CN106504162A - Same pedestrian's association analysis method and device based on station MAC scan datas - Google Patents
Same pedestrian's association analysis method and device based on station MAC scan datas Download PDFInfo
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Abstract
The embodiment of the invention discloses a kind of same pedestrian's association analysis method and device based on station MAC scan datas.Wherein included based on same pedestrian's association analysis method of station MAC scan datas：Current n item collections are generated according to station MAC scan datas, according to the relative frequency that station MAC scan datas calculate n item collections, frequent n item collections based on n item collections are determined according to relative frequency, whole frequent item sets are searched for by the alternative manner that successively searches for, the frequent item set in addition to frequent 1 item collection in whole frequent item sets is analyzed, the colleague's relationship between target person is obtained.The scheme of the embodiment of the present invention can achieve to analyze the colleague's relationship in statistical significance by extremely limited station MAC scan data information excavatings, meet user's needs to a certain extent, the efficiency that public security department's criminal investigation is traced can be especially effectively improved, and facilitates station public network safety management.
Description
Technical field
The present invention relates to network public safety and data mining technology field, more particularly to a kind of based on station MAC scannings
Same pedestrian's association analysis method of data and device.
Background technology
Public security department is found out, with target person, there is going together for relation of going together usually through being analyzed to target data set
People.
In the analysis of network public safety, the incidence relation excavated between observed personnel is often based upon some specific bases
Plinth data carrying out, for example, by the booking data of railway operation system, resident his identity registration data, hotel ccommodation registration
Data etc..Mining analysis are carried out based on above-mentioned basic data, tends to grasp very intuitive and accurate association relationship.
But in specific implementation process, due to the cooperation problem between the administration section of data source, public security officer
The data that can be transferred typically do not include above-mentioned intuitive and accurate basic data information, when these basic data information are lacked,
Prior art then can not be according to the method for incidence relation between the observed personnel of the data message mining analysis on surface.
Content of the invention
For solving Related Technical Issues, the present invention provides a kind of same pedestrian's association analysiss based on station MAC scan datas
Method and apparatus, with the data collected according to station MAC scanning devices, excavates between observed personnel on behavioral pattern
Certain common denominator, and analyze the incidence relation between the observed personnel of these common denominators acquisitions.
For achieving the above object, the embodiment of the present invention is adopted the following technical scheme that：
In a first aspect, the embodiment of the present invention provides a kind of same pedestrian's association analysis method based on station MAC scan datas,
Including：
Current n item collections are generated according to the station MAC scan datas, wherein, the n item collections are client mac address
Set, n is positive integer, the station MAC scan datas include client mac address, scanning device MAC Address, history SSID,
Acquisition time and collecting location numbering
According to the relative frequency that the station MAC scan datas calculate the n item collections；
Frequent n item collections based on the n item collections are determined according to the relative frequency；
Whole frequent item sets are searched for by the alternative manner that successively searches for；
The frequent item set in addition to frequent 1 item collection in whole frequent item sets is analyzed, is obtained same between target person
Pedestrian's relation.
Second aspect, the embodiment of the present invention also correspondingly provide a kind of same pedestrian's association point based on station MAC scan datas
Analysis apparatus, including：
N item collection generation modules, for generating current n item collections, wherein, the n item collections according to the station MAC scan datas
For the set of client mac address, n is positive integer, and the station MAC scan datas include client mac address, scanning device
MAC Address, history SSID, acquisition time and collecting location numbering
Relative frequency computing module, for calculating the relative frequency of the n item collections according to the station MAC scan datas；
Frequent n item collection determining modules, for determining the frequent n item collections based on the n item collections according to the relative frequency；
Frequent item set search module, searches for whole frequent item sets for the alternative manner by successively searching for；
Colleague's relationship analysis module, for analyzing the frequent episode in whole frequent item sets in addition to frequent 1 item collection
Collection, obtains the colleague's relationship between target person.
