JP2021174272A - Travel plan proposal program - Google Patents

Abstract

To propose a travel plan at a tourist place where a traveler is scheduled to travel.SOLUTION: A travel plan proposal program for proposing a travel plan at a tourist place where a traveler is scheduled to travel causes a computer to execute: an information acquisition step of acquiring desired conditions information including a visit spot and how to spend desired by a traveler at the tourist place and fees information on desired fees; and a proposal step of referring to a combination having reference desired conditions information including the visit spot and how to spend at the tourist place and reference fees information on the fees, and an association degree of three or more levels with the travel plan to output a travel plan to be proposed based on the reference desired conditions information in accordance with the desired conditions information acquired in the information acquisition step and the reference fees information in accordance with the acquired fees information.SELECTED DRAWING: Figure 1

Description

The present invention relates to a travel plan proposal program that proposes a travel plan at a tourist destination where a tourist plans to travel.

As part of the services of a travel agency, there is a concierge service that creates a travel plan after understanding the desired conditions, preferences, and budget, including the tourist spots and ways of spending. In addition, there is a service that arranges reservations for airline tickets and accommodations necessary to realize the created travel plan.

However, if all of these tasks are to be done manually, the burden of labor will increase and labor costs will increase accordingly. For this reason, it is necessary to reduce the burden of labor and costs by making all of these tasks carried out by artificial intelligence, but in the past, artificial intelligence proposed a travel plan for tourist destinations where tourists plan to travel. The current situation is that no system has been proposed to do so.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is to provide a travel plan proposal program that proposes a travel plan at a tourist destination where a tourist plans to travel. It is in.

In order to solve the above-mentioned problems, the travel plan proposal program according to the present invention is a travel plan proposal program in which a traveler proposes a travel plan at a tourist destination where the tourist plans to travel. Information acquisition step to acquire desired condition information including spots and how to spend, charge information regarding desired charges, reference desired condition information including visited spots and ways of spending at the above tourist spots, and reference charge information regarding the charges Referencing the combination with and the degree of association with the travel plan in three or more stages, the desired condition information for reference according to the desired condition information acquired in the above information acquisition step, and the reference charge according to the acquired charge information. It is characterized by having a computer execute a proposal step that outputs a travel plan to be proposed based on the information.

Even if you do not have any special skills or experience, it will be possible to automatically and accurately propose the optimal travel plan according to the desired conditions including the tourist spots and ways of spending, and the desired price. ..

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the travel plan proposal program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a travel plan proposal system 1 in which a travel plan proposal program to which the present invention is applied is implemented. The travel plan proposal system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person who uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the search device 2 described later. The information acquisition unit 9 outputs the detected information to the search device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be composed of a communication interface for acquiring data on the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and this body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biological data of animals as well as humans. Further, the information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading it from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects an odor or scent.

Database 3 stores various information necessary for making a travel plan proposal. Information necessary for making a travel plan proposal includes information on desired conditions for reference including visiting spots and ways of spending at tourist destinations, information on reference charges for the charges, reference attribute information on past tourist attributes, and past. Reference term academic ability information regarding the language ability of tourists, reference preference information regarding past tourist preferences, reference time information regarding the time when the charges in the above reference fee information were planned, events occurring at the above tourist destinations The reference event information regarding the reference, the reference external environment information regarding the external environment at the time of the charge specified in the above reference charge information, and the data set of the travel plan are stored.

That is, in the database 3, such reference desired condition information, reference charge information, reference attribute information, reference term academic ability information, reference preference information, reference timing information, reference event information, reference external environment Any one or more of the information and the travel plan are stored in association with each other. By the way, this travel plan judged in the past sets not only the data actually judged in the past but also a fictitious persona, and the travel plan that each institution or trader will propose about this is used as learning data. May be included.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation in the travel plan proposal system 1 having the above-described configuration will be described.

