JP2022067095A - Building repair method estimation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately and automatically determine a repair method for a building without depending on manual work.

SOLUTION: A building repair method estimation program for estimating a repair method for a building causes a computer to execute an information acquisition step of acquiring renovation possibility information on a possibility of renovation of a building to be estimated, and an estimation step of estimating a repair method by using a learned model in which the three or more stages of degrees of association between reference renovation possibility information on the possibility of renovation of the building acquired in advance and a repair method are defined and having the reference renovation possibility information as input and the repair method as output, and prioritizing one having a higher degree of association on the basis of reference renovation possibility information according to the renovation possibility information acquired in the information acquisition step.

SELECTED DRAWING: Figure 1

COPYRIGHT: (C)2022,JPO&INPIT

Description

The present invention relates to a building repair period estimation program that estimates a building repair method.

The building may be damaged, broken down or dirty, and even if it is not damaged, it may be damaged or broken if left as it is. In such a case, the repair will be outsourced to a repair company. However, since it is often not possible to immediately grasp how much the repair cost will be, if the repair cost cannot be estimated and the repair period and repair method cannot be immediately grasped, the building will be repaired. Sometimes I hesitate about things. As a result, if you continue to use the building even though it has already been damaged or broken down, you will have to live in the building with a lot of inconvenience, and inevitably the failure or damage will occur. If you neglect to perform repairs and maintenance in advance, you will also feel inconvenience based on this. In order to prevent such a situation from occurring, a technique that can immediately grasp the repair cost, repair period, and repair method for damage or failure has been desired.

In the past, the current situation is that no technique has been proposed that can immediately grasp the repair cost, repair period, and repair method of such a building.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is a building capable of automatically and accurately discriminating a building repair method without relying on human hands. To provide a repair period estimation program.

The building repair method estimation program according to the present invention includes an information acquisition step for acquiring renovation possibility information regarding the possibility of renovation of the building to be estimated in the building repair method estimation program for estimating the building repair method, and an information acquisition step acquired in advance. The reference renovation possibility information regarding the possibility of building renovation and the degree of association with the repair method are specified in three or more stages, and the input is used as the reference renovation possibility information and the output is used as the repair method. Then, based on the renovation possibility information for reference according to the renovation possibility information acquired in the information acquisition step, the computer is made to execute the estimation step of estimating the repair method by giving priority to the one having the higher degree of association. It is characterized by that.

Anyone can easily estimate the repair cost, repair period, and repair method of a building with high accuracy and automatically without any special skill or experience.

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 the estimation 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. 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.

First Embodiment Hereinafter, the occupancy recommendation company proposal program (real estate transaction price 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 occupancy recommendation company proposal system 1 in which a occupancy recommendation company proposal program to which the present invention is applied is implemented. The move-in recommendation company proposal system 1 includes an information acquisition unit 9, an estimation device 2 connected to the information acquisition unit 9, and a database 3 connected to the estimation device 2. Information may be transmitted / received between the information acquisition unit 9 and the estimation device 2 and between the estimation device 2 and the database 3 via a public communication network such as the Internet.

The information acquisition unit 9 is a device for a person using 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 estimation device 2 described later. The information acquisition unit 9 outputs the detected information to the estimation device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information.

Database 3 stores various information about real estate to be rented and real estate to be bought and sold. Real estate is land, buildings (buildings, condominiums, detached houses), etc. Regional characteristic information about each of these real estates (address, nearest station, how many minutes walk from the station, surrounding facility information, surrounding environment information, surrounding image information around the real estate, ground information, past disaster information, nearest Distance information from the station, traffic volume information around the real estate), property information (area information about the size of the real estate, age information about the age of the real estate, inside information about the inside of the real estate, the real estate Appearance image information that captures the appearance of the real estate, brand information about the brand of the building structure of the real estate, new construction price information about the price at the time of new construction of the real estate) is stored. The property information (reference property information) may be composed of information (location of the property, condominium, building name, floor) for specifying the property itself, in addition to the above-mentioned example. Examples of the inside information inside the real estate include, for example, a layout, a flow line, equipment, an exterior, an image taken indoors, and the like. Further, the external environment information is stored in this database 3. This external environmental information includes all information related to the external environment such as politics, economy, and society, apart from individual real estate, and includes, for example, market price information. This market price information includes rent, office vacancy rate, tsubo unit price, transaction price of condominiums and houses, and information on changes over time.

In addition, the database 3 also stores industry information indicating the type of business of the business operator who recommends moving in. The type of business to be recommended may be classified into a relatively broad category such as law offices, restaurants, convenience stores, coffee shops, retail stores, etc. For example, in restaurants, taverns, restaurants, bars, etc. It may be included in detailed classifications such as for standing eating soba restaurants and chain stores.

The estimation 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 the one to be converted. The user can obtain a search solution by this estimation device 2.

FIG. 2 shows a specific configuration example of the estimation device 2. The estimation device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire estimation device 2 and an operation unit 25 for inputting various control commands via an operation button, 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 estimation 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 according 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 is responsible for searching for a business operator to be proposed and estimating the price for pricing real estate. 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 configured by 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 will be read and executed by the control unit 24.

The operation in the move-in recommendation company proposal system 1 having the above-mentioned configuration will be described.

