CN113946748A - Big data-based intelligent tourism target matching system - Google Patents
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Abstract
The invention discloses a big data-based intelligent tourism target matching system which comprises a user information input module, a tourism destination pre-screening module and an optimal tourism destination selecting module, wherein the user information input module is used for a user to input a plurality of candidate tourism destinations and predicted tourism dates in advance, whether the user is predicted to carry children or not in a tourism process is obtained, the tourism destination pre-screening module is used for pre-screening the candidate tourism destinations when the user is predicted to carry the children, and the optimal tourism destination selecting module is used for collecting weather conditions, pedestrian volume conditions and scenic spot distribution conditions of the screened candidate tourism destinations and selecting the optimal tourism destination to push to the user.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a big data-based intelligent tourism target matching system.
Background
The travel is travel, go out, namely a process of spatially traveling from a first place to a second place for achieving a certain purpose; the tour is going out for touring, sightseeing and entertainment, namely the tour for achieving the purposes. The two methods are combined, namely, the emotion among family members can be improved by touring parent-child tourism, the cognitive ability of the child can be cultivated, the child can be informed, the language ability of the child can be exercised, the communication ability of the child can be enhanced, and the method for enhancing the environment adaptation ability of the child from the physical and mental aspects by cultivating an emotier can be realized. However, in the prior art, when a child travels, the selection of a travel destination is a difficult problem.
Disclosure of Invention
The invention aims to provide a big data-based intelligent tourism target matching system and a big data-based intelligent tourism target matching method, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the matching system comprises a user information input module, a tourist destination pre-screening module and an optimal tourist destination selecting module, wherein the user information input module is used for a user to input a plurality of candidate tourist destinations and predicted tourist dates in advance, whether the user is predicted to carry children during travelling is obtained, when the user is predicted to carry children, the tourist destination pre-screening module is used for pre-screening the candidate tourist destinations, and the optimal tourist destination selecting module is used for collecting weather conditions, pedestrian volume conditions and scenic spot distribution conditions of the candidate tourist destinations after screening and selecting the optimal tourist destination to push to the user.
Further, the tourist destination pre-screening module comprises a child body condition acquisition and comparison module, a tourist destination adverse reaction acquisition module and a preferred tourist destination selection module, wherein the child body condition acquisition module is used for acquiring the illness frequency of a carried child in a recent period of time, comparing the illness frequency with an illness frequency threshold, and when the illness frequency is greater than or equal to the illness frequency threshold, enabling the tourist destination adverse reaction acquisition module to calculate the adverse reaction rate of a candidate tourist destination according to the number of tourists which are out of water and soil and are generated by the candidate tourist destination and the total number of the tourists at the candidate tourist destination, and selecting the candidate tourist destination as the preferred tourist destination when the adverse reaction rate of the preferred tourist destination selection module at a certain candidate tourist destination is less than or equal to the adverse reaction rate threshold.
Furthermore, the optimal tourist destination selecting module comprises a weather factor acquiring module, a people flow factor acquiring module, a scenic spot factor acquiring module, a comprehensive reference factor calculating module and a sequencing comparison module, wherein the weather factor acquiring module acquires weather conditions of each optimal tourist destination within expected tourist dates and acquires weather factors according to the weather conditions; the people flow factor acquiring module comprises a first people flow factor acquiring module, a second people flow factor acquiring module and a people flow total factor calculating module, wherein the first people flow factor acquiring module calculates a first people flow factor of a preferred tourism destination according to the sum of the number of tourism people of the preferred tourism destination in the same year and the number of tourism people of the preferred tourism destination in the same year, the second people flow factor acquiring module calculates a second people flow factor of the preferred tourism destination according to the number of tourism people of a certain preferred tourism destination in the same year and the number of local residents of the preferred tourism destination, and the people flow total factor calculating module calculates the people flow total factor of the preferred tourism destination according to the first people flow factor and the second people flow factor; the scenic spot factor acquisition module acquires scenic spot factors according to the geographical position condition of each scenic spot in the preferred tourist destination; the comprehensive reference factor calculation module calculates comprehensive reference factors of all the preferred tourist destinations according to weather factors, people flow factors and scenic spot factors, the sorting comparison module sorts the comprehensive reference factors of all the preferred tourist destinations in descending order, the first preferred tourist destination is selected as the best tourist destination, and the best tourist destination is pushed to the user.