The travel plan proposal system 1 proposes an actual specific travel plan to the tourist from among the candidates for each travel plan included in the tourist destination designated by the tourist. When booking a travel plan, tourists usually specify a tourist destination, and among the travel plan candidates included in the tourist destination area, the trip that best suits their financial and service content. Select a plan. Therefore, when implementing the travel plan proposal system 1, it is premised that the tourist destinations designated by the tourists are narrowed down. In addition to tourist destinations, tourists usually specify when they will actually travel, but it may be a prerequisite that candidates are narrowed down according to the designated date. That is, the travel plan proposal system 1 according to the present invention implements a place where the optimum travel plan is selected from the travel plan candidates whose tourist destinations and times are narrowed down in advance.

In the travel plan proposal system 1, for example, as shown in FIG. 3, it is premised that a combination of the desired condition information for reference and the charge information for reference is formed. Desired condition information for reference is defined as desired conditions including visiting spots and ways of spending desired by tourists in tourist spots. If the tourist destination is Kyoto, the desired condition information for reference includes information on the spots that you actually want to visit, such as Kinkakuji, Ginkakuji, and Kiyomizudera, as more specifically desired visit spots. This visiting spot is not limited to the case where it is specified only by such a specific spot name, for example, a temple, a shrine, a place with a good view, a place with abundant nature, a ranch, a spot where children can play, etc. The genre you want to visit, such as an amusement park, an aquarium, or a museum, may be defined in a higher-level concept. This spot also contains information about accommodation. For example, there are large communal baths and open-air baths, massage services, breakfast included, check-in and check-out times, dinner shows and contents, daycare facilities, and cleaning facilities. The way of spending is, for example, a pattern that you want to spend romantically with a couple, a pattern that you want to emphasize children's play for families, a pattern that sticks to yourself such as leisure and hobbies, a group such as a company or group travel party It is classified into various patterns such as patterns that you want to move all at once.

The desired condition information for reference is formed by such a visiting spot and a pattern of how to spend.

The reference fee information is information on the accommodation fee, food and drink fee, transportation fee, tour operator and other staff members provided by each travel plan. The accommodation fee referred to here may include not only the accommodation fee but also the meal fee and the service fee. The accommodation charge specified in the reference charge information may be composed of the average value of the accommodation charges provided by the travel plan, or may be composed of the accommodation charges set for each room. Further, it may be indicated by the degree of increase / decrease of the accommodation fee acquired in chronological order over a certain period, or may be composed of the rate ratio with other travel plans, etc., instead of the accommodation fee itself.

As the input data, such reference desired condition information and reference charge information are arranged side by side. The intermediate node shown in FIG. 3 is a combination of the desired condition information for reference and the charge information for reference as such input data. Each intermediate node is further linked to the output. In this output, the travel plan as the output solution is displayed.

Specifically, the travel plan referred to here is what time and minute you arrive at any spot in a tourist spot, how you spend at that spot, what time and minute you arrive at any restaurant, what you eat, and what souvenir shop. It shows what to buy at and what kind of accommodation to arrive at when and at what time. The travel plan also indicates what means of transportation will be used as a means of transportation between the spots. The travel plan referred to here is the departure station and arrival liquid of the means of transportation that the restaurants that take time-series visits, lunch, and dinner stay from what time to what time, and then to the next destination. It may be shown in detail up to the time of, or only the course to be visited is proposed, and the departure / arrival time and staying time are rough or not shown. May be good.

For this travel plan, Plan A is "9:30 departure from Tokyo Station → Tokaido Shinkansen ●● → 12:00 arrival at Kyoto Station → 12:30 to 13:15 lunch at restaurant ○○ → bus → 13:30 Arrive at Kiyomizu-dera → 13:30 to 14:30 Spend at Kiyomizu-dera → Bus → Arrive at Ginkakuji at 15:00 → Spend at Ginkakuji from 15:00 to 16:00 → Bus → Arrive at Hotel △△ Zhuang at 17:00, Plan B "9:00 Depart from Tokyo Station → Tokaido Shinkansen △ ● → Arrive at Kyoto Station at 11:30 → Spend at Honganji Temple from 11:30 to 12:00 → Bus → Lunch at restaurant ○○ → Bus → 13:00 Arrive at Kinkakuji Temple → 13:30 to 14:30 Spend at Kinkakuji Temple → Bus → Spend at Kyoto Tower from 15:00 to 16:00 → Bus → Arrive at 16:30 XX Ryokan ..

