CN115130735A - Scenic spot number prediction method and device based on big data - Google Patents
Scenic spot number prediction method and device based on big data Download PDFInfo
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Abstract
本发明涉及旅游应用技术领域，具体涉及一种基于大数据的景点人数预测方法及装置；通过景点旅游数据、旅游团旅客数据、景点周边酒店入住数据，多维度得到第一平均增长率，然后再根据明日订单数据，以及今日预测数据与实际数据的差异度进行修正，得到第二平均增长率和第三平均增长率，生成预测人数的范围，最后根据收集的天气情况和政策情况进行最后的修正，得到较为精确的景点明日预测人数的范围；使管理人员可以得知景点明日人数后，能对应安排工作人员进行管理，便于景区管理，防止产生景点内拥挤、产生安全隐患的问题，也可提前疏导景区周边交通，降低交通拥堵的情况；使游客得到较好的游玩体验，推进旅游业的可持续发展。
The invention relates to the technical field of tourism applications, in particular to a method and a device for predicting the number of people in scenic spots based on big data; the first average growth rate is obtained from multiple dimensions through scenic spot tourism data, tourist group passenger data, and hotel occupancy data around the scenic spot, and then According to tomorrow's order data and the difference between today's forecast data and actual data, the second average growth rate and the third average growth rate are obtained, and the range of predicted number of people is generated. Finally, the final correction is made according to the collected weather conditions and policy conditions. , to get a more accurate range of the predicted number of people in the scenic spot tomorrow; so that the management personnel can arrange the staff to manage accordingly after knowing the number of people in the scenic spot tomorrow, which is convenient for the management of the scenic spot and prevents the problem of crowding in the scenic spot and potential safety hazards. Drain the traffic around the scenic spot and reduce traffic congestion; enable tourists to have a better play experience and promote the sustainable development of tourism.
Description
Technical Field
The invention relates to the technical field of tourism application, in particular to a scenic spot population prediction method and device based on big data.
Background
With the continuous improvement of the national economic level, tourism becomes a favorite leisure and entertainment project for people, which not only can release pressure and relieve mood, but also can feel popular culture and local characteristics of different places.
However, in some hot scenic spots, frequent visitors are more, managers cannot accurately predict the number of visitors in the bright days, and cannot correspondingly arrange workers for management, so that congestion in the scenic spots is easily caused, and even potential safety hazards are generated; traffic around scenic spots cannot be commanded in time, and traffic jam can also occur; therefore, the method not only influences the playing experience and safety of the tourists, but also influences the sustainable development of urban traffic and tourism industry.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a scenic spot number prediction method and device based on big data.
In one aspect, the invention provides a scenic spot number prediction method based on big data, which comprises the following steps:
s1: obtaining scenic spot tourism data, and calculating to obtain a first growth rate of yesterday in the same year and a second growth rate of today in the same year;
s2: acquiring tourist group passenger data, and calculating to obtain a third growth rate of yesterday in the same year and a fourth growth rate of today in the same year;
s3: obtaining hotel check-in data around the scenic spot, and calculating to obtain a fifth growth rate of yesterday in the same year and a sixth growth rate of today in the same year;
s4: obtaining a first average growth rate according to the first growth rate, the second growth rate, the third growth rate, the fourth growth rate, the fifth growth rate and the sixth growth rate;
s5: obtaining scenery spot bright day order data, peripheral hotel bright day order data and tourist party bright day order data in real time, correcting the first average growth rate according to the scenery spot bright day order data, peripheral hotel bright day order data and tourist party bright day order data, and obtaining a corrected second average growth rate;
s6: acquiring current prediction data and actual data, and correcting the second average growth rate according to the current prediction data and the actual data to obtain a corrected third average growth rate;
s7: automatically generating the range of the predicted number of the scenery spot tomorrow on the next day according to the data of the last year and the next day, the second average growth rate and the third average growth rate;
s8: and collecting weather conditions and policy conditions in real time, and correcting the range of the number of the forecasted scenic spot tomorrow people.
