CN106982411A - A kind of real-time passenger flow statistical method based on WIFI probe datas - Google Patents
A kind of real-time passenger flow statistical method based on WIFI probe datas Download PDFInfo
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- CN106982411A CN106982411A CN201710164635.9A CN201710164635A CN106982411A CN 106982411 A CN106982411 A CN 106982411A CN 201710164635 A CN201710164635 A CN 201710164635A CN 106982411 A CN106982411 A CN 106982411A
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- passenger flow
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Classifications
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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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/53—Network services using third party service providers
Abstract
The invention discloses a kind of real-time passenger flow statistical method based on WIFI probe datas, step includes S1, within the time of one day, the WIFI probe datas in real-time collecting shop, and WIFI probe datas are stored in server, and server is counted to WIFI probe datas；S2, server are screened to WIFI probe datas, and the mac addresses of passenger flow number computation rule are not met for filtering；S3, server are counted to the mac addresses left after screening, and corresponding mac number of addresses is just the quantity for obtaining passenger flow number；In passenger flow number, if not being registered as the mac addresses of passenger flow number in the time before today, this mac address is designated as into shop number, and count the quantity into shop number；S4, one day terminate when, server to passenger flow number and enter shop number data carry out persistent storage.The present invention is capable of the mobile population of accurate statistics retail shop, has the advantages that real-time is high, calculating speed is fast, it is accurate to count.
Description
Technical field
Statistics crowd's technical field is applied to the present invention relates to mobile device, more particularly to it is a kind of based on WIFI probe datas
Real-time passenger flow statistical method.
Background technology
Mobile device such as mobile phone, flat board etc., when accessing WIFI network, it is necessary to send Probe request probe datas
The mac address information of terminal is contained in bag, these packets, the equipment such as special router by designing can obtain end
The mac addresses at end, field strength RSSI, and current time information.WIFI probe datas can be applied in big stream of people's early warning, indoor
In the field such as positioning and trajectory analysis, generally these applications are all the mac address informations by being collected to router, count one section
The signal intensity of some mac address is carried out in time.
With advances in technology, major trade companies provide WIFI to supply customer to connect internet all inside shop now,
If the information such as the volume of the flow of passengers in each shop can be counted using WIFI probe datas, it would be beneficial in trade company to oneself shop
Management.When above-mentioned described technology is applied on passenger flow statisticses, discovery has poor real, and processing is slow, history number
The defects such as renewal are responded according to that can not be set according to user.
The content of the invention
In order to overcome the shortcoming and deficiency that prior art is present, the present invention provides a kind of based on the real-time of WIFI probe datas
Passenger flow statistical method, is capable of the mobile population of accurate statistics retail shop, with real-time is high, calculating speed is fast, count accurate etc. excellent
Point.
In order to solve the above technical problems, the present invention provides following technical scheme：It is a kind of based on the real-time of WIFI probe datas
Passenger flow statistical method, comprises the following steps：
S1, within the time of one day, the WIFI probe datas in real-time collecting shop, and WIFI probe datas are stored in clothes
It is engaged in device, and server is counted to WIFI probe datas；
S2, server are screened to WIFI probe datas, the mac of passenger flow number computation rule is not met for filtering
Location；
S3, server are counted to the mac addresses left after screening, and corresponding mac number of addresses is just to obtain passenger flow
The quantity of number；In passenger flow number, if not being registered as the mac addresses of passenger flow number in the time before today,
This mac address is designated as into shop number, and counts the quantity into shop number；
S4, one day terminate when, server to passenger flow number and enter shop number data carry out persistent storage.
Further, the step S1, uses the WIFI probe datas in WIFI hot spot equipment real-time collecting shop, server
WIFI probe datas are counted, the statistics WIFI probe datas include：A certain mac addresses are in a retail shop gateway
Under occurrence number, field strength RSSI and time.
Further, the mac addresses of passenger flow number computation rule are met in the step S2, including：
Occurrence number of the mac addresses in retail shop reaches given threshold, and the difference of time that arbitrary continuation occurs twice is not
More than one hour；
Further, the given threshold be 4 times and more than.
Further, in the step S3, passenger flow number and shop number will be entered as statistical result, statistical result now
It is so that retail shop ID and time point is the passenger flow numbers under keyword, difference RSSI intensity and to enter shop number be actual value values
It is buffered in memory database；In the step S4, carry out persistent storage when, using retail shop Id and field strength RSSI as
Passenger flow number under keyword, different time points and enter shop number as value value persistent storages in server.
After adopting the above technical scheme, the present invention at least has the advantages that：The present invention is by using WIFI probes
Data, count different retail shops and the passenger flow number of Each point in time and entered shop number in one day, and based on above-mentioned statistical result,
There is provided close friend, the statistical result display systems with user mutual；Statistical method in the present invention has amount of calculation and storage
The advantages of required amount is small, real-time, statistical result is accurate, error is small；In 3 stylobates in can just be reached on common commercial PC
To the processing speed of middle 5W bars per second, while extension process speed can be continued according to the extension of hardware, it disclosure satisfy that big absolutely
Application in most scenes.
Brief description of the drawings
Fig. 1 is a kind of step flow chart of the real-time passenger flow statistical method based on WIFI probe datas of the present invention；
Fig. 2 shows for a kind of passenger flow statisticses display systems of the real-time passenger flow statistical method based on WIFI probe datas of the present invention
It is intended to；
Fig. 3 is statistics passenger flow number in a kind of real-time passenger flow statistical method based on WIFI probe datas of the present invention and enters shop
The line chart used after number.
Embodiment
It should be noted that in the case where not conflicting, the feature in embodiment and embodiment in the application can phase
Mutually combine, the application is described in further detail with specific embodiment below in conjunction with the accompanying drawings.
As shown in figure 1, the invention provides a kind of real-time passenger flow statistical method based on WIFI probe datas, its specific step
It is rapid as follows：
1st,, can be by WIFI number of probes after the WIFI hot spot equipment collection WIFI probe datas in some shop in one day
According to storage in the server, the first step to do is to the probe data come in real time in server according to mac addresses and retail shop
ID is counted, and the number of times that same mac addresses occur in different retail shops is separated statistics.For speed up processing,
There is the smaller explanation mac addresses of the value apart from this nearlyer characteristic of WIFI hot spot equipment using field strength RSSI, respectively to every
The RSSI of individual grade carries out statistics number, then remembers the last item probe records of the mac addresses under this retail shop ID
Time, it can facilitate and greatly reduce amount of calculation in follow-up passenger flow calculating.
2nd, in order to reduce amount of calculation, the passenger flow number at each time point uses the side that timing is calculated with the calculating for entering shop number
Formula is carried out, it is to avoid carrying out a WIFI probe data just calculates a passenger flow number and enter shop number, in order that the reality of system
Shi Xinggeng is high, and the interval that timing is calculated is generally 3-5 minutes.Within a timing counting period, calculated advise according to passenger flow first
Incongruent mac addresses record is then excluded, the amount of calculation in downstream and the expense of transmission data is reduced.Passenger flow computation rule is such as
Under：Some occurrence numbers of mac addresses A under some retail shop reaches some threshold value, while must be in a continuous period
It is interior, and the time of last time appearance and current time can not differ by more than some threshold value.Specifically, a mac address is in business
Occurrence number is more than or equal to 4 times in paving, and no more than one hour of difference of any time occurred twice.Preferably, this
Number of times and time difference in rule in practice can be with self-defined adjustment.
3rd, recorded for meeting the mac addresses of passenger flow computation rule, according to different fields of the mac addresses under this retail shop
Strong intensity RSSI number of times statistic record, ascending mac addresses occurrence number added up under different field strength RSSI, when
More than one threshold value of number of times that the mac addresses occur, RSSI intensity now is statisfied_rssi, when the mac addresses exist
Last time of occurrence and current time under statisfied_rssi intensity are spaced in certain threshold value, then be considered as this
Mac addresses all meet passenger flow in all RSSI intensity higher than statisfied_rssi (including statisfied_rssi) intensity
Number design conditions, are included in passenger flow number, and this mac address is now stored in the visitor under the current point in time of this retail shop
In person who lives in exile's ordered series of numbers table, and record the RSSI intensity statisfied_rssi of satisfaction.
4th, the mac number of addresses that meets passenger flow number of the retail shop at each time point is counted, with regard to that can show that the retail shop exists
Then these mac addresses for meeting passenger flow calculating are carried out after duplicate removal by the passenger flow number of difference RSSI intensity under each time point,
Earliest time point when passenger flow is met with mac addresses, the shop time is entered as the mac addresses, it is possible to is drawn when different
Shop number is entered by Jian Dian retail shops.
Wherein, on passenger flow number and entering dividing into for shop number：If a mac addresses A is credited to the visitor in retail shop B
In person who lives in exile's number, then the passenger flow number of current point in time needs+1, when this mac addresses A does not have in one day in retail shop B
Time point in the past occurred, that is, was not credited to passerby person who lives in exile's number, and it is in this time to be considered as this mac address A
In the retail shop B that point enters, the shop number of entering of current point in time needs+1, when this prior times of mac addresses A in retail shop B
Point is credited in passerby person who lives in exile's number, then current point in time enters shop number and need not count this mac address A.
5th, retail shop is counted in the passenger flow number of different time points and after entering shop number, and persistent storage is carried out to it,
Follow-up displaying is to need to show the passenger flow number of the retail shop under different RSSI intensity and enter shop number, so need will be above-mentioned
Statistical result is major key according to retail shop ID and different RSSI intensity, with the visitor of retail shop's Each point in time under each RSSI intensity
Person who lives in exile's number is the data line in tables of data with shop number is entered, and is stored in database.Each point in time for retail shop expires
The mac address sets of sufficient passenger flow, it is also desirable to which carrying out persistent storage, there is provided the query function of mac addresses.
The passenger flow statisticses display systems of the present invention are as shown in Fig. 2 the passenger flow statisticses result after real-time calculate is stored
Afterwards, display systems are according to the user-defined RSSI strength requests for meeting passenger flow, from database by this RSSI under the retail shop
The passenger flow number of the Each point in time of intensity is shown with shop number is entered using the mode of line chart.As shown in figure 3, being certain shop one
Passenger flow number in it and enter shop number line chart, wherein abscissa is the time, and ordinate is number, and dotted line represents passenger flow people
Number, solid line is represented into shop number.
The real-time passenger flow statistical method of the present invention can calculate retail shop in real time in one day with minimum time interval
The passenger flow number of Each point in time and enter shop number, and the mac address sets for meeting passenger flow can be viewed, and by certain
Rule-based filtering fallen the mac addresses of falseness, passenger flow statisticses result is accurate, and real-time, calculating speed is fast.
Although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
Understand, can carry out a variety of equivalent changes to these embodiments without departing from the principles and spirit of the present invention
Change, change, replace and modification, the scope of the present invention is limited by appended claims and its equivalency range.
Claims (5)
1. a kind of real-time passenger flow statistical method based on WIFI probe datas, it is characterised in that comprise the following steps：
S1, within the time of one day, the WIFI probe datas in real-time collecting shop, and WIFI probe datas are stored in server
In, and server counts to WIFI probe datas；
S2, server are screened to WIFI probe datas, and the mac addresses of passenger flow number computation rule are not met for filtering；
S3, server are counted to the mac addresses left after screening, and corresponding mac number of addresses is just to obtain passenger flow number
Quantity；In passenger flow number, if the mac addresses of passenger flow number are not registered as in the time before today, this
Mac addresses are designated as into shop number, and count the quantity into shop number；
S4, one day terminate when, server to passenger flow number and enter shop number data carry out persistent storage.
2. a kind of real-time passenger flow statistical method based on WIFI probe datas according to claim 1, it is characterised in that institute
Step S1 is stated, using the WIFI probe datas in WIFI hot spot equipment real-time collecting shop, server is carried out to WIFI probe datas
Statistics, the statistics WIFI probe datas include：Occurrence number, field strength of a certain mac addresses under a retail shop gateway are strong
Spend RSSI and time.
3. a kind of real-time passenger flow statistical method based on WIFI probe datas according to claim 2, it is characterised in that institute
The mac addresses for meeting passenger flow number computation rule in step S2 are stated, including：Occurrence number of the mac addresses in retail shop, which reaches, to be set
Determine threshold value, and no more than one hour of difference of time that arbitrary continuation occurs twice.
4. a kind of real-time passenger flow statistical method based on WIFI probe datas according to claim 3, it is characterised in that institute
State given threshold for 4 times and more than.
5. a kind of real-time passenger flow statistical method based on WIFI probe datas according to claim 1, it is characterised in that institute
State in step S3, passenger flow number and shop number will be entered as statistical result, statistical result now is with retail shop ID and time point
For the passenger flow number under keyword, difference RSSI intensity and to enter shop number be that actual value values are buffered in memory database；
In the step S4, carry out persistent storage when, using retail shop Id and field strength RSSI as keyword, different time points under
Passenger flow number and enter shop number as value value persistent storages in server.
