JP2021174265A - Change rate determination program of accommodation charges - Google Patents

Abstract

To automatically and highly accurately determine a change rate of an accommodation charge of an accommodation facility.SOLUTION: A change rate determination program of accommodation charges that determines a change rate of an accommodation charge of an accommodation facility allows a computer to perform: an information obtaining step for obtaining time information related to a time when the change rate of the accommodation charge at the accommodation facility is determined; and a determination step for determining the change rate of the accommodation charge of the accommodation facility on the basis of the time information obtained by the information obtaining step, in reference to an association degree of three or more levels between reference time information related to past individual time periods and the change rates of the accommodation charge which are extracted from a history of previously obtained past time-series change rates of the accommodation charge of the accommodation facility.SELECTED DRAWING: Figure 5

Description

The present invention relates to an accommodation fee increase / decrease rate determination program for determining an accommodation fee increase / decrease rate of an accommodation facility.

The rate of increase / decrease in accommodation charges for accommodation facilities such as hotels and inns varies greatly depending on the time of day, month, summer vacation, Golden Week, year-end and New Year holidays, etc. In addition to these timing factors, the rate of increase / decrease in accommodation charges at accommodation facilities depends on the external environment such as the spread of infectious diseases, international affairs, social affairs, and political affairs. Because they are different, the rate of increase or decrease in the accommodation fee will change. In addition to this, the rate of increase / decrease in the accommodation fee will change because the degree of congestion of reservations will differ depending on the events and transportation conditions in the area where the accommodation facility is located.

In this way, the rate of increase / decrease in accommodation charges for accommodation facilities differs depending on the external environment, events, and transportation, in addition to seasonal factors. In other words, since the rate of increase / decrease in accommodation charges changes due to various factors, it has been a heavy burden on the management side of the accommodation facility to set the optimum rate of increase / decrease in accommodation charges accordingly. In other words, if the rate of increase / decrease in accommodation charges is too high, the number of reservations will decrease. Will decrease. Further, even for a user who actually wants to stay at an accommodation facility, if the rate of increase / decrease in the accommodation fee of the accommodation facility can be predicted in advance, the convenience when estimating the cost of a business trip or travel can be improved. However, there has been a problem that a method for predicting the rate of increase / decrease in the accommodation fee of each accommodation facility with high accuracy has not been established in the past.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is an accommodation fee capable of automatically and highly accurately determining the rate of increase or decrease in the accommodation fee of an accommodation facility. The purpose is to provide an increase / decrease rate determination program.

In order to solve the above-mentioned problems, the accommodation fee increase / decrease rate determination program according to the present invention is the accommodation fee increase / decrease rate determination program for determining the accommodation fee increase / decrease rate of the accommodation facility. The information acquisition step for acquiring the timing information regarding the timing for determining the rate, the reference timing information consisting of the history of the past time-series reservations of the accommodation facility acquired in advance, and the rate of increase / decrease in the accommodation fee of the above accommodation facility. It is characterized in that the computer executes a determination step of determining the rate of increase / decrease in the accommodation fee of the accommodation facility based on the timing information acquired in the information acquisition step with reference to the degree of association of three or more stages with.

Even if you do not have any special skills or experience, it is possible to automatically and accurately determine the rate of increase or decrease in accommodation charges at accommodation facilities.

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. 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 accommodation fee increase / decrease rate determination 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 an accommodation fee increase / decrease rate determination system 1 in which an accommodation fee increase / decrease rate determination program to which the present invention is applied is implemented. The accommodation fee increase / decrease rate determination 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 discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the determination 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.

The database 3 stores various information necessary for determining the rate of increase / decrease in accommodation charges. Information necessary for determining the rate of increase / decrease in accommodation charges includes reference period information for each past period extracted from the history of the rate of increase / decrease in accommodation charges in the past time series of the above accommodation facilities acquired in advance. Reference reservation information regarding the past reservation status of the accommodation facility, reference event information regarding events held in the area where the accommodation facility is located, and the external environment at the time specified in the reference time information are shown in chronological order. External environmental information for reference, market information for reference showing past market conditions in chronological order, population estimation information for reference regarding population estimation in the area where the accommodation facility is located, reference for transportation in the area where the accommodation facility is located A data set of transportation information and the rate of increase / decrease in accommodation charges for accommodation facilities is stored.

That is, in addition to such reference timing information, the database 3 contains any of reference reservation information, reference event information, reference external environment information, reference market condition information, reference population estimation information, and reference transportation. Or 1 or more and the rate of increase / decrease in the accommodation fee determined in the past are stored in association with each other. By the way, the rate of increase / decrease in accommodation charges determined in the past is not limited to individuals actually determined in the past, but a fictitious persona is set, and the rate of increase / decrease in accommodation charges determined by each institution or accommodation facility. May be included in the training data.

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 of the accommodation fee increase / decrease rate determination system 1 having the above-described configuration will be described.

In the accommodation fee increase / decrease rate determination system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between the reference time information and the accommodation fee increase / decrease rate are set and acquired in advance. Become. The reference time information is information about each past time extracted from the history of the past time-series rate of increase / decrease in accommodation charges at a certain accommodation facility when focusing on one accommodation facility. This reference time information is time-distinguished by past days or weeks. The temporal unit of this reference timing information may be composed of any unit such as daily, weekly, monthly, and the like. Such reference time information may be specified by day, for example, May 26, June 15, etc., or may be specified in the week of May 17, the first week of July, etc. It may be configured on a monthly basis, such as September and December.

