CN107145962A - A kind of sight spot domestic visitors forecasting system - Google Patents
A kind of sight spot domestic visitors forecasting system Download PDFInfo
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- CN107145962A CN107145962A CN201710214721.6A CN201710214721A CN107145962A CN 107145962 A CN107145962 A CN 107145962A CN 201710214721 A CN201710214721 A CN 201710214721A CN 107145962 A CN107145962 A CN 107145962A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/14—Travel agencies
Abstract
The invention provides a kind of sight spot domestic visitors forecasting system, including data acquisition module, data prediction module and flow indication module；The data acquisition module is used to gather prediction data；The data prediction module is provided with the passenger flow forecast model based on LSTM, for predicting sight spot domestic visitors according to the prediction data of data collecting module collected；The flow indication module is used to show the sight spot domestic visitors data.This programme can carry out the prediction of the domestic visitors of different time sections.
Description
Technical field
The present invention relates to technical field of data prediction, more particularly to a kind of sight spot domestic visitors forecasting system.
Background technology
In China, due to being influenceed by many external factor such as natural climate, national economic situation, scenic environment construction,
The short-term volume of the flow of passengers of travelling shows the complicated feature such as non-linear, seasonal, randomness.On by the factors such as nature, weather influenceed compared with
Big tourist attraction scenic spot such as Mount Huang, Jiu Zhaigou, the Huashan, the increase of tourist flows amount is not that presentation is uniformly distributed state
Gesture, many factors such as festivals or holidays influence in addition so that the volume of the flow of passengers is more notable in the imbalance of different times.
Due to tourist flows amount be one mainly participated in by visitor, complicated, uncertain nonlinear system, by all
Such as influence of natural climate, festivals or holidays and Tourism Accident Forecasting many factors, the characteristics of different times are presented different, therefore
This brings great difficulty for the prediction of the volume of the flow of passengers.It is conventional using year, season and the moon as the tourism of yardstick in the long-term volume of the flow of passengers it is pre-
Survey, mainly the assurance to volume of the flow of passengers general characteristic and variation tendency, be only the guidance that tourist attraction provides macroscopic aspect, it is difficult
Direct information reference is provided with the routine decision for scenic spot management person, it is impossible to carry out the prediction of the domestic visitors of different time sections.
The content of the invention
In view of this, the technical problem to be solved in the present invention is to provide a kind of sight spot domestic visitors forecasting system, it can carry out
The prediction of the domestic visitors of different time sections.
The technical proposal of the invention is realized in this way：
A kind of sight spot domestic visitors forecasting system, including data acquisition module, data prediction module and flow indication module；
The data acquisition module is used to gather prediction data；
The data prediction module is provided with the passenger flow forecast model based on LSTM, for being adopted according to data acquisition module
The prediction data prediction sight spot domestic visitors of collection；
The flow indication module is used to show the sight spot domestic visitors data.
It is preferred that, in addition to data scrubbing module；
The data scrubbing module is used to clear up the data of the data collecting module collected.
It is preferred that, the data scrubbing module includes format content cleaning unit, numerical transformation unit, noise data cleaning
Unit, repetition values cleaning unit, logic error cleaning unit and normalized unit.
It is preferred that, the flow indication module shows that the sight spot domestic visitors data include：
Generate and show the prediction curve of the sight spot domestic visitors.
It is preferred that, the prediction data includes：
Visitor's related data, scenic spot related data, scenic spot periphery related data, economic related data.
Domestic visitors forecasting system in sight spot proposed by the present invention, by gathering prediction data, passes through the volume of the flow of passengers based on LSTM
Forecast model carries out domestic visitors prediction, because the passenger flow forecast model based on LSTM can take into full account temporal aspect, so that
The prediction data for the different time sections that can be gathered carries out the prediction of the domestic visitors of different time sections.
