WO2022264273A1

WO2022264273A1 - Processing device and processing method

Info

Publication number: WO2022264273A1
Application number: PCT/JP2021/022714
Authority: WO
Inventors: 豊松枝; 健太久瀬; 奈々穂大澤
Original assignee: 三菱電機ビルソリューションズ株式会社; 三菱電機株式会社
Priority date: 2021-06-15
Filing date: 2021-06-15
Publication date: 2022-12-22
Also published as: CN117546189A; JPWO2022264273A1; JP7336582B2

Abstract

This processing device (100) processes information pertaining to maintaining a building facility (10) installed in a building (1). The processing device (100) comprises an acquisition unit (130), a classification unit (131), and an output unit (133). The acquisition unit (130) acquires a plurality of pieces of history information. The classification unit (131) classifies the plurality of pieces of history information into a plurality of groups by means of a clustering technique. The output unit (133) outputs the classification result from the classification unit (131). Each of the plurality of pieces of history information includes a handling time from the arrival of a maintenance worker at the building (1) until the work pertaining to the building facility (10) is completed. The plurality of groups include a first group that does not include a non-work time and a second group including the non-work time. The classification unit (131) classifies the plurality of pieces of history information into at least the first group and the second group on the basis of the handling time.

Description

Processing equipment and processing method

The present disclosure relates to processing apparatuses and processing methods.

　Regarding the maintenance of building equipment installed in buildings, the history of maintenance work is accumulated as history information. The history information includes the response time from the arrival of maintenance personnel to the completion of work on building equipment.

Conventionally, methods have been devised to predict the above-mentioned response time from accumulated history information. For example, there is a method of using an average value of past response times as a predicted value of response time. Further, as a technique for estimating the working time, for example, there is a technique described in Japanese Patent Laying-Open No. 2017-151490 (Patent Document 1).

JP 2017-151490 A

However, the response time may include not only working time but also non-working time such as the time until entering the building and the time when work is interrupted to arrange the parts necessary for the work. When non-work time with a different nature from the actual work time is included, it is difficult to predict the response time from a simple average value. Moreover, in the technique described in Patent Document 1, no consideration is given to a method of predicting the response time taking into account cases where time with different characteristics is included.

The present disclosure has been made to solve such problems, and the purpose thereof is to analyze the response time even when time of different nature such as non-work time is included. An object of the present invention is to provide a processing apparatus and a processing method that can be suitably performed.

A processing device according to the present disclosure processes information related to maintenance of building equipment installed in a building. The processing device comprises an acquisition unit, a classification unit, and an output unit. The acquisition unit acquires a plurality of pieces of history information. The classification unit classifies multiple pieces of history information into multiple groups by a clustering method. The output unit outputs a classification result of the classification unit. Each of the pieces of history information includes the response time from the arrival of maintenance personnel to the completion of the work on the building equipment. The multiple groups include a first group that does not include non-working time and a second group that includes non-working time. The classification unit classifies the pieces of history information into at least a first group and a second group based on the corresponding time.

The processing method according to the present disclosure is a method of processing information related to maintenance of building equipment installed in a building. The processing method includes the steps of acquiring a plurality of pieces of history information, classifying the pieces of history information into a plurality of groups by a clustering method, and outputting a classification result of the classifying step. Each of the pieces of history information includes the response time from the arrival of maintenance personnel to the completion of the work on the building equipment. The multiple groups include a first group that does not include non-working time and a second group that includes non-working time. The classifying step classifies the plurality of pieces of history information into at least a first group and a second group based on corresponding time.

According to the present disclosure, it is possible to suitably analyze response time even when time with different characteristics such as non-work time is included.

