JP5755159B2 - Parameter and equipment recommendation equipment for elevators - Google Patents

Description

  TECHNICAL FIELD The present invention relates to an elevator parameter and device recommendation device that provides information necessary for determining appropriate control parameters and optional devices for an elevator, and adding or changing the recommended control parameters and optional devices. .

  In recent years, with the progress of computerization of society, the amount of information that can be touched by humans has become enormous. “Information retrieval” is one of means for obtaining necessary information from such a large amount of information. However, the information search only determines whether or not the search keyword matches, and cannot consider whether or not the information obtained as a result of the search is useful for the search user.

  In contrast to the above, the user's content browsing history and product purchase history are collected, and based on the collected history, other users with similar preferences are extracted as similar users. "Collaborative filtering technology (hereinafter also simply referred to as collaborative filtering)" that recommends unknown content or products to users based on information that has been browsed and determined to be useful or information on products purchased in addition to the products There is. Collaborative filtering is often used in shopping sites and the like.

  For example, conventionally, a user who has browsed an article of net news is allowed to input an evaluation of the article in five stages, and the evaluation value is accumulated. Based on the evaluation value of the other users who evaluated the user similar to the evaluation value for the article that the user evaluated in the past, the user who has not yet evaluated the user Some articles recommend an article that is predicted to have a high evaluation value by predicting the evaluation value of the article (see, for example, Non-Patent Document 1).

  It also has a product database that stores products and levels, allows the user to input the importance of the product attributes, calculates the weighted indexing level by multiplying the level by the importance, and the weighted indexing level. In some cases, the distance between the virtual product that matches the actual product and each actual product is calculated, and the products are displayed to the user in ascending order of the distance (see, for example, Patent Document 1).

  In addition, let the user select a category, extract other users who have accessed the same information as the information that the user has accessed in the past, and that the other user has accessed from the information group of the category Some information is extracted and recommended (for example, see Patent Document 2).

  Also, based on user content items such as still images, videos, articles, applications, etc. that are used or viewed by the user, positive or negative feedback from the user is received, and the characteristic information of the user content item and on the server There is one that automatically updates a user profile that defines a genre or the like of interest by comparing with characteristic information of content items (for example, Patent Document 3).

JP 2007-41971 A Japanese Patent No. 3443090 Special table 2009-527831

Rennick, P.M. et al. GroupLens, “An Architecture for Collaborative Filtering of Netnews, Proceeding of CSCW '94'”, 1994, pp. 197 175-186

  Non-Patent Document 1 does not consider the genre or category that the user is interested in, so even if the evaluation value of the user is low, it was not interested in the genre in the first place, or was interested in the genre. Cannot distinguish whether the content of the article was not interesting.

  Further, in Patent Document 1, although a product suitable for the user's request can be recommended from a group of products made up of a plurality of competing products, it is necessary to set attributes and importance desired by the user himself / herself. The procedure for obtaining recommended products is complicated.

  Also, in Patent Document 2, since the user must first select a category, appropriate information cannot be recommended unless the user himself / herself understands the category of information.

  Moreover, in patent document 3, although the genre in which a user is interested can be estimated automatically and the information suitable for a user's request can be provided, since a user profile itself is updated, a user's constant It is not possible to distinguish between temporary interests and temporary interests.

  The appropriate values of the elevator control parameters and the necessity / unnecessity of optional equipment vary depending on the usage status and maintenance status of each building or elevator. For example, in condominiums and commercial facilities where there are many children and elderly people, it is generally effective to add sensors around the door and slow down the door opening and closing speed in order to reduce user accidents. In an elevator in an office, changes in control parameters that give priority to transportation efficiency and increase waiting time may be avoided. On the other hand, even if it is an elevator in an office, when interest in energy saving is high, operation with high equipment efficiency is preferred even if waiting time is slightly increased. The necessity / unnecessity of such control parameters and optional equipment changes not only according to the purpose and location of the building but also according to the intention of the building owner.

  However, in the recommendation device using the conventional collaborative filtering described above, other elevators with similar usage conditions are extracted based on the elevator equipment status, operation history, etc., and the elevator control parameters for which various functions are prepared When adjusting control parameters adopted by similar similar elevators extracted from retrofit optional equipment and suggesting optional retrofit equipment, make recommendations that reflect the application status of other elevators. Yes, but in order to obtain a recommendation result that reflects the intention of the building owner or a recommendation result that solves the problem specific to the building, it was necessary for the building owner to enter a category of interest in advance. .

  The present invention has been made to solve these problems, and it is an object of the present invention to provide a parameter and equipment recommendation device for an elevator that can make an accurate recommendation reflecting the needs of a building owner. .