The rate of increase / decrease in accommodation charges may consist of the rate of increase / decrease in accommodation charges for each room in the accommodation facility. In such cases, the rate of increase / decrease in accommodation charges is the average value of some or all rooms in the accommodation facility. It may be composed of the rate of increase / decrease of. In such a case, the average value of some or all the rooms of the accommodation facility is indicated by + 10% for A, -15% for B, and the like.

In other words, through this reference time information and the data set of the rate of increase / decrease of the accommodation fee, it can be known at what rate of increase / decrease of the accommodation fee was actually sold to the customer for each period shown in the reference time information. In other words, the data set is the time described in the reference time information and the rate of increase / decrease in the accommodation fee. Therefore, by collecting the reference time information and the data set of the rate of increase / decrease of the accommodation fee, it is possible to know at what time in the past and how much the rate of increase / decrease of the accommodation fee was.

In the example of FIG. 3, it is assumed that the input data is, for example, reference timing information P01 to P03. The reference timing information as such input data is linked to the output. In this output, the rate of increase / decrease in the accommodation fee as the output solution is displayed.

The reference time information is related to each other through three or more levels of association with the rate of increase / decrease in the accommodation fee as the output solution. Reference time information is arranged on the left side via this degree of association, and the rate of increase / decrease in accommodation charges is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which rate of increase / decrease in accommodation charges with respect to the reference time information arranged on the left side. In other words, this degree of association is an index showing what kind of accommodation fee increase / decrease rate is likely to be associated with each reference period information, and the most probable accommodation rate increase / decrease rate from the reference period information. It shows the accuracy in selecting. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 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 rate of increase / decrease in the accommodation fee 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 rate of increase / decrease in the accommodation fee as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data on which of the reference time information and the rate of increase / decrease in the accommodation fee in that case is adopted in determining the actual search solution, and analyzes these. , The degree of association shown in FIG. 3 is created by analysis.

For example, it is assumed that the rate of increase / decrease in the accommodation fee for a certain reference time information at a certain time (the second week of February) is + 16%. In such a situation, it is assumed that in the reference time information consisting of (February 2nd week), the rate of increase / decrease in the accommodation fee is + 16% as well. In such a case, the rate of increase / decrease in accommodation charges + 16% will be more closely linked in the second week of February. On the other hand, in the pattern (classification) of the exact same reference time information (February 2nd week), it is assumed that most of them were judged to be -12% and few were judged to be + 16%. In such a case, the degree of association of -12% becomes stronger and the degree of association of + 16% becomes lower.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference time information P01, analysis is performed from the data of the rate of increase / decrease in the past accommodation charges. This may be extracted from, for example, data on the rate of increase / decrease in past accommodation charges stored at each institution or each accommodation facility. In any case, when extracting the reference time information and the data set of the rate of increase / decrease of the accommodation fee, for example, as shown in FIG. 4, the time is the horizontal axis and the rate of increase / decrease of the accommodation fee is the vertical axis. Data may be extracted from the history of the rate of increase / decrease in the past time-series accommodation charges. A large amount of data can be obtained by going back several years in the past, such as 2019, 2018, etc., for historical data consisting of such a period and the rate of increase / decrease in accommodation charges. In addition, by acquiring data sets not only from one accommodation facility but also from other accommodation facilities, it is possible to acquire a large number of reference timing information and the rate of increase / decrease in accommodation charges.

If there are many cases of the rate of increase / decrease of the accommodation fee A in the case of the reference time information P01, the degree of association leading to the rate of increase / decrease of the accommodation fee A is set higher, and the case of the rate of increase / decrease of the accommodation fee B is the case. If there are many, the degree of association that leads to the rate of increase / decrease B of the accommodation fee is set higher. For example, in the example of reference time information P01, the rate of increase / decrease in accommodation charges A and the rate of increase / decrease in accommodation charges C are linked. , The degree of association of w14, which leads to the rate of increase / decrease C of the accommodation fee, is set to 2 points.

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.

In such a case, as shown in FIG. 5, reference time information is input as input data, the increase / decrease rate of each accommodation fee is output as output data, and at least one or more hidden layers are output between the input node and the output node. May be provided and machine learning may be performed. On the contrary, the rate of increase / decrease in the accommodation fee may be input and the reference time information may be output. Such a degree of association is what is called learned data in artificial intelligence.

After creating such learned data, the rate of increase / decrease in accommodation charges will be predicted using the above-mentioned learned data in actually determining the rate of increase / decrease in accommodation charges. In such a case, the time information regarding the time when the rate of increase / decrease of the accommodation fee is actually determined at the accommodation facility for which the rate of increase / decrease of the accommodation fee is actually newly determined is acquired. This time information may be composed of any time unit such as daily, weekly, or monthly.

The time information to be newly acquired is input by the information acquisition unit 9 described above. The information acquisition unit 9 may acquire such timing information as electronic data. For example, if the date for acquiring the information is April 10, and the rate of increase / decrease in the accommodation fee on April 15 in the future is searched, enter April 15. If you want to search for the rate of increase / decrease in the weekly accommodation charges starting from April 16th, enter the week starting from April 16th.

