JP2022033575A - Moving cost estimation program - Google Patents

Abstract

To determine the cost of moving with high accuracy.SOLUTION: A moving cost estimation program for estimating a moving cost causes a computer to execute: an information acquisition step of acquiring transport amount information associated with the types and/or amount of household goods to be transported from a house during moving and external environment information on an external environment during the moving; and an estimation step of using three or more degrees of association of the moving cost with a combination having transport amount information for reference associated with the types and/or amount of household goods to be transported from a house during moving acquired in the past and external environment information for reference on an external environment during the acquisition of the transport amount information for reference, and based on the transport amount information for reference according to the transport amount information acquired in the information acquisition step and the external environment information for reference according to the external environment information, prioritizing the higher degree of association to estimate the moving cost.SELECTED DRAWING: Figure 12

Description

The present invention relates to a moving cost estimation program suitable for estimating moving costs with high accuracy.

Moving services provided by various businesses are often performed in the flow of requesting a quote, making a quote, making a contract, and moving. Requests for quotations are often made over the phone or on the web. Quotations are generally made by staff visiting the home of the moving client and previewing the room.

However, the burden of labor for the staff of the moving service company to visit the client's home is excessive each time a moving estimate is made, and the client side is also in the space of a private home just for the moving estimate. There is a growing demand for businesses not to be included in the service. A technology is known that can estimate a move with a simple process without imposing a burden on the moving customer (see, for example, Patent Document 1), but a system that can perform a more accurate cost estimate. Has been desired in the past.

Japanese Unexamined Patent Publication No. 2005-266904

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is to provide a moving cost estimation program capable of estimating moving costs with higher accuracy. be.

The moving cost estimation program according to the present invention is the moving cost estimation program for estimating the moving cost, in which the transportation amount information associated with the type and / or amount of the household goods to be transported in the house of the moving source and the outside at the time of moving are used. Information acquisition step to acquire external environmental information about the environment, reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and the above reference transportation. Using the combination of the external environment information for reference regarding the external environment at the time of acquisition of the quantity information and the degree of association with the moving cost in three or more stages, the transport for reference according to the transport amount information acquired in the above information acquisition step. Based on the quantity information and the external environment information for reference according to the external environment information, the one having the higher degree of association is prioritized, and the estimation step for estimating the moving cost is executed by the computer.

Anyone can easily estimate the moving cost with high accuracy 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 a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the moving cost estimation program to which the present invention is applied will be described in detail with reference to the drawings.

The first embodiment FIG. 1 is a block diagram showing an overall configuration of a moving cost estimation system 1 to which a moving cost estimation program to which the present invention is applied is implemented. The moving cost estimation 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.

The information acquisition unit 9 is a device for a person who utilizes 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 taking 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. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be configured by a communication interface for acquiring data about the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the information acquisition unit 9 may be configured as a device for acquiring information such as drawings by scanning or reading from a database.

The database 3 stores various information necessary for estimating the moving cost. As information necessary for estimating the moving cost, by analyzing the image of the house of the moving source taken in the past, the reference transportation amount information associated with the type and amount of the household goods to be transported, and the moving amount. Reference distance information from the original to the moving destination, reference date and time information regarding the moving date and time, reference packing information regarding the degree of provision of packing services at the time of moving, reference building structure information regarding the structure of the building of the moving source or moving destination, A data set of reference attribute information regarding the attributes of the moving requester and the moving cost for which a judgment is actually made is stored.

That is, in the database 3, in addition to such reference transport amount information, any one or more of reference distance information, reference date and time information, reference packing information, reference building structure information, and reference attribute information is stored. Moving costs are linked and remembered.

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

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 moving cost estimation system 1 having the above-described configuration will be described.

In the moving cost estimation system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between the reference transport amount information and the moving cost are set in advance. The reference transportation amount information is obtained by analyzing an image of the inside of the house of the moving source, and is associated with the type and amount of household goods to be transported. This reference transport amount information is extracted by analyzing an image of the inside of the house of the moving source, for example, how many tables, how many chairs, and how many shelves there are through image analysis technology. At this time, artificial intelligence is used to learn image data of household goods such as desks, chairs, shelves, sofas, and televisions, and when actually acquiring information on the amount of transport for reference, these learned images are used. The type and amount may be determined by comparing with the data. At this time, if household goods are stored in the shelves, the type and amount of the stored household goods may be similarly determined by utilizing artificial intelligence.

It should be noted that the method of obtaining the reference transport amount information is not limited to the case of obtaining the image by analyzing the image of the house of the moving source. For example, the reference transport amount information itself, or the type and amount of household goods constituting the information may be manually input through a keyboard or a touch panel, and there are many tables, many chairs, and so on. The number of shelves may be selected by the user. Further, the text information of the answer obtained to the question to the user such as chat may be subjected to morphological analysis and syntactic analysis, and the reference transport amount information may be acquired from the words or the like extracted from each. Further, the voice data emitted by the user may be acquired, converted into text information, and the reference transport amount information may be acquired in the same manner.

Similarly, the method of acquiring the transport amount information may be performed by the user's voice or manual input.

Further, in the following description, the case where the reference transport amount information and the transport amount information are configured from the two types and amounts of the household goods to be transported will be described as an example, but the description is not limited to this. , The reference transport amount information and the transport amount information may be configured from any one of the types or quantities of the household goods to be transported.

In addition, as a method of acquiring the reference transport amount information and the transport amount information, it may be related from the size of the house of the moving source. That is, the size of the house of the moving source acquired in the past and the reference transportation amount information are associated with each other. For example, as the size of the house increases, a calibration curve or the like is created so that the amount of transportation in the reference transportation amount information becomes larger. Then, referring to the calibration curve, the reference transport amount information can be calculated from the size of the house. Similarly, the amount of transportation information is derived from the size of the house using the calibration curve. The size of this house may be reference size information and size information, which will be described later, and the reference size information and the reference transport amount information are associated with each other, and the size information is also transported. The information may be associated with each other.

In addition to this, as a method of acquiring the reference transport amount information and the transport amount information, the association may be performed from the attribute of the moving requester. That is, the attributes of the moving requester acquired in the past and the reference transportation amount information are associated with each other. For example, as an attribute of the moving requester, a calibration curve or the like is created so that the amount of transportation in the reference transportation amount information increases as the number of family members increases. Then, referring to the calibration curve, the reference transport amount information can be calculated from the size of the house. The attributes of the move requester may be reference attribute information and attribute information described later, and the reference attribute information and the reference transport amount information are associated with each other, and similarly, the attribute information is used as the transport amount information. You may try to associate them.

The moving cost is the moving cost paid by the client to the moving company. The moving cost may be set in detail up to the level of 1 yen, or may be set in three or more stages of roughly high, medium, and low. Further, as an alternative to the moving cost, it may be displayed by replacing it with an index such as a work amount or a work time. Further, the moving cost is not limited to the moving cost paid by the client to the moving company, and may be the cost price of the moving company. In other words, when the moving company actually estimates the moving cost to the client, the profit is added to the cost actually borne by the moving company to make a proposal. By learning the cost before adding the profit as learning data, the cost of moving can be obtained as a search solution. The moving company can add the profit to be taken to the cost of this moving and propose it to the client.

In the example of FIG. 3, it is assumed that the input data is, for example, reference transfer amount information P01 to P03. The reference transport amount information P01 to P03 as such input data is linked to the moving cost as output. In this output, the moving cost as an output solution is displayed.

The reference transport amount information is linked to each other through three or more levels of linkage with respect to the moving cost as the output solution. The reference transport amount information is arranged on the left side through this degree of association, and each moving cost is arranged on the right side through this degree of association. The degree of association indicates which moving cost is more relevant to the reference transportation amount information arranged on the left side. In other words, this degree of association is an index showing what kind of moving cost each reference transportation amount information is likely to be associated with, and is used to select the most probable moving cost from the reference transportation amount 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 moving cost 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 a past data set as to which of the reference transport amount information and the moving cost in that case is adopted and evaluated in determining the actual search solution, and stores these. By analyzing and analyzing, the degree of association shown in FIG. 3 is created.

