CN108288106A - A kind of tourist flows prediction technique based on big data - Google Patents
A kind of tourist flows prediction technique based on big data Download PDFInfo
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Abstract
The tourist flows prediction technique based on big data that the invention discloses a kind of, comprises the steps of：Data acquire, and acquire customer position information；Customer position information and user location state are associated by data correlation；Data processing is eliminated interference data, is calibrated to statistical data by specific model to the volume of the flow of passengers of specific region；Passenger data passenger flow is counted and predicted using Match algorithms；Based on statistical analysis and data digging method, estimation model is established, and then calculate and specify region full dose customer flow；Step 6：Data statistic analysis based on passenger flow, statistical history data passenger flow data, establishes Passenger flow forecast model, according to scenic spot tourist's feature, establishes the computation model as unit of week, and assist being corrected with the data in year.The present invention realizes real-time dynamic monitoring and tourist's source analysis to unit tourist flow of travelling by building tourist flow dynamic monitoring system.
Description
Technical field
The present invention relates to a kind of passenger flow forecasting, especially a kind of tourist flows prediction technique based on big data.
Background technology
Traditional passenger flow statistics mode has so several：Artificial passenger flow statistics, infrared induction passenger flow statistics, tripod turnstile visitor
Stream statistics, gravity sensing passenger flow statistics etc..
Artificial statistical：The volume of the flow of passengers is counted by manually, this method has very big drawback.
Disadvantage：First, the attention of statistician can not possibly keep high concentration for a long time, easily when tired
Leak number customer number.Second, in terms of being the time, impossible prolonged, the continual work of statistician, for market general 12
For business hours more than a hour, it is difficult to accomplish all-round statistics.Third, in terms of being cost, counted by the way of artificial
Manpower wages cost caused by passenger flow is unquestionably more high than using the cost of device statistics, and equipment belongs to
Disposable input, and human cost belongs to duration input.Therefore, what artificial passenger flow statistics mode can only be as certain period is general
Number statistics, lacks comprehensive and validity.
Infrared induction statistical：Infrared induction passenger flow statistics equipment can be divided into：Infrared emission mode, infrared external reflection
The equipment such as mode, the main human body being achieved in that passing through from infrared induction region, cut-out or blocking infrared ray make it
The specific infrared ray of resistance variations or the 10um sent out by detection human body or so is generated to judge human body quantity.This mode
Cost is more moderate, can be when people free in and out doorway, and system obtains passenger flow data automatically, and equipment is smaller and installation is beautiful
It sees.
Disadvantage：First, since infrared light is highly prone to extraneous factor interference, its statistical data is made to generate large error；It is right
In wider doorway, more people also easy to produce leakage number phenomenon when process simultaneously；Second, due to itself technical reason,
Infrared mode can not judge that customer is to enter or go out well, and it is someone's process that can only count on, therefore data acquisition
Unicity influences the result of passenger flow analysing.
Tripod turnstile mode：Tripod turnstile mode mainly uses mechanical system, customer to enter required for dependent field by rolling lock
Mouthful, rolling lock rolls once, thus records one into and out of personnel.
Disadvantage：Tripod turnstile mode is more accurate for data statistics, but due to needing to install tripod turnstile at entrance
Machine lacks aesthetics for market, and convenience is inadequate, can not quickly pass in and out, therefore be not suitable for market etc.
It applies in place.
Gravity sensing mode：Gravity sensing mainly installs gravity sensing device on floor, calculates when human body was trampled
When going, passenger flow number is calculated.But due to the problem of installation relative requirement is higher, of high cost and its stability aspect, very
It is few to be used by commercial user.
In summary several traditional passenger flow statistics modes are all difficult preferably to meet the needs of commercial user.
Invention content
The tourist flows prediction technique based on big data that technical problem to be solved by the invention is to provide a kind of, by hand
Machine big data realizes tourist's distribution, traveller's analysis, behavioural analysis, early warning monitoring function.
