CN106211071B - Group activity method of data capture and system based on multi-source space-time trajectory data - Google Patents

Group activity method of data capture and system based on multi-source space-time trajectory data Download PDF

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CN106211071B
CN106211071B CN201610517438.6A CN201610517438A CN106211071B CN 106211071 B CN106211071 B CN 106211071B CN 201610517438 A CN201610517438 A CN 201610517438A CN 106211071 B CN106211071 B CN 106211071B
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data
activity
point
trajectory
time
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CN106211071A (en
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涂伟
曹劲舟
李清泉
乐阳
曹瑞
王振声
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Shenzhen University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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Abstract

本发明公开了基于多源时空轨迹数据的群体活动数据收集方法及系统，方法包括：后台获取原始移动终端信令数据和原始社交软件签到数据并进行预处理，生成符合特定格式的待处理信令数据和待处理签到数据；后台从待处理信令数据得到的活动点轨迹数据；构建并学习群体活动规律的先验信息；获取活动点轨迹数据，获取活动地点数据；后台根据活动点轨迹数据、群体活动规律的先验信息、活动地点数据，采用基于贝叶斯模型进行活动点轨迹语义信息标记，生成活动时空轨迹链。本发明采用贝叶斯模型进行个体活动的推断，并考虑了时空活动轨迹中前一时刻活动类型对后一时刻活动类型的影响，实现大范围、海量群体活动的准确、快速、高效提取与收集。

The invention discloses a method and a system for collecting group activity data based on multi-source spatiotemporal trajectory data. The method includes: acquiring original mobile terminal signaling data and original social software check-in data in the background, preprocessing, and generating pending signaling conforming to a specific format Data and check-in data to be processed; activity point trajectory data obtained from the signaling data to be processed in the background; construct and learn the prior information of group activity patterns; obtain activity point trajectory data, and obtain activity location data; The prior information and activity location data of the group activity law are used to mark the semantic information of the activity point trajectory based on the Bayesian model to generate the activity spatiotemporal trajectory chain. The invention adopts the Bayesian model to infer individual activities, and considers the influence of the activity type at the previous moment on the activity type at the next moment in the space-time activity trajectory, so as to achieve accurate, fast and efficient extraction and collection of large-scale and massive group activities .

Description

Group activity method of data capture and system based on multi-source space-time trajectory data
Technical field
The present invention relates to technical field of data processing, more particularly to the group activity data based on multi-source space-time trajectory data Collection method and system.
Background technique
Traditional movable gathering method depends on activity log or activity survey, and sample size is few, and the collection time is long, time-consuming consumption Power.The outburst of space-time trajectory data provides new tool for the movable acquisition of large-scale groups.Space-time data analyzes correlative study The individual activity identification being primarily upon in realistic space, especially travel activity, lack the extraction to activity essential attribute information. Need to develop the group activity extracting method of fusion multi-source space-time trajectory data, to establish based on the movable urban science research of magnanimity Determine data basis.Space-time trajectory data (such as mobile phone signaling data, vehicle GPS data, social activity register data) is although comprising rich Rich temporal information and location information, but semantic information is opposite to be lacked, and spatial and temporal resolution is different, can not directly provide Group activity information.
Therefore, the existing technology needs to be improved and developed.
Summary of the invention
In view of the deficiencies in the prior art, it is an object of that present invention to provide a kind of, and the group based on multi-source space-time trajectory data is living Dynamic method of data capture and system.
Technical scheme is as follows:
A kind of group activity method of data capture based on multi-source space-time trajectory data, wherein method includes:
A, backstage obtains originating mobile terminal signaling data and original social software and registers data, respectively to it is original it is mobile eventually End signaling data and original social software data of registering are pre-processed, and the correspondence of generation meets the signaling to be processed of specific format Data and data to be processed of registering；
B, from the background by presetting the rule of time and space, moving point is extracted from signaling data to be processed, is obtained Moving point track data；According to the classification information of registering in data to be processed of registering, constructs and learn group activity rule Prior information；Moving point track data is obtained, activity venue data are obtained；
C, from the background according to moving point track data, the prior information of group activity rule, activity venue data, using being based on Bayesian model carry out activity locus of points semantic information label, generation activity space-time trajectory chain.
The group activity method of data capture based on multi-source space-time trajectory data, wherein the A is specifically included:
A1, backstage obtain originating mobile terminal signaling data, carry out quality cleaning to originating mobile terminal signaling data, go Except repeated data, the data of attribute missing, the data of removal time and space not within the predefined range, removal user's points are removed Amount is less than or greater than the user data of certain threshold value, generates pretreatment signaling data；
A2, original social software is obtained from the background registering data, quality cleaning is carried out to original social software data of registering, is gone Except repeated data, the data of attribute missing are removed, remove the data of time and space not in research range, removal user registers The user data that quantity is only registered in one place in a certain range of user data, removal generates and pre-processes data of registering；
A3, signaling data will be pre-processed and pre-process the spatial resolution for data of registering according to the scale of pre-defined rule grid Resolution ratio converted, generate corresponding signaling data to be processed and data to be processed of registering.
The group activity method of data capture based on multi-source space-time trajectory data, wherein by preparatory in the B The rule of setting time and space extracts moving point from signaling data to be processed, and obtained moving point track data specifically wraps It includes:
B11, signaling data to be processed is obtained from the background, people and time are ranked up according to specific time rule, obtained People sequential track；
B12, the sequential track according to people calculate the time that people enters and leaves specific position, successively enter people each A position is set as moving point, and first position that people enters is set as first moving point in the movable locus of points；
B13, the space length and time difference for calculating every bit and existing moving point in sequential track, if space length Less than given threshold, and time difference is less than given threshold, then moving point is added in the point and the point is otherwise set as new Moving point, until in sequential track all the points all calculate finish, obtain the candidate active locus of points；
B14, obtain the candidate active locus of points in candidate active point, when the entry time for detecting candidate active point and from The difference of ETAD expected time of arrival and departure will then correspond to candidate active point after removing in the candidate active locus of points, generate less than the second given threshold Moving point track data.