Based on the newly acquired time information in this way, the optimum rate of increase / decrease in accommodation charges is searched for. In such a case, the corresponding reference time information is specified from the acquired time information. The reference time information is organized in chronological order as described above. For example, when the time information is April 15, the reservation status of the past April 15 is organized as reference time information. Then, when the reference time information according to the time information of April 15 is the same as or similar to P02, the increase / decrease rate B is w15 and the increase / decrease rate C is the degree of association through the degree of association. It is associated with w16. In such a case, the rate of increase / decrease B having the highest 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 increase / decrease rate C in which the degree of association is low 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 this way, it is possible to search for the rate of increase / decrease in the accommodation fee to be determined from the newly acquired timing information and display it to the user or the operator of the accommodation facility. By looking at the search results, the user and the like can formulate an accommodation plan based on the rate of increase / decrease in the searched accommodation charges, and the operator of the accommodation facility can consider the accommodation charges. By the way, in the process of outputting the rate of increase / decrease of this accommodation fee, in addition to simply displaying the rate of increase / decrease of the searched accommodation fee, the specific accommodation fee itself based on the rate of increase / decrease of this accommodation fee is displayed. You may. In such a case, the accommodation fee for each accommodation facility or for each room at the accommodation facility may be corrected according to the rate of increase or decrease.

In the example of FIG. 6, it is assumed that the input data is, for example, reference timing information P01 to P03 and reference reservation information P14 to 17. The intermediate node shown in FIG. 6 is a combination of reference reservation information and reference timing information as such input data. Each intermediate node is further linked to the output. In this output, the rate of increase / decrease of each accommodation fee as an output solution is displayed.

In the example of FIG. 6, it is premised that the combination of the reference timing information and the reference reservation information is formed. The reference reservation information is information indicating the past reservation status of a certain accommodation facility when the focus is on the accommodation facility. This reference reservation information can be extracted from the past daily or weekly time-series reservation history. The temporal unit of the reservation history may be composed of any unit such as daily, weekly, monthly, and the like. The temporal unit of the reservation history may be the same as the temporal unit of the reference timing information. The reservation status indicates the situation in which the room of the accommodation facility is filled with the reservation through the actual reservation. As shown in FIG. 7, the reservation rate is displayed as the ratio of the number of rooms for which reservations have actually been made (hereinafter referred to as the reservation rate) to the number of rooms that can accept guests at the accommodation facility. There may be. Further, the reference reservation information may be derived from the so-called room surplus situation, which is not actually filled with reservations, for the number of rooms that can accept guests in a certain accommodation facility. Further, the reference reservation information may be composed of the actual sales of the accommodation facility itself, or may be derived from the sales. As the number of reservations increases, the sales increase accordingly, and since the amount of reservations and the sales are data that correlate with each other, they may be derived from the sales. Further, the reference reservation information may be derived from the number of guests themselves or the number of guests.

The reference reservation information may reflect the number of reservation cancellations with respect to the number of past time-series reservations of the accommodation facility. In such a case, the number of reservation cancellations is measured in chronological order. As the number of reservation cancellations increases, it is possible that some factor that does not want to stay at the accommodation facility is working, which acts to reduce the accommodation fee. On the other hand, when the number of reservation cancellations is small, it is considered that there is a factor that strongly desires accommodation for the accommodation facility, which acts to increase the accommodation fee.

Further, the reference reservation information may reflect the number of desired reservations in the past in chronological order. The number of reservations requested here is the number of reservations made to each room of the accommodation facility, as well as the number of reservations made to the accommodation facility, but the number actually exceeds the capacity of each room and cannot be reserved. Is also included. That is, the desired number of reservations is represented by the sum of the number of reservations and the number of requests for reservations but could not be reserved. The number of people who requested a reservation but could not make a reservation may be obtained by actually calling the accommodation facility to apply for a reservation and counting the number of refusals, or the accommodation site on the Internet. The number of people who actually tried to apply through the site but could not apply due to overcapacity may be obtained from the operation information and click information on the site and totaled.

In the example of FIG. 6, it is assumed that the input data is, for example, reference timing information P01 to P03 and reference reservation information P14 to 17. The intermediate node shown in FIG. 6 is a combination of reference reservation information and reference timing information as such input data. Each intermediate node is further linked to the output. In this output, the rate of increase / decrease in the accommodation fee as the output solution is displayed.

Each combination (intermediate node) of the reference timing information and the reference reservation information is associated with each other through three or more levels of association with the rate of increase / decrease in the accommodation fee as the output solution. The reference time information and the reference reservation information are arranged on the left side through this degree of association, and the rate of increase / decrease in the accommodation fee is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the rate of increase / decrease of each accommodation fee with respect to the reference timing information and the reference reservation information arranged on the left side. In other words, this degree of association is an index indicating what kind of accommodation fee increase / decrease rate each reference time information and reference reservation information is likely to be associated with, and is a reference time information and reference reservation. It shows the accuracy in selecting the most probable increase / decrease rate of each accommodation fee from the information. In addition to the timing information, the optimal accommodation fee will change depending on the actual reservation status. For example, it is assumed that the reservation rate is low (for example, 50%), and the rate of increase / decrease in past accommodation charges is + 15% in that case, and the degree of association is highest at the rate of increase / decrease in accommodation charges C. .. In such a case, the lower the accommodation fee, the higher the reservation rate, and as a result, the sales can be increased. In such a case, it may be adjusted to lower the degree of association of + 15%, which is the highest degree of association in the past, and increase the rate of increase / decrease of the lower accommodation fee (for example, the degree of association of -10%).

