CN109918582A - A kind of user's list point of interest knowledge method for distinguishing based on space-time data - Google Patents
A kind of user's list point of interest knowledge method for distinguishing based on space-time data Download PDFInfo
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- CN109918582A CN109918582A CN201910168620.9A CN201910168620A CN109918582A CN 109918582 A CN109918582 A CN 109918582A CN 201910168620 A CN201910168620 A CN 201910168620A CN 109918582 A CN109918582 A CN 109918582A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract
The invention discloses a kind of, and user's list point of interest based on space-time data knows method for distinguishing, comprising the following steps: 1) filtering error data；2) DBSCAN is clustered；Cluster judgement；3) correlated variables calculates；4) family or work address list class algorithm.The present invention, which only passes through run-length data, can excavate family's work address of most of user.The present invention goes out the main interest point of user as potential family's work address by the DBSCAN cluster result to main interest point.Further, the present invention is filtered all users for not reaching cluster condition, reduces flow and calculates cost.Further, the present invention is used for subsequent algorithm judgement by accurately calculating correlated variables, and raising judges precision.Further, the present invention determines that it is family, work address or is not by carrying out probability calculation to single cluster.
Description
Technical field
The present invention relates to a kind of user's list points of interest to know method for distinguishing, and it is single to particularly belong to a kind of user based on space-time data
Point of interest knows method for distinguishing, belongs to data mining technology field.
Background technique
In the modern life, self-driving trip has become one of most important means of conveyance of people.As vehicle intelligent is set
The standby development with smart phone, more and more equipment have satellite positioning capability, to record wheelpath.
The data that base existing in the prior art then passes through acquisition user's all the period of time carry out correlated judgment, with user couple
The attention of privacy, the data of all the period of time are increasingly difficult to acquire.On the one hand the GPS data of user can be used for a long time, on the other hand
It is to need the data volume of client big, privacy information is also more.And in the prior art, there are no commercialized can use run-length data
Directly excavate the method for family's work address.
Summary of the invention
The purpose of the present invention is to provide a kind of more results of more points of interest of the user based on space-time data to know method for distinguishing, should
Method can be avoided acquisition user's GPS data for a long time, and the data volume needed is few, and the user privacy information needed is also few.
To achieve the above object, the invention provides the following technical scheme: a kind of more points of interest of user based on space-time data
More results know method for distinguishing, comprising the following steps:
One, filtering error data；
Two, DBSCAN is clustered；Cluster judgement；
Three, correlated variables calculates；
Four, family or work address list class algorithm (SAA).
As the further technical solution of the present invention: space-time data is the complete run-length data of user, includes the every of user
One history travel information includes more accurate starting point longitude and latitude, starting point GPS time, terminal longitude and latitude in each history stroke
Degree, terminal GPS time, time are accurate to time-division date.
As the further technical solution of the present invention: in the step 1, to user's latent fault run-length data bag filter
Contain but be not limited to the user less to head office's number of passes and is filtered and reorders to the stroke for backward occur.Because of head office's number of passes
The less and user that is filtered is without any subsequent step, to reduce flow and calculate cost.
As the further technical solution of the present invention: in the step 2, carrying out DBSCAN cluster is with existing
DBSCAN algorithm clusters the terminal of user, finds out in the potential family that can be used for address excavation or work address class and class
The latitude and longitude coordinates of the heart.
As the further technical solution of the present invention: in the step 2, cluster judgement is carried out according to the result after cluster,
User null for cluster numbers reduces flow and server carrying cost without any subsequent step.For cluster numbers
User equal to one carries out the subsequent step in the present invention；User for cluster numbers greater than one a kind of is dug with stroke referring to inventing
Dig the method or a kind of method for excavating multiple families and work address with stroke of single family and work address.
As the further technical solution of the present invention: in the step 3, being carried out to potential family or work address class
Correlated variables calculates, and the variable of calculating includes: total residence time.
