WO2022075274A1

WO2022075274A1 - Program for predicting frequency of occurrence of air conditioning issues

Info

Publication number: WO2022075274A1
Application number: PCT/JP2021/036666
Authority: WO
Inventors: 綾子澤田
Original assignee: Ａｓｓｅｓｔ株式会社
Priority date: 2020-10-06
Filing date: 2021-10-04
Publication date: 2022-04-14

Abstract

[Problem] To predict, in regional units, the frequency of occurrence of air conditioning issues in a building structure. [Solution] A program for predicting the frequency of the occurrence of air conditioning issues, that predicts, in regional units, the frequency of occurrence of issues in air conditioning equipment in a building structure. The program is characterized by causing a computer to execute: an information acquisition step in which region specification information, for specifying a building structure for which the frequency of occurrence of air conditioning issues is to be predicted or a region in which the structure is located, is obtained; and a search step that searches for an air conditioner issue occurrence frequency having a higher degree of connection set between reference sales data for business dispatched in response to air conditioning issues in each region and reference sales data that refers to at least three levels of association to the frequency of occurrence of air conditioning issues and corresponds to past regional sales data in the region specification information obtained in the information acquisition step.

Description

Air conditioning trouble occurrence frequency prediction program

The present invention relates to an air conditioning trouble occurrence frequency prediction program.

Examples of air-conditioning troubles in building structures such as buildings, condominiums, and detached houses include air-conditioning equipment failures, water leaks in air-conditioning equipment piping, filter dust accumulation in air-conditioning equipment, and abnormal noise generated from air-conditioning equipment. , There is a strange odor drifting from the air conditioner. Since there are many cases where air-conditioning problems in such building structures cannot be solved by the resident 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 when the air-conditioning equipment is broken on a hot day or a cold day, if the dispatch of the contractor is delayed, it may cause a big hindrance to normal life and work. Since it is often necessary to deal with such air-conditioning 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 handle air conditioning, it is necessary to predict the frequency of air conditioning problems in each area. However, the current situation is that a technique for predicting the frequency of such air conditioning 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 an air conditioning trouble occurrence frequency prediction program for predicting the occurrence frequency of air conditioning troubles in a building structure on a regional basis. Is to provide. In addition to this, a trouble occurrence frequency prediction program that predicts the frequency of gas equipment troubles in a building structure on a regional basis, or predicts the frequency of electrical equipment troubles in a building structure on a regional basis. Is to provide.

The air-conditioning trouble occurrence frequency prediction program according to the present invention is a building structure or a building structure that predicts the occurrence frequency of air-conditioning trouble in the air-conditioning trouble occurrence frequency prediction program that predicts the occurrence frequency of air-conditioning equipment trouble in the building structure on a regional basis. There are three stages: the information acquisition step to acquire area-specific information to identify the area where it is located, the reference sales data of the vendor dispatched to deal with air-conditioning troubles in each area, and the frequency of air-conditioning troubles. With reference to the above degree of association, the occurrence of air conditioning trouble in which a higher degree of association is set with the reference sales data corresponding to the past sales data of the area in the area identification information acquired in the above information acquisition step. It is characterized by having a computer perform a search step for searching for frequency.

Even if you do not have any special skills or experience, you can predict the frequency of air conditioning troubles 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.

Hereinafter, the air conditioning trouble occurrence frequency prediction program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of an air conditioning trouble occurrence frequency system 1 in which an air conditioning trouble occurrence frequency prediction program to which the present invention is applied is implemented. The air conditioning 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 air-conditioning troubles, and is used for 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 accordingly.

Database 3 stores various information necessary for performing air conditioning trouble occurrence frequency. The information required to determine the frequency of air-conditioning troubles is the reference sales data of the vendors dispatched to respond to air-conditioning troubles in each region, the reference refusal rate of the vendors requested to respond to air-conditioning troubles, and each. Reference population estimation data in each region, reference weather information in each region, reference trouble type information regarding the type of trouble in each region, reference statistical information regarding the type and years of use of air conditioning equipment in each region, outside temperature in each region Information on the outside temperature for reference is accumulated in relation to the frequency of air conditioning troubles as output data.

