JP6797434B1 - Moving cost estimation program and system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To determine the moving cost with high accuracy [Solution] In a moving cost estimation program for estimating a moving cost, by analyzing an image of the house of the moving source, the type of household goods to be transported and the type of household goods to be transported By analyzing the information acquisition step of acquiring the transportation amount information associated with the amount and the image of the house of the moving source taken in the past, the reference transportation amount associated with the type and amount of the household goods to be transported. Using the three or more levels of association between the information and the moving cost, the computer executes an estimation step to estimate the moving cost based on the reference transportation amount information according to the transportation amount information acquired in the above information acquisition step. It is characterized by letting it. [Selection diagram] Fig. 3

Description

The present invention relates to a moving cost estimation program and system 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 by 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 also only needs to estimate the move in the space of a private home. There is a growing demand for not wanting to include businesses in. A system 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 capable of making 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 and system capable of estimating moving costs with higher accuracy. There is.

The moving cost estimation program according to the present invention is a moving cost estimation program for estimating a moving cost, and by analyzing an image of the inside of the house of the moving source, the transportation associated with the type and amount of the household goods to be transported is used. By analyzing the information acquisition step to acquire the quantity information and the image of the house of the moving source taken in the past, the transportation amount information for reference associated with the type and amount of the household goods to be transported, and the moving cost Estimating the moving cost by prioritizing the one with the higher degree of association based on the reference transportation amount information according to the transportation amount information acquired in the above information acquisition step using the three or more levels of association. It is characterized by having a computer perform steps.

The moving cost estimation system according to the present invention is a moving cost estimation system for estimating moving costs, and by analyzing an image of the inside of the house of the moving source, the transportation associated with the type and amount of household goods to be transported is used. By analyzing the information acquisition means for acquiring the quantity information and the image of the house of the moving source taken in the past, the transportation amount information for reference associated with the type and amount of the household goods to be transported, and the moving cost The moving cost is estimated by giving priority to the one with the higher degree of association based on the reference transportation amount information according to the transportation amount information acquired by the above information acquisition means by using the three or more levels of association. It is characterized by having an estimation means.

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.

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

FIG. 1 is a block diagram showing an overall configuration of a moving cost estimation system 1 in 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 uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the 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 composed of 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 this body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading it from a database.

The database 3 stores various information necessary for estimating the moving cost. Information necessary for estimating the moving cost includes reference transportation amount information associated with the type and amount of household goods to be transported by analyzing the images of the house of the moving source taken in the past, and moving. 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 transportation 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 remembered in association with each other.

The estimation device 2 is composed of electronic devices such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the 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 operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the 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 in response to the operation via the operation unit 25.

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

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

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

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

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 transportation amount information and the moving cost are set in advance. The reference transportation amount information is obtained by analyzing an image 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 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, using artificial intelligence, image data of household goods such as desks, chairs, shelves, sofas, and televisions are trained, and when actually acquiring transportation amount information for reference, these trained images are trained. 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.

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.

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 associated with each other through three or more levels of association with the moving cost as the output solution. Reference transport volume information is arranged on the left side via this degree of association, and each moving cost is arranged on the right side via 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 and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference transportation amount information P01, analysis is performed from various data as a result of evaluating the past moving cost. In the case of the transportation amount information P01 for reference, 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, but from the previous case, the degree of association of w13, which leads to the moving cost of 156,000 yen, is 7 points, and the moving cost is 95,000. 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 data set of images of the house to be moved before and the actually estimated moving cost, the above-mentioned is used to actually determine the moving cost from now on. 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 information acquisition unit 9 described above. 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 reference transport amount information described above.

The moving cost is determined based on the newly acquired transportation 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 association degree w16 through the association degree. .. 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, and the moving cost of 95,000 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 as long as it is based on the degree of association, it may be selected in any other priority.

In this way, the most suitable moving cost can be searched for and displayed to the user from the newly acquired transportation 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.