It is a figure which shows an example of the functional block diagram of the processing apparatus which concerns on 1st Embodiment. It is a figure which shows an example of the hardware constitutions of the processing apparatus which concerns on 1st Embodiment. It is a graph for explaining distribution of correspondence time. 4 is a flowchart of processing executed by the processing device according to the first embodiment; FIG. 7 is a diagram showing a display example of classification results according to the first embodiment; It is a figure which shows the example of a display of the calculation result which concerns on 1st Embodiment. It is a figure which shows the example of a display of the calculation result which concerns on 1st Embodiment. It is a figure which shows the example of a display of the calculation result which concerns on 1st Embodiment. It is a figure which shows an example of the functional block diagram of the processing apparatus which concerns on 2nd Embodiment. 9 is a flowchart of processing executed by a processing device according to the second embodiment;

Embodiments will be described below with reference to the drawings. In the following description, the same parts are given the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated.

[First Embodiment]
First, the processing apparatus 100 according to the first embodiment will be described. FIG. 1 is a diagram showing an example of a functional block diagram of a processing device 100 according to the first embodiment. FIG. 2 is a diagram showing an example of the hardware configuration of the processing device 100 according to the first embodiment.

The processing device 100 in the first embodiment is a device that processes information related to maintenance of building equipment installed in a building. Specifically, the processing device 100 classifies a plurality of pieces of work history information (also referred to as “history information”) stored in the storage unit 114, calculates statistical values based on the classified results, and The result is displayed on the display device 201 .

History information is information that records the contents of the work performed in response to inquiries, complaints, etc. as a history. As shown in FIG. 2, the history information records information on a plurality of buildings including buildings 1a to 1c.

Each of the multiple pieces of history information includes the response time. The response time is the time from the arrival of the maintenance personnel at the building (1a to 1c, etc.) to the completion of work on the building equipment (10a to 10c, etc.). For example, when maintenance work is performed in the building 1a, the response time is the time from when maintenance personnel arrive at the building 1a until the work on the building facility 10a is completed.

Each of the pieces of history information includes, in addition to the response time, information such as work date and time, elevator type (model), and elevator failure type (failure type).

　History information and response time will be explained in detail using Figures 3 to 8. In the following description, buildings such as buildings 1a to 1c are collectively referred to as "building 1", and building facilities such as buildings 10a to 10c are collectively referred to as "building facilities 10". Further, here, an elevator such as an elevator is assumed as the building equipment 10, but other building equipment may be used.

As shown in FIG. 1, the processing device 100 includes a storage unit 114, an acquisition unit 130, a classification unit 131, a calculation unit 132, and an output unit 133. Acquisition unit 130 acquires a plurality of pieces of history information stored in storage unit 114 .

The classification unit 131 classifies a plurality of pieces of history information and outputs classification results. The calculation unit 132 calculates a calculation result (statistical value) using the classification result. The output unit 133 outputs classification results and calculation results. The display device 201 displays the classification result and the calculation result output by the output unit 133 .

The display examples of the display device 201 in FIG. 1 are the display example of the classification result described later using FIG. 5 and the display example of the calculation result described later using FIG. In this display example, after classifying a plurality of pieces of history information into two groups, the results of calculating statistical values are displayed.

As shown in FIG. 2, the processing device 100 includes a CPU (Central Processing Unit) 111, a ROM (Read Only Memory) 112, a RAM (Random Access Memory) 113, a storage unit 114, and an I/O interface 120. have These are communicably connected to each other via a bus.

The CPU 111 comprehensively controls the entire processing device 100 . The CPU 111 develops a program stored in the ROM 112 in the RAM 113 and executes it. The ROM 112 stores a program describing the processing procedure of the processing performed by the processing device 100 .

The RAM 113 serves as a work area when the CPU 111 executes programs, and temporarily stores programs and data used when executing the programs. The storage unit 114 is a non-volatile storage device such as a HDD (Hard Disk Drive) or an SSD (Solid State Drive).

The I/O interface 120 is an interface for connecting the CPU 111 with the display device 201 or the input device 202 . A display device 201 and an input device 202 are connected to the processing device 100 .

The display device 201 is, for example, a display. The display device 201 displays the results output by the output unit 133 . Input device 202 is, for example, a keyboard or a mouse. For example, by operating the input device 202 , it is possible to cause the processing device 100 to execute a history information classification process or the like, or to select the content to be displayed on the display device 201 .