As the further technical solution of the present invention: in the step 4, using family or work address list class algorithm
(SAA) the cluster total residence time singly clustered when has to be larger than certain threshold value, otherwise it is assumed that user's stroke is unreliable without rear
Continuous step.
As the further technical solution of the present invention: in the step 4, using family or work address list class algorithm
(SAA) point in single cluster is looped to determine when, and with calculating potential place of working and potential family probability.
As the further technical solution of the present invention: in the step 4, using family or work address list class algorithm
(SAA) when to the point in single cluster loop to determine in judgement include that the end time, whether festivals or holidays judged, the end time
Whether reach company's judgement and whether the end time reaches family's judgement.
As the further technical solution of the present invention: in the step 4, using family or work address list class algorithm
(SAA) made the following judgment when: if potential family probability be greater than certain threshold value, determine cluster for home address；If potential
Work address probability is greater than certain threshold value, then determines cluster for work address；If two potential address probability are respectively less than certain threshold
Value, then abandon judging.
Compared with prior art, the beneficial effects of the present invention are: the present invention, which only passes through run-length data, can excavate big portion
Divide family's work address of user.The present invention goes out the main interest point of user by the DBSCAN cluster result to main interest point
As potential family's work address.Further, the present invention is filtered all users for not reaching cluster condition, reduces
Flow and calculating cost.Further, the present invention is used for subsequent algorithm judgement by accurately calculating correlated variables, and raising is sentenced
Disconnected precision.Further, the present invention determines that it is family, work address or is not by carrying out probability calculation to single cluster.
Detailed description of the invention
Fig. 1 is that implementation is illustrated with the process for the method that stroke excavates single family or work address in the embodiment of the present invention 1
Figure；
Fig. 2 is the flow diagram of the calculating of correlated variables in the embodiment of the present invention 1；
Fig. 3 is the flow diagram of family or work address list class algorithm (SAA) in the embodiment of the present invention 1.
Specific embodiment
Below in conjunction with the embodiment of the present invention, technical scheme in the embodiment of the invention is clearly and completely described,
Obviously, described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based in the present invention
Embodiment, every other embodiment obtained by those of ordinary skill in the art without making creative efforts, all
Belong to the scope of protection of the invention.
A kind of user's list point of interest knowledge method for distinguishing based on space-time data, comprising the following steps:
1) filtering error data；
2) DBSCAN is clustered；Cluster judgement；
3) correlated variables calculates；
4) family or work address list class algorithm.
The space-time data is the complete run-length data of user, each history travel information comprising user, each
It include more accurate starting point longitude and latitude, starting point GPS time, terminal longitude and latitude, terminal GPS time in history stroke, the time is accurate
To time-division date.
In the step 1), user's latent fault run-length data is filtered including but not limited to the use less to head office's number of passes
Family is filtered and reorders to the stroke for backward occur.
In the step 2), carrying out DBSCAN cluster is to be clustered with existing DBSCAN algorithm to the terminal of user,
Find out the latitude and longitude coordinates of the potential family or work address class and class center that can be used for address excavation.
In the step 2), cluster judgement is carried out according to the result after cluster, user null for cluster numbers not into
Any subsequent step of row, reduces flow and server carrying cost.
In the step 3), correlated variables calculating is carried out to potential family or work address class, the variable of calculating includes:
Total residence time.
In the step 4), the cluster total residence time singly clustered when using family or work address list class algorithm must be big
In certain threshold value, otherwise it is assumed that user's stroke is unreliable without subsequent step.
In the step 4), the point in single cluster is looped to determine using when family or work address list class algorithm,
And with calculating potential place of working and potential family probability.
In the step 4, described to loop to determine whether festivals or holidays judge comprising the end time, whether the end time reaches public affairs
Whether department's judgement and end time reach family's judgement.
In the step 4), the potential place of working being calculated is used when using family or work address list class algorithm
With potential family probability makes the following judgment: if potential family probability be greater than certain threshold value, determine cluster for family
Location；If potential work address probability is greater than certain threshold value, determine cluster for work address；If two potential address probability are small
In certain threshold value, then abandon judging.