Optionally, the step of obtaining historical scenic spot travel data and calculating to obtain a first growth rate of yesterday and a second growth rate of today and the same year includes: obtaining historical tourist data of scenic spots, recording data of the day before the last year as M, recording data of the last year and the current day as N, and recording data of the day after the last year and the current day as Z; obtaining yesterday scenic spot tourism data and today's scenic spot tourism data, comparing the yesterday scenic spot tourism data with the M to obtain a first growth rate, and comparing the today's scenic spot tourism data with the N to obtain a second growth rate; and acquiring real-time weather conditions and policy conditions for correction to obtain an actual first growth rate and an actual second growth rate.
Optionally, the step of obtaining the tourist data of the tourist group and calculating to obtain a third growth rate of yesterday that is the same year in the last year and a fourth growth rate of today that is the same year in the last year includes: acquiring historical passenger data of a tourist party, recording the data of the previous day of the last year as Q, and recording the data of the current day of the last year as W; acquiring the data of the traveling group passenger at yesterday and the data of the traveling group passenger at this day, comparing the data of the traveling group passenger at yesterday with Q to obtain a third growth rate, and comparing the data of the traveling group passenger at this day with N to obtain a fourth growth rate; acquiring the promotion activity condition of the tourist group, wherein if the promotion activities of yesterday, this day and tomorrow are the same, the third growth rate and the fourth growth rate are unchanged; and if the promotion activities of yesterday, this day and the tomorrow are different, correspondingly correcting the third growth rate and the fourth growth rate.
Optionally, the obtaining of the hotel occupancy data around the scenic spot and the calculating of the fifth growth rate of yesterday in the same year in the last year and the sixth growth rate of today in the same year in the last year include: acquiring historical check-in data of peripheral hotels, recording the data of the day before the last year as X, and recording the data of the day before the last year as Y; acquiring yesterday ambient hotel stay data and today ambient hotel stay data, comparing the yesterday ambient hotel stay data with the X to obtain a fifth growth rate, and comparing the today ambient hotel stay data with the Y to obtain a sixth growth rate; acquiring activity information around the hotel in real time, wherein the activity information comprises examinations, commercial activities and festival activities, and if the activity information exists, correcting the corresponding growth rate of the current day according to the scale of the activity information; if there is no activity information, the fifth growth rate and the sixth growth rate are unchanged.
Optionally, the step of obtaining the scenic spot bright day order data, the peripheral hotel bright day order data, and the tourist party bright day order data in real time, correcting the first average growth rate according to the scenic spot bright day order data, the peripheral hotel bright day order data, and the tourist party bright day order data, and obtaining a corrected second average growth rate further includes: obtaining the order data of the scenic spot today, the order data of the surrounding hotels today and the order data of the tourist party today, and processing the order data to obtain a union L; obtaining the number of simulated people according to the L and the history coefficient I, comparing the number of simulated people with the number of actual people, and adjusting the history coefficient I to obtain a new history coefficient I; obtaining the number of the simulated people in the tomorrow according to the new history coefficient I, the scenery spot tomorrow order data, the surrounding hotel tomorrow order data and the tourist party tomorrow order data; and correcting the first average growth rate according to the number of the tomorrow simulated people to obtain a second average growth rate.
Optionally, the history coefficient I is a numerical value obtained in the previous day, and the history coefficient is obtained comprehensively according to the proportion of the number of the simulated people to the number of the actual people, the economic situation and the policy.
Optionally, the step of obtaining the prediction data and the actual data of this day, correcting the second average growth rate according to the prediction data and the actual data of this day, and obtaining a corrected third average growth rate further includes: calculating the ratio F of the current predicted data to the actual data; obtaining the recent economic situation and policy, and adjusting the proportion F according to the recent economic situation and policy; and correcting the second average growth rate according to the adjusted ratio F to obtain a corrected third average growth rate.
Optionally, the step of automatically generating the range of the predicted number of the scenery spot tomorrow according to the data of the day after the last year, the second average growth rate and the third average growth rate includes: recording the data of the day after the current year as Z; obtaining a first predicted population according to the Z and the second average growth rate; obtaining a second predicted number of people according to the Z and the third average growth rate; and obtaining the range of the number of the forecasted scenery bright days according to the first forecasted number and the second forecasted number, and displaying from small to large.