In the move-in recommendation company proposal system 1, for example, as shown in FIG. 3, three or more levels of association between the reference area characteristic information and the type of business information indicating the type of business of the business that recommends the move-in are set and acquired in advance. It is premised that it is. The reference area characteristic information includes information such as the address where the real estate is located, the nearest station, and how many minutes walk from the station. Also, as reference area characteristic information, how many minutes walk to schools, stores (supermarkets, shopping malls, convenience stores, libraries, public halls, hospitals, restaurants), etc. around the real estate, or what is actually It contains information about the surrounding facilities as to whether they are located at a distance of meters. In addition, as reference area characteristic information, for example, information on the surrounding location environment such as pubs, restaurants, stores specializing in sex customs, simple inns, etc., and information on the natural environment such as sunlight and wind direction (hereinafter, surrounding environment information). ) Is also included. In addition, ground information regarding the ground of the real estate and past disaster information that describes the presence or absence of damage due to a disaster at the location of the real estate in the past, the degree of damage, etc. may be included in this reference area characteristic information. .. It is essential that this reference area characteristic information includes all of the address, the nearest station, how many minutes walk from the station, surrounding facility information, surrounding environmental information, ground information, and past disaster information. However, any one or more may be included. The reference area characteristic information also includes the traffic volume of vehicles and people. In addition, the reference area characteristic information includes information on what kind of industry the company has moved in in the past. In addition, the reference area characteristic information includes vibration information regarding the vibration of the ground in the area where the real estate is located. This vibration information may be composed of data obtained by measuring the ground shaking and vibration caused by the traveling of the vehicle with a vibration meter. The reference area characteristic information also includes the annual income information regarding the annual income of the residents in the area where the real estate is located. For this annual income information, for example, the average annual income data aggregated for each area (prefecture, city, ward, town, village unit) where the real estate is located may be used. In addition, population estimation information regarding population estimation in the area where the real estate is located and vacant house rate information regarding the vacant house rate in the area where the real estate is located may also be included in this reference area characteristic information. Further, as the reference area characteristic information, disaster risk information regarding the disaster risk in the area where the real estate is located may be included. For this disaster risk information, the history of suffering natural disasters such as tsunamis, floods, typhoons, landslides, etc. in the past and the possibility of such disasters may be aggregated as risk evaluation values. Further, noise information regarding noise around the real estate may be included in the reference area characteristic information, and the data measured by the sound level meter may be used for this noise information.

In the example of FIG. 3, it is assumed that the input data is, for example, reference area characteristic information P01 to P03. The reference area characteristic information as such input data is linked to the output. In this output, industry information as an output solution is displayed. For example, the industry information Q1 is assigned to a restaurant, the industry information Q2 is assigned to an office, and the like.

The reference area characteristic information is associated with each other through three or more levels of association with the industry information as this output solution. The reference area characteristic information is arranged on the left side through this degree of association, and each industry information is arranged on the right side through this degree of association. The degree of association indicates the degree of which industry information is highly relevant to the reference area characteristic information arranged on the left side. In other words, this degree of association is an index showing what kind of industry information each reference area characteristic information is likely to be associated with, and is used to select the most probable industry information from the reference area characteristic information. It shows the accuracy in. 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 price 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 price as an output.

The estimation device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the estimation device 2 accumulates past data on how much the reference area characteristic information and the industry information in that case were in determining the actual search solution, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 3 is created.

For example, it is assumed that the reference area characteristic information P01 is the nearest station XX, a 5-minute walk from the station, and an average of 30 people per 5 minutes of traffic. At this time, it is investigated what kind of company in the past such real estate was included.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference area characteristic information P01, the type of business of the business operator who has moved in in the past is analyzed from the past data. If there are many taverns, the degree of association that leads to the industry information indicating this tavern is set higher, and if there are many cases of law firms and there are few cases of taverns, it leads to industry information indicating the law firm. Set the degree of association high and the degree of association low that leads to industry information indicating izakaya.

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. After creating such learned data, when actually introducing a real estate property to a new business operator, when estimating what kind of business operator should be introduced, the above-mentioned learned data is used. The industry information will be determined. In such a case, the regional characteristic information of the real estate to be traded is newly acquired.

The newly acquired regional characteristic information is input by the above-mentioned information acquisition unit 9. The details of this regional characteristic information are the same as those for reference regional characteristic information described above.

Based on the newly acquired regional characteristic information in this way, it is estimated which type of business should actually be introduced. 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 regional characteristic information is the same as or similar to P02, the industry information Q2 is associated with w15 and the industry information Q3 is associated with the association degree w16 via the degree of association. In such a case, the industry information Q2 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 industry information Q3, 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 any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, the newly acquired regional characteristic information is consistent with the reference regional characteristic information as the type of information. Therefore, when the new regional characteristic information is acquired, it is the same or the same as the regional characteristic information. It is possible to immediately refer to similar regional characteristic information for reference and estimate the optimal industry for recommending occupancy.

By the way, when acquiring any one or more of the surrounding image information around the real estate, the distance information from the nearest station, and the traffic volume information about the traffic volume around the real estate as the regional attribute information, as the reference regional characteristic information. , It is necessary to learn the surrounding image information, the distance information, and the traffic volume information corresponding to these from the industry information in advance. Then, the industry information is searched for through the reference area characteristic information according to the actually acquired surrounding image information, distance information, and traffic volume information.

At this time, the above-mentioned degree of association may be configured by any combination of two or more of the surrounding image information around the real estate, the distance information from the nearest station, and the traffic volume information regarding the traffic volume around the real estate. Then, the solution may be searched by learning the industry information as the search solution for the combination.

In the example of FIG. 4, it is premised that the combination of the reference area attribute information and the reference property information is formed.

In the example of FIG. 4, it is assumed that the input data is, for example, reference area attribute information P11 to P13 and reference property information P14 to 17. The intermediate node shown in FIG. 4 is a combination of reference property information and reference area attribute information as such input data. Each intermediate node is further linked to the output. In this output, industry information as an output solution is displayed.

Each combination (intermediate node) of the reference area attribute information and the reference property information is associated with each other through three or more levels of association with the industry information as this output solution. The reference area attribute information and the reference property information are arranged on the left side through this degree of association, and the industry information is arranged on the right side through this degree of association. The degree of association indicates the degree of which industry information is highly relevant to the reference area attribute information and the reference property information arranged on the left side. In other words, this degree of association is an index showing what kind of industry information each reference area attribute information and reference property information is likely to be associated with, and is a reference area attribute information and reference property information. It shows the accuracy in selecting the most probable industry information from. In the example of FIG. 4, 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 price as an output, and conversely. The closer to one point, the less related each combination as an intermediate node to the price as an output.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the estimation device 2 accumulates past data on how much the reference area attribute information, the reference property information, and the industry information in that case were in determining the actual search solution, and these By analyzing and analyzing, the degree of association shown in FIG. 4 is created.

For example, it is assumed that the real estate traded in the past is the reference area attribute information P11. At this time, when the actual area of the real estate is "50 tsubo" as the property information, the industry information is investigated in the previous data.