In order to solve the above technical problems, the technical solution adopted in the present invention is：
A kind of tourist flows prediction technique based on big data, it is characterised in that comprise the steps of：
Step 1：Data acquire, and acquire customer position information；
Step 2：Customer position information and user location state are associated by data correlation；
Step 3：Data processing eliminates interference data, to statistics by specific model to the volume of the flow of passengers of specific region
Data are calibrated；
Step 4：Passenger data passenger flow is counted and predicted using Match algorithms；
Step 5：Based on statistical analysis and data digging method, estimation model is established, and then calculate and region full dose is specified to use
Family flow；
Step 6：Data statistic analysis based on passenger flow, statistical history data passenger flow data, establishes Passenger flow forecast model,
According to scenic spot tourist's feature, the computation model as unit of week is established, and assists being corrected with the data in year.
Further, the step 1 specifically,
Acquisition user position update information in real time is docked with signaling shared platform in a manner of socket interfaces and obtains position
It updates the data；
The operator of acquisition in real time call signaling data, docks acquisition fortune in a manner of socket interfaces with signaling shared platform
Seek quotient's call signaling data；
Using number, generation time and the logical relation in different signaling interface signalings, establish IMSI, TMSI and
Correspondence between MSISDN, and record the renewal time of the correspondence；
According to correspondence and its renewal time, the number that IMSI or MSISDN is carried out to the signaling message of reception backfills,
Preserve the signaling message after the number backfill.
Further, the step 2 specifically,
By the data acquired in real time, user location state table, the last state of real-time update user location are established, and be
The source of user is tagged, some region of volume of the flow of passengers situation of real-time statistics, and nonlocal user source distribution situation；
By updating the data the statistical analysis of historical data to position, daily, week, the moon mode count districts and cities' dimension or spy
Determine region dimension, periodic passenger flow situation of change.
Further, in the step 3, pass through the acquisition and processing to operator call real time phone call, real-time statistics weight
The network quality situation of point base stations, while being docked with warning system, when emphasis base station network quality when something goes wrong, it is immediately pre-
Police simultaneously handles network problem immediately.
Further, the step 4 Match algorithms specifically,
It defines r and indicates that tourist's quantity, t indicate predicted time, then calculating contemporaneous data weighting according to historical data is averaged
Value, i.e.,：
Using weekly data, the moon data, annual data and festivals or holidays data multidimensional degree statistical forecast are as correcting.Correspondingly,
The corresponding weighted of data of different dimensions.The data for defining week, the moon, year and festivals or holidays are respectively rw rm ry rh.Pass through
The comparison of the offset of history real data and historical forecast data, adjusts the size of weight parameter.
Δ r=| rr-rp| wherein rrIndicate truthful data, rpIndicate prediction data
Corresponding weight parameter after definition adjustment is abcd, the week as prediction of selection, Month And Year data amount check point
It is not j kl.Obtaining final predictive equation is：
Further, in the step 6, according to historical data, it is to be overlapped statistics in the period with week, excludes festivals or holidays
Data, such as Monday data prediction, then the data trend of statistical history all Mondays, is overlapped, and weighting takes in chronological order
Centre is worth to change curve trend, and increase and decrease degree is calculated by change curve trend further according to current value, following several small with prediction
When passenger flow variation.
Compared with prior art, the present invention haing the following advantages and effect：
1, by build tourist flow dynamic monitoring system, realize to travel unit tourist flow real-time dynamic monitoring and
Tourist's source analysis；
2, the early warning monitoring management function of each scenic spot tourist is realized；
3, the multi dimensional analysis of each scenic spot tourist is provided, realize the transverse direction to each scenic spot, ring than analysis；
4, travel unit progress targetedly marketing and management are helped, standard is provided for the analysis and decision of tourism management departments
True decision data is supported.
Description of the drawings
Fig. 1 is a kind of system framework figure of tourist flows prediction technique based on big data of the present invention.
Fig. 2 is the number geographic location association figure of the present invention.
Fig. 3 is the cloud computing shared platform schematic diagram of the present invention.
Fig. 4 is the carrier data analysis model figure of the present invention.
Fig. 5 is the sample size and precision relational graph of the embodiment of the present invention.
Fig. 6 is the predictablity rate figure of the embodiment of the present invention.
Fig. 7 is the final passenger flow estimation design sketch of the embodiment of the present invention.
Specific implementation mode
The present invention is described in further detail below in conjunction with the accompanying drawings and by embodiment, and following embodiment is to this hair
Bright explanation and the invention is not limited in following embodiments.