Optionally, the step of collecting weather conditions and policy conditions in real time and correcting the range of the number of the scenery spot forecasted on the next day further includes: obtaining the range of the final number of the scenery spot tomorrow forecasted people; extracting the maximum predicted number of people in the range; judging whether the maximum number of forecasted people exceeds a preset threshold value of a scenic spot; and if the threshold value is exceeded, early warning is carried out, and a manager is reminded.
In another aspect, the present invention provides a device for predicting number of persons in scenic spots based on big data, wherein the device comprises:
the scenic spot data processing unit is used for acquiring scenic spot tourism data and calculating to obtain a first growth rate of yesterday in the same year and a second growth rate of today in the same year;
the tourist group data processing unit is used for acquiring tourist group passenger data and calculating to obtain a third growth rate of yesterday on the same day in the last year and a fourth growth rate of today on the same day in the last year;
the peripheral hotel data processing unit is used for acquiring the stay data of the peripheral hotels of the scenic spots and calculating to obtain the fifth growth rate of yesterday on the same day in the last year and the sixth growth rate of yesterday on the same day in the last year;
the first average growth rate calculating unit is used for obtaining a first average growth rate according to the first growth rate, the second growth rate, the third growth rate, the fourth growth rate, the fifth growth rate and the sixth growth rate;
the second average growth rate calculation unit is used for acquiring scenery spot bright day order data, peripheral hotel bright day order data and tourist party bright day order data in real time, correcting the first average growth rate according to the scenery spot bright day order data, peripheral hotel bright day order data and tourist party bright day order data, and obtaining a corrected second average growth rate;
a third average growth rate calculation unit, configured to obtain the current predicted data and the actual data, and correct the second average growth rate according to the current predicted data and the actual data to obtain a corrected third average growth rate;
the prediction range calculation unit is used for automatically generating the range of the predicted number of the scenery spot tomorrow according to the data of the day after the last year, the second average growth rate and the third average growth rate;
the forecast data correction unit is used for collecting weather conditions and policy conditions in real time and correcting the range of the forecast number of the scenic spot on the next day;
the prediction early warning management unit is used for acquiring the range of the number of the final scenery spot tomorrow forecasted people; extracting the maximum predicted number of people in the range; judging whether the maximum number of predicted people exceeds a preset threshold value of a scenic spot; and if the threshold value is exceeded, early warning is carried out, and a manager is reminded.
The invention has the following beneficial effects: according to the scenic spot tourist data, tourist group passenger data and hotel check-in data around the scenic spot, obtaining a first average growth rate in multiple dimensions, then correcting according to the data of the open-day order and the difference degree between the current predicted data and the actual data to obtain a second average growth rate and a third average growth rate, generating a predicted people number range according to the data of the day after the current year, and finally correcting according to the collected weather conditions and policy conditions to obtain a more accurate scenic spot open-day predicted people number range; the number of workers can be correspondingly arranged for management after the manager can know the number of the workers at the bright day of the scenic spot, so that the management of the scenic spot is facilitated, the problems of congestion and potential safety hazards in the scenic spot are prevented, the traffic around the scenic spot can be dredged in advance, and the traffic jam condition is reduced; the tourists can get better playing experience, and the sustainable development of the tourism industry is promoted.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic flow chart of a method for predicting the number of persons in a scenic spot based on big data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a device for predicting the number of persons in the scenic spots based on big data according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
With the continuous improvement of the national economic level, tourism becomes a favorite leisure and entertainment project for people, not only can release pressure and relieve mood, but also can feel popular culture and local characteristics of different places. However, in some hot scenic spots, frequent visitors are more, managers cannot accurately predict the number of visitors in the bright days, and cannot correspondingly arrange workers for management, so that congestion in the scenic spots is easily caused, and even potential safety hazards are generated; traffic around scenic spots cannot be commanded in time, and traffic jam can also occur; therefore, the playing experience and the safety of the tourists are influenced, and the sustainable development of urban traffic and tourism industry is also influenced; in order to solve the above problems, it is necessary to develop a method and an apparatus for predicting the number of people in a scenic spot based on big data.