The processing device 100 stores work history information (history information) of the building equipment 10 (10a to 10c, etc.) such as elevators in the storage unit 114.

Details of history information and response time are described below. FIG. 3 is a graph for explaining the distribution of response times. In FIG. 3, the vertical axis indicates response time, and the horizontal axis indicates work date and time.

This response time may include non-work time (also referred to as "waiting time") unrelated to the above work. For example, the non-work time includes at least one of non-work time A, non-work time B, and non-work time C.

　Non-work time A is the time during which the work is suspended in order to arrange the parts necessary for the work. For example, if the board of the elevator is out of order, it may be necessary to arrange for board replacement. The non-work time B is the time from when maintenance personnel arrive at the building 1 until they enter the building 1 . The non-work time C is a time during which work is interrupted in order to obtain confirmation from the owner of the building 1 when exchanging paid parts. If the parts to be replaced are charged, it is necessary to check with the owner. This confirmation may necessitate a return visit to the building.

In the graph of FIG. 3, as non-working time, data including non-working time A (data with "wait" in the figure) and data without non-working time A (data with "no waiting" in the figure) is plotted.

As mentioned above, when arranging parts, maintenance personnel often pull up Building 1 once to make arrangements, and then work on a different day when the parts are available. In this case, even if the work would normally be completed in 1 to 2 hours, it may take several days to complete the work because the work must be arranged over a period of days.

In this way, when data with different characteristics such as data including non-working time A and data not including non-working time A are mixed, even if the average time is obtained as a statistical value, It is difficult to grasp the time required for

Therefore, in the first embodiment, the processing device 100 performs statistical processing after classifying such data with different properties. Processing executed by the processing device 100 and contents displayed on the display device 201 according to the first embodiment will be specifically described below with reference to FIGS. 4 to 8. FIG.

FIG. 4 is a flowchart of processing executed by the processing device 100 according to the first embodiment. FIG. 5 is a diagram showing a display example of classification results according to the first embodiment. 6 to 8 are diagrams showing display examples of calculation results according to the first embodiment.

The processing executed by the processing device 100 may be started by, for example, an operation by the user using the processing device 100 (an operation by the input device 202). Hereinafter, "step" is also simply referred to as "S".

As shown in FIG. 4, when the process executed by the processing device 100 starts, in S1, the acquisition unit 130 of the processing device 100 acquires a plurality of pieces of history information stored in the storage unit 114, and advances the process to S2.

In S2, the classification unit 131 of the processing device 100 classifies multiple pieces of history information into multiple groups by a clustering method, and advances the process to S3. Specifically, the plurality of groups includes a first group (no waiting) that does not include non-working time and a second group (with waiting) that includes non-working time. The classification unit 131 classifies a plurality of pieces of history information into at least a first group and a second group based on the corresponding time.

In S2, the classification unit 131 clusters the data based on the properties of the data (the degree of data gathering, etc.). In the present embodiment, clustering is performed using a Gaussian Mixture Model (GMM). This makes it possible to obtain a plurality of Gaussian distribution models (two of the first group and the second group in the example of FIG. 5, which will be described later). The clustering method is not limited to this, and may use SOM (self-organizing map), hierarchical clustering, or the like.

In S3, the classification unit 131 of the processing device 100 calculates the boundary time indicating the boundary between the first group and the second group based on the classified first group and second group, and advances the process to S4. .

In S4, the output unit 133 of the processing device 100 outputs the classification result of the classification unit 131, and advances the process to S5. Thereby, the display device 201 displays the output classification result.

For example, as shown in FIG. 5, the display device 201 displays a graph plotting the corresponding time on the horizontal axis and the frequency on the vertical axis. As shown in the figure, by clustering, the data is classified into the first group (no waiting) that has a peak when the response time is short, and the second group (with a wait) that has a peak when the response time is longer than the first group. The graph is displayed with

In the figure, each corresponding time is plotted (square markers) on the horizontal axis, and it can be seen that the appearance frequency is high near the peak of the first group and near the peak of the second group. Boundary times are also shown on the graph. In the present embodiment, the boundary time is defined as the time when the frequency of the first group and the frequency of the second group are equal (the time when the probabilities change in magnitude).