This analysis may be performed by artificial intelligence. In such a case, for example, when the reference area attribute information P11 and the reference property information P16 "50 tsubo", the industry information is analyzed from the past data. If the industry information has many cases of convenience stores, the degree of association that leads to the industry information indicating this convenience store is set higher, and if there are many cases of restaurants and there are few cases of convenience stores, the restaurant is indicated. Set the degree of association that leads to industry information high, and the degree of association that leads to industry information that indicates convenience stores low. For example, in the example of the intermediate node 61a, the output of the industry information Q1 and the industry information Q2 is linked, but from the previous case, the degree of association of w13 connected to the industry information Q1 is set to 7 points, and the degree of association of w13 connected to the industry information Q2 is set to 7. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 4 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. 4, the node 61b is a node in which the reference property information P14 is combined with the reference area attribute information P11, the degree of association of the industry information Q3 is w15, and the association of the industry information Q5. The degree is w16. The node 61c is a node that is a combination of the reference property information P15 and P17 with respect to the reference area attribute information P12, and the degree of association of the industry information Q2 is w17 and the degree of association of the industry information Q4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the price will be estimated using the above-mentioned learned data when actually estimating the type of business of the business operator to be newly recommended from now on. In such a case, the regional characteristic information of the recommended real estate is newly acquired and the property information is acquired.

The newly acquired regional characteristic information and property information may be acquired via a user interface such as a keyboard.

Based on the newly acquired regional characteristic information and property information in this way, the type of business that should actually be newly recommended is estimated. In such a case, the degree of association shown in FIG. 4 (Table 1) acquired in advance is referred to. For example, when the newly acquired regional characteristic information is the same as or similar to P12 and the property information is P17, the node 61d is associated with the node 61d via the degree of association. The node 61d is associated with the industry information Q3 by w19 and the industry information Q4 by the degree of association w20. In such a case, the industry information Q3 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 industry information Q4, 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 any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, an example of the degree of association w1 to w12 extending from the input is shown in Table 2 below.

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.

By the way, as the property information, one or more of the area information regarding the size of the real estate, the age information regarding the age of the real estate, the internal information regarding the inside of the real estate, and the external image information obtained by imaging the appearance of the real estate is acquired. In this case, it is necessary to learn in advance the area information, the age information, the internal information, and the external image information corresponding to these as the reference property information with the industry information. Then, the industry information is searched for through the actually acquired area information, age information, internal information, and reference property information according to the external image information.

At this time, the above-mentioned degree of association may be configured by any combination of two or more of the breadth information, the age information, the internal information, and the appearance image information. Then, the solution may be searched by learning the industry information as the search solution for the combination.

FIG. 5 shows an example in which the combination of the above-mentioned reference area characteristic information, the reference external environment information, and the industry information for the combination are set to three or more levels of association.

As the input data, such reference area characteristic information and reference external environment information are arranged side by side. The intermediate node shown in FIG. 5 is a combination of the reference area characteristic information and the reference external environment information as such input data.

The reference external environment information includes all information related to the external environment such as politics, economy, and society, apart from individual real estate, and includes, for example, market price information. This market price information includes office vacancy rate, unit price per tsubo, and information on changes over time.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in actually estimating the industry information, the estimation device 2 includes the reference area characteristic information, the reference external environment information, the industry of the business operator who actually moved in in that case, and the introduced business operator. By accumulating data on what kind of industry was, and analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

In the example of the degree of association shown in FIG. 5, the node 61b is a node in which the reference external environment information P18 is combined with the reference area characteristic information P11, the degree of association of the industry information Q3 is w15, and the industry information Q5. The degree of association is w16.

Similarly, when such a degree of association is set, regional characteristic information is newly acquired and external environment information is acquired. The regional characteristic information corresponds to the reference regional characteristic information, and the external environmental information corresponds to the reference external environmental information.

In estimating the industry information, the degree of association shown in FIG. 5 acquired in advance is referred to. For example, when the acquired regional characteristic information is the same as or similar to the reference regional characteristic information P12 and the acquired external environment information corresponds to the reference external environment information P19, the combination is associated with the node 61c. In this node 61c, the industry information Q2 is associated with the association degree w17, and the industry information Q4 is associated with the association degree w18. As a result of such a degree of association, based on w17 and w18, the industry information at the time when the new reference area characteristic information and the reference external environment information are actually acquired will be estimated.

In FIG. 6, in addition to the above-mentioned reference area characteristic information and reference property information, a combination of reference external environment information and industry information for the combination are set to three or more levels of association. An example is shown.

In such a case, as shown in FIG. 6, the degree of association is such that the set of combinations of the reference area characteristic information, the reference property information, and the reference external environment information is the node 61a to 61e of the intermediate node as described above. Will be expressed as.

For example, in FIG. 6, in the node 61c, the reference area characteristic information P12 is associated with the association degree w3, the reference property information P15 is associated with the association degree w7, and the reference external environment information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference area characteristic information P13 is associated with the association degree w5, the reference property information P15 is associated with the association degree w8, and the reference external environment information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, the type of industry is estimated based on the newly acquired regional characteristic information, property information, and external environmental information.

In estimating this industry, the degree of association shown in FIG. 6 acquired in advance is referred to. For example, the acquired regional characteristic information is the same as or similar to the reference regional characteristic information P12, the acquired property information corresponds to the reference property information P15, and the acquired external environment information corresponds to the reference external environment information P21. In this case, the node 61c is associated with the combination, and the industry information Q2 is associated with the association degree w17 and the industry information Q4 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 a combination of the above-mentioned reference area characteristic information, reference property information, and a transaction price for the combination are set to three or more levels of association. The transaction price here includes the rent as well as the sale price.

As the input data, such reference area characteristic information and reference property information are arranged side by side. The intermediate node shown in FIG. 7 is a combination of reference property information and reference area characteristic information as such input data.

The reference property information used here includes information on the contents of the property, and includes, for example, floor plans, flow lines, equipment, exteriors, images taken indoors, and the like.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the estimation device 2 accumulates reference area characteristic information, reference property information, and data on what the past transaction price was in that case when actually estimating the industry information. By analyzing, analyzing, and learning these, the degree of association shown in FIG. 7 is created.