Brief description of the drawings
Fig. 1 is the structured flowchart for the sight spot domestic visitors forecasting system that the embodiment of the present invention is proposed.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
As shown in figure 1, the embodiment of the present invention proposes a kind of sight spot domestic visitors forecasting system 1, including data acquisition module
101st, data prediction module 102 and flow indication module 103；
Data acquisition module 101 is used to gather prediction data；
Data prediction module 102 is provided with based on LSTM (Long Short-Term Memory, long memory network in short-term)
Passenger flow forecast model, the prediction data for being gathered according to data acquisition module 101 predicts sight spot domestic visitors；
Flow indication module 103 is used to show sight spot domestic visitors data.
It can be seen that, domestic visitors forecasting system in sight spot proposed by the present invention, by gathering prediction data, passes through the visitor based on LSTM
Flux prediction model carries out domestic visitors prediction, because the passenger flow forecast model based on LSTM can take into full account temporal aspect,
So as to the prediction of the domestic visitors of the prediction data progress different time sections of the different time sections of collection.
Wherein, the passenger flow forecast model based on LSTM successively include LSTM layers, Dropout layers, full articulamentum and
Sigmod active coatings.
In a preferred embodiment of the invention, in addition to data scrubbing module；
Data scrubbing module is used to clear up the data of data collecting module collected.
Wherein, data scrubbing module include format content cleaning unit, numerical transformation unit, noise data cleaning unit,
Repetition values cleaning unit, logic error cleaning unit and normalized unit.
The method for cleaning of data includes：
Step 1, format content cleaning, for carrying out format content cleaning, are substantially carried out the cleaning of three aspects：
It is a, the forms such as time, date, numerical value, full half-angle is inconsistent, uniformly it is modified as specified format；
The character that should not exist in b, cleaning content, such as excess space, unnecessary punctuation mark, unnecessary measure word；
C, cleaning and place field require the data that content is not inconsistent, and such as Region field is write as non-geographic position, price and write as
Chinese character etc..
Step 2, numerical transformation.Nonumeric type data are artificially mainly mapped into data type data to facilitate at subsequent analysis
Reason.
Step 3, noise data cleaning.For the method using statistics with histogram analyze data distribution characteristics come identification data
The noise data of concentration, deletes some exceptional values.
Step 4, repetition values cleaning.For some multiple reported datas caused by human factor to be identified and deleted
Repeated data.
Step 5, logic error cleaning.For removing some numbers that just can be directly pinpointed the problems using simple logic reasoning
According to being divided into three aspects：A, remove duplicate contents；B, the irrational value of removal, are carried out clear according to the span of field restriction
Wash；The contradictory content of c, modification.
Step 6, normalized.For by data not being mapped in the range of 0~1 in 0~1 scope, facilitating follow-up
Calculating is handled, and using min-max standardized methods, calculation formula is
X*=(x-min)/(max-min)
Wherein, X*For the value obtained after normalized.
In the present embodiment, after the cleaning for completing data, the data after cleaning using this can be spliced into spy of the length as N
Vector is levied, the length for passenger flow forecast trained is input in short-term in memory network (LSTM), provides and predict the outcome,
As a result it is the pre- quantitation of the volume of the flow of passengers in multiple periods in the future.
In a preferred embodiment of the invention, flow indication module shows that sight spot domestic visitors data include：
Generate and show the prediction curve of sight spot domestic visitors.
Data display is carried out by curve, can intuitively display data situation of change.
In a preferred embodiment of the invention, prediction data includes：
Visitor's related data, scenic spot related data, scenic spot periphery related data, economic related data.
Wherein, visitor's related data include visitor's sex, visitor area of source, visitor hotel pay per capita, visitor's diet
Expenditure, visitor's age, visitor's educational background, visitor's feedback, visitor's mood etc. per capita.
