JP2022061135A - Plumbing trouble occurrence frequency prediction program - Google Patents

Abstract

To predict an occurrence frequency of a plumbing trouble in a building structure in units of regions.SOLUTION: A plumbing trouble occurrence frequency prediction program for predicting an occurrence frequency of a plumbing trouble in a building structure in units of regions makes a computer perform: an information acquisition step for acquiring geographical information of regions where the occurrence frequency of the plumbing trouble is predicted; and a search step. The search step searches the occurrence frequency of the plumbing trouble in which a higher association degree is set between: reference geographical information in each of the regions; and reference geographical information corresponding to the geographical information acquired by the information acquisition step in reference to an association degree of three levels or more with the occurrence frequency of the plumbing trouble.SELECTED DRAWING: Figure 18

Description

The present invention relates to a water-related trouble occurrence frequency prediction program.

Examples of water problems in building structures such as buildings, condominiums, and detached houses include clogging of toilets, water leaks, malfunctions, clogging of kitchens and washrooms, water leaks, malfunctions, clogging of bath drains, and showers. There is a water leak in the faucet or a malfunction. Since there are many cases where water problems in such building structures cannot be solved by the residents alone, there are many cases where a specialist is dispatched and the work is outsourced.

However, in recent years, the labor shortage of specialists has become more serious, and there are an increasing number of cases where residents are unable to respond even if they contact them. Especially in the case of a clogged toilet, the toilet cannot be used unless the clog is cleared, and if the dispatch of the contractor is delayed, it may seriously hinder normal life. The same applies to clogging and water leaks in kitchens and washrooms other than toilets. Since it is often necessary to deal with such water-related troubles earlier, the companies that deal with them are also making efforts such as optimizing the staffing for each corresponding region.

When considering how many people should be assigned to which area in order to actually deal with water around, it is necessary to predict the frequency of water around troubles in each area. However, the current situation is that a technique for predicting the frequency of such water troubles in each region has not yet been devised.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is to predict the frequency of water troubles in a building structure on a regional basis. To provide a forecasting program.

The water-related trouble occurrence frequency prediction program according to the present invention is a water-related trouble occurrence frequency prediction program that predicts the occurrence frequency of water-related troubles in a building structure on a regional basis, and is a region that predicts the occurrence frequency of water-related troubles. The geographical information acquired in the above information acquisition step by referring to the information acquisition step for acquiring the geographical information of the above, the geographical information for reference in each region, and the degree of association of three or more stages between the frequency of occurrence of water troubles. It is characterized by having a computer perform a search step of searching for the frequency of occurrence of troubles around water, which has a higher degree of association with the reference geographical information corresponding to the information.

Even if you do not have any special skills or experience, you can predict the frequency of water problems in building structures on a regional basis without relying on human hands.

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. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the water-related trouble occurrence frequency prediction 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 water circulation trouble occurrence frequency system 1 to which a water circulation trouble occurrence frequency prediction program to which the present invention is applied is implemented. The water trouble occurrence frequency system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of 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 search device 2 described later. The information acquisition unit 9 outputs the detected information to the search device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be 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 body sensor may acquire biometric data of not only humans but also animals. 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. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects odors and scents.

In addition, the information acquisition unit 9 is dispatched to deal with water-related troubles, such as sales data for each region recorded in the database of a company that solves problems by actually performing work, the number of dispatches for each region, and the like. It may be configured by means for acquiring the dispatch frequency data calculated according to the above.

The database 3 stores various information necessary for performing the frequency of water troubles. The information necessary for determining the frequency of water-related troubles is the sales data for reference of the vendors dispatched for the water-related troubles in each region, and the reference of the vendors dispatched for the water-related troubles in each region. Dispatch frequency data, reference refusal rate of vendors requested to respond to water troubles, reference population estimation data in each region, reference geographical information in each region, reference troubles related to the type of trouble in each region Type information, reference statistical information on the types of building structures in each area, etc. are accumulated in relation to the frequency of water-related troubles as output data.

That is, in the database 3, in addition to the reference sales data and the reference dispatch frequency data of the vendor, the reference rejection rate of the vendor, the population estimation data for reference, the geographical information for reference, and the trouble type for reference are stored in the database 3. Any one or more of the information and the reference statistical information regarding the type of the building structure and the frequency of occurrence of troubles around the water are stored in association with each other.

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

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

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 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 discrimination 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 discrimination unit 27 discriminates the search solution. The discriminating 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 discriminating operation. The discriminating unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

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

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

The operation in the water trouble occurrence frequency system 1 having the above-described configuration will be described.

In the water-related trouble occurrence frequency system 1, for example, as shown in FIG. 3, there are three or more stages of reference sales data of a vendor dispatched to deal with water-related troubles in each region and the occurrence frequency of water-related troubles. It is assumed that the degree of association is set in advance. A contractor dispatched to deal with water problems in each area is actually a site dispatched to a request for water problems from a resident of a building structure (building, condominium, detached house, apartment, etc.). It is a contractor who performs repair work at. Problems around water include clogging of toilets, water leaks, malfunctions, clogging of kitchens and washrooms, water leaks, malfunctions, clogging of bath drains, water leaks of showers and faucets, and malfunctions. Such vendors often manage sales in each region. The unit of each area may be classified in detail into a region, a prefecture, a municipality, a town, a street number, a number, and even a building or a condominium unit. Sales are managed on a yearly, monthly, weekly, daily, etc. basis. First, the reference sales data for each region of such a trader is acquired for the learning data. In addition, this reference sales data may be represented by the average value or standard deviation of a certain period such as yearly, monthly, weekly, daily, etc., or may be represented by fluctuation trend data or fluctuation transition data. good.

The frequency of water-related troubles indicates how often water-related troubles can occur in each region. The frequency of occurrence of this water trouble may be composed of any denominator such as yearly, monthly, weekly, daily, and five-year units. The occurrence of water-related troubles may be counted as one time each time the resident of the building structure notifies the contractor that there was a water-related trouble. The frequency of such troubles around water can be obtained after the fact by counting the frequency of such troubles around the water and recording it in the database held by the vendor. The frequency of troubles around water is organized by region as described above.

In the example of FIG. 3, it is assumed that the input data is the reference sales data P01, P02, and P03 in each region. The reference sales data P01, P02, and P03 as such input data are linked to the frequency of troubles around the water as output.

The reference sales data P01, P02, and P03 are associated with each other through the degree of association of three or more levels with respect to the trouble occurrence frequencies A to D around the water as the output solution. The frequency of occurrence of this trouble is shown, for example, 5 times a month for A, 20 times a month for B, etc., but this is not limited to the monthly frequency, and may be any period unit frequency. The sales data for reference is arranged on the left side via this degree of association, and the frequency of troubles around each water is arranged on the right side via the degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference sales data arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference sales data is likely to be associated with, and selects the most probable trouble occurrence frequency for each reference sales data. It shows the accuracy above. 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 frequency of trouble occurrence as output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node and the frequency of trouble occurrence as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates a past data set as to which of the reference sales data of each region and the trouble occurrence frequency in that case is adopted and evaluated in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 3 is created.

For example, it is assumed that the reference sales data in the past is shown by the fluctuation transition shown by the line graph. In the case of such reference sales data shown by a certain line graph, it is assumed that A actually has the highest frequency of water troubles in the area. By collecting and analyzing such data sets, the degree of association with reference sales data in each region becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference sales data P01, analysis is performed from past sales and various data of trouble occurrence frequency. If the annual average sales in the area P01 are 5.6 million yen and there are many cases of trouble occurrence frequency A, the degree of association that leads to the evaluation of this trouble occurrence frequency is set higher, and the trouble occurrence frequency B is set. If there are many cases, set a higher degree of association that leads to the evaluation of the frequency of trouble occurrence. For example, in the example of the reference sales data for the region P01, the trouble occurrence frequency A and the trouble occurrence frequency C are linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points, and the trouble occurs. The degree of association of w14 connected to the occurrence frequency C is set to 2 points.

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

In such a case, as shown in FIG. 4, reference sales data for each region is input as input data, trouble occurrence frequency is output as output data, and at least one hidden layer between the input node and the output node is output. May be provided and machine learning may be performed. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data through a data set of sales data for reference in each region before and the frequency of troubles around water, it is necessary to actually determine the frequency of troubles in the future. The trouble occurrence frequency will be searched using the above-mentioned learned data. These data sets may be created by reading from a database managed by the vendor.