In S5, the calculation unit 132 of the processing device 100 calculates statistical values regarding the response time for each of the plurality of groups classified by the classification unit 131, and advances the process to S6. The statistics include the average response time of the first group and the average response time of the second group.

In S6, the calculation unit 132 of the processing device 100 predicts current or future statistical values from the time-series information of the statistical values, and advances the process to S7.

In S7, the output unit 133 of the processing device 100 outputs the calculation result of the calculation unit 132, and the process ends. The output unit 133 outputs time-series information of statistical values.

For example, as shown in FIG. 6, the display device 201 displays a graph in which the horizontal axis plots the work date and time, and the vertical axis plots the corresponding time. The graph plots the corresponding times (circular markers) of the first group and shows the transition of the average time X of the first group over time. In addition, the graph plots the corresponding time (square markers) of the second group and shows the transition of the average time Z of the second group over time.

For example, from the graph in FIG. 6, it is possible to analyze each situation, such as the average time X when waiting for parts arrangement etc. occurs does not change, but the average time X when no waiting occurs gradually improves. .

Here, the average value obtained in S5 may be, for example, the average time for each of the first group and the second group divided by month or year, or the average value of all data. may be

Furthermore, in S6, the predicted time XP, which is the predicted value of the average time X, is displayed as the statistical value predicted from the time-series information. The most recent average time X may be used as the predicted time XP, or the present or future predicted time XP may be estimated from the transition of the average time X using a method such as the least squares method.

In addition, it is possible to display statistical value information as shown in FIG. The pie chart shows the probability that the response time is within 1 hour (75%), the probability that the response time is 1-3 hours (12.5%), the probability that the response time is 3 hours or more (12.5%). ) are each shown as a pie chart.

In this example, the boundary time is calculated as 3 hours. Therefore, the probability that the response time is 3 hours or more=probability that non-work time due to parts arrangement occurs (also referred to as "waiting occurrence probability").

The average response time when there is no waiting (first group) is X hours, the probability of waiting is Y%, and the average response time when there is waiting (second group) is Z hours. It has been shown that The statistical values shown above are calculated in S5.

Also, as shown in FIG. 8, a graph showing the time transition of the boundary time may be displayed. The display device 201 displays a graph in which the horizontal axis plots the work date and time, and the vertical axis plots the corresponding time.

In the figure, the corresponding times of the first group (circle markers) and the corresponding times of the second group (square markers) are plotted. Boundary times are also indicated at these boundaries. In this example, the maintenance method is changed at the illustrated timing. For example, change the maintenance method by improving the maintenance manual or reviewing the parts ordering method.

In the figure, the boundary time drops when the maintenance method is changed. As a result, it is possible to grasp the effect of improving the response time by changing the maintenance method.

As described above, by classifying a plurality of pieces of history information based on corresponding time into at least a first group that does not include non-working time and a second group that includes non-working time, Even when time is included, the analysis regarding response time can be preferably performed. Furthermore, by calculating and outputting statistical values relating to response time for each of a plurality of classified groups, it is possible to perform statistics relating to response time after excluding non-work time that is unrelated to work. . In this case, it is also possible to predict the average response time (predicted time XP in FIG. 6) and grasp the improvement effect of changing the maintenance method (FIG. 8).

[Second embodiment]
FIG. 9 is a diagram showing an example of a functional block diagram of the processing device 100 according to the second embodiment. FIG. 10 is a flowchart of processing executed by the processing device 100 according to the second embodiment.

In the first embodiment, the processing device 100 includes a storage unit 114, an acquisition unit 130, a classification unit 131, a calculation unit 132, and an output unit 133. In the second embodiment, the processing device 100 further includes a dividing section 140 and a determining section 141. FIG. The second embodiment will be described below with reference to FIGS. 9 and 10. FIG. In the description of the second embodiment, points different from the first embodiment will be described, and descriptions of common parts will be omitted.