Each combination (intermediate node) of the desired condition information for reference and the charge information for reference is associated with each other through three or more levels of association with the travel plan as this output solution. The desired condition information for reference and the charge information for reference are arranged on the left side via this degree of association, and each travel plan is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which of the travel plans A to E with respect to the reference desired condition information and the reference fee information arranged on the left side. In other words, this degree of association is an index indicating what kind of travel plan each reference desired condition information and reference charge information is likely to be associated with, and is a reference desired condition information and reference charge information. It shows the accuracy in selecting the most probable travel plan from. In the example of FIG. 3, w13 to w22 are shown as the degree of association. These w13 to w22 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the travel plan as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the travel plan as an output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 stores the desired condition information for reference, the charge information for reference, and the data set in which the travel plan is associated with the information in determining the actual travel plan, and stores these. By analyzing and analyzing, the degree of association shown in FIG. 3 is created. Such a data set is obtained by extracting information on reference fee information provided by each travel plan in the past and information on desired conditions for reference including visiting spots and ways of spending from information on the Internet. You may keep it, or you may extract it from the past information held by the travel agency or each institution.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference desired condition information P01 and the reference fee information P16, what kind of travel plan is actually associated with such information is analyzed from the past data. For example, in the example of the intermediate node 61a to which the reference desired condition information P01 and the reference charge information P16 are connected, the travel plan A and the output of the travel plan B are linked. Since there are many cases where such service contents and accommodation charges are adopted, the degree of association of w13 connected to travel plan A is set to 7 points, and the degree of association of w14 connected to travel plan B is set to 2 points.

Such learning data can be obtained from information posted on SNS and various Internet sites, such as spots and restaurants that are popular among people who have actually traveled to the tourist spots in the past and local people. The latest plans such as play may actually be included in the travel plan. Automatically or visually check for these latest plans to see if they are similar in price, previously typified spots and spending patterns, or similar in genre. It may be artificially associated with each other.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. That is, the reference desired condition information regarding the service content of each travel plan and the reference charge information regarding the accommodation fee of each travel plan are stored in association with each of the above travel plans. Then, a judgment model in which the combination of the stored reference desired condition information and the reference charge information and the above travel plan are used as teacher data, the input is the reference desired condition information and the reference charge information, and the output is the travel plan. Is generated by machine learning. After creating such learned data, when actually determining a new travel plan at a railroad crossing, the travel plan will be determined using the above-mentioned learned data. In such a case, the desired condition information is newly acquired and the charge information is acquired.

The newly acquired desired condition information and charge information are acquired through the user interface via the mouse, touch panel, and keyboard in the information acquisition unit 9 described above.

Based on the newly acquired desired condition information and the fare information in this way, the travel plan at the time when the newly acquired desired condition information and the fare information are actually acquired is obtained. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired desired condition information is the same as or similar to P02, and the charge information arriving most recently at the time of acquisition is the same as or similar to the reference charge information P17. Is associated with the node 61d via the degree of association, and the node 61d is associated with the travel plan C by w19 and the travel plan D by the degree of association w20. In such a case, the travel plan C having a higher degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the travel plan D, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In addition, Table 2 below shows examples of the degrees of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

Instead of accepting the input of the desired condition information described above from the tourist, the travel history information regarding the past travel history of the tourist may be acquired. This travel history information is information on tourist destinations actually visited by the traveler in the past, as well as spots visited, restaurants, and accommodations where the traveler stayed. It is also possible to actually extract these information from the travel history information and search for a travel plan to be proposed through the same or similar reference desired condition information.