The invention designs a scenic spot number prediction method based on big data, which comprises the steps of obtaining a first average growth rate through scenic spot tourism data, tourist group passenger data and hotel check-in data around the scenic spot in multiple dimensions, then correcting according to the data of an open-day order and the difference between the current prediction data and actual data to obtain a second average growth rate and a third average growth rate, generating a predicted number range according to the data of the day after the last year, and finally correcting according to collected weather conditions and policy conditions to obtain a more accurate scenic spot open-day predicted number range; the number of workers can be correspondingly arranged for management after the manager can know the number of the workers at the bright day of the scenic spot, so that the management of the scenic spot is facilitated, the problems of congestion and potential safety hazards in the scenic spot are prevented, the traffic around the scenic spot can be dredged in advance, and the traffic jam condition is reduced; the tourists can get better playing experience, and the sustainable development of the tourism industry is promoted.
The specific implementation mode of the invention provides a scenic spot number prediction method based on big data, and the method is shown in figure 1 and comprises the following steps:
in step S1, the sight spot travel data is acquired, and a first growth rate of yesterday that is the same day of the last year and a second growth rate of this day that is the same day of the last year are calculated.
In the embodiment of the invention, historical tourist data of scenic spots is obtained firstly, data of the day before the last year and the present day is recorded as M, data of the day after the last year and the present day is recorded as N, and data of the day after the last year and the present day is recorded as Z; acquiring yesterday scenic spot tourism data and today's scenic spot tourism data, comparing the yesterday scenic spot tourism data with the M to obtain a first growth rate, and comparing the today's scenic spot tourism data with the N to obtain a second growth rate; finally, acquiring real-time weather conditions and policy conditions for correction, for example, the weather conditions of yesterday are much better than those of the same period of the last year, and correspondingly reducing the growth rate; the weather is equivalent to the weather in the same period of the last year, the adjustment is not carried out, the weather is worse than the weather in the same period of the last year, and the growth rate is correspondingly increased; the policy condition is compared with the policy condition of the same period in the last year; resulting in actual first and second growth rates, which may also be negative.
In step S2, the tourist group traveler data is acquired, and the third growth rate of yesterday that is the same year before and the fourth growth rate of this day that is the same year before are calculated.
In the embodiment of the invention, historical passenger data of a tourist party is obtained firstly, the data of the previous day of the last year is recorded as Q, and the data of the current day of the last year is recorded as W; acquiring yesterday tourist group passenger data and today tourist group passenger data, comparing the yesterday tourist group passenger data with the Q to obtain a third growth rate, and comparing the today scenic spot tourist data with the N to obtain a fourth growth rate; finally, acquiring the promotion activity condition of the tourist group, wherein if the promotion activities of yesterday, this day and tomorrow are the same, the third growth rate and the fourth growth rate are unchanged; and if the promotion activities of yesterday, this day and the tomorrow are different, correspondingly correcting the third growth rate and the fourth growth rate.
In step S3, hotel stay data around the spot is acquired, and the fifth growth rate of yesterday earlier than the same day of the last year and the sixth growth rate of this day earlier than the same day of the last year are calculated.
In the embodiment of the invention, the historical check-in data of the peripheral hotel is firstly obtained, the data of the day before the last year is recorded as X, and the data of the day before the last year and the current year is recorded as Y; acquiring yesterday ambient hotel check-in data and today ambient hotel check-in data, comparing the yesterday ambient hotel check-in data with the X to obtain a fifth growth rate, and comparing the today ambient hotel check-in data with the Y to obtain a sixth growth rate; finally, acquiring activity information around the hotel in real time, wherein the activity information comprises examinations, commercial activities and festival activities, and if the activity information exists, correcting the corresponding growth rate of the current day according to the scale of the activity information; if there is no activity information, the fifth growth rate and the sixth growth rate are unchanged.
In step S4, a first average growth rate is obtained from the first, second, third, fourth, fifth, and sixth growth rates.
In the embodiment of the invention, the first average growth rate can be calculated according to the average value, and the first average growth rate can be calculated after the highest score and the lowest score are removed.
In step S5, the scenery spot bright day order data, the surrounding hotel bright day order data, and the tourist party bright day order data are acquired in real time, and the first average growth rate is corrected according to the scenery spot bright day order data, the surrounding hotel bright day order data, and the tourist party bright day order data, so as to obtain a corrected second average growth rate.
In the embodiment of the invention, the present order data of the scenic spot, the present order data of the peripheral hotels and the present order data of the tourist groups are obtained first and processed to obtain a union set L; obtaining the number of simulated people according to the L and the history coefficient I, comparing the number of simulated people with the number of actual people, and adjusting the history coefficient I to obtain a new history coefficient I; then obtaining the number of the simulated people in the tomorrow according to the new history coefficient I, the scenery spot tomorrow order data, the surrounding hotel tomorrow order data and the tourist party tomorrow order data; finally, correcting the first average growth rate according to the number of the simulated people on the tomorrow to obtain a second average growth rate; the history coefficient I is a numerical value obtained in the previous day, and is obtained comprehensively according to the proportion of the number of the simulated people to the number of the actual people, economic situation and policy.
In step S6, the predicted data and the actual data of this day are obtained, and the second average growth rate is corrected according to the predicted data and the actual data of this day, so as to obtain a corrected third average growth rate.
In the embodiment of the invention, the present predicted data and the actual data are obtained firstly; then calculating the ratio F of the prediction data and the actual data; then obtaining the recent economic situation and policy, and adjusting the proportion F according to the recent economic situation and policy; and finally, correcting the second average growth rate according to the adjusted ratio F to obtain a corrected third average growth rate.
In step S7, a range of the predicted number of the scenery spot tomorrow is automatically generated according to the data of the day after the current day of the last year, the second average growth rate and the third average growth rate.
In the embodiment of the invention, the data of the day after the last year is recorded as Z; obtaining a first predicted population according to the Z and the second average growth rate; obtaining a second predicted number of people according to the Z and the third average growth rate; and finally, obtaining the range of the number of the forecasted scenery bright days according to the first forecasted number and the second forecasted number, and displaying from small to large.
In step S8, weather conditions and policy conditions are collected in real time, and the range of the predicted number of people in the spot tomorrow is corrected.
In the embodiment of the invention, the tomorrow weather condition and the policy condition are collected through each channel, the tomorrow weather condition and the policy condition are compared with today and yesterday one by one, and the range of the predicted number point is adjusted correspondingly.
After the predicted number range is corrected, the method also comprises the step of early warning; obtaining the range of the final number of the scenery spot tomorrow forecasted people; extracting the maximum predicted number of people in the range; judging whether the maximum number of forecasted people exceeds a preset threshold value of a scenic spot; and if the threshold value is exceeded, early warning is carried out, and a manager is reminded.
According to the scenic spot tourist data, tourist group passenger data and hotel check-in data around the scenic spot, obtaining a first average growth rate in multiple dimensions, then correcting according to the data of the open-day order and the difference degree between the current predicted data and the actual data to obtain a second average growth rate and a third average growth rate, generating a predicted people number range according to the data of the day after the current year, and finally correcting according to the collected weather conditions and policy conditions to obtain a more accurate scenic spot open-day predicted people number range; the number of workers can be correspondingly arranged for management after the manager can know the number of the workers at the bright day of the scenic spot, so that the management of the scenic spot is facilitated, the problems of congestion and potential safety hazards in the scenic spot are prevented, the traffic around the scenic spot can be dredged in advance, and the traffic jam condition is reduced; the tourists can get better playing experience, and the sustainable development of the tourism industry is promoted.
Referring to fig. 2, fig. 2 provides a device for scenic spot number prediction based on big data, the device includes:
the scenic spot data processing unit 21 is configured to obtain scenic spot travel data, and calculate a first growth rate of yesterday that is the same year in the last year and a second growth rate of today that is the same year in the last year.
Optionally, the scenic spot data processing unit 21 is further configured to obtain historical scenic spot travel data, record data of the day before this year as M, record data of the day before this year as N, and record data of the day after this year as Z; obtaining yesterday scenic spot tourism data and today's scenic spot tourism data, comparing the yesterday scenic spot tourism data with the M to obtain a first growth rate, and comparing the today's scenic spot tourism data with the N to obtain a second growth rate; and acquiring real-time weather conditions and policy conditions for correction to obtain an actual first growth rate and an actual second growth rate.