A finer classification may be set as the type of household goods to be identified in this transportation amount information. 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 precaution, whether it is a fragile product, or whether it is a piano or not. May be determined. If it is an electrical 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 antiques) are included, some items require some care and effort, and the amount of 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 precautions and cracks, and the learning data may be compared for discrimination, or the labels of the cracks and cracks may be labeled. If it is attached to a cardboard, it may be determined 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, cracks, 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 discriminated as the transportation amount information includes the size of the household goods. For example, even for the same television, the burden of transportation labor varies greatly depending on its size. 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. For example, it may be displayed in km units, or a region such as Tokyo (moving source) to Osaka (moving destination). It may be displayed in units of city, region, prefecture, city, ward, town, and point name.

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 transportation 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 transport 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 via this degree of association, and the moving cost is arranged on the right side via 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 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 transportation 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 transportation 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 and 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 association that leads to the yen high and the degree of association 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, but from the previous case, the degree of association of w13, which leads to the moving cost of 156000 yen, is reduced to 7 points, and the moving cost is reduced to 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 transport amount information P01, 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. 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 transportation 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 as long as it is based on the degree of association, it may be selected in any other priority.

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

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the 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 transportation 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 transport amount information and the reference date and 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 via this degree of association, and the moving cost is arranged on the right side via 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 and analysis may be performed by artificial intelligence. In such a case, for example, when the reference transport amount information P01 is the reference date and time information P20, the moving cost is analyzed from the past data. If there are many cases of moving cost of 156,000 yen, set a higher degree of association that leads to this moving cost of 156,000 yen, and if there are many cases of moving cost of 32,000 yen and few cases of moving cost of 156,000 yen, , 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, the output is linked to the moving cost of 32,000 yen and the moving cost of 156,000 yen, but from the previous case, the degree of association of w13, which leads to the moving cost of 156,000 yen, is reduced to 7 points, and the moving cost is reduced to 32,000 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 transport amount information P01, 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. 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 requester 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. The node 61d is associated with a moving cost of 95,000 yen at w19 and a moving cost of 233,000 yen at a 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 as long as it is based on the degree of association, it may be selected in any other priority.

In FIG. 6, in addition to the above-mentioned reference transportation 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, indicates how much the moving business operator provides the packing-related service when actually 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 forming the degree of association by combining the packing information for reference.

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 transportation 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 of the reference transport amount information, the reference packing 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. 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 trader 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 as long as it is based on the degree of association, it may be selected in any other priority.

In FIG. 7, in addition to the above-mentioned reference transportation 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. An example is shown.

This reference building structure information includes all information regarding the structure of the building of the moving source or all information regarding the structure of the building of the moving destination. The information to be converted into the structure of the building of the moving source or the moving destination is a house, a condominium, a building, a one-story house, a multi-story building, a building or a condominium, the presence or absence of an elevator, and the entrance. All information is included, such as the distance to the building and the availability of parking spaces. In the case of a building without an elevator, 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 forming the degree of association by combining the reference building structure information.

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 transportation 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 transportation 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 of the reference transport amount information, the reference building structure 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. 7 is created.

For example, it is assumed that the reference building structure information is "a three-story condominium with no 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 transportation amount information P01, 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 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 through 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 a 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 as long as it is based on the degree of association, it may be selected in any other priority.

Further, in the present invention, in addition to the above-mentioned reference transport 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 attributes of the requester who requests the move. For example, the requester is a corporation or an individual, if it is a corporation, the type of business or the number of employees, and if it is an individual, it is a single person. It indicates a person, a householder, or if it is a householder, the number of members of the family. Even if it is a corporation, if the number of employees is large, the number of fixtures 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 according to the input attribute information by using the combination including the reference attribute information and the three or more levels of association with the moving cost.

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 determine this search solution with higher accuracy than that performed by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

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

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 transportation 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 transportation amount information. You may. In addition to the reference transportation amount information, the degree of association includes any one or more of the reference distance information, the reference date and time information, the reference packing information, the reference building structure information, and the reference attribute information, and 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.