The beneficial effect that technical scheme provided in an embodiment of the present invention is brought：
In the technical program, generate n item collections and calculate corresponding relative frequency according to station MAC scan datas, according to phase
Frequency and preset rules are determined with the frequent n item collections of n item collections, whole frequent item sets are searched for by the alternative manner that successively searches for,
Frequent item set of the analysis in addition to frequent 1 item collection, obtains the colleague's relationship between target person；This programme is can achieve according to ten
The colleague's relationship for dividing limited data message mining analysis to go out in statistical significance, meets user's needs, especially to a certain extent
The efficiency that public security department's criminal investigation is traced can be effectively improved.
Description of the drawings
For the technical scheme being illustrated more clearly that in the embodiment of the present invention, below will be to institute in embodiment of the present invention description
The accompanying drawing for using is needed to be briefly described, it should be apparent that, drawings in the following description are only some enforcements of the present invention
Example, for those of ordinary skill in the art, on the premise of not paying creative work, can be with according to present invention enforcement
The content of example and these accompanying drawings obtain other accompanying drawings.
Fig. 1 is the stream of the same pedestrian's association analysis method based on station MAC scan datas that the embodiment of the present invention one is provided
Journey schematic diagram；
Fig. 2A is the stream of the same pedestrian's association analysis method based on station MAC scan datas that the embodiment of the present invention two is provided
Journey schematic diagram；
Fig. 2 B are the schematic flow sheets of the optional embodiment of S230 in Fig. 2A；
Fig. 2 C are the schematic flow sheets of the optional embodiment of S240 in Fig. 2A；
Fig. 3 is the frame of the same pedestrian's association analysis device based on station MAC scan datas that the embodiment of the present invention three is provided
Structure schematic diagram；
Fig. 4 A are the framves of the same pedestrian's association analysis device based on station MAC scan datas that the embodiment of the present invention four is provided
Structure schematic diagram；
Fig. 4 B are the configuration diagrams of the optional embodiment of relative frequency computing module 430 in Fig. 4 A；
Fig. 4 C are the configuration diagrams of the optional embodiment of frequent n item collections determining module 440 in Fig. 4 A.
Specific embodiment
For make present invention solves the technical problem that, the technical scheme that adopts and the technique effect that reaches clearer, below
Accompanying drawing will be combined to be described in further detail the technical scheme of the embodiment of the present invention, it is clear that described embodiment is only
It is a part of embodiment of the invention, rather than whole embodiments.Embodiment in based on the present invention, those skilled in the art exist
The every other embodiment obtained under the premise of not making creative work, belongs to the scope of protection of the invention.
Embodiment one
Fig. 1 is refer to, which is the same pedestrian's association analysiss based on station MAC scan datas that the embodiment of the present invention one is provided
The schematic flow sheet of method.The method of the present embodiment can be executed by computer, it is adaptable to which railway public safety management is public
The criminal investigation of peace department such as traces at the application scenarios.A kind of this embodiment scheme improved method mainly using Apirori algorithms is excavating
Analysis is with pedestrian's incidence relation.
Same pedestrian based on station MAC (Media/Medium Access Control, medium access control) scan data
Association analysis method, may include steps of：
S110：Current n item collections are generated according to station MAC scan datas.
Exemplary, station MAC scan datas include client mac address, scanning device MAC Address, history SSID
The data such as (Service Set Identifier, service set), acquisition time and collecting location numbering, client mac ground
Location represents the MAC Address at the handheld client such as the mobile phone of target person, flat board end, its identity as target person, scanning
Device mac address represents the MAC Address of the MAC scanning devices at station, and acquisition time is the scanning client collection of MAC scanning devices
To time point during client mac address, collecting location numbering is place of the MAC scanning devices present position AT STATION in region
Numbering.The collection of item is collectively referred to as item collection, and the item collection comprising n item is referred to as n item collections, set of the n item collections for client mac address, and n is
Positive integer.According to each client mac address in the MAC scan datas of station, current n item collections are generated.
S120：According to the relative frequency that station MAC scan datas calculate n item collections.
Exemplary, due to when excavating with pedestrian's incidence relation, it is desirable to which that found is the multiple clients while occurring
End MAC Address, and for the number of times that this multiple client MAC Address occurs simultaneously, occur in statistics all clients MAC Address
Number of times in proportion do not require.Therefore the probability that some specific item collection occurs relative to whole data set is for final
As a result without any impact, according to this point, former Apirori need not be introduced during following S130 determine frequent item set
" support " this concept of algorithm, then " relative frequency " is used as the judgment criteria of screening frequent item set, for example,
The physical significance of relative frequency is：When two personnel are occurred in station, if they the most of the time all together, can sentence
Colleague's relation in the two artificial statistical significances fixed.Set according to the client mac address in the MAC scan datas of station, scanning
Standby MAC Address, acquisition time and collecting location numbering, calculate the relative frequency of n item collections.
S130：Frequent n item collections based on n item collections are determined according to relative frequency.
Exemplary, determine that the method for frequent item set is similar with former Apirori algorithms, replaced with relative frequency former
When support in Apirori algorithms, i.e. relative frequency are not less than predetermined threshold value, then corresponding with relative frequency n item collections
For frequent n item collections.
S140：Whole frequent item sets are searched for by the alternative manner that successively searches for.
Exemplary, identical with the processing mode of former Apirori algorithms, find out frequent 1 item collection first, by frequent 1
Frequent 2 item collection found out by collection, finds out frequent 3 item collection by frequent 2 item collection, so successively searches for, until finding new k items
Collection, so far can find out whole frequent item sets.Generate k item collections mode be：Existing k-1 item collections are carried out from connection, principle
It is to ensure that front k-2 items are identical, and connects according to lexicographic order.
S150：The frequent item set in addition to frequent 1 item collection in whole frequent item sets is analyzed, is obtained same between target person
Pedestrian's relation.
Exemplary, as this programme actual demand is for excavating with pedestrian's incidence relation, therefore frequent 1 item collection is not analyzed
Meaning, analyzes the frequent item set in addition to frequent 1 item collection in whole frequent item sets, you can obtain the same pedestrian between target person
Relation.For example, for frequent 2 item collection, then two mesh corresponding to two client mac address in frequent 2 item collection
Mark personnel are colleague's relationship.
It should be noted that according to the actual requirements, as long as the scheme of the present embodiment obtains colleague's relationship of target person
, when occurring for target person X, target person Y is also while there is (i.e. X->Y unidirectional incidence relation) is simultaneously lost interest in,
Therefore the last incidence relation extraction step of former Apirori algorithms is eliminated in the scheme of the present embodiment.
To sum up, in the technical program, n item collections and corresponding relative frequency are generated according to station MAC scan datas, according to
Relative frequency determines the frequent n item collections of n item collections, searches out whole frequent item sets by the alternative manner that successively searches for, to which
In frequent item set in addition to frequent 1 item collection be analyzed, obtain the colleague's relationship between target person；The scheme of this enforcement
Can achieve to analyze the colleague's relationship in statistical significance by extremely limited station MAC scan data information excavatings, necessarily
User's needs are met in degree, can especially effectively improve the efficiency that public security department's criminal investigation is traced.
Embodiment two
Fig. 2A, Fig. 2 B and Fig. 2 C are refer to, wherein, Fig. 2A is scanning based on station MAC for the offer of the embodiment of the present invention two
The schematic flow sheet of same pedestrian's association analysis method of data.The present embodiment is differred primarily in that with embodiment one, be increased
The content of station MAC scan datas is obtained from data base, and further provides the optional enforcement of S230 and S240 in Fig. 2A
Mode.
Based on same pedestrian's association analysis method of station MAC scan datas, may include steps of：
S210：Station MAC scan datas are obtained from data base.
Exemplary, station MAC scan datas are collected by the MAC scanning devices being exclusively provided in each region in station, are lifted
For example, the frequency of MAC scanning device gathered datas is 2 times per minute, is to reduce because the time difference of each MAC scanning devices causes to miss
Difference and target person successively occur in the impact in the range of MAC scanning devices to collection result, take each acquisition time window
Window width is 1 minute, the client that will be scanned within each minute in different stations, by different MAC scanning devices
It is stored in data base after MAC Address re-scheduling, while preserving corresponding scanning device MAC Address, history SSID, acquisition time and adopting
The data messages such as collection location number, call so as to follow-up.In actual process, the MAC scan datas at different stations are deposited
Enter in different database tables to carry out respectively the process such as re-scheduling, because not possessing relatedness between the data at each station, this
Sample process can reduce operand, improve computational efficiency.
S220：Current n item collections are generated according to station MAC scan datas.
S230：According to the relative frequency that station MAC scan datas calculate n item collections.
S240：Frequent n item collections based on n item collections are determined according to relative frequency.
S250：Whole frequent item sets are searched for by the alternative manner that successively searches for.
S260：The frequent item set in addition to frequent 1 item collection in whole frequent item sets is analyzed, is obtained same between target person
Pedestrian's relation.
Optionally, Fig. 2 B refer to, and S230 can include two steps of S231 and S232, wherein：
S231：According to whole client macs in station MAC scan datas statistics n item collections C={ C1, C2 ... C (n-1), Cn }
The number of times that address occurs simultaneously, and the number of times of i-th client mac address Ci appearance, wherein Ci ∈ C.
S232：According to statistical result, the relative frequency of n item collections is calculated.
Exemplary, relative frequency can be calculated according to equation below：
Wherein, S represents relative frequency；T (C) represents the number of times that all client mac address occurs simultaneously in n item collections C；T
(Ci) number of times of i-th client mac address Ci appearance in n item collections C is represented.
For example, for 2 item collections { a, b }, its relative frequency S2 is calculated as follows：
Wherein, T (a) and T (b) are illustrated respectively in client mac address a and client mac ground in whole data for counting
The number of times that location b each occurs, T (ab) represent that client mac address a and client mac address b is same in the data for all counting
When the number of times that occurs.The implication that relative frequency S2 is represented is：Client mac address a and client mac address b occurs simultaneously
Number of times, the average of proportion in the total degree that client mac address a and client mac address b each occur.
Optionally, Fig. 2 C refer to, and S240 can include two steps of S241 and S242, wherein：
S241：It is candidate's n item collections of nonmatching grids to non-NULL Son item set, carries out delete processing.
Exemplary, due to being to determine frequent item set by the alternative manner that successively searches in this programme, if therefore
Certain non-NULL Son item set of candidate's n item collections is nonmatching grids, then the candidate n item collections must not be frequent item set, and which is carried out
Delete processing.
S242：To relative frequency less than candidate's n item collections of preset threshold value, delete processing is carried out, obtain frequent n item collections.
Exemplary, preset threshold value is an empirical value.Judge relative frequency not less than preset threshold value candidate n item collections as
Frequent n item collections；Judge that candidate n item collection of the relative frequency less than preset threshold value, as nonmatching grids, carries out delete processing to which.Through
Cross two judgement deletion actions of S241 and S242, the frequent n item collections needed for final acquisition.
To sum up, in the technical program, station MAC scan datas are obtained from data base, according to station MAC scan datas
N item collections and corresponding relative frequency is generated, according to the frequent n item collections that relative frequency determines n item collections, by the iteration that successively searches for
Method searches out whole frequent item sets, and the frequent item set wherein in addition to frequent 1 item collection is analyzed, and obtains target person
Between colleague's relationship；The scheme of this enforcement is can achieve by extremely limited station MAC scan datas information excavating analysis
The colleague's relationship gone out in statistical significance, meets user's needs to a certain extent, can especially effectively improve public security department's criminal investigation and chase after
The efficiency that looks into.
It is below the enforcement of the same pedestrian's association analysis device based on station MAC scan datas provided in an embodiment of the present invention
Example, the same pedestrian's association analysis device based on station MAC scan datas are closed with the above-mentioned same pedestrian based on station MAC scan datas
Connection analysis method belongs to an inventive concept, and in the embodiment of device, the detail content of not detailed description, please investigate above-mentioned
The embodiment of method.
Embodiment three
Fig. 3 is refer to, which is the same pedestrian's association analysiss based on station MAC scan datas that the embodiment of the present invention three is provided
The configuration diagram of device.
Based on same pedestrian's association analysis device 300 of station MAC scan datas, following content can be included：
N item collections generation module 310, for generating current n item collections according to station MAC scan datas, wherein, n item collections are visitor
The set of family end MAC Address, n is positive integer, station MAC scan datas include client mac address, scanning device MAC Address,
History SSID, acquisition time and collecting location numbering.
Relative frequency computing module 320, for calculating the relative frequency of n item collections according to station MAC scan datas.
Frequent n item collections determining module 330, for determining the frequent n item collections based on n item collections according to relative frequency.
Frequent item set search module 340, searches for whole frequent item sets for the alternative manner by successively searching for.
Colleague's relationship analysis module 350, for analyzing the frequent episode in whole frequent item sets in addition to frequent 1 item collection
Collection, obtains the colleague's relationship between target person.
To sum up, in the technical program, n item collections and corresponding relative frequency are generated according to station MAC scan datas, according to
Relative frequency determines the frequent n item collections of n item collections, searches out whole frequent item sets by the alternative manner that successively searches for, to which
In frequent item set in addition to frequent 1 item collection be analyzed, obtain the colleague's relationship between target person；The scheme of this enforcement
Can achieve to analyze the colleague's relationship in statistical significance by extremely limited station MAC scan data information excavatings, necessarily
User's needs are met in degree, can especially effectively improve the efficiency that public security department's criminal investigation is traced.
Example IV
Refer to Fig. 4 A, Fig. 4 B and Fig. 4 C, wherein Fig. 4 A be the embodiment of the present invention four provide number is scanned based on station MAC
According to same pedestrian's association analysis device configuration diagram.The present embodiment differred primarily in that with embodiment three, increased number
According to acquisition module 410, and further provide relative frequency computing module 430 and frequent n item collections determining module 440 in Fig. 4 A
Optional embodiment.
Based on same pedestrian's association analysis device 400 of station MAC scan datas, following content can be included：
Data acquisition module 410, for obtaining station MAC scan datas from data base.
N item collections generation module 420, for generating current n item collections according to station MAC scan datas, wherein, n item collections are visitor
The set of family end MAC Address, n is positive integer, station MAC scan datas include client mac address, scanning device MAC Address,
History SSID, acquisition time and collecting location numbering.
Relative frequency computing module 430, for calculating the relative frequency of n item collections according to station MAC scan datas.
Frequent n item collections determining module 440, for determining the frequent n item collections based on n item collections according to relative frequency.
Frequent item set search module 450, searches for whole frequent item sets for the alternative manner by successively searching for.
Colleague's relationship analysis module 460, for analyzing the frequent episode in whole frequent item sets in addition to frequent 1 item collection
Collection, obtains the colleague's relationship between target person.
Optionally, Fig. 4 B refer to, and relative frequency computing module 430 can include statistic unit 431 and computing unit
432, wherein：
Statistic unit 431, for according in station MAC scan datas statistics n item collections C={ C1, C2 ... C (n-1), Cn }
The number of times that all client mac address occurs simultaneously, and the number of times of i-th client mac address Ci appearance, wherein Ci ∈ C.
Computing unit 432, for according to statistical result, calculating the relative frequency of n item collections.
Wherein, relative frequency is calculated according to the following equation：
Wherein, S represents relative frequency；T (C) represents the number of times that all client mac address occurs simultaneously in n item collections C；T
(Ci) number of times of i-th client mac address Ci appearance in n item collections C is represented.