Scenic spot related data includes entrance ticket price, scenic spot position, scenic spot campaign item, scenic spot country level, scenic spot
Season the stream of people, scenic spot microblog label, scenic spot travel notes quantity, scenic spot travel notes temperature, scenic spot volumes of searches, scenic spot microblogging attention rate, scape
Area searching temperature, scenic spot media report quantity etc..
Scenic spot periphery related data include hotel's price, hotel occupancy rate, hotel room number, traffic route open situation,
Travel agency's quantity on order, area traffic convenience degree, regional public security degree of stability etc..
Economic related data includes national season GDP, inhabitant's consumption level, regional economy level, regional expenditure etc..
The embodiment of the present invention propose sight spot domestic visitors forecasting system be predicted when, can predict as needed when
Between section gather correspondence visitor's related data of period, scenic spot related data, scenic spot periphery related data, economic dependency number
According to；The data of collection are subjected to data scrubbing again, the data after cleaning are spliced into the characteristic vector that length is N, are input to
The length for passenger flow forecast trained in memory network (LSTM), is provided and predicted the outcome in short-term, in flow indication mould
Block shows the curve map of sight spot domestic visitors data.
In summary, the embodiment of the present invention can at least realize following effect：
In embodiments of the present invention, by gathering prediction data, swum by the passenger flow forecast model based on LSTM
Volume of passenger traffic is predicted, because the passenger flow forecast model based on LSTM can take into full account temporal aspect, so as to the difference of collection
The prediction data of period carries out the prediction of the domestic visitors of different time sections.
In embodiments of the present invention, domestic visitors forecasting system in sight spot also includes data scrubbing module；Data scrubbing module is used
Cleared up in the data to data collecting module collected, consequently facilitating the prediction of the passenger flow forecast model based on LSTM.
In embodiments of the present invention, prediction data includes visitor's related data, scenic spot related data, scenic spot periphery dependency number
According to, economic related data, multi-source data comprehensive analysis is thus allowed for, the analysis prediction at various scenic spots is can adapt to.
It is last it should be noted that：Presently preferred embodiments of the present invention is the foregoing is only, the skill of the present invention is merely to illustrate
Art scheme, is not intended to limit the scope of the present invention.Any modification for being made within the spirit and principles of the invention,
Equivalent substitution, improvement etc., are all contained in protection scope of the present invention.
Claims (5)
1. a kind of sight spot domestic visitors forecasting system, it is characterised in that aobvious including data acquisition module, data prediction module and flow
Show module；
The data acquisition module is used to gather prediction data；
The data prediction module is provided with the passenger flow forecast model based on LSTM, for according to data collecting module collected
Prediction data predicts sight spot domestic visitors；
The flow indication module is used to show the sight spot domestic visitors data.
2. domestic visitors forecasting system in sight spot as claimed in claim 1, it is characterised in that also including data scrubbing module；
The data scrubbing module is used to clear up the data of the data collecting module collected.
3. domestic visitors forecasting system in sight spot as claimed in claim 2, it is characterised in that the data scrubbing module includes form
Content cleaning unit, numerical transformation unit, noise data cleaning unit, repetition values cleaning unit, logic error cleaning unit and
Normalized unit.
4. domestic visitors forecasting system in sight spot as claimed in claim 1, it is characterised in that the flow indication module shows described
Sight spot domestic visitors data include：
Generate and show the prediction curve of the sight spot domestic visitors.