When searching for a new trouble occurrence frequency, the input of the area to be searched is accepted. Since the past sales data is associated and stored for each region, when the input of the region is accepted, it can be acquired by reading the sales data associated with the region.

Next, the read sales data is collated with the reference sales data. 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 sales data is the same as or similar to P02, the trouble occurrence frequency B is associated with w15 and the trouble occurrence frequency C is associated with the association degree w16 through the association degree. In such a case, the trouble occurrence frequency B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency C in which the degree of association itself is recognized although the degree of association is low 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.

By the way, the collation of sales data and reference sales data is based on whether or not the sales average is within the range of ± 10% if these data are represented by the sales average for a certain period. It may be determined whether they are the same or similar. Further, as long as the sales data is shown in a time-series transition graph, it may be determined based on the similarity of the trends.

In this way, it is possible to search for the most suitable trouble occurrence frequency from the newly acquired sales data and display it to the user. By looking at this search result, it is possible to determine in advance what kind of trouble occurrence frequency may occur in the area in the future, and it is possible to consider the allocation of workers in each area.

In the example of FIG. 5, the degree of association between the reference sales data and the reference rejection rate is formed. The reference refusal rate is the probability that a resident of a building structure actually requested a dispatch based on a water supply trouble to the contractor, and the contractor could not accept the request and refused it. .. This refusal rate is expressed by the number of refusals for the number of dispatch requests. The number of dispatch requests, the number of times of refusal, and each region are managed by the contractor on the database 3. The refusal rate can be obtained by reading the number of refusals for the number of dispatch requests from the database 3 for the area where the refusal rate is actually desired to be known.

In addition to sales in the area, the frequency of water-related troubles depends on the refusal rate, because if the number of dispatch requests is too large, there are many cases of refusal. Therefore, by combining the reference rejection rate with the learning data in addition to the reference sales data, the trouble occurrence frequency can be determined with higher accuracy. Therefore, in addition to the reference sales data, the reference rejection rate is combined to form the above-mentioned degree of association.

In the example of FIG. 5, it is assumed that the input data is, for example, reference sales data P01 to P03 and reference rejection rates P14 to 17. The intermediate node shown in FIG. 5 is a combination of the reference sales data and the reference rejection rate as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination (intermediate node) of the reference sales data and the reference rejection rate is associated with each other through three or more levels of association with the trouble occurrence frequency as this output solution. The reference sales data and the reference rejection rate are arranged on the left side through this degree of association, and the trouble occurrence frequency is arranged on the right side through this degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference sales data and the reference rejection rate arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference sales data and reference rejection rate are likely to be associated with, and is based on the reference sales data and the reference rejection rate. It shows the accuracy in selecting the most probable trouble occurrence frequency. Therefore, the optimum trouble occurrence frequency is searched for by combining these reference sales data and the reference rejection rate.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference sales data, the reference rejection rate, and the frequency of trouble occurrence in that case is suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the reference sales data P01 and the reference rejection rate P16, the trouble occurrence frequency is analyzed from the past data. If there are many cases where the trouble occurrence frequency is A, the degree of association leading to this trouble occurrence frequency A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, trouble occurs. The degree of association leading to the occurrence frequency B is set high, and the degree of association leading to the trouble occurrence frequency A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. 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. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 5, the node 61b is a node in which the reference sales data P01 is combined with the reference rejection rate P14, and the trouble occurrence frequency C has a connection degree of w15 and the trouble occurrence frequency E. The degree of association is w16. The node 61c is a node in which the reference rejection rates P15 and P17 are combined with respect to the reference sales data P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the trouble occurrence frequency from now on, the above-mentioned learned data will be used. In such a case, the area where the trouble occurrence frequency is actually to be determined is input in the same manner. Then, the sales data and the error rate organized for each region in the database 3 are acquired.

Based on the newly acquired sales data and error rate in this way, the optimum trouble occurrence frequency 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, if the newly acquired sales data is the same as or similar to P02 and the error rate is the same as or similar to P17, the node 61d is associated via the degree of association. The node 61d is associated with the trouble occurrence frequency C by w19 and the trouble occurrence frequency D by the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

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

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

In FIG. 6, in addition to the above-mentioned reference sales data, the combination with the reference population estimation data instead of the above-mentioned reference rejection rate and the frequency of trouble occurrence for the combination are set to three or more levels of association. Here is an example.

This reference population estimation data, which is added as an explanatory variable instead of the reference rejection rate, shows the population estimation in the area, and is the population pyramid (a diagram showing the distribution of population by age group and gender) and its time series. This data may include the transition, the number of in-migrants, the number of out-migrants, the number of in-migrant households, the number of out-migrant households in the area, and the classification by occupation for each population. The frequency of troubles is affected by such population estimates in addition to sales data. The larger the elderly population, the more often it is not possible to deal with clogged toilets, etc., and the chances of requesting dispatch may increase. In addition, as the number of in-migrants-the number of out-migrants increases, the population is increasing, and it is possible that the frequency of troubles will increase accordingly. Such reference population estimation data is managed in the database 3 for each region.

Since such population estimation also affects the frequency of trouble occurrence, it is possible to improve the discrimination accuracy by combining it with the reference sales data and discriminating the frequency of trouble occurrence through the degree of association.

In the example of FIG. 6, it is assumed that the input data is, for example, reference sales data P01 to P03 and reference population estimation data P18 to 21. The intermediate node shown in FIG. 6 is a combination of reference sales data and reference population estimation data as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination (intermediate node) of the reference sales data and the reference population estimation data is associated with each other through three or more levels of association with the trouble occurrence frequency as this output solution. The reference sales data and the reference population estimation data are arranged on the left side through this degree of association, and the trouble occurrence frequency is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the frequency of trouble occurrence with respect to the reference sales data and the reference population estimation data arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference sales data and reference population estimation data are likely to be associated with, and is a reference sales data and reference population estimation. It shows the accuracy in selecting the most probable trouble occurrence frequency from the data.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the search device 2 determines which of the reference sales data, the reference population estimation data obtained when acquiring the reference sales data, and the frequency of trouble occurrence in that case. Whether it was suitable or not, the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference sales data P01 and the reference population estimation data P20, the trouble occurrence frequency is analyzed from the past data. If there are many cases of trouble occurrence frequency A, the degree of association that this trouble occurrence frequency leads to A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, , The degree of association where the trouble occurrence frequency leads to B is set high, and the degree of association where the trouble occurrence frequency leads to A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 6, the node 61b is a node of the combination of the reference sales data P01 and the reference population estimation data P18, and the trouble occurrence frequency C has a connection degree of w15 and the trouble occurrence frequency E. The degree of association is w16. The node 61c is a node that is a combination of the reference population estimation data P19 and P21 with respect to the reference sales data P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D 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 searching for the frequency of trouble occurrence from now on, the above-mentioned learned data will be used. In such a case, by actually inputting the area to be determined for the trouble occurrence frequency, the sales data and the population estimation data in that area are acquired.

Based on the newly acquired sales data and population estimation data in this way, the optimum trouble occurrence frequency 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, if the newly acquired sales data is the same as or similar to P02 and the population estimation data is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the trouble occurrence frequency C by w19 and the trouble occurrence frequency D by the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low 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 sales data, the combination with the reference geographical information instead of the above-mentioned reference rejection rate and the trouble occurrence frequency for the combination are set to three or more levels of association. Here is an example.

This reference geographic information, which is added as an explanatory variable instead of the reference rejection rate, indicates all geographic information in the area, including rivers, presence or absence of the sea, location, distance, area, and how many meters above sea level. , Information on contour lines, information on roads, relative positional relationship of building structures to rivers, etc. Such reference geographical information is managed in the database 3 for each region.

Such geographical information also affects the frequency of water troubles. If it is close to a river, there is a high possibility that problems around the water will occur, so by combining this with sales data for reference, the frequency of problems can be determined through the degree of association. The discrimination accuracy can be improved.