In addition, when the reservation rate is high (for example, 100%), it is assumed that the degree of association is highest at the rate D of increase / decrease in the past accommodation fee of -8% in that case. .. In such a case, rather, by increasing the rate of increase / decrease in the accommodation fee, it is often possible to increase sales as a result even if the reservation rate is slightly lowered. In such a case, it may be adjusted to lower the degree of association of -8%, which is the highest degree of association in the past, and increase the degree of association of a higher rate of increase / decrease in accommodation charges (for example, + 13%).

In other words, the higher the reservation rate, the heavier the weighting on the higher rate of increase / decrease in accommodation charges, and the lower the reservation rate, the heavier the weighting on the lower rate of increase / decrease in accommodation charges. You may try to adjust. In such a case, the reservation rate may be identified in the process of training the data set, and the increase / decrease rate of the output itself may be increased or decreased according to the identified reservation rate, or the weighting of the above-mentioned degree of association may be changed. You may let it.

In the example of FIG. 6, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the 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 accumulates past data as to which of the reference timing information, the reference reservation information, and the rate of increase / decrease in the accommodation fee in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference timing information P01 and the reference reservation information P16, the rate of increase / decrease in the accommodation fee is analyzed from the past data. If there are many cases where the rate of increase / decrease in accommodation charges is A, the degree of association that leads to A is set higher, and if there are many cases of B and there are few cases of A, the degree of association that leads to B is increased. , Set the degree of association that leads to A low. For example, in the example of the intermediate node 61a, it is linked to the outputs of A and B, but from the previous case, the degree of association of w13 connected to A is set to 7 points, and the degree of association of w14 connected to B is set to 2 points. There is.

Further, the degree of association shown in FIG. 6 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.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference reservation information P14 is combined with the reference time information P01, the degree of association of the increase / decrease rate C is w15, and the degree of association of the increase / decrease rate E. Is w16. The node 61c is a node in which the reference reservation information P15 and P17 are combined with the reference timing information P02, and the degree of association of the accommodation charge increase / decrease rate B is w17 and the degree of association of the accommodation charge increase / decrease rate D is w18. It has become.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used in the actual search for determining the rate of increase / decrease in accommodation charges. In such a case, the timing information will be obtained from the company that newly determines the rate of increase / decrease in the accommodation fee.

Based on the newly acquired time information in this way, the optimum rate of increase / decrease in accommodation charges is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired time information is the same as or similar to P02, and the reference reservation information consisting of the reservation status according to the time specified in the time information is the same as or similar to P17. In the case similar to, the node 61d is associated with each other through the degree of association, and the node 61d is associated with the increase / decrease rate C by w19 and the increase / decrease rate D by the degree of association w20. In such a case, the increase / decrease rate C having the highest 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 increase / decrease rate D in which the degree of association is low 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.

For reference reservation information consisting of the reservation status according to the time specified in the acquired time information, for example, if the acquisition time is the first week of May, the past reservation status in the first week of May is past. It is read from the past time-series reservation status as shown in FIG. As a result of reading, if the reservation status in the first week of May is a reservation rate of 50%, the solution search will be performed via the reference reservation information corresponding to the reservation rate.

In order to obtain the reservation information, the number of reservation cancellations with respect to the number of past time-series reservations of the accommodation facility and the number of past time-series reservation requests may be included. The method for obtaining the number of canceled reservations and the desired number of reservations is the same as described above.

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.

FIG. 8 shows an example in which three or more levels of association between the combination of the above-mentioned reference timing information and the reference event information and the rate of increase / decrease in the accommodation fee for the combination are set.

Reference event information is information about an event held in the area where the accommodation facility is located. This reference event information can be obtained from information in which information on past events held in each region is described in chronological order. For example, this includes events that are common to heyday events such as December 24th, which is Christmas, but it also includes fireworks festivals and festivals held in each region.

Since the degree of congestion of accommodation facilities changes depending on whether or not they actually hit such an event, by learning including such reference event information, the rate of increase or decrease in accommodation charges can be searched with higher accuracy. It becomes possible to do.

In the reference event information P18, for example, the events of the region α are described and learned in chronological order, and in the reference event information P19, for example, the events of the region β are described in chronological order and are referred to. In the event information P20, the events of the region γ may be described in chronological order. In this way, the reference event information may be defined separately for each region. In addition to this, the reference event information may be divided in event units. For example, the reference event information P18 is Christmas, the reference event information P19 is the date of the international competition, and the reference event information P20. May be the day of a tour of a sporting event, etc. Even when separated into events, each of these events is similar in that the events in the region are obtained from the information shown in chronological order, in other words, all of these events are linked to the region. It is attached.

In the example of FIG. 8, it is assumed that the input data is, for example, reference timing information P01 to P03 and reference event information P18 to 21. The intermediate node shown in FIG. 8 is a combination of reference event information and reference timing information as such input data. Each intermediate node is further linked to the output. In this output, the rate of increase / decrease in the accommodation fee as the output solution is displayed.

