JP2023075555A - Maintenance cost prediction program - Google Patents

Abstract

To provide a maintenance cost prediction program that allows making a prediction about maintenance cost for various facilities including electricity, gas, and water supply without depending on manual work even without specific skills and experiences.SOLUTION: In a maintenance contractor selection system 1 comprising an information acquisition unit 9, a search device 2 connected with the information acquisition unit 9, and a database 3 connected with the search device 2, a maintenance cost prediction program for predicting maintenance cost for a facility causes a computer to execute an information acquisition step of acquiring facility data from a facility on which maintenance is performed, and a prediction step of predicting maintenance cost by using the three or more stages of relevance between reference facility data acquired from the facility in the past and the maintenance cost, based on reference facility data according to the facility data acquired in the information acquisition step.SELECTED DRAWING: Figure 1

Description

The present invention relates to a maintenance cost prediction program for predicting the cost of maintaining equipment.

Various facilities such as electricity, gas, and water supply are essential infrastructures for forming a lifeline. Such various equipment also deteriorates after being used for a long period of time, and eventually the time will come when maintenance is required. Maintenance of such various facilities is often entrusted to a company specializing in such maintenance. Such maintenance companies also have their own areas of specialty, and whether or not they can immediately respond to the busyness of their work becomes a problem, and maintenance costs also change accordingly.

For this reason, there has long been a demand for a system that can minimize the effort required to select a contractor each time the user entrusts maintenance of equipment in relation to maintenance costs, but such a technique has not yet been proposed. The current situation was that there was none. In addition, there are many cases where the user wants to predict the cost of maintenance every time the maintenance of equipment is outsourced, but no technology has yet been devised that can handle this.

Therefore, the present invention has been devised in view of the above-mentioned problems, and its object is to provide electricity, gas, water, etc., without relying on human labor, even without special skills and experience. To provide a maintenance cost prediction program capable of predicting maintenance costs of various facilities.

A maintenance cost prediction program according to the present invention is a maintenance cost prediction program for predicting the cost of maintaining equipment, which includes an information acquisition step of acquiring equipment data from equipment to be maintained, and reference equipment data obtained from the equipment in the past. and a prediction step of predicting maintenance costs based on reference facility data corresponding to the facility data acquired in the information acquisition step using three or more levels of association with maintenance costs. and

Even if you do not have special skills or experience, you can automatically and highly accurately predict maintenance costs for various facilities such as electricity, gas, and water without relying on human labor.

1 is a block diagram showing the overall configuration of a system to which the present invention is applied; FIG. It is a figure which shows the specific structural example of a search apparatus. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention.

FIRST EMBODIMENT Hereinafter, a maintenance company selection 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 the overall configuration of a maintenance company selection system 1 in which a maintenance company selection program to which the present invention is applied is installed. The maintenance company selection 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. 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 imaging device capable of capturing an image, such as a camera. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper document. Moreover, the information acquisition part 9 may be integrated with the searching device 2 which will be described later. The information acquisition unit 9 outputs the detected information to the searching device 2 . Further, the information acquisition unit 9 may be configured by means for specifying position information by scanning map information. Further, the information acquisition unit 9 may be configured with an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. The information acquisition unit 9 may also be configured with a communication interface that acquires weather data from the Meteorological Agency or a private weather forecast company. The information acquisition unit 9 may be composed of a body sensor worn on 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 configured with a sensor for Also, the body sensor may acquire biological data not only of humans but also of animals. The information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading from a database. The information acquisition unit 9 may be configured by an odor sensor that detects odors and scents in addition to these.

The database 3 accumulates various information necessary for solution search.

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

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

The control section 24 is a so-called central control unit for controlling each component implemented in the search device 2 by transmitting control signals via the internal bus 21 . In addition, the control unit 24 transmits commands for various controls through the internal bus 21 according to operations through the operation unit 25 .

The operation unit 25 is embodied by a keyboard or a touch panel, and a user inputs an execution command for executing a program. The operation unit 25 notifies the control unit 24 when the execution command is input by the user. Upon receiving this notification, the control unit 24 cooperates with each component including the determination unit 27 to execute desired processing operations. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

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

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

When the storage unit 28 is configured with a hard disk, predetermined information is written to each address based on the control by the control unit 24, and this information is read as necessary. The storage unit 28 also stores a program for executing the present invention. This program is read and executed by the control unit 24 .

The operation of the maintenance company selection system 1 configured as described above will be described.

In the maintenance company selection system 1, for example, as shown in FIG. 3, it is assumed that three or more degrees of association between the reference facility data and the degree of deterioration of the water pipe are set in advance. Reference facility data is directly obtained from the facilities (devices) necessary for creating an environment where people live, move, work, communicate, play, and hold various ceremonies and events. This is the data obtained. In other words, this reference facility data consists of all data obtained from various facilities of various architectural structures (houses, buildings, transportation facilities, various facilities, commercial facilities, factories, and plants). Equipment here refers to various types of equipment via electricity, gas, water, etc., which are installed integrally and inseparably inside a building structure, and are subject to independent transactions such as home appliances. does not include

Electrical equipment includes, but is not limited to, lighting, power distribution systems, air conditioners, bathroom dryers or electric heaters, kitchens, toilets, ventilation systems, factory electrical control systems, etc. Facilities are covered. Maintenance of electrical equipment includes repair, replacement, maintenance, restoration work, detection of failures in advance, etc. of such electrical equipment. The maintenance of water supply equipment is all equipment that uses water, regardless of so-called water supply, sewerage, or plant, and the maintenance target is clogging of toilets, water leakage, breakdown, clogging of kitchens and washrooms, Repair, replacement, maintenance, recovery work, and detection of failures in advance for water leaks, failures, clogged bath drains, water leaks in showers and faucets, and failures. Gas facilities are gas facilities used in baths, kitchens, and the like, and maintenance includes repair, replacement, care, restoration work, and detection of failures in advance of water supply facilities.