Embodiment 1
Referring to Fig. 1, in the embodiment of the present invention, a kind of user's list point of interest knowledge method for distinguishing based on space-time data, packet
Include following steps:
Step S10, filtering error data.
In the present embodiment, coarse filtration and arrangement are carried out for user's run-length data of input.To prevent user's number of strokes
According to there is backward, both earlier than nth stroke at the beginning of (n+1)th stroke, when starting first to user data according to stroke
Between be ranked up.If user's total kilometres quantity is less than 5, without subsequent step, abandon judging.
Step S20, DBSCAN cluster.
In the present embodiment, directly go over the terminal of 3 months all strokes to user using existing DBSCAN method
It is clustered, for the cluster radius threshold value set as r, clustering threshold value of at least counting in range is c.
The above-mentioned threshold value being specifically defined includes:
1) 500 meters of cluster radius threshold value；
2) it clusters in range and at least counts as 5 points.
Step S30, cluster judgement.
In the present embodiment, the first time filtering after the completion of being clustered.If cluster sum is equal to 0, without rear
Continuous step, abandons judging；If cluster numbers have and only 1, sequentially execution step S40；Otherwise, a kind of stroke is used referring to inventing
Excavate the method or a kind of method for excavating multiple families and work address with stroke of single family and work address.
Step S40, correlated variables calculate.
As shown in Fig. 2, the calculating of the correlated variables comprises the following steps:
S4001: judge stroke list to be calculated whether not for sky.If so, reading this stroke and next adjacent rows
Journey and sequence execution step S4002；Otherwise, terminate to calculate, export the total residence time that this single cluster calculation goes out, and execute step
S50。
S4002: calculating this stroke end and next start of a run time difference and sequence executes step S4003.
S4003: the time difference calculated in judgment step S4002 whether less than 0 or the time difference be greater than 3 days.If so, returning
Return step S4001；Otherwise, sequence executes step S4004.
S4004: calculating the actual range of this stroke end and next start of a run and sequence executes step S4005.
S4005: whether the distance calculated in judgment step S4004 is less than 5 kilometers.If so, sequence executes step
S4006；Otherwise, step S4009 is directly executed.
S4006: whether the time difference calculated in judgment step S4002 is greater than 1.5 days.If so, sequence executes step
S4007；Otherwise, step S4008 is directly executed.
S4007: increase by 1.5 days on original cluster total residence time variable (being initially 0), and be back to step
S4001。
S4008: increase the time calculated in step S4002 on original cluster total residence time variable (being initially 0)
Difference, and it is back to step S4001.
S4009: all starts of a run belonged to clustered in 3 kilometers in this calculating and its are found by cluster centre point first
The corresponding departure time.Then the terminal arrival time of the stroke in sorting to all departure times, taking median simultaneously and calculate
Calculate the time difference.If the time difference less than 0, increases by 24 hours on its basis until the time difference is positive, then sequence executes step
Rapid S4010.
S4010: the time difference calculated in judgment step S4009 whether less than 16 hours.If so, sequence executes step
S4011；Otherwise, it is back to step S4001.
S4011: it is calculated in increase 0.5* step S4009 on original cluster total residence time variable (being initially 0)
Time difference, and it is back to step S4001.
Step S50, family or work address list class algorithm (SAA).
As shown in figure 3, the family or work address list class algorithm (SAA) comprise the following steps:
S5001: whether calculated total residence time is greater than 10 hours in judgment step S40.If so, sequence executes
Step S5002；Otherwise, it abandons judging.
S5002: judge whether that cycle calculations reunite all terminals in class.If so, directly executing step S5007；It is no
Then, continue to calculate next terminal and sequence executes step S5003.
S5003: whether the end time for judging the affiliated stroke of this terminal is China national legal festivals and holidays or in 17 points
In~23 points of section.If so, sequence executes step S5004；Otherwise, step S5005 is directly executed.