For example, the reference transportation amount information is 1 table, 4 chairs, 1 TV stand, 1 TV, 1 sofa set, 1 office desk, 1 office use, 8 futons, and a bed. It is assumed that there are two units and 40 boxes of cardboard. It is assumed that the moving cost of 32,000 yen is highly evaluated as the moving cost for such reference transportation amount information. By collecting and analyzing such data sets, reference transportation amount information (1 table, 4 chairs, 1 TV stand, 1 TV, 1 sofa set, 1 office desk, office work) The degree of connection with one used, eight futons, two beds, and 40 cardboard boxes) will be stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference transport amount information P01, analysis is performed from various data as a result of evaluating the past moving cost. In the case of this reference transport amount information P01, if there are many cases of moving cost of 156,000 yen, the degree of association that leads to the evaluation of this moving cost is set higher, and if there are many cases of moving cost of 95,000 yen, , Set a higher degree of association that leads to the evaluation of this moving cost. For example, in the example of the reference transportation amount information P01, the moving cost is 156,000 yen and the moving cost is 95,000 yen. The degree of association of w14 connected to the circle is set to 2 points.

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

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data through a dataset of images of the house to be moved before and the actually estimated moving cost, the above-mentioned method for actually determining the moving cost from now on will be described above. The moving cost will be searched for using the learned data. In such a case, the transport amount information is newly acquired in the area to be actually discriminated. The newly acquired transport amount information is input by the above-mentioned information acquisition unit 9. The transportation amount information is performed by determining the type and amount of household goods to be moved by capturing an image of the inside of the house for which the moving cost is to be determined. This determination method may be performed by the same method as the above-mentioned reference transport amount information.

The moving cost is determined based on the newly acquired transport amount information in this way. 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 transport amount information is the same as or similar to P02, the moving cost of 32,000 yen is associated with w15 and the moving cost of 95,000 yen is associated with the linkage degree w16 via the degree of association. .. In such a case, the moving cost of 32,000 yen, which has 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, or to prioritize the solution search with the one with the highest degree of association, and although the degree of association is low, the association itself is recognized. Moving cost 95,000 The circle may be selected as the optimal 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 most suitable moving cost can be searched for and displayed to the user from the newly acquired transport amount information. By looking at this search result, the user, that is, the client or the moving company, can determine the moving cost based on the searched moving cost, or indirectly determine the man-hours and time of moving. Can be done.

In addition, as the type of household goods to be discriminated in this transportation amount information, a finer classification may be set. In such a case, when determining the type of household goods, one or more of whether it is an electric product, whether it is a handling caution item, whether it is a fragile item, or whether it is a piano or not. May be determined. If it is an electric product, it includes TVs, personal computers, microwave ovens, refrigerators, washing machines, etc. that require some care and effort when transporting, and the work man-hours will increase accordingly. As a result, moving costs will increase. In discriminating these, images may be learned in advance for each type of these electric appliances, and the learning data may be compared for discrimination.

Similarly, if handling precautions or fragile items (for example, expensive jars or antique items) are included, some items require some care and effort, and the work man-hours will increase accordingly. , Moving costs will increase. In discriminating these, images may be learned in advance for each type of these handling caution items and cracked items, and the learning data may be compared for discrimination, or the labels of the cracked items and handling caution items may be used. If it is attached to a cardboard, it may be identified by reading it.

In addition, the burden of labor is excessive for transporting the piano, and a crane or the like may be required. As for the discrimination of the piano, the image may be learned in the same manner to improve the discrimination accuracy.

The same applies by collecting a learning data set with moving costs when these electrical products, handling precautions, fragile items, and pianos are included as types of household goods, and forming the above-mentioned degree of association. It is possible to estimate the moving cost.

The type of household goods to be determined as the transportation amount information includes the size of the household goods. For example, even if the same television is used, the burden of transportation labor varies greatly depending on the size of the television. Therefore, when determining the type of household goods, the size may be included in the determination. For example, in the case of a television, it may be classified according to the size of the screen (for example, a type), and in the case of a refrigerator, it may be classified according to its volume.

When determining the size of the type of household goods to be transported in the information acquisition step, it is necessary to learn in advance including the size of these household goods in order to acquire the reference transportation amount information in advance. ..

In the example of FIG. 4, it is premised that a combination of the reference transport amount information and the reference distance information is formed. The reference distance information is all information related to the distance from the moving source to the moving destination, and may be displayed in km units, for example, a region such as Tokyo (moving source) to Osaka (moving destination), etc. It may be displayed in units of cities, regions, prefectures, cities, wards, towns and villages, and point names.

The moving cost varies depending on the distance from the moving source to the moving destination in addition to the reference transportation amount information. The longer the distance, the more time it takes to move, and the higher the cost of gasoline, etc., so the moving cost increases. Therefore, the moving cost can be evaluated with high accuracy by defining the moving cost in combination with the reference distance information in addition to the reference transport amount information.

In the example of FIG. 4, it is assumed that the input data is, for example, reference transport amount information P01 to P03 and reference distance information P14 to 17. The intermediate node shown in FIG. 4 is a combination of reference distance information and reference transport amount information as such input data. Each intermediate node is further linked to the output. In this output, the moving cost as an output solution is displayed.

Each combination (intermediate node) of the reference transfer amount information and the reference distance information is associated with each other through three or more levels of association with the moving cost as this output solution. The reference transport amount information and the reference distance information are arranged on the left side through this degree of association, and the moving cost is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the moving cost with respect to the reference transport amount information and the reference distance information arranged on the left side. In other words, this degree of association is an index indicating what kind of moving cost each reference transport amount information and reference distance information is likely to be associated with, and is a reference transport amount information and reference distance information. It shows the accuracy in selecting the most probable moving cost from. Therefore, the optimum moving cost will be searched for by combining the reference transport amount information and the reference distance information.

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 output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the 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 as to which of the reference transport amount information, the reference distance information, and the moving cost in that case is suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 4 is created.

For example, the reference transport amount information in the actual case in the past is 2 tables, 2 chairs, 1 refrigerator, 1 washing machine, 3 futons, 1 bed, and 20 cardboard boxes. And. Further, it is assumed that the reference distance information is 150 km. In such a case, the moving cost, which indicates how much the moving cost was actually, is learned as a data set and defined in the form of the above-mentioned degree of association. In addition, such reference transport amount information and reference distance information may be extracted from the management database managed by the moving company.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference transport amount information P01 and the reference distance information P16, the moving cost is analyzed from the past data. If there are many cases where the moving cost is 95,000 yen, the degree of association that leads to this 95,000 yen is set higher, and if there are many cases where the moving cost is 233,000 yen and there are few cases where the moving cost is 95,000 yen, the moving cost is 233000. Set the degree of linkage that leads to the yen high and the degree of linkage that leads to the moving cost of 95,000 yen low. For example, in the example of the intermediate node 61a, it is linked to the output of the moving cost of 156000 yen and the moving cost of 32000 yen. The degree of association of the connected w14 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 distance information P14 is combined with the reference transfer amount information P01, the association degree of the moving cost of 95,000 yen is w15, and the moving cost is 119000 yen. The degree of association is w16. The node 61c is a node that is a combination of the reference position information P15 and P17 with respect to the reference transport amount information P02, and the degree of association of the moving cost of 32,000 yen is w17 and the degree of association of the moving cost of 233,000 yen is w18. There is.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the moving cost from now on, the above-mentioned learned data will be used. In such a case, the transportation amount information and the distance information are actually acquired. The distance information may be extracted based on the address of the moving source and the moving destination actually entered through the moving requester.

Based on the newly acquired transport amount information and distance information in this way, the optimum moving cost is searched for. 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 transport amount information is the same as or similar to P02 and the distance information is P17, the node 61d is associated via the degree of association. The node 61d is associated with a moving cost of 95,000 yen by w19 and a moving cost of 233,000 yen by the degree of association w20. In such a case, the moving cost of 95,000 yen, which has 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 moving cost of 233,000 yen, 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 association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

In FIG. 5, in addition to the above-mentioned reference transport amount information, a combination with the reference date and time information instead of the above-mentioned reference distance information and a moving cost for the combination are set to three or more levels of association. An example is shown.