Each combination (intermediate node) of the reference timing information and the reference event information is associated with each other through three or more levels of association with the rate of increase / decrease in the accommodation fee as this output solution. The reference time information and the reference event information are arranged on the left side via the degree of association, and the rate of increase / decrease in the accommodation fee is arranged on the right side via the degree of association. The degree of association indicates the degree of relevance to the rate of increase / decrease in the accommodation fee with respect to the reference time information and the reference event information arranged on the left side. In other words, this degree of association is an index indicating what kind of accommodation fee increase / decrease rate is likely to be associated with each reference time information and reference event information, and is a reference time information and reference event information. It shows the accuracy in selecting the most probable increase / decrease rate of each accommodation fee. In addition to timing information, the likelihood of cash recovery will vary depending on the actual market potential. Therefore, by combining the reference timing information and the reference event information, the optimum rate of increase / decrease in the accommodation fee will be searched for.

In the example of FIG. 8, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the 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 accumulates past data as to which of the reference time information, the reference event information, and the rate of increase / decrease in the accommodation fee in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference time information P01 and the reference event information P20, the rate of increase / decrease in the accommodation fee is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of the increase / decrease rate A and the increase / decrease rate B is linked. The degree is set to 2 points.

Further, the degree of association shown in FIG. 8 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.

In the example of the degree of association shown in FIG. 8, the node 61b is a node in which the reference event information P18 is combined with the reference time information P01, the degree of association of the increase / decrease rate C is w15, and the degree of association of the increase / decrease rate E. Is w16. The node 61c is a node in which the reference event information P19 and P21 are combined with respect to the reference timing information P02, and the degree of association of the increase / decrease rate B is w17 and the degree of association of the increase / decrease rate D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned timing information, the area information regarding the area where the accommodation facility for determining the increase / decrease rate is located is acquired. The output solution is searched based on the reference event information corresponding to the regional event specified in the regional information. If the reference event information is divided by region, select the reference event information according to the region. Then, each reference event information is shown in chronological order, and at that time, any event is held in the area at that time by referring to the one specified in the input time information. It becomes possible to specify.

Based on the newly acquired time information and area information in this way, the rate of increase / decrease in accommodation charges is searched for. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired time information is the same as or similar to P02, and the newly acquired reference event information according to the region is P21, the association degree is used. A node 61d is associated, and the node 61d is associated with an increase / decrease rate C of w19 and an increase / decrease rate D of a degree of association w20. In such a case, the increase / decrease rate C having the highest 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 increase / decrease rate D in which the degree of association is low 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.

FIG. 9 shows an example in which the combination of the above-mentioned reference timing information, the reference external environment information, and the rate of increase / decrease in the accommodation fee for the combination are set to three or more levels of association.

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. The external environmental information includes all information outside the accommodation facility, in addition to the information that reflects some or all of these data. The external environment information for reference may be categorized as the external environment itself.

In the example of FIG. 9, it is assumed that the input data is, for example, reference timing information P01 to P03 and reference external environment information P18 to 21. The intermediate node shown in FIG. 9 is a combination of reference external environment information and reference timing information as such input data. Each intermediate node is further linked to the output. In this output, the rate of increase / decrease in the accommodation fee as the output solution is displayed. The reference external environment information may be associated with the time specified in the reference time information.

Each combination (intermediate node) of the reference timing information and the reference external environment information is associated with each other through three or more levels of association with the rate of increase / decrease in the accommodation fee as this output solution. The reference time information and the reference external environment information are arranged on the left side through this degree of association, and the rate of increase / decrease in the accommodation fee is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the rate of increase / decrease in the accommodation fee with respect to the reference timing information and the reference external environment information arranged on the left side. In other words, this degree of association is an index showing what kind of accommodation fee increase / decrease rate is likely to be associated with each reference time information and reference external environment information, and is a reference time information and reference external environment. It shows the accuracy in selecting the most probable increase / decrease rate of each accommodation fee from the environmental information. In addition to the timing information, the number of guests changes and the accommodation fee changes depending on the actual state of the external environment. Therefore, by combining these reference timing information and reference external environment information, the optimum rate of increase / decrease in accommodation charges will be searched for.

In the example of FIG. 9, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the 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 accumulates past data as to which of the reference timing information, the reference external environment information, and the rate of increase / decrease in the accommodation fee in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 9 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference time information P01 and the reference external environment information P20, the rate of increase / decrease in the accommodation fee is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of the increase / decrease rate A and the increase / decrease rate B is linked. The degree is set to 2 points.

In the example of the degree of association shown in FIG. 9, the node 61b is a node in which the reference external environment information P18 is combined with the reference timing information P01, and the degree of association of the increase / decrease rate C is w15 and the association of the increase / decrease rate E. The degree is w16. The node 61c is a node that is a combination of the reference external environment information P19 and P21 with respect to the reference timing information P02, and the degree of association of the increase / decrease rate B is w17 and the degree of association of the increase / decrease rate D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned timing information, external environmental information regarding the outside of the accommodation facility for determining the rate of increase / decrease is acquired. This external environment information is composed of the same type of data as the above-mentioned reference external environment information. The external environment actually acquired is the external environment at the time specified in the time information, but if the time information is one year later, the external environment cannot be predicted as a matter of course. In the example, it is applicable when the time information is the latest (for example, the day when the accommodation is reserved is tomorrow, the day after tomorrow, one week later, etc.), and the external environment in such a case should be entered at the time of the search for the rate of increase / decrease. It may be.