FIG. 4 shows an example in which, in addition to the above-mentioned desired condition information for reference and charge information for reference, a combination of attribute information for reference and a travel plan for the combination are set to three or more levels of association. Is shown. Reference attribute information is any information about the attributes of past tourists. Examples of this reference attribute information are, for example, age, gender, occupation, annual income, whether or not the work has a lot of business trips, family information, hometown information, and the like. Such attribute information should be obtained from a database managed by each travel plan, which records information on attributes that tourists who have booked in the past or stayed in the past at check-in or at the time of booking in each travel plan. You may. In creating the learning data of this reference attribute information, not only the data actually judged in the past but also a fictitious persona is set, and the travel plan that each institution or trader will propose about this is used as the learning data. May be included.

In such a case, as shown in FIG. 4, the degree of association is expressed as nodes 61a to 61e of the intermediate node as a set of combinations of desired condition information for reference, charge information for reference, and attribute information for reference as described above. Will be done.

For example, in FIG. 4, in the node 61c, the reference desired condition information P02 is associated with the association degree w3, the reference charge information P15 is associated with the association degree w7, and the reference attribute information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference desired condition information P03 is associated with the association degree w5, the reference charge information P15 is associated with the association degree w8, and the reference attribute information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, attribute information is newly acquired in addition to the newly acquired desired condition information and charge information. This attribute information, like the desired condition information and the price information, is acquired through the information acquisition unit 9, which is information to be acquired from a tourist who is about to propose a travel plan.

Based on the reference attribute information associated with the attribute defined in the acquired attribute information, the solution search is performed while referring to the degree of association shown in FIG.

In proposing this travel plan, the degree of association shown in FIG. 4 acquired in advance is referred to. For example, the acquired desired condition information is the same as or similar to the reference desired condition information P02, the acquired charge information corresponds to the reference charge information P15, and the reference attribute information corresponding to the attribute of the acquired attribute information is P21. In the case of, the combination is associated with the node 61c, in which the travel plan B is associated with the association degree w17 and the travel plan D is associated with the association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

FIG. 5 shows an example in which, in addition to the above-mentioned desired condition information for reference and fee information for reference, a combination of reference term academic ability information and a travel plan for the combination have three or more levels of association. Is shown. The reference term academic ability information is information indicating the degree of language ability of each country's language, and may be indicated by, for example, an English proficiency test or a score of a private company.

When a tourist destination is actually a foreign country, the width and contents of the actual travel plan may differ depending on how much you have learned the language of the country of the tourist destination. If you have mastered the language of the country of the tourist destination at a high level, it is possible to propose a travel plan that includes language exchanges with local people. The reference term academic ability information may indicate the linguistic ability of each language of each country. Then, when the country of the tourist destination is actually decided, the linguistic ability of the language of that country will be used as an explanatory variable.

In such a case, as shown in FIG. 5, the degree of association is expressed as nodes 61a to 61e of the intermediate node as a set of combinations of desired condition information for reference, fee information for reference, and academic ability information for reference as described above. Will be done.

For example, in FIG. 5, in the node 61c, the reference desired condition information P02 is associated with the reference degree w3, the reference charge information P15 is associated with the association degree w7, and the reference term academic ability information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference desired condition information P03 is associated with the reference degree w5, the reference charge information P15 is associated with the association degree w8, and the reference term academic ability information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, language ability information is newly acquired in addition to the newly acquired desired condition information and fee information. This language proficiency information is acquired through the information acquisition unit 9, which is information to be acquired from a tourist who is going to propose a travel plan, like the desired condition information and the price information.

Based on the reference term academic ability information associated with the attributes defined in the acquired language ability information, the solution search is performed while referring to the degree of association shown in FIG.