Further, the weather factor acquisition module comprises a first weather factor acquisition module, a second weather factor acquisition module and a weather factor calculation module, wherein the first weather factor acquisition module calculates a first weather factor according to the sum of days of a certain preferred travel destination in an expected travel date, wherein the days of the certain preferred travel destination in the expected travel date are sunny days and cloudy days, and the total days of the expected travel date, the second weather factor acquisition module calculates a second weather factor according to the average temperature of the child frequent location in the last month and the average temperature of each preferred travel destination in the expected travel, and the weather factor calculation module calculates the weather factor of each preferred travel destination according to the first weather factor and the second weather factor; the scenic spot factor acquisition module comprises a first scenic spot factor acquisition module, a second scenic spot factor acquisition module and a scenic spot factor calculation module, wherein the first scenic spot factor acquisition module calculates first scenic spot factors according to the number of scenic spots of each preferred tourist destination, which are plain in terms of the terrain of the scenic spot, and the number of scenic spots of each preferred tourist location, the second scenic spot factor acquisition module calculates second scenic spot factors of each preferred tourist destination according to the distance between two adjacent scenic spots in each preferred tourist destination, and the scenic spot factor calculation module calculates the scenic spot factors of each preferred tourist destination according to the first scenic spot factors and the second scenic spot factors of each preferred tourist destination.
A big data-based intelligent travel target matching method comprises the following steps:
the method comprises the steps that a user inputs a plurality of candidate tourist destinations and predicted tourist dates in advance, whether the user is predicted to carry children during tourism is obtained, and if the user is predicted to carry children, the candidate tourist destinations are screened in advance;
and collecting the weather conditions, the pedestrian volume conditions and the scenic spot distribution conditions of the screened candidate tourist destinations, and selecting the optimal tourist destination according to the weather conditions, the pedestrian volume conditions and the scenic spot distribution conditions and pushing the optimal tourist destination to the user.
Further, the pre-screening of candidate travel destinations includes the following:
the method comprises the steps of collecting the physical condition of a carried child in a recent period of time, collecting the adverse reaction rate of a certain candidate tourist destination if the illness frequency of the carried child in the recent period of time is more than or equal to an illness frequency threshold value, and if the adverse reaction rate of the candidate tourist destination is less than or equal to an adverse reaction rate threshold value, determining that the candidate tourist destination is a preferred tourist destination.
Further, the selecting the optimal travel destination to push to the user includes the following steps:
step A: respectively acquiring the weather conditions of each preferred tourist destination within the expected tourist date, and acquiring weather factors q according to the weather conditions;
and B: collecting the number b1 of tourists of each preferred tourism destination in the same period of the last year, and calculating a first person flow factor r1 of 1-b1/bz of each preferred tourism destination, wherein bz is the sum of the number of the tourists of each preferred tourism destination in the same period of the last year, and a second person flow factor r2 of each preferred tourism destination is bv/(bv + b1), wherein bv is the local resident number of the preferred tourism destination;
then the gross factor of people flow r is 0.7 r1+0.3 r 2;
and C: respectively collecting the geographical position conditions of each scenic spot in the preferred tourist destination, and acquiring a scenic spot factor s according to the geographical position conditions;
step D: then the comprehensive reference factors Z of the preferred tourist destinations are ranked from big to small, the first preferred tourist destination is selected as the optimal tourist destination, and the optimal tourist destination is pushed to the user.