Reference equipment data includes any data obtained by directly or indirectly attaching sensors or measuring instruments to such equipment. For example, the power, electricity, voltage, vibration, sound, light, radio waves (hereinafter collectively referred to as physical data) supplied to the various facilities mentioned above, the flow rate of air and liquids, the amount of wastewater in drainage facilities, etc. This is equipment data for reference. Data consisting of any one or more of physical data for operating equipment, gas flow rate data for supplying or discharging gas, solar power generation data, and light intensity for irradiation is detected. By detecting these data, it is possible to grasp whether the current state of the environment is normal or whether some kind of abnormality has occurred. By detecting these motion data, it is also possible to estimate whether there is a possibility that the environment will become abnormal in the near future, or whether the environment will remain normal.

In the vibration data of the reference facility data, how to detect the vibration wave is, for example, in addition to the vibration sensor and the strain sensor, if the vibration appears as a sound wave, it is detected by an ultrasonic sensor, etc. can be anything. Equipment data composed of electromagnetic waves and light may be composed of ordinary image data or spectral data. The reference equipment data also includes data using all waves used in non-destructive inspection.

Further, the facility data for reference and the facility data may be data obtained by interviewing a customer who needs maintenance, in addition to data detected by a camera or a sensor. In such a case, convert the text data of the customer's phone call to the call center, or acquire the text data entered by the customer in the e-mail or inquiry website and perform natural language analysis on it. Analyze the content. Intentions and degrees are actually obtained from the relationships between verbs, noun phrases, adjectives, etc., and converted into data.

These reference facility data can be managed in any chronological units such as yearly, monthly, weekly, daily, hourly, and minutely.

The trader (contractor) to be selected is a trader that performs the various types of maintenance described above as a business, and is composed of a company name or an individual name. The trader to be selected may indicate not only the specific company name but also what kind of office or branch office it is.

In the example of FIG. 3, it is assumed that the input data are reference facility data P01, P02, and P03. The reference facility data P01, P02, and P03 as such input data are linked to the vendor as output.

The facility data for reference P01, P02, and P03 are associated with each other through a degree of association of three or more levels with respect to vendors A to D as the output solutions. The traders are shown, for example, A is 95% trader, B is 60% trader, and so on. The facility data for reference is arranged on the left side through this degree of association, and each trader is arranged on the right side through the degree of association. The degree of relevance indicates the degree of relevance with which trader the facility data for reference arranged on the left side is highly related. In other words, the degree of association is an index indicating to which trader each reference equipment data is likely to be linked, and is an accurate indicator for selecting the most probable trader for each reference equipment data. It shows the sex. In the example of FIG. 3, w13 to w19 are shown as association degrees. 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 correlation between each combination as an intermediate node and the trader as an output, Conversely, the closer to one point, the lower the degree of mutual association between each combination as an intermediate node and the trader as an output.

The search device 2 acquires three or more degrees of association w13 to w19 shown in FIG. 3 in advance. In other words, in determining the actual search solution, the search device 2 accumulates the reference equipment data and the past data sets as to which of the vendors in that case was adopted and evaluated, analyzes these, By analyzing, the degree of association shown in FIG. 3 is created. Note that the reference facility data is not limited to, for example, time-series data, and may be data that has been FFT-transformed and transformed into a frequency axis.

This analysis may be performed by artificial intelligence. In such a case, each reference equipment data and the trader's data set in that case are learned. In the case of facility data P01, if there are many cases of trader A, the degree of association leading to evaluation of this trader is set higher, and if there are many cases of trader B, the degree of correlation leading to evaluation of this trader is set. set higher. For example, in the example of the reference equipment data for the equipment data P01, it is linked to the contractor A and the contractor C, but from the previous example, the degree of association of w13 connected to the contractor A is 7 points, and the degree of association of w14 connected to the contractor C The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degree of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

In such a case, as shown in FIG. 4, reference equipment data for each region is input as input data, vendors are output as output data, and at least one hidden layer is provided between the input node and the output node. It may be machine-learned. Either or both of the input nodes and the hidden layer nodes are set with the above-mentioned degree of relevance, which serves as a weighting for each node, based on which the output is selected. Then, when the degree of association exceeds a certain threshold, the output may be selected.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data is used to search for a trader in order to actually determine a new trader. It may be determined to which of the trader's previously categorized classifications the trader falls under.

When searching for a new trader, input of facility data is accepted. Details of the equipment data will be omitted below by citing the description of the reference equipment data described above.

Next, this input equipment data is collated with the reference equipment data. In such a case, reference is made to the degree of association shown in FIG. 3 (Table 1), which has been acquired in advance. For example, when the newly acquired facility data is the same as or similar to P02, the vendor B is associated with w15 and the vendor C with the degree of association w16. In such a case, trader B with 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 trader C whose degree of association is low but whose association itself is recognized may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

By the way, collation of equipment data and reference equipment data depends on whether or not the average value is within ±10% if these data are represented by the average value for a certain period. You may make it discriminate|determine whether it is the same and similar. Moreover, if the facility data is indicated by a time-series transition graph, it may be determined based on the similarity of the trend.

In this way, it is possible to search for the most suitable trader from the newly acquired facility data and display it to the user. By looking at the results of this search, it is possible to propose to which trader it is preferable to outsource the equipment.

The example of FIG. 5 is an example in which the degree of association is formed by combining the reference equipment data and the reference vendor status information regarding the status of the vendor. The reference vendor status information includes the order status of the vendor's work, the degree of congestion, the customer's reputation so far, the arrangement status and progress of other maintenance orders if they are currently receiving orders, and the genre of each maintenance. It consists of repair record and repair history, information on the quality of the repair, service engineer's current location information, scheduled arrival time at the customer site, scheduled repair start time, scheduled repair completion time, location of the dealer's branch office, and the like. This reference trader status information may be obtained from a database managed by the trader itself.