Optionally, Fig. 4 C refer to, and frequent n item collections determining module 440 can include that the first deletion unit 441 and second is deleted
Unit 442 is removed, wherein：
First deletes unit 441, for being candidate's n item collections of nonmatching grids to non-NULL Son item set, carries out delete processing.
Second deletes unit 442, less than candidate's n item collections of preset threshold value, carries out delete processing, obtain for relative frequency
Obtain frequent n item collections.
To sum up, in the technical program, station MAC scan datas are obtained from data base, according to station MAC scan datas
N item collections and corresponding relative frequency is generated, according to the frequent n item collections that relative frequency determines n item collections, by the iteration that successively searches for
Method searches out whole frequent item sets, and the frequent item set wherein in addition to frequent 1 item collection is analyzed, and obtains target person
Between colleague's relationship；The scheme of this enforcement is can achieve by extremely limited station MAC scan datas information excavating analysis
The colleague's relationship gone out in statistical significance, meets user's needs to a certain extent, can especially effectively improve public security department's criminal investigation and chase after
The efficiency that looks into.
Note, above are only presently preferred embodiments of the present invention and institute's application technology principle.It will be appreciated by those skilled in the art that
The invention is not restricted to specific embodiment described here, can carry out for a person skilled in the art various obvious changes,
Readjust and substitute without departing from protection scope of the present invention.Therefore, although the present invention is carried out by above example
It is described in further detail, but the present invention is not limited only to above example, without departing from the inventive concept, also
Other Equivalent embodiments more can be included, and the scope of the present invention is determined by scope of the appended claims.
Claims (10)
1. a kind of same pedestrian's association analysis method based on station MAC scan datas, it is characterised in that include：
Current n item collections are generated according to the station MAC scan datas, wherein, set of the n item collections for client mac address,
N is positive integer, and the station MAC scan datas include client mac address, scanning device MAC Address, history SSID, collection
Time and collecting location numbering；
According to the relative frequency that the station MAC scan datas calculate the n item collections；
Frequent n item collections based on the n item collections are determined according to the relative frequency；
Whole frequent item sets are searched for by the alternative manner that successively searches for；
The frequent item set in addition to frequent 1 item collection in whole frequent item sets is analyzed, the same pedestrian between target person is obtained
Relation.
2. the method for claim 1, it is characterised in that described the n items are calculated according to the station MAC scan datas
The relative frequency of collection, including：
Whole client mac address in n item collections C={ C1, C2 ... C (n-1), Cn } are counted according to the station MAC scan datas
The number of times for occurring simultaneously, and the number of times of i-th client mac address Ci appearance, wherein Ci ∈ C；
According to statistical result, the relative frequency of the n item collections is calculated.
3. method as claimed in claim 2, it is characterised in that the relative frequency is calculated according to the following equation：
Wherein, S represents the relative frequency；T (C) represents whole client mac address in n item collections C while occurred is secondary
Number；T (Ci) represents the number of times of i-th client mac address Ci appearance in n item collections C.
4. the method for claim 1, it is characterised in that before the current n item collections of the generation, also include：
The station MAC scan datas are obtained from data base.
5. the method as described in any one of claim 1-4, it is characterised in that described determined according to the relative frequency be based on institute
The frequent n item collections of n item collections are stated, including：
It is candidate's n item collections of nonmatching grids to non-NULL Son item set, carries out delete processing；
To relative frequency less than candidate's n item collections of preset threshold value, delete processing is carried out, obtain frequent n item collections.
6. a kind of same pedestrian's association analysis device based on station MAC scan datas, it is characterised in that include：
N item collection generation modules, for generating current n item collections according to the station MAC scan datas, wherein, the n item collections are visitor
The set of family end MAC Address, n is positive integer, and the station MAC scan datas include client mac address, scanning device MAC
Address, history SSID, acquisition time and collecting location numbering；
Relative frequency computing module, for calculating the relative frequency of the n item collections according to the station MAC scan datas；
Frequent n item collection determining modules, for determining the frequent n item collections based on the n item collections according to the relative frequency；
Frequent item set search module, searches for whole frequent item sets for the alternative manner by successively searching for；
Colleague's relationship analysis module, for analyzing the frequent item set in whole frequent item sets in addition to frequent 1 item collection, obtains
Obtain the colleague's relationship between target person.