Further, the obtaining the weather factor q according to the above includes the following:
step A1: respectively acquiring the sum of days of sunny days and cloudy days of each preferred tourist destination within the expected tourist date d1, and then obtaining a first weather factor q1 which is d1/dz, wherein dz is the total days of the expected tourist date;
step A2: collecting the average temperature T0 of the frequent children premises in the last month, the average temperature Tc of each preferred tourist destination in the prospective tourism, and calculating a second weather factor q2 | Tc-T0|/T0 of each preferred tourist destination;
step A3: then the weather factor q for each preferred travel destination is 0.4 q1+0.6 q 2.
Further, the obtaining the sight factor s according to the above includes:
step C1: respectively counting the number sp of scenic spots of each preferred tourist destination, wherein the terrain of the scenic spots is plain, and then a first scenic spot factor s1 of each preferred tourist destination is sp/sz, wherein sz is the number of scenic spots of each preferred tourist location;
step C2: the distance between two adjacent sights in each preferred travel destination is collected, and then the average distance between two adjacent sights in a preferred travel destination
Wherein d isiThe distance between the ith attraction and the (i + 1) th attraction of the preferred travel destination is defined as e, the number of the attractions of the preferred travel destination is subtracted by 1,
then the second sight factor s2 for a preferred travel destination is (D1-Dmin)/(Dmax-Dmin), where D1 is the average distance between two adjacent sights of the preferred travel destination, Dmin is the minimum of the average distances between two adjacent sights of all preferred travel destinations, and Dmax is the maximum of the average distances between two adjacent sights of all preferred travel destinations;
step C3: then the attraction factor s for each preferred travel destination is 0.5 s1+0.5 s 2.
Further, the adverse reaction rate p of the certain candidate tourist destination is v/w, wherein v is the number of tourists suffering from water and soil intolerance at the candidate tourist destination, and w is the number of tourists at the candidate tourist destination.
Compared with the prior art, the invention has the following beneficial effects: according to the method and the device, the optimal tourist destination suitable for carrying the children is selected according to weather factors, people flow factors and scenic spot factors of the tourist destination, and meanwhile, the tourist destination is screened before the optimal tourist destination is selected, so that the probability of discomfort of the children in the tourism process is reduced, and the tourism experience is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of a big data based intelligent travel object matching system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: the matching system comprises a user information input module, a tourist destination pre-screening module and an optimal tourist destination selecting module, wherein the user information input module is used for a user to input a plurality of candidate tourist destinations and predicted tourist dates in advance, whether the user is predicted to carry children during travelling is obtained, when the user is predicted to carry children, the tourist destination pre-screening module is used for pre-screening the candidate tourist destinations, and the optimal tourist destination selecting module is used for collecting weather conditions, pedestrian volume conditions and scenic spot distribution conditions of the candidate tourist destinations after screening and selecting the optimal tourist destination to push to the user.
The tourist destination pre-screening module comprises a child body condition acquisition and comparison module, a tourist destination adverse reaction acquisition module and a preferred tourist destination selection module, wherein the child body condition acquisition module is used for acquiring the illness frequency of a carried child in a recent period of time, comparing the illness frequency with an illness frequency threshold, and when the illness frequency is greater than or equal to the illness frequency threshold, enabling the tourist destination adverse reaction acquisition module to calculate the adverse reaction rate of a candidate tourist destination according to the number of tourists which are not taken by water and soil of the candidate tourist destination and the total number of the tourists at the candidate tourist destination, and selecting the candidate tourist destination as the preferred tourist destination when the adverse reaction rate of the preferred tourist destination selection module at a certain candidate tourist destination is less than or equal to the adverse reaction rate threshold.