The reference trader status information may be composed of data obtained by subjecting the above data to various statistical processing, may be composed of average values, etc., or may be composed of standard deviations, etc. of data measured in chronological order. may have been Alternatively, it may be composed of the change tendency itself of the data measured in time series, or a typed version thereof.

Vendors rely on vendor status information in addition to facility data. Therefore, in addition to the reference facility data, by combining the reference vendor status information with the learning data, it is possible to determine the vendor with higher accuracy. For this reason, in addition to reference equipment data, reference trader status information is combined to form the above-described degree of association.

In the example of FIG. 5, it is assumed that the input data are reference facility data P01 to P03 and reference trader situation information P14 to P17. The intermediate node shown in FIG. 5 is a combination of reference facility data as input data and reference vendor status information. Each intermediate node is also connected to an output. In this output, the vendor is displayed as the output solution.

Each combination (intermediate node) of the facility data for reference and the business situation information for reference is associated with each other through a degree of association of three or more levels with respect to the business as the output solution. The facility data for reference and the business status information for reference are arranged on the left side through the degree of association, and the business operators are arranged on the right side through the degree of association. The degree of relevance indicates the degree of high relevance to the trader with respect to the reference equipment data and the reference trader status information arranged on the left side. In other words, the degree of association is an index indicating to which trader the respective reference equipment data and reference trader status information are likely to be linked. It indicates accuracy in selecting the most probable trader. Therefore, the optimal trader is searched for by combining these reference facility data and reference trader status information.

In the example of FIG. 5, w13 to w22 are shown as association degrees. These w13 to w22 are shown in 10 stages as shown in Table 1. The closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the output. The closer it is, the lower the degree of correlation between each combination as an intermediate node and the output.

The search device 2 acquires in advance three or more degrees of association w13 to w22 shown in FIG. In other words, the search device 2 accumulates past data as to whether the reference facility data, the reference trader status information, and the trader in that case was suitable for determining the actual search solution, By 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 case of reference equipment data P01 and reference trader status information P16, the trader is analyzed from past data. If there are many cases of trader A, the degree of association leading to this trader A is set higher. , the degree of association leading to trader A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of trader A and trader B, but from the previous example, the degree of relevance of w13, which is connected to trader A, is 7 points, and the degree of relevance of w14, which is connected to trader B, is 2 points. is set to

Further, the degree of association shown in FIG. 5 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degree of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence. In addition, the configuration related to artificial intelligence is the same as described in FIG.

In the example of the degree of association shown in FIG. 5, the node 61b is a node of the combination of the facility data for reference P01 and the supplier status information for reference P14. It is w16. The node 61c is a node of a combination of the reference vendor status information P15 and P17 with respect to the reference equipment data P02.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually discriminating a trader from now on. In such a case, the region where the trader is actually to be discriminated is similarly entered. Then, it acquires the facility data and vendor status information arranged for each region in the database 3 .

Based on the newly acquired facility data and vendor status information, the optimum vendor is searched for. In such a case, reference is made to the degrees of association shown in FIG. 5 (Table 1) that have been acquired in advance. For example, when the newly acquired facility data is the same as or similar to P02, and the supplier status information is the same or similar to P17, the node 61d is associated via the degree of association. This node 61d is associated with dealer C with w19 and with dealer D with degree of association w20. In such a case, trader C with 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 trader D whose degree of association is low but whose association itself is recognized may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

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

Intermediate nodes 61 may be selected based on degrees of association w1 to w12 extending from this input. In other words, the greater the degree of association w1 to w12, the heavier the weight in selecting the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weighting in selecting the intermediate nodes 61 may all be the same.

According to the present invention, in addition to the above-mentioned reference facility data, instead of the above-mentioned reference business situation information, a combination of reference weather information related to the weather at the location of the facility, and 3 A solution search may be performed based on the degrees of association of stages or higher.

This reference weather information, which is added as an explanatory variable instead of the reference business condition information, is all information about the weather at the location of the facility, such as weather (clear, cloudy, rainy), disaster (typhoon, heavy rain, etc.). , temperature, humidity, etc. In addition to the necessity and urgency of maintenance, the weather also affects the time at which a contractor can arrive, so this is also included as reference information. The reference weather information and weather information may be obtained by taking in the weather at that time from data provided by the Meteorological Agency or a private company, or by inputting the weather ascertained by oneself.

When searching for a solution, the facility data of the facility to be proposed and the weather information at the location of the facility are actually acquired. Based on the newly acquired facility data and weather information, we will search for the most suitable supplier. In such a case, the pre-obtained association degree is referred to, and the trader is searched based on the method described above.

According to the present invention, in addition to the above-mentioned reference facility data, a combination of reference location information regarding the location of the facility instead of the above-mentioned reference vendor status information, and three or more stages of the combination with the vendor for the combination A solution search may be performed based on the degree of association.

This reference location information, which is added as an explanatory variable instead of the reference business situation information, is information about the location of the facility to be proposed, and is displayed, for example, by GPS coordinates or an address. The position of this facility is also added as an explanatory variable because it is governed by how fast the contractor can rush to the site depending on the degree of urgency.

At the time of searching for the solution, the location information of the facility is actually acquired together with the facility data. Based on newly acquired equipment data and location information, search for the most suitable supplier. In such a case, the pre-obtained association degree is referred to, and the trader is searched based on the method described above.

According to the present invention, in addition to the reference equipment data described above, instead of the reference operator condition information described above, a combination of reference congestion condition information relating to the traffic congestion condition of the road leading to the location of the equipment, and The solution search may be performed based on three or more levels of association with the trader for the combination.

This reference traffic congestion information, which is added as an explanatory variable instead of the reference business situation information, obtains the congestion degree of vehicles on the road up to the location of the facility from the data provided by the road traffic management organization. Alternatively, the state of traffic congestion may be determined from an image of the road captured by a camera installed on the road. In such a case, image analysis may be performed, or a well-known image analysis technique or a well-known deep learning technique may be used. In this way, the density of vehicles on the road is measured, statistically processed as necessary, and then quantified as the degree of congestion.