In the example of FIG. 7, it is assumed that the input data is, for example, reference sales data P01 to P03 and reference geographical information P18 to 21. The intermediate node shown in FIG. 7 is a combination of reference sales data and reference geographical information as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination of reference sales data and reference geographical information (intermediate node) is associated with each other through three or more levels of association with the frequency of trouble occurrence as this output solution. The reference sales data and the reference geographical information are arranged on the left side through this degree of association, and the trouble occurrence frequency is arranged on the right side through this degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference sales data and the reference geographical information arranged on the left side. In other words, this degree of association is an indicator of what kind of trouble frequency each reference sales data and reference geographic information is likely to be associated with, and is a reference sales data and reference geographic information. It shows the accuracy in selecting the most probable trouble occurrence frequency from the information.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, in determining the actual search solution, the search device 2 determines which of the reference sales data, the reference geographical information obtained when acquiring the reference sales data, and the frequency of trouble occurrence in that case. Whether it was suitable or not, the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference sales data P01 and the reference geographical information P20, the trouble occurrence frequency is analyzed from the past data. If there are many cases of trouble occurrence frequency A, the degree of association that this trouble occurrence frequency leads to A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, , The degree of association where the trouble occurrence frequency leads to B is set high, and the degree of association where the trouble occurrence frequency leads to A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 7 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. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 7, the node 61b is a node of the combination of the reference sales data P01 and the reference geographical information P18, and the trouble occurrence frequency C has a connection degree of w15 and the trouble occurrence frequency E. The degree of association is w16. The node 61c is a node in which the reference geographical information P19 and P21 are combined with respect to the reference sales data P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D 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 searching for the frequency of trouble occurrence from now on, the above-mentioned learned data will be used. In such a case, by actually inputting the area to be determined for the trouble occurrence frequency, the sales data and the geographical information in the area are acquired.

Based on the newly acquired sales data and geographical information in this way, the optimum trouble occurrence frequency 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, if the newly acquired sales data is the same as or similar to P02 and the geographical 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 the trouble occurrence frequency C by w19 and the trouble occurrence frequency D by the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low 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. 8, in addition to the above-mentioned reference sales data, a combination with the reference trouble type information and the trouble occurrence frequency for the combination are set in three or more stages instead of the above-mentioned reference rejection rate. Here is an example.

This reference trouble type information, which is added as an explanatory variable instead of the reference rejection rate, indicates information about all types of trouble in the area. The types of this trouble are classified into types such as clogging of toilets, water leaks, malfunctions, clogging of kitchens and washrooms, malfunctions, clogging of bath drains, water leaks of showers and faucets, malfunctions, etc. ing. Such reference trouble type information is managed in the database 3 for each region.

Such trouble type information also affects the frequency of water troubles. Since the frequency of troubles may differ depending on whether there are many water leaks in the water pipes or the clogging of the drainage outlet, this can be combined with the sales data for reference to determine the frequency of troubles through the degree of association. , The discrimination accuracy can be improved.

In the example of FIG. 8, it is assumed that the input data is, for example, reference sales data P01 to P03 and reference trouble type information P18 to 21. The intermediate node shown in FIG. 8 is a combination of reference sales data and reference trouble type information as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination (intermediate node) of the reference sales data and the reference trouble type information is associated with each other through three or more levels of association with the trouble occurrence frequency as this output solution. The reference sales data and the reference trouble type information are arranged on the left side via the degree of association, and the trouble occurrence frequency is arranged on the right side via the degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference sales data and the reference trouble type information arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference sales data and reference trouble type information is likely to be associated with, and is a reference sales data and reference trouble type. It shows the accuracy in selecting the most probable trouble occurrence frequency from the information.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the search device 2 determines which of the reference sales data, the reference trouble type information obtained when acquiring the reference sales data, and the trouble occurrence frequency in that case. Whether it was suitable or not, the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference sales data P01 and the reference trouble type information P20, the trouble occurrence frequency is analyzed from the past data. If there are many cases of trouble occurrence frequency A, the degree of association that this trouble occurrence frequency leads to A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, , The degree of association where the trouble occurrence frequency leads to B is set high, and the degree of association where the trouble occurrence frequency leads to A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 8, the node 61b is a node in which the reference trouble type information P18 is combined with the reference sales data P01, the trouble occurrence frequency C has a relation degree of w15, and the trouble occurrence frequency E. The degree of association is w16. The node 61c is a node in which the reference trouble type information P19 and P21 are combined with respect to the reference sales data P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D 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 searching for the frequency of trouble occurrence from now on, the above-mentioned learned data will be used. In such a case, by actually inputting the area to be determined for the trouble occurrence frequency, the sales data in the area and the trouble type information are acquired.

Based on the newly acquired sales data and the trouble type information in this way, the optimum trouble occurrence frequency is searched for. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired sales data is the same as or similar to P02 and the trouble type 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 the trouble occurrence frequency C by w19 and the trouble occurrence frequency D by the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low 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. 9, in addition to the above-mentioned reference sales data, the combination with the reference statistical information instead of the above-mentioned reference rejection rate and the trouble occurrence frequency for the combination are set to three or more levels of association. An example is shown.

This reference statistic, which is added as an explanatory variable instead of the reference denial rate, is statistic about the type of building structure in the area. In addition to major categories such as buildings, condominiums, detached houses, and apartments, the types of building structures are statistically based on the age and construction method (lightweight steel frame, heavy steel frame, reinforced concrete structure, wooden structure, etc.) of each building structure. Has been analyzed. Each of these types, construction methods, and age ratios are statistically analyzed to facilitate comparative analysis between regions. Such reference statistical information is managed in the database 3 for each region. In addition, this statistical information may be composed of statistical information regarding the age of building structures in the area.

Such statistical information also affects the frequency of water troubles. The longer the building is, the more water leaks in the water pipes and the more clogging of the drainage outlet. Therefore, by combining this with the sales data for reference and determining the frequency of trouble occurrence through the degree of association, the discrimination accuracy is improved. Can be made to.

In the example of FIG. 9, it is assumed that the input data is, for example, reference sales data P01 to P03 and reference statistical information P18 to 21. The intermediate node shown in FIG. 9 is a combination of reference sales data and reference statistical information as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination (intermediate node) of the reference sales data and the reference statistical information is associated with each other through three or more levels of association with the trouble occurrence frequency as this output solution. Reference sales data and reference statistics are arranged on the left side via this degree of association, and trouble occurrence frequencies are arranged on the right side via this degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference sales data and the reference statistical information arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference sales data and reference statistical information is likely to be associated with, and is based on the reference sales data and reference statistical information. It shows the accuracy in selecting the most probable trouble occurrence frequency.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the search device 2 prefers the reference sales data, the reference statistical information obtained when acquiring the reference sales data, and the frequency of trouble occurrence in that case. Or, by accumulating past data and analyzing and analyzing these, the degree of association shown in FIG. 9 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference sales data P01 and the reference statistical information P20, the trouble occurrence frequency is analyzed from the past data. If there are many cases of trouble occurrence frequency A, the degree of association that this trouble occurrence frequency leads to A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, , The degree of association where the trouble occurrence frequency leads to B is set high, and the degree of association where the trouble occurrence frequency leads to A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 9 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. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 9, the node 61b is a node in which the reference sales data P01 is combined with the reference statistical information P18, the trouble occurrence frequency C is related to w15, and the trouble occurrence frequency E is related. The degree is w16. The node 61c is a node in which the reference statistical information P19 and P21 are combined with respect to the reference sales data P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the frequency of trouble occurrence from now on, the above-mentioned learned data will be used. In such a case, by actually inputting the area to be determined for the trouble occurrence frequency, the sales data and the statistical information in the area are acquired.

Based on the newly acquired sales data and statistical information in this way, the optimum trouble occurrence frequency is searched for. In such a case, the degree of association shown in FIG. 9 (Table 1) acquired in advance is referred to. For example, if the newly acquired sales data is the same as or similar to P02 and the statistical 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 the trouble occurrence frequency C by w19 and the trouble occurrence frequency D by the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low 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 addition, as a substitute for the above-mentioned reference information (reference rejection rate, reference population estimation data, reference geographical information, reference trouble type information, reference statistical information, etc.), it is related to the timing when reference sales data was acquired. Reference time information may be used. The reference time information referred to here is composed of all data indicating the time such as month, week, day, season, etc. as the time when the reference sales data is acquired. Since there is a time when water troubles are likely to occur at this time as well, it affects the frequency of water troubles, so it is possible to realize a more accurate solution search by making a judgment including this.

In such a case, a combination having reference sales data of a trader who has been dispatched to deal with water circulation troubles in each region in the past and reference timing information regarding the time when the above reference sales data was acquired, and water circulation Acquire in advance the degree of association with the frequency of trouble occurrence at three or more levels. Then, the area-specific information for specifying the building structure for predicting the frequency of occurrence of water-related troubles or the area where the area-specific information is located, and the time information regarding the time when the area-specific information was acquired are acquired. Next, based on the reference sales data corresponding to the past sales data of the region in the acquired region specific information and the reference timing information corresponding to the acquired timing information, based on the degree of association shown in FIGS. 5 to 9. Search for the frequency of troubles around the water.