As shown in FIG. 10, when the process executed by the processing device 100 starts, in S11, the acquisition unit 130 of the processing device 100 acquires a plurality of pieces of history information stored in the storage unit 114, and advances the process to S12. This process is the same as S1.

Here, each of the plurality of pieces of history information includes a plurality of related information related to the building 1 (1a-1c, etc.) or the building equipment 10 (10a-10c, etc.) in addition to the response time. The multiple pieces of related information include the type of building 1 (type of building), the number of years the elevator has been in operation, the type (model) of the elevator, the type of elevator failure, the type of maintenance contract, and the skills of the maintenance staff. including at least one of

The types of buildings are classified into, for example, commercial buildings and office buildings. The types of elevator failures (failure types) are classified into, for example, door-related failures, brake-related failures, controller failures, and the like. Types of maintenance contracts include, for example, a contract to replace specified parts for a fee (hereinafter referred to as "A contract") or a contract to replace these parts free of charge (hereinafter referred to as "B contract"). be. The skills of maintenance personnel may be classified into, for example, 3 years or less of experience as maintenance personnel, 3 to 7 years, and 7 years or more.

In S12, the determining unit 141 of the processing device 100 determines information used by the dividing unit 140 to divide the plurality of pieces of history information from among the plurality of pieces of related information, and advances the process to S13. For example, the determining unit 141 determines to divide history information for each elevator model. Alternatively, or in addition, it may be decided to divide the history information by contract. For example, if there are model A, model B, contract A, and contract B, they are divided into four: model A and contract A, model A and contract B, model B and contract A, and model B and contract B.

The determining unit 141 may divide based on division information specified by the user. Further, the determination unit 141 may determine whether or not to divide based on the division information designated by the user, using a test or the like. When determination is made using a test, for example, the t-test, the Mann-Whitney U-test, or the like may be used. If the amount of data is small, it may not be possible to properly classify data with different properties (whether waiting occurs or not). In such a case, measures are taken to reintegrate the data divided above.

In S13, the dividing unit 140 of the processing device 100 divides the plurality of history information based on at least one of the plurality of related information, and advances the process to S14. Specifically, a plurality of pieces of history information are divided based on the information determined in S12.

In S14, the classification unit 131 of the processing device 100 classifies each of the pieces of history information divided by the division unit 140 into a plurality of groups, and advances the process to S15.

In S15, the classification unit 131 of the processing device 100 calculates the boundary time indicating the boundary between the first group and the second group based on the classified first group and second group, and advances the process to S16. . This process is the same as S3.

In S16, the output unit 133 of the processing device 100 outputs the classification result of the classification unit 131, and advances the process to S17. This process is the same as S4. As a result, for example, a graph showing the frequencies of the first group and the second group as shown in FIG. 5 may be shown for each divided piece of history information. For example, the above graph may be displayed for each type of maintenance contract.

In S17, the determining unit 141 of the processing device 100 determines information to be used by the dividing unit 140 to divide the plurality of pieces of history information from among the plurality of pieces of related information, and advances the process to S18. The determination unit 141 divides based on the division information designated by the user. This may be the same as or different from the decision of the decision unit 141 in S12. For example, in S12, the history information may be divided for each elevator model, and in S17, the history information may be divided for each elevator model and for each contract (divided into four as described above). In both S12 and S17, the history information may be divided for each model of the elevator and for each contract.

In S18, the dividing unit 140 of the processing device 100 further divides each of the plurality of groups classified by the classifying unit 131 based on at least one of the plurality of related information, and advances the process to S19. Specifically, each of the plurality of groups classified by the classification unit 131 is divided based on the information determined in S17.

In S19, the calculation unit 132 of the processing device 100 calculates statistical values for each of the multiple groups divided by the division unit 140 and classified by the classification unit 131, and advances the process to S20.

At S20, the output unit 133 of the processing device 100 outputs the calculation result of the calculation unit 132, and the process ends. The output unit 133 outputs time-series information of statistical values. This process is similar to S7.

For example, as shown in FIG. 9, a pie chart similar to the upper part of FIG. 7 may be displayed. In this example, the history data is divided for each type of maintenance contract, and a pie chart is displayed for each type of maintenance contract.