When searching for a solution, actual facility data and traffic congestion information on roads leading to the position of the proposed facility are acquired. The method of acquiring the traffic congestion information is the same as the method of acquiring the traffic congestion information for reference described above. At the time of acquisition of the traffic congestion information, it may be determined by utilizing the image analysis described above, and by extension, artificial intelligence. Next, based on the newly acquired facility data and the traffic congestion information, the optimum dealer is searched for. In such a case, the pre-obtained association degree is referred to, and the trader is searched based on the method described above.

According to the present invention, in addition to the above-mentioned reference facility data, a combination of reference external environment information related to the external environment instead of the above-described reference business situation information, and three or more steps with the business for the combination. A solution search may be performed based on the degree of association.

This external environmental information for reference added as an explanatory variable instead of the business situation information for reference includes economic data (GDP, employment statistics, industrial production index, capital investment, labor force survey, etc.), household data (household consumption survey, Household budget data, average working hours per week, statistical data on savings, statistical data on annual income, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market, land price, vacant house data, etc.), natural environment data (disaster data) , temperature data, precipitation data, wind direction data, humidity data, etc.). The external environmental information includes all external information of the company to be screened, in addition to the part or the whole of these data. The external environment information for reference may categorize the external environment itself. For example, it may be classified by separating data in employment statistics. Moreover, it may be categorized according to a pattern (for example, a pattern such as whether the growth rate of GDP increases rapidly or gradually).

When searching for a solution, actual equipment data and external environment information at the time of the proposal are acquired. The acquisition method of the external environment information is the same as the acquisition method of the reference external environment information described above. Next, based on the newly acquired facility data and the external environment information, a search is made for the most suitable supplier. In such a case, the pre-obtained association degree is referred to, and the trader is searched based on the method described above.

According to the present invention, in addition to the above-mentioned reference facility data, there are three stages: a combination of reference usage status information regarding facility usage status instead of the above-described reference vendor status information, and a vendor for the combination. A solution search may be performed based on the degree of association described above.

This reference usage status information, which is added as an explanatory variable instead of the reference vendor status information, indicates the usage status of the equipment, and consists of data such as the number of years the equipment has been used, the date of installation, and the frequency of use. . The reference usage status information may be categorized by usage frequency patterns (for example, patterns such as whether the growth rate of time-series changes in usage increases rapidly or gradually). Reference usage information and usage information may be obtained from a database managed by an individual or corporation that owns the equipment, or from the reference equipment data and equipment data described above. good.

At the time of searching for a solution, the equipment data and usage information of the target equipment are actually acquired. The method of acquiring the usage status information is the same as the method of acquiring the reference usage status information described above. Next, based on the newly acquired facility data and the usage information, the most suitable supplier is searched for. In such a case, the pre-obtained association degree is referred to, and the trader is searched based on the method described above.

According to the present invention, in addition to the above-mentioned reference facility data, instead of the above-mentioned reference vendor situation information, a combination of reference failure history information related to the failure history of the facility up to now, and a vendor for the combination The solution search may be performed based on the degrees of association of three or more stages.

This reference failure history information, which is added as an explanatory variable instead of the reference vendor status information, indicates the failure history of the facility, and indicates whether or not the facility has ever failed, and if so, , the date, number of times, frequency, details of failure, etc. The failure history information for reference may be categorized according to the pattern of the number of failures (for example, patterns such as whether the growth rate of the time-series change in the number of failures increases rapidly or gradually). Reference failure history information and failure history information may be obtained by using a database managed by an individual or corporation that owns the equipment, or by obtaining the reference equipment data and equipment data described above. good.

When searching for a solution, actually acquire equipment data and failure history information of the target equipment. A method of acquiring the failure history information is the same as the method of acquiring the reference failure history information described above. Next, based on the newly acquired equipment data and the failure history information, the optimum dealer is searched for. In such a case, the pre-obtained association degree is referred to, and the trader is searched based on the method described above.

According to the present invention, in addition to the reference equipment data described above, there are three or more stages of combinations of reference equipment type information related to equipment types in place of the reference contractor status information described above, and contractors for the combination. The solution search may be performed based on the degree of association between .

This reference equipment type information, which is added as an explanatory variable instead of the reference contractor status information, indicates the type of equipment, such as lighting equipment, air conditioning equipment, ventilation equipment, lifting equipment such as elevators, water equipment, etc. In addition to the general information, the information may be subdivided to the model number level of the products that make up the facility. The reference equipment type information and the equipment type information can be obtained by using a database managed by the individual or corporation that owns the equipment, or by using the reference equipment data and equipment data described above. good.

When searching for a solution, the equipment data and the equipment type information of the target equipment are actually acquired. The acquisition method of the facility type information is the same as the acquisition method of the reference facility type information described above. Next, based on the newly acquired equipment data and the equipment type information, an optimum trader is searched for. In such a case, the pre-obtained association degree is referred to, and the trader is searched based on the method described above.

According to the present invention, in addition to the above-mentioned reference facility data, instead of the above-mentioned reference business situation information, a combination of reference customer desire information regarding the customer's desire to use the facility, and a business operator for the combination You may make it search for a solution based on the degree of association of three or more steps with.

This customer request information for reference, which is added as an explanatory variable instead of the information on the status of reference contractors, indicates all the customer's wishes regarding equipment maintenance, including delivery dates, costs, actual maintenance methods, and desired contractors or It indicates the type of equipment such as the person in charge and the attributes of the person in charge (degree of experience, gender, age, etc.) In addition to equipment, it may be subdivided to the model number level of the products that make up the facility. The reference customer request information may be obtained by inputting the request received from the customer who actually ordered the maintenance into the database.

When searching for a solution, the actual facility data and the customer's desired information obtained from the customer who uses the target facility are obtained. The method of acquiring customer desired information is the same as the method of acquiring customer desired information for reference described above. Next, based on the newly acquired equipment data and customer's desired information, the optimum trader is searched for. In such a case, the pre-obtained association degree is referred to, and the trader is searched based on the method described above.