In addition, as a substitute for the above-mentioned reference information (reference rejection rate, reference population estimation data, reference geographical information, reference trouble type information, reference statistical information, etc.), the weather at the time when the area specific information was acquired. Reference weather information for reference may be used. The reference weather information referred to here is all information related to the weather at the time when the reference sales data was acquired, and is composed of all data related to climate, temperature, humidity, weather, wind direction, wind speed, thunderstorm, typhoon, drought, etc. Will be done. Since the weather is also a factor that affects water problems, this is added to the explanatory variables.

In such a case, a combination having reference sales data of a trader who has been dispatched to deal with water problems in each region in the past and reference weather information regarding the weather at the time when the above reference sales data was acquired, and water. Obtain in advance the degree of association with the frequency of occurrence of troubles around you in three or more stages. Then, the area-specific information for specifying the building structure for predicting the frequency of water-related troubles or the area where the area-specific information is located and the weather information regarding the weather at the time when the area-specific information is acquired are acquired. Next, based on the reference sales data corresponding to the past sales data of the region in the acquired region specific information and the reference weather information corresponding to the acquired weather information, based on the degree of association shown in FIGS. 5 to 9. Search for the frequency of water problems.

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 determine and search for the frequency of trouble occurrence 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 input data and the output data described above do not exist completely the same in the process of learning, the information may be classified by type between the input data and the output data. 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 dataset may be created between the data and the output data and trained.

In the above-mentioned degree of association, in addition to the reference sales data, the reference rejection rate of the vendor, the reference population estimation data, the reference geographical information, the reference trouble type information, and the reference statistical information regarding the type of the building structure. Although the explanation has been given by taking the case of being configured in combination with any of the above as an example, the present invention is not limited to this. In other words, the degree of association is one of the reference sales data, the reference rejection rate of the vendor, the reference population estimation data, the reference geographical information, the reference trouble type information, and the reference statistical information regarding the type of building structure. It may be composed of a combination of two or more. In addition, the degree of association is the reference sales data or, in addition to this, the reference rejection rate of the contractor, the reference population estimation data, the reference geographical information, the reference trouble type information, and the reference statistical information regarding the type of the building structure. In addition to any one or more of the above, other factors may be added to this combination to form the degree of association.

In either case, data is input according to the reference information of the degree of association, and the frequency of trouble occurrence is calculated using the degree of association.

Further, as shown in FIG. 10, the present invention determines the frequency of trouble occurrence 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 sales data, and the reference information V is the reference rejection rate of the vendor, the reference population estimation data, the reference geographical information, the reference trouble type information, and the reference regarding the type of the building structure. It shall be one of the statistical information.

At this time, as shown in FIG. 10, the output obtained for the reference information U is used as input data as it is, and is associated with the output (trouble occurrence frequency) via the intermediate node 61 in combination with the reference information V. May be good. For example, for reference information U (reference sales data), after outputting an output solution 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. The output (frequency of trouble occurrence) may be searched.

Further, according to the present invention, instead of obtaining the trouble occurrence frequency as an output solution, the warning information such as an alarm, an alarm, etc. based on the trouble occurrence frequency may be transmitted. The higher the frequency of troubles, the higher the degree of alerting of the alerting information. As a result, it is possible to efficiently alert the outside to the fact that the student is in a dangerous state.

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, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

By determining the most probable trouble occurrence frequency based on the degree of association expressed by the numerical values of three or more stages, the degree of association is high under the situation where there are multiple possible candidates for the search solution. It is also possible to search and display in order. If the user can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

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

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

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided, for example, via a public communication network such as the Internet. In addition, when each reference information such as reference sales data is acquired and knowledge, information, and data regarding the frequency of trouble occurrence for these are acquired, the degree of association is increased or decreased according to these.

In such a case, collect the reference information including the reference sales data and the case of the judgment result of the risk degree and the sign, and the degree of association according to the number of cases. Raise or lower. At this time, when the above-mentioned sales data, rejection rate, population estimation data, geographical information, trouble type information, and reference statistical information regarding the type of building structure are acquired and determined, they are updated based on these. May be done.

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

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

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

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

In the second embodiment, the data set of the reference refusal rate and the frequency of occurrence of troubles around water is learned.

In the example of FIG. 11, it is assumed that the input data is the reference refusal rates P01, P02, and P03 in each region. The reference rejection rates P01, P02, and P03 as such input data are linked to the frequency of troubles around water as an output.

The reference refusal rates P01, P02, and P03 are associated with each other through three or more levels of association with the water-related trouble occurrence frequencies A to D as the output solution. The reference refusal rate is arranged on the left side through this degree of association, and the frequency of troubles around each water is arranged on the right side through this degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference refusal rate arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference rejection rate is likely to be associated with, and the most probable trouble occurrence frequency is selected for each reference rejection rate. It shows the accuracy above. In the example of FIG. 11, 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 frequency of trouble occurrence as output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node and the frequency of trouble occurrence as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates a past data set as to which of the reference refusal rate of each region and the frequency of trouble occurrence in that case is adopted and evaluated in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 11 is created.

Further, the degree of association shown in FIG. 11 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. 12, the reference refusal rate of each region is input as input data, the trouble occurrence frequency is output as output data, and at least one hidden layer between the input node and the output node is output. May be provided and machine learning may be performed. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data through a data set of the reference refusal rate of each region before and the frequency of troubles around water, it is necessary to actually determine the frequency of troubles from now on. The trouble occurrence frequency will be searched using the above-mentioned learned data. These data sets may be created by reading from a database managed by the vendor.

When searching for a new trouble occurrence frequency, the input of the area to be searched is accepted. Since the past refusal rate is associated and stored for each region, when the input of the region is accepted, it can be obtained by reading the refusal rate associated with this.

Next, this read-out refusal rate is collated with the reference refusal rate. In such a case, the degree of association shown in FIG. 11 (Table 1) acquired in advance is referred to. For example, when the newly acquired rejection rate is the same as or similar to P02, the trouble occurrence frequency B is associated with the association degree w15 and the trouble occurrence frequency C is associated with the association degree w16 via the association degree. In such a case, the trouble occurrence frequency B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency C in which the degree of association itself is recognized although the degree of association is low 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.

By the way, the collation of the refusal rate and the refusal rate for reference is based on whether or not the sales average is within the range of ± 10% if these data are represented by the sales average for a certain period. It may be determined whether they are the same or similar. Further, as long as the refusal rate is shown in the time-series transition graph, it may be discriminated based on the similarity of the trends.

In this way, it is possible to search for the most suitable trouble occurrence frequency from the newly acquired rejection rate and display it to the user. By looking at this search result, it is possible to determine in advance what kind of trouble occurrence frequency may occur in the area in the future, and it is possible to consider the allocation of workers in each area.

FIG. 13 shows an example in which, in addition to the above-mentioned reference refusal rate, a combination with the reference population estimation data and a trouble occurrence frequency for the combination are set to three or more levels of association.

In the example of FIG. 13, it is assumed that the input data is, for example, reference refusal rates P01 to P03 and reference population estimation data P18 to 21. The intermediate node shown in FIG. 13 is a combination of the reference population estimation data and the reference refusal rate as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination (intermediate node) of the reference refusal rate and the reference population estimation data is associated with each other through three or more levels of association with the trouble occurrence frequency as this output solution. The reference refusal rate and the reference population estimation data are arranged on the left side through this degree of association, and the trouble occurrence frequency is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the frequency of trouble occurrence with respect to the reference refusal rate and the reference population estimation data arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference refusal rate and reference population estimation data are likely to be associated with, and the reference refusal rate and reference population estimation. It shows the accuracy in selecting the most probable trouble occurrence frequency from the data.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the search device 2 determines which of the reference refusal rate, the reference population estimation data obtained when acquiring the reference refusal rate, and the frequency of trouble occurrence in that case. Whether it was suitable or not, the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 13 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, when the reference refusal rate P01 and the reference population estimation data P20, the trouble occurrence frequency is analyzed from the past data. If there are many cases of trouble occurrence frequency A, the degree of association that this trouble occurrence frequency leads to A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, , The degree of association where the trouble occurrence frequency leads to B is set high, and the degree of association where the trouble occurrence frequency leads to A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 13 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. 13, the node 61b is a node in which the reference population estimation data P18 is combined with the reference rejection rate P01, the trouble occurrence frequency C has a relation degree of w15, and the trouble occurrence frequency E. The degree of association is w16. The node 61c is a node in which the reference population estimation data P19 and P21 are combined with respect to the reference refusal rate P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D 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 searching for the frequency of trouble occurrence from now on, the above-mentioned learned data will be used. In such a case, by actually inputting the area to be determined of the trouble occurrence frequency, the refusal rate in the area and the population estimation data are acquired.

Based on the newly acquired refusal rate and the population estimation data in this way, the optimum trouble occurrence frequency 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, if the newly acquired rejection rate is the same as or similar to P02, and the population estimation data is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the trouble occurrence frequency C by w19 and the trouble occurrence frequency D by the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low 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, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference geographical information may be used.

In such a case, obtain in advance three or more levels of association between the reference refusal rate associated with each region in the past, the combination of the reference geographical information, and the frequency of water-related troubles. Keep it. Then, the refusal rate associated with the building structure that predicts the frequency of water troubles or the area where it is located and the geographical information are acquired. Next, based on the reference rejection rate corresponding to the regional rejection rate in the acquired regional specific information and the reference geographical information corresponding to the acquired geographical information, the water supply is based on the degree of association shown in FIG. Search for the frequency of troubles.

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference trouble type information may be used.

In such a case, the degree of association of three or more levels of the combination of the reference refusal rate associated with each region in the past, the reference trouble type information, and the frequency of water troubles is acquired in advance. Keep it. Then, the refusal rate associated with the building structure for predicting the frequency of water troubles or the area where the water trouble occurs and the trouble type information are acquired. Next, based on the reference refusal rate corresponding to the regional refusal rate in the acquired area specific information and the reference trouble type information corresponding to the acquired trouble type information, the water circulation is based on the degree of association shown in FIG. Search for the frequency of troubles.

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference statistical information may be used.

In such a case, the combination of the reference refusal rate associated with each area in the past, the above-mentioned reference statistical information on the type and age of the building structure, and the frequency of water troubles. Obtain the degree of association of 3 or more levels in advance. Then, the refusal rate associated with the building structure that predicts the frequency of water troubles or the area where it is located and the statistical information are acquired. Next, based on the reference refusal rate corresponding to the regional refusal rate in the acquired area specific information and the reference statistical information corresponding to the acquired statistical information, the trouble around the water is based on the degree of association shown in FIG. Search for the frequency of occurrence of.

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference timing information may be used.

In such a case, obtain in advance the degree of association of three or more levels of the combination of the reference refusal rate associated with each region in the past, the reference timing information, and the frequency of water-related troubles. back. Then, the refusal rate associated with the building structure that predicts the frequency of water troubles or the area where it is located, and the timing information are acquired. Next, based on the reference refusal rate corresponding to the regional refusal rate in the acquired area specific information and the reference timing information corresponding to the acquired timing information, the trouble around the water is based on the degree of association shown in FIG. Search for the frequency of occurrence of.

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference weather information may be used.

In such a case, obtain in advance the degree of association of three or more levels of the combination of the reference refusal rate linked to each area in the past, the reference weather information, and the frequency of water troubles. back. Then, the refusal rate associated with the building structure that predicts the frequency of water troubles or the area where it is located and the weather information are acquired. Next, based on the reference refusal rate corresponding to the regional refusal rate in the acquired area specific information and the reference weather information corresponding to the acquired weather information, the trouble around the water is based on the degree of association shown in FIG. Search for the frequency of occurrence of.

Third Embodiment Hereinafter, the third embodiment will be described. In executing this third embodiment, the trouble occurrence frequency prediction 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.

The third embodiment focuses on one area. It also predicts how often water problems may occur in the area. In the third embodiment, the reference timing information and the data set of the frequency of occurrence of water troubles are learned.

In the example of FIG. 14, it is assumed that the input data is reference timing information P01, P02, P03 in each region. The reference timing information P01, P02, and P03 as such input data are linked to the frequency of troubles around the water as an output.

The reference timing information P01, P02, and P03 are associated with each other through the degree of association of three or more levels with respect to the trouble occurrence frequencies A to D around the water as the output solution. The reference timing information is arranged on the left side via this degree of association, and the frequency of troubles around each water is arranged on the right side via the degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference timing information arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference timing information is likely to be associated with, and selects the most probable trouble occurrence frequency for each reference timing information. It shows the accuracy above. In the example of FIG. 14, 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 frequency of trouble occurrence as output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node and the frequency of trouble occurrence as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates a past data set as to which of the reference timing information of each region and the trouble occurrence frequency in that case is adopted and evaluated in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 14 is created.

Further, the degree of association shown in FIG. 14 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 the data set of the reference timing information of each region before and the frequency of troubles around the water, in order to actually determine the frequency of troubles newly from now on. The trouble occurrence frequency will be searched using the above-mentioned learned data. These data sets may be created by reading from a database managed by the vendor.

When searching for a new trouble occurrence frequency, input of time information regarding the predicted time is accepted.

Next, the received time information is collated with the reference time information. 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 timing information is the same as or similar to P02, the trouble occurrence frequency B is associated with w15 and the trouble occurrence frequency C is associated with the association degree w16 via the association degree. In such a case, the trouble occurrence frequency B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency C in which the degree of association itself is recognized although the degree of association is low 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.

By the way, the collation of the timing information and the reference timing information is based on whether or not the sales average is within the range of ± 10% if these data are represented by the sales average for a certain period. It may be determined whether they are the same or similar. Further, if the time information is shown in a time-series transition graph, it may be discriminated based on the similarity of the trends.

In this way, it is possible to search for the most suitable trouble occurrence frequency from the newly acquired timing information and display it to the user. By looking at this search result, it is possible to determine in advance what kind of trouble occurrence frequency may occur in the area in the future, and it is possible to consider the allocation of workers in each area.

FIG. 15 shows an example in which, in addition to the above-mentioned reference timing information, a combination with the reference population estimation data and a trouble occurrence frequency for the combination are set to three or more levels of association.

In the example of FIG. 15, it is assumed that the input data is, for example, reference timing information P01 to P03 and reference population estimation data P18 to 21. The intermediate node shown in FIG. 15 is a combination of reference population estimation data and reference timing information as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination (intermediate node) of the reference timing information and the reference population estimation data is associated with each other through three or more levels of association with the trouble occurrence frequency as this output solution. The reference timing information and the reference population estimation data are arranged on the left side through this degree of association, and the trouble occurrence frequency is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the frequency of trouble occurrence with respect to the reference timing information and the reference population estimation data arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference time information and reference population estimation data are likely to be associated with, and is a reference time information and reference population estimation. It shows the accuracy in selecting the most probable trouble occurrence frequency from the data.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the search device 2 determines which of the reference time information, the reference population estimation data obtained when acquiring the reference time information, and the frequency of trouble occurrence in that case. Whether it was suitable or not, the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 15 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference time information P01 and the reference population estimation data P20, the trouble occurrence frequency is analyzed from the past data. If there are many cases of trouble occurrence frequency A, the degree of association that this trouble occurrence frequency leads to A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, , The degree of association where the trouble occurrence frequency leads to B is set high, and the degree of association where the trouble occurrence frequency leads to A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 15 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. 15, the node 61b is a node in which the reference population estimation data P18 is combined with the reference time information P01, the trouble occurrence frequency C has a relation degree of w15, and the trouble occurrence frequency E. The degree of association is w16. The node 61c is a node that is a combination of the reference population estimation data P19 and P21 with respect to the reference timing information P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D 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 searching for the frequency of trouble occurrence from now on, the above-mentioned learned data will be used. In such a case, by actually inputting the area to be determined of the trouble occurrence frequency, the time information in the area and the population estimation data are acquired.

Based on the newly acquired timing information and population estimation data in this way, the optimum trouble occurrence frequency 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, if the newly acquired time information is the same as or similar to P02 and the population estimation data is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the trouble occurrence frequency C by w19 and the trouble occurrence frequency D by the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low 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, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference geographical information may be used.

In such a case, the degree of association of three or more levels of the combination of the reference time information acquired for a certain area in the past, the reference geographical information, and the frequency of occurrence of water troubles is acquired in advance. .. Then, the time information regarding the predicted time and the geographical information of the predicted area are acquired. Next, based on the reference time information corresponding to the acquired time information and the reference geographical information corresponding to the acquired geographical information, the frequency of occurrence of water troubles is determined based on the degree of association shown in FIG. Explore.

