candidate tracks of the query set Q.
For example, there are two leafy node, the first node and the second node, the sequence in Priority Queues is：Second node,
First node.The tracing point under second node is traversed first with multiprobe LSH index technologies, then is searched under the first node
Tracing point.
Specifically, the multiprobe LSH index technologies utilize detection sequence (carefully derived probing
Sequence), obtain and the approximate multiple Hash buckets of query point.According to the property of LSH, if we understand and query point q phases
Close data are no and query point q is mapped in the same bucket, it is probably mapped in the bucket of surrounding (i.e. two buckets
Cryptographic Hash there was only little difference), target in this way be to position these buckets closed on, search neighbour's data to increase
Chance.
Each query point is inquired according to above-mentioned steps (1), (2), obtains each query point in the query set Q
Candidate tracks.
By above-mentioned implementation, it is based on the corresponding theme probability distribution of each query point, calculates each query point and semantic rail
The similarity of tracing point in mark data set concentrates rail to characterize query point based on the similarity of theme distribution with semantic track data
Semantic association between mark point, enabling more fully understand the intrinsic meaning of text description.For example query specification " drinks coffee in this way
Coffee " and tracing point description " Startbuck " will be considered related because of its similar theme distribution.Inquiry can be made more acurrate in this way.
Candidate tracks collection of the sorting module 23 based on the query set Q, calculates the query set Q and the query set
The candidate tracks of Q concentrate the distance of each track, and according to apart from size, each wait is concentrated to the candidate tracks of the query set Q
Track is selected to be ranked up.
Specifically, semantic track data collection a τ, an a series of limited theme collection Z, including query points are looked into are given
Ask collection Q, shaping variable k that a user specifies (k ＜ | τ |), the then similar inquiry in semantic track that a user oriented is intended to
(User-oriented Trajectory Similarity Query, UTSQ), returns to independent k track from τ, and this k
Trajectory distance query set Q has the distance D of top-k minimumsQ(Tr)。
Preferably, the sorting module 23 calculate the query set Q and query set Q candidate tracks concentrate it is any one
The distance of a candidate tracks Tr includes：
(1) calculate each query point in the candidate tracks Tr at a distance from each tracing point.
Specifically, give a query point q containing text entry q.W and geographical location q.l, from a tracing point p to
Its distance can according between them spatial proximity and topic relativity measure, specific formula for calculation is as follows：
D (q, p)=λ DS(q,p)+(1-λ)·DT(q, p),
Wherein, λ ∈ [0,1] are that user specifies parameter to be used to adjust the weight of spatial proximity and topic similarity；DS
(q, p) is space Euclidean distance, herein equally using the sigmoid function specifications distance between section [0,1]；DT(q, p),
It is DTThe simplification of (q.W, p.W) represents the theme distance between q and p text entries.
(2) according to each query point at a distance from each tracing point, calculated in the candidate tracks Tr each query point with
The distance of the candidate tracks.
Specifically, an a query point q and track Tr is given, if one of tracing point p ∈ Tr, for the track
In for other any one tracing point p', we have d (q, p)≤d (q, p'), then tracing point p can be expressed as in the track
With the maximally related tracing points of query point q (Most Relevant Point, MRP), it is defined as Tr.MRP (q).Then from most reference point
The distance between Tr.MRP (q) to tracing point q is indicated as the query point to the distance of track, under specifically can define：
Dmrp(q, Tr)=minp∈TrD (q, p),
(3) query set and the candidate tracks are calculated at a distance from the candidate tracks Tr according to each query point
The distance of Tr.
Specifically, the query set Q={ q for including m query point are given1,q2,…qmAnd a track Tr, we define and look into
Ask the distance D of collection Q to track TrQ(Tr) it is each query point qi(i ∈ [1, m]) arrives the sum of the distance of track Tr, calculates as follows：
It can be seen that the most related point set MRPs of each query point is formed the most related point set of the inquiry in inquiry,
Tr.MRPs (Q), therefore the most related point set MRPs for finding a query set Q can be broken down into search and each be inquired in the inquiry
The most reference point MRP of point.