As described above, the "A contract" is a contract to exchange predetermined parts for a fee, and the "B contract" is a contract to exchange for free. In contract A, the probability that the response time is within 1 hour is 75%, the probability that the response time is 1 to 3 hours is 12.5%, and the probability that the response time is 3 hours or more is 12.5%. %.

On the other hand, in contract B, the probability that the response time is within 1 hour is 85%, the probability that the response time is 1 to 3 hours is 10%, and the probability that the response time is 3 hours or more is 5%. From this graph, it can be read that the response time is shorter for Contract B than for Contract A.

When replacing parts for a fee, it is necessary to check with the owner who has a maintenance contract, and there is a waiting time for that confirmation. Therefore, in the case of the B contract, which can be replaced free of charge, the response time is shorter than that of the A contract because no waiting time for confirmation occurs. For example, when proposing a maintenance contract, it is possible to suggest a contract B with a shorter response time while presenting this graph comparing contract A and contract B.

In addition, the lower part of FIG. 7 and the information may be displayed. In this example, the history data is divided for each failure type, and the average response time and the like are displayed for each failure type.

When the failure type is failure type C, the average response time when there is no waiting (first group) is X1 hours, and the probability of non-working time due to parts arrangement (waiting occurrence probability) is Y1%. It is indicated that the average response time when waiting occurs (second group) is Z1 time.

When the failure type is failure type D, the average response time when there is no waiting (first group) is X2 hours, and the probability of non-working time due to parts arrangement (waiting occurrence probability) is Y2%. It is shown that the average response time when there is a wait (second group) is Z2 time.

For example, in the case of door-related troubles (failures), there are many minor failures that can be resolved on the spot, such as simply having something stuck in the door. Minor failures include those that can be resolved by removing dust, poor contact of connectors, and the like. On the other hand, for example, it may take longer to complete work than door-related troubles due to brake-related or circuit board failures that require adjustment or parts replacement. In this way, it is possible to analyze and predict the response time for each failure type with different characteristics.

Regarding the type of building (building), for example, if it is a commercial building, there are restrictions on entry time during business hours, and depending on the building, there are some that can be entered 24 hours a day, and some that cannot be entered after hours. In this way, the nature of data also differs depending on the type of building.

Regarding the number of years the elevator has been in operation, the longer it is in operation, the more likely it is to malfunction due to aging. Regarding the model of the elevator, there are differences such as that there is no stock of parts in the case of an old model, or that it takes time to arrange. Also, it is assumed that the response time will vary depending on the skill (years of experience) of the maintenance staff.

As described above, after excluding non-work time that is unrelated to work, it is possible to suitably analyze the response time for each of a plurality of related information related to the building facility 10.

For example, the classification by the classification unit 131 as shown in FIGS. 9 and 10 may be performed in advance. Then, when a trouble occurs in the elevator, the control device of the elevator may report the model information of the elevator and the failure type, and the processing device 100 may be configured to receive the reported information. When receiving the notification information, the processing device 100 causes the calculation unit 132 to calculate the statistical value of the failure type in the notified model. By checking the average response time calculated by the calculation unit 132, the maintenance staff can quickly grasp the predicted value of the response time required to deal with the trouble, or the probability of occurrence of waiting time due to parts arrangement, etc. be able to.

In the event of an elevator malfunction, we are required to shorten the time the elevator is stopped due to the malfunction as much as possible (shorten the response time). By configuring as described above, for example, if it seems that the response time for arranging parts will be long, the response time can be shortened by arranging the parts early. Analysis of the response time can be performed more precisely and accurately for each of a plurality of related information related to the building equipment 10 as the past history information is accumulated. Thus, in this embodiment, it is possible to shorten the response time while effectively utilizing the accumulated past history information.

[Main configuration and effects]
Main configurations and effects of the above-described embodiment will be described below.