In the example of the degree of association shown in FIG. 7, the node 61b is a node of the combination of the reference property information P14 with respect to the reference area characteristic information P11, the degree of association of the transaction price Q3 is w15, and the association of the transaction price Q5. The degree is w16.

Similarly, when such a degree of association is set, regional characteristic information is newly acquired and property information is acquired.

In estimating the transaction price, the degree of association shown in FIG. 7 acquired in advance is referred to. For example, when the acquired regional characteristic information is the same as or similar to the reference regional characteristic information P12 and the acquired property information corresponds to the reference property information P15, the combination is associated with the node 61c. In this node 61c, the transaction price Q2 is associated with the degree of association w17, and the transaction price Q4 is associated with the degree of association w18. As a result of such a degree of association, the transaction price at the time when the new reference area characteristic information and the reference property information are actually acquired will be estimated based on w17 and w18.

Also when estimating this transaction price, as shown in FIG. 6, in addition to the reference area characteristic information and the reference property information, the combination of the reference external environment information and the transaction price for the combination are used. By learning the degree of association of three or more levels, it is possible to estimate the transaction price for newly acquired regional characteristic information, property information, and external environmental information.

Further, the transaction price may be estimated together with the estimation of the industry information. As a result, it is possible to estimate the type of business of the business to be recommended and the transaction price at the same time. At this time, the transaction price itself may be changed based on the estimated industry. In such a case, for example, a weighting coefficient may be set for each industry, and the transaction price may be calculated based on the weighting according to the estimated industry.

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 estimate the type of real estate to be introduced and price the real estate without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than that made 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.

Further, according to the present invention, there is a feature that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by a numerical value from 0 to 100%, for example, in addition to the above-mentioned 5 stages, but is not limited to this, and any stage can be described as long as it can be described by a numerical value of 3 or more stages. It may be configured.

Under the circumstances where there are multiple possible candidates for the search solution by searching for the most probable real estate industry estimation and real estate price based on the degree of association expressed by such three or more levels of numerical values. In, it is also possible to search and display in descending order of the degree of association. If the user can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

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 high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized 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, for example, via a public communication network such as the Internet. In addition, when regional characteristic information is acquired, and in addition to this, property information, external environmental information, and knowledge, information, and data regarding the industry to be introduced and the price of real estate are acquired, the degree of association is increased accordingly. Or lower it.

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 done by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from public communication networks. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating a 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.

Further, the degree of association of each combination described above is the degree of association of a combination having one factor and another factor, and it goes without saying that other elements other than these may be associated with the degree of association.

Second Embodiment Hereinafter, the second embodiment will be described. In executing this second embodiment, the move-in recommendation company proposal system 1, the information acquisition unit 9, the search device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by citing the description of the first embodiment.

In the second embodiment, the reference property information is learned.

In the example of FIG. 8, it is assumed that the reference property information P01, P02, P03 is used as the input data. The reference property information P01, P02, and P03 as such input data are linked to the transaction price as output.

The degree of association indicates the degree of which transaction price is highly relevant to the reference property information arranged on the left side. In other words, this degree of association is an indicator of what transaction price each reference property information is likely to be associated with, and in selecting the most probable transaction price for each reference property information. It shows the accuracy of. In the example of FIG. 8, w13 to w19 are shown as the degree of association. As shown in Table 1, these w13 to w19 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 transaction price as an output, and vice versa. The closer to one point, the lower the degree of relevance of each combination as an intermediate node to the transaction price 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 the past data set as to which of the reference property information of each region and the transaction price in that case is adopted and evaluated in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

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 such a case, as shown in FIG. 9, reference property information of each region is input as input data, transaction price is output as output data, and at least one hidden layer is formed between the input node and the output node. It may be provided and machine learning may be performed. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data through the data set of the reference property information of each region before and the transaction price, the above-mentioned trained data is used to actually determine the transaction price from now on. It will be used to search for the transaction price. These data sets may be created by reading from a database managed by the vendor.

When searching for a new transaction price, the input of the property information to be searched is accepted.

Next, this property information is collated with the reference property information. 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 property information is the same as or similar to P02, the transaction price B is associated with w15 and the transaction price C is associated with the association degree w16 via the degree of association. In such a case, the transaction price 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 transaction price C, which has the lowest degree of association but is recognized for the association itself, 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 any other output solution may be selected in any other priority as long as it is based on the degree of association.

In the example of FIG. 10, it is an example of using the degree of association between the reference property information and the transaction price in three or more stages. When focusing only on this degree of association, it is the same as in FIG. 8, but in this example, other reference information different from the reference property information is further associated with this transaction price.

It is premised that the reference property information and the transaction price are linked to each other to form a degree of association. In the example of FIG. 10, it is assumed that the input data is, for example, reference property information P11 to P13. The reference property information as such input data is linked to the output. In this output, it is assumed that the transaction price is the output solution.

The reference property information is associated with each other through three or more levels of association with the transaction price as the output solution. The reference property information is arranged on the left side through this degree of association, and each transaction price is arranged on the right side through this degree of association.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, in determining the actual search solution, the discrimination device 2 accumulates past data on what kind of transaction price was obtained at the time of the reference property information captured and acquired in the past, and analyzes these. By analyzing, the degree of association shown in FIG. 10 is created.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the solution will be searched. In such a case, the captured property information is acquired, and each information corresponding to other reference information is also acquired in the same manner.

First, the transaction price is searched based on the newly acquired property information. In such a case, the degree of association shown in FIG. 10 acquired in advance is used. For example, when the newly acquired property information is the same as or similar to the reference property information P12, the transaction price of 30% is the degree of association w15 and the transaction price C is the degree of association w16. Associated with. In such a case, the transaction price B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the solution having the highest degree of association as the optimum solution, and quality C, which has a low degree of association but is recognized in the association itself, may be selected as the solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association. Further, the output solution to be selected is not limited to one, and two or more may be selected. In such a case, two or more may be selected in order from the highest degree of association, but the present invention is not limited to this, and may be based on the priority of any other degree of association.

The transaction price obtained through the degree of association may be further modified based on the reference information or the weighting may be changed.

The reference information referred to here includes all the reference information described in the first embodiment, and is, for example, reference area characteristic information, external environment information, and the like in the first embodiment.