This reference date and time information, which is added as an explanatory variable instead of the reference position information, is any information about the date and time of the move. The reference date and time information may be specified up to the start time and end time in addition to the specific date and day of the week, or may be roughly specified in the range of months and seasons (for example, June to August). good. It may or may not be crowded depending on the date and time of the move. For example, because there are employment and admission, and it is the so-called moving season, moving requests are flooded and workers are busy, so the unit price of moving tends to rise. Therefore, the purpose is to improve the accuracy of determining the moving cost by forming the degree of association by combining the reference date and time information.

In the example of FIG. 5, it is assumed that the input data is, for example, reference transport amount information P01 to P03 and reference date and time information P18 to 21. The intermediate node shown in FIG. 5 is a combination of reference date and time information with reference transport amount information as such input data. Each intermediate node is further linked to the output. In this output, the moving cost as an output solution is displayed.

Each combination (intermediate node) of the reference transfer amount information and the reference date / time information is associated with each other through three or more levels of association with the moving cost as this output solution. The reference transport amount information and the reference date and time information are arranged on the left side through this degree of association, and the moving cost is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the moving cost with respect to the reference transportation amount information and the reference date and time information arranged on the left side. In other words, this degree of association is an index indicating what kind of moving cost each reference transportation amount information and reference date and time information is likely to be associated with, and is a reference transportation amount information and reference date and time information. It shows the accuracy in selecting the most probable moving cost from.

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 as to which of the reference transport amount information, the reference date and time information, and the moving cost in that case was more suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

For example, it is assumed that the reference date and time information is "the afternoon of March 15" for a certain reference transportation amount information at the time of evaluating the actual moving cost in the past. In such a case, if there are many cases where the moving cost is determined to be 233,000 yen, these are learned as a data set and defined in the form of the above-mentioned degree of association.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference transfer amount information P01 and the reference date / time information P20, the moving cost is analyzed from the past data. If there are many cases where the moving cost is 156,000 yen, the degree of association that this moving cost leads to 156,000 yen is set higher, and if there are many cases where the moving cost is 32,000 yen and there are few cases where the moving cost is 156,000 yen, there are few cases. , The degree of association that leads to the moving cost of 32,000 yen is set high, and the degree of association that leads to the moving cost of 156,000 yen is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the moving cost of 32000 yen and the moving cost of 156000 yen. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 5 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. 5, the node 61b is a node in which the reference date and time information P18 is combined with the reference transfer amount information P01, and the connection degree of the moving cost of 95,000 yen is w15 and the moving cost is 119000 yen. The degree of association is w16. The node 61c is a node that is a combination of the reference date and time information P19 and P21 with respect to the reference transport amount information P02, and the degree of association of the moving cost of 32,000 yen is w17 and the degree of association of the moving cost of 233,000 yen is w18. There is. Such a degree of association is what is called learned data in artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for moving costs from now on, the above-mentioned learned data will be used. In such a case, the transportation amount information to be determined for the moving cost and the date and time information are actually acquired. Here, as the date and time information, the date and time of the move desired by the client may be input when actually estimating the moving cost.

Based on the newly acquired transportation amount information and the date and time information in this way, the optimum moving cost is searched for. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired transport amount information is the same as or similar to P02 and the date and time information is the same as or similar to P21, the node 61d is associated via the degree of association. In this node 61d, the moving cost of 95,000 yen is associated with w19, and the moving cost of 233,000 yen is associated with the degree of association w20. In such a case, the moving cost of 95,000 yen, which has 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 moving cost 233000, 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 FIG. 6, in addition to the above-mentioned reference transport amount information, a combination with the reference packing information instead of the above-mentioned reference distance information and a moving cost for the combination are set to three or more levels of association. An example is shown.

This reference packing information, which is added as an explanatory variable instead of the reference distance information, shows how much the moving business operator provides the packing-related service in the actual moving. For example, the amount of work varies greatly depending on whether all the packing in cardboard is undertaken or only the packing of futons is undertaken, and the moving cost also increases or decreases accordingly. Therefore, the purpose is to improve the accuracy of determining the moving cost by combining the packing information for reference to form the degree of association.

In the example of FIG. 6, it is assumed that the input data is, for example, reference transport amount information P01 to P03 and reference packing information P22 to 25. The intermediate node shown in FIG. 6 is a combination of reference packing information and reference transport amount information as such input data. Each intermediate node is further linked to the output. In this output, the moving cost as an output solution is displayed.

Each combination (intermediate node) of the reference transport amount information and the reference packing information is associated with each other through three or more levels of association with the moving cost as this output solution. The reference transport amount information and the reference packing information are arranged on the left side through this degree of association, and the moving cost is arranged on the right side through this degree of association.

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 as to which is more suitable for the reference transport amount information, the reference packing information, and the moving cost in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

For example, at the time of evaluating the actual moving cost in the past, it is assumed that the reference packing information is "the vendor side packs all the packages" for a certain reference transportation amount information. In such a case, if there are many cases where the moving cost is 233,000 yen, these are learned as a data set and defined in the form of the above-mentioned degree of association.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference packing information P22 is combined with the reference transport amount information P01, and the association degree of the moving cost of 95,000 yen is w15 and the moving cost is 119000 yen. The degree of association is w16.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for moving costs from now on, the above-mentioned learned data will be used. In such a case, the transportation amount information to be determined for the moving cost and the packing information are actually acquired. Here, the packing information may be input each time based on the request of the requester for moving.

Based on the newly acquired transportation amount information and packing information in this way, the optimum moving cost is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired transport amount information is the same as or similar to P02 and the packing information corresponds to P25, the node 61d is associated via the degree of association. The node 61d is associated with a moving cost of 95,000 yen by w19 and a moving cost of 233,000 yen by the degree of association w20. In such a case, the moving cost of 95,000 yen, which has 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 moving cost of 233,000 yen, 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 FIG. 7, in addition to the above-mentioned reference transport amount information, a combination with the reference building structure information instead of the above-mentioned reference distance information and a moving cost for the combination are set to three or more levels of association. Here is an example.

This reference building structure information includes all information about the structure of the building of the moving source or all information about the structure of the building of the moving destination. Information on the structure of the building you are moving to or moving to is whether it is a single-family home, condominium, building, single-family home, one-story building, multi-story building, or if it is a building or condominium, the presence or absence of an elevator, and the distance from the entrance. , Whether there is a parking space, etc., all information is included. If the building does not have an elevator, the luggage must be transported through the stairs, which increases the labor burden and increases the moving cost. Therefore, the purpose is to improve the accuracy of determining the moving cost by combining the reference building structure information to form the degree of association.

In the example of FIG. 7, it is assumed that the input data is, for example, reference transport amount information P01 to P03 and reference building structure information P22 to 25. The intermediate node shown in FIG. 7 is a combination of the reference building structure information and the reference transport amount information as such input data. Each intermediate node is further linked to the output. In this output, the moving cost as an output solution is displayed.

Each combination (intermediate node) of the reference transport amount information and the reference building structure information is associated with each other through three or more levels of association with the moving cost as this output solution. The reference transport amount information and the reference building structure information are arranged on the left side through this degree of association, and the moving cost is arranged on the right side through this degree of association.

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 past data as to which is more suitable for the reference transport amount information, the reference building structure information, and the moving cost in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

For example, it is assumed that the reference building structure information is "a three-story condominium without an elevator" for a certain reference transportation amount information at the time of evaluating the actual moving cost in the past. In such a case, if there are many cases where the moving cost is 233,000 yen, these are learned as a data set and defined in the form of the above-mentioned degree of association.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference building structure information P22 is combined with the reference transfer amount information P01, the connection degree of the moving cost of 95,000 yen is w15, and the moving cost is 119000 yen. The degree of association is w16.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for moving costs from now on, the above-mentioned learned data will be used. In such a case, the transportation amount information to be determined for the moving cost and the building structure information are actually acquired. Here, the building structure information may be input each time based on the request of the requester for moving.