The group activity method of data capture based on multi-source space-time trajectory data, wherein according to wait locate in the B The classification information of registering in data of registering is managed, constructs and the prior information for learning group activity rule specifically includes:
B21, according to social activity register platform register classification and user's data in different time periods of registering in one day it is total Amount, is calculated different groups activity and is distributed in intraday intensive probable；
B22, the data of registering according to user calculate the different groups activity movable transition probability under different time point Cloth；
B23, the data of registering according to user calculate different regions and carry out the movable probability distribution of different groups.
The group activity method of data capture based on multi-source space-time trajectory data, wherein acquisition activity in the B Locus of points data obtain activity venue data and specifically include:
B31, preset people activity venue time identification window, be denoted as respectively the first active window, second activity Window；
B32, the moving point track data for obtaining people are living with the first active window and second respectively by the moving point duration Dynamic window is matched, if the duration of moving point falls in a certain active window, and accounts for total activity window time length 50% or more, then the moving point corresponds to the corresponding activity venue of the active window as candidate active position；
B33, activity venue data of the match time longest candidate active position as user are obtained.
The group activity method of data capture based on multi-source space-time trajectory data, wherein the C is specifically included:
C1, according to Bayesian model, and after the Activity Type of given position, time and previous moment, generate Subsequent time carries out the new probability formula of a certain type of activity；
C2, according to each moving point in moving point track data, different movable probability sizes are engaged in calculating, are obtained most The activity mark of maximum probability is the maximum probability Activity Type of the moving point；
C3, by moving point track data all moving points label after, output activity space-time trajectory chain.
A kind of group activity data gathering system based on multi-source space-time trajectory data, wherein system includes:
Preprocessing module, for obtaining originating mobile terminal signaling data from the background and original social software is registered data, point Other to pre-process to originating mobile terminal signaling data and original social software data of registering, the correspondence of generation meets particular bin The signaling data to be processed of formula and data to be processed of registering；
Activity venue data acquisition module, for backstage by presetting the rule of time and space, from letter to be processed It enables and extracts moving point in data, obtained moving point track data；According to the classification information of registering in data to be processed of registering, structure Build and learn the prior information of group activity rule；Moving point track data is obtained, activity venue data are obtained；
Semantic marker module, for backstage according to moving point track data, the prior information of group activity rule, actively Point data is marked, generation activity space-time trajectory chain using based on Bayesian model carry out activity locus of points semantic information.
The group activity data gathering system based on multi-source space-time trajectory data, wherein the preprocessing module It specifically includes:
Signaling data processing unit, for obtaining originating mobile terminal signaling data from the background, to originating mobile terminal signaling Data carry out quality cleaning, remove repeated data, and the data of removal attribute missing remove time and space not within the predefined range Data, removal user's point quantity be less than or greater than certain threshold value user data, generate pretreatment signaling data；
It registers data processing unit, registers data for obtaining original social software from the background, register to original social software Data carry out quality cleaning, remove repeated data, and the data of removal attribute missing remove time and space not in research range Data, removal user registers quantity in a certain range of user data, and the user data that removal is only registered in one place is raw It registers data at pretreatment；
Resolution conversion unit, for that will pre-process signaling data and pre-process the spatial resolution for data of registering according to pre- The resolution ratio for determining the scale of regular grid is converted, and corresponding signaling data to be processed and data to be processed of registering are generated.
The group activity data gathering system based on multi-source space-time trajectory data, wherein described actively to count It is specifically included according to module is obtained:
Sequencing unit carries out people and time according to specific time rule for obtaining signaling data to be processed from the background Sequence, the sequential track of obtained people；
Moving point marking unit calculates the time that people enters and leaves specific position for the sequential track according to people, according to The secondary each position for entering people is set as moving point, and first first position that people enters be set as in the movable locus of points A moving point；
Candidate active locus of points generation unit, for calculate the space of every bit and existing moving point in sequential track away from From with time difference, if space length be less than given threshold, and time difference be less than given threshold, then by it is described point addition activity Otherwise the point is set as new moving point by point, until in sequential track all the points all calculate finish, obtain candidate active The locus of points；
Moving point track data processing unit, for obtaining the candidate active point in the candidate active locus of points, when detecting The entry time of candidate active point and the difference of time departure will then correspond to candidate active point from candidate less than the second given threshold After removing in the movable locus of points, moving point track data is generated；
First probability calculation unit, for according to social activity register platform register classification and user it is different in one day when Between section total amount of data of registering, different groups activity is calculated and is distributed in intraday intensive probable；
Second probability calculation unit calculates different groups activity under different time for the data of registering according to user Movable transfering probability distribution；
It is living to calculate different region progress different groups for the data of registering according to user for third probability calculation unit Dynamic probability distribution；
Unit is preset, the time identification window of the activity venue for presetting people is denoted as the first activity respectively Window, the second active window；
Candidate active location determination unit distinguishes the moving point duration for obtaining the moving point track data of people It is matched with the first active window and the second active window, if the duration of moving point falls in a certain active window, and 50% or more of total activity window time length is accounted for, then the moving point corresponds to the corresponding activity venue of the active window as time Select moving position；
Activity venue data capture unit, for obtaining work of the match time longest candidate active position as user Dynamic locality data.
The group activity data gathering system based on multi-source space-time trajectory data, wherein the semantic marker mould Block specifically includes:
4th probability calculation unit is used for according to Bayesian model, and given position, time and previous moment Activity Type after, generate subsequent time and carry out the new probability formula of a certain type of activity；
Maximum probability Activity Type marking unit, for according to each moving point in moving point track data, calculate from The different movable probability sizes of thing, the activity mark for obtaining maximum probability is the maximum probability Activity Type of the moving point；
Activity space-time trajectory chain generation unit, for exporting after all moving points label in moving point track data Activity space-time trajectory chain.