For example, as one of the reference information, in the reference area characteristic information, the noise level in the area is higher than the average, or the traveling amount of the vehicle is higher than the average. With such a structure, the transaction price is often low. At this time, it is set in advance to perform a process of increasing the weight for the lower transaction price searched from the property information via the degree of association, in other words, a process of leading to a search solution having a low transaction price. Keep it. On the other hand, in a quiet environment where the noise level is lower than the average, the lower transaction price searched from the property information via the degree of association is processed to lower the weight, in other words, the transaction price is higher. It is set in advance to perform processing that leads to a high search solution.

For example, it is assumed that the reference information G is an analysis result suggesting a lower transaction price, and the reference information F is an analysis result suggesting a higher transaction price. In this way, after setting with the reference information, if the actually acquired information is the same as or similar to the reference information G, a process of increasing the weighting of the low transaction price is performed, in other words, the transaction price. Performs the process of lowering itself. On the other hand, when the actually acquired information is the same as or similar to the reference information F, a process of increasing the weighting of the high transaction price is performed, in other words, a process of increasing the transaction price itself is performed. That is, the degree of association itself that leads to the transaction price may be controlled based on the reference information F to H, or the transaction price is first obtained independently between the property information and the transaction price, and this Modifications may be made to the obtained search solution based on the reference information F to H. In the latter case, how to modify the transaction price as a search solution based on the reference information F to H will reflect the one designed by the system side each time.

Further, the reference information is not limited to the case where it is composed of any one type, and the transaction price may be searched based on two or more types of reference information. Similarly, in such a case, the higher the transaction price suggested by the reference information is, the higher the transaction price as the search solution obtained through the degree of association is corrected, and the transaction suggested by the reference information. The lower the price, the lower the transaction price as the search solution obtained through the degree of association.

In the example shown in FIG. 11, it is assumed that the input data is, for example, reference area characteristic information P11 to P13, as in the first embodiment. The reference area characteristic information as such input data is linked to the output. In this output, it is assumed that the transaction price is the output solution. As a method for obtaining this transaction price, the description in the first embodiment and the present embodiment will be quoted, and the description below will be omitted. The transaction price obtained through the degree of association may be further modified based on the reference information or the weighting may be changed.

The reference information referred to here includes all the reference information described in the first embodiment, and is, for example, reference property information, external environment information, and the like in the first embodiment. Similarly, in such a case, it is assumed that the reference information G is an analysis result suggesting a lower transaction price, and the reference information F is an analysis result suggesting a higher transaction price. In this way, after setting with the reference information, if the actually acquired information is the same as or similar to the reference information G, a process of increasing the weighting of the low transaction price is performed, in other words, the transaction price. Performs the process of lowering itself. On the other hand, when the actually acquired information is the same as or similar to the reference information F, a process of increasing the weighting of the high transaction price is performed, in other words, a process of increasing the transaction price itself is performed.

FIG. 12 shows an example in which the reference area characteristic information is replaced with the reference external environment information in the degree of association of the transaction price with respect to the combination of the reference area characteristic information and the reference property information shown in FIG. 7. .. Similarly, in such a case, the degree of association of the transaction price with respect to the combination of the reference external environment information and the reference property information is learned in advance, and when the external environment information and the property information are newly acquired, the association is obtained. The transaction price is derived as a search solution by referring to the degree. In such a case, the transaction price can be searched in the same manner.

The present invention is not limited to the above-described embodiment. In both the first embodiment and the second embodiment, the output of the above-mentioned degree of association may be used as an input, and a search solution corresponding to the input of the degree of association may be searched. For example, in the case of the example of FIG. 8, the input of the desired transaction price corresponding to the transaction price is accepted. Then, using the degree of association shown in FIG. 8, the reference property information for the transaction price corresponding to the acquired desired transaction price may be reversely searched.

Further, the rate of increase / decrease of the transaction price may be learned as an alternative to the transaction price as the output of the degree of association described in the first embodiment and the second embodiment. This rate of increase / decrease is what percentage of the transaction price of the real estate will rise or fall in one year, two years, three years, ... n years (n is a positive integer). Is shown. This rate of increase / decrease may indicate the rate of increase / decrease with respect to the current transaction price as a percentage, or may indicate the expected future transaction price obtained by multiplying the transaction price by the rate of increase / decrease.

In such a case, as shown in FIG. 13, as described in the first embodiment and the second embodiment described above, the reference property information or the reference information (reference area characteristic information, reference). For two or more combinations of external environment information, the rate of increase / decrease in the transaction price is learned through the degree of association. When learning the rate of increase / decrease in the transaction price, there is a certain point in the past (for example, 3 years). You may learn the current rate of increase / decrease with respect to the previous), but it is not limited to this, and as shown in Fig. 14, it is a time-series transition of the transaction price of the property specified by the reference property information. It may be configured.

For two or more combinations of such reference property information or each reference information (reference area characteristic information, reference external environment information), the rate of increase / decrease in transaction price is learned through the degree of association. If you actually want to predict the future rate of increase / decrease of the property, enter the property information. Refer to the above-mentioned degree of association for the rate of increase / decrease of the transaction price according to the reference property information that is close to this property information. Based on the rate of increase / decrease thus obtained, the rate of increase / decrease of the property in the future (for example, n months, n years; n is a positive number) is predicted. May be good.

At this time, the time-series transition of the transaction price is shown by the rate of increase / decrease of the current transaction price with respect to the transaction price of one year ago. If is one year later and the rate of increase / decrease Q1 is searched for, the expected rate of increase / decrease may be similarly predicted to be 5% down based on the result of the 5% decrease. ..

Further, as shown in FIG. 15, the time-series transition of the transaction price is shown by the time-series transition of the transaction price over the past five years, and the learned rate of increase / decrease Q1 is the time-series as shown in FIG. In the case of a target transition, if the rate of increase / decrease to be predicted is three years later and the rate of increase / decrease Q1 is searched, the predicted value will increase / decrease according to the time-series transition shown in FIG. It may be predicted as a rate.

In addition to the reference property information, the transaction price increase / decrease rate is predicted by using the transaction price increase / decrease rate for two or more combinations of reference information (reference area characteristic information and reference external environment information). By learning, it is possible to further improve the prediction accuracy.