That is, in the database 3, in addition to the reference sales data of such a vendor, the reference rejection rate of the vendor, the population estimation data for reference, the weather information for reference, the trouble type information for reference, the type of air conditioning equipment and the number of years of use. Any one or more of the reference statistical information and the reference outside temperature information regarding the reference, and the frequency of occurrence of air conditioning troubles 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 technology.

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

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

The operation of the air conditioning trouble occurrence frequency system 1 having the above-mentioned configuration will be described.

In the air-conditioning trouble occurrence frequency system 1, for example, as shown in FIG. 3, the degree of association between the reference sales data of the vendor dispatched for the air-conditioning trouble response in each region and the frequency of the air-conditioning trouble occurrence is three or more levels. It is assumed that it is set in advance. The contractor dispatched to deal with air conditioning troubles in each area is dispatched to respond to requests for air conditioning troubles from residents of building structures (buildings, condominiums, detached houses, apartments, etc.) and actually repaired at the site. It is a contractor who does the work. Air-conditioning troubles include malfunction of air-conditioning equipment, water leakage from piping of air-conditioning equipment, accumulation of dust on filters in air-conditioning equipment, generation of abnormal noise from air-conditioning equipment, and offensive odor drifting from air-conditioning equipment. It is not a thing and includes any other troubles related to air conditioning. 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 air-conditioning troubles indicates how often air-conditioning troubles can occur in each region. The frequency of occurrence of this air conditioning trouble may be composed of any denominator such as yearly, monthly, weekly, daily, and five-year units. The occurrence of air-conditioning trouble may be counted as one time each time the resident of the building structure notifies the contractor that there was an air-conditioning trouble. The frequency of such air-conditioning troubles can be obtained after the fact by counting the frequency of such air-conditioning troubles by the vendor itself and recording it in the database held by the vendor. The frequency of air conditioning troubles 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 trouble occurrence of air conditioning as output.

The reference sales data P01, P02, and P03 are associated with each other through three or more levels of association with the air conditioning trouble occurrence frequencies A to D as the output solution. The frequency of occurrence of this trouble is shown, for example, A is 5 times a month, B is 20 times a month, and the like, but this is not limited to the monthly frequency, and may be any period unit frequency. The reference sales data is arranged on the left side via this degree of association, and the trouble occurrence frequency of each air conditioner 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 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 sales data for reference 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 air conditioning 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 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 air-conditioning troubles, the above-mentioned method is used to actually determine the frequency of troubles. The trouble occurrence frequency will be searched for using the 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, accept the input of the area you want to search. 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, collate this read sales data 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 the resident of the building structure actually requested the contractor to dispatch based on the air conditioning trouble, 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.

The frequency of air-conditioning troubles depends on the sales in the area, as well as 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 refusal rate 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 of reference sales data and reference rejection rate (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 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 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 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.

Search for the optimum trouble occurrence frequency based on the newly acquired sales data and error rate in this way. 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.

Table 2 below shows an example of the degree of association w1 to w12 extending from the input.

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

According to the present invention, 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 have three or more levels of association. The solution may be searched based on the above.

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 respond to air conditioner failures, 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 sales data for reference and determining the frequency of trouble occurrence through the degree of association. At the time of solution search, 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. Search for the optimal trouble occurrence frequency based on newly acquired sales data and population estimation data. In such a case, the trouble occurrence frequency is searched based on the above-mentioned method with reference to the degree of association acquired in advance.

According to the present invention, in addition to the above-mentioned reference sales data, the combination with the reference weather information instead of the above-mentioned reference refusal rate and the frequency of trouble occurrence for the combination have three or more levels of association. The solution search may be performed based on the above.

This reference weather information, which is added as an explanatory variable instead of the reference refusal rate, indicates all the weather information in the area, and the past weather information such as fine weather, cloudiness, and rain in each area is chronological. It is organized and stored in (daily, weekly, monthly, yearly, etc.). In addition, the weather such as heavy rain, typhoons, strong winds, and snowfalls are also organized and memorized. Such reference weather information is managed in the database 3 for each region.

Such weather information also affects the frequency of air conditioning troubles. If there is snowfall, there is a high possibility that air conditioning troubles will occur accordingly, so by combining this with sales data for reference and determining the frequency of troubles through the degree of association, The discrimination accuracy can be improved. At the time of solution search, by actually inputting the area to be determined for the trouble occurrence frequency, the sales data and the weather information in that area are acquired. Search for the optimum frequency of trouble occurrence based on newly acquired sales data and weather information. In such a case, the trouble occurrence frequency is searched based on the above-mentioned method with reference to the degree of association acquired in advance.