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference trouble type information may be used.

In such a case, the degree of association of three or more levels of the combination of the reference timing information acquired for a certain area in the past, the reference trouble type information, and the frequency of occurrence of water troubles is acquired in advance. .. Then, the timing information regarding the predicted timing and the trouble type information are acquired. Next, based on the reference timing information corresponding to the acquired timing information and the reference trouble type information corresponding to the acquired trouble type information, the frequency of occurrence of water troubles is determined based on the degree of association shown in FIG. Explore.

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference statistical information may be used.

In such a case, the combination of the reference timing information acquired for a certain area in the past, the reference statistical information regarding the type and age of the above-mentioned building structure, and the frequency of occurrence of water troubles 3 Obtain the degree of association above the stage in advance. Then, the timing information regarding the predicted timing and the statistical information are acquired. Next, based on the reference timing information corresponding to the acquired timing information and the reference statistical information corresponding to the acquired statistical information, the frequency of occurrence of water troubles is searched for based on the degree of association shown in FIG. ..

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference weather information may be used.

In such a case, the degree of association of three or more levels of the combination of the reference time information acquired for a certain area in the past, the reference weather information, and the frequency of occurrence of water troubles is acquired in advance. Then, the refusal rate associated with the building structure that predicts the frequency of water troubles or the area where it is located and the weather information are acquired. Next, based on the time information regarding the predicted time and the reference weather information corresponding to the acquired weather information, the frequency of occurrence of water troubles is searched for based on the degree of association shown in FIG.

In the example of FIG. 16, it is assumed that the input data is the reference weather information P01, P02, P03 in each area. The reference weather information P01, P02, and P03 as such input data are linked to the frequency of troubles around the water as an output.

The reference weather information P01, P02, and P03 are associated with each other through the degree of association of three or more levels with respect to the trouble occurrence frequencies A to D around the water as the output solution. Reference weather information is arranged on the left side via this degree of association, and the frequency of troubles around each water is arranged on the right side via the degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference weather information arranged on the left side. In other words, this degree of association is an indicator of what trouble frequency each reference weather information is likely to be associated with, and selects the most probable trouble occurrence frequency for each reference weather information. It shows the accuracy above. In the example of FIG. 16, 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 frequency of trouble occurrence as output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node and the frequency of trouble occurrence as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates a past data set as to which of the reference weather information of each region and the frequency of trouble occurrence in that case is adopted and evaluated in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 16 is created.

Further, the degree of association shown in FIG. 16 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 the previous reference weather information for each region and the frequency of water troubles, it is necessary to actually determine the frequency of new troubles from now on. The trouble occurrence frequency will be searched using the above-mentioned learned data. These data sets may be created by reading from a database managed by the vendor.

When searching for a new trouble occurrence frequency, input of weather information at the predicted time is accepted.

Next, the received weather information is collated with the reference weather information. In such a case, the degree of association shown in FIG. 16 (Table 1) acquired in advance is referred to. For example, when the newly acquired weather information is the same as or similar to P02, the trouble occurrence frequency B is associated with w15 and the trouble occurrence frequency C is associated with the association degree w16 via the association degree. In such a case, the trouble occurrence frequency B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency C in which the degree of association itself is recognized although the degree of association is low 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.

By the way, the collation of the weather information and the reference weather information is based on whether or not the sales average is within the range of ± 10% if these data are represented by the sales average for a certain period. It may be determined whether they are the same or similar. Further, as long as the weather information is shown in a time-series transition graph, it may be discriminated based on the similarity of the trends.

In this way, it is possible to search for the most suitable trouble occurrence frequency from the newly acquired weather information and display it to the user. By looking at this search result, it is possible to determine in advance what kind of trouble occurrence frequency may occur in the area in the future, and it is possible to consider the allocation of workers in each area.

FIG. 17 shows an example in which, in addition to the above-mentioned reference weather information, a combination with the reference population estimation data and a trouble occurrence frequency for the combination are set to three or more levels of association.

In the example of FIG. 17, it is assumed that the input data are, for example, reference weather information P01 to P03 and reference population estimation data P18 to 21. The intermediate node shown in FIG. 17 is a combination of reference weather information and reference population estimation data as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination (intermediate node) of the reference weather information and the reference population estimation data is associated with each other through three or more levels of association with the trouble occurrence frequency as this output solution. Reference weather information and reference population estimation data are arranged on the left side via this degree of association, and trouble occurrence frequencies are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the frequency of trouble occurrence with respect to the reference weather information and the reference population estimation data arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference weather information and reference population estimation data are likely to be associated with, and is a reference weather information and reference population estimation. It shows the accuracy in selecting the most probable trouble occurrence frequency from the data.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the search device 2 determines which of the reference weather information, the reference population estimation data obtained when acquiring the reference weather information, and the frequency of trouble occurrence in that case. Whether it was suitable or not, the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 15 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference weather information P01 and the reference population estimation data P20, the trouble occurrence frequency is analyzed from the past data. If there are many cases of trouble occurrence frequency A, the degree of association that this trouble occurrence frequency leads to A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, , The degree of association where the trouble occurrence frequency leads to B is set high, and the degree of association where the trouble occurrence frequency leads to A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 17 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. 17, the node 61b is a node in which the reference population estimation data P18 is combined with the reference weather information P01, the trouble occurrence frequency C has a connection degree of w15, and the trouble occurrence frequency E. The degree of association is w16. The node 61c is a node that is a combination of the reference population estimation data P19 and P21 with respect to the reference weather information P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D 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 searching for the frequency of trouble occurrence from now on, the above-mentioned learned data will be used. In such a case, by actually inputting the area to be determined of the trouble occurrence frequency, the weather information in the area and the population estimation data are acquired.

Based on the newly acquired weather information and population estimation data in this way, the optimum trouble occurrence frequency 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, if the newly acquired weather information is the same as or similar to P02 and the population estimation data is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the trouble occurrence frequency C by w19 and the trouble occurrence frequency D by the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low 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, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference geographical information may be used.

In such a case, the degree of association of three or more levels of the combination of the reference weather information acquired for a certain area in the past, the reference geographical information, and the frequency of occurrence of water troubles is acquired in advance. .. Then, the weather information regarding the predicted time and the geographical information of the predicted area are acquired. Next, based on the reference weather information corresponding to the acquired weather information and the reference geographical information corresponding to the acquired geographical information, the frequency of occurrence of water troubles is determined based on the degree of association shown in FIG. Explore.

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference trouble type information may be used.

In such a case, the degree of association of three or more levels of the combination of the reference weather information acquired for a certain area in the past, the reference trouble type information, and the frequency of occurrence of water troubles is acquired in advance. .. Then, the weather information regarding the predicted time and the trouble type information are acquired. Next, based on the reference weather information corresponding to the acquired weather information and the reference trouble type information corresponding to the acquired trouble type information, the frequency of occurrence of water troubles is determined based on the degree of association shown in FIG. Explore.

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference statistical information may be used.

In such a case, the combination of the reference weather information acquired for a certain area in the past, the reference statistical information regarding the type and age of the above-mentioned building structure, and the frequency of occurrence of water troubles 3 Obtain the degree of linkage above the stage in advance. Then, the weather information and the statistical information regarding the predicted time are acquired. Next, based on the reference weather information corresponding to the acquired weather information and the reference statistical information corresponding to the acquired statistical information, the frequency of occurrence of water troubles is searched for based on the degree of association shown in FIG. ..

Fourth Embodiment Hereinafter, the fourth embodiment will be described. In executing this fourth embodiment, the trouble occurrence frequency prediction 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 fourth embodiment, the reference geographical information and the data set of the frequency of occurrence of water troubles are learned.

In the example of FIG. 18, it is assumed that the input data is the reference geographical information P01, P02, P03 in each region. The reference geographical information P01, P02, and P03 as such input data are linked to the frequency of troubles around the water as an output.