In proposing this travel plan, the degree of association shown in FIG. 5 acquired in advance is referred to. For example, the acquired desired condition information is the same as or similar to the reference desired condition information P02, the acquired charge information corresponds to the reference charge information P15, and the reference term academic ability information corresponding to the acquired language ability information is on P21. Correspondingly, the combination is associated with a node 61c, to which the travel plan B is associated with a degree of association w17 and the travel plan D is associated with a degree of association w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

FIG. 5 shows an example in which, in addition to the above-mentioned desired condition information for reference and fee information for reference, a combination of reference term academic ability information and a travel plan for the combination have three or more levels of association. Is shown. The reference term academic ability information is information indicating the degree of language ability of each country's language, and may be indicated by, for example, an English proficiency test or a score of a private company.

When a tourist destination is actually a foreign country, the width and contents of the actual travel plan may differ depending on how much you have learned the language of the country of the tourist destination. If you have mastered the language of the country of the tourist destination at a high level, it is possible to propose a travel plan that includes language exchanges with local people. The reference term academic ability information may indicate the linguistic ability of each language of each country. Then, when the country of the tourist destination is actually decided, the linguistic ability of the language of that country will be used as an explanatory variable.

In such a case, as shown in FIG. 5, the degree of association is expressed as nodes 61a to 61e of the intermediate node as a set of combinations of desired condition information for reference, fee information for reference, and academic ability information for reference as described above. Will be done.

For example, in FIG. 5, in the node 61c, the reference desired condition information P02 is associated with the reference degree w3, the reference charge information P15 is associated with the association degree w7, and the reference term academic ability information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference desired condition information P03 is associated with the reference degree w5, the reference charge information P15 is associated with the association degree w8, and the reference term academic ability information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, language ability information is newly acquired in addition to the newly acquired desired condition information and fee information. This language proficiency information is acquired through the information acquisition unit 9, which is information to be acquired from a tourist who is going to propose a travel plan, like the desired condition information and the price information.

Based on the reference term academic ability information associated with the attributes defined in the acquired language ability information, the solution search is performed while referring to the degree of association shown in FIG.

In proposing this travel plan, the degree of association shown in FIG. 5 acquired in advance is referred to. For example, the acquired desired condition information is the same as or similar to the reference desired condition information P02, the acquired charge information corresponds to the reference charge information P15, and the reference term academic ability information corresponding to the acquired language ability information is on P21. Correspondingly, the combination is associated with a node 61c, to which the travel plan B is associated with a degree of association w17 and the travel plan D is associated with a degree of association w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

FIG. 6 shows an example in which, in addition to the above-mentioned desired condition information for reference and fee information for reference, a combination of preference information for reference and a travel plan for the combination are set to have three or more levels of association. Is shown. Reference preference information is all information related to tourist preferences, such as hobbies such as skiing, ceramics, visiting shrines, viewing art, fishing, driving, golf, etc., or sticking to gourmet food, sticking to hot springs, etc. , It may be indicated by a strong commitment to the inn.

Such reference preference information may be obtained through interviews with past tourists, or may use data entered by past tourists as answers to questionnaires. good. In addition, preferences may be extracted as data based on a well-known method through the search history and browsing history of past tourists on the Internet, or from the words actually input by past tourists on the Internet search engine. Preference may be extracted as data.

In such a case, as shown in FIG. 6, the degree of association is expressed as nodes 61a to 61e of the intermediate node as a set of combinations of desired condition information for reference, charge information for reference, and preference information for reference as described above. Will be done.

For example, in FIG. 6, in the node 61c, the reference desired condition information P02 is associated with the association degree w3, the reference charge information P15 is associated with the association degree w7, and the reference preference information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference desired condition information P03 is associated with the reference degree w5, the reference charge information P15 is associated with the association degree w8, and the reference preference information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, the preference information is newly acquired in addition to the newly acquired desired condition information and the charge information. This preference information, like the desired condition information and the price information, is information to be acquired from a tourist who is about to propose a travel plan, and is acquired through the information acquisition unit 9. The method of acquiring the preference information is the same as the method of acquiring the reference preference information described above.