In addition, the above-mentioned reference property information and property information are not limited to newly built properties, used properties where people actually live, or properties where tenants and residents are scheduled to leave the property soon, so-called so-called uninhabited properties. Reference property information and property information regarding vacant house properties are also included.

In such a case, the property information of the vacant house includes size information regarding the size of the vacant house, age information regarding the age of the vacant house, internal information regarding the inside of the vacant house, external image information obtained by capturing the appearance of the vacant house, and the vacant house. Brand information about the brand of the building structure, new construction price information about the price at the time of new construction of the above vacant house, resident information about the resident before the vacant house, history information about the background of becoming vacant house, renovation possibility information about the possibility of renovation, deterioration It consists of deterioration degree information regarding the degree. The reference property information of the vacant house includes the reference area information regarding the size of the vacant house, the reference age information regarding the age of the vacant house, the reference internal information regarding the inside of the vacant house, and the reference appearance that images the appearance of the vacant house. Image information, reference brand information about the brand of the building structure of the vacant house, reference new construction price information about the price at the time of new construction of the vacant house, reference resident information about the residents before the vacant house, reference background information about the history of becoming vacant house, It consists of renovation possibility information for reference regarding the possibility of renovation and deterioration degree information for reference regarding the degree of deterioration.

Here, the reference resident information is obtained from the data stored by the municipality, real estate company, etc., such as the name and age of the resident before the vacant house, family structure and period of residence, reason for leaving, etc. There may be. Similarly, reference history information regarding the circumstances of becoming an unoccupied house may be obtained from data recorded by a trader such as a municipality or a real estate company. This reference background information includes the reason why the house is vacant. For example, after a resident or tenant has left the house, no one can buy it and leave it as it is, or an accident in which some incident occurred. Whether or not the property is a property may also be included in this reference background information. For reference renovation possibility information regarding the possibility of renovation, the result of having the renovation company or its expert judge the possibility may be converted into data, or an image of the vacant house may be actually taken and the floor plan may be taken. The degree of aging, whether or not renovation is possible from the viewpoint of the structure of the house, and the extent to which the renovation can be reflected may be configured. The reference deterioration degree information indicates the degree of deterioration. The deterioration degree information for reference is an index of the degree of deterioration such as mold and dew condensation inside and outside the vacant house, the degree of rain leakage, the degree of dirt and damage on the walls and ceiling, pillars and each room, the degree of peeling of the outer wall, the condition of water circulation, etc. It is a ghost. The deterioration information for reference may be automatically determined by imaging the inside and outside of an unoccupied house and analyzing the image. In such a case, an event indicating deterioration such as scratches and stains, mold and dew condensation on the image may be detected as a feature amount, and discriminated and extracted by using a deep learning technique and a machine learning technique.

Using the three or more levels of association with the transaction price of any one of the reference property information, the above-mentioned size information, the above-mentioned internal information, and the above-mentioned external image according to the newly acquired reference property information. The transaction price to be proposed may be searched based on any one of the information, the brand information, the new construction price information, the resident information, the background information, the renovation possibility information, and the deterioration degree information. ..

Further, as shown in FIG. 16, this reference property information includes reference area information, reference age information, reference internal information, and reference external image information as reference information constituting the reference property information. , Reference brand information, reference new construction price information, reference resident information, reference history information, reference renovation possibility information, reference deterioration degree information, and the transaction price for the combination You may use the degree of association of 3 or more levels. The example of FIG. 16 is an example of configuring the degree of association of the combination having the reference history information and the reference deterioration degree information, but it is substituted with the reference information constituting any other reference property information. You may. After forming such a degree of association, size information, internal information, appearance image information, brand information, new construction price information, resident information, background information, renovation possibility information, deterioration degree according to the combination of the degree of association Based on the information, search for the transaction price to be proposed. The method of this search is the same as described above.

Similarly, property information other than vacant houses also includes reference area information, reference age information, reference internal information, reference exterior image information, and reference brand information as reference information that constitutes reference property information. , A combination having any two or more of the new construction price information for reference and a transaction price for the combination may be associated with three or more levels. After forming such a degree of association, the transaction price to be proposed is searched for based on the size information, the inside information, the appearance image information, the brand information, and the new construction price information according to the combination of the degree of association. The method of this search is the same as described above.

The present invention is not limited to the above-described embodiment, and for example, as shown in FIG. 17, the present invention utilizes three or more levels of association between the reference information as the keynote and the transaction price and the rate of increase / decrease. You may try to do it. In such a case, the solution search will be performed based on the degree of association with the transaction price and the rate of increase / decrease according to the newly acquired information in three or more stages. As the reference information as the keynote, for example, all the reference information after the first embodiment can be applied.

Similarly, in these cases, when the information corresponding to the reference information used as the learning data is input, the solution search is performed based on the above-mentioned method.

The search solution obtained through the degree of association may be further modified based on other reference information, or the weighting may be changed.

When any of the above-mentioned reference information is used as the keynote reference information, the other reference information here corresponds to any reference information other than the keynote reference information.

For example, as one of the other reference information, it is assumed that in a certain reference information P02, B was previously determined as the transaction price. When a transaction price corresponding to such reference information P02 is newly acquired, the search solution B as the transaction price is subjected to a process of increasing the weight, in other words, the search solution B as the transaction price is connected. It is set in advance to perform the process of making.

For example, another reference information G is an analysis result that suggests a search solution C as a transaction price, and reference information F is an analysis result that suggests a search solution D as a transaction price. Suppose there is. After the setting with the reference information in this way, if the actually acquired information is the same as or similar to the reference information G, a process of increasing the weighting of the search solution C as the transaction price is performed. On the other hand, when the actually acquired information is the same as or similar to the reference information F, a process of increasing the weighting of the search solution D as the transaction price is performed. That is, the degree of association itself leading to the transaction price may be controlled based on the reference information F to H. Alternatively, after the transaction price is determined only by the above-mentioned degree of association, the search solution obtained may be modified based on the reference information F to H. In the latter case, how to modify the transaction price as a search solution based on the reference information F to H will reflect the one designed on the system side each time.