The optimal tourist destination selecting module comprises a weather factor acquiring module, a people flow factor acquiring module, a scenic spot factor acquiring module, a comprehensive reference factor calculating module and a sequencing comparison module, wherein the weather factor acquiring module acquires the weather condition of each optimal tourist destination within an expected tourist date and acquires weather factors according to the weather condition; the people flow factor acquiring module comprises a first people flow factor acquiring module, a second people flow factor acquiring module and a people flow total factor calculating module, wherein the first people flow factor acquiring module calculates a first people flow factor of a preferred tourism destination according to the sum of the number of tourism people of the preferred tourism destination in the same year and the number of tourism people of the preferred tourism destination in the same year, the second people flow factor acquiring module calculates a second people flow factor of the preferred tourism destination according to the number of tourism people of a certain preferred tourism destination in the same year and the number of local residents of the preferred tourism destination, and the people flow total factor calculating module calculates the people flow total factor of the preferred tourism destination according to the first people flow factor and the second people flow factor; the scenic spot factor acquisition module acquires scenic spot factors according to the geographical position condition of each scenic spot in the preferred tourist destination; the comprehensive reference factor calculation module calculates comprehensive reference factors of all the preferred tourist destinations according to weather factors, people flow factors and scenic spot factors, the sorting comparison module sorts the comprehensive reference factors of all the preferred tourist destinations in descending order, the first preferred tourist destination is selected as the best tourist destination, and the best tourist destination is pushed to the user.
The weather factor acquisition module comprises a first weather factor acquisition module, a second weather factor acquisition module and a weather factor calculation module, wherein the first weather factor acquisition module calculates a first weather factor according to the sum of the days of a certain preferred tourist destination in an expected tourist date, namely a sunny day and a cloudy day, and the total days of the expected tourist date, the second weather factor acquisition module calculates a second weather factor according to the average temperature of the usual child station in the latest month and the average temperature of each preferred tourist destination in the expected tourist, and the weather factor calculation module calculates the weather factor of each preferred tourist destination according to the first weather factor and the second weather factor; the scenic spot factor acquisition module comprises a first scenic spot factor acquisition module, a second scenic spot factor acquisition module and a scenic spot factor calculation module, wherein the first scenic spot factor acquisition module calculates first scenic spot factors according to the number of scenic spots of each preferred tourist destination, which are plain in terms of the terrain of the scenic spot, and the number of scenic spots of each preferred tourist location, the second scenic spot factor acquisition module calculates second scenic spot factors of each preferred tourist destination according to the distance between two adjacent scenic spots in each preferred tourist destination, and the scenic spot factor calculation module calculates the scenic spot factors of each preferred tourist destination according to the first scenic spot factors and the second scenic spot factors of each preferred tourist destination.
A big data-based intelligent travel target matching method comprises the following steps:
the method comprises the steps that a user inputs a plurality of candidate tourist destinations and predicted tourist dates in advance, whether the user is predicted to carry children during tourism is obtained, and if the user is predicted to carry children, the candidate tourist destinations are screened in advance;
the pre-screening candidate travel destinations include the following:
acquiring the physical condition of the carried child in the latest period of time, acquiring the adverse reaction rate of a certain candidate tourist destination if the illness frequency of the carried child in the latest period of time is more than or equal to an illness frequency threshold, and if the adverse reaction rate of the candidate tourist destination is less than or equal to an adverse reaction rate threshold, determining that the candidate tourist destination is the preferred tourist destination;
the adverse reaction rate p of the certain candidate tourist destination is equal to v/w, wherein v is the number of tourists suffering from water and soil intolerance in the candidate tourist destination, and w is the number of tourists in the candidate tourist destination; the condition that the tourists suffer from diarrhea and vomiting in places where the tourists are travelling is indicated, the physical quality of the children is weaker than that of adults, and therefore, when the tourists travel with the children, the places with lower probability of suffering from water and soil inadequacy, better natural environment and milder dietary culture are selected as the travel destinations.