According to the present invention, in addition to the above-mentioned reference facility data, instead of the above-mentioned reference business situation information, a combination of reference atmosphere information regarding the atmosphere around the facility, and 3 A solution search may be performed based on the degrees of association of stages or higher.

This reference atmosphere information, which is added as an explanatory variable instead of the reference operator status information, is composed of all data relating to the atmosphere around the facility, including temperature, humidity, sound, and the like. The temperature and humidity are measured by a thermometer and a hygrometer, and the sound is measured by a sonic meter, a sound sensor, and the like.

When searching for a solution, actual equipment data and atmosphere information obtained from customers who use the target equipment are acquired. The atmosphere information acquisition method is the same as the reference atmosphere information acquisition method described above. Next, based on the newly acquired equipment data and the atmosphere information, the optimum dealer is searched for. In such a case, the pre-obtained association degree is referred to, and the trader is searched based on the method described above.

In the above-mentioned degree of relevance, the degree of relevance is expressed by a 10-level evaluation, but it is not limited to this, and it may be expressed by a degree of relevance of 3 or more stages. 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 associated with each other, either 1 or 0.

According to the present invention configured as described above, anyone can easily determine and search for a trader without special skills or experience. Further, according to the present invention, it is possible to judge the search solution with a higher degree of accuracy than a human being does. Furthermore, by configuring the degree of association described above with artificial intelligence (neural network, etc.) and making it learn, it is possible to further improve the discrimination accuracy.

Since the above-described input data and output data may not be exactly the same in many cases during the learning process, information obtained by classifying these input data and output data according to type may be used. That is, the information P01, P02, . . . P15, 16, . A data set may be created between the data and the output data for learning.

Further, according to the present invention, as shown in FIG. 6, a trader is determined based on the degree of relevance of a combination of two or more types of information, ie, information for reference U and information for reference V. FIG. It is assumed that this reference information U is the reference equipment data, and the reference information V is any reference information other than the reference equipment data.

At this time, the output obtained for the reference information U may be used as input data as it is, and may be associated with the output (vendor) through the intermediate node 61 of the combination with the reference information V. FIG. For example, after generating an output solution for reference information U (reference equipment data) as shown in FIG. You may make it search an output (vendor).

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

By discriminating the most probable trader based on the degree of association represented by numerical values of three or more levels, search is performed in order of the degree of association in a situation where there are multiple candidates for the possibility of a search solution. It is also possible to display If it is possible to display to the user in descending order of degree of association in this way, it is also possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to make a judgment without overlooking even a very low output discrimination result such as a correlation degree of 1%. Remind the user that even discrimination results with an extremely low degree of association are connected as slight signs, and that once in dozens or hundreds of times, the discrimination results may be useful. be able to.

Furthermore, according to the present invention, there is an advantage that a search policy can be determined by setting thresholds by performing a search based on three or more degrees of association. If the threshold value is lowered, even if the degree of association is 1%, it can be picked up without omission. be. On the other hand, if the threshold is set high, there is a high possibility that the optimal search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to place emphasis on based on the way of thinking of the user side and the system side, and it is possible to increase the degree of freedom in selecting such points to place emphasis on.

Furthermore, in the present invention, the degree of association described above may be updated. This update may reflect information provided via a public communication network such as the Internet, for example. In addition, when each reference information including reference equipment data is acquired, and knowledge, information, and data related to the vendor are acquired, the degree of association is increased or decreased according to these.

In such a case, collect examples of whether or not the reference equipment data and other reference information actually met, and the selection result of the vendor, and increase the degree of association according to the number of examples, or lower it. At this time, information corresponding to the reference information, including the facility data described above, may be acquired and updated based on these when the determination is made.

In other words, this update corresponds to learning in terms of artificial intelligence. Since new data is acquired and reflected in the learned data, it can be said that it is a learning act.

Also, the process of initially building a trained model and the above-described updating 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 data sets of input data and output data and learning it, we read information corresponding to the input data and make it learn, from which the degree of association related to the output data is self-formed. You can let it run.

In addition, according to the present invention, the selected construction company may be automatically arranged for the maintenance work. In such a case, the name of the proposed trader (company name, personal name, office, branch office, etc.) is obtained as text data. Then, the document is completed by inserting the text data of the name into the electronic data document format of any of the quotation, request, completion, and bill prepared in advance. In that case, insert the date and request content into the document as appropriate. The content of the request may be extracted from the reference information described above, or may be obtained from the customer's desired status.

The completed electronic data documents will be sent to the email address of the company actually selected, or if managed on a website, uploaded on the website.

Second Embodiment A second embodiment will be described below. In carrying out the second embodiment, the vendor proposal system 1, the information acquisition unit 9, the search device 2, and the database 3 used in the first embodiment are used in the same manner. Description of each of these configurations is omitted below by citing the description of the first embodiment.

In the second embodiment, as a search solution, maintenance costs associated with the maintenance described above are determined. The maintenance cost is the price to be paid to the contractor who actually entrusted the maintenance. When constructing learning data, actually paid maintenance costs may be accumulated, read out, and included in the data set.

In such a case, first, the reference equipment data equipment data and the maintenance cost data set are learned.

In the example of FIG. 7, it is assumed that the input data are reference facility data P01, P02, and P03 in each area. Such reference facility data P01, P02, P03 as input data are linked to maintenance costs as output.

The reference facility data P01, P02, and P03 are associated with each other through three or more degrees of association with the maintenance costs A to B as output solutions. The facility data for reference is arranged on the left side through this degree of association, and each maintenance cost is arranged on the right side through the degree of association. The degree of relevance indicates the degree of relevance with which maintenance cost the reference equipment data arranged on the left side is highly related. In other words, the degree of association is an index indicating which maintenance cost each reference equipment data is likely to be associated with, and is used in selecting the most probable maintenance cost for each reference equipment data. It indicates the accuracy of

Also, this degree of association may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degree of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data through a data set of previous reference equipment data for each region and maintenance costs, the above-mentioned learned data is used to actually determine new maintenance costs from now on. It will be used to search for maintenance costs. These data sets may be created by reading from a database managed by a vendor. Since the solution search method is the same as that of the above-described first embodiment, the description thereof will be omitted.