(1) The processing device 100 processes information related to maintenance of the building facilities 10 (10a to 10c, etc.) installed in the building 1 (1a to 1c, etc.). The processing device 100 includes an acquisition unit 130 , a classification unit 131 and an output unit 133 . Acquisition unit 130 acquires a plurality of pieces of history information. The classification unit 131 classifies multiple pieces of history information into multiple groups by a clustering method. The output unit 133 outputs the classification result of the classification unit 131 . Each of the plurality of pieces of history information includes the response time from the arrival of the maintenance personnel to the building 1 until the completion of the work on the building equipment 10 . The multiple groups include a first group that does not include non-working time and a second group that includes non-working time. The classification unit 131 classifies a plurality of pieces of history information into at least a first group and a second group based on the corresponding time. In this way, by classifying a plurality of pieces of history information into at least a first group that does not include non-working time and a second group that includes non-working time based on the corresponding time, it is possible to Even if it is included, it is possible to preferably perform analysis on response time.

(2) Based on the classified first group and second group, the classification unit 131 calculates the boundary time indicating the boundary between the first group and the second group. This makes it possible to grasp the time that is the boundary between the first group and the second group.

(3) The non-work time is the time from when maintenance personnel arrive at building 1 (1a to 1c, etc.) until they enter building 1 (1a to 1c, etc.), and to arrange the parts necessary for work. It includes at least one of the time during which the work is suspended and the time during which the work is suspended for obtaining confirmation from the owner of the building 1 when replacing the charged parts. As a result, it becomes possible to analyze the response time after excluding the time for arranging parts that is unrelated to the work.

(4) The processing device 100 further includes a calculator 132 . The calculation unit 132 calculates a statistical value regarding the response time for each of the plurality of groups classified by the classification unit 131 . The output unit 133 further outputs the calculation result of the calculation unit 132 . As a result, non-work time unrelated to work can be excluded, and statistics regarding response time can be obtained.

(5) The statistics include the average response time of the first group and the average response time of the second group. As a result, it is possible to grasp the average value of the response time after excluding the non-work time unrelated to work.

(6) The output unit 133 outputs time-series information of statistical values. As a result, it is possible to grasp the passage of time of the statistical value after excluding the non-work time unrelated to the work.

(7) The calculation unit 132 predicts current or future statistical values from the time-series information of the statistical values. Predict current or future statistics by excluding non-work time that is not related to work.

(8) Each of the plurality of history information includes, in addition to the response time, a plurality of related information related to the building 1 (1a-1c, etc.) or the building equipment 10 (10a-10c, etc.). The processing device 100 further includes a dividing section 140 . The dividing unit 140 divides a plurality of pieces of history information based on at least one of the plurality of pieces of related information. The classification unit 131 classifies each of the pieces of history information divided by the division unit 140 into a plurality of groups. As a result, it is possible to suitably analyze the response time for each of the plurality of related information related to the building facility 10 .

(9) The division unit 140 further divides each of the plurality of groups classified by the classification unit 131 based on at least one of the plurality of related information. The calculation unit 132 calculates a statistical value for each of the plurality of groups divided by the division unit 140 and classified by the classification unit 131 . As a result, statistical values regarding the response time can be grasped for each of a plurality of pieces of related information related to the building equipment 10 .

(10) The plurality of related information includes at least one of the type of building 1 (1a to 1c, etc.), the type of elevator, the type of elevator failure, and the type of maintenance contract. As a result, statistical values relating to response time can be grasped for each type of building 1, type of elevator, type of elevator failure, and type of maintenance contract.

(11) The processing device 100 further includes a determination unit 141 . The determining unit 141 determines information to be used by the dividing unit 140 to divide the plurality of pieces of history information from among the plurality of pieces of related information. When the information to be used for dividing multiple pieces of history information is determined by testing, there is no need to consider how to divide the pieces of history information appropriately.