Output module 24 exports result to user according to the candidate tracks collection after sequence.
Preferably, the output module 24 shows the candidate tracks collection after sequence on a user interface, after the sequence
Candidate tracks collection is ranked up from small to large according to distance.Maximally related result can thus be shown up front for
User is intuitive to see maximally related query result.
The present invention is converted to each look by the similarity measurements flow function based on theme distribution, by the description of each query point
Ask the corresponding theme probability distribution with position and time tag of point, the corresponding theme probability based on each query point point
Each query point is scanned for matching, searches the candidate rail of the query set Q by cloth with the semantic track data collection in database
Mark collection, based on the candidate tracks collection of the query set Q, the candidate tracks for calculating the query set Q and query set Q are concentrated often
The distance of a track is ranked up the candidate tracks collection of the query set Q, according to apart from size according to the candidate after sequence
Track collection exports result to user.The present invention indicates that model indicates the tracing point in query point and database using semantic track,
Tracing point and the description of the text of query point are converted into be the theme probability distribution, the i.e. a series of master with position and time tag
Inscribe probability distribution, enabling more fully understand text description intrinsic meaning, and by the similarity based on theme distribution come
The semantic association for characterizing them, to improve retrieval precision.
The above-mentioned integrated unit realized in the form of software function module, can be stored in one and computer-readable deposit
In storage media.Above-mentioned software function module is stored in a storage medium, including some instructions are used so that a computer
It is each that equipment (can be personal computer, server or the network equipment etc.) or processor (processor) execute the present invention
The part steps of embodiment the method.
As shown in figure 3, the electronic equipment 3 includes at least one sending device 31, at least one processor 32, at least one
A processor 33, at least one reception device 34 and at least one communication bus.Wherein, the communication bus is for realizing this
Connection communication between a little components.
The electronic equipment 3 be it is a kind of can according to the instruction for being previously set or storing, it is automatic carry out numerical computations and/or
The equipment of information processing, hardware include but not limited to microprocessor, application-specific integrated circuit (Application Specific
Integrated Circuit, ASIC), programmable gate array (Field-Programmable Gate Array, FPGA), number
Word processing device (Digital Signal Processor, DSP), embedded device etc..The electronic equipment 3 may also include network
Equipment and/or user equipment.Wherein, the network equipment includes but not limited to single network server, multiple network servers
The server group of composition or the cloud being made of a large amount of hosts or network server for being based on cloud computing (Cloud Computing),
Wherein, cloud computing is one kind of Distributed Calculation, a super virtual computing being made of the computer collection of a group loose couplings
Machine.
The electronic equipment 3, which may be, but not limited to, any type, to pass through keyboard, touch tablet or voice-operated device with user
Etc. modes carry out the electronic product of human-computer interaction, for example, tablet computer, smart mobile phone, personal digital assistant (Personal
Digital Assistant, PDA), intellectual Wearable, picture pick-up device, the terminals such as monitoring device.
Network residing for the electronic equipment 3 includes, but are not limited to internet, wide area network, Metropolitan Area Network (MAN), LAN, virtual
Dedicated network (Virtual Private Network, VPN) etc..
Wherein, the reception device 34 and the sending device 31 can be wired sending ports, or wirelessly set
It is standby, such as including antenna assembly, for other equipment into row data communication.
The memory 32 is for storing program code.The memory 32 can not have physical form in integrated circuit
The circuit with store function, such as RAM (Random-Access Memory, random access memory), FIFO (First In
First Out) etc..Alternatively, the memory 32 can also be the memory with physical form, such as memory bar, TF card
(Trans-flash Card), smart media card (smart media card), safe digital card (secure digital
Card), storage facilities such as flash memory cards (flash card) etc..
The processor 33 may include one or more microprocessor, digital processing unit.The processor 33 is adjustable
With the program code stored in memory 32 to execute relevant function.For example, the modules described in Fig. 2 are stored in institute
The program code in memory 32 is stated, and performed by the processor 33, to realize a kind of track querying method.The processing
Device 33 is also known as central processing unit (CPU, Central Processing Unit), is one piece of ultra-large integrated circuit, is fortune
Calculate core (Core) and control core (Control Unit).
The embodiment of the present invention also provides a kind of computer readable storage medium, is stored thereon with computer instruction, the finger
It enables when being executed by the electronic equipment including one or more processors, electronic equipment is made to execute as described in embodiment of the method above
Track querying method.
In conjunction with shown in Fig. 1 and Fig. 2, the memory 32 in the electronic equipment 3 stores multiple instruction to realize one kind
Track querying method, the processor 33 can perform the multiple instruction to realize：
Obtain the query set for the description for including each query point；The description of each query point is converted into each query point pair
The theme probability distribution with position and time tag answered；Corresponding theme probability distribution based on each query point, will be every
A query point scans for matching with the semantic track data collection in database, searches the candidate tracks collection of the query set；Base
In the candidate tracks collection of the query set, the candidate tracks for calculating the query set and the query set concentrate each candidate tracks
Distance concentrate each candidate tracks to be ranked up the candidate tracks of the query set according to apart from size；After sequence
Candidate tracks collection, export result to user.
The characteristic means of present invention mentioned above can be realized by integrated circuit, and control above-mentioned of realization
The function of track querying method described in embodiment of anticipating.That is, the integrated circuit of the present invention is installed in the electronic equipment, make institute
Electronic equipment is stated to play the following functions：Obtain the query set for the description for including each query point；The description of each query point is turned
It is changed to the corresponding theme probability distribution with position and time tag of each query point；Corresponding master based on each query point
Probability distribution is inscribed, each query point is scanned for matching with the semantic track data collection in database, searches the query set
Candidate tracks collection；Candidate tracks collection based on the query set, calculates the candidate tracks of the query set and the query set
The distance for concentrating each candidate tracks, according to apart from size, to the candidate tracks of the query set concentrate each candidate tracks into
Row sequence；According to the candidate tracks collection after sequence, result is exported to user.
Function achieved by the track querying method described in any embodiment can be transferred through the integrated circuit of the present invention
It is installed in the electronic equipment, the electronic equipment is made to play achieved by track querying method described in any embodiment
Function, this will not be detailed here.
It should be noted that for each method embodiment above-mentioned, for simple description, therefore it is all expressed as a series of
Combination of actions, but those skilled in the art should understand that, the present invention is not limited by the described action sequence because
According to the present invention, certain steps can be performed in other orders or simultaneously.Secondly, those skilled in the art should also know
It knows, embodiment described in this description belongs to preferred embodiment, and involved action and module are not necessarily of the invention
It is necessary.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment
Point, it may refer to the associated description of other embodiment.
In several embodiments provided herein, it should be understood that disclosed device, it can be by another way
It realizes.For example, the apparatus embodiments described above are merely exemplary, for example, the unit division, it is only a kind of
Division of logic function, formula that in actual implementation, there may be another division manner, such as multiple units or component can combine or can
To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be by some interfaces, the INDIRECT COUPLING or communication connection of device or unit,
Can be electrical or other forms.
The unit illustrated as separating component may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, you can be located at a place, or may be distributed over multiple
In network element.Some or all of unit therein can be selected according to the actual needs to realize the mesh of this embodiment scheme
's.
In addition, each functional unit in various embodiments of the present invention can be integrated in a processing unit, also may be used
It, can also be during two or more units be integrated in one unit to be that each unit physically exists alone.It is above-mentioned integrated
The form that hardware had both may be used in unit is realized, can also be realized in the form of SFU software functional unit.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can be stored in a computer read/write memory medium.Based on this understanding, technical scheme of the present invention is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or network equipment etc.) execute each embodiment the method for the present invention whole or
Part steps.And storage medium above-mentioned includes：USB flash disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), mobile hard disk, magnetic disc or CD etc. are various can to store program code
Medium.
The above, the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to before
Stating embodiment, invention is explained in detail, it will be understood by those of ordinary skill in the art that：It still can be to preceding
The technical solution recorded in each embodiment is stated to modify or equivalent replacement of some of the technical features；And these
Modification or replacement, the range for various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution.
Claims (10)
1. a kind of track querying method, which is characterized in that the method includes：
Obtain the query set for the description for including each query point；
The description of each query point is converted into the corresponding theme probability distribution with position and time tag of each query point；
Corresponding theme probability distribution based on each query point, by the semantic track data collection in each query point and database
It scans for matching, searches the candidate tracks collection of the query set；
Candidate tracks collection based on the query set, the candidate tracks for calculating the query set and the query set concentrate each wait
The distance for selecting track concentrates each candidate tracks to be ranked up the candidate tracks of the query set according to apart from size；
According to the candidate tracks collection after sequence, result is exported to user.
2. track querying method as described in claim 1, which is characterized in that the semanteme track data concentrates each tracing point
Including position coordinates and theme distribution information, based on each tracing point include position coordinates and the foundation of theme distribution information includes empty
The hierarchical index structure of interbed and subject layer, wherein space layer establish index structure using quaternary tree, for each leafy node
The multiple tracing points indicated establish the corresponding LSH index structures of each leafy node based on position sensing Hash in subject layer.
3. track querying method as claimed in claim 2, which is characterized in that the corresponding theme based on each query point
Each query point is scanned for matching, searches the query set by probability distribution with the semantic track data collection in database
Candidate tracks collection includes：
Corresponding theme probability distribution based on each query point, by the semantic track data collection in each query point and database
It scans for matching, searches the candidate tracks of each query point, the candidate tracks of each query point are determined as the query set
Candidate tracks collection；
The wherein corresponding theme probability distribution based on each query point, by the semantic track number in each query point and database
It scans for matching according to collection, the candidate tracks of one query point q of inquiry include：
Based on the corresponding theme probability distribution of query point q, the leafy node of quaternary tree, obtains Priority Queues described in recursive traversal,
The Priority Queues is ranked up according to the ascending order of mdist (q, N), and the mdist (q, N) indicates query point q and leafy node N
Indicate the minimum range of multiple tracing points；
Each leafy node in the Priority Queues is traversed successively, and each leaf knot is traversed using multiprobe LSH index technologies
The corresponding LSH index structures of point, obtain the candidate tracks point of the query point, by the candidate tracks point comprising the query point
Candidate tracks of the track as the query point, and using the candidate tracks of the query point as the candidate tracks of the query set
A part.
4. track querying method as claimed in claim 3, which is characterized in that the calculation formula of the mdist (q, N) is as follows：
Mdist (q, N)=λ Ds(q,N)+(1-λ)·DT(q,N)；
Wherein DS(q, N) is the minimum boundary matrix N .rect based on leafy node N, most from query point q to leafy node N
Small space length, DT(q, N) from q to the leafy node N the multiple tracing points indicated minimum theme distance, λ ∈ [0,1]
For weight parameter.
5. track querying method as described in claim 1, which is characterized in that calculating the query set Q and query set Q
Candidate tracks concentrate candidate tracks apart from when, including：
Calculate each query point in the candidate tracks Tr at a distance from each tracing point；
According to each query point at a distance from each tracing point, calculate each query point and the candidate in the candidate tracks Tr
The distance of track Tr；
According to each query point at a distance from the candidate tracks Tr, calculate the query set and the candidate tracks Tr away from
From.
6. track querying method as claimed in claim 5, which is characterized in that described to calculate each query point and the candidate rail
The distance of each tracing point includes in mark Tr：
Give a query point q containing text entry q.W and geographical location q.l, from a tracing point p to query point q away from
From can according between tracing point p to query point q spatial proximity and topic relativity measure, calculation formula is as follows：
D (q, p)=λ DS(q,p)+(1-λ)·DT(q, p), wherein λ ∈ [0,1] are that user specifies parameter to be used to adjust sky
Between the degree of approach and topic similarity weight；DS(q, p) is space Euclidean distance；DT(q, p) represents the master between q and p text entries
Inscribe distance.
7. track querying method as claimed in claim 5, which is characterized in that in each query point of basis and candidate tracks
The distance of each tracing point, calculate each query point includes at a distance from the candidate tracks Tr：
Given an a query point q and track Tr, one of tracing point p ∈ Tr, in the track it is other any one
For tracing point p', there is d (q, p)≤d (q, p'), then tracing point p is expressed as in the track and the maximally related tracing points of query point q
Tr.MRP (q) is then indicated as the query point to rail the distance between from maximally related tracing point Tr.MRP (q) to tracing point q
The distance of mark.
8. track querying method as claimed in claim 5, which is characterized in that each query point of basis and the candidate rail
The distance of mark, calculate the query set includes at a distance from the candidate tracks Tr：
The given inquiry Q={ q for including m inquiry point set1,q2,…qmAnd a track Tr, inquire the distance D of Q to track TrQ
(Tr) it is each query point qi(i ∈ [1, m]) arrives the sum of the distance of track Tr, calculates as follows：
The most related point set MRPs of each query point is formed the most related point set Tr.MRPs (Q) of the inquiry in inquiry.
9. a kind of electronic equipment, which is characterized in that the electronic equipment includes memory and processor, and the memory is for depositing
At least one instruction is stored up, the processor is for executing at least one instruction to realize such as any one of claim 1 to 8
The track querying method.
10. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has at least one
Instruction, at least one instruction realize the track issuer as described in any one of claim 1 to 8 when being executed by processor
Method.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810303902.0A CN108536813B (en) | 2018-04-04 | 2018-04-04 | Track querying method, electronic equipment and storage medium |
| PCT/CN2018/100149 WO2019192120A1 (en) | 2018-04-04 | 2018-08-13 | Track query method, electronic device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810303902.0A CN108536813B (en) | 2018-04-04 | 2018-04-04 | Track querying method, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108536813A true CN108536813A (en) | 2018-09-14 |
| CN108536813B CN108536813B (en) | 2019-07-16 |
Family
ID=63482647
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810303902.0A Active CN108536813B (en) | 2018-04-04 | 2018-04-04 | Track querying method, electronic equipment and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN108536813B (en) |
| WO (1) | WO2019192120A1 (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109492150A (en) * | 2018-10-30 | 2019-03-19 | 石家庄铁道大学 | Reverse nearest neighbor queries method and device based on semantic track big data |
| CN110543457A (en) * | 2019-09-11 | 2019-12-06 | 北京明略软件系统有限公司 | Track type document processing method and device, storage medium and electronic device |
| CN111221353A (en) * | 2020-04-16 | 2020-06-02 | 上海特金信息科技有限公司 | Unmanned aerial vehicle flight trajectory processing method and device, electronic equipment and storage medium |
| CN111241217A (en) * | 2018-11-29 | 2020-06-05 | 阿里巴巴集团控股有限公司 | Data processing method, device and system |
| CN112487256A (en) * | 2020-12-10 | 2021-03-12 | 中国移动通信集团江苏有限公司 | Object query method, device, equipment and storage medium |
| CN112597190A (en) * | 2020-12-28 | 2021-04-02 | 京东城市(北京)数字科技有限公司 | Point neighbor track query method and device, electronic equipment and readable storage medium |
| CN114117260A (en) * | 2021-12-02 | 2022-03-01 | 中国人民解放军国防科技大学 | Spatiotemporal trajectory indexing and query processing method, device, equipment and medium |
| CN115510303A (en) * | 2022-11-02 | 2022-12-23 | 深圳市瑞达飞行科技有限公司 | Method and system for identifying off-site program, computer equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102214215A (en) * | 2011-06-07 | 2011-10-12 | 陆嘉恒 | Rapid reverse nearest neighbour search method based on text information |
| CN104834679A (en) * | 2015-04-14 | 2015-08-12 | 苏州大学 | Representation and inquiry method of behavior track and device therefor |
-
2018
- 2018-04-04 CN CN201810303902.0A patent/CN108536813B/en active Active
- 2018-08-13 WO PCT/CN2018/100149 patent/WO2019192120A1/en active Application Filing