As shown in Figure 1, a kind of tourist flows prediction technique based on big data of the present invention, with the position of signaling shared platform
It sets and updates the data as data source, by data preprocessing phase, by full dose User ID, the label informations such as position are stored in unification
In data warehouse；Based on data warehouse, make the statistical analyses such as inlet flow rate's distribution based on a variety of dimensions.With talk times, fall
The indexs such as line rate count corresponding position network quality situation, carry out the network quality traffic-operating period visual control of region dimension.
It comprises the steps of：
Step 1：Data acquire, and acquire customer position information；
Acquisition user position update information in real time is docked with signaling shared platform in a manner of socket interfaces and obtains position
It updates the data, data receive to complete in second grade；
The operator of acquisition in real time call signaling data, docks acquisition fortune in a manner of socket interfaces with signaling shared platform
Quotient's call signaling data is sought, data receive to complete in second grade；
Using number, generation time and the logical relation in different signaling interface signalings, establish IMSI, TMSI and
Correspondence between MSISDN, and record the renewal time of the correspondence；
According to correspondence and its renewal time, the number that IMSI or MSISDN is carried out to the signaling message of reception backfills,
Preserve the signaling message after the number backfill.
Step 2：Customer position information and user location state are associated by data correlation；
By the data acquired in real time, user location state table, the last state of real-time update user location are established, and be
The source of user is tagged, some region of volume of the flow of passengers situation of real-time statistics, and nonlocal user source distribution situation；
By updating the data the statistical analysis of historical data to position, daily, week, the moon mode count districts and cities' dimension or spy
Determine region dimension, periodic passenger flow situation of change.Number geographic location association is shown in Fig. 2.
Step 3：Data processing eliminates interference data, to statistics by specific model to the volume of the flow of passengers of specific region
Data are calibrated, such as：Permanent resident population, removal passerby are removed, permanent Migrant women etc. is removed；
By the acquisition and processing to operator call real time phone call, the network quality situation of real-time statistics emphasis base station,
Simultaneously dock with warning system, when emphasis base station network quality when something goes wrong, can immediately early warning and immediately handle network ask
Topic.Cloud computing shared platform is shown in Fig. 3.
Step 4：Passenger data passenger flow is counted and predicted using Match algorithms；
Match algorithms specifically,
It defines r and indicates that tourist's quantity, t indicate predicted time, then calculating contemporaneous data weighting according to historical data is averaged
Value, i.e.,：
Using weekly data, the moon data, annual data and festivals or holidays data multidimensional degree statistical forecast are as correcting.Correspondingly,
The corresponding weighted of data of different dimensions.The data for defining week, the moon, year and festivals or holidays are respectively rw rm ry rh.Pass through
The comparison of the offset of history real data and historical forecast data, adjusts the size of weight parameter.
Δ r=| rr-rp| wherein rrIndicate truthful data, rpIndicate prediction data
Corresponding weight parameter after definition adjustment is abcd, the week as prediction of selection, Month And Year data amount check point
It is not j kl.Obtaining final predictive equation is：
The accuracy of sample estimated value, it is in close relations with the absolute size of sample size, it is closed with ratio of the sample in totality
System is little, and in practical sampling process, the sample size of sampling is overall 25% (the market occupancy volume of telecommunications) sampling accuracy
Reach 99.9%.Therefore theoretically, have and counter push away full dose feasibility.Carrier data analysis model is shown in Fig. 4.
Step 5：Based on statistical analysis and data digging method, estimation model is established, and then calculate and region full dose is specified to use
Family flow；Sample size and the relationship of precision are shown in Fig. 5.
Step 6：Data statistic analysis based on passenger flow, statistical history data passenger flow data, establishes Passenger flow forecast model,
According to scenic spot tourist's feature, the computation model as unit of week is established, and assists being corrected with the data in year.
It is to be overlapped statistics the period with week according to historical data, exclusion festivals or holidays data, such as Monday data prediction, then
The data trend of statistical history all Mondays, is overlapped, and weighting takes centre to be worth to change curve trend in chronological order,
Increase and decrease degree is calculated by change curve trend further according to current value, to predict that the passenger flow of coming few hours changes.Predictablity rate
See Fig. 6.