In the second embodiment, as a search solution, a maintenance period related to the period required for maintenance may be obtained as an alternative to the maintenance cost. In such a case, similarly, when constructing the learning data, the actually required maintenance period may be accumulated, read out and included in the data set.

In the second embodiment, the search solution may be a maintenance method that replaces the maintenance cost. In such a case, similarly, when constructing the learning data, the actually required maintenance method may be accumulated, read out, and included in the data set.

In the second embodiment, the search solution may be maintenance frequency instead of maintenance cost. The maintenance frequency referred to here indicates the frequency according to the number of times the contractor performs maintenance in a predetermined period. In such a case, similarly, when constructing the learning data, the number of times of maintenance actually required is accumulated, and the maintenance frequency for a predetermined period is calculated by reading it out and included in the data set. good.

These search solutions may be classified by region and type of maintenance.

Further, in the second embodiment, the insurance conditions linked to the obtained maintenance cost may be calculated. The insurance referred to here means that a business operator who acts as an insurance policyholder for a customer's insured interests takes out maintenance cost compensation insurance in advance. is received. As a result, there is no need for customers to pay maintenance fees directly to contractors.

Such insurance conditions include all information and requirements that make up the insurance scheme, such as the insurance period, premiums, compensation conditions, exclusion clauses, insurance claims (maintenance cost compensation), payment limits, etc. Denotes various specific concepts that define an item.

Such insurance conditions are set so as to be optimal in relation to maintenance cost compensation. Therefore, as a continuity requirement of the insurance contract, the insurance premium rate applied to each contract year is appropriately adjusted by the result rating method according to the increase or decrease in the insurance payment performance as compensation for maintenance costs. is desirable.

In such a case, insurance conditions may be linked to individual maintenance costs, and the insurance conditions linked to the obtained maintenance costs may be calculated.

In addition, maintenance costs and other related information obtained by the method described above are stored for a predetermined period of time. Then, from the above accumulated information, it is possible to make trial calculations of the annual average amount of maintenance costs, statistically analyze the frequency of failure occurrence, and modify the applicable insurance conditions. In either case, the various applicable insurance terms may be based on any known method. The purpose of using information is to balance the income and expenditure of the business based on the law of large numbers and to achieve a long-term leveled overall cost burden structure based on stable insurance contracts.

In such a case, the maintenance costs obtained by the method described above are accumulated for a predetermined period, and the total amount, average value, maximum value and median value of the accumulated maintenance costs within the predetermined period are obtained. Based on these obtained values, insurance conditions are obtained. As a method of obtaining this insurance condition, the insurance period, insurance premium, compensation conditions, exemption clause, and insurance amount are determined according to changes in the total amount, average value, maximum value, and median value of the accumulated maintenance costs within the above-mentioned predetermined period. , a conversion formula that changes the payment limit, etc., and the total amount, average value, maximum value, and median value of the maintenance cost within the above-mentioned predetermined period, the insurance period, insurance premium, compensation conditions, and disclaimer , an insurance amount, a payment limit, etc. may be prepared in advance, and the insurance conditions may be obtained by referring to the template.

Note that the above-described predetermined period for summing up the obtained maintenance costs may be composed of any period (days, months, years).

Even when the maintenance frequency is used as a search solution, the number of times of maintenance obtained by the method described above is accumulated for a predetermined period, and the maintenance frequency is obtained from the accumulated number of times of maintenance. Then, the average value, maximum value, and median value of this maintenance frequency are obtained. Based on these obtained values, insurance conditions are obtained. As a method of obtaining this insurance condition, the insurance period, insurance premium, compensation conditions, disclaimer, insurance money, payment limit, etc. change according to changes in the average value, maximum value, and median value of accumulated maintenance frequency. The insurance period, insurance premiums, compensation conditions, disclaimers, insurance claims, payment limits, etc. A template may be prepared in advance and the insurance conditions may be obtained by referring to the template.

Also, the insurance conditions may be obtained based on both the maintenance frequency and the maintenance cost described above. In such a case, both the maintenance frequency and the maintenance cost obtained by searching by the above-described method may be used, but it is not limited to this, and either one is obtained by another method. Alternatively, the conventional method may be used to measure the maintenance frequency and maintenance cost for a predetermined period.

In the present invention, such insurance conditions may be automatically obtained and the calculation results may be output. In such a case, for example, it is conceivable to prepare in advance a table of basic insurance premiums (insurance cost excluding costs and profits corresponding to insurance companies) corresponding to total maintenance costs for a predetermined period.

In addition, in the second embodiment, when searching for applicable insurance conditions, as shown in FIG. may be calculated. The maintenance work content here is a type of what kind of maintenance work is actually performed, and in addition to major classifications such as replacement, maintenance, repair, replacement, etc. It may be limited to

Also, the maintenance work content may be developed into situation organizing information that actually organizes the situation of the facility, or may be substituted for this. Here, the status sorting information is a typed representation of the actual status of the facility, and indicates the extent to which the facility has deteriorated, the soundness of the facility, and the status of the facility. This is a categorization of items that require a certain degree of maintenance and work.

Such maintenance work content and situation sorting information may be determined and classified by an expert in the field or a person skilled in the art.