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

By determining the most probable moving cost based on the degree of association represented by the numerical values of three or more levels, in a situation where there are multiple possible candidates for the search solution, the order of the degree of association is high. It is also possible to search and display. If the users 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. is there. 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 via a public communication network such as the Internet. In addition, we acquire reference transportation amount information, reference distance information, reference date and time information, reference packing information, reference building structure information, and reference attribute information, and obtain knowledge, information, and data on moving costs and improvement measures for these. 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, treatises, 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 the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

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

Claims (10)

In the moving cost estimation program that estimates the moving cost
An information acquisition step to acquire the transportation amount information associated with the type and amount of household goods to be transported by analyzing the image of the moving house.
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 degree of association between the moving cost and the three levels or more are used. Based on the reference transportation amount information according to the transportation amount information acquired in the above information acquisition step, the computer is made to execute the estimation step of estimating the moving cost by giving priority to the one having the higher degree of association. Moving cost estimation program. In the above information acquisition step and the above estimation step, the types of household appliances to be transported are whether or not they are electrical products, whether or not they are handling precautions, whether or not they are fragile, and whether or not they are pianos. The moving cost estimation program according to claim 1, wherein any one or more of the above is determined. The moving cost estimation program according to claim 1 or 2, wherein in the information acquisition step and the estimation step, the size of the household goods is determined as the type of the household goods to be transported. In the above information acquisition step, the distance information from the moving source to the moving destination is acquired.
In the estimation step, there are three stages: the combination of the reference transport amount information, the reference distance information from the moving source to the moving destination in moving when acquiring the reference transport amount information, and the moving cost. The present invention according to any one of claims 1 to 3, wherein the moving cost is estimated based on the reference distance information corresponding to the distance information acquired in the above information acquisition step by using the above degree of association. Moving cost estimation program. In the above information acquisition step, the date and time information regarding the moving date and time is acquired.
In the estimation step, the combination of the reference transportation amount information, the reference date and time information regarding the moving date and time in moving when acquiring the reference transportation amount information, and the degree of association of the moving cost with three or more stages. utilizing, further based on the reference time information corresponding to the acquired date and time information in the information acquisition step, moving the cost estimation of any one of claims 1 to 3, characterized in that estimating the moving costs program. In the above information acquisition step, packing information regarding the degree of provision of packing service at the time of moving is acquired.
In the estimation step, the combination of the reference transportation amount information, the reference packing information regarding the degree of provision of the packing service at the time of moving when acquiring the reference transportation amount information, and the moving cost 3 One of claims 1 to 3, characterized in that the moving cost is estimated based on the reference packing information according to the packing information acquired in the above information acquisition step by using the degree of association of one or more stages. The moving cost estimation program described. In the above information acquisition step, the building structure information regarding the structure of the building of the moving source or the moving destination is acquired.
In the estimation step, the combination of the reference transportation amount information and the reference building structure information regarding the structure of the moving source or the moving destination when acquiring the reference transportation amount information, and the moving cost. Claims 1 to 3 characterized in that the moving cost is estimated based on the reference building structure information according to the building structure information acquired in the above information acquisition step by utilizing the degree of association of three or more levels with. The moving cost estimation program described in any one of them. In the above information acquisition step, attribute information related to the attributes of the above moving requester is acquired.
In the estimation step, there are three or more stages of the combination of the reference transport amount information, the reference attribute information regarding the attribute of the moving requester when acquiring the reference transport amount information, and the moving cost. The moving according to any one of claims 1 to 3, wherein the moving cost is estimated based on the reference attribute information corresponding to the attribute information acquired in the above information acquisition step by using the degree of association. Cost estimation program. 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 cost estimation system that estimates the moving cost
An information acquisition means for acquiring transportation amount information associated with the type and amount of household goods to be transported by analyzing an image of the house of the moving source.
By analyzing the images taken inside the house of the moving source in the past, we can use the reference transportation amount information associated with the type and amount of household goods to be transported and the degree of association between the moving cost and three levels or more. , Based on the reference transportation amount information according to the transportation amount information acquired by the information acquisition means , the moving cost is estimated by giving priority to the one having a higher degree of association. Moving cost estimation system.

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