Recent passenger flow changing rule has similitude, and the reference value of closer time is bigger, so according to time order and function
Sequence setting weight, from closely to far successively decreasing as unit of the moon.And the coefficient of lapse rate with float up and down 10% as a comparison,
The accuracy rate in the case of three kinds is calculated, most accurate coefficient is selected.The passenger flow variation model obtained is counted according to historical data, into
The data Average Accuracy of row passenger flow estimation is 85% or so.
It is illustrated below with specific case.
Case 1：Long Island smart travel
Pictorialization format shows important travel statistics data, historical data can be called to compare and analyze.Pass through operator
Locate the passenger flow obtained, traveller's information, the real-time passenger flow statistics in scenic spot, scenic spot reception of visitor seniority among brothers and sisters statistics, scenic spot reception people can be carried out
The year-on-year ring of number is resident duration analysis, tourist's visit than analysis, preferred scenic spot seniority among brothers and sisters statistics, tourist's age layer analysis, tourist scenic spot
Circuit seniority among brothers and sisters statistics.All kinds of statistical data are stored and are inquired with list mode, in a manner of block diagram, line chart, pie chart etc.
Carry out effect demonstration.Final passenger flow estimation design sketch is shown in Fig. 7
Real-time passenger flow analysing：The module provides Long Island in real time and tourist's quantity, data update frequency are received in all sight spots in real time
Rate：Update in 15 minutes is primary.
Tourist's source analysis：The module daily provides source province ranking and each province trip in city and scenic spot visiting tourist
Objective proportion shows that tourist is in national each province's distribution situation in one week in the form of thermal map.With the temperature height at its scenic spot of city
To assist Tourism Marketing department to formulate corresponding regulating by market mechanism policy, the data of update in 15 minutes.
Tourist's attributive analysis：The module daily counts Long Island and its all sight spot scenic spot tourist's genders and age level distribution feelings
Condition analyze, by the ages of tourist be divided into 0-20 Sui, 20-30 Sui, 30-40 Sui, 40-50 Sui, 50-60 Sui, 60 years old and with
On.Data show each age group tourist's accounting with cake chart, tourist industry personalized service and precision marketing are supported, daily with new one
Secondary data statistics.
Residence time counts:The module daily counts tourist and has the average stay time at scenic spot, displaying nearly one under its command in city
Average stay time ranking of all tourists at scenic spot.
Passenger flow comparative analysis：The module provides city and scenic spot festivals or holidays (maximum number of such persons) with the normal volume of the flow of passengers to score
The comparative analysis of tourist's quantity is realized in analysis on the basis of data accumulation.
Visit scenic spot statistics first：The module counts other provinces tourist respectively with scenic spot dimension and this province tourist visits first in the city
Scenic spot ranking and tourist's accounting.
It analyzes travelling route：According to each city hot topic of visitor location trajectory analysis travelling route Top10 rankings, displaying is each
Itinerary ranking and tourist's accounting.
Case 2：Xuzhou smart travel
The real-time passenger flow statistics in scenic spot, scenic spot reception of visitor seniority among brothers and sisters statistics, the year-on-year ring of scenic spot reception number are than analysis, preferred scape
Rank statistics, tourist's age layer analysis, the resident duration analysis in tourist scenic spot, tourist's touring line seniority among brothers and sisters statistics in area.All kinds of statistics
Data are stored and are inquired with list mode, and effect demonstration is carried out in a manner of block diagram, line chart, pie chart etc..
System needs to handle more than 1600 general-purpose family signaling datas (native client containing Jiangsu and Roaming Client), busy signaling
Flow is more than 800Mbps.
It is demonstrated experimentally that 85% mankind track can utilize the algorithm correctly predicted.On this basis, historical data is proposed
The anti-method pushed away further improves the accuracy rate of prediction, realizes tourist's distribution, traveller's analysis, behavioural analysis, early warning monitoring work(
Can, provide science accurate decision-making foundation for the management and marketing of tourist resources.
Described in this specification above content is only illustrations made for the present invention.Technology belonging to the present invention
The technical staff in field can do various modifications or supplement to described specific embodiment or substitute by a similar method, only
The guarantor of the present invention should all be belonged to without departing from the content or beyond the scope defined by this claim of description of the invention
Protect range.