In order to solve the above-described problems, an apparatus for recommending parameters and devices in an elevator according to the present invention includes at least one of an operation history of an elevator user and an operation history of optional equipment used in the elevator. and operation history data storage unit for storing history data, and building equipment parameter option information storage section for storing the building equipment parameter option information is information configurable parameters and can be employed optional equipment to the elevator, elevator configured Appropriate for the recommended elevator based on the building equipment information data storage unit that stores the building equipment information data that is the parameters and information on the adopted optional equipment, and the operation history data, building equipment parameter option information, and building equipment information data Para A primary recommendation candidate extraction unit that extracts over data and optional equipment, or the category that contains the parameters and optional equipment as the primary recommendation candidates, the primary recommendation candidates extracted by the primary recommendation candidate extraction unit Is output as the primary recommendation result and presented to the user, and the primary recommendation selected by the user among the primary recommendation results presented by the primary recommendation result output unit A candidate selection receiving unit that receives candidates, a data correction unit that temporarily corrects building facility information data or operation history data based on the primary recommendation candidates received by the candidate selection receiving unit, and building facility parameter option information If, on the basis of the corrected building equipment information data and the operating history data in the data correction unit, appropriate to the recommendation target elevator parameters and options A secondary recommendation candidate extraction unit that extracts a mobile device as a secondary recommendation candidate, and outputs a secondary recommendation candidate extracted by the secondary recommendation candidate extraction unit as a secondary recommendation result to the user. A secondary recommendation result output unit to be presented , and the building facility information data includes elevator specification information that is information on specifications and usage status of the elevator, and elevator setting information that is information on parameters and optional equipment. It shall be the feature.

According to the present invention, an operation history data accumulation unit that accumulates operation history data including at least one of an operation history of an elevator user and an operation history of an optional device that has been adopted in the elevator, and can be set in the elevator and building equipment parameter option information storage section for storing the building equipment parameter option information is information parameters and can be employed optional equipment, a building equipment information data is configured of parameters and information already adopted optional equipment elevator storage Ca include a building facility information data storage unit which, operating history data, building equipment parameter option information, and based on the building equipment information data, appropriate parameters and optional equipment the recommendation target elevator, or the parameters and optional equipment A primary recommendation candidate extraction unit that extracts a gore as a primary recommendation candidate, and a primary recommendation candidate extracted by the primary recommendation candidate extraction unit is output as a primary recommendation result and presented to the user Among the primary recommendation result output unit and the primary recommendation result presented by the primary recommendation result output unit, a candidate selection receiving unit that receives a primary recommendation candidate selected by the user, and a candidate selection receiving unit Based on the first recommended candidates received in this way, a data correction unit that temporarily corrects building facility information data or operation history data, building facility parameter option information, and building facility information data corrected by the data correction unit and based on the operation history data, and the second recommended candidate extracting unit that extracts the appropriate parameters and optional equipment the recommendation target lift as the second recommended candidate, the second recommended candidate And a second recommendation result output unit for the second recommendation candidates extracted by the detecting section presented to the user by outputting the second recommendation result, building equipment information data, specifications and usage in Elevator Since it includes the elevator specification information that is information and the elevator setting information that is information of parameters and optional equipment, it is possible to make an accurate recommendation reflecting the request of the building owner.

It is a block diagram of the recommendation apparatus by Embodiment 1 of this invention. It is a figure which shows an example of the building equipment information data accumulate | stored in the building equipment information data storage part by Embodiment 1 of this invention. It is a figure which shows an example of the building equipment parameter option information accumulate | stored in the building equipment parameter option storage part by Embodiment 1 of this invention. It is a figure which shows an example of the operation history data accumulate | stored in the operation history data storage part by Embodiment 1 of this invention. It is a flowchart which shows operation | movement of the primary recommendation candidate extraction part by Embodiment 1 of this invention. It is a figure which shows an example of the building equipment matrix produced | generated in the primary recommendation candidate extraction part by Embodiment 1 of this invention. It is a flowchart which shows the operation | movement of the extraction of the primary recommendation candidate in the primary recommendation candidate extraction part by Embodiment 1 of this invention. It is a figure which shows an example of the screen which presents a primary recommendation result with respect to the user by Embodiment 1 of this invention. It is a figure which shows an example of the building equipment matrix produced | generated in the primary recommendation candidate extraction part by Embodiment 1 of this invention. It is a flowchart which shows operation | movement of the data correction part by Embodiment 1 of this invention. It is a flowchart which shows operation | movement of the secondary recommendation candidate extraction part by Embodiment 1 of this invention. It is a figure which shows an example of the screen which shows a secondary recