CN106970976A - A kind of real-time dynamic passenger flow volume statistical method in scenic spot based on visitor's mobile signaling protocol data - Google Patents
A kind of real-time dynamic passenger flow volume statistical method in scenic spot based on visitor's mobile signaling protocol data Download PDFInfo
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- CN106970976A CN106970976A CN201710190179.5A CN201710190179A CN106970976A CN 106970976 A CN106970976 A CN 106970976A CN 201710190179 A CN201710190179 A CN 201710190179A CN 106970976 A CN106970976 A CN 106970976A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24552—Database cache management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W8/00—Network data management
- H04W8/02—Processing of mobility data, e.g. registration information at HLR [Home Location Register] or VLR [Visitor Location Register]; Transfer of mobility data, e.g. between HLR, VLR or external networks
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Abstract
The present invention relates to a kind of real-time dynamic passenger flow volume statistical method in scenic spot based on visitor's mobile signaling protocol data, comprise the following steps：S1：LTE A monitors of eating dishes without rice or wine gather the signaling data of visitor in scenic spot in real time, and data are preserved into telefile, and a signaling data file is preserved with the time granularity of one minute；S2：Whether there is file renewal by Flume assemblies monitors telefile, if there is renewal, file is recorded to collection one by one；S3：Data in the latest document of renewal are sent and carry out data buffering into Kafka data buffering components by Flume components one by one, until the data in the latest document after updating all are sent, bulk data that the data flow is packed is used as Spark data input stream；S4：In the distributed internal memory Computational frames of Spark, by the position of base station where relatively more previous minute and one minute after visitor come visitor's total amount in real-time statistics scenic spot；S5：The volume of the flow of passengers result of real-time statistics per minute is exported and stored.
Description
Technical field
The invention belongs to communication technical field, particularly mobile Internet and computer communication field, it is related to one kind and is based on
The real-time dynamic passenger flow volume statistical method in the scenic spot of visitor's mobile signaling protocol data.
Background technology
In recent years, achieved with cloud computing, Internet of Things, mobile communication etc. for the generation information technology of representative great prominent
It is broken, start to be widely used in all trades and professions.Technology of Internet of things breaches " on line " limitation of internet, virtual world and reality
The world is joined together；Mobile communication technology realizes the wireless connection of real time data between the systems, between remote equipment, is nothing
Place not full-range service provide condition；Cloud computing solves the mass data storage that internet development brought and asked with processing
Topic.The popularization and application of these technologies, powerful support is provided for the development of Digitalized Tourist Information System, has brought human society into one
With the new stage that " PB " (1024TB) is unit, the big data epoch arise at the historic moment.Big data not only have updated Digitalized Tourist Information System institute
The technology needed, the understanding of the mankind has more been reformed from idea and thinking, thinking, business, managerial change is realized.
With the development of China's economic, the growth and the pursuit to quality of the life of people's income, tourist industry is sent out at home
Open up quickly, obtained the support energetically of country, tourist industry enters fulminant build phase, as new pillar industry.Trip
Trip has become the important component of people life style, but has at the same time triggered the potential safety hazard of some scenic spot managements,
The super flow reception visitor in scenic spot has not been rare phenomenon.The tread event that across year Shanghai at night in 2015 occurs, exposes China
The scarcity of ability in terms of a large amount of visitor managements.National Tourism Administration is stricter to scenic spot safety management afterwards, especially in scape
In area's volume of the flow of passengers control.Therefore, either for country or Tourism Bureau's aspect, scenic spot needs a set of for scenic spot visitor's
Security monitoring scheme.
It is poor that the dynamic passenger flow volume statistical method in traditional scenic spot embodies in terms of real-time, and the statistics scenic spot volume of the flow of passengers is
Extracted by being analyzed from traditional database.And traditional database has that efficiency is not high enough, readable not high, data time
Not enough the shortcomings of, causing the magnanimity visitor signaling data in analysis conventional database to cause, inquiry velocity is especially slow, count speed
The shortcomings of degree is especially slow.So traditional database is not suitable for storage analysis mass data, it is unfavorable for tackling short time internal cause
Visitor, which increases severely, the security incident of initiation such as causes congestion, tramples.
Flume is a distribution, the system of the massive logs polymerization of reliable and High Availabitity, is supported in log system
Various types of data sender is customized, for collecting data；Meanwhile, Flume is provided carries out simple process to data, and writes various numbers
According to the ability of reciever (customizable).
Kafka is that a kind of distributed post of high-throughput subscribes to message system, and it can handle the net of consumer's scale
Everything flow data in standing.Kafka can record the data collected from metadata acquisition tool Flume in real time, and conduct disappears
Breath Buffer Unit provides authentic data support for the real-time Computational frame in upstream.
Spark is a distributed internal memory Computational frame, is characterized in that large-scale data can be handled, calculating speed is fast.
Spark needs integrated Hadoop distributed file system to operate, and the MapReduce that it has continued Hadoop calculates mould
Type, by contrast Spark calculating process be maintained in internal memory, reduce disk read-write, can by it is multiple operation merge
After calculate, therefore improve calculating speed.Spark must be ridden on Hadoop clusters, and its data source is HDFS, substantially
It is a Computational frame on Yarn, as MapReduce.Spark cores are divided into RDD.Spark SQL、Spark
The core components such as Streaming, MLlib, GraphX, SparkR solve the problems, such as many big datas, its perfect framework day
It is welcome.Its corresponding ecological environment in terms of visualization, just grows stronger day by day including zepplin etc..Spark read and write process unlike
Hadoop overflows write-in disk, is all based on internal memory, therefore speed is quickly.The width of other DAG job scheduling systems, which is relied on, to be allowed
Spark speed is improved.
This method monitors the passenger flow in scenic spot using quick mass data processing framework and corresponding parser in real time
Situation, the measure such as dredges, shunts, so as to reach the mesh of scenic spot safety management so that scenic spot and the local people's government can take in time
's.
The content of the invention
In view of this, it is an object of the invention to provide a kind of scenic spot based on visitor's mobile signaling protocol data, dynamic is objective in real time
Flow statistical method, this method for the dynamic passenger flow volume statistical method in traditional scenic spot embodied in terms of real-time it is poor,
Mass data is not suitable for being stored in the problems such as being analyzed in traditional database, by monitoring the volume of the flow of passengers in scenic spot in real time, has
Help scenic spot visitor's sharp increase situation in the reply short time, be easy to scenic spot and local government to take in time and the measure such as dredge, shunt, from
And the purpose of scenic spot safety management is reached, while Experience Degree of the lifting visitor to scenic spot to a greater degree.
To reach above-mentioned purpose, the present invention provides following technical scheme：
A kind of real-time dynamic passenger flow volume statistical method in scenic spot based on visitor's mobile signaling protocol data, this method includes following step
Suddenly：
S1：LTE-A monitors of eating dishes without rice or wine gather the signaling data of visitor in scenic spot in real time, and data are preserved to telefile
In, a signaling data file was preserved with the time granularity of one minute；
S2：Whether there is file renewal by Flume assemblies monitors telefile, if there is renewal, file is recorded to receipts one by one
Collection；
S3：Data in the latest document of renewal are sent into Kafka data buffering components progress by Flume components one by one
Data buffering, until update after latest document in data be all sent, using the data flow pack bulk data as
Spark data input stream；
S4：In the distributed internal memory Computational frames of Spark, pass through base where relatively more previous minute and one minute after visitor
Visitor's total amount in real-time statistics scenic spot is carried out in the position stood；
S5：The volume of the flow of passengers result of real-time statistics per minute is exported and stored, MySQL/Oracle numbers are arrived in such as storage
According in storehouse.
Further, in step sl, the LTE-A eat dishes without rice or wine monitor collection scenic spot in visitor mobile signaling protocol data, bag
Including IMS I storehouses, the time of signaling generation, the base station position information at place, and by mobile signaling protocol data with the time granularity of one minute
Preserve a signaling data file.
Further, this method is further comprising the steps of：S6：The real-time results for being stored into database are exported to application displaying
In layer, user is facilitated to check in the form of visualization interface.
The beneficial effects of the present invention are：
1) present invention is data by the real-time mobile signaling protocol data storage of magnanimity of visitor in scenic spot in Telefile
Pretreatment and data analysis and processing module provide data and prepared, and the result after data analysis is stored into traditional database,
So as to significantly reduce the pressure that traditional database directly stores magnanimity real time data.
2) present invention is based on Hadoop platform, and the data operation of magnanimity is carried out in Spark distributed memory Computational frames
The pressure that data operation is carried out in traditional database, is transferred to efficiently quickly Spark distributed memory calculation blocks by analysis
In frame, so as to ensure that the real-time monitoring in scenic spot to visitor.
3) present invention can be used for the passenger flow feature become more meticulous in analysis scenic spot, by the lot number for reusing Kafka bufferings
According to processing stream, multiple RDD conversion are generated in Spark, each RDD is converted for a kind of analysis of passenger flow feature, forms respective
Service logic.
Brief description of the drawings
In order that the purpose of the present invention, technical scheme and beneficial effect are clearer, the present invention provides drawings described below and carried out
Explanation：
Fig. 1 is the data prediction flow chart in the present invention；
Fig. 2 calculates process chart in real time for data in the present invention；
Fig. 3 is the passenger flow signature analysis FB(flow block) that becomes more meticulous in the present invention；
The design of tables of data after Fig. 4 is handled for data calculating in real time in the present invention.
Embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.
Fig. 1 is the data prediction flow chart in the present invention, and Fig. 2 calculates process chart in real time for data in the present invention,
As illustrated, the invention provides a kind of real-time dynamic passenger flow volume statistical method in scenic spot based on visitor's mobile signaling protocol data, should
Method comprises the following steps：
Step 1：LTE-A eat dishes without rice or wine monitor gather in real time visitor in scenic spot signaling data IMSI number and (LAC, CI) (move
Dynamic terminal IMSI represents unique mark of the visitor at scenic spot, and the base station location (LAC, CI) where mobile terminal is represented where visitor
Position), and data are preserved into long-range file, a signaling data file were preserved with the time granularity of one minute.
Step 2：Whether there is file renewal by Flume assemblies monitors telefile, if there is renewal, file is recorded one by one
Collect.
Step 3：Flume components send the data in the latest document of renewal into Kafka data buffering components one by one
Data buffering is carried out, until the data in the latest document after updating all are sent, by data flow packing bulk data
It is used as Spark data input stream.
Step 4：In the distributed internal memory Computational frames of Spark, pass through relatively more previous minute and one minute after visitor institute
Carry out visitor's total amount in real-time statistics scenic spot in the position of base station.When foreground zone visitor total amount be each base station under visitor's total amount it
With.As visitor out of scenic spot one base station to another base station, scenic spot total amount is constant；When before visitor one minute after all at the scenic spot
In interior same base station range, scenic spot total amount is constant；When visitor is from a base station outside scenic spot to a base station in scenic spot,
Scenic spot total amount+1；When visitor from a base station in scenic spot to a base station outside scenic spot, scenic spot total amount -1.
Step 5：The volume of the flow of passengers result of real-time statistics per minute is exported and stored, MySQL/Oracle is arrived in such as storage
In database.The design structure of table is as shown in Figure 4 in database.
Step 6：The real-time results of database are stored into export into application presentation layer.
The present invention can be used for the passenger flow feature become more meticulous in analysis scenic spot, by the batch data for reusing Kafka bufferings
Processing stream, generates multiple RDD conversion in Spark, and each RDD is converted for a kind of analysis of passenger flow feature, formed respective
Service logic.As shown in Figure 3.
Finally illustrate, preferred embodiment above is merely illustrative of the technical solution of the present invention and unrestricted, although logical
Cross above preferred embodiment the present invention is described in detail, it is to be understood by those skilled in the art that can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (3)
1. a kind of real-time dynamic passenger flow volume statistical method in scenic spot based on visitor's mobile signaling protocol data, it is characterised in that：This method
Comprise the following steps：
S1：LTE-A monitors of eating dishes without rice or wine gather the signaling data of visitor in scenic spot in real time, and data are preserved into telefile,
One signaling data file was preserved with the time granularity of one minute；
S2：Whether there is file renewal by Flume assemblies monitors telefile, if there is renewal, file is recorded to collection one by one；
S3：Data in the latest document of renewal are sent and carry out data into Kafka data buffering components by Flume components one by one
Buffering, up to the data in the latest document after updating all are sent, regard data flow packing bulk data as Spark
Data input stream；
S4：In the distributed internal memory Computational frames of Spark, pass through base station where relatively more previous minute and one minute after visitor
Visitor's total amount in real-time statistics scenic spot is carried out in position；
S5：The volume of the flow of passengers result of real-time statistics per minute is exported and stored.
2. the real-time dynamic passenger flow volume statistical method in a kind of scenic spot based on visitor's mobile signaling protocol data as claimed in claim 1,
It is characterized in that：In step sl, the LTE-A eat dishes without rice or wine monitor collection scenic spot in visitor mobile signaling protocol data, including
IMSI storehouses, the time of signaling generation, the base station position information at place, and mobile signaling protocol data were protected with the time granularity of one minute
Deposit a signaling data file.
3. the real-time dynamic passenger flow volume statistical method in a kind of scenic spot based on visitor's mobile signaling protocol data as claimed in claim 1,
It is characterized in that：This method is further comprising the steps of：S6：The real-time results for being stored into database are exported to applying presentation layer
In, facilitate user to check in the form of visualization interface.
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| CN108288106A (en) * | 2017-10-30 | 2018-07-17 | 江苏鸿信系统集成有限公司 | A kind of tourist flows prediction technique based on big data |
| TWI776257B (en) * | 2020-10-19 | 2022-09-01 | 遠傳電信股份有限公司 | Mobile service diagnostic assistance system |
| CN117314119A (en) * | 2023-11-07 | 2023-12-29 | 北京凯泰铭科技文化发展有限公司 | Precise online real-time analysis system based on number of tourists in universe and scenic spot |
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Application publication date: 20170721 |
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