CN103593361A - Movement space-time trajectory analysis method in sense network environment - Google Patents
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Abstract
The invention relates to the technical field of movement behavioral analysis and prediction in a sense network environment, and specifically to a movement space-time trajectory analysis method in the sense network environment. The movement space-time trajectory analysis method in the sense network environment comprises data reception of receiving trajectory movement position data generated by a positioning device and resolving the data format into a data format applicable to data treatment; semantic treatment of performing clustering operation on the semantic trajectory data; space-time correlation of performing characteristic analysis and statistics on clustered semantic trajectory data in a time domain and a space domain, and performing time-space correlation analysis in combination with the time domain and the space domain; correlation similarity analysis of calculating space-time correlation similarity of the semantic trajectory and performing analysis and calculation on the correlation among different space domains and different movement objects; outputting a result. The movement space-time trajectory analysis method in the sense network environment solves the problem of continuous treatment and mutual correlation of time and space dimensions in a traditional transactional database, and meets the need from a sense network application service to real-time analysis of trajectory movement data.
Description
Technical field
The present invention relates to respond to mobile behavior analysis and the electric powder prediction of net environment, specifically a kind of induction net environment Mobile Space-time trajectory analysis method.
Background technology
At present, along with the universal and wireless transmission of the shift position harvesters such as GPS, the development of ubiquitous computing technique, the research in the space-time trajectory analysis fields such as the behavior pattern excavation based on moving object position information, real time position service, shift position prediction more and more causes the concern of academia and industrial community, and its relevant application is also increasingly extensive.At present in intelligent traffic administration system with scheduling, the monitoring processing of sudden colony event, the aspects such as the variation perception of ecologic environment, mobile network's value-added service have relevant base application.Because space-time track data has respectively continuation property on time dimension and Spatial Dimension, simultaneously spacetime correlation is present in the process of trajectory analysis widely, therefore in conjunction with Spatial dimensionality distribution character, carries out trajectory analysis very necessary.But current existing track moves analytical approach or only considers the distribution characteristics of Spatial Dimension, or only consider that shift position is according to the spatial distribution characteristic of time order and function order, for spacetime correlation track aspect, there is no ripe analytical approach.Simultaneously based on background knowledge (as regional population's statistical distribution, shift position point semantic expressiveness etc.), the interrelated similarity cluster of time and space, the aspects such as the mobility model of semantic space represents lack relevant analysis and research method, and this moves for track the application of analyzing with Information Mobile Service and has brought a huge difficult problem.Therefore, in the urgent need to a kind of method of trajectory analysis effectively, for mobile location information, carry out profound signature analysis and pattern extraction.
Summary of the invention
For the above-mentioned problems in the prior art, the invention provides a kind of systematic method based on shift position trajectory analysis, by the semantic background of spacetime correlation analysis and locus, carry out the analysis of track and the discovery of pattern, efficiently solve the deficiency of the association analysis aspect existence of the moving object position trace information under LBS, met the needs of Information Mobile Service application for aspects such as real-time, complicacy, integration, actuality.
The technical scheme that the present invention adopted is for achieving the above object: induction net environment Mobile Space-time trajectory analysis method, comprises the following steps:
Data receiver and parsing: the track mobile position data that receiving positioner produces, comprises shift position point data and corresponding time data with it; Noise data, redundant data, misdata and deficiency of data are wherein carried out to filtering cleaning; Data after cleaning are carried out to linear interpolation operation, adjacent position data time spacing value is surpassed the data of threshold value, linearization point of addition point data between adjacent position, this threshold value combines and is provided by user with concrete application background;
Semantic processes: the track mobile position data after resolving is converted into the semantic track data in abstract meaning, mobile position data space-time three-dimensional coordinate being represented carries out the semantic conversion under two-dimensional coordinate, be specially the space two-dimensional element being represented by GPS longitude, dimension is converted to the region semantic one-dimensional element under geography information, corresponding time dimension element is constant; On this basis, the close regional location data in semantic track data are sorted out;
Spacetime correlation: in time domain and spatial domain, the data after semantic processes are carried out to similarity analysis statistics by distribution characteristics and density feature respectively, binding time territory and spatial domain are carried out the analysis of spacetime correlation degree; Described associated similarity analysis is: the spacetime correlation similarity of computing semantic track, for the degree of association between different spaces territory, between different mobile object, calculate respectively;
Output: set up probability model according to above-mentioned associated similarity analysis result, carry out semantic integrated approach for found trajectory model, produce readable Output rusults; Calculate the space-time track probability of mobile object individual and group, predict the space-time track position that it is following.
The object of described parsing comprises the various criterion of being obtained by different shift positions collecting device, the track mobile position data of different-format.
Described locus semantic knowledge information represents social satellite information, comprises that demographics distributed intelligence, economic society information and mechanism arrange, region is divided.
Described semantic track data after semantic processes is stored in semantic knowledge-base.
The track mobile position data that described locating device produces sends on corresponding mobile device in the mode of prompting message.
Described prompting message comprises data transmission, message transfer and abnormal conditions prompting.
Described spacetime correlation is specially:
Position between produce similarity measure matrix: the trajectory range 1) motion track being covered carries out location network calculating in the mode of network interconnection, trajectory range is divided into n mutual disjunct regional ensemble, and any two regions during this is gathered are based on its track linking number of topological relationship calculation;
2) corresponding to above-mentioned divided region, calculate its region interest measure:
Value＝f(N
in,N
out,ΔT)
N wherein
inrepresent to enter the track number in this region, N
outrepresent to leave the track number in this region, Δ T represents the accumulated time that track stops, and Value represents the interest measure based on the drawn area of space of above-mentioned three parameters;
3) on the basis being connected with interregional track in region interest-degree tolerance, carry out cluster between similar position, thus set up region and time correlation quantitative relationship, and draw the region with same trajectories access characteristic;
Wherein, l
i, jrepresent the similarity value between i region and j region, and diagonal of a matrix element is constantly equal to 1;
4) utilize the position under Spatial Semantics, to similar matrix, cluster is carried out in locus, make the locus with similar semanteme be classified as a class;
Between mobile object, set up similarity measure matrix: based on mobile object trajectory range network topology, mobile object similarity relation in zones of different is set up to metric matrix, this metric matrix is in order to represent the movement similar incidence relation of n mobile object on a certain region, as the residence time, travel frequency, initial and final position
Wherein, m
i, jrepresent the similarity value between i mobile object and j mobile object, diagonal of a matrix element m
i, ibe constantly equal to 1.
Described data receiver, semantic processes, spacetime correlation, associated similarity analysis and result output are all to carry out under monitor state, for abnormal and error situation, carry out alarm.
Track data after described parsing carries out semantic conversion processing, and the semantic stream data after conversion are compared with historical track data on the one hand, carry out respectively individual track association analysis and colony's track association analysis; Utilize on the other hand predefined known event condition to carry out Condition Matching, if meet certain predefined event structure condition, think the generation of some definite events, thereby produce the operations such as abnormal alarm.
The anomalous event that described monitoring obtains is kept in historical data base to carry out historical events renewal.
The present invention has the following advantages:
The acquisition and recording to mobile object self mobile message by shift position perception mancarried device or equipment, can reflect the relevant information of mobile object external environment condition of living in and the relevant information of mobile colony indirectly.Track moves as directly perceived, the dynamic message form of induction net environment not only can provide the application services such as location-based service, mobile message be mutual to mobile object, and can carry out perception and prediction to the inherent law of mobile colony and variation tendency, can reflect in real time environmental information so that manage, optimize construction and the maintenance of public infrastructure simultaneously.
The analytical approach of track mobile location information has produced larger inhibition with disappearance to the related application under induction network in the reduction of spacetime correlation dimension, become to a certain extent the bottleneck that induction network related application is extensively promoted, especially for complicated integrated service on real-time position information basis be applied in that real time service provides, the aspects such as comprehensive extraction of the fast detecting of abnormal conditions and tracking, mobile behavior pattern have formed obvious restriction.
Provided by the present invention based on the time, the Spatial Dimension trajectory analysis method that is mutually related, semantic expressiveness and relevant position demographics distributed intelligence in conjunction with trajectory location points, at track historical time record, the different aspects such as real-time time record and following finite time interval are to single individuality, between same community, between different groups, analyze relatively, solved the semantic background knowledge in position, between demographics distributed intelligence and locus point in conjunction with undertighten, time dimension information and spatial positional information cannot efficient associations, between space-time track mobile behavior pattern and real knowledge excavation, cannot wait alternately relevant issues.
Accompanying drawing explanation
Fig. 1 is method flow diagram of the present invention;
Fig. 2 is that spacetime correlation of the present invention is processed schematic diagram;
Fig. 3 is the real-time processing engine principle assumption diagram of track flow data of the present invention;
Fig. 4 is the integrated system structural drawing that the present invention applies.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail.
As shown in Figure 1, be method flow diagram of the present invention.Spacetime correlation is analyzed (Spatial-Temporal Correlation Analysis, STCA) semanteme based on semantic knowledge-base, track data being carried out on Spatial dimensionality transforms and represents, association analyzer by time domain, spatial domain relatively calculate diverse location between, associated similarity between different mobile object, with the object as a comparison of the track data in historical data base, final generative semantics track behavior pattern.Spacetime correlation analysis is mainly by following module composition:
1. track data receives: the track mobile position data producing for receiving positioner.
2. resolve: for resolving the various criterion of being obtained by different shift positions collecting device, the track position data of different-format.
3. semantic knowledge-base: storage track moves the locus semantic knowledge information of institute overlay area, comprise the social satellite informations such as demographics distributed intelligence, economic society information and mechanism's setting, region division, and the mobile statistical distribution knowledge on time dimension etc., semantic knowledge-base as a setting knowledge is that the operations such as the pre-service of space-time track, division, cluster provide support.
4. semantic processes: carry out alternately with semantic knowledge-base, carry out the operations such as semantic expressiveness, semantic coordinate transformation, Semantic Clustering for resolving the track data producing; Semantic expressiveness is for to be converted into the semantic track data in abstract meaning by initial trace data, semantic coordinate transformation is that the two-dimensional coordinate that the space-time three-dimensional coordinate of initial trace data is converted under semantic coordinate represents, the cluster operation of Semantic Clustering for semantic track being carried out on this basis.One side eliminate redundancy track data, track on the other hand notes abnormalities.
5. message produces: produce the data transmission that sends on relevant mobile device and the prompting message of message transfer and abnormal conditions.
6. spacetime correlation analysis: in time domain and spatial domain, semantic track data is carried out to signature analysis and statistics respectively, binding time territory and spatial domain are carried out the analysis of spacetime correlation degree simultaneously.
7. associated similarity analysis: for the spacetime correlation similarity of computing semantic track, carry out analytical calculation for the degree of association between different spaces region, between different mobile object.
8. flow data processing engine: real-time Knowledge Discovery and the application service support function of oriented locus flow data are provided, and have carried out Data Update, transmission, mutual with portable running fix equipment.
9. monitoring management: be responsible for specially the operations such as track data receiver, parsing, semantic conversion and processing, spacetime correlation analysis, similarity calculating are monitored.
10. output: be responsible for setting up probability model according to similarity counting statistics rule, for found trajectory model, carry out semantic integrated approach to produce the integrated Output rusults of semanteme more succinct readability, more abstract complexity, based on probability model, for the following track of mobile object individual and group, move and behavior is analyzed and predict simultaneously.
Space-time track data flow chart of data processing is as shown in Figure 2: after sending to trajectory analysis system receiving terminal, analytic system is by carrying out the identifying operation of space-time mobile behavior and track data by historical data base to it, semantic knowledge-base will carry out the associated identifying of semantic knowledge information to track data afterwards, thereafter will be respectively position between and between mobile object, produce similarity measure matrix.For position between similarity measure matrix, first spacetime correlation analysis is moved track covered trajectory range and is carried out location network calculating in the mode of network interconnection, utilizes afterwards Spatial Semantics information to carry out Semantic Clustering to locus.In cluster process, the locus with similar semanteme will be classified as a class, and abnormal semantic locations is named a person for a particular job and is sent to semantic association analysis part and surveys abnormal track behavior.By semantic coordinate transformation process, semantic locations track is extracted as semantic space probability model, the region in built vertical statistical significance and time correlation quantitative relationship.Similar matrix between mobile object calculates and first calculates mobile object network topology, and then takes the semantic conversion process of mobile object to draw the Semantic Clustering relation of mobile object.By position between similarity measure can find to have the region of same trajectories access characteristic, and then in conjunction with semantic knowledge, can be to track data analysis, explain and predict.By the similarity measure between mobile object, can carry out cluster analysis to thering is individuality and the colony of similar mobile behavior, set up the mobile kinetics model knowledge in colony's meaning, for mobile application service provides more targeted, more selectively service support.
The principle assumption diagram of the track flow data processing engine module in the present invention under spacetime correlation analysis (STAC) is as shown in Figure 3: when the collected individual flow data of running fix station acquisition device are sent to trajectory analysis system, after preliminary pre-service, passing to flow data processing engine, flow data processing engine is utilized its inner semantic knowledge to manage plug-in unit stream data to carry out semantic conversion processing.Semantic stream data after conversion are compared with historical track data on the one hand, carry out respectively individual track association analysis and colony's track association analysis; Semantic stream data are passed through event monitor on the other hand, the known event condition of setting before utilizing is carried out Condition Matching, once meet the event structure condition that certain sets, think the generation of some definite events, thereby produce the operations such as abnormal alarm, to realize the object of Real-Time Monitoring mobile object behavior.The anomalous event obtaining of monitoring will be kept in historical data base to carry out historical events renewal simultaneously.
The designed track of the present invention move flow data spacetime correlation analyze with integrated morphology as shown in Figure 4: mobile portable terminal and various application platform are connected with track BMAT server by cloud network, the track Mobile data on the one hand self being gathered is by wireless network transmissions to the cloud network platform, and background server provides the relevant mobile computation service support of real-time for it on the other hand.Space-time track flow data passes to space-time trajectory analysis system in real time by transmission platform, system is after carrying out preliminary pre-service and backup preservation operation to it, Spatial Semantics database by space-time track association analyzer, location database behavioral pattern data storehouse carries out spacetime correlation analysis to real-time streaming data, after feature clustering relation by more semantic track data on time dimension and Spatial Dimension, comprehensive spacetime correlation relation is carried out behavioural analysis research to it, it is integrated and operation associated that integrating engine and spacetime correlation engine carry out higher level semanteme to the semantic behavior of be drawn into movement, final track knowledge is kept in knowledge base so that rule base is upgraded, export available Integration Services in the representation of knowledge simultaneously.
Claims (10)
1. an induction net environment Mobile Space-time trajectory analysis method, is characterized in that, comprises the following steps:
Data receiver and parsing: the track mobile position data that receiving positioner produces, comprises shift position point data and corresponding time data with it; Noise data, redundant data, misdata and deficiency of data are wherein carried out to filtering cleaning; Data after cleaning are carried out to linear interpolation operation, adjacent position data time spacing value is surpassed the data of threshold value, linearization point of addition point data between adjacent position, this threshold value combines and is provided by user with concrete application background;
Semantic processes: the track mobile position data after resolving is converted into the semantic track data in abstract meaning, mobile position data space-time three-dimensional coordinate being represented carries out the semantic conversion under two-dimensional coordinate, be specially the space two-dimensional element being represented by GPS longitude, dimension is converted to the region semantic one-dimensional element under geography information, corresponding time dimension element is constant; On this basis, the close regional location data in semantic track data are sorted out;
Spacetime correlation: in time domain and spatial domain, the data after semantic processes are carried out to similarity analysis statistics by distribution characteristics and density feature respectively, binding time territory and spatial domain are carried out the analysis of spacetime correlation degree; Described associated similarity analysis is: the spacetime correlation similarity of computing semantic track, for the degree of association between different spaces territory, between different mobile object, calculate respectively;
Output: set up probability model according to above-mentioned associated similarity analysis result, carry out semantic integrated approach for found trajectory model, produce readable Output rusults; Calculate the space-time track probability of mobile object individual and group, predict the space-time track position that it is following.