The reference geographical information P01, P02, and P03 are associated with each other through the degree of association of three or more levels with respect to the trouble occurrence frequencies A to D around the water as the output solution. Geographical information for reference is arranged on the left side through this degree of association, and the frequency of troubles around each water is arranged on the right side via this degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference geographical information arranged on the left side. In other words, this degree of association is an indicator of what trouble frequency each reference geographic information is likely to be associated with, and is the most probable trouble occurrence frequency for each reference geographic information. It shows the accuracy of selection. In the example of FIG. 18, 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 frequency of trouble occurrence as output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node and the frequency of trouble occurrence as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data sets as to which of the geographical information for reference in each region and the frequency of trouble occurrence in that case is adopted and evaluated in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 18 is created.

Further, the degree of association shown in FIG. 18 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 the previous geographical information for reference in each region and the frequency of troubles around the water, it is necessary to actually determine the frequency of troubles from now on. , The frequency of trouble occurrence will be searched using the above-mentioned learned data. These data sets may be created by reading from a database managed by the vendor.

When searching for a new trouble occurrence frequency, the input of geographical information regarding the predicted time is accepted.

Next, the received geographical information is collated with the reference geographical information. In such a case, the degree of association shown in FIG. 18 (Table 1) acquired in advance is referred to. For example, when the newly acquired geographical information is the same as or similar to P02, the trouble occurrence frequency B is associated with w15 and the trouble occurrence frequency C is associated with the association degree w16 via the association degree. .. In such a case, the trouble occurrence frequency B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency C in which the degree of association itself is recognized although the degree of association is low 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.

By the way, the matching of geographical information and reference geographical information is whether or not the sales average is within the range of ± 10% if these data are represented by the sales average for a certain period. It may be determined whether or not they are the same or similar. Further, as long as the geographical information is shown by a time-series transition graph, it may be discriminated based on the similarity of the trends.

In this way, it is possible to search for the most suitable trouble occurrence frequency from the newly acquired geographical information and display it to the user. By looking at this search result, it is possible to determine in advance what kind of trouble occurrence frequency may occur in the area in the future, and it is possible to consider the allocation of workers in each area.

FIG. 19 shows an example in which, in addition to the above-mentioned reference geographical information, a combination with the reference population estimation data and a trouble occurrence frequency for the combination are set to three or more levels of association.

In the example of FIG. 19, it is assumed that the input data is, for example, reference geographical information P01 to P03 and reference population estimation data P18 to 21. The intermediate node shown in FIG. 19 is a combination of reference geographic information and reference population estimation data as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination (intermediate node) of the reference geographical information and the reference population estimation data is associated with each other through three or more levels of association with the frequency of trouble occurrence as this output solution. The reference geographical information and the reference population estimation data are arranged on the left side through this degree of association, and the trouble occurrence frequency is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the frequency of trouble occurrence with respect to the reference geographical information and the reference population estimation data arranged on the left side. In other words, this degree of association is an indicator of what kind of trouble frequency each reference geographical information and reference population estimation data are likely to be associated with, and is a reference geographical information and reference. It shows the accuracy in selecting the most probable trouble occurrence frequency from the population estimation data.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in the search device 2, the reference geographic information, the reference population estimation data obtained when acquiring the reference geographical information, and the trouble occurrence frequency in that case are used in determining the actual search solution. By accumulating past data and analyzing and analyzing which one was more suitable, the degree of association shown in FIG. 19 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference geographical information P01 and the reference population estimation data P20, the trouble occurrence frequency is analyzed from the past data. If there are many cases of trouble occurrence frequency A, the degree of association that this trouble occurrence frequency leads to A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, , The degree of association where the trouble occurrence frequency leads to B is set high, and the degree of association where the trouble occurrence frequency leads to A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 19 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. 19, the node 61b is a node in which the reference population estimation data P18 is combined with the reference geographical information P01, the trouble occurrence frequency C has a connection degree of w15, and the trouble occurrence frequency E. The degree of association is w16. The node 61c is a node that is a combination of the reference population estimation data P19 and P21 with respect to the reference geographical information P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the frequency of trouble occurrence from now on, the above-mentioned learned data will be used. In such a case, by actually inputting the area to be determined of the trouble occurrence frequency, the geographical information in the area and the population estimation data are acquired.

Based on the newly acquired geographical information and population estimation data in this way, the optimum trouble occurrence frequency is searched for. In such a case, the degree of association shown in FIG. 19 (Table 1) acquired in advance is referred to. For example, if the newly acquired geographical information is the same as or similar to P02 and the population estimation data is the same as or similar to P21, the node 61d is associated via the degree of association. In this node 61d, the trouble occurrence frequency C is associated with w19, and the trouble occurrence frequency D is associated with the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low 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, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference trouble type information may be used.

In such a case, the degree of association of three or more levels of the combination of the reference geographical information acquired for a certain area in the past, the reference trouble type information, and the frequency of occurrence of water troubles is acquired in advance. back. Then, the geographical information about the predicted area and the trouble type information are acquired. Next, based on the reference geographical information corresponding to the acquired geographical information and the reference trouble type information corresponding to the acquired trouble type information, the occurrence of water troubles based on the degree of association shown in FIG. Search for frequency.

Further, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference statistical information may be used.

In such a case, the combination of the reference geographical information acquired for a certain area in the past, the reference statistical information regarding the type and age of the above-mentioned building structure, and the frequency of occurrence of water troubles. Obtain the degree of association of 3 or more levels in advance. Then, the geographical information about the predicted area and the statistical information are acquired. Next, based on the reference geographical information corresponding to the acquired geographical information and the reference statistical information corresponding to the acquired statistical information, the frequency of occurrence of water troubles is determined based on the degree of association shown in FIG. Explore.

In the example of FIG. 20, it is assumed that the input data is reference statistical information P01, P02, P03 regarding the type of building structure in each area. The reference statistical information P01, P02, and P03 as such input data are linked to the frequency of troubles around water as an output.

The reference statistical information P01, P02, and P03 are related to each other through the degree of association of three or more levels with respect to the trouble occurrence frequencies A to D around the water as the output solution. Reference statistics are arranged on the left side via this degree of association, and the frequency of troubles around each water is arranged on the right side via the degree of association. The degree of association indicates the degree of trouble occurrence frequency and the degree of relevance to the reference statistical information arranged on the left side. In other words, this degree of association is an indicator of what trouble frequency each reference statistic is likely to be associated with, and selects the most probable trouble occurrence frequency for each reference statistic. It shows the accuracy above. In the example of FIG. 20, 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 frequency of trouble occurrence as output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node and the frequency of trouble occurrence as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 20. That is, the search device 2 accumulates a past data set as to which of the reference statistical information of each region and the frequency of trouble occurrence in that case is adopted and evaluated in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 20 is created.

Further, the degree of association shown in FIG. 20 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 the data set of the reference statistical information of each region before and the frequency of troubles around the water, in order to actually determine the frequency of troubles newly from now on. The trouble occurrence frequency will be searched using the above-mentioned learned data. These data sets may be created by reading from a database managed by the vendor.

When searching for a new trouble occurrence frequency, input of statistical information in the predicted area is accepted.

Next, the received statistical information is collated with the reference statistical information. In such a case, the degree of association shown in FIG. 20 (Table 1) acquired in advance is referred to. For example, when the newly acquired statistical information is the same as or similar to P02, the trouble occurrence frequency B is associated with w15 and the trouble occurrence frequency C is associated with the association degree w16 via the association degree. In such a case, the trouble occurrence frequency B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency C in which the degree of association itself is recognized although the degree of association is low 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.

By the way, the collation of statistical information and reference statistical information is based on whether or not the sales average is within the range of ± 10% if these data are represented by the sales average for a certain period. It may be determined whether they are the same or similar. Further, as long as the statistical information is shown in a time-series transition graph, it may be discriminated based on the similarity of the trends.

In this way, it is possible to search for the most suitable trouble occurrence frequency from the newly acquired statistical information and display it to the user. By looking at this search result, it is possible to determine in advance what kind of trouble occurrence frequency may occur in the area in the future, and it is possible to consider the allocation of workers in each area.
The reference statistical information and the statistical information are not limited to the statistics regarding the type of the building structure, and may reflect various statistics such as the age of the building.

FIG. 21 shows an example in which, in addition to the above-mentioned reference statistical information, a combination with the reference population estimation data and a trouble occurrence frequency for the combination are set to three or more levels of association.

In the example of FIG. 21, it is assumed that the input data is, for example, reference statistical information P01 to P03 and reference population estimation data P18 to 21. The intermediate node shown in FIG. 21 is a combination of reference statistical information and reference population estimation data as such input data. Each intermediate node is further linked to the output. In this output, the frequency of trouble occurrence as an output solution is displayed.