In proposing this travel plan, the degree of association shown in FIG. 6 acquired in advance is referred to. For example, the acquired desired condition information is the same as or similar to the reference desired condition information P02, the acquired charge information corresponds to the reference charge information P15, and the reference preference information corresponding to the acquired preference information corresponds to P21. When the combination is performed, the node 61c is associated with the node 61c, and the travel plan B is associated with the association degree w17 and the travel plan D is associated with the association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

FIG. 7 shows an example in which, in addition to the above-mentioned desired condition information for reference and fee information for reference, a combination of time information for reference and a travel plan for the combination are set to three or more levels of association. Is shown. The reference time information is information regarding the time when the charge in the above reference charge information is planned. In other words, it is information about the time when the fee was actually paid in the reference fee information, and in any unit, any event time such as date, week, month, season, Golden Week, summer vacation, Christmas season, New Year holiday, Halloween season, etc. It may be as shown. Especially during Golden Week and summer vacation season, congestion is expected and travel charges during that period will be very high, so this time information is also added to the explanatory variables in association with the price information. Similarly, this reference time information is also extracted from the data of the date when the charge was charged and the date of the estimate when the reference charge information was acquired.

In such a case, as shown in FIG. 7, the degree of association is expressed as nodes 61a to 61e of the intermediate node as a set of combinations of desired condition information for reference, charge information for reference, and timing information for reference as described above. Will be done.

For example, in FIG. 7, in the node 61c, the reference desired condition information P02 is associated with the association degree w3, the reference charge information P15 is associated with the association degree w7, and the reference time information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference desired condition information P03 is associated with the association degree w5, the reference charge information P15 is associated with the association degree w8, and the reference timing information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, in addition to the newly acquired desired condition information and charge information, new timing information is acquired. This time information, like the desired condition information and the price information, is information to be acquired from a tourist who is going to propose a travel plan from now on, and is acquired through the information acquisition unit 9. The method of acquiring the timing information is the same as the method of acquiring the reference timing information described above.

In proposing this travel plan, the degree of association shown in FIG. 7 acquired in advance is referred to. For example, the acquired desired condition information is the same as or similar to the reference desired condition information P02, the acquired charge information corresponds to the reference charge information P15, and the reference time information corresponding to the acquired time information corresponds to P21. When the combination is performed, the node 61c is associated with the node 61c, and the travel plan B is associated with the association degree w17 and the travel plan D is associated with the association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

In the present invention, as an alternative to the reference timing information shown in FIG. 7, the following reference event information is learned in association with the degree of association by a combination of reference desired condition information and reference charge information. It may be.

The reference event information is information about an event that occurs in the tourist spot. For example, if Aomori is a tourist destination, the Nebuta Festival is an event, and the fireworks display held at each tourist destination is also included in the event. The event also includes various festivals, festivals, concerts and shows held at each tourist destination. Such reference event information is acquired in advance for each tourist destination in chronological order.

In such a case, the combination of the desired condition information for reference, the charge information for reference, and the event information for reference, and the degree of association with the travel plan at three or more levels are acquired in advance. At the time of actual exploration, information on events to be held at tourist destinations selected by tourists is acquired. This event information corresponds to the above-mentioned reference event information, and the acquisition method is also the same. Then, after referring to the degree of association, the travel plan is searched in the same manner as described above based on the newly acquired event information.

In the present invention, as an alternative to the reference timing information shown in FIG. 7, the following reference external environment information is learned in association with the degree of association by a combination of reference desired condition information and reference charge information. It may be the one that is made up of.

The reference external environment information is various information related to the external environment information. The external environmental information here is economic data (GDP, employment statistics, mining and industry production index, capital investment, labor force survey, etc.), household data (household consumption situation survey, household data, average working hours per week, savings amount). Statistical data, annual income statistical data, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation data, wind direction data, humidity data, etc.) Etc.). External environmental information may include the presence or absence and extent of various events related to infectious diseases, natural disasters, politics, economy, and society. External environmental information includes all information outside the travel plan, in addition to some or all of this data being reflected. The external environment information for reference may be categorized as the external environment itself.