As an alternative to the reference weather information, the above-mentioned degree of association may be formed between the reference outside air temperature information regarding the outside air temperature in the area and the reference sales data. Reference outside air temperature information provides all data on past outside air temperature in the area. The reference outside air temperature information is shown by hourly, daily, weekly, monthly, and yearly changes, changes, and averages of past outside temperature in the area.

In such a case, create in advance three or more levels of association between the reference sales data, the reference outside air temperature information regarding the outside air temperature in each of the above areas, and the frequency of air conditioning troubles. Then, along with the sales data of the area in the acquired area-specific information, the outside air temperature information regarding the past outside air temperature of the area is acquired. The details of this outside air temperature information are the same as those for reference outside air temperature information described above.

When the outside air temperature information is acquired, the degree of association is referred to, and the reference sales data corresponding to the past sales data of the region in the acquired area specific information and the reference outside air temperature information corresponding to the acquired outside air temperature information. Search for the frequency of air temperature troubles that have a higher degree of association with the combination with. The method of this search is the same as the method described above.

According to the present invention, in addition to the above-mentioned reference sales data, the combination with the reference trouble type information instead of the above-mentioned reference rejection rate and the trouble occurrence frequency for the combination have three or more levels of association. The solution may be searched based on the above.

This reference trouble type information, which is added as an explanatory variable instead of the reference refusal rate, indicates information on all types of trouble in the area. The types of this trouble are classified into types such as air-conditioning equipment failure, water leakage from air-conditioning equipment piping, accumulation of filter dust in air-conditioning equipment, generation of abnormal noise from air-conditioning equipment, and offensive odor drifting from air-conditioning equipment. ing. Such reference trouble type information is managed in the database 3 for each region.

Such trouble type information also affects the frequency of air conditioning troubles. Since the frequency of troubles may differ depending on whether there are many water leaks in the piping of the air conditioning equipment or the clogging of the drainage port, this is combined with the sales data for reference to determine the frequency of troubles through the degree of association. Therefore, the discrimination accuracy can be improved. At the time of solution search, the sales data in the area where the trouble occurrence frequency is actually determined and the trouble type information are acquired. Search for the optimum trouble occurrence frequency based on the newly acquired sales data and trouble type information. In such a case, the trouble occurrence frequency is searched based on the above-mentioned method with reference to the degree of association acquired in advance.

According to the present invention, 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 frequency of trouble occurrence for the combination have three or more levels of association. The solution search may be performed based on the above.

This reference statistical information, which is added as an explanatory variable instead of the reference refusal rate, is statistical information regarding the type of air conditioning equipment in the area. The types of air conditioning equipment are all-air system, air / water combined system, all-water system, refrigerant system, single duct constant air volume system, single duct variable air volume system, 4-way ceiling-embedded cassette type, 1-way ceiling embedding. Built-in cassette type, ceiling built-in cassette type, ceiling-embedded duct type, ceiling-suspendable type, wall-mounted type, and floor-standing type are statistically analyzed. The proportions of each of these types are statistically analyzed to facilitate comparative analysis between regions. Such reference statistical information is managed in the database 3 for each region.

Further, this reference statistical information may be composed of statistical data regarding the number of years of use of the air conditioning equipment. Statistical data such as frequency distribution, average, and standard deviation are managed for each region for each year of use of this air conditioning equipment.

Such statistical information also affects the frequency of air conditioning troubles. The longer the product has been used, the more water leaks from the equipment and the more failures may occur. Therefore, by combining this with sales data for reference and determining the frequency of trouble occurrence through the degree of association, it is possible to improve the discrimination accuracy. can. In addition, since the frequency of trouble occurrence may change depending on the type of air-conditioning equipment, the accuracy of discrimination can be improved by combining this with sales data for reference and determining the frequency of trouble occurrence through the degree of association.

At the time of solution search, sales data and statistical information in the area where the trouble occurrence frequency is actually determined are acquired. Search for the optimal trouble occurrence frequency based on newly acquired sales data and statistical information. In such a case, the trouble occurrence frequency is searched based on the above-mentioned method with reference to the degree of association acquired in advance.