Based on the newly acquired transportation amount information and the building structure information in this way, the optimum moving cost is searched for. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired transport amount information is the same as or similar to P02 and the building structure information corresponds to P25, the node 61d is associated via the degree of association. The node 61d is associated with a moving cost of 95,000 yen by w19 and a moving cost of 233,000 yen by the degree of association w20. In such a case, the moving cost of 95,000 yen, which has 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 moving cost of 233,000 yen, 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, in the present invention, in addition to the above-mentioned reference transfer amount information, a combination with the reference attribute information instead of the above-mentioned reference distance information and a moving cost for the combination are set to three or more levels of association. May be good. The reference attribute information referred to here indicates the attribute of the requester who requests the move, for example, whether the requester is a corporation or an individual, if it is a corporation, its industry and number of employees, and if it is an individual, it is a single person. It indicates the number of members of the family and the family composition (father, mother, 3rd grade of junior high school, 5th grade of elementary school, etc.). Even if it is a corporation, if the number of employees is large, the number of furniture such as desks and lockers will increase, and if it is a manufacturing industry, the number of goods to be transported such as manufacturing equipment and inventory will tend to increase compared to the service industry. Therefore, the work man-hours will increase and the moving cost will increase accordingly.

By setting the degree of association between the combination including the reference attribute information and the moving cost in three or more stages, the moving request is made in the case of estimating the moving cost, as in FIGS. 4 to 7 above. Accepts input of attribute information about a person. Then, the moving cost is newly estimated based on the reference attribute information corresponding to the input attribute information by using the combination including the reference attribute information and the degree of association of the moving cost in three or more stages.

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

According to the present invention having the above-described configuration, anyone can easily search for moving costs 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.

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

The degree of association described above is composed of a combination of reference distance information, reference date and time information, reference packing information, reference building structure information, and reference attribute information in addition to the reference transport amount information. The explanation was given by taking the case where there is an example, but the explanation is not limited to this. In other words, the degree of association is composed of a combination of any two or more of reference distance information, reference date and time information, reference packing information, reference building structure information, and reference attribute information, in addition to reference transport amount information. You may. In addition to the reference transportation amount information, the degree of association includes any one or more of reference distance information, reference date and time information, reference packing information, reference building structure information, and reference attribute information, as well as other factors. A degree of association may be formed in addition to this combination.

In either case, data is input according to the reference information of the degree of association, and the moving cost is calculated using the degree of association.

The present invention is not limited to the above-described embodiment. In the search solution, that is, the output of the neural network, as an alternative to the moving cost, the amount of work required for moving may be learned, for example, as shown in FIG. The amount of work referred to here is, for example, how many hours it takes when a normal worker performs all the moving work alone, or how many hours it takes when all the moving work is performed by a predetermined number of workers, etc. The work amount A is composed of 19 hours by 3 workers, the work amount B is composed of 8 hours by 3 workers, and the like. As an alternative to this amount of work, for example, it may be configured by the amount of time when all the moving work is performed by a predetermined number of workers. In addition to being indicated by time, the amount of work may be composed of the number and amount of luggage to be transported, the apparent volume of luggage to be transported, and the like.

In addition to the above-mentioned reference transport amount information and the reference transport amount information, such work amount is added to the reference distance information, the reference date and time information, the reference packing information, the reference building structure information, and the reference attribute information. By learning by the degree of association with any one or more of the above, the amount of work for moving can be estimated in the same manner as described above. Then, if this amount of work can be estimated, it is possible to estimate the type and amount of necessary materials, physical resources and human resources to be explained, and further, it is possible to estimate the cost.

The present invention is not limited to the above-described embodiment. In the search solution, that is, the output of the neural network, as an alternative to the moving cost, a moving resource consisting of physical resources and / or human resources necessary for moving may be learned, for example, as shown in FIG. As for the physical resources in the moving resources mentioned here, how many types of light trucks, 3-ton trucks, 4-ton trucks, etc. are required as necessary transportation means, necessary packing materials, types and amounts of cardboard, and necessary Information on the types and amounts of all necessary items for moving, such as the types and amounts of materials for curing. The human resources required for moving are information indicating the number of workers required and what kind of specialization workers are required. For example, the removal and installation of air conditioners, the transportation of luggage by crane, the transportation of pianos, house cleaning, the installation of large office furniture, etc. must be handled by workers with experience and expertise according to each. Information is also included in this human resource. For example, in FIG. 9, the moving resource A has one light truck as a physical resource, 30 boxes of cardboard, curing parts XX, five workers as a human resource, and the moving resource B has two 4-ton trucks as a physical resource. , 200 boxes of cardboard, curing parts XX, 20 workers as human resources, 2 of whom are office furniture installation personnel.

In addition to the above-mentioned reference transportation amount information and the reference transportation amount information, such moving resources are added to the reference distance information, the reference date and time information, the reference packing information, the reference building structure information, and the reference attribute information. By learning by the degree of association with any one or more of the above, it is possible to estimate the moving resources required for moving in the same manner as described above. This moving resource is not limited to the case where it is composed of both physical resources and human resources, and may be composed of either one.

Further, the above-mentioned degree of association 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. 10, reference transport amount information is input as input data, moving cost (other work amount, etc.) is output as output data, and at least 1 between the input node and the output node. The above hidden layer 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.

Further, as shown in FIG. 11, the present invention determines the meat quality based on the degree of association between two or more types of information, the reference information U and the reference information V. The reference information Y is the reference transport amount information, and the reference information V is any one of the reference distance information, the reference date and time information, the reference packing information, the reference building structure information, the reference attribute information, and the like. It shall be.

At this time, as shown in FIG. 11, even if the output obtained for the reference information U is used as input data as it is and is associated with the output (moving cost) via the intermediate node 61 in combination with the reference information V. good. For example, for reference information U (reference transport amount information), after an output solution is output as shown in FIG. 3, this is used as an input as it is, and the degree of association with other reference information V is used. , You may want to search the output.

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 10 levels, 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 levels. It may be configured.

By determining the most probable moving cost based on the degree of association expressed by such three or more levels of numerical values, in a situation where there are multiple possible candidates for search solutions, the order of the degree of association is high. It is also possible to search and display. 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%. Remind 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, the transportation amount information for reference, the distance information for reference, the date and time information for reference, the packing information for reference, the building structure information for reference, and the attribute information for reference are acquired, and the knowledge, information, and data regarding the moving cost and improvement measures for these are acquired. When acquired, the degree of association is increased or decreased according to these.

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 the public communication network. 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 training 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.

Second Embodiment Hereinafter, the second embodiment of the moving cost estimation program to which the present invention is applied will be described. In executing this second embodiment, the moving cost estimation 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, for example, as shown in FIG. 12, a combination of the above-mentioned reference transport amount information, reference external environment information, and a moving cost for the combination are set by three or more levels of association. Is.

The reference external environment information is various information related to the external environment information. The external environmental information here is economic data (GDP, employment statistics, mining and industry production index, capital investment, labor force survey, etc.), household data (household consumption status survey, household data, average working hours per week, savings amount). Statistical data, annual income statistical data, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation data, wind direction data, humidity data, etc.) Etc.). The external environmental information includes all information outside the company to be examined, in addition to the information that reflects a part or all of these data. The reference external environment information may be classified by categorizing the external environment itself, for example, by separating it with data in employment statistics. Further, it may be categorized by a pattern (for example, a pattern in which the growth rate of GDP increases rapidly or gradually) or the like.

In the example of FIG. 12, it is assumed that the input data is, for example, reference transport amount information P01 to P03 and reference external environment information P22 to 25. The intermediate node shown in FIG. 12 is a combination of reference external environment information and reference transport amount information as such input data. Each intermediate node is further linked to the output. In this output, the moving cost as an output solution is displayed.

Each combination (intermediate node) of the reference transport amount information and the reference external environment information is associated with each other through three or more levels of association with the moving cost as this output solution. The reference transport amount information and the reference external environment information are arranged on the left side through this degree of association, and the moving cost is arranged on the right side through this degree of association.

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 as to which is more suitable for the reference transport amount information, the reference external environment information, and the moving cost in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 12 is created.

For example, at the time of evaluating the actual moving cost in the past, if there are many cases where the moving cost is 233000 yen for a certain reference external environment information for a certain reference transport amount information, there are many cases. These are trained as a data set and defined in the form of the above-mentioned degree of association.

In the example of the degree of association shown in FIG. 12, the node 61b is a node in which the reference external environment information P22 is combined with the reference transfer amount information P01, the association cost of 95,000 yen is w15, and the moving cost is 119000 yen. The degree of association is w16.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for moving costs from now on, the above-mentioned learned data will be used. In such a case, the transportation amount information to be determined for the moving cost and the external environment information are actually acquired. Here, the external environment information may be input each time based on the request of the requester who requests the move.

Based on the newly acquired transportation amount information and external environment information in this way, the optimum moving cost is searched for. In such a case, the degree of association shown in FIG. 12 (Table 1) acquired in advance is referred to. For example, when the newly acquired transport amount information is the same as or similar to P02 and the external environment information corresponds to P25, the node 61d is associated via the degree of association. The node 61d is associated with a moving cost of 95,000 yen by w19 and a moving cost of 233,000 yen by the degree of association w20. In such a case, the moving cost of 95,000 yen, which has 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 moving cost of 233,000 yen, 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 the second embodiment, for example, as shown in FIG. 13, the degree of association between the combination of the above-mentioned reference transport amount information, the reference market condition information, and the moving cost for the combination is set. be.

Reference market information is various information related to market conditions. The market conditions referred to here may target any range to one company, the entire industry including that company, the entire Japan, or the entire world. Examples of this reference market information include interest rates, exchange rates, stock prices of each stock, crude oil, futures, precious metals, and price movements of Bitcoin. This reference market information may be displayed as a time-series chart, a line graph, or the like for these objects. Further, information such as Bollinger band, MACD, moving average line and the like may be attached. In addition, this market information may include fundamental indicators of each brand's company, and in addition to annual sales, profits, and costs, PER (price-earnings ratio), PBR (price-to-book value ratio), ROE ( Indicators such as return on equity) may be included. Information such as a chart showing price movements between currencies, Bollinger Bands, MACD, and moving averages may be attached to the foreign exchange. The reference market information may be categorized as the market potential itself. That is, the reference market information P26 to P29 in FIG. 8 may be information classified by type, and may be classified by dividing them according to whether or not the growth rate of the stock price is 0% or more per year, for example. Further, it may be categorized by a pattern (for example, a pattern of whether the growth rate of the stock price increases rapidly or gradually) or the like.

In the example of FIG. 13, it is assumed that the input data is, for example, reference transport amount information P01 to P03 and reference market condition information P22 to 25. The intermediate node shown in FIG. 13 is a combination of the reference market condition information and the reference transport amount information as such input data. Each intermediate node is further linked to the output. In this output, the moving cost as an output solution is displayed.

Each combination (intermediate node) of the reference transport amount information and the reference market condition information is associated with each other through three or more levels of association with the moving cost as this output solution. The reference transportation amount information and the reference market condition information are arranged on the left side through this degree of association, and the moving cost is arranged on the right side through this degree of association.

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 as to which is more suitable for the reference transport amount information, the reference market condition information, and the moving cost in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 13 is created.

For example, at the time of evaluating the actual moving cost in the past, if there are many cases where the moving cost is 233000 yen for a certain reference market condition information for a certain reference transportation amount information, these are used. Is trained as a data set, and is defined in the form of the above-mentioned degree of association.

In the example of the degree of association shown in FIG. 13, the node 61b is a node in which the reference market condition information P22 is combined with the reference transport amount information P01, and the connection degree of the moving cost of 95,000 yen is w15 and the moving cost is 119000 yen. The degree of association is w16.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for moving costs from now on, the above-mentioned learned data will be used. In such a case, the transportation amount information to be determined for the moving cost and the market condition information are actually acquired. Here, the market condition information may be input each time based on the request of the requester for moving.

Based on the newly acquired transportation amount information and market condition information in this way, the optimum moving cost is searched for. In such a case, the degree of association shown in FIG. 13 (Table 1) acquired in advance is referred to. For example, when the newly acquired transport amount information is the same as or similar to P02 and the market condition information corresponds to P25, the node 61d is associated via the degree of association. The node 61d is associated with a moving cost of 95,000 yen by w19 and a moving cost of 233,000 yen by the degree of association w20. In such a case, the moving cost of 95,000 yen, which has 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 moving cost of 233,000 yen, 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 the second embodiment, for example, as shown in FIG. 14, a combination of the above-mentioned reference transport amount information, reference road condition information, and a moving cost for the combination are set by three or more levels of association. Is.

The reference road condition information and road condition information are information on the road condition of the moving source and / or the moving destination. Information on the width of the roadway and sidewalk facing the house of the moving source and the house of the moving destination, the traffic of surrounding people, the traffic volume of vehicles and people, the existence of parking space, and the like. This reference road condition information may also reflect the conditions of other houses around the new house and the original house. Such road conditions affect the parking position and workability of the vehicle used for moving, and may be reflected in the moving cost. Therefore, these are included as explanatory variables.

In the example of FIG. 14, it is assumed that the input data is, for example, reference transport amount information P01 to P03 and reference road condition information P22 to 25. The intermediate node shown in FIG. 14 is a combination of reference road condition information and reference transport amount information as such input data. Each intermediate node is further linked to the output. In this output, the moving cost as an output solution is displayed.

Each combination (intermediate node) of the reference transport amount information and the reference road condition information is associated with each other through three or more levels of association with the moving cost as this output solution. The reference transportation amount information and the reference road condition information are arranged on the left side through this degree of association, and the moving cost is arranged on the right side through this degree of association.

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 as to which of the reference transport amount information, the reference road condition information, and the moving cost in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 14 is created.

For example, at the time of evaluating the actual moving cost in the past, if there are many cases where the moving cost is 233000 yen for a certain reference road condition information for a certain reference transportation amount information, there are many cases. These are trained as a data set and defined in the form of the above-mentioned degree of association.

In the example of the degree of association shown in FIG. 14, the node 61b is a node in which the reference road condition information P22 is combined with the reference transport amount information P01, the connection degree of the moving cost of 95,000 yen is w15, and the moving cost is 119000 yen. The degree of association is w16.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for moving costs from now on, the above-mentioned learned data will be used. In such a case, the transportation amount information to be determined for the moving cost and the road condition information are actually acquired.

Based on the newly acquired transportation amount information and road condition information in this way, the optimum moving cost is searched for. In such a case, the degree of association shown in FIG. 14 (Table 1) acquired in advance is referred to. For example, when the newly acquired transport amount information is the same as or similar to P02 and the road condition information corresponds to P25, the node 61d is associated via the degree of association. The node 61d is associated with a moving cost of 95,000 yen by w19 and a moving cost of 233,000 yen by the degree of association w20. In such a case, the moving cost of 95,000 yen, which has 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 moving cost of 233,000 yen, 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 the second embodiment, for example, as shown in FIG. 15, the combination of the above-mentioned reference transport amount information and the reference area information and the moving cost for the combination are set by three or more levels of association. Is.

The reference area information and the area information are information on the area of the house from which the moving source is moved, and are represented by, for example, the number of tsubo, the occupied area (mm ² ), and the floor plan (for example, 3LDK). May be good. If the reference area information is added in addition to the reference transport amount information, the actual transport amount can be estimated with higher accuracy, so this is added as an explanatory variable.

In the example of FIG. 15, it is assumed that the input data is, for example, reference transfer amount information P01 to P03 and reference area information P22 to 25. The intermediate node shown in FIG. 15 is a combination of reference size information and reference transfer amount information as such input data. Each intermediate node is further linked to the output. In this output, the moving cost as an output solution is displayed.