Further, the reference information is not limited to the case where it is composed of any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the transaction price as the search solution obtained through the degree of association may be modified higher as the case leads to the transaction price suggested by the reference information.

Similarly, as shown in FIG. 18, even in the case of forming a degree of association with the transaction price for a combination having the underlying reference information and other reference information, the underlying reference information is still available. Any reference information from the first embodiment described above can be applied. Other reference information includes any reference information other than the underlying reference information.

At this time, if the reference information that is the keynote is the reference distance information, the other reference information includes any other reference information.

In such a case as well, the transaction price can be estimated by performing a solution search in the same manner. At this time, as shown in FIG. 17 described above, the transaction price is corrected through further other reference information (reference information F, G, H, etc.) for the search solution obtained through the degree of association. You may do it.

As shown in FIGS. 19 and 20, the solution search may be performed by using the reference information as the keynote and the degree of association between the transaction price and the transaction price in three or more stages.

The transaction price is determined only from the reference information. For example, as shown in FIGS. 19 and 20, the degree of association between the reference information acquired in the past (including any reference information after the first embodiment) and the transaction price actually determined in the past is three or more levels. To use.

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 when actually determining a new transaction price from now on. In such a case, new information corresponding to the reference information is acquired.

The transaction price is determined based on the newly acquired information in this way. In such a case, the degree of association shown in FIGS. 19 and 20 acquired in advance is referred to. Since the specific method for estimating the transaction price is the same as described above, the description below will be omitted.

Third Embodiment In the third embodiment, instead of searching for the transaction price and the rate of increase / decrease, the repair cost, the repair period, and the repair method are searched. In this embodiment, the target is further narrowed down from real estate and limited to buildings.

The repair cost refers to the repair cost in the event of damage or failure in any system or device mounted on the exterior or inside of the building, or inside or outside the building. The repairs mentioned here are, for example, those that have actually had scratches or dents on the exterior of the building, and those that have not actually been damaged at this point, but that would lead to damage if left as they are. Including the case of repair and maintenance in advance. In addition, the repair includes the case where the dirt is attached although it is not damaged and it is removed. In addition, although it is not damaged, repairs that remove odors and defects that have occurred at the time of purchase are also included in this repair.

The repair period indicates the period until the repair is completed based on the date when the repair is sent or the date when the repair is applied for in the case of the above-mentioned damage. The repair method is the specific content of repair for the above-mentioned damage, and includes total replacement, partial replacement, replacement with a compatible product, re-tightening of bolts, and even one painting.

For a detailed explanation of the solution search when any one of the repair cost, the repair period, and the repair method is used as the search solution, the transaction price and the rate of increase / decrease in the above-mentioned first embodiment to the second embodiment are referred to as the repair cost, the repair period, and the repair period. The explanation will be replaced with the repair method, and the explanation below will be omitted.

Specifically, the solution search can be performed in the same manner by learning the repair cost, the repair period, and the learning data set of the repair method in the same manner for each of the above-mentioned reference information.

The reference contract information may be applied as the reference information applicable in the third embodiment. Reference contract information is any information regarding the contractual content of building repair support. When purchasing or renting a building, various warranty and repair support contracts are often concluded, but in such repair support, it is clearly stated what kind of damage and how much repair is supported. It is often done. In the reference contract information, the specific repair support content for the damage condition, the cost borne by the contractor and the customer, and the ratio thereof are expressed. Specifically, repair support conditions (period, cost, ratio) and the like may be extracted and learned. In the case of learning such reference contract information, the contract information is acquired when a new solution search is performed. The type of data of this contract information is the same as that of the reference contract information.

As reference information applicable in this third embodiment, reference internal image information may be applied. The reference internal image information is image information obtained by photographing the inside of the building. Specifically, it may be composed of images taken of damaged parts (walls, stairs, ceilings, floor photographs, doors, windows, curtain rails) inside the building. In the case of learning such reference internal image information, the internal image information is acquired when a new solution search is performed. The type of data of this internal image information is the same as that of the internal image information for reference.

As the reference information applicable in this third embodiment, the reference odor information may be applied. This reference odor information is information obtained as a result of sensing the odor in the building and the odor. This reference odor information can be detected by, for example, an odor sensor, and the oxidation-reduction reaction of odor molecules is used to detect reducing odors such as hydrogen sulfide, acetaldehyde, and ammonia on the surface of the conductor. A semiconductor-type odor sensor that utilizes changes in the resistance value of semiconductors due to adsorption of odor molecules and surface reactions, and an odor-sensitive film made of a lipid film made of natural or synthetic lipids that selectively adsorbs molecules is attached to the surface of the crystal oscillator. It may be composed of a attached crystal oscillator type odor sensor, a molecular selection FET biosensor in the air, or the like. In the case of learning such reference odor information, the odor information is acquired when a new solution search is performed. The type of data of this odor information is the same as that of the reference odor information.

The reference sound information may be applied as the reference information applicable in the third embodiment. The reference sound information is a recording of the sound emitted when walking in the building. That is, this reference sound information includes creaking that occurs when actually walking in a building, loudness of abnormal walking sound, resonance, resonance, abnormal sound, and the like. The sound data constituting this reference sound information may be composed of, for example, sound wave data with respect to the time axis, but it represents what band of sound is generated by converting to the frequency axis. You may. In the case of learning such reference sound information, the sound information is acquired when a new solution search is performed. The type of data of this sound information is the same as that of the reference sound information.

In the third embodiment, as shown in the first to second embodiments, the quality of the building may be searched, and the repair cost, repair period, and repair method may be searched from the searched quality. .. As mentioned above, the quality here is an evaluation of the condition of the building from various viewpoints such as beauty, worn-out, usability, odor, scratches, sound, and damage status. In addition to the above, in the third embodiment, if the damage has occurred, the degree of the damage, the content of the damage, and if there is a feeling of strangeness or a defect, the degree of the damage may be indicated.

The method for searching for a solution for the quality of a building omits the description below by replacing the search for a solution for the search solution in the above-mentioned first embodiment to the second embodiment with the quality of the building.