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-mentioned 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.

Note that the above-mentioned input data and output data may not be completely the same in the process of learning, so that the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A 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, any of the reference refusal rate, the reference population estimation data, the reference weather information, the reference trouble type information, and the reference statistical information regarding the type of air conditioning equipment. The explanation has been given by taking the case of being composed of a combination of heels as an example, but the explanation is not limited to this. In other words, in addition to the reference sales data, the degree of association is any two or more of the reference rejection rate of the vendor, the reference population estimation data, the reference weather information, the reference trouble type information, and the reference statistical information regarding the type of air conditioning equipment. It may be configured in combination with. In addition, the degree of association is any of the reference sales data, or in addition to this, the reference refusal rate, the reference population estimation data, the reference weather information, the reference trouble type information, and the reference statistical information regarding the type of air conditioning equipment. In addition to 1 or more, 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. 6, 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 U is the reference sales data, and the reference information V is the reference rejection rate, the reference population estimation data, the reference weather information, the reference trouble type information, and the reference statistics regarding the type of air conditioning equipment. It shall be one of the information.

At this time, as shown in FIG. 6, 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 in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but 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 such numerical values of three or more stages, in a situation where there are multiple possible candidates for search solutions, the order of the degree of association is high. It is also possible to search and display. If the user can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the extremely low output such as the degree of association of 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 a merit 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, the above-mentioned sales data, refusal rate, population estimation data, weather information, trouble type information, and reference statistical information regarding the type of air conditioning equipment are acquired, and when the determination is made, the update is performed based on these. You may do so.

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

Second Embodiment Hereinafter, the second embodiment 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 reference rejection rate and the data set of the frequency of occurrence of air conditioning troubles are learned.

In the example of FIG. 7, 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 trouble occurrence of air conditioning as output.

The reference refusal rates P01, P02, and P03 are associated with each other through three or more levels of association with the air conditioning 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 trouble occurrence frequency of each air conditioner 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. 7, 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 having three or more stages. That is, the search device 2 accumulates a past data set as to which of the reference refusal rate in each region and the trouble occurrence frequency in that case was adopted and evaluated in determining the actual search solution. , Analyzing these is to create a degree of association.

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

In such a case, as shown in FIG. 8, 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 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 air-conditioning troubles, the above-mentioned is used to actually determine the frequency of troubles from now on. The trouble occurrence frequency will be searched for using the 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, accept the input of the area you want to search. 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, collate this read-out refusal rate with the reference refusal rate. In such a case, the degree of association 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 refusal 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.

Further, both the first embodiment and the second embodiment are not limited to the above-described embodiment, and as shown in FIG. 9, for example, the reference information as the keynote and the frequency of occurrence of air conditioning troubles. You may try to use three or more levels of association. In such a case, the solution search is performed based on the degree of association between the reference information according to the newly acquired information and the frequency of occurrence of air conditioning troubles in three or more stages. The basic reference information includes all the above-mentioned reference information (reference sales data, reference rejection rate, reference population estimation data, reference weather information, reference trouble type information, reference statistical information, etc.). Applicable.

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

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

The other reference information referred to here corresponds to any reference information other than the reference information which is the keynote when any of the above-mentioned reference information is used as the keynote reference information.

For example, as one of the other reference information, it is assumed that in a certain reference weather information F, the frequency B of the air conditioning trouble was previously determined. When the weather information corresponding to the reference weather information F is newly acquired, the search solution B as the frequency of occurrence of air conditioning troubles is subjected to a process of increasing the weight, in other words, the occurrence of air conditioning troubles. It is set in advance to perform a process that leads to the frequency search solution B.

For example, the other reference information G is an analysis result that suggests the search solution C as the frequency of occurrence of air conditioning troubles, and the reference information F is the search solution D as the frequency of occurrence of air conditioning troubles. It is assumed that the analysis result suggests. After the setting with the reference information in this way, if the actually acquired information is the same as or similar to the reference information G, a process of increasing the weighting of the occurrence frequency C of the air conditioning trouble is performed. On the other hand, when the actually acquired information is the same as or similar to the reference information F, a process of increasing the weighting of the occurrence frequency D of the air conditioning trouble is performed. That is, the degree of association itself, which leads to the frequency of air conditioning troubles, may be controlled based on the reference information F to H. Alternatively, after determining the frequency of occurrence of air conditioning trouble only by the above-mentioned degree of association, the search solution obtained may be modified based on the reference information F to H. In the latter case, how to correct the frequency of air conditioning troubles as a search solution based on the reference information F to H will reflect what was designed on the system side each time. ..