In such a case, the combination of the desired condition information for reference, the charge information for reference, and the external environment information for reference, and the degree of association with the travel plan at three or more levels are acquired in advance. At the time of the actual search, the external environmental information performed in the tourist destination selected by the tourist is acquired. This external environment information acquires information about the external environment at the present time when exploring a travel plan. It corresponds to the above-mentioned external environment information for reference, and the acquisition method is the same. Then, after referring to the degree of association, the travel plan is searched in the same manner as described above based on the newly acquired external environment information.

In addition, when acquiring any two or more of desired condition information, price information, attribute information, language ability information, preference information, time information, event information, and external environment information, the information to be acquired depends on the two or more information to be acquired. In addition, two or more reference information (desired condition information for reference, charge information for reference, attribute information for reference, academic ability information for reference, preference information for reference, timing information for reference, event information for reference, external environment for reference) By creating learning data consisting of a combination with (information, etc.) and a travel plan for the combination with three or more levels of association, a solution search can be performed in the same manner.

In addition to desired condition information and price information, attribute information, language ability information, preference information, time information, event information, external environment information, etc., in addition to any one or more, and other information may be acquired. A solution search can be performed in the same way.

Further, as shown in FIG. 8, the present invention determines the rate of increase / decrease of each accommodation fee based on the degree of association between two or more types of information, the reference information U and the reference information V. It is assumed that the reference information U is the reference desired condition information and the reference information V is the reference charge information. Alternatively, it is assumed that the reference information U is the reference charge information and the reference information V is the desired condition information for reference.

At this time, as shown in FIG. 8, the output obtained for the reference information U may be used as the input data as it is, and may be associated with the output via the intermediate node 61 in combination with the reference information V. For example, for the reference information U, after the output solution is output, this may be used as an input as it is, and the output may be searched by using the degree of association with other reference information V.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the rate of increase / decrease in the accommodation fee of a company considering financing without any special skill or experience. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since the above-mentioned input data and output data often do not exist exactly the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

Further, according to the present invention, there is a feature that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but the degree of association is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

Candidates for the possibility of search solutions by determining the rate of increase / decrease in accommodation charges, which is more credible and less misleading, based on the degree of association expressed by such numerical values of three or more levels. It is also possible to search and display in descending order of the degree of association under the situation where there are a plurality of possible situations.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition to desired condition information and price information, when attribute information, language ability information, preference information, and knowledge, information, and data related to time information, event information, external environment information, etc. are acquired, the degree of association is determined accordingly. Raise or lower.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is not based on the information that can be obtained from the public communication network, but is also updated by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

1 Travel plan proposal system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 Node

Claims (11)

In the travel plan proposal program that proposes travel plans for tourist destinations where tourists plan to travel
Information acquisition step to acquire desired condition information including visiting spots and ways of spending desired by tourists in the above tourist spots, and charge information regarding desired charges, and
In the above information acquisition step, refer to the combination of the desired condition information for reference including the visited spots and how to spend at the tourist spot, the reference charge information regarding the charge, and the degree of association with the travel plan in three or more stages. It is characterized by having a computer execute a proposal step that outputs a travel plan to be proposed based on the desired condition information for reference according to the acquired desired condition information and the reference charge information according to the acquired charge information. Travel plan proposal program to do. In the above information acquisition step, the attribute information of the above tourist is acquired, and the above information acquisition step is performed.
In the above-mentioned proposal step, the above-mentioned combination having the above-mentioned desired condition information for reference, the above-mentioned reference charge information, and the above-mentioned reference attribute information regarding the attributes of past travelers, and the degree of association with the above-mentioned travel plan in three or more stages are obtained. The travel plan proposal program according to claim 1, wherein the travel plan to be proposed is output based on the reference attribute information according to the attribute information acquired in the above information acquisition step. In the above information acquisition step, the language proficiency information related to the above tourist's language proficiency is acquired, and the information is acquired.
In the above-mentioned proposal step, the above-mentioned combination having the above-mentioned desired condition information for reference, the above-mentioned reference fee information, and reference terminology proficiency information regarding the language proficiency of past tourists, and the degree of association with the above-mentioned travel plan in three or more stages. The travel plan proposal program according to claim 1, wherein the travel plan to be proposed is output based on the reference term academic ability information corresponding to the language ability information acquired in the above information acquisition step. In the above information acquisition step, preference information regarding the preferences of the above tourists is acquired, and the preference information is acquired.
In the above-mentioned proposal step, the above-mentioned combination having the above-mentioned desired condition information for reference, the above-mentioned reference charge information, and the above-mentioned reference preference information regarding the tastes of past tourists, and the degree of association with the above-mentioned travel plan in three or more stages are obtained. The travel plan proposal program according to claim 1, wherein the travel plan to be proposed is output based on the reference preference information according to the preference information acquired in the above information acquisition step. In the above information acquisition step, time information regarding the time when the above trip is planned is acquired, and the above information acquisition step is performed.
In the above-mentioned proposal step, the above-mentioned combination having the above-mentioned desired condition information for reference, the above-mentioned reference charge information, and the above-mentioned reference time information regarding the time when the charge in the above-mentioned reference charge information was planned, and the above-mentioned travel plan 3 The travel plan proposal program according to claim 1, wherein the travel plan to be proposed is output based on the reference timing information according to the timing information acquired in the above information acquisition step by referring to the degree of association of the stages or higher. .. In the above information acquisition step, travel history information regarding the past travel history of the above tourist is acquired, and the travel history information is acquired.
The travel plan proposal program according to claim 1, wherein the proposal step outputs a travel plan to be proposed based on desired condition information for reference according to the travel history information acquired in the information acquisition step. In the above information acquisition step, event information regarding an event occurring in the tourist destination where the above trip is planned is acquired, and the event information is acquired.
In the above-mentioned proposal step, the above-mentioned combination having the above-mentioned desired condition information for reference, the above-mentioned reference charge information, and the above-mentioned reference event information regarding an event occurring in the above-mentioned tourist spot, and the above-mentioned travel plan have three or more levels of association. The travel plan proposal program according to claim 1, wherein the travel plan to be proposed is output based on the reference event information corresponding to the event information acquired in the above information acquisition step. In the above information acquisition step, the external environment information about the current external environment is acquired, and the information is acquired.
In the above-mentioned proposal step, the above-mentioned combination having the above-mentioned desired condition information for reference, the above-mentioned reference charge information, and the above-mentioned external environment information for reference regarding the external environment at the time of the charge specified in the above-mentioned reference charge information, and the above-mentioned travel. Claim 1 is characterized in that a travel plan to be proposed is output based on the reference external environment information corresponding to the external environment information acquired in the above information acquisition step by referring to the degree of association with the plan in three or more stages. The travel plan proposal program described. Any of claims 1 to 8, further comprising an arrangement step for arranging reservations for each of the accommodation, transportation means, and restaurant proposed in the travel plan output by the above proposal step. Travel plan proposal program described in item 1. The travel plan for the accommodation fee according to any one of claims 1 to 9, wherein in the above-mentioned proposed step, the above-mentioned degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. Proposal program. In the travel plan proposal system that proposes travel plans for tourist destinations where tourists plan to travel
A storage means for storing information on desired conditions for reference, including visiting spots and ways of spending at the tourist destination, and information on reference charges related to the charges, respectively, in association with each of the above travel plans.
The combination of the desired condition information for reference and the charge information for reference stored in the storage means and the travel plan are used as teacher data, the input is the desired condition information for reference and the charge information for reference, and the output is the travel plan. A model generation means for generating a judgment model to be performed by machine learning,
A reception means that accepts input of desired condition information including visit spots and ways of spending desired by tourists in the above tourist spots, and charge information regarding desired charges.
Using the determination model generated by the model generation means, it is estimated from the reference desired condition information according to the desired condition information input to the reception means and the reference charge information according to the input charge information. A travel plan proposal system characterized by having a processing means for outputting a travel plan.

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