Based on the quality of the building obtained in this way, the repair cost, repair period, and repair method are calculated. In such a case, prepare a template in which the repair cost, repair period, and repair method are linked to each quality of the building in advance. That is, if the quality of the building is obtained, the repair cost, the repair period, and the repair method can be easily obtained by referring to the template. This makes it possible to obtain the quality of the building from various information and estimate the repair cost, repair period, and repair method from it.

Similarly, the quality of this building may be used to determine the premium paid to the contractor or customer. Similarly, in such a case, prepare a template in which insurance premiums are associated with each quality of the building in advance. If the quality of the building is desired, it will be easy to obtain the insurance premium according to it by referring to the template. This makes it possible to obtain the quality of the building from various information and estimate the insurance premium from it.

When seeking insurance premiums, the repair cost, repair period, and repair method may be searched for, and the insurance premium may be obtained from there. Similarly, in such a case, prepare a template in which insurance premiums are associated with each vehicle repair cost, repair period, and repair method in advance. If the repair cost, repair period, and repair method are determined, the template can be referred to to make it easy to obtain the corresponding insurance premium. This makes it possible to obtain the repair cost, repair period, and repair method from various information, and estimate the insurance premium from them.

Of course, in the third embodiment as well, the solution search may be performed as shown in FIGS. 17 to 20. In addition to buildings, the targets for searching for repair costs, repair periods, and repair methods are, of course, buildings, machines, water areas (washrooms, baths, kitchens, toilets), equipment, and home appliances.

1 Tenant recommendation company proposal system 2 Estimator 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 node

Claims (9)

In the renovation possibility estimation program that estimates the possibility of building renovation
An information acquisition step to acquire exterior image information obtained by capturing an image of the exterior of the building to be estimated, and
Three or more levels of association between the reference exterior image information obtained by capturing the image of the exterior of the building acquired in advance and the possibility of renovation are defined, the input is the exterior image information for reference, and the output is the possibility of renovation. An estimation step that estimates the possibility of renovation by prioritizing those with a higher degree of association based on the reference appearance image information according to the appearance image information acquired in the above information acquisition step using the trained model. A renovation possibility estimation program characterized by having a computer execute and. In the above information acquisition step, the internal image information obtained by capturing the image of the inside of the building to be estimated is acquired.
In the estimation step, the combination of the reference internal image information obtained by capturing the image of the inside of the building acquired in advance and the reference external image information, and the possibility of renovation are defined by three or more levels of association. , The input is the external image information for reference and the internal image information for reference, the output is a trained model with the possibility of renovation, and the internal image information for reference according to the internal image information acquired in the above information acquisition step. The renovation possibility estimation program according to claim 1, wherein the possibility of renovation is estimated based on the above. In the building repair method estimation program that estimates the building repair method,
The information acquisition step to acquire the renovation possibility information regarding the renovation possibility of the building to be estimated, and the information acquisition step.
It has been learned that the renovation possibility information for reference regarding the possibility of renovation of the building acquired in advance and the degree of association with the repair method are defined in three or more stages, the input is the renovation possibility information for reference, and the output is the repair method. Using the model, based on the renovation possibility information for reference according to the renovation possibility information acquired in the above information acquisition step, the computer has an estimation step of estimating the repair method by giving priority to the one with the higher degree of association. A building repair method estimation program characterized by having a computer run. In the above information acquisition step, the internal image information obtained by capturing the image of the inside of the building to be estimated is acquired.
In the estimation step, a combination having the reference internal image information obtained by capturing an image of the inside of the building acquired in advance, the reference renovation possibility information, and the degree of association with the repair method in three or more stages are defined. The input is the renovation possibility information for reference and the internal image information for reference, the trained model with the output as the repair method is used, and the internal image information for reference according to the internal image information acquired in the above information acquisition step is used. , The building method estimation program according to claim 3, wherein the repair method is estimated. In the above information acquisition step, the internal image information obtained by capturing the image of the inside of the building to be estimated is acquired.
The building method estimation program according to claim 3, wherein the estimation step uses the trained model and estimates a repair method based on the internal image information acquired in the information acquisition step. In the building repair method estimation program that estimates the building repair method,
An information acquisition step for acquiring external image information obtained by capturing an image of the appearance of the building to be estimated and renovation possibility information regarding the possibility of renovation of the building to be estimated.
The combination of the reference exterior image information obtained by capturing the image of the exterior of the building acquired in advance and the reference renovation possibility information regarding the possibility of renovation of the building acquired in advance, and the degree of association with the repair method in three or more stages. Is specified, the input is the appearance image information for reference and the renovation possibility information for reference, and the trained model with the output as the repair method is used, and the appearance image for reference according to the appearance image information acquired in the above information acquisition step. Based on the information and the renovation possibility information for reference according to the renovation possibility information, the building characterized by having the computer perform an estimation step of estimating the repair method by giving priority to the one with the higher degree of association. Repair method estimation program. In the building repair method estimation program that estimates the building repair method,
An information acquisition step for acquiring external image information obtained by capturing an image of the appearance of the building to be estimated and renovation possibility information regarding the possibility of renovation of the building to be estimated.
A trained model in which the degree of association between the reference exterior image information obtained by capturing an image of the exterior of the building acquired in advance and the repair method is defined, and the input is the reference exterior image information and the output is the repair method. Based on the reference appearance image information according to the appearance image information acquired in the above information acquisition step, priority is given to the one having a higher degree of association, and the above renovation possibility information acquired in the above information acquisition step is used. Based on, a building repair method estimation program characterized by having a computer perform an estimation step to estimate the repair method. In the building repair method estimation program that estimates the building repair method,
An information acquisition step for acquiring internal image information obtained by capturing an image of the inside of the building to be estimated and renovation possibility information regarding the possibility of renovation of the building to be estimated.
A trained model that defines the degree of association between the internal image information for reference, which is an image of the exterior of the building acquired in advance, and the repair method, and uses the input as the internal image information for reference and the output as the repair method. Based on the internal image information for reference according to the internal image information acquired in the information acquisition step, priority is given to the information having a higher degree of association, and the renovation possibility information acquired in the information acquisition step is used. Based on, a building repair method estimation program characterized by having a computer perform an estimation step to estimate the repair method. The building repair method estimation program according to any one of claims 1 to 8, wherein in the estimation 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. ..

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