The present invention provides a kind of group activity method of data capture and system based on multi-source space-time trajectory data, this hairs The bright deduction that individual activity is carried out using Bayesian model, and previous moment Activity Type is considered in spatio-temporal activity track to rear A wide range of, the accurate, quick of magnanimity group activity, high efficiency extraction and collection are realized in the influence of one moment Activity Type.
Detailed description of the invention
Fig. 1 is a kind of preferable implementation of group activity method of data capture based on multi-source space-time trajectory data of the invention The flow chart of example.
Fig. 2 is a kind of preferable implementation of group activity data gathering system based on multi-source space-time trajectory data of the invention The functional schematic block diagram of example.
Specific embodiment
To make the purpose of the present invention, technical solution and effect clearer, clear and definite, below to the present invention further specifically It is bright.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
The present invention provides a kind of preferable implementations of group activity method of data capture based on multi-source space-time trajectory data The flow chart of example, as shown in Figure 1, wherein method includes:
Step S100, backstage obtains originating mobile terminal signaling data and original social software and registers data, respectively to original Beginning mobile terminal signaling data and original social software data of registering are pre-processed, the correspondence of generation meet specific format to Handle signaling data and data to be processed of registering.Wherein mobile terminal is preferably mobile phone.
In further embodiment, specifically included in step S100:
Step S101, backstage obtains originating mobile terminal signaling data, carries out quality to originating mobile terminal signaling data Cleaning removes repeated data, the data of removal attribute missing, the data of removal time and space not within the predefined range, removal User's point quantity is less than or greater than the user data of certain threshold value, generates pretreatment signaling data；
Step S102, original social software is obtained from the background to register data, and quality is carried out to original social software data of registering Cleaning, removes repeated data, and the data of removal attribute missing remove the data of time and space not in research range, removal User registers quantity in a certain range of user data, removes the user data only registered in one place, generates pretreatment label To data；
Step S103, signaling data will be pre-processed and pre-process the spatial resolution for data of registering according to pre-defined rule grid The resolution ratio of scale converted, generate corresponding signaling data to be processed and data to be processed of registering.
When it is implemented, pre-processing mobile phone signaling data and social activity data of registering, obtain locating after being allowed to meet Desired data are managed, particular content includes:
Quality cleaning, including removal repeated data, the data of removal attribute missing, when removal are carried out to mobile phone signaling data Between and data of the space not in research range, removal user's point quantity be less than or greater than certain threshold value user data；Threshold value Selection depend on specific data type, data format, the quality of data.Preferably, threshold value value range is every less than 3 It, is greater than 100 daily.
Quality cleaning, including removal repeated data, the data of removal attribute missing, when removal are carried out to social activity data of registering Between and data of the space not in research range；Removal user registers user data of the quantity less than 2 times, greater than 100 times；Removal The user data only registered in one place；
For multi-source space-time trajectory data, the influence of spatial resolution is considered.Mobile phone signaling data and social activity are registered number According to spatial resolution be uniformly converted to the scale of rule-based grid.The scale size of regular grid generally depends on above two The spatial resolution of class data itself.Preferential scale selection is 500m*500m.
Step S200, from the background by presetting the rule of time and space, the extraction activity from signaling data to be processed Point, obtained moving point track data；According to the classification information of registering in data to be processed of registering, constructs and learn group activity The prior information of rule；Moving point track data is obtained, activity venue data are obtained.
Further, it is mentioned from signaling data to be processed in step S200 by presetting the rule of time and space Moving point is taken, obtained moving point track data specifically includes:
Step S211, signaling data to be processed is obtained from the background, and people and time are ranked up according to specific time rule, The sequential track of obtained people；
Step S212, according to the sequential track of people, the time that people enters and leaves specific position is calculated, successively enters people Each position be set as moving point, and first position that people enters is set as first moving point in the movable locus of points；
Step S213, the space length and time difference for calculating every bit and existing moving point in sequential track, if empty Between distance be less than given threshold, and time difference be less than given threshold, then by it is described point be added moving point, otherwise, by the point Be set as new moving point, until in sequential track all the points all calculate finish, obtain the candidate active locus of points；
Step S214, the candidate active point in the candidate active locus of points is obtained, when detecting the entrance of candidate active point Between and time departure difference less than the second given threshold, then corresponding candidate active point is removed from the candidate active locus of points Afterwards, moving point track data is generated.
When it is implemented, by extracting the moving point of people, obtaining the work of people for passing through processed mobile phone signaling data Moving point trace.The method for extracting moving point mainly determines that the specific method is as follows by setting time and the rule in space:
It for the mobile phone signaling data of generation, is ranked up according to people and time, obtains the sequential track of people；
Using the sequential track of people, its time for entering and leaving each position (grid) is calculated, first position is set as First moving point in the movable locus of points；
As the time is mobile, calculate in sequential track the space of every bit and the moving point in the existing movable locus of points away from From with time difference；If space length is less than given threshold, and time difference is less than given threshold, then the point is added to this Moving point；Otherwise, which is set as new moving point；Until in sequential track all the points all calculate finish, obtain candidate active The locus of points；The range of preferred given threshold is 500m-1000m.
For the candidate active point in the candidate active locus of points, if the entry time of the point and the difference of time departure are less than Certain threshold value, then it is assumed that the point is not moving point, it is removed from the candidate active locus of points, the moving point rail finally obtained Mark.Preferably, threshold value value range is -3 hours 1 hour.
Further, it constructs according to the classification information of registering in data to be processed of registering in step S200 and learns group The prior information of mechanics specifically includes:
Step S221, according to classification and the user number in different time periods of registering in one day of registering of social platform of registering According to total amount, different groups activity is calculated and is distributed in intraday intensive probable；
Step S222, according to the data of registering of user, it is general to calculate activity transfer of the different groups activity under different time Rate distribution；
Step S223, it according to the data of registering of user, calculates different regions and carries out the movable probability distribution of different groups.