Collecting the weather conditions, the pedestrian volume conditions and the scenic spot distribution conditions of the screened candidate tourist destinations, and selecting the optimal tourist destination to push to the user according to the weather conditions, the pedestrian volume conditions and the scenic spot distribution conditions;
the selecting the optimal travel destination to push to the user comprises the following steps:
step A: respectively acquiring the weather conditions of each preferred tourist destination within the expected tourist date, and acquiring weather factors q according to the weather conditions;
accordingly, acquiring the weather factor q includes the following:
step A1: respectively acquiring the sum of days of sunny days and cloudy days of each preferred tourist destination within the expected tourist date d1, and then obtaining a first weather factor q1 which is d1/dz, wherein dz is the total days of the expected tourist date;
step A2: collecting the average temperature T0 of the frequent children premises in the last month, the average temperature Tc of each preferred tourist destination in the prospective tourism, and calculating a second weather factor q2 | Tc-T0|/T0 of each preferred tourist destination;
step A3: then the weather factor q for each preferred travel destination is 0.4 q1+0.6 q 2; the weather is sunny or cloudy, so that the child can conveniently carry out traveling at a travel destination, the second weather factor is obtained, and the probability that the child cannot adapt to the condition caused by overlarge temperature difference between the two places is reduced;
and B: collecting the number b1 of tourists of each preferred tourism destination in the same period of the last year, and calculating a first person flow factor r1 of 1-b1/bz of each preferred tourism destination, wherein bz is the sum of the number of the tourists of each preferred tourism destination in the same period of the last year, and a second person flow factor r2 of each preferred tourism destination is bv/(bv + b1), wherein bv is the local resident number of the preferred tourism destination;
then the gross factor of people flow r is 0.7 r1+0.3 r 2; if the flow of people at the tourist destination is too much, the children are easy to get away, and the travel experience is influenced by too much flow of people;
and C: respectively collecting the geographical position conditions of each scenic spot in the preferred tourist destination, and acquiring the scenic spot factor s according to the geographical position conditions
Obtaining the sight factors s includes:
step C1: respectively counting the number sp of scenic spots of each preferred tourist destination, wherein the terrain of the scenic spots is plain, and then a first scenic spot factor s1 of each preferred tourist destination is sp/sz, wherein sz is the number of scenic spots of each preferred tourist location;
step C2: the distance between two adjacent sights in each preferred travel destination is collected, and then the average distance between two adjacent sights in a preferred travel destination
Wherein d isiThe distance between the ith sight spot and the (i + 1) th sight spot of the preferred tourist destination is defined as e, and the number of the sight spots of the preferred tourist destination is subtracted by 1; the average distance between two adjacent scenic spots of a certain preferred tourist destination is the difference of the sum of the distances between two adjacent scenic spots of the preferred tourist destination divided by the number of the scenic spots of the preferred tourist destination minus one;
then the second sight factor s2 for a preferred travel destination is (D1-Dmin)/(Dmax-Dmin), where D1 is the average distance between two adjacent sights of the preferred travel destination, Dmin is the minimum of the average distances between two adjacent sights of all preferred travel destinations, and Dmax is the maximum of the average distances between two adjacent sights of all preferred travel destinations;
step C3: then the attraction factor s for each preferred travel destination is 0.5 s1+0.5 s 2; the physical strength of the children is weaker than that of adults, so that the places with gentle landforms and the places with relatively short distances of the scenic spots are selected, the physical strength of the children is prevented from being excessively consumed, and the children are prevented from being tired;
step D: then the comprehensive reference factors Z of the preferred tourist destinations are ranked from big to small, the first preferred tourist destination is selected as the optimal tourist destination, and the optimal tourist destination is pushed to the user.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. The intelligent tourism target matching system based on big data is characterized by comprising a user information input module, a tourism destination pre-screening module and an optimal tourism destination selection module, wherein the user information input module is used for a user to input a plurality of candidate tourism destinations and predicted tourism dates in advance, whether the user is predicted to carry children or not in a tourism process is obtained, when the user is predicted to carry children, the tourism destination pre-screening module is used for pre-screening the candidate tourism destinations, and the optimal tourism destination selection module is used for collecting weather conditions, pedestrian volume conditions and scenic spot distribution conditions of the screened candidate tourism destinations and selecting the optimal tourism destination to push to the user;