Further, the reference information is not limited to the case where it is composed of any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the more the case leads to the occurrence frequency of the air conditioning trouble suggested by the reference information, the higher the modification of the discrimination type as the search solution obtained through the degree of association may be made.

Similarly, as shown in FIG. 10, when forming the degree of association between the reference information as the keynote and the frequency of occurrence of air conditioning troubles for the combination having the other reference information, the reference as the keynote. The information used is any reference information (reference sales data, reference rejection rate, reference population estimation data, reference weather information, reference trouble type information, reference statistical information) in the first embodiment and the second embodiment. Etc.) are also applicable. Other reference information includes any reference information in the first and second embodiments other than the underlying reference information.

At this time, if the reference information that is the keynote is the reference trouble type information, the other reference information includes any reference information in the other first embodiment and the second embodiment.

In such a case, the frequency of air conditioning troubles can be estimated by searching for a solution in the same way. At this time, as shown in FIG. 9 described above, the frequency of occurrence of air conditioning troubles is obtained through further other reference information (reference information F, G, H, etc.) for the search solution obtained through the degree of association. May be modified.

At this time, the degree of association may be learned by combining not only 1 but also 2 or more other reference information.

Further, as shown in FIG. 11, the degree of association may be formed between only the reference information that is the keynote and the frequency of occurrence of air conditioning troubles. This reference information as a keynote is any reference information (reference sales data, reference rejection rate, reference population estimation data, reference weather information, reference trouble type information) in the first embodiment and the second embodiment. , Reference statistics, etc.) are also applicable. The solution search method of FIG. 11 is omitted below by quoting the explanation of FIG.

In addition, this embodiment is based on the reference refusal rate, the reference weather information, and the reference outside air temperature information, and other reference information (reference population estimation data, reference trouble type information, etc.). The explanation has been given by taking as an example the case where the degree of association with the frequency of occurrence of air conditioning troubles and the degree of association of three or more levels are acquired in advance by combining the above, but the present invention is not limited to this. All reference information in the first to second embodiments (reference sales data, reference rejection rate of vendors, reference population estimation data, reference weather information, reference trouble type information, reference regarding types of air conditioning equipment). Other reference information (in addition to reference sales data, reference rejection rate, reference population estimation data, reference weather information, reference trouble type information, air conditioning equipment) Of course, it is also possible to obtain in advance the degree of association with the frequency of occurrence of air conditioning troubles and the degree of association with the frequency of occurrence of air conditioning troubles in combination with reference statistical information regarding the types of the above, and to search for a solution.

In both the first embodiment and the second embodiment, the frequency of occurrence of troubles in electrical equipment may be searched in addition to the frequency of occurrence of troubles in air conditioning. The method for searching for the frequency of occurrence of troubles in electrical equipment is the same as described above, and it is premised that three or more levels of association between each reference information and the frequency of occurrence of troubles in electrical equipment are preset. ..

Electrical equipment includes all electrical equipment (lighting equipment, air conditioning equipment, indoor dryers, ventilation equipment, sprinkler equipment, underfloor heating equipment, indoor heating equipment, indoor air circulation equipment, etc.) installed in the building structure. Is done. Although this electrical equipment can be separated from the building structure and transported, it is premised that it is installed in the building structure, and air conditioning equipment such as an air conditioner is also included in this. On the other hand, electric appliances (personal computers, refrigerators, microwaves, vacuum cleaners, television receivers, Internet communication devices, etc.) released from building structures are simply connected to outlets. It does not include those that are not actually attached to the building structure. The troubles of this electric equipment include the above-mentioned failure of the electric equipment, water leakage of the piping to the electric equipment, accumulation of dust of the filter in the electric equipment, generation of abnormal noise from the electric equipment, and a strange odor drifting from the electric equipment. However, it is not limited to these, and includes any other troubles related to electrical equipment. 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 electrical equipment troubles indicates how often electrical equipment troubles can occur in each region. The frequency of occurrence of this electrical equipment trouble may be composed of any denominator such as yearly, monthly, weekly, daily, and five-year units. The occurrence of electrical equipment trouble may be counted as one time each time the resident of the building structure notifies the contractor that there was an electrical equipment trouble. The frequency of such troubles in electrical equipment can be obtained after the fact by counting the frequency of troubles by the contractor and recording it in the database held by the contractor. The frequency of troubles in this electrical equipment is organized by region as described above.