When it is implemented, it is rich in classification information abundant of registering using it for registering data by processed social activity, The prior information of building and study group activity rule.The specific method is as follows:
It is registered according to social activity classification and the user data in different time periods of registering in one day of registering provided by platform Total amount is calculated different groups activity in intraday intensive probable and is distributed Pr (ATi| t), indicate are as follows:
checkins(ATi, t) and indicate that moment t Activity Type is the quantity of registering of i, ∑tcheckins(ATi, t) and it is one day Interior each moment is engaged in the quantity of registering that Activity Type is i, wherein ATiTo be engaged in the number of registering that the class of activity is i, according to user Track of registering, movable transfering probability distribution of the different groups activity under different time is calculated, is expressed as Pr (ATi,t| ATj, t-1), wherein i, j indicate movable classification, and t indicates the time.ATi, it is registering for i that t expression, which is engaged in Activity Type in t moment, Quantity, (ATj, t-1) and it indicates to be engaged in the number of registering that Activity Type is j, probability P r (AT at the t-1 momenti,t|ATj, t-1) meaning Justice is to be engaged in the probability of movable i in moment t in the case where known previous moment t-1 is engaged in movable j；Pr (X) indicates event X Probability announce；
According to the track of registering of user, different mesh regions are calculated and carry out the movable probability distribution of different groups, table It is shown as: Pr (Gridm|ATi, t), wherein m is grid serial number, GridmIndicate m-th of grid, i is the class of activity, and t is the time.
In further embodiment, moving point track data is obtained in the step S200, obtains activity venue data tool Body includes:
Step S231, the time identification window for presetting the activity venue of people, is denoted as the first active window, second respectively Active window；
Step S232, obtain people moving point track data, by the moving point duration respectively with the first active window and Second active window is matched, if the duration of moving point falls in a certain active window, and accounts for total activity window time 50% or more of length, then the moving point corresponds to the corresponding activity venue of the active window as candidate active position；
Step S233, activity venue data of the match time longest candidate active position as user are obtained.
When it is implemented, obtained moving point track data, detects house and the work activities of people.The specific method is as follows:
According to common sense, the identification window of setting house activity and work activities is set to: 0. -7 point, 9. -17 points；
For the moving point track data of people, the duration of moving point is matched with two above identification window, If the duration of the moving point falls in identification window, and 50% or more of the total identification window time span of Zhan, then it is assumed that With success, as candidate house or work activities position；
Find match time longest house or work activities position house and work activities position as the user；If There is no successful match, then it is assumed that the user does not find house or work activities position.
Step S300, from the background according to moving point track data, the prior information of group activity rule, activity venue data, It is marked using based on Bayesian model carry out activity locus of points semantic information, generation activity space-time trajectory chain.
Using the movable locus of points by obtaining, obtained group activity temporal prior information, the house work of obtained people Make action message, marked based on Bayesian model carry out activity locus of points semantic information, the action message of label mainly includes occupying Family, work, other (such as: amusement/shopping/study/leisure/trip), obtain activity space-time trajectory chain.
Obtained spatio-temporal activity track chain has important meaning for research urban planning and urban function region dynamic change Justice.According to the variation of spatio-temporal activity, tune can quickly be made in time for the dynamic change for the urban function region planned Whole and prediction.
Further embodiment, step S300 are specifically included:
Step S301, according to Bayesian model, and given position, the Activity Type of time and previous moment Afterwards, the new probability formula that subsequent time carries out a certain type of activity is generated；
Step S302, according to each moving point in moving point track data, different movable probability sizes are engaged in calculating, The activity mark for obtaining maximum probability is the maximum probability Activity Type of the moving point；
Step S303, after all moving points in moving point track data being marked, output activity space-time trajectory chain.
When it is implemented, according to Bayesian model, in the activity class of given specific location, time and previous moment Under type, the lower moment at a moment will carry out the probability of a certain type of activity are as follows:
Wherein, m is grid serial number, and j is the Activity Type at previous moment, and t is current time, and i is current time activity Type.
For Pr (Gridm|ATi,t,ATj), it is believed that ATjWith GridmCondition is unrelated, then the formula can simplify are as follows:
Pr(Gridm|ATi,t,ATj)=Pr (Gridm|ATi,t) (2)
For Pr (ATi|t,ATj), which can rewrite are as follows:
Pr(ATi|t,ATj)=Pr (ATi,t|ATj, t-1) and (3)
In conjunction with formula (2) (3), formula (1) is converted are as follows:
Pr(ATi|Gridm,t,ATj) ∝ Pr (Gridm|ATi, t) and Pr(ATi,t|ATj,t-1)Pr(ATj|t)(5)
It for the movable locus of points, is sequentially inputted in formula (5), different movable probability sizes are engaged in calculating, take maximum The activity mark of probability is the maximum probability Activity Type of the moving point；
Particularly, for have been marked as at home or work activities type grid position, then by Pr (Gridm|ATi,t) It is set as 1, and by ATj, t-1=AThomeorATworking, continue the label processing for being input to next moving point.Until all work Moving point in moving point trace is marked, and output obtains movable Space-time Chain.AThomeIndicate that Activity Type is AT at homeworking Indicate that Activity Type is to work.
Wherein, moving point track extraction method depends on the spatial and temporal resolution of specific data type, data, is not limited to The method that the present invention introduces；
The observation duration of space-time data is limited to work activities detection method at home, the selection of threshold value is not limited to this hair The method of bright introduction；
The prior information of building and study group activity rule is not limited to social media and registers data, can also use residence The modes such as people's survey data, GPS track data, volunteer's data.
The present invention proposes a kind of completely new group activity collection method based on multi-source space-time trajectory data, using Bayes Model carries out the deduction of individual activity, solves existing method the problems such as taking time and effort, is at high cost, sample size is small, realize it is a wide range of, The accurate, quick of magnanimity group activity, high efficiency extraction and collection.Group activity deduction of the invention not only allows for city space The Factors on Human class such as middle time, position movable constraint, it is also contemplated that previous moment Activity Type is to rear in spatio-temporal activity track The influence of one moment Activity Type considers movable deduction in mankind's spatio-temporal activity chain.
The present invention also provides a kind of preferable realities of group activity data gathering system based on multi-source space-time trajectory data The functional schematic block diagram of example is applied, as shown in Fig. 2, system includes:
Preprocessing module 100, for obtaining originating mobile terminal signaling data from the background and original social software is registered data, Originating mobile terminal signaling data and original social software data of registering are pre-processed respectively, the correspondence of generation meets specific The signaling data to be processed of format and data to be processed of registering；Specifically as described in embodiment of the method.
Activity venue data acquisition module 200, for backstage by presetting the rule of time and space, to be processed Moving point is extracted in signaling data, obtained moving point track data；According to the classification information of registering in data to be processed of registering, Construct and learn the prior information of group activity rule；Moving point track data is obtained, activity venue data are obtained；Specific such as side Described in method embodiment.
Semantic marker module 300, for backstage according to moving point track data, the prior information of group activity rule, work Dynamic locality data, is marked, generation activity space-time trajectory chain using based on Bayesian model carry out activity locus of points semantic information；Tool Body is as described in embodiment of the method.
The group activity data gathering system based on multi-source space-time trajectory data, wherein the preprocessing module It specifically includes:
Signaling data processing unit, for obtaining originating mobile terminal signaling data from the background, to originating mobile terminal signaling Data carry out quality cleaning, remove repeated data, and the data of removal attribute missing remove time and space not within the predefined range Data, removal user's point quantity be less than or greater than certain threshold value user data, generate pretreatment signaling data；Specific such as side Described in method embodiment.
It registers data processing unit, registers data for obtaining original social software from the background, register to original social software Data carry out quality cleaning, remove repeated data, and the data of removal attribute missing remove time and space not in research range Data, removal user registers quantity in a certain range of user data, and the user data that removal is only registered in one place is raw It registers data at pretreatment；Specifically as described in embodiment of the method.
Resolution conversion unit, for that will pre-process signaling data and pre-process the spatial resolution for data of registering according to pre- The resolution ratio for determining the scale of regular grid is converted, and corresponding signaling data to be processed and data to be processed of registering are generated；Tool Body is as described in embodiment of the method.
The group activity data gathering system based on multi-source space-time trajectory data, wherein described actively to count It is specifically included according to module is obtained:
Sequencing unit carries out people and time according to specific time rule for obtaining signaling data to be processed from the background Sequence, the sequential track of obtained people；Specifically as described in embodiment of the method.
Moving point marking unit calculates the time that people enters and leaves specific position for the sequential track according to people, according to The secondary each position for entering people is set as moving point, and first first position that people enters be set as in the movable locus of points A moving point；Specifically as described in embodiment of the method.
Candidate active locus of points generation unit, for calculate the space of every bit and existing moving point in sequential track away from From with time difference, if space length be less than given threshold, and time difference be less than given threshold, then by it is described point addition activity Otherwise the point is set as new moving point by point, until in sequential track all the points all calculate finish, obtain candidate active The locus of points；Specifically as described in embodiment of the method.
Moving point track data processing unit, for obtaining the candidate active point in the candidate active locus of points, when detecting The entry time of candidate active point and the difference of time departure will then correspond to candidate active point from candidate less than the second given threshold After removing in the movable locus of points, moving point track data is generated；Specifically as described in embodiment of the method.
First probability calculation unit, for according to social activity register platform register classification and user it is different in one day when Between section total amount of data of registering, different groups activity is calculated and is distributed in intraday intensive probable；Specific such as method is implemented Described in example.
Second probability calculation unit calculates different groups activity under different time for the data of registering according to user Movable transfering probability distribution；Specifically as described in embodiment of the method.
It is living to calculate different region progress different groups for the data of registering according to user for third probability calculation unit Dynamic probability distribution；Specifically as described in embodiment of the method.
Unit is preset, the time identification window of the activity venue for presetting people is denoted as the first activity respectively Window, the second active window；Specifically as described in embodiment of the method.
Candidate active location determination unit distinguishes the moving point duration for obtaining the moving point track data of people It is matched with the first active window and the second active window, if the duration of moving point falls in a certain active window, and 50% or more of total activity window time length is accounted for, then the moving point corresponds to the corresponding activity venue of the active window as time Select moving position；Specifically as described in embodiment of the method.
Activity venue data capture unit, for obtaining work of the match time longest candidate active position as user Dynamic locality data；Specifically as described in embodiment of the method.
The group activity data gathering system based on multi-source space-time trajectory data, wherein the semantic marker mould Block specifically includes:
4th probability calculation unit is used for according to Bayesian model, and given position, time and previous moment Activity Type after, generate subsequent time and carry out the new probability formula of a certain type of activity；Specifically as described in embodiment of the method.
Maximum probability Activity Type marking unit, for according to each moving point in moving point track data, calculate from The different movable probability sizes of thing, the activity mark for obtaining maximum probability is the maximum probability Activity Type of the moving point；Tool Body is as described in embodiment of the method.
Activity space-time trajectory chain generation unit, for exporting after all moving points label in moving point track data Activity space-time trajectory chain；Specifically as described in embodiment of the method.