When obtaining the insurance conditions, the insurance conditions are linked to such maintenance work details and situation sorting information, and the insurance conditions linked to the maintenance work details and situation sorting information found in the solution search are output. can be

In addition to this, the content of maintenance work and status information are stored for a predetermined period of time. Statistical data, such as the total number of times and averages of each type of maintenance work content accumulated for a predetermined period of time and the situation sorting information, etc., may be output, and based on this, the applied revised insurance conditions may be planned. Since maintenance costs are often related to such maintenance work details and situation sorting information, insurance conditions can also be similarly narrowed down based on the maintenance work details and situation sorting information. In any case, the calculation method of the insurance conditions may be based on any known method. In other words, considering the relationship between the insurance premium collected from the business operator, the content of maintenance work, the status information, and the amount of work and costs based on this, any well-known method should be used to calculate the insurance premium and insurance conditions. good too. The search for the insurance conditions is not limited to the above-described embodiment, and the insurance conditions may be learned instead of the content of the maintenance work as the output of the search solution in FIG. In such a case, each reference information described above and the actual insurance conditions at that time are obtained from customers or dealers, and these are used as a data set for learning. This method also makes it possible to search for applicable modified insurance conditions.

Both the first embodiment and the second embodiment are not limited to the embodiments described above. For example, as shown in FIG. (maintenance cost, maintenance method, maintenance period, maintenance frequency, maintenance work content, situation sorting information, insurance conditions, etc.) may be used at three or more levels of association. In such a case, a solution search is performed based on three or more levels of degree of association between the reference information corresponding to the newly acquired information and the trader. The basic reference information includes all of the above-mentioned reference information (reference facility data, reference business situation information, reference weather information, reference location information, reference traffic congestion information, reference external environment information, reference use status information for reference, failure history information for reference, equipment type information for reference, customer request information for reference, atmosphere information for reference, etc.) can be applied.

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

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

The other reference information referred to here corresponds to any reference information other than the basic reference information when any of the reference information described above is used as the basic reference information.

For example, it is assumed that in the reference weather information F, which is one of the other reference information, the trader B was often identified in the past. When weather information corresponding to such reference weather information F is newly acquired, processing is performed to increase the weight of the search solution B as the trader, in other words, the search solution B of the trader is connected. Set in advance to process.

For example, the other reference information G is an analysis result that suggests a search solution C more as a trader, and the reference information F is an analysis result that more suggests a search solution D as a trader. and After the setting with the reference information in this way, when the actually acquired information is the same as or similar to the reference information G, processing to increase the weight of the trader C is performed. On the other hand, when the actually acquired information is the same as or similar to the reference information F, processing is performed to increase the weight of the trader D. FIG. In other words, the degree of association itself leading to a trader may be controlled based on the reference information FH. Alternatively, after the dealer is determined only by the degree of association described above, the obtained search solution may be modified based on the reference information FH. In the latter case, how and with what weight the trader as the search solution is to be modified based on the reference information F to H is to reflect what is designed on the system side each time.

The reference information is not limited to 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 a case leads to a trader suggested by the reference information, the higher the discriminant pattern as a search solution obtained through the degree of association may be corrected.

Similarly, as shown in FIG. 10, even in the case of forming the degree of relevance with a trader for a combination of reference information that is the keynote and other reference information, the reference information that is the keynote is any Information for reference (facility data for reference, operator status information for reference, weather information for reference, location information for reference, congestion status information for reference, external environment information for reference, usage status information for reference, failure history information for reference, reference equipment type information for reference, customer desired information for reference, atmosphere information for reference, etc.) can also be applied. Other reference information includes any reference information other than the underlying reference information.

At this time, if the basic reference information is the reference facility data, the other reference information includes any other reference information.

In such a case as well, the trader can be estimated by searching for solutions in the same way. At this time, as shown in FIG. 9 described above, for the search solution obtained through the degree of association, the vendor is corrected through further reference information (reference information F, G, H, etc.). may

At this time, the degree of association may be learned by combining not only one piece of other reference information but also two or more pieces of information.

Further, as shown in FIG. 11, the degree of association may be formed between only the basic reference information and the trader. This basic reference information is any reference information in the first and second embodiments (reference facility data, reference business situation information, reference weather information, reference location information, reference traffic congestion information, etc.). , external environment information for reference, use status information for reference, failure history information for reference, equipment type information for reference, customer desire information for reference, atmosphere information for reference, etc.) are also applicable. Description of the solution search method in FIG. 11 will be omitted by citing the description of FIG.

In the present invention, as shown in FIG. 8, after the content of maintenance work or the information for sorting out the situation is searched for solutions, each of the above-mentioned searched solutions (contractors, maintenance workers, cost, maintenance method, maintenance frequency, maintenance period, insurance conditions).

In such a case, the input is the content of maintenance work or status information, the output is each search solution (contractor, maintenance cost, maintenance method, maintenance period, maintenance frequency, insurance conditions), and the input and output are related as described above. You may make it associate and learn via degree. Then, as shown in FIG. 8, after performing a solution search for the content of maintenance work or the information for sorting out the situation, by inputting the content of the maintenance work searched for the solution or the information for sorting out the situation, a search solution (contractor , maintenance cost, maintenance method, maintenance period, maintenance frequency, insurance conditions). In such a case, the maintenance work is performed based on the reference information corresponding to the obtained information by using the first association degree of three or more levels between the above-mentioned reference information and the categorized maintenance work content or situation arrangement information. Identify content or contextual information. Next, a second degree of association of three or more levels between reference maintenance work details or reference situation organization information acquired in the past and search solutions (contractor, maintenance cost, maintenance method, maintenance period, maintenance frequency, insurance conditions) is calculated. A search solution is obtained based on the maintenance work content for reference or the situation arrangement information for reference corresponding to the maintenance work content or situation arrangement information that is used and searched for a solution.

In addition, the present invention learns two or more of the search solutions (contractor, maintenance cost, maintenance method, maintenance period, maintenance frequency, insurance conditions, maintenance work content, situation arrangement information) as one search solution. good too. In other words, the dealer and its maintenance cost may be learned as one search solution. As a result, the trader to be selected and the maintenance fee are output as an estimation result.

As a result, when automatically making arrangements with a contractor, insert the estimated contractor and maintenance fee into the format of any electronic data document such as an estimate, request, completion form, or invoice. can complete these documents.

1 maintenance company selection system 2 search device 21 internal bus 23 display unit 24 control unit 25 operation unit 26 communication unit 27 determination unit 28 storage unit 61 node

Claims (16)

In a maintenance cost prediction program that predicts the cost of maintaining equipment,
an information acquisition step of acquiring equipment data from equipment to be maintained;
Predict maintenance costs based on the reference equipment data acquired in the above information acquisition step, using three or more levels of association between reference equipment data acquired from equipment in the past and maintenance costs. A maintenance cost prediction program characterized by causing a computer to execute a prediction step. In the information acquisition step, facility data consisting of one or more of electric power, electricity, voltage, vibration, and sound is acquired from the facility;
2. The method according to claim 1, wherein the prediction step uses three or more levels of association between reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound, and maintenance costs. Maintenance cost prediction program. In the information acquisition step, the vendor status information regarding the status of the vendor is acquired,
In the prediction step, a combination of the reference equipment data and the reference contractor status information on the contractor status and the degree of association with maintenance costs at three levels or more are used, and the above obtained in the information acquisition step is used. 3. The maintenance cost prediction program according to claim 1, wherein the maintenance cost is predicted based on the reference vendor status information corresponding to the cost status information. In the information acquisition step, the vendor status information regarding the status of the vendor is acquired,
3. The maintenance cost according to claim 1 or 2, wherein in the prediction step, priority is given to those with a higher degree of association, and the maintenance cost is predicted based on the vendor status information acquired in the information acquisition step. prediction program. The information acquisition step acquires weather information relating to the weather at the location of the facility;
The prediction step uses a combination of the reference facility data and the reference weather information related to the weather at the location of the facility, and the degree of association with maintenance costs at three or more levels, and furthermore, the information acquired in the information acquisition step. 3. The program for predicting maintenance costs according to claim 1, wherein maintenance costs are predicted based on reference weather information corresponding to said weather information. In the information acquisition step, location information regarding the location of the equipment is acquired,
In the prediction step, a combination of the reference facility data and the reference location information related to the location of the facility and three or more levels of association with maintenance costs are used, and the location acquired in the information acquisition step is used. 3. The maintenance cost prediction program according to claim 1 or 2, wherein the maintenance cost is predicted based on the reference positional information corresponding to the information. The information acquisition step acquires traffic congestion information regarding traffic congestion on roads leading to the location of the facility,
In the prediction step, a combination of the reference equipment data and the reference traffic congestion information regarding the traffic congestion condition of the road leading to the location of the equipment and the degree of association with the maintenance cost of three or more stages are used, and 3. The maintenance cost prediction program according to claim 1 or 2, wherein the maintenance cost is predicted based on reference traffic congestion information corresponding to the traffic congestion information acquired in the information acquisition step. In a maintenance cost prediction program that predicts the cost of maintaining equipment,
an information acquisition step of acquiring vendor status information relating to the status of the vendor;
The maintenance cost is predicted based on the reference situation information corresponding to the situation information acquired in the above information acquisition step, using three or more levels of correlation between the reference situation information concerning the situation of the contractor and the costs to be estimated. A maintenance cost prediction program characterized by causing a computer to execute a prediction step. In the information acquisition step, facility data is acquired from the facility to be maintained,
9. The maintenance cost prediction program according to claim 8, wherein in the prediction step, priority is given to data with a higher degree of association, and maintenance costs are predicted based on the equipment data acquired in the information acquisition step. The program for predicting maintenance costs according to any one of claims 1 to 9, further comprising a calculation step of calculating insurance conditions based on the maintenance costs predicted in the prediction step. The maintenance cost prediction program according to any one of claims 1 to 10, wherein the degree of association is composed of nodes of a neural network in artificial intelligence. In a maintenance cost prediction program that predicts the cost of maintaining equipment,
an information acquisition step of acquiring equipment data from equipment to be maintained;
Based on the reference equipment data acquired in the above information acquisition step, using the first association degree of three or more levels between the reference equipment data acquired from the equipment in the past and the categorized maintenance work content , a specific step for identifying the content of the maintenance work;
Maintenance cost is calculated based on the reference maintenance work content corresponding to the maintenance work content acquired in the above information acquisition step by using the second degree of association between the maintenance work content for reference acquired in the past and the maintenance cost in three or more stages. A maintenance cost prediction program characterized by causing a computer to execute a prediction step for prediction. In the maintenance period prediction program that predicts the maintenance period for equipment,
an information acquisition step of acquiring equipment data from equipment to be maintained;
Predict the maintenance period based on the reference equipment data acquired in the above information acquisition step, using the reference equipment data acquired from the equipment in the past and the degree of association with the maintenance period in three or more stages. A maintenance period prediction program characterized by causing a computer to execute a prediction step. In a maintenance method estimation program for estimating a method for maintaining equipment,
an information acquisition step of acquiring equipment data from equipment to be maintained;
The maintenance method is estimated based on the reference equipment data corresponding to the equipment data obtained in the above information obtaining step, using three or more levels of association between the reference equipment data obtained from the equipment in the past and the maintenance method. A maintenance method estimation program characterized by causing a computer to execute an estimation step. an information acquisition step of acquiring equipment data from equipment to be maintained;
Using the reference equipment data acquired from equipment in the past and the first degree of association with maintenance costs in three or more stages, based on the reference equipment data corresponding to the equipment data acquired in the above information acquisition step, maintenance costs are calculated. an estimation step to estimate;
an insurance condition calculation step of accumulating maintenance costs estimated in the estimating step for a predetermined period of time and calculating insurance conditions based on one of the total amount, average value, maximum value, and median value of the accumulated maintenance costs; An insurance condition proposal program characterized by being executed. In the information acquisition step, the maintenance frequency is further acquired,
16. The insurance conditions proposal program according to claim 15, wherein said insurance conditions calculation step further calculates insurance conditions based on said maintenance frequency.

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