Each combination (intermediate node) of the reference transport amount information and the reference area information is associated with each other through three or more levels of association with the moving cost as this output solution. The reference transport amount information and the reference size information are arranged on the left side through this degree of association, and the moving cost is arranged on the right side through this degree of association.

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 as to which is more suitable for the reference transport amount information, the reference area information, and the moving cost in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 15 is created.

For example, at the time of evaluating the actual moving cost in the past, if there are many cases where the moving cost is 233000 yen for a certain reference area information for a certain reference transportation amount information, there are many cases. These are trained as a data set and defined in the form of the above-mentioned degree of association.

In the example of the degree of association shown in FIG. 15, the node 61b is a node in which the reference area information P22 is combined with the reference transfer amount information P01, and the connection degree of the moving cost of 95,000 yen is w15 and the moving cost is 119000 yen. The degree of association is w16.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for moving costs from now on, the above-mentioned learned data will be used. In such a case, the transportation amount information to be determined for the moving cost and the area information are actually acquired.

Based on the newly acquired transportation amount information and the area information in this way, the optimum moving cost is searched for. In such a case, the degree of association shown in FIG. 15 (Table 1) acquired in advance is referred to. For example, when the newly acquired transport amount information is the same as or similar to P02 and the area information corresponds to P25, the node 61d is associated via the degree of association. The node 61d is associated with a moving cost of 95,000 yen by w19 and a moving cost of 233,000 yen by the degree of association w20. In such a case, the moving cost of 95,000 yen, which has 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 moving cost of 233,000 yen, 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, in the present invention, in addition to the above-mentioned reference transport amount information, the combination with the reference resource availability information instead of the above-mentioned reference external environment information and the moving cost for the combination have three or more levels of association. May be set. The reference resource securing possibility information referred to here is information on the securing possibility of moving resources consisting of the above-mentioned physical resources and / or human resources at the time of past moving. It shows how busy or vacant the truck is as a physical resource, how much the reservation status is, and how much the necessary materials and cardboard inventory and stockpile are. Regarding human resources, it is information on the degree of work entry of workers, the degree of schedule filling in the schedule, the number of free workers, and the like. If there is no room for physical resources and human resources, the unit price for moving tends to rise, so these are also added to the explanatory variables.

In the case of estimating the moving cost, as in FIGS. 12 to 15 above, by setting the degree of association between the combination including the resource availability information for reference and the moving cost in three or more stages. , Accepts input of resource availability information regarding the availability of moving resources consisting of physical resources and / or human resources necessary for moving at the time of moving. Then, using the combination including the reference resource security possibility information and the degree of linkage of three or more levels of the moving cost, a new reference resource security possibility information according to the input resource security possibility information is used. Estimate moving costs.

Further, in the present invention, in addition to the above-mentioned reference transport amount information, a combination with the reference financial information instead of the above-mentioned reference external environment information and a moving cost for the combination are set to three or more levels of association. You may. The reference financial information referred to here is the reference financial information that reflects the past financial contents of the company that has been examined before the past move. Examples of this reference financial information include data related to all financial statements, including the financial statements of the moving operating company for which the financial statements were provided, and sales and profits held by the moving agency. It is a numerical value of the book, for example, the numerical value of the book information and financial information described in the financial statements is the center, and in addition to sales and profit, the liquid ratio, the liquid debt, the equity capital ratio, the shareholders' equity, the total capital, the gearing ratio, Interest-bearing debt, equity capital, ordinary income margin, ordinary income, sales, total capital ordinary income ratio, ordinary income, total capital, net income, ordinary income increase rate, ordinary income, previous term ordinary income, sales, Repayment capacity, debt repayment years, interest-bearing debt, operating funds, cash flow, operating income, depreciation expenses, etc. In addition to the figures in these financial statements, the reference financial information may include whether or not the debt is insolvent, and how much borrowing from officers and inventories (inventory) are. These reference financial information may be for one year or several years. If sales, etc. are not increasing, customers may be acquired even if the price is reduced a little, which will affect the moving cost, so this is added as an explanatory variable.

By setting the degree of association between the combination including the reference financial information and the moving cost in three or more stages, the moving agent is used in the case of estimating the moving cost as in FIGS. 12 to 15 above. Accepts the input of the vendor's financial information. Then, the moving cost is newly estimated based on the reference financial information according to the input financial information by using the combination including the reference financial information and the three or more levels of association with the moving cost.

Further, in the present invention, in addition to the above-mentioned reference transport amount information, the combination with the reference option information regarding the moving option instead of the above-mentioned reference external environment information, and the moving cost for the combination are linked in three or more stages. You may set the degree. The reference option information referred to here includes all services that can be selected by the customer who outsources the move to a contractor, for example, the presence / absence and degree of attachment / detachment of an air conditioner and transportation of luggage by a crane. , Whether or not pianos are transported, whether or not disused items are collected, unpacking, house cleaning, whether or not cars and two-wheeled vehicles are transported and their amount, whether or not office furniture is installed and how much, etc.

By setting the degree of association between the combination including the reference option information and the moving cost in three or more stages, the option information is used in the case of estimating the moving cost as in FIGS. 12 to 15 above. Accepts input. Then, the moving cost is newly estimated based on the reference option information according to the input option information by using the combination including the reference option information and the three or more levels of association with the moving cost.

Further, in the present invention, in addition to the above-mentioned reference transport amount information, a combination with the reference address information regarding each address of the moving source or the moving destination in the moving instead of the above-mentioned reference external environment information, and the moving for the combination. You may set three or more levels of association with the cost. The reference address information referred to here is information relating to the address of the moving source or the moving destination. Since the unit cost of moving may differ depending on the address, this is added as an explanatory variable. Address information may be referred to at the finer town, chome, and street level, but is not limited to this, but has been expanded to the municipal level, prefectural level, and even local level. May be.

By setting the degree of association between the combination including the reference address information and the moving cost in three or more stages, the address information is estimated in the case of estimating the moving cost as in FIGS. 12 to 15 above. Accepts input. Then, the moving cost is newly estimated based on the reference address information corresponding to the input address information by using the combination including the reference address information and the three or more levels of association with the moving cost.

In addition to the transportation volume information, when acquiring any two or more of external environment information, market condition information, road condition information, area information, resource securing possibility information, financial information, option information, and address information, , Create learning data consisting of three or more levels of association between the combination of two or more reference information according to the two or more information to be acquired with the reference transportation amount information and the moving cost for the combination. By setting it, it is possible to search for a solution to the moving cost in the same way.

In addition to the transportation volume information, in addition to any one or more of external environment information, market condition information, road condition information, area information, resource securing possibility information, financial information, option information, and address information, the first implementation Learning data consisting of three or more levels of association between the combination of reference distance information, reference date and time information, reference packing information, reference building structure information, and reference attribute information in the form, and the moving cost for the combination. By creating, you can search for a solution to the moving cost in the same way.

Further, also in the second embodiment, in the search solution, that is, in the output of the neural network, as an alternative to the moving cost, for example, as shown in FIG. 8, the amount of work required for moving may be learned. Such a work amount is learned by the degree of association with any one or more of the reference information described in the second embodiment in addition to the above-mentioned reference transport amount information and the reference transport amount information. By setting this, the amount of work required for moving can be estimated in the same manner as described above.

Further, also in the second embodiment, as an alternative to the moving cost in the search solution, that is, the output of the neural network, as shown in FIG. 9, for example, a moving resource consisting of physical resources and / or human resources necessary for moving. May be made to learn. Such moving resources are learned by the degree of association with any one or more of the reference information described in the second embodiment in addition to the above-mentioned reference transport amount information and the reference transport amount information. By doing so, it is possible to estimate the moving resources required for moving in the same manner as described above. This moving resource is not limited to the case where it is composed of both physical resources and human resources, and may be composed of either one.