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| CN201710164635.9A CN106982411A (en) | 2017-03-20 | 2017-03-20 | A kind of real-time passenger flow statistical method based on WIFI probe datas |
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| CN201710164635.9A CN106982411A (en) | 2017-03-20 | 2017-03-20 | A kind of real-time passenger flow statistical method based on WIFI probe datas |
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Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107635006A (en) * | 2017-09-28 | 2018-01-26 | 上海盈联电信科技有限公司 | Business synthesis cloud application system based on wireless connection |
| CN107845259A (en) * | 2017-10-24 | 2018-03-27 | 东南大学 | Public transport operation situation real-time feedback system and public transport real-time running data processing method |
| CN108011761A (en) * | 2017-12-06 | 2018-05-08 | 易居（中国）企业集团股份有限公司 | The method of collection and analysis visitor's data based on big data |
| CN108133386A (en) * | 2017-12-19 | 2018-06-08 | 上海云信拓客信息科技有限公司 | A kind of visitor's behaviortrace analysis system |
| CN108306962A (en) * | 2018-01-30 | 2018-07-20 | 河海大学常州校区 | A kind of business big data analysis system |
| CN108492152A (en) * | 2018-04-17 | 2018-09-04 | 广州建翎电子技术有限公司 | A kind of shop analysis and processing method based on big data |
| CN108600965A (en) * | 2018-05-11 | 2018-09-28 | 南京邮电大学 | A kind of passenger flow data prediction technique based on guest location information |
| CN108664620A (en) * | 2018-05-14 | 2018-10-16 | 南京邮电大学 | A kind of shop passenger flow forecast method |
| CN108668225A (en) * | 2018-04-11 | 2018-10-16 | 北京天正聚合科技有限公司 | Object properties determine method and intercept strategy adjusting method, device and electronic equipment |
| CN109199780A (en) * | 2018-06-14 | 2019-01-15 | 浙江九点健康科技有限公司 | A kind of massage armchair of smart charge |
| CN109935082A (en) * | 2019-04-30 | 2019-06-25 | 长安大学 | A method of its acquisition data of Transportation Data Collection Terminal and use based on WiFi |
| CN109951804A (en) * | 2019-02-26 | 2019-06-28 | 北京空中信使信息技术有限公司 | A kind of stream of people's amount estimation method and device |
| CN110087255A (en) * | 2019-04-22 | 2019-08-02 | 新华三技术有限公司 | Information statistical method and device |
| CN110290465A (en) * | 2018-12-27 | 2019-09-27 | 上海君来软件有限公司 | A kind of real-time passenger flow monitoring method in park based on WiFi sniff data |
| CN110366200A (en) * | 2019-08-01 | 2019-10-22 | 上海重盟信息技术有限公司 | A kind of method and device of terminal calibration |
| CN110517063A (en) * | 2019-07-19 | 2019-11-29 | 阿里巴巴集团控股有限公司 | Method, apparatus and server are determined into shop consumption person-time |
| CN110535891A (en) * | 2018-05-24 | 2019-12-03 | 北京智慧图科技有限责任公司 | A method of identification user's visiting |
| CN113438686A (en) * | 2021-06-18 | 2021-09-24 | 杭州宇链科技有限公司 | Method for intelligently monitoring floating population |
| CN113536256A (en) * | 2021-07-27 | 2021-10-22 | 江西高创保安服务技术有限公司 | Statistical analysis method and device for population mobility data and electronic equipment |
| CN114157986A (en) * | 2021-12-02 | 2022-03-08 | 重庆交通大学 | Close contact people identification method and device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103458456A (en) * | 2013-08-27 | 2013-12-18 | 中国科学院信息工程研究所 | Method and device for user behavior detection based on mobile terminal Wi-Fi data |
| CN104778642A (en) * | 2015-03-20 | 2015-07-15 | 北京邮电大学 | Data processing method, server and monitoring system for campus users based on WiFi |
| CN105187240A (en) * | 2015-08-19 | 2015-12-23 | 广东中兴新支点技术有限公司 | Pedestrian volume detection method and device thereof, |
| CN205721956U (en) * | 2016-05-11 | 2016-11-23 | 上海创畴互联网科技有限公司 | The rate that reaches and on-the-spot resident data statistics terminal are touched in a kind of intelligent advertisement |
-
2017
- 2017-03-20 CN CN201710164635.9A patent/CN106982411A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103458456A (en) * | 2013-08-27 | 2013-12-18 | 中国科学院信息工程研究所 | Method and device for user behavior detection based on mobile terminal Wi-Fi data |
| CN104778642A (en) * | 2015-03-20 | 2015-07-15 | 北京邮电大学 | Data processing method, server and monitoring system for campus users based on WiFi |