S5004: it is back to step S5002 together in family's trip count (initial value 0) increase.
S5005: judge whether the end time of the affiliated stroke of this terminal is in 6 points~12 points of section.If so, suitable
Sequence executes step S5006；Otherwise, it is back to step S5002.
S5006: (initial value 0) increase is counted in impulse stroke and is back to step S5002 together.
S5007: judge whether family's number of strokes is greater than specific threshold divided by the ratio of head office's number of passes.If so, directly sentencing
This fixed cluster is home address and exports；Otherwise, sequence executes step S5008.
S5008: judge whether working line number of passes is greater than specific threshold divided by the ratio of head office's number of passes.If so, directly sentencing
This fixed cluster is work address and exports；Otherwise, it abandons judging.
It is necessary to meet following condition in principle for specific threshold in step S5007 and S5008:
1) threshold value is greater than 50%；
2) threshold value is less than or equal to 100%；
The method for excavating single family or work address with stroke in the present embodiment is counted first, in accordance with stroke and is carried out
It filters and clusters and find out potential family and work address, then carry out cluster judgement and correlated variables accurately calculates, finally
Judge that this cluster is family or work address or can not judge by family or work address list class algorithm (SAA).With existing skill
Art is compared, and the method for excavating single family or work address with stroke in the present embodiment solves the number that must use all the period of time
The method of subscriber household work address is obtained accordingly.It, can be only by making in the epoch that user increasingly payes attention to data-privacy
The rough home address of user or work address, which are found, with run-length data provides support for the user tag in later period and user's portrait.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (10)
1. a kind of user's list point of interest based on space-time data knows method for distinguishing, which comprises the following steps:
1) filtering error data；
2) DBSCAN is clustered；Cluster judgement；
3) correlated variables calculates；
4) family or work address list class algorithm.
2. a kind of user's list point of interest based on space-time data according to claim 1 knows method for distinguishing, it is characterised in that:
The space-time data is the complete run-length data of user, each history travel information comprising user, each history stroke
In include more accurate starting point longitude and latitude, starting point GPS time, terminal longitude and latitude, terminal GPS time, the time is accurate to the date
Time-division.
3. a kind of user's list point of interest based on space-time data according to claim 1 knows method for distinguishing, it is characterised in that:
In the step 1), user's latent fault run-length data is filtered and was carried out including but not limited to the less user of head office's number of passes
It filters and reorders to the stroke for backward occur.
4. a kind of user's list point of interest based on space-time data according to claim 1 knows method for distinguishing, which is characterized in that
In the step 2), carrying out DBSCAN cluster is to be clustered with existing DBSCAN algorithm to the terminal of user, is found out available
In the latitude and longitude coordinates of potential family or work address class and class center that address is excavated.
5. a kind of user's list point of interest based on space-time data according to claim 1 knows method for distinguishing, which is characterized in that
In the step 2), cluster judgement is carried out according to the result after cluster, user null for cluster numbers is without after any
Continuous step, reduces flow and server carrying cost.
6. a kind of user's list point of interest based on space-time data according to claim 1 knows method for distinguishing, which is characterized in that
In the step 3), correlated variables calculating is carried out to potential family or work address class, when the variable of calculating includes: total stop
Between.
7. a kind of user's list point of interest based on space-time data according to claim 1 knows method for distinguishing, which is characterized in that
In the step 4), the cluster total residence time singly clustered when using family or work address list class algorithm has to be larger than certain threshold
Value, otherwise it is assumed that user's stroke is unreliable without subsequent step.
8. a kind of user's list point of interest based on space-time data according to claim 1 knows method for distinguishing, which is characterized in that
In the step 4), the point in single cluster is looped to determine using when family or work address list class algorithm, and is calculated latent
Probability in place of working and potential family.
9. a kind of user's list point of interest based on space-time data according to claim 8 knows method for distinguishing, which is characterized in that
In the step 4, it is described loop to determine comprising the end time whether festivals or holidays judge, the end time whether reach company judgement and
Whether the end time reaches family's judgement.