In both the first embodiment and the second embodiment, the frequency of occurrence of gas equipment trouble may be searched in addition to the frequency of occurrence of air conditioning trouble. The method for searching for the frequency of occurrence of gas equipment trouble is the same as described above, and it is premised that three or more levels of association between each reference information and the frequency of occurrence of gas equipment trouble are set in advance.

Gas equipment includes all gas equipment (lighting equipment, air conditioning equipment, gas stoves, water heaters, baths, etc.) arranged in building structures. Although this gas facility can be separated from the building structure and transported, it is premised that it is installed in the building structure, and air conditioning equipment such as an air conditioner is also included in this. On the other hand, gas products released from building structures (gas stoves, etc.) that are simply connected to outlets and are not actually attached to building structures are included. I can't. The troubles of this gas equipment include the above-mentioned failure of the gas equipment, water leakage of the piping to the gas equipment, accumulation of dust on the filter in the gas equipment, generation of abnormal noise from the gas equipment, and offensive odor drifting from the gas equipment. Is not limited to these, but includes any other troubles related to gas equipment. 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 gas equipment troubles indicates how often gas equipment troubles can occur in each region. The frequency of occurrence of this gas equipment trouble may be composed of any denominator such as yearly, monthly, weekly, daily, and five-year units. The occurrence of gas equipment trouble may be counted as one time each time the resident of the building structure notifies the contractor that there was a gas equipment trouble. The frequency of troubles in such gas equipment can be obtained after the fact by counting the frequency of troubles by the gas equipment and recording it in the database it owns. The frequency of troubles in this gas facility is organized by region as described above.