Based on the newly acquired time information and external environmental information in this way, the rate of increase / decrease in accommodation charges is searched for. In such a case, the degree of association shown in FIG. 9 (Table 1) acquired in advance is referred to. For example, when the newly acquired time information is the same as or similar to P02, and the reference external environment information corresponding to the newly acquired external environment is P21, the degree of association is determined. A node 61d is associated with the node 61d, and the node 61d is associated with an increase / decrease rate C of w19 and an increase / decrease rate D of a degree of association w20. In such a case, the increase / decrease rate C having the highest 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 increase / decrease rate D in which the degree of association is low 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.

FIG. 10 shows an example in which a combination of the above-mentioned reference time information and the reference market condition information, and an increase / decrease rate of the accommodation fee for the combination are set to three or more levels of association.

What is reference market information? Reference market information is various information related to market conditions. The market conditions referred to here may target any range including one company, the entire industry including the company, the entire Japan, and the entire world. Examples of this reference market information include interest rates, exchange rates, stock prices of each stock, crude oil, futures, precious metals, and price movements of Bitcoin. This reference market information may be displayed as a time-series chart, a line graph, or the like for these targets. Further, information such as Bollinger band, MACD, moving average line and the like may be attached. In addition, this market information may include fundamental indicators of each brand's company, and in addition to annual sales, profits, and costs, PER (price-earnings ratio), PBR (price-to-book value ratio), ROE ( Indicators such as return on equity) may be included. Information such as a chart showing price movements between currencies, Bollinger Bands, MACD, and moving averages may be attached to the exchange rate. The reference market information may categorize the market potential itself. That is, the reference market information in FIG. 10 may be information classified by type, and for example, it may be classified by dividing it according to whether or not the growth rate of the stock price is 0% or more per year. Further, it may be categorized by a pattern (for example, a pattern of whether the growth rate of the stock price increases rapidly or gradually) or the like.

In the example of FIG. 10, it is assumed that the input data is, for example, reference time information P01 to P03 and reference market condition information P18 to 21. The intermediate node shown in FIG. 10 is a combination of reference market condition information and reference time information as such input data. Each intermediate node is further linked to the output. In this output, the rate of increase / decrease in the accommodation fee as the output solution is displayed. The reference market information may be associated with the time specified in the reference time information.

Each combination (intermediate node) of the reference time information and the reference market condition information is associated with each other through three or more levels of association with the rate of increase / decrease in the accommodation fee as this output solution. Reference time information and reference market information are arranged on the left side via this degree of association, and the rate of increase / decrease in accommodation charges is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the rate of increase / decrease in accommodation charges with respect to the reference time information and the reference market condition information arranged on the left side. In other words, this degree of association is an index showing what kind of accommodation fee increase / decrease rate is likely to be associated with each reference time information and reference market condition information, and is a reference time information and reference market condition information. It shows the accuracy in selecting the most probable increase / decrease rate of each accommodation fee. In addition to the time information, the number of passengers will change and the accommodation fee will change depending on the actual market conditions. Therefore, by combining these reference time information and reference market condition information, the optimum rate of increase / decrease in accommodation charges will be searched for.

In the example of FIG. 10, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the 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 accumulates past data as to which of the reference time information, the reference market condition information, and the rate of increase / decrease in the accommodation fee in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 10 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference time information P01 and the reference market condition information P20, the rate of increase / decrease in the accommodation fee is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of the increase / decrease rate A and the increase / decrease rate B is linked. The degree is set to 2 points.

In the example of the degree of association shown in FIG. 10, the node 61b is a node that is a combination of the reference time information P01 and the reference market condition information P18, and the degree of association of the increase / decrease rate C is w15 and the degree of association of the increase / decrease rate E. Is w16. The node 61c is a node that is a combination of the reference market condition information P19 and P21 with respect to the reference time information P02, and the degree of association of the increase / decrease rate B is w17 and the degree of association of the increase / decrease rate D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, market information is acquired in addition to the above-mentioned timing information. This market condition information is composed of the same kind of data as the above-mentioned reference market condition information. The market information actually acquired is the market information at the time specified in the time information, but if the time information is one year later, the external environment cannot be predicted as a matter of course. In the example, it is applicable when the time information is the latest (for example, the day when the accommodation is reserved is tomorrow, the day after tomorrow, one week later, etc.), and the market information in such a case should be entered at the time of the search for the rate of increase / decrease. It may be.

Based on the newly acquired time information and market condition information in this way, the rate of increase / decrease in accommodation charges is searched for. In such a case, the degree of association shown in FIG. 10 (Table 1) acquired in advance is referred to. For example, when the newly acquired time information is the same as or similar to P02, and the newly acquired reference market information according to the external environment is P21, the degree of association is used. The node 61d is associated with the node 61d, and the increase / decrease rate C is associated with w19 and the increase / decrease rate D is associated with the degree of association w20. In such a case, the increase / decrease rate C having the highest 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 increase / decrease rate D in which the degree of association is low 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 the present invention, as an alternative to the reference event information shown in FIG. 10, the reference population estimation information regarding the population estimation in the area where the accommodation facility is located is learned by being associated with the degree of association by the combination of the reference timing information. It may be made up of.

The reference population estimation information is information on population estimation in the area where the accommodation facility is located. The area where the accommodation facility is located may be composed of any area unit such as a municipal unit, a prefecture unit, a local unit, and the like. Population estimation to such an area shows the increasing and decreasing trends of the population, as well as the status of moving in and out of the region, and the status of the pyramid of the population. This population estimate may be presented as time-series information.

In such a case, the degree of association between the combination of the reference time information and the reference market condition information and the rate of increase / decrease in the accommodation fee is obtained in advance. Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually performing a search from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned timing information, the population estimation information regarding the population estimation of the area where the accommodation facility for determining the increase / decrease rate is located is acquired. This population estimation information is composed of the same type of data as the above-mentioned reference population estimation information. The population estimation information actually acquired is the time specified in the time information and the population estimation in the area, but since the population estimation cannot be predicted if it is 5 years later, in this example, It is applicable when the time information is within about one year, and the population estimation information in such a case may be input at the time of searching for the increase / decrease rate.

The population estimation information to be acquired corresponds to the above-mentioned reference population estimation information, and the acquisition method is the same. Then, after referring to the degree of association, the rate of increase / decrease in the accommodation fee is determined in the same manner as described above based on the newly acquired time information and the population estimation information.

In the present invention, as an alternative to the reference event information shown in FIG. 10, reference transportation information regarding transportation in the area where the accommodation facility is located is learned by being associated with the degree of association by a combination of reference timing information. It may be made up of.

The reference transportation information indicates all information regarding the means of transportation to the area where the accommodation facility is located, or all information regarding the means of transportation within the area where the accommodation facility is located. If there is a train as one of the means of transportation to the area, it shows all the information about the train, for example, in addition to the information about the timetable, the number of passengers and the degree of congestion, it is connected by the area or the train. If there are more flights in response to events in other areas, information about them is also included. In addition to this, the reference transportation information includes information on delays in transportation means, information on accidents, information on traffic congestion, and the like. For inns located in the suburbs away from the station, automobiles may be the only means of transportation, but in such cases, the degree of congestion depends on traffic information such as traffic congestion leading up to the inn. May change.

In such a case, the degree of association of three or more levels of the combination of the reference transportation information and the reference time information and the rate of increase / decrease in the accommodation fee is acquired in advance. Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually performing a search from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned timing information, the transportation information regarding the transportation in the area where the accommodation facility for determining the increase / decrease rate is located is acquired. This transportation information is composed of the same type of data as the above-mentioned reference transportation information. The transportation information that is actually acquired is the information about the time specified in the time information and the transportation in the area, but it cannot be predicted if the transportation situation is several months later. In this example, the time information can be applied when the time information is one day, two days later, or one week or less, and the transportation information in such a case may be input at the time of searching for the rate of increase / decrease.

The transportation information to be acquired corresponds to the above-mentioned reference transportation information, and the acquisition method is also the same. Then, after referring to the degree of association, the rate of increase / decrease in the accommodation fee is determined in the same manner as described above based on the newly acquired time information and the transportation information.

The present invention is not limited to the case where the rate of increase / decrease in the accommodation fee is obtained as a search solution, and the possibility of congestion in the reservation of the accommodation facility may be obtained. Accommodation charges tend to rise when accommodation reservations are crowded, and decrease when there are few accommodation reservations. In other words, the congestion status of accommodation reservations correlates with the accommodation charges. Therefore, when creating a data set, as an alternative to the rate of increase / decrease in accommodation charges, the congestion status of accommodation reservations is learned. As an example of the congestion status of reservations, it may be roughly classified into very crowded, slightly crowded, normal, easy to do, very busy, etc., and the higher the score is, the higher the score is. It may be displayed as a score for determining that it is crowded. Then, these scores can be obtained by extracting from data such as the filling degree of past reservations at the accommodation facility. By learning through such a data set, it is possible to output the possibility of reservation congestion as a search solution instead of the rate of increase / decrease in the accommodation fee.

In addition to the time information, when acquiring any two or more of event information, external environment information, market condition information, population estimation information, transportation information, etc., 2 or more according to the acquired 2 or more information. The above reference information (reference reservation information, reference event information, reference external environment information, reference market information, reference population estimation information, reference transportation information, etc.) can be combined with reference timing information. By creating learning data consisting of three or more levels of association with the rate of increase / decrease for the combination, the solution search can be performed in the same manner.

In addition to the time information, in addition to any one or more of event information, external environment information, market condition information, population estimation information, transportation information, etc., and when other information is acquired, the acquisition is also performed in the same manner. Reference reservation information according to information, reference event information, reference external environment information, reference market information, reference population estimation information, reference transportation information, etc., and reference information according to other information to be acquired By creating learning data consisting of three or more levels of association between the combination with and the degree of demand for the combination, the solution search can be performed in the same manner.

Further, as shown in FIG. 11, 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. The reference information U is the reference time information, and the reference information V is the reference reservation information, the reference event information, the reference external environment information, the reference market condition information, the reference population estimation information, and the reference transportation. It shall be information. Alternatively, the reference information U is the reference reservation information, the reference event information, the reference external environment information, the reference market information, the reference population estimation information, and the reference transportation information, and the reference information V is the reference information. It shall be time information.