| CN105187240A (en) * | 2015-08-19 | 2015-12-23 | 广东中兴新支点技术有限公司 | Pedestrian volume detection method and device thereof, |
| CN205721956U (en) * | 2016-05-11 | 2016-11-23 | 上海创畴互联网科技有限公司 | The rate that reaches and on-the-spot resident data statistics terminal are touched in a kind of intelligent advertisement |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107635006A (en) * | 2017-09-28 | 2018-01-26 | 上海盈联电信科技有限公司 | Business synthesis cloud application system based on wireless connection |
| CN107845259A (en) * | 2017-10-24 | 2018-03-27 | 东南大学 | Public transport operation situation real-time feedback system and public transport real-time running data processing method |
| CN108011761A (en) * | 2017-12-06 | 2018-05-08 | 易居（中国）企业集团股份有限公司 | The method of collection and analysis visitor's data based on big data |
| CN108133386A (en) * | 2017-12-19 | 2018-06-08 | 上海云信拓客信息科技有限公司 | A kind of visitor's behaviortrace analysis system |
| CN108306962A (en) * | 2018-01-30 | 2018-07-20 | 河海大学常州校区 | A kind of business big data analysis system |
| CN108306962B (en) * | 2018-01-30 | 2021-09-28 | 河海大学常州校区 | Commercial big data analysis system |
| CN108668225A (en) * | 2018-04-11 | 2018-10-16 | 北京天正聚合科技有限公司 | Object properties determine method and intercept strategy adjusting method, device and electronic equipment |
| CN108668225B (en) * | 2018-04-11 | 2023-12-01 | 北京天正聚合科技有限公司 | Object attribute determining method, interception policy adjusting method and device and electronic equipment |
| CN108492152A (en) * | 2018-04-17 | 2018-09-04 | 广州建翎电子技术有限公司 | A kind of shop analysis and processing method based on big data |
| CN108600965A (en) * | 2018-05-11 | 2018-09-28 | 南京邮电大学 | A kind of passenger flow data prediction technique based on guest location information |
| CN108664620A (en) * | 2018-05-14 | 2018-10-16 | 南京邮电大学 | A kind of shop passenger flow forecast method |
| CN108664620B (en) * | 2018-05-14 | 2022-07-22 | 南京邮电大学 | Shop passenger flow volume prediction method |
| CN110535891A (en) * | 2018-05-24 | 2019-12-03 | 北京智慧图科技有限责任公司 | A method of identification user's visiting |
| CN109199780A (en) * | 2018-06-14 | 2019-01-15 | 浙江九点健康科技有限公司 | A kind of massage armchair of smart charge |
| CN110290465A (en) * | 2018-12-27 | 2019-09-27 | 上海君来软件有限公司 | A kind of real-time passenger flow monitoring method in park based on WiFi sniff data |
| CN109951804B (en) * | 2019-02-26 | 2020-09-22 | 北京莱利时空科技有限公司 | People flow estimation method and device |
| CN109951804A (en) * | 2019-02-26 | 2019-06-28 | 北京空中信使信息技术有限公司 | A kind of stream of people's amount estimation method and device |
| CN110087255B (en) * | 2019-04-22 | 2022-03-01 | 新华三技术有限公司 | Information statistical method and device |
| CN110087255A (en) * | 2019-04-22 | 2019-08-02 | 新华三技术有限公司 | Information statistical method and device |
| CN109935082A (en) * | 2019-04-30 | 2019-06-25 | 长安大学 | A method of its acquisition data of Transportation Data Collection Terminal and use based on WiFi |
| CN110517063A (en) * | 2019-07-19 | 2019-11-29 | 阿里巴巴集团控股有限公司 | Method, apparatus and server are determined into shop consumption person-time |
| CN110517063B (en) * | 2019-07-19 | 2023-04-28 | 创新先进技术有限公司 | Store consumer time determining method, store consumer time determining device and store consumer time determining server |
| CN110366200A (en) * | 2019-08-01 | 2019-10-22 | 上海重盟信息技术有限公司 | A kind of method and device of terminal calibration |
| CN110366200B (en) * | 2019-08-01 | 2022-11-18 | 上海重盟信息技术有限公司 | Terminal calibration method and device |
| CN113438686A (en) * | 2021-06-18 | 2021-09-24 | 杭州宇链科技有限公司 | Method for intelligently monitoring floating population |
| CN113536256A (en) * | 2021-07-27 | 2021-10-22 | 江西高创保安服务技术有限公司 | Statistical analysis method and device for population mobility data and electronic equipment |
| CN113536256B (en) * | 2021-07-27 | 2023-02-24 | 江西高创保安服务技术有限公司 | Statistical analysis method and device for population mobility data and electronic equipment |
| CN114157986A (en) * | 2021-12-02 | 2022-03-08 | 重庆交通大学 | Close contact people identification method and device, electronic equipment and storage medium |
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Application publication date: 20170725 |