7. device as claimed in claim 6, it is characterised in that the relative frequency computing module, including：
Statistic unit, for counting in n item collections C={ C1, C2 ... C (n-1), Cn } all according to the station MAC scan datas
The number of times that client mac address occurs simultaneously, and the number of times of i-th client mac address Ci appearance, wherein Ci ∈ C；
Computing unit, for according to statistical result, calculating the relative frequency of the n item collections.
8. device as claimed in claim 7, it is characterised in that the relative frequency is calculated according to the following equation：
Wherein, S represents the relative frequency；T (C) represents whole client mac address in n item collections C while occurred is secondary
Number；T (Ci) represents the number of times of i-th client mac address Ci appearance in n item collections C.
9. method as claimed in claim 6, it is characterised in that also include：
Data acquisition module, for obtaining the station MAC scan datas from data base.
10. the device as described in any one of claim 6-9, it is characterised in that the frequent n item collections determining module, including：
First deletes unit, for being candidate's n item collections of nonmatching grids to non-NULL Son item set, carries out delete processing；
Second deletes unit, less than candidate's n item collections of preset threshold value, carries out delete processing, obtain frequent n for relative frequency
Item collection.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610900214.3A CN106504162A (en) | 2016-10-14 | 2016-10-14 | Same pedestrian's association analysis method and device based on station MAC scan datas |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610900214.3A CN106504162A (en) | 2016-10-14 | 2016-10-14 | Same pedestrian's association analysis method and device based on station MAC scan datas |
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| CN109345748A (en) * | 2018-10-31 | 2019-02-15 | 北京锐安科技有限公司 | User device association method, apparatus, server-side, detection device and medium |
| CN109344281A (en) * | 2018-10-12 | 2019-02-15 | 元力云网络有限公司 | A kind of data analysing method based on WIFI probe Yu camera technology |
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| CN109783531A (en) * | 2018-12-07 | 2019-05-21 | 北京明略软件***有限公司 | A kind of relationship discovery method and apparatus, computer readable storage medium |
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| CN111078922A (en) * | 2019-10-15 | 2020-04-28 | 深圳市商汤科技有限公司 | Information processing method and device and storage medium |
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- 2016-10-14 CN CN201610900214.3A patent/CN106504162A/en not_active Withdrawn
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| CN109344281A (en) * | 2018-10-12 | 2019-02-15 | 元力云网络有限公司 | A kind of data analysing method based on WIFI probe Yu camera technology |
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| CN109345748A (en) * | 2018-10-31 | 2019-02-15 | 北京锐安科技有限公司 | User device association method, apparatus, server-side, detection device and medium |
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| CN109523796A (en) * | 2018-12-03 | 2019-03-26 | 南京诚勤教育科技有限公司 | The method for often using rider using information of vehicles and wireless network card address information |
| CN109783531A (en) * | 2018-12-07 | 2019-05-21 | 北京明略软件***有限公司 | A kind of relationship discovery method and apparatus, computer readable storage medium |
| CN111385527A (en) * | 2018-12-28 | 2020-07-07 | 成都云天励飞技术有限公司 | Method for judging peer and related products |
| CN109902934A (en) * | 2019-01-29 | 2019-06-18 | 特斯联（北京）科技有限公司 | City personnel's compartmentalization based on multi-source big data is deployed to ensure effective monitoring and control of illegal activities management method and system |
| CN109947793A (en) * | 2019-03-20 | 2019-06-28 | 深圳市北斗智能科技有限公司 | Analysis method, device and the storage medium of accompanying relationship |
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| CN111078922A (en) * | 2019-10-15 | 2020-04-28 | 深圳市商汤科技有限公司 | Information processing method and device and storage medium |
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