(12) The processing method is a method of processing information related to maintenance of the building equipment 10 (10a-10c, etc.) installed in the building 1 (1a-1c, etc.). The processing method includes the steps of acquiring a plurality of pieces of history information, classifying the pieces of history information into a plurality of groups by a clustering method, and outputting a classification result of the classifying step. Each of the plurality of pieces of history information includes the response time from the arrival of the maintenance personnel to the building 1 until the completion of the work on the building equipment 10 . The multiple groups include a first group that does not include non-working time and a second group that includes non-working time. The classifying step classifies the plurality of pieces of history information into at least a first group and a second group based on corresponding time. In this way, by classifying a plurality of pieces of history information into at least a first group that does not include non-working time and a second group that includes non-working time based on the corresponding time, it is possible to Even if it is included, it is possible to preferably perform analysis on response time.

The embodiments disclosed this time should be considered illustrative in all respects and not restrictive. The scope of the present disclosure is indicated by the scope of claims rather than the above description, and is intended to include all changes within the meaning and scope of equivalence to the scope of claims.

1, 1a to 1c building, 10, 10a to 10c building equipment, 100 processing device, 111 CPU, 112 ROM, 113 RAM, 114 storage unit, 120 I/O interface, 201 display device, 202 input device, 130 acquisition unit, 131 classification unit, 133 output unit, 132 calculation unit, 140 division unit, 141 determination unit.

Claims

A processing device for processing information related to maintenance of building equipment installed in a building,
an acquisition unit that acquires a plurality of pieces of history information;
a classification unit that classifies the plurality of pieces of history information into a plurality of groups by a clustering method;
an output unit that outputs a classification result of the classification unit;
each of the plurality of pieces of history information includes a response time from arrival at the building by maintenance personnel to completion of work related to the building equipment;
The plurality of groups includes a first group that does not include non-working time and a second group that includes the non-working time,
The processing device, wherein the classification unit classifies the plurality of pieces of history information into at least the first group and the second group based on the response time.
2. The processing device according to claim 1, wherein said classification unit calculates a boundary time indicating a boundary between said first group and said second group based on said first group and said second group classified. .
The non-work time includes the time from when the maintenance worker arrives at the building until he enters the building, the time during which the work is interrupted for arranging the parts necessary for the work, and the costly parts. 3. The processing apparatus according to claim 1 or claim 2, including at least one of a time during which the work is interrupted to obtain confirmation from the owner of the building when replacing.
further comprising a calculation unit that calculates a statistical value regarding the response time for each of the plurality of groups classified by the classification unit;
The processing device according to any one of claims 1 to 3, wherein said output unit further outputs a calculation result of said calculation unit.
The processing device according to claim 4, wherein said statistical value includes an average value of said response times of said first group and an average value of said response times of said second group.
The processing device according to claim 4 or 5, wherein the output unit outputs time-series information of the statistical values.
The processing device according to claim 6, wherein the calculation unit predicts the current or future statistical value from time-series information of the statistical value.
each of the plurality of history information includes a plurality of related information related to the building or the building equipment in addition to the response time;
The processing device further comprises a dividing unit that divides the plurality of history information based on at least one of the plurality of related information,
The processing device according to any one of claims 4 to 7, wherein said classifying unit classifies each of said plurality of pieces of history information divided by said dividing unit into said plurality of groups.
The dividing unit further divides each of the plurality of groups classified by the classifying unit based on at least one of the plurality of related information,
9. The processing device according to claim 8, wherein said calculating unit calculates said statistical value for each of said plurality of groups divided by said dividing unit and classified by said classifying unit.
The plurality of related information according to claim 8 or claim 9, wherein the plurality of related information includes at least one of the building type, the elevator type, the elevator failure type, and the maintenance contract type. processing equipment.
11. The process according to any one of claims 8 to 10, further comprising a determining unit that determines, from among the plurality of related information, information to be used by the dividing unit to divide the plurality of pieces of history information. Device.
A processing method for processing information related to maintenance of building equipment installed in a building,
obtaining a plurality of historical information;
classifying the plurality of history information into a plurality of groups by a clustering method;
and outputting a classification result of the classifying step,
each of the plurality of pieces of history information includes a response time from arrival at the building by maintenance personnel to completion of work related to the building equipment;
The plurality of groups includes a first group that does not include non-working time and a second group that includes the non-working time,
The processing method, wherein the classifying step classifies the plurality of pieces of history information into at least the first group and the second group based on the corresponding time.