The tourist group data processing unit 22 is used for acquiring tourist group data and calculating to obtain a third growth rate of yesterday in the same year and a fourth growth rate of today in the same year;
optionally, the data processing unit 22 of the tourist group is further configured to obtain historical tourist data of the tourist group, record data of the day before the last year as Q, and record data of the day before the last year as W; acquiring yesterday tourist group passenger data and today tourist group passenger data, comparing the yesterday tourist group passenger data with the Q to obtain a third growth rate, and comparing the today scenic spot tourist data with the N to obtain a fourth growth rate; acquiring the promotion activity condition of the tourist group, wherein if the promotion activities of yesterday, this day and tomorrow are the same, the third growth rate and the fourth growth rate are unchanged; and if the promotion activities of yesterday, this day and the tomorrow are different, correspondingly correcting the third growth rate and the fourth growth rate.
The peripheral hotel data processing unit 23 is configured to obtain hotel attendance data around the scenic spot, and calculate a fifth growth rate of yesterday that is the same day as the last year and a sixth growth rate of this day that is the same day as the last year;
optionally, the peripheral hotel data processing unit 23 is further configured to obtain historical check-in data of the peripheral hotel, record data of the day before the last year as X, and record data of the day after the last year as Y; acquiring yesterday peripheral hotel stay data and today peripheral hotel stay data, comparing the yesterday peripheral hotel stay data with the X to obtain a fifth growth rate, and comparing the today peripheral hotel stay data with the Y to obtain a sixth growth rate; acquiring activity information around the hotel in real time, wherein the activity information comprises examinations, commercial activities and festival activities, and if the activity information exists, correcting the corresponding growth rate of the current day according to the scale of the activity information; if there is no activity information, the fifth growth rate and the sixth growth rate are unchanged.
A first average growth rate calculation unit 24, configured to obtain a first average growth rate according to the first growth rate, the second growth rate, the third growth rate, the fourth growth rate, the fifth growth rate, and the sixth growth rate;
the second average growth rate calculation unit 25 is configured to obtain scenery spot bright day order data, peripheral hotel bright day order data, and tourist party bright day order data in real time, correct the first average growth rate according to the scenery spot bright day order data, peripheral hotel bright day order data, and tourist party bright day order data, and obtain a corrected second average growth rate;
optionally, the second average growth rate calculating unit 25 is further configured to obtain today's order data of the scenic spot, today's order data of the peripheral hotel, and today's order data of the tourist party, and process the obtained result to obtain a union L; obtaining the number of simulated people according to the L and the history coefficient I, comparing the number of simulated people with the number of actual people, and adjusting the history coefficient I to obtain a new history coefficient I; obtaining the number of the simulated tomorrow people according to the new history coefficient I, the scenery spot tomorrow order data, the peripheral hotel tomorrow order data and the tourism group tomorrow order data; and correcting the first average growth rate according to the number of the tomorrow simulated people to obtain a second average growth rate. The history coefficient I is a numerical value obtained in the previous day, and is obtained comprehensively according to the proportion of the number of the simulated people to the number of the actual people, economic situation and policy.
A third average growth rate calculation unit 26, configured to obtain the current predicted data and the actual data, and correct the second average growth rate according to the current predicted data and the actual data to obtain a corrected third average growth rate;
optionally, the third average growth rate calculating unit 26 is further configured to calculate a ratio F between the today's predicted data and the actual data; obtaining the recent economic situation and policy, and adjusting the proportion F according to the recent economic situation and policy; and correcting the second average growth rate according to the adjusted ratio F to obtain a corrected third average growth rate.
The prediction range calculation unit 27 is configured to automatically generate a range of the predicted number of the scenery spot tomorrow according to data of the day after the last year, the second average growth rate and the third average growth rate;
optionally, the prediction range calculating unit 27 is further configured to record data of the day after this day of the last year as Z; obtaining a first predicted population according to the Z and the second average growth rate; obtaining a second predicted number of people according to the Z and the third average growth rate; and obtaining the range of the number of the forecasted scenery bright days according to the first forecasted number and the second forecasted number, and displaying from small to large.