At this time, as shown in FIG. 11, 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 time information, when event information, external environment information, market information, population estimation information, and transportation information knowledge, information, and data are acquired, the degree of association is increased or decreased accordingly.

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 Accommodation charge increase / decrease rate determination 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 (10)

In the accommodation fee increase / decrease rate determination program that determines the rate of increase / decrease in accommodation charges
An information acquisition step for acquiring timing information regarding the timing for determining the rate of increase / decrease in accommodation charges at the above accommodation facilities,
Three or more levels of association between the reference time information for each past period and the rate of increase / decrease in the rate of increase / decrease in the accommodation fee, extracted from the history of the rate of increase / decrease in the rate of increase / decrease in the past time-series of accommodation facilities acquired in advance. A program for determining the rate of increase / decrease in accommodation charges, which is characterized by having a computer execute a determination step for determining the rate of increase / decrease in accommodation charges of the accommodation facility based on the timing information acquired in the above information acquisition step. In the determination step, the combination of the reference timing information and the reference reservation information regarding the past reservation status of the accommodation facility and the rate of increase / decrease in the accommodation fee of the accommodation facility are referred to three or more levels of association. However, the claim is characterized in that the rate of increase / decrease in the accommodation fee of the above accommodation facility is determined based on the reference reservation information according to the reservation status according to the time specified in the time information acquired in the above information acquisition step. The rate of increase / decrease judgment program for the accommodation charges described in 1. In the above information acquisition step, the area information about the area where the accommodation facility for which the increase / decrease rate is located is located is acquired, and the area information is acquired.
In the above determination step, there are three or more stages of association between the combination of the reference timing information and the reference event information regarding the event held in the area where the accommodation facility is located, and the rate of increase / decrease in the accommodation fee of the accommodation facility. A claim characterized in that the rate of increase / decrease in the accommodation fee of the above accommodation facility is determined based on the reference event information corresponding to the event in the area specified in the area information acquired in the above information acquisition step with reference to the degree. The rate of increase / decrease judgment program for the accommodation charges described in 1. In the above information acquisition step, the external environment information regarding the external environment at the time when the above increase / decrease rate is determined is acquired.
In the determination step, a combination having the reference time information and the reference external environment information in which the external environment at the time specified in the reference time information is shown in chronological order, and the accommodation fee of the accommodation facility. The feature is that the rate of increase / decrease of the accommodation fee of the above accommodation facility is determined based on the reference event information according to the external environmental information acquired in the above information acquisition step by referring to the degree of association with the rate of increase / decrease of 3 or more levels. The rate of increase / decrease determination program for accommodation charges according to claim 1. In the above information acquisition step, market information regarding the market conditions at the time when the above increase / decrease rate is determined is acquired.
In the above determination step, there are three or more levels of association between the combination of the reference time information and the reference market information showing the past market conditions in chronological order, and the rate of increase / decrease in the accommodation charges of the accommodation facility. The rate of increase / decrease in the accommodation fee according to claim 1, which is characterized in that the rate of increase / decrease in the accommodation fee of the above accommodation facility is determined based on the reference market condition information according to the market condition information acquired in the above information acquisition step. Judgment program. In the above information acquisition step, the population estimation information regarding the population estimation of the area where the accommodation facility for determining the increase / decrease rate is located is acquired.
In the determination step, there are three or more stages of association between the combination of the reference timing information and the reference population estimation information regarding the population estimation in the area where the accommodation facility is located, and the rate of increase / decrease in the accommodation fee of the accommodation facility. The accommodation fee increase / decrease rate determination program according to claim 1, wherein the rate of increase / decrease in the accommodation fee of the accommodation facility is determined based on the population estimation information acquired in the above information acquisition step with reference to the degree. In the above information acquisition step, the transportation information related to the transportation in the area where the accommodation facility where the accommodation facility is located is acquired to determine the increase / decrease rate.
In the determination step, there are three or more stages of association between the combination of the reference timing information and the reference transportation information regarding the transportation in the area where the accommodation facility is located, and the rate of increase / decrease in the accommodation fee of the accommodation facility. The accommodation fee increase / decrease rate determination program according to claim 1, wherein the rate of increase / decrease of the accommodation fee of the accommodation facility is determined based on the transportation information acquired in the above information acquisition step with reference to the degree. In the determination step, there are three stages: the combination having the reservation information for reference reflecting the number of reservation cancellations with respect to the number of reservations in the past time series of the accommodation facility, and the rate of increase / decrease in the accommodation fee of the accommodation facility. The accommodation fee increase / decrease rate determination program according to claim 2, wherein the rate of increase / decrease in the accommodation fee of the above accommodation facility is determined with reference to the above degree of association. In the determination step, there are three or more stages of association between the combination having the reference reservation information reflecting the past time-series reservation request number of the accommodation facility and the increase / decrease rate of the accommodation fee of the accommodation facility. The accommodation fee increase / decrease rate determination program according to claim 2, wherein the rate of increase / decrease in the accommodation fee of the above accommodation facility is determined with reference to the degree. The rate of increase / decrease in the accommodation fee according to any one of claims 1 to 9, wherein in the determination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. Judgment program.

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