Third Embodiment Hereinafter, the third embodiment of the moving cost estimation program to which the present invention is applied will be described. In executing this third embodiment, the moving cost estimation 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 third embodiment, in the input data, that is, the input of the neural network, as an alternative to the reference transport amount information shown in FIG. 3, the reference area information is used as the output solution as the moving cost as shown in FIG. You may try to learn in a relationship. The reference area information and the area information described below are the same as those described in the second embodiment.

The larger the size of the house you are moving from, the more household goods you have, and the larger the amount of transportation may be. In the present embodiment, the moving cost as a search solution is searched for through the reference area information, not the parameter of the transport amount. Since the specific search method is the same as that of the first embodiment, the description below will be omitted by quoting the description.

In FIG. 17, the degree of association between the combination of the above-mentioned reference area information and the reference distance information and the moving cost for the combination is set in three or more stages.

In the example of FIG. 17, it is assumed that the input data is, for example, reference area information P01 to P03 and reference distance information P22 to 25. The intermediate node shown in FIG. 17 is a combination of the reference area information and the reference distance information as such input data. Each intermediate node is further linked to the output. In this output, the moving cost as an output solution is displayed.

Each combination (intermediate node) of the reference area information and the reference distance information is associated with each other through three or more levels of association with the moving cost as this output solution. The reference area information and the reference distance information are arranged on the left side through this degree of association, and the moving cost is arranged on the right side through this degree of association.

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 as to which is more suitable for the reference area information, the reference distance information, and the moving cost in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 17 is created.

For example, at the time of evaluating the actual moving cost in the past, if there are many cases where the moving cost is 233000 yen for a certain reference area information and a certain reference distance information, these are the cases. Is trained as a data set, and is defined in the form of the above-mentioned degree of association.

In the example of the degree of association shown in FIG. 17, the node 61b is a node in which the reference area information P01 and the reference distance information P22 are combined, and the degree of association of the moving cost of 95,000 yen is w15 and the moving cost is 119000 yen. The degree of association is w16.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for moving costs from now on, the above-mentioned learned data will be used. In such a case, the area information of the object to be determined for the moving cost and the distance information are actually acquired.

Based on the newly acquired area information and distance information in this way, the optimum moving cost is searched for. In such a case, the degree of association shown in FIG. 17 (Table 1) acquired in advance is referred to. For example, when the newly acquired area information is the same as or similar to P02 and the distance information corresponds to P25, the node 61d is associated via the degree of association. The node 61d is associated with a moving cost of 95,000 yen by w19 and a moving cost of 233,000 yen by the degree of association w20. In such a case, the moving cost of 95,000 yen, which has 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 moving cost of 233,000 yen, 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.

As described above, in addition to the reference area information, in the reference distance information, the reference date and time information, the reference packing information, the reference building structure information, the reference attribute information, or the second embodiment, in the first embodiment. With any one or more of the described external environment information for reference, market condition information for reference, road condition information for reference, resource availability information for reference, financial information for reference, option information for reference, and address information for reference. By creating learning data consisting of three or more levels of association with the moving cost for the combination, it is possible to search for a solution to the moving cost in the same manner.

In the third embodiment, in the input data, that is, the input of the neural network, as an alternative to the reference transport amount information shown in FIG. 3, the reference attribute information is used as an output solution in relation to the moving cost as shown in FIG. You may let them learn at. The reference attribute information and the attribute information described below are all the same as those described in the first embodiment.

Among the reference attribute information, the amount of household goods may be determined by the family structure, and the amount of household goods may be controlled by the number of family members, and the amount of transportation may change accordingly. In the present embodiment, the moving cost as a search solution is searched for through the reference attribute information, not the parameter of the transport amount. Since the specific search method is the same as that of the first embodiment, the description below will be omitted by quoting the description.

Further, as shown in FIG. 17, in addition to the reference attribute information, the reference distance information, the reference date and time information, the reference packing information, the reference building structure information, or the second embodiment will be described. Combination with any one or more of external environment information for reference, market condition information for reference, road condition information for reference, resource availability information for reference, financial information for reference, option information for reference, and address information for reference. By creating learning data consisting of three or more levels of association with the moving cost for the combination, it is possible to search for a solution to the moving cost in the same manner.

Further, also in the third embodiment, in the search solution, that is, in the output of the neural network, as an alternative to the moving cost, for example, as shown in FIG. 8, the amount of work required for moving may be learned. In addition to the above-mentioned reference area information and reference attribute information, the reference area information, and reference attribute information, such work amount is added to any one or more of the reference information described in each embodiment. By learning by the degree of association with the combination with, the amount of work required for moving can be estimated in the same manner as described above.

Further, also in the third embodiment, as an alternative to the moving cost in the search solution, that is, the output of the neural network, as shown in FIG. 9, for example, a moving resource consisting of physical resources and / or human resources necessary for moving. May be made to learn. Such a moving resource is added to the above-mentioned reference area information, reference attribute information, the reference area information, and reference attribute information, and any one of the reference information described in the second embodiment. By learning by the degree of association in combination with the above, it is possible to estimate the moving resources required for moving in the same manner as described above. This moving resource is not limited to the case where it is composed of both physical resources and human resources, and may be composed of either one.

In the example of the fourth embodiment FIG. 19, it is an example of using the degree of association between the reference transport amount information and the moving cost in three or more stages. When focusing only on this degree of association, it is the same as in FIG. 3, but in this example, other reference information different from the reference transport amount information is further associated with this moving cost.

It is premised that the degree of association between the reference transportation amount information and the moving cost is formed. In the example of FIG. 19, it is assumed that the input data is, for example, reference transfer amount information P11 to P13. The reference transport amount information as such input data is linked to the output. In this output, it is assumed that it is the moving cost as an output solution.

The reference transport amount information is linked to each other through three or more levels of linkage with respect to the moving cost as the output solution. The reference transport amount information is arranged on the left side through this degree of association, and each moving cost 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, the discriminating device 2 accumulates past data on what kind of moving cost was in the case of the reference transport amount information captured and acquired in the past in discriminating the actual search solution, and analyzes these. , The degree of association shown in FIG. 19 is created by analysis.

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 transport amount information is acquired, and each information corresponding to other reference information is also acquired in the same manner.

First, the moving cost is searched for based on the newly acquired transportation amount information. In such a case, the degree of association shown in FIG. 19 acquired in advance is used. For example, when the newly acquired transport amount information is the same as or similar to the reference transport amount information P12, the moving cost 30% is associated with the linkage degree w15 and the moving cost C is associated with the linkage degree. It is associated with degree w16. In such a case, the moving cost 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 moving costs required through the degree of association may be further modified or weighted based on the reference information.

The reference information referred to here includes all the reference information described in the first embodiment and the second embodiment, and for example, the reference distance information, the reference date and time information, and the reference packing information in the first embodiment. , Reference building structure information, reference attribute information, or reference external environment information described in the second embodiment, reference market condition information, reference road condition information, reference resource availability information, reference financial information, This includes any one or more of the reference option information and the reference address information.

For example, as one of the reference information, in the reference building structure information, the moving source or the moving destination is the second floor of the condominium, but the condominium has a structure without an elevator. With such a structure, it is necessary to transport the cargo using the stairs, which increases the burden of labor and the number of work man-hours, which often increases the cost. At this time, the weighting is lowered for the lower moving cost (32000 yen) searched from the transport amount information via the degree of association, in other words, the search solution with a low moving cost is not connected. Set in advance to perform processing. On the other hand, for example, in the reference resource securing possibility information, both physical resources and human resources are surplus, and it is assumed that the situation can be easily secured. At this time, if the discrimination result from the transport amount information via the degree of association is a higher moving cost, a process of lowering the weighting of the moving cost is performed, in other words, it does not lead to a search solution with a low moving cost. Set in advance to perform processing.