In conclusion the present invention provides a kind of group activity method of data capture based on multi-source space-time trajectory data and System, method include: that backstage obtains originating mobile terminal signaling data and original social software and registers and data and pre-processed, Generate the signaling data to be processed for meeting specific format and data to be processed of registering；The work that backstage is obtained from signaling data to be processed Moving point trace data；Construct and learn the prior information of group activity rule；Moving point track data is obtained, activity venue is obtained Data；Backstage is according to moving point track data, the prior information of group activity rule, activity venue data, using based on pattra leaves This model carry out activity locus of points semantic information label, generation activity space-time trajectory chain.The present invention is carried out using Bayesian model The deduction of individual activity, and previous moment Activity Type is considered in spatio-temporal activity track to the shadow of later moment in time Activity Type It rings, realizes a wide range of, the accurate, quick of magnanimity group activity, high efficiency extraction and collection.
It should be understood that the application of the present invention is not limited to the above for those of ordinary skills can With improvement or transformation based on the above description, all these modifications and variations all should belong to the guarantor of appended claims of the present invention Protect range.

Claims (6)

1.一种基于多源时空轨迹数据的群体活动数据收集方法，其特征在于，所述方法包括：1. a group activity data collection method based on multi-source spatiotemporal trajectory data, is characterized in that, described method comprises: A、后台获取原始移动终端信令数据和原始社交软件签到数据，分别对原始移动终端信令数据和原始社交软件签到数据进行预处理，生成的对应符合特定格式的待处理信令数据和待处理签到数据；A. Obtain original mobile terminal signaling data and original social software sign-in data in the background, preprocess the original mobile terminal signaling data and original social software sign-in data respectively, and generate corresponding to-be-processed signaling data and pending-processed signaling data that conform to a specific format. sign-in data; B、后台通过预先设定时间和空间的规则，从待处理信令数据中提取活动点，得到的活动点轨迹数据；根据待处理签到数据中的签到类别信息，构建并学习群体活动规律的先验信息；获取活动点轨迹数据，获取活动地点数据；B. The background uses preset time and space rules to extract activity points from the signaling data to be processed, and obtain the activity point trajectory data; according to the check-in category information in the to-be-processed check-in data, build and learn the group activity rules. check information; obtain activity point trajectory data, and obtain activity location data; C、后台根据活动点轨迹数据、群体活动规律的先验信息、活动地点数据，采用基于贝叶斯模型进行活动点轨迹语义信息标记，生成活动时空轨迹链；C. In the background, according to the activity point trajectory data, the prior information of the group activity law, and the activity location data, the Bayesian model is used to mark the activity point trajectory semantic information to generate the activity spatiotemporal trajectory chain; 所述B中通过预先设定时间和空间的规则，从待处理信令数据中提取活动点，得到的活动点轨迹数据具体包括：In said B, by presetting the rules of time and space, the active point is extracted from the signaling data to be processed, and the obtained active point trajectory data specifically includes: B11、后台获取待处理信令数据，将人和时间按照特定的时间规则进行排序，得到的人的时序轨迹；B11. The background obtains the signaling data to be processed, sorts the people and time according to a specific time rule, and obtains the time sequence track of the person; B12、根据人的时序轨迹，计算人进入和离开特定位置的时间，依次将人进入的各个位置设置为活动点，并将人进入的第一个位置设为活动点轨迹中的第一个活动点；B12. According to the time sequence trajectory of the person, calculate the time for the person to enter and leave a specific position, set each position entered by the person as the activity point in turn, and set the first position where the person enters as the first activity in the trajectory of the activity point point; B13、计算时序轨迹中每一点与已有的活动点的空间距离与时间差值，若空间距离小于设定阈值，且时间差值小于设定阈值，则将所述点加入活动点，否则，将所述点设为新的活动点，直到时序轨迹中所有点全部计算完毕，得到候选活动点轨迹；B13. Calculate the spatial distance and time difference between each point in the time sequence trajectory and the existing active point. If the spatial distance is less than the set threshold and the time difference is less than the set threshold, the point is added to the active point, otherwise, The point is set as a new active point, until all points in the time sequence trajectory are calculated, and the candidate active point trajectory is obtained; B14、获取候选活动点轨迹中的候选活动点，当检测到候选活动点的进入时间和离开时间的差值小于第二设定阈值，则将对应候选活动点从候选活动点轨迹中移除后，生成活动点轨迹数据；B14. Obtain the candidate activity points in the candidate activity point trajectory. When it is detected that the difference between the entry time and the departure time of the candidate activity point is less than the second set threshold, the corresponding candidate activity point is removed from the candidate activity point trajectory. , to generate active point trajectory data; 所述B中根据待处理签到数据中的签到类别信息，构建并学习群体活动规律的先验信息具体包括：According to the check-in category information in the check-in data to be processed, the prior information for constructing and learning the law of group activity in said B specifically includes: B21、根据社交签到平台的签到类别以及用户在一天内不同时间段的签到数据总量，计算得到不同群体活动在一天内的强度概率分布；B21. According to the check-in categories of the social check-in platform and the total amount of check-in data of users in different time periods within a day, calculate the intensity probability distribution of different group activities within a day; B22、根据用户的签到数据，计算不同群体活动在不同时间下的活动转移概率分布；B22. According to the user's check-in data, calculate the activity transition probability distribution of different group activities at different times; B23、根据用户的签到数据，计算不同的区域进行不同群体活动的概率分布；B23. Calculate the probability distribution of different groups of activities in different areas according to the user's check-in data; 所述B中获取活动点轨迹数据，获取活动地点数据具体包括：The activity point trajectory data obtained in the B, the acquisition of the activity location data specifically includes: B31、预先设定人的活动地点的时间识别窗口，分别记为第一活动窗口、第二活动窗口；B31. Preset the time identification window of the person's activity location, which are respectively recorded as the first activity window and the second activity window; B32、获取人的活动点轨迹数据，将活动点持续时间分别与第一活动窗口和第二活动窗口进行匹配，若活动点的持续时间落在某一活动窗口内，并占总活动窗口时间长度的50%以上，则该活动点对应所述活动窗口对应的活动地点作为候选活动位置；B32. Acquire the trajectory data of the activity point of the person, and match the duration of the activity point with the first activity window and the second activity window respectively. If the duration of the activity point falls within a certain activity window, it accounts for the length of the total activity window. more than 50%, the activity point corresponds to the activity location corresponding to the activity window as the candidate activity location; B33、获取匹配时间最长的的候选活动位置作为用户的活动地点数据。