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102214215A (en) * | 2011-06-07 | 2011-10-12 | 陆嘉恒 | Rapid reverse nearest neighbour search method based on text information |
| CN104834679A (en) * | 2015-04-14 | 2015-08-12 | 苏州大学 | Representation and inquiry method of behavior track and device therefor |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109492150A (en) * | 2018-10-30 | 2019-03-19 | 石家庄铁道大学 | Reverse nearest neighbor queries method and device based on semantic track big data |
| CN109492150B (en) * | 2018-10-30 | 2021-07-27 | 石家庄铁道大学 | Reverse nearest neighbor query method and device based on semantic track big data |
| CN111241217A (en) * | 2018-11-29 | 2020-06-05 | 阿里巴巴集团控股有限公司 | Data processing method, device and system |
| CN111241217B (en) * | 2018-11-29 | 2023-05-30 | 阿里巴巴集团控股有限公司 | Data processing method, device and system |
| CN110543457A (en) * | 2019-09-11 | 2019-12-06 | 北京明略软件系统有限公司 | Track type document processing method and device, storage medium and electronic device |
| CN111221353A (en) * | 2020-04-16 | 2020-06-02 | 上海特金信息科技有限公司 | Unmanned aerial vehicle flight trajectory processing method and device, electronic equipment and storage medium |
| CN112487256A (en) * | 2020-12-10 | 2021-03-12 | 中国移动通信集团江苏有限公司 | Object query method, device, equipment and storage medium |
| CN112487256B (en) * | 2020-12-10 | 2024-05-24 | 中国移动通信集团江苏有限公司 | Object query method, device, equipment and storage medium |
| CN112597190A (en) * | 2020-12-28 | 2021-04-02 | 京东城市(北京)数字科技有限公司 | Point neighbor track query method and device, electronic equipment and readable storage medium |
| CN114117260A (en) * | 2021-12-02 | 2022-03-01 | 中国人民解放军国防科技大学 | Spatiotemporal trajectory indexing and query processing method, device, equipment and medium |
| CN115510303A (en) * | 2022-11-02 | 2022-12-23 | 深圳市瑞达飞行科技有限公司 | Method and system for identifying off-site program, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108536813B (en) | 2019-07-16 |
| WO2019192120A1 (en) | 2019-10-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108536813B (en) | Track querying method, electronic equipment and storage medium | |
| CN104123332B (en) | The display methods and device of search result | |
| Ma et al. | Big graph search: challenges and techniques | |
| CN106128100B (en) | A kind of short-term traffic flow forecast method based on Spark platform | |
| CN110162695A (en) | A kind of method and apparatus of information push | |
| CN107346326A (en) | For generating the method and system of neural network model | |
| CN104834693A (en) | Depth-search-based visual image searching method and system thereof | |
| CN109086413A (en) | For searching for the method, equipment and readable storage medium storing program for executing of block chain data | |
| CN104391908B (en) | Multiple key indexing means based on local sensitivity Hash on a kind of figure | |
| CN106294815B (en) | A kind of clustering method and device of URL | |
| CN104021125A (en) | Search engine sorting method and system and search engine | |
| CN107315984B (en) | Pedestrian retrieval method and device | |
| Cong | Personalized recommendation of film and television culture based on an intelligent classification algorithm | |
| CN110232133A (en) | A kind of image of clothing search method and system classified based on Fusion Features and style | |
| CN110427404A (en) | A kind of across chain data retrieval system of block chain | |
| CN103902549A (en) | Search data sorting method and device and data searching method and device | |
| Xuan et al. | Constrained range search query processing on road networks | |
| JP7393475B2 (en) | Methods, apparatus, systems, electronic devices, computer readable storage media and computer programs for retrieving images | |
| CN108509545A (en) | A kind of comment processing method and system of article | |
| Sun et al. | On efficient aggregate nearest neighbor query processing in road networks | |
| CN105354264B (en) | A kind of quick adding method of theme label based on local sensitivity Hash | |
| Mei et al. | Instance-level object retrieval via deep region CNN | |
| CN109684418A (en) | Date storage method and device based on power grid chart database | |
| CN109726615A (en) | A kind of recognition methods of road boundary and device | |
| CN114491088A (en) | Spatial data semantic query method and device |
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 |