The forecast data correction unit 28 is used for collecting weather conditions and policy conditions in real time and correcting the range of the forecast number of the scenic spot on the next day;
the prediction early warning management unit 29 is used for obtaining the range of the number of the final scenic spot sunday predictions; extracting the maximum predicted number of people in the range; judging whether the maximum number of forecasted people exceeds a preset threshold value of a scenic spot; and if the threshold value is exceeded, early warning is carried out, and a manager is reminded.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A scenic spot number prediction method based on big data is characterized by comprising the following steps:
s1: obtaining scenic spot tourism data, and calculating to obtain a first growth rate of yesterday in the same year and a second growth rate of today in the same year;
s2: acquiring tourist group passenger data, and calculating to obtain a third growth rate of yesterday in the same year and a fourth growth rate of today in the same year;
s3: acquiring check-in data of hotels around the scenic spots, and calculating to obtain a fifth growth rate of yesterday in the same year and a sixth growth rate of today in the same year;
s4: obtaining a first average growth rate according to the first growth rate, the second growth rate, the third growth rate, the fourth growth rate, the fifth growth rate and the sixth growth rate;
s5: obtaining scenery spot bright day order data, peripheral hotel bright day order data and tourist party bright day order data in real time, correcting the first average growth rate according to the scenery spot bright day order data, peripheral hotel bright day order data and tourist party bright day order data, and obtaining a corrected second average growth rate;
s6: acquiring current prediction data and actual data, and correcting the second average growth rate according to the current prediction data and the actual data to obtain a corrected third average growth rate;
s7: automatically generating the range of the predicted number of the scenery spot tomorrow on the next day according to the data of the last year and the next day, the second average growth rate and the third average growth rate;
s8: and collecting weather conditions and policy conditions in real time, and correcting the range of the number of the forecasted scenic spot tomorrow people.
2. The method of claim 1, wherein the step of obtaining the historical sight travel data and calculating a first growth rate of yesterday from the same day of the last year and a second growth rate of today from the same day of the last year comprises:
obtaining historical tourist data of the scenic spot, recording the data of the day before the last year as M, recording the data of the last year and the present day as N, and recording the data of the day after the last year and the present day as Z;
obtaining yesterday scenic spot tourism data and today's scenic spot tourism data, comparing the yesterday scenic spot tourism data with the M to obtain a first growth rate, and comparing the today's scenic spot tourism data with the N to obtain a second growth rate;
and acquiring real-time weather conditions and policy conditions for correction to obtain an actual first growth rate and an actual second growth rate.
3. The method of claim 1, wherein the step of obtaining tourist group passenger data and calculating a third growth rate of yesterday from the same date in the last year and a fourth growth rate of today from the same date in the last year comprises:
acquiring historical tourist data of a tourist party, recording the data of the previous day of the last year as Q, and recording the data of the current day of the last year as W;
acquiring yesterday tourist group passenger data and today tourist group passenger data, comparing the yesterday tourist group passenger data with the Q to obtain a third growth rate, and comparing the today scenic spot tourist data with the N to obtain a fourth growth rate;
acquiring the promotion activity condition of the tourist group, wherein if the promotion activities of yesterday, this day and tomorrow are the same, the third growth rate and the fourth growth rate are unchanged; and if the promotion activities of yesterday, this day and the tomorrow are different, correspondingly correcting the third growth rate and the fourth growth rate.
4. The method of claim 1, wherein the obtaining the hotel stay data around the attraction and calculating a fifth growth rate of yesterday from the same day of the last year and a sixth growth rate of today from the same day of the last year comprises:
acquiring historical check-in data of peripheral hotels, recording the data of the day before the last year as X, and recording the data of the day before the last year as Y;
acquiring yesterday peripheral hotel stay data and today peripheral hotel stay data, comparing the yesterday peripheral hotel stay data with the X to obtain a fifth growth rate, and comparing the today peripheral hotel stay data with the Y to obtain a sixth growth rate;
acquiring activity information around the hotel in real time, wherein the activity information comprises examinations, commercial activities and festival activities, and if the activity information exists, correcting the corresponding growth rate of the current day according to the scale of the activity information; if there is no activity information, the fifth growth rate and the sixth growth rate are unchanged.