B33. Obtain the candidate activity location with the longest matching time as the activity location data of the user. 2.根据权利要求1所述的基于多源时空轨迹数据的群体活动数据收集方法，其特征在于，所述A具体包括：2. The method for collecting group activity data based on multi-source spatiotemporal trajectory data according to claim 1, wherein the A specifically comprises: A1、后台获取原始移动终端信令数据，对原始移动终端信令数据进行质量清洗，去除重复数据，去除属性缺失的数据，去除时间和空间不在预定范围内的数据，去除用户点数量小于或大于一定阈值的用户数据，生成预处理信令数据；A1. Acquire the original mobile terminal signaling data in the background, clean the quality of the original mobile terminal signaling data, remove duplicate data, remove data with missing attributes, remove data whose time and space are not within a predetermined range, remove user points less than or greater than User data with a certain threshold, generate preprocessing signaling data; A2、后台获取原始社交软件签到数据，对原始社交软件签到数据进行质量清洗，去除重复数据，去除属性缺失的数据，去除时间和空间不在研究范围内的数据，去除用户签到数量在一定范围的用户数据，去除只在一个地点签到的用户数据，生成预处理签到数据；A2. Obtain the original social software check-in data in the background, clean the original social software check-in data, remove duplicate data, remove data with missing attributes, remove data whose time and space are not within the scope of the study, and remove users whose check-in number is within a certain range Data, remove user data that only checks in at one location, and generate preprocessing check-in data; A3、将预处理信令数据与预处理签到数据的空间分辨率根据预定规则格网的尺度的分辨率进行转换，生成对应的待处理信令数据和待处理签到数据。A3. Convert the spatial resolution of the preprocessed signaling data and the preprocessed check-in data according to the resolution of the scale of the predetermined rule grid, and generate corresponding to-be-processed signaling data and to-be-processed check-in data. 3.根据权利要求1所述的基于多源时空轨迹数据的群体活动数据收集方法，其特征在于，所述C具体包括：3. The method for collecting group activity data based on multi-source spatiotemporal trajectory data according to claim 1, wherein the C specifically comprises: C1、根据贝叶斯模型，以及给定的位置、时间以及前一个时刻的活动类型后，生成下一时刻进行某一类型活动的概率公式；C1. Generate a probability formula for a certain type of activity at the next moment according to the Bayesian model and the given location, time and activity type at the previous moment; C2、根据活动点轨迹数据中的各个活动点，计算从事不同活动的概率大小，获取最大概率的活动标记为所述活动点的最大概率活动类型；C2. Calculate the probability of engaging in different activities according to each activity point in the activity point trajectory data, and obtain the activity with the highest probability and mark it as the activity type with the highest probability of the activity point; C3、将活动点轨迹数据中的所有活动点标记后，输出活动时空轨迹链。C3. After marking all the active points in the active point trajectory data, output the active spatiotemporal trajectory chain. 4.一种基于多源时空轨迹数据的群体活动数据收集系统，其特征在于，系统包括：4. a group activity data collection system based on multi-source spatiotemporal trajectory data, is characterized in that, the system comprises: 预处理模块，用于后台获取原始移动终端信令数据和原始社交软件签到数据，分别对原始移动终端信令数据和原始社交软件签到数据进行预处理，生成的对应符合特定格式的待处理信令数据和待处理签到数据；The preprocessing module is used to obtain the original mobile terminal signaling data and the original social software sign-in data in the background, preprocess the original mobile terminal signaling data and the original social software sign-in data respectively, and generate corresponding to-be-processed signaling conforming to a specific format. data and pending check-in data; 活动地点数据获取模块，用于后台通过预先设定时间和空间的规则，从待处理信令数据中提取活动点，得到的活动点轨迹数据；根据待处理签到数据中的签到类别信息，构建并学习群体活动规律的先验信息；获取活动点轨迹数据，获取活动地点数据；The activity location data acquisition module is used in the background to extract activity points from the signaling data to be processed by pre-setting time and space rules, and obtain the activity point trajectory data; Learn the prior information of group activity law; obtain activity point trajectory data, and obtain activity location data; 语义标记模块，用于后台根据活动点轨迹数据、群体活动规律的先验信息、活动地点数据，采用基于贝叶斯模型进行活动点轨迹语义信息标记，生成活动时空轨迹链；Semantic tagging module is used in the background to use Bayesian model-based semantic information tagging of activity point trajectories based on activity point trajectory data, prior information of group activity rules, and activity location data to generate activity spatiotemporal trajectory chains; 所述活动地点数据获取模块具体包括：The activity location data acquisition module specifically includes: 排序单元，用于后台获取待处理信令数据，将人和时间按照特定的时间规则进行排序，得到的人的时序轨迹；The sorting unit is used to obtain the signaling data to be processed in the background, sort the people and time according to specific time rules, and obtain the time sequence trajectory of the people; 活动点标记单元，用于根据人的时序轨迹，计算人进入和离开特定位置的时间，依次将人进入的各个位置设置为活动点，并将人进入的第一个位置设为活动点轨迹中的第一个活动点；The activity point marking unit is used to calculate the time when the person enters and leaves a specific position according to the time sequence trajectory of the person, and sequentially sets each position where the person enters as the activity point, and sets the first position where the person enters as the activity point track. the first activity point of ; 候选活动点轨迹生成单元，用于计算时序轨迹中每一点与已有的活动点的空间距离与时间差值，若空间距离小于设定阈值，且时间差值小于设定阈值，则将所述点加入活动点，否则，将所述点设为新的活动点，直到时序轨迹中所有点全部计算完毕，得到候选活动点轨迹；The candidate activity point trajectory generation unit is used to