For example, it is assumed that the reference information G is an analysis result suggesting a lower moving cost, and the reference information F is an analysis result suggesting a higher moving cost. 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, the processing to reduce the weighting of the moving cost is performed, in other words, the moving cost itself. Performs the process of lowering. 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 moving cost is performed, in other words, a process of increasing the moving cost itself is performed. That is, the degree of association itself that leads to the moving cost may be controlled based on the reference information F to H, or the moving amount information and the moving cost may be independently obtained for the moving cost. 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 moving cost as a search solution based on the reference information F to H will reflect what was 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 moving cost may be searched based on two or more types of reference information. Similarly, in such a case, the higher the moving cost suggested by the reference information is, the higher the moving cost as the search solution obtained through the degree of association is corrected, and the moving cost suggested by the reference information is corrected. The lower the cost, the lower the cost of moving as a search solution obtained through the degree of association.

Further, in the fourth embodiment, the degree of association of the moving cost with the reference transportation amount information is used to the last, and the moving cost is modified through other reference information, but the present invention is limited to this. However, as described in the third embodiment, the degree of association of the moving cost with any one of the reference area information and the reference attribute information is used, and the moving is performed via the other reference information. The cost may be modified.

Further, in the fourth embodiment, the moving cost may be determined in combination with the first to third embodiments. That is, in the fourth embodiment, any reference information corresponding to the input of the neural network is combined with any reference information corresponding to the input of the neural network in the first to third embodiments, and the moving cost corresponding to the output is searched. good.

Further, also in the fourth embodiment, in the search solution, that is, in the output of the neural network, as an alternative to the moving cost, for example, as shown in FIG. 8, the amount of work required for moving may be learned. Also in the fourth embodiment, in the search solution, that is, in the output of the neural network, as an alternative to the moving cost, for example, as shown in FIG. 9, the moving resource consisting of the physical resources and / or the human resources necessary for moving is learned. You may let them do it.

1 Moving cost estimation system 2 Estimator 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimator unit 28 Storage unit 61 Node

Claims (12)

In the moving cost estimation program that estimates the moving cost
An information acquisition step for acquiring transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source and external environment information regarding the external environment at the time of moving.
Reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and reference external environment information regarding the external environment at the time of acquisition of the above reference transportation amount information. Using the combination of and the three or more levels of association with the moving cost, the reference transport amount information according to the transport amount information acquired in the above information acquisition step and the reference external environment according to the external environment information. A moving cost estimation program characterized by having a computer perform an estimation step for estimating moving costs, giving priority to those with a higher degree of association based on information. In the moving cost estimation program that estimates the moving cost
An information acquisition step for acquiring transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source and market information regarding market conditions at the time of moving, and
The reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and the reference market condition information regarding the market conditions at the time of acquisition of the above reference transportation amount information. Based on the reference transportation amount information according to the transportation amount information acquired in the above information acquisition step and the reference market condition information according to the market condition information, using the combination of the combination and the degree of association of three or more stages with the moving cost. , A moving cost estimation program characterized by having a computer perform an estimation step for estimating moving costs, giving priority to those with a higher degree of association. In the moving cost estimation program that estimates the moving cost
An information acquisition step for acquiring transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source and road condition information regarding the road condition of the moving source and / or the moving destination.
Reference transportation amount information associated with the type and / or amount of household goods to be transported inside the house of the moving source acquired in the past, and reference road regarding the road conditions of the moving source and / or the moving destination acquired in the past. For reference according to the transportation amount information acquired in the above information acquisition step, and for reference according to the road condition information, using the combination with the situation information and the degree of association with the moving cost in three or more stages. A moving cost estimation program characterized by having a computer perform an estimation step for estimating moving costs, giving priority to those with a higher degree of association based on road condition information. In the moving cost estimation program that estimates the moving cost
An information acquisition step for acquiring transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source and size information regarding the size of the house of the moving source.
Reference transportation amount information related to the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and reference size information regarding the size of the house of the moving source acquired in the past. The reference transport amount information according to the transport amount information acquired in the above information acquisition step and the reference size information according to the size information by using the combination having the above and the degree of association with the moving cost in three or more stages. Based on the above, a moving cost estimation program characterized by having a computer perform an estimation step for estimating moving costs, giving priority to those with a higher degree of association. In the moving cost estimation program that estimates the moving cost
Concerning the possibility of securing moving resources consisting of physical resources and / or human resources necessary for moving at the time of moving, and information on the amount of household goods to be transported in the house of the moving source, which is associated with the type and / or amount of household goods. Information acquisition step to acquire resource security possibility information,
Consists of reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and physical resources and / or human resources necessary for the above-mentioned moving in the past. Reference transportation according to the transportation amount information acquired in the above information acquisition step by using the combination with the reference resource securing possibility information regarding the availability of moving resources and the degree of association with the moving cost in three or more stages. Based on the quantity information and the resource availability information for reference according to the resource availability information, prioritize the one with the higher degree of association and let the computer perform the estimation step for estimating the moving cost. A featured moving cost estimation program. In the moving cost estimation program that estimates the moving cost
An information acquisition step to acquire information on the amount of transportation associated with the type and / or amount of household goods to be transported in the house of the moving source and financial information on the financial status of the contractor.
It has reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and reference financial information regarding the financial status of the above-mentioned moving contractor in the past. Based on the reference transportation amount information according to the transportation amount information acquired in the above information acquisition step and the reference financial information according to the financial information, using the degree of association between the combination and the moving cost in three or more stages. A moving cost estimation program characterized by having a computer perform an estimation step for estimating moving costs, giving priority to those with a higher degree of association. In the moving cost estimation program that estimates the moving cost
An information acquisition step for acquiring transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source and optional information regarding moving options, and
A combination of reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and reference option information regarding the above moving options in the past, and moving. Using the degree of association with the cost in three or more stages, the degree of association is based on the reference transfer amount information according to the transfer amount information acquired in the above information acquisition step and the reference option information according to the option information. A moving cost estimation program characterized by having a computer perform an estimation step to estimate the moving cost, giving priority to the higher one. In the moving cost estimation program that estimates the moving cost
An information acquisition step for acquiring transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source and address information regarding each address of the moving source or the moving destination.
Reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and reference address for each address of the moving source or moving destination in the above-mentioned moving in the past. Using the combination with information and the degree of association with the moving cost in three or more stages, the reference transportation amount information according to the transportation amount information acquired in the above information acquisition step and the reference address information according to the address information. Based on the above, a moving cost estimation program characterized by having a computer perform an estimation step for estimating a moving cost, giving priority to the one with a higher degree of association. The moving cost 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. In the moving work amount estimation program that estimates the moving work amount
An information acquisition step for acquiring transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source and external environment information regarding the external environment at the time of moving.
Reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and reference external environment information regarding the external environment at the time of acquisition of the above reference transportation amount information. Using the combination of the above and the work amount required for moving in three or more stages, the reference transport amount information according to the transport amount information acquired in the above information acquisition step and the external environment information are supported. A moving work amount estimation program characterized by having a computer perform an estimation step for estimating work amount by giving priority to the one with a higher degree of association based on the external environment information for reference. In the moving resource estimation program that estimates the moving resources required for moving
An information acquisition step for acquiring transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source and external environment information regarding the external environment at the time of moving.
Reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and reference external environment information regarding the external environment at the time of acquisition of the above reference transportation amount information. For reference according to the amount of transportation information acquired in the above information acquisition step, using the degree of association between the combination with and the moving resource consisting of physical resources and / or human resources necessary for moving. Based on the transportation amount information and the external environment information for reference according to the external environment information, the one with the higher degree of association is prioritized, and the estimation step for estimating the moving resource is executed by the computer. Moving cost estimation program. In the moving cost estimation system that estimates the moving cost,
Information acquisition means for acquiring transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source and external environment information regarding the external environment at the time of moving.
Reference transportation amount information associated with the type and / or amount of household goods to be transported in the house of the moving source acquired in the past, and reference external environment information regarding the external environment at the time of acquisition of the above reference transportation amount information. Using the combination of and the three or more levels of association with the moving cost, the reference transport amount information according to the transport amount information acquired by the above information acquisition means and the reference external according to the external environment information. A moving cost estimation program characterized by providing an estimation means for estimating moving costs by giving priority to those having a higher degree of association based on environmental information.

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