the matching method of the matching system comprises the following steps:
the method comprises the steps that a user inputs a plurality of candidate tourist destinations and predicted tourist dates in advance, whether the user is predicted to carry children during tourism is obtained, and if the user is predicted to carry children, the candidate tourist destinations are screened in advance;
collecting the weather conditions, the pedestrian volume conditions and the scenic spot distribution conditions of the screened candidate tourist destinations, and selecting the optimal tourist destination to push to the user according to the weather conditions, the pedestrian volume conditions and the scenic spot distribution conditions;
the pre-screening candidate travel destinations include the following:
acquiring the physical condition of the carried child in the latest period of time, acquiring the adverse reaction rate of a certain candidate tourist destination if the illness frequency of the carried child in the latest period of time is more than or equal to an illness frequency threshold, and if the adverse reaction rate of the candidate tourist destination is less than or equal to an adverse reaction rate threshold, determining that the candidate tourist destination is the preferred tourist destination;
the selecting the optimal travel destination to push to the user comprises the following steps:
step A: respectively acquiring the weather conditions of each preferred tourist destination within the expected tourist date, and acquiring weather factors q according to the weather conditions;
and B: collecting the number b1 of tourists of each preferred tourism destination in the same period of the last year, and calculating a first person flow factor r1 of 1-b1/bz of each preferred tourism destination, wherein bz is the sum of the number of the tourists of each preferred tourism destination in the same period of the last year, and a second person flow factor r2 of each preferred tourism destination is bv/(bv + b1), wherein bv is the local resident number of the preferred tourism destination;
then the gross factor of people flow r is 0.7 r1+0.3 r 2;
and C: respectively collecting the geographical position conditions of each scenic spot in the preferred tourist destination, and acquiring a scenic spot factor s according to the geographical position conditions;
step D: then the comprehensive reference factors Z of each preferred travel destination are sorted according to the sequence from big to small, the first preferred travel destination is selected as the optimal travel destination, and the optimal travel destination is pushed to the user;
the obtaining of the weather factor q according to the above includes the following:
step A1: respectively acquiring the sum of days of sunny days and cloudy days of each preferred tourist destination within the expected tourist date d1, and then obtaining a first weather factor q1 which is d1/dz, wherein dz is the total days of the expected tourist date;
step A2: collecting the average temperature T0 of the frequent children premises in the last month, the average temperature Tc of each preferred tourist destination in the prospective tourism, and calculating a second weather factor q2 | Tc-T0|/T0 of each preferred tourist destination;
step A3: then the weather factor q for each preferred travel destination is 0.4 q1+0.6 q 2;
the obtaining of the sight factors s according to the above method includes:
step C1: respectively counting the number sp of scenic spots of each preferred tourist destination, wherein the terrain of the scenic spots is plain, and then a first scenic spot factor s1 of each preferred tourist destination is sp/sz, wherein sz is the number of scenic spots of each preferred tourist location;
step C2: the distance between two adjacent sights in each preferred travel destination is collected, and then the average distance between two adjacent sights in a preferred travel destination
Wherein d isiThe distance between the ith attraction and the (i + 1) th attraction of the preferred travel destination is defined as e, the number of the attractions of the preferred travel destination is subtracted by 1,
then the second sight factor s2 for a preferred travel destination is (D1-Dmin)/(Dmax-Dmin), where D1 is the average distance between two adjacent sights of the preferred travel destination, Dmin is the minimum of the average distances between two adjacent sights of all preferred travel destinations, and Dmax is the maximum of the average distances between two adjacent sights of all preferred travel destinations;
step C3: then the attraction factor s for each preferred travel destination is 0.5 s1+0.5 s 2.
2. The big data based intelligent travel target matching method as claimed in claim 1, wherein: and the adverse reaction rate p of the certain candidate tourist destination is v/w, wherein v is the number of tourists suffering from water and soil erosion in the candidate tourist destination, and w is the number of tourists in the candidate tourist destination.
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