2. induction net environment Mobile Space-time trajectory analysis method according to claim 1, is characterized in that, the object of described parsing comprises the various criterion of being obtained by different shift positions collecting device, the track mobile position data of different-format.
3. induction net environment Mobile Space-time trajectory analysis method according to claim 1, it is characterized in that, described locus semantic knowledge information represents social satellite information, comprises that demographics distributed intelligence, economic society information and mechanism arrange, region is divided.
4. induction net environment Mobile Space-time trajectory analysis method according to claim 1, is characterized in that, described semantic track data after semantic processes is stored in semantic knowledge-base.
5. induction net environment Mobile Space-time trajectory analysis method according to claim 1, is characterized in that, the track mobile position data that described locating device produces sends on corresponding mobile device in the mode of prompting message.
6. induction net environment Mobile Space-time trajectory analysis method according to claim 5, is characterized in that, described prompting message comprises data transmission, message transfer and abnormal conditions prompting.
7. induction net environment Mobile Space-time trajectory analysis method according to claim 1, is characterized in that, described spacetime correlation is specially:
Position between produce similarity measure matrix: the trajectory range 1) motion track being covered carries out location network calculating in the mode of network interconnection, trajectory range is divided into n mutual disjunct regional ensemble, and any two regions during this is gathered are based on its track linking number of topological relationship calculation;
2) corresponding to above-mentioned divided region, calculate its region interest measure:
Value＝f(N
in,N
out，ΔT)
N wherein
inrepresent to enter the track number in this region, N
outrepresent to leave the track number in this region, Δ T represents the accumulated time that track stops, and Value represents the interest measure based on the drawn area of space of above-mentioned three parameters;
3) on the basis being connected with interregional track in region interest-degree tolerance, carry out cluster between similar position, thus set up region and time correlation quantitative relationship, and draw the region with same trajectories access characteristic;
Wherein, l
i, jrepresent the similarity value between i region and j region, and diagonal of a matrix element is constantly equal to 1;
4) utilize the position under Spatial Semantics, to similar matrix, cluster is carried out in locus, make the locus with similar semanteme be classified as a class;
Between mobile object, set up similarity measure matrix: based on mobile object trajectory range network topology, mobile object similarity relation in zones of different is set up to metric matrix, this metric matrix is in order to represent the movement similar incidence relation of n mobile object on a certain region, as the residence time, travel frequency, initial and final position
Wherein, m
i, jrepresent the similarity value between i mobile object and j mobile object, diagonal of a matrix element m
i, jbe constantly equal to 1.
8. induction net environment Mobile Space-time trajectory analysis method according to claim 1, it is characterized in that, described data receiver, semantic processes, spacetime correlation, associated similarity analysis and result output are all to carry out under monitor state, for abnormal and error situation, carry out alarm.
9. induction net environment Mobile Space-time trajectory analysis method according to claim 1, it is characterized in that, track data after described parsing carries out semantic conversion processing, semantic stream data after conversion are compared with historical track data on the one hand, carry out respectively individual track association analysis and colony's track association analysis; Utilize on the other hand predefined known event condition to carry out Condition Matching, if meet certain predefined event structure condition, think the generation of some definite events, thereby produce the operations such as abnormal alarm.
10. induction net environment Mobile Space-time trajectory analysis method according to claim 9, is characterized in that, the anomalous event that described monitoring obtains is kept in historical data base to carry out historical events renewal.
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