calculate the spatial distance and time difference between each point in the time sequence trajectory and the existing activity point. If the spatial distance is less than the set threshold and the time difference is less than the set threshold, the said The point is added to the active point, otherwise, the point is set as a new active point, until all points in the time sequence trajectory are all calculated, and the candidate active point trajectory is obtained; 活动点轨迹数据处理单元，用于获取候选活动点轨迹中的候选活动点，当检测到候选活动点的进入时间和离开时间的差值小于第二设定阈值，则将对应候选活动点从候选活动点轨迹中移除后，生成活动点轨迹数据；The activity point trajectory data processing unit is used to obtain candidate activity points in the candidate activity point trajectory. When it is detected that the difference between the entry time and the departure time of the candidate activity point is less than the second set threshold, the corresponding candidate activity point is removed from the candidate activity point. After the active point trajectory is removed, the active point trajectory data is generated; 第一概率计算单元，用于根据社交签到平台的签到类别以及用户在一天内不同时间段的签到数据总量，计算得到不同群体活动在一天内的强度概率分布；The first probability calculation unit is used to calculate the intensity probability distribution of different group activities within a day according to the check-in category of the social check-in platform and the total amount of check-in data of users in different time periods within a day; 第二概率计算单元，用于根据用户的签到数据，计算不同群体活动在不同时间下的活动转移概率分布；The second probability calculation unit is used to calculate the activity transition probability distribution of different group activities at different times according to the user's check-in data; 第三概率计算单元，用于根据用户的签到数据，计算不同的区域进行不同群体活动的概率分布；The third probability calculation unit is used to calculate the probability distribution of different groups of activities in different areas according to the user's check-in data; 预先设定单元，用于预先设定人的活动地点的时间识别窗口，分别记为第一活动窗口、第二活动窗口；The presetting unit is used for presetting the time identification window of the activity place of the person, which is respectively denoted as the first activity window and the second activity window; 候选活动位置判定单元，用于获取人的活动点轨迹数据，将活动点持续时间分别与第一活动窗口和第二活动窗口进行匹配，若活动点的持续时间落在某一活动窗口内，并占总活动窗口时间长度的50%以上，则该活动点对应所述活动窗口对应的活动地点作为候选活动位置；The candidate activity position determination unit is used to obtain the trajectory data of the activity point of the person, and match the duration of the activity point with the first activity window and the second activity window respectively. If the duration of the activity point falls within a certain activity window, and accounts for more than 50% of the total activity window time length, then the activity point corresponds to the activity location corresponding to the activity window as a candidate activity location; 活动地点数据获取单元，用于获取匹配时间最长的的候选活动位置作为用户的活动地点数据。The activity location data acquisition unit is configured to acquire the candidate activity location with the longest matching time as the activity location data of the user. 5.根据权利要求4所述的基于多源时空轨迹数据的群体活动数据收集系统，其特征在于，所述预处理模块具体包括:5. the group activity data collection system based on multi-source spatiotemporal trajectory data according to claim 4, is characterized in that, described preprocessing module specifically comprises: 信令数据处理单元，用于后台获取原始移动终端信令数据，对原始移动终端信令数据进行质量清洗，去除重复数据，去除属性缺失的数据，去除时间和空间不在预定范围内的数据，去除用户点数量小于或大于一定阈值的用户数据，生成预处理信令数据；The signaling data processing unit is used to obtain the original mobile terminal signaling data in the background, clean the quality of the original mobile terminal signaling data, remove duplicate data, remove data with missing attributes, remove data whose time and space are not within a predetermined range, and remove For user data whose number of user points is less than or greater than a certain threshold, preprocessing signaling data is generated; 签到数据处理单元，用于后台获取原始社交软件签到数据，对原始社交软件签到数据进行质量清洗，去除重复数据，去除属性缺失的数据，去除时间和空间不在研究范围内的数据，去除用户签到数量在一定范围的用户数据，去除只在一个地点签到的用户数据，生成预处理签到数据；The check-in data processing unit is used to obtain the original social software check-in data in the background, clean the quality of the original social software check-in data, remove duplicate data, remove data with missing attributes, remove data whose time and space are not within the scope of the study, and remove the number of user check-ins In a certain range of user data, remove the user data that only checks in at one location, and generate preprocessing check-in data; 分辨率转换单元，用于将预处理信令数据与预处理签到数据的空间分辨率根据预定规则格网的尺度的分辨率进行转换，生成对应的待处理信令数据和待处理签到数据。The resolution conversion unit is configured to convert the spatial resolution of the preprocessed signaling data and the preprocessed check-in data according to the resolution of the scale of the predetermined rule grid, and generate corresponding to-be-processed signaling data and to-be-processed check-in data. 6.根据权利要求4所述的基于多源时空轨迹数据的群体活动数据收集系统，其特征在于，所述语义标记模块具体包括：6. The system for collecting group activity data based on multi-source spatiotemporal trajectory data according to claim 4, wherein the semantic tagging module specifically comprises: 第四概率计算单元，用于根据贝叶斯模型，以及给定的位置、时间以及前一个时刻的活动类型后，生成下一时刻进行某一类型活动的概率公式；The fourth probability calculation unit is used to generate a probability formula for a certain type of activity at the next moment according to the Bayesian model and the given location, time and activity type at the previous moment; 最大概率活动类型标记单元，用于根据活动点轨迹数据中的各个活动点，计算从事不同活动的概率大小，获取最大概率的活动标记为所述活动点的最大概率活动类型；The maximum probability activity type marking unit is used to calculate the probability of engaging in different activities according to each activity point in the activity point trajectory data, and obtain the activity mark with the maximum probability as the maximum probability activity type of the activity point; 活动时空轨迹链生成单元，用于将活动点轨迹数据中的所有活动点标记后，输出活动时空轨迹链。The active spatiotemporal trajectory chain generation unit is used to output the active spatiotemporal trajectory chain after marking all active points in the active point trajectory data.
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