5. the sight spot domestic visitors forecasting system as described in claim any one of 1-4, it is characterised in that the prediction data bag
Include：
Visitor's related data, scenic spot related data, scenic spot periphery related data, economic related data.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108376260A (en) * | 2018-01-30 | 2018-08-07 | 陕西师范大学 | A kind of SVR tourism demand prediction techniques based on optimal subset optimization |
| CN108416464A (en) * | 2018-01-31 | 2018-08-17 | 山东汇贸电子口岸有限公司 | A kind of passenger flow forecasting and system based on mobile terminal of mobile telephone |
| CN108513308A (en) * | 2018-02-26 | 2018-09-07 | 山东汇贸电子口岸有限公司 | A kind of scenic spot passenger flow analysis system and method |
| CN109165779A (en) * | 2018-08-12 | 2019-01-08 | 北京清华同衡规划设计研究院有限公司 | A kind of size of population prediction technique based on multi-source big data Yu shot and long term Memory Neural Networks model |
| CN109299825A (en) * | 2018-09-26 | 2019-02-01 | 重庆英传智能科技研究院有限公司 | A kind of prediction technique and forecasting system based on the real-time passenger flow of rail traffic |
| CN109636609A (en) * | 2019-01-04 | 2019-04-16 | 广州市本真网络科技有限公司 | Stock recommended method and system based on two-way length memory models in short-term |
| CN110443314A (en) * | 2019-08-08 | 2019-11-12 | 中国工商银行股份有限公司 | Scenic spot passenger flow forecast method and device based on machine learning |
| CN110738361A (en) * | 2019-09-29 | 2020-01-31 | 浙江浙大中控信息技术有限公司 | Dynamic peak period prediction method based on line passenger capacity |
| CN111027771A (en) * | 2019-12-10 | 2020-04-17 | 浙江力石科技股份有限公司 | Scenic spot passenger flow volume estimation method, system and device and storable medium |
| CN111144652A (en) * | 2019-12-26 | 2020-05-12 | 浙江力石科技股份有限公司 | Tour comfort degree algorithm and trend prediction method, system and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108376260A (en) * | 2018-01-30 | 2018-08-07 | 陕西师范大学 | A kind of SVR tourism demand prediction techniques based on optimal subset optimization |
| CN108416464A (en) * | 2018-01-31 | 2018-08-17 | 山东汇贸电子口岸有限公司 | A kind of passenger flow forecasting and system based on mobile terminal of mobile telephone |
| CN108513308A (en) * | 2018-02-26 | 2018-09-07 | 山东汇贸电子口岸有限公司 | A kind of scenic spot passenger flow analysis system and method |
| CN109165779B (en) * | 2018-08-12 | 2022-04-08 | 北京清华同衡规划设计研究院有限公司 | Population quantity prediction method based on multi-source big data and long-short term memory neural network model |
| CN109165779A (en) * | 2018-08-12 | 2019-01-08 | 北京清华同衡规划设计研究院有限公司 | A kind of size of population prediction technique based on multi-source big data Yu shot and long term Memory Neural Networks model |
| CN109299825A (en) * | 2018-09-26 | 2019-02-01 | 重庆英传智能科技研究院有限公司 | A kind of prediction technique and forecasting system based on the real-time passenger flow of rail traffic |
| CN109636609A (en) * | 2019-01-04 | 2019-04-16 | 广州市本真网络科技有限公司 | Stock recommended method and system based on two-way length memory models in short-term |
| CN110443314A (en) * | 2019-08-08 | 2019-11-12 | 中国工商银行股份有限公司 | Scenic spot passenger flow forecast method and device based on machine learning |
| CN110738361A (en) * | 2019-09-29 | 2020-01-31 | 浙江浙大中控信息技术有限公司 | Dynamic peak period prediction method based on line passenger capacity |
| CN110738361B (en) * | 2019-09-29 | 2023-08-04 | 浙江中控信息产业股份有限公司 | Dynamic peak period prediction method based on line passenger traffic |
| CN111027771A (en) * | 2019-12-10 | 2020-04-17 | 浙江力石科技股份有限公司 | Scenic spot passenger flow volume estimation method, system and device and storable medium |
| CN111144652A (en) * | 2019-12-26 | 2020-05-12 | 浙江力石科技股份有限公司 | Tour comfort degree algorithm and trend prediction method, system and device |
| CN111144652B (en) * | 2019-12-26 | 2023-08-08 | 浙江力石科技股份有限公司 | Tour comfort algorithm and trend prediction based method, system and device |
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Application publication date: 20170908 |