5. The method of claim 1, wherein the step of obtaining the scenery spot bright day order data, the peripheral hotel bright day order data, and the tourist party bright day order data in real time, and correcting the first average growth rate according to the scenery spot bright day order data, the peripheral hotel bright day order data, and the tourist party bright day order data to obtain a corrected second average growth rate further comprises:
obtaining the order data of the scenic spot today, the order data of the surrounding hotels today and the order data of the tourist party today, and processing the order data to obtain a union L;
obtaining the number of simulated people according to the L and the history coefficient I, comparing the number of simulated people with the number of actual people, and adjusting the history coefficient I to obtain a new history coefficient I;
obtaining the number of the simulated people in the tomorrow according to the new history coefficient I, the scenery spot tomorrow order data, the surrounding hotel tomorrow order data and the tourist party tomorrow order data;
and correcting the first average growth rate according to the number of the simulated people in the tomorrow to obtain a second average growth rate.
6. The method as claimed in claim 5, wherein the history coefficient I is a value obtained from the previous day, and the history coefficient is obtained by integrating the ratio of the number of the simulated people to the number of the actual people, economic situation and policy.
7. The method according to claim 1, wherein the step of obtaining the predicted data and the actual data of this day, correcting the second average growth rate according to the predicted data and the actual data of this day, and obtaining the corrected third average growth rate further comprises:
calculating the ratio F of the prediction data and the actual data;
obtaining the recent economic situation and policy, and adjusting the proportion F according to the recent economic situation and policy;
and correcting the second average growth rate according to the adjusted ratio F to obtain a corrected third average growth rate.
8. The method of claim 1, wherein the step of automatically generating a range of the predicted number of sightseeing tomorrow based on the data of the day after the last year, the second average growth rate, and the third average growth rate comprises:
recording the data of the day after the last year as Z;
obtaining a first predicted population according to the Z and the second average growth rate;
obtaining a second predicted number of people according to the Z and the third average growth rate;
and obtaining the range of the predicted number of the scenic spot tomorrow according to the first predicted number and the second predicted number, and displaying the range from small to large.
9. The method of claim 1, wherein the step of collecting weather and policy conditions in real time and modifying the range of the number of the sightseeing tomorrow predictors further comprises:
obtaining the range of the final number of the scenery spot tomorrow forecasted people;
extracting the maximum predicted number of people in the range;
judging whether the maximum number of predicted people exceeds a preset threshold value of a scenic spot;
and if the threshold value is exceeded, early warning is carried out, and a manager is reminded.
10. An apparatus for spot size prediction based on big data, the apparatus comprising:
the scenic spot data processing unit is used for acquiring scenic spot tourism data and calculating to obtain a first growth rate of yesterday in the same year and a second growth rate of today in the same year;
the tourist group data processing unit is used for acquiring tourist group data and calculating to obtain a third growth rate of yesterday in the same year and a fourth growth rate of today in the same year;
the peripheral hotel data processing unit is used for acquiring the check-in data of the peripheral hotels of the scenic spots and calculating to obtain a fifth growth rate of yesterday in the same year and a sixth growth rate of today in the same year;
the first average growth rate calculation unit is used for obtaining a first average growth rate according to the first growth rate, the second growth rate, the third growth rate, the fourth growth rate, the fifth growth rate and the sixth growth rate;
the second average growth rate calculation unit is used for acquiring scenery spot bright day order data, peripheral hotel bright day order data and tourist party bright day order data in real time, correcting the first average growth rate according to the scenery spot bright day order data, peripheral hotel bright day order data and tourist party bright day order data, and acquiring a corrected second average growth rate;
a third average growth rate calculation unit, configured to obtain the current predicted data and the actual data, and correct the second average growth rate according to the current predicted data and the actual data to obtain a corrected third average growth rate;
the prediction range calculation unit is used for automatically generating the range of the predicted number of the scenery spot tomorrow according to the data of the day after the last year, the second average growth rate and the third average growth rate;
the forecast data correction unit is used for collecting weather conditions and policy conditions in real time and correcting the range of the forecast number of the scenic spot on the next day;
the prediction early warning management unit is used for acquiring the range of the number of the final scenery spot tomorrow forecasted people; extracting the maximum predicted number of people in the range; judging whether the maximum number of forecasted people exceeds a preset threshold value of a scenic spot; and if the threshold value is exceeded, early warning is carried out, and a manager is reminded.
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