10. a kind of user's list point of interest based on space-time data according to claim 8 knows method for distinguishing, feature exists
In in the step 4), using using the potential place of working being calculated and latent when family or work address list class algorithm
In family probability makes the following judgment: if potential family probability be greater than certain threshold value, determine cluster for home address；If
Potential work address probability is greater than certain threshold value, then determines cluster for work address；If two potential address probability are respectively less than one
Determine threshold value, then abandons judging.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021103626A1 (en) * | 2019-11-26 | 2021-06-03 | 深圳壹账通智能科技有限公司 | Method and apparatus for generating user geographical portrait, computer device, and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140280085A1 (en) * | 2013-03-15 | 2014-09-18 | Mapquest, Inc. | Systems and methods for point of interest data ingestion |
| US20160069697A1 (en) * | 2014-09-10 | 2016-03-10 | Volkswagen Ag | Apparatus, system and method for clustering points of interest in a navigation system |
| CN107529135A (en) * | 2016-06-20 | 2017-12-29 | 同济大学 | User Activity type identification method based on smart machine data |
| CN107580069A (en) * | 2017-09-22 | 2018-01-12 | 挖财网络技术有限公司 | The determination method and device of station address |
| CN108256914A (en) * | 2018-01-17 | 2018-07-06 | 华东师范大学 | A kind of point of interest category forecasting method based on tensor resolution model |
| CN108596202A (en) * | 2018-03-08 | 2018-09-28 | 清华大学 | The method for calculating personal commuting time based on mobile terminal GPS positioning data |
| CN108650632A (en) * | 2018-04-28 | 2018-10-12 | 广州市交通规划研究院 | It is a kind of based on duty live correspondence and when space kernel clustering stationary point judgment method |
| CN109034187A (en) * | 2018-06-12 | 2018-12-18 | 上海中通吉网络技术有限公司 | A kind of subscriber household work address excavation process |
-
2019
- 2019-03-06 CN CN201910168620.9A patent/CN109918582B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140280085A1 (en) * | 2013-03-15 | 2014-09-18 | Mapquest, Inc. | Systems and methods for point of interest data ingestion |
| US20160069697A1 (en) * | 2014-09-10 | 2016-03-10 | Volkswagen Ag | Apparatus, system and method for clustering points of interest in a navigation system |
| CN107529135A (en) * | 2016-06-20 | 2017-12-29 | 同济大学 | User Activity type identification method based on smart machine data |
| CN107580069A (en) * | 2017-09-22 | 2018-01-12 | 挖财网络技术有限公司 | The determination method and device of station address |
| CN108256914A (en) * | 2018-01-17 | 2018-07-06 | 华东师范大学 | A kind of point of interest category forecasting method based on tensor resolution model |
| CN108596202A (en) * | 2018-03-08 | 2018-09-28 | 清华大学 | The method for calculating personal commuting time based on mobile terminal GPS positioning data |
| CN108650632A (en) * | 2018-04-28 | 2018-10-12 | 广州市交通规划研究院 | It is a kind of based on duty live correspondence and when space kernel clustering stationary point judgment method |
| CN109034187A (en) * | 2018-06-12 | 2018-12-18 | 上海中通吉网络技术有限公司 | A kind of subscriber household work address excavation process |
Non-Patent Citations (3)
| Title |
|---|
| KYOSUKE NISHIDA 等: "Probabilistic identification of visited point-of-interest for personalized automatic check-in", ACM * |
| 方英兰;杨勇;韩兵;: "基于多关联度的移动用户POI个性化推荐", 北方工业大学学报 * |
| 韩昊英;于翔;龙瀛;: "基于北京公交刷卡数据和兴趣点的功能区识别", 城市规划 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021103626A1 (en) * | 2019-11-26 | 2021-06-03 | 深圳壹账通智能科技有限公司 | Method and apparatus for generating user geographical portrait, computer device, and storage medium |
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