Each combination (intermediate node) of the reference statistical information and the reference population estimation data is associated with each other through three or more levels of association with the trouble occurrence frequency as this output solution. Reference statistics and reference population estimation data are arranged on the left side via this degree of association, and trouble occurrence frequencies are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the frequency of trouble occurrence with respect to the reference statistical information and the reference population estimation data arranged on the left side. In other words, this degree of association is an index showing what kind of trouble occurrence frequency each reference statistical information and reference population estimation data are likely to be associated with, and is a reference statistical information and reference population estimation. It shows the accuracy in selecting the most probable trouble occurrence frequency from the data.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 21. That is, in determining the actual search solution, the search device 2 determines which of the reference statistical information, the reference population estimation data obtained when acquiring the reference statistical information, and the frequency of trouble occurrence in that case. Whether it was suitable or not, the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 21 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference statistical information P01 and the reference population estimation data P20, the trouble occurrence frequency is analyzed from the past data. If there are many cases of trouble occurrence frequency A, the degree of association that this trouble occurrence frequency leads to A is set higher, and if there are many cases of trouble occurrence frequency B and there are few cases of trouble occurrence frequency A, , The degree of association where the trouble occurrence frequency leads to B is set high, and the degree of association where the trouble occurrence frequency leads to A is set low. For example, in the example of the intermediate node 61a, the output of the trouble occurrence frequency A and the trouble occurrence frequency B is linked, but from the previous case, the degree of association of w13 connected to the trouble occurrence frequency A is set to 7 points and the trouble occurrence frequency B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 21 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. 21, the node 61b is a node in which the reference population estimation data P18 is combined with the reference statistical information P01, and the trouble occurrence frequency C has a connection degree of w15 and the trouble occurrence frequency E. The degree of association is w16. The node 61c is a node that is a combination of the reference population estimation data P19 and P21 with respect to the reference statistical information P02, and the degree of association of the trouble occurrence frequency B is w17 and the degree of association of the trouble occurrence frequency D 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 searching for the frequency of trouble occurrence from now on, the above-mentioned learned data will be used. In such a case, by actually inputting the area to be determined of the trouble occurrence frequency, the statistical information in the area and the population estimation data are acquired.

Based on the statistical information newly acquired in this way and the population estimation data, the optimum trouble occurrence frequency is searched for. In such a case, the degree of association shown in FIG. 21 (Table 1) acquired in advance is referred to. For example, if the newly acquired statistical information is the same as or similar to P02 and the population estimation data is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the trouble occurrence frequency C by w19 and the trouble occurrence frequency D by the association degree w20. In such a case, the trouble occurrence frequency C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the trouble occurrence frequency D in which the degree of association itself is recognized although the degree of association is low 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, as a substitute for the above-mentioned reference information (reference population estimation data, etc.), reference trouble type information may be used.

In such a case, the degree of association of three or more levels of the combination of the reference statistical information acquired for a certain area in the past, the reference trouble type information, and the frequency of occurrence of water troubles is acquired in advance. .. Then, the statistical information about the predicted time and the trouble type information are acquired. Next, based on the reference statistical information corresponding to the acquired statistical information and the reference trouble type information corresponding to the acquired trouble type information, the frequency of occurrence of water troubles is determined based on the degree of association shown in FIG. Explore.

In this embodiment, the reference geographical information and the reference statistical information are used as the keynote, and other reference information (reference population estimation data, reference trouble type information, etc.) are combined with the water. The explanation has been given by taking as an example the case where the degree of association with the frequency of occurrence of surrounding troubles is acquired in advance, but the present invention is not limited to this. Based on all the reference information in the first to fourth embodiments, and in combination with other reference information, the degree of association with the frequency of water-related troubles at three or more levels is acquired in advance. Of course, it is possible to search for a solution.

1 Frequency of water troubles System 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Discrimination unit 28 Storage unit 61 Node

Claims (9)

In the water trouble occurrence frequency prediction program that predicts the occurrence frequency of water troubles in building structures on a regional basis
An information acquisition step to acquire geographical information of the area that predicts the frequency of water troubles,
Between the geographical information for reference in each region and the geographical information for reference corresponding to the geographical information acquired in the above information acquisition step by referring to the degree of association between the frequency of occurrence of water troubles and the degree of association of three or more levels. A water-related trouble occurrence frequency prediction program characterized by having a computer execute a search step for searching for the occurrence frequency of water-related troubles for which a higher degree of association is set in. In the above information acquisition step, the population estimation data of the area to be predicted is acquired, and the population estimation data is acquired.
In the search step, the combination of the reference geographical information and the reference population estimation data in each region and the degree of association between the frequency of occurrence of water troubles are referred to in three or more stages, and the information acquisition step is performed. Trouble around the water where a higher degree of association is set between the combination of the reference geographic information corresponding to the acquired geographical information and the reference population estimation data corresponding to the acquired population estimation data. The water-related trouble occurrence frequency prediction program according to claim 1, wherein the occurrence frequency of water is searched. In the above information acquisition step, trouble type information regarding the type of water trouble in the area to be predicted is acquired.
In the above search step, the combination of the above reference geographical information and the reference trouble type information regarding the type of water trouble in each region and the degree of association between the frequency of occurrence of water trouble are referred to three or more stages. However, a higher degree of association is set between the combination of the reference geographical information corresponding to the geographical information acquired in the above information acquisition step and the reference trouble type information corresponding to the acquired trouble type information. The water-related trouble occurrence frequency prediction program according to claim 1, wherein the frequency of occurrence of water-related troubles is searched for. In the above information acquisition step, statistical information regarding the types of building structures in the area to be predicted is acquired.
In the above search step, the combination of the above reference geographical information and the reference statistical information regarding the type of building structure in each area and the degree of association between the frequency of occurrence of water troubles and the degree of association of three or more levels are referred to. , Water for which a higher degree of association is set between the combination having the reference geographical information corresponding to the geographical information acquired in the above information acquisition step and the reference statistical information corresponding to the acquired statistical information. The water-related trouble occurrence frequency prediction program according to claim 1, wherein the frequency of occurrence of surrounding troubles is searched for. In the above information acquisition step, statistical information on the building age of the building structure in the predicted area is acquired.
In the above search step, the combination of the above reference geographical information and the reference statistical information regarding the age of the building structure in each region and the frequency of occurrence of water troubles are referred to three or more levels of association. However, a higher degree of association is set between the combination of the reference geographical information corresponding to the geographical information acquired in the above information acquisition step and the reference statistical information corresponding to the acquired statistical information. The water-related trouble occurrence frequency prediction program according to claim 1, wherein the water-related trouble occurrence frequency is searched for. In the water trouble occurrence frequency prediction program that predicts the occurrence frequency of water troubles in building structures on a regional basis
An information acquisition step to acquire statistical information on the type or age of building structures in the area that predicts the frequency of water problems, and
Refer to the reference statistical information regarding the type or age of the building structure in each area and the degree of association between the frequency of occurrence of water troubles at three levels or more, and refer to the statistical information acquired in the above information acquisition step. A water-related trouble occurrence frequency prediction program characterized by having a computer execute a search step for searching for the occurrence frequency of water-related troubles that have a higher degree of association with the statistical information. In the above information acquisition step, the population estimation data of the area to be predicted is acquired, and the population estimation data is acquired.
In the above search step, the combination of the above reference statistical information and the reference population estimation data in each region and the degree of association between the frequency of occurrence of water troubles are referred to in three or more stages, and in the above information acquisition step. The frequency of water troubles where a higher degree of association is set between the combination of the reference statistical information corresponding to the acquired statistical information and the reference population estimation data corresponding to the acquired population estimation data. The water-related trouble occurrence frequency prediction program according to claim 6, which comprises searching for. In the above information acquisition step, trouble type information regarding the type of water trouble in the area to be predicted is acquired.
In the search step, the combination of the reference statistical information and the reference trouble type information regarding the type of water trouble in each region and the degree of association between the frequency of occurrence of water trouble and the degree of association of three or more stages are referred to. , Water for which a higher degree of association is set between the combination having the reference statistical information corresponding to the statistical information acquired in the above information acquisition step and the reference trouble type information corresponding to the acquired trouble type information. The water-related trouble occurrence frequency prediction program according to claim 1, wherein the frequency of occurrence of surrounding troubles is searched for. The water-related trouble occurrence frequency prediction program according to any one of claims 1 to 8, wherein the degree of association is composed of nodes of a neural network in artificial intelligence.

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