1 Air conditioning trouble occurrence frequency 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

In the air-conditioning trouble occurrence frequency prediction program that predicts the occurrence frequency of air-conditioning equipment troubles in building structures on a regional basis.
An information acquisition step to acquire area-specific information for identifying a building structure that predicts the frequency of air-conditioning troubles or the area where it is located, and
Refer to the reference sales data of the vendor dispatched for the response to the air conditioning trouble in each region and the degree of association between the frequency of occurrence of the air conditioning trouble in three or more stages, and refer to the regional specific information acquired in the above information acquisition step. Air conditioning trouble occurrence frequency characterized by having a computer perform a search step to search for the occurrence frequency of air conditioning troubles that have a higher degree of association with reference sales data corresponding to past sales data. Prediction program.
In the above information acquisition step, the refusal rate of the contractor who was requested to be dispatched to deal with the past air conditioning troubles in the area in the acquired area specific information is acquired.
In the above search step, the combination of the reference sales data of the contractor dispatched for the air conditioning trouble response in each region and the reference refusal rate of the contractor dispatched for the air conditioning trouble response in each region, and the air conditioning. Refer to the degree of association with the frequency of occurrence of troubles in three or more stages, and correspond to the reference sales data corresponding to the past sales data of the region in the region specific information acquired in the above information acquisition step and the acquired refusal rate. The air-conditioning trouble occurrence frequency prediction program according to claim 1, wherein a higher degree of association with a reference rejection rate is set to search for the occurrence frequency of air-conditioning troubles.
In the air-conditioning trouble occurrence frequency prediction program that predicts the occurrence frequency of air-conditioning troubles in building structures on a regional basis
An information acquisition step to acquire area-specific information for identifying a building structure that predicts the frequency of air-conditioning troubles or the area where it is located, and
Area-specific information acquired in the above information acquisition step by referring to the degree of association of three or more levels, which has the reference refusal rate of the contractor requested to be dispatched to deal with air-conditioning troubles in each region and the frequency of occurrence of air-conditioning troubles. Air-conditioning trouble occurrence characterized by having a computer perform a search step to search for the frequency of air-conditioning troubles that have a higher degree of association with the reference refusal rate corresponding to the regional refusal rate in Frequency prediction program.
In the air-conditioning trouble occurrence frequency prediction program that predicts the occurrence frequency of air-conditioning troubles in building structures on a regional basis
An information acquisition step to acquire weather information on the local weather that predicts the frequency of air conditioning troubles,
Referencing the reference weather information regarding the local weather acquired in the past and the three or more levels of linkage with the frequency of occurrence of air conditioning troubles, and the reference weather information corresponding to the weather information acquired in the above information acquisition step. An air-conditioning trouble occurrence frequency prediction program characterized by having a computer execute a search step for searching for the frequency of occurrence of air-conditioning troubles having a higher degree of association between them.
In the air-conditioning trouble occurrence frequency prediction program that predicts the occurrence frequency of air-conditioning troubles in building structures on a regional basis
An information acquisition step to acquire outside air temperature information related to the outside air temperature in the area that predicts the frequency of air conditioning troubles,
Referencing the reference outside air temperature information regarding the outside air temperature of the area acquired in the past and the degree of association of three or more levels with the frequency of occurrence of air conditioning troubles, and the reference weather corresponding to the weather information acquired in the above information acquisition step. An air-conditioning trouble frequency prediction program characterized by having a computer perform a search step to search for the frequency of air-conditioning troubles that have a higher degree of association with information.
In the electrical equipment trouble occurrence frequency prediction program that predicts the occurrence frequency of electrical equipment troubles in building structures on a regional basis
An information acquisition step to acquire area-specific information for identifying a building structure that predicts the frequency of electrical equipment troubles or the area where it is located, and
Refer to the three or more levels of association between the reference sales data of the vendor dispatched to deal with electrical equipment troubles in each region and the frequency of occurrence of electrical equipment troubles, and in the region-specific information acquired in the above information acquisition step. Electricity characterized by having a computer perform a search step to search for the frequency of troubles in electrical equipment that has a higher degree of association with reference sales data that corresponds to past sales data for the region. Equipment trouble occurrence frequency prediction program.
In the above information acquisition step, statistical information regarding the number of years of use of local electrical equipment in the acquired area-specific information is acquired.
In the above prediction step, the combination of the reference sales data of the supplier dispatched to deal with the electrical equipment trouble in each region and the reference statistical information regarding the years of use of the electrical equipment in each region, and the trouble of the electrical equipment Referencing the degree of association with the frequency of occurrence in three or more stages, the reference sales data corresponding to the past sales data of the region in the region specific information acquired in the above information acquisition step, and the reference statistics corresponding to the acquired statistical information. The electrical equipment trouble occurrence frequency prediction program according to claim 6, wherein the frequency of occurrence of electrical equipment troubles for which a higher degree of association with information is set is searched for.
In the gas equipment trouble occurrence frequency prediction program that predicts the occurrence frequency of gas equipment troubles in building structures on a regional basis
An information acquisition step to acquire area-specific information for identifying a building structure that predicts the frequency of gas facility troubles or the area where it is located, and
In the area-specific information acquired in the above information acquisition step, refer to the reference sales data of the vendors dispatched to deal with gas equipment troubles in each region and the degree of association between the frequency of occurrence of gas equipment troubles in three or more stages. A gas characterized by having a computer perform a search step to search for the frequency of troubles in gas equipment that has a higher degree of association with reference sales data that corresponds to past sales data for the region. Equipment trouble occurrence frequency prediction program.
In the above information acquisition step, the outside air temperature information regarding the outside air temperature of the area in the acquired area specific information is acquired.
In the above prediction step, the combination of the reference sales data of the vendor dispatched to deal with the gas equipment trouble in each region and the reference outside temperature information regarding the outside temperature in each region, and the frequency of occurrence of the gas equipment trouble. Reference sales data corresponding to the past sales data of the region in the region specific information acquired in the above information acquisition step and reference outside temperature information corresponding to the acquired outside temperature information by referring to the degree of association of three or more stages of. The gas equipment trouble occurrence frequency prediction program according to claim 8, wherein the frequency of occurrence of troubles of gas equipments having a higher degree of association with the combination with and is searched for.