Claims (6)
1. a kind of tourist flows prediction technique based on big data, it is characterised in that comprise the steps of：
Step 1：Data acquire, and acquire customer position information；
Step 2：Customer position information and user location state are associated by data correlation；
Step 3：Data processing eliminates interference data, to statistical data by specific model to the volume of the flow of passengers of specific region
It is calibrated；
Step 4：Passenger data passenger flow is counted and predicted using Match algorithms；
Step 5：Based on statistical analysis and data digging method, estimation model is established, and then calculates and specifies region full dose user stream
Amount；
Step 6：Data statistic analysis based on passenger flow, statistical history data passenger flow data, establishes Passenger flow forecast model, according to
Scenic spot tourist's feature establishes the computation model as unit of week, and assists being corrected with the data in year.
2. a kind of tourist flows prediction technique based on big data described in accordance with the claim 1, it is characterised in that：The step
One specifically,
Acquisition user position update information in real time is docked with signaling shared platform in a manner of socket interfaces and obtains location updating
Data；
The operator of acquisition in real time call signaling data, is docked with signaling shared platform in a manner of socket interfaces and obtains operator
Call signaling data；
Using number, generation time and the logical relation in different signaling interface signalings, establish between IMSI, TMSI and MSISDN
Correspondence, and record the renewal time of the correspondence；
According to correspondence and its renewal time, the number that IMSI or MSISDN is carried out to the signaling message of reception is backfilled, is preserved
Signaling message after the number backfill.
3. a kind of tourist flows prediction technique based on big data described in accordance with the claim 1, it is characterised in that：The step
Two specifically,
By the data acquired in real time, user location state table, the last state of real-time update user location are established, and is user
Source it is tagged, some region of volume of the flow of passengers situation of real-time statistics, and nonlocal user source distribution situation；
By updating the data the statistical analysis of historical data to position, daily, week, the moon mode count districts and cities' dimension or specifically
The situation of change of domain dimension, periodic passenger flow.
4. a kind of tourist flows prediction technique based on big data described in accordance with the claim 1, it is characterised in that：The step
In three, by the acquisition and processing to operator call real time phone call, the network quality situation of real-time statistics emphasis base station, simultaneously
Docked with warning system, when emphasis base station network quality when something goes wrong, instant early warning simultaneously handles network problem immediately.
5. a kind of tourist flows prediction technique based on big data described in accordance with the claim 1, it is characterised in that：The step
Four Match algorithms specifically,
It defines r and indicates that tourist's quantity, t indicate predicted time, then calculating contemporaneous data weighting according to historical data is averaged,
I.e.：
Using weekly data, the moon data, annual data and festivals or holidays data multidimensional degree statistical forecast are as correcting, the number of different dimensions
According to corresponding weighted；The data for defining week, the moon, year and festivals or holidays are respectively rw rm ry rh, pass through history real data
With the comparison of the offset of historical forecast data, the size of weight parameter is adjusted；
Δ r=| rr-rp| wherein rrIndicate truthful data, rpIndicate that prediction data, the corresponding weight parameter after definition adjustment are a
B c d, selection is j k l respectively as the week of prediction, Month And Year data amount check；Obtaining final predictive equation is：
6. a kind of tourist flows prediction technique based on big data described in accordance with the claim 1, it is characterised in that：The step
In six, according to historical data, it is to be overlapped statistics in the period with week, excludes festivals or holidays data and then counted such as Monday data prediction
The data trend of history all Mondays, is overlapped, and weighting takes centre to be worth to change curve trend, then root in chronological order
Increase and decrease degree is calculated by change curve trend according to current value, to predict that the passenger flow of coming few hours changes.
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| CN109345042A (en) * | 2018-11-19 | 2019-02-15 | 中国联合网络通信集团有限公司 | A kind of prediction technique and device of the variation of passenger flow heating power degree |
| CN110674210A (en) * | 2019-09-23 | 2020-01-10 | 钟栎娜 | Method for comprehensively evaluating tourist destinations based on big data |
| CN111126736A (en) * | 2018-11-01 | 2020-05-08 | 百度在线网络技术（北京）有限公司 | Enterprise passenger flow determining method and device, server and storage medium |
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| CN116227760A (en) * | 2023-05-11 | 2023-06-06 | 北京大也智慧数据科技服务有限公司 | Method, device, storage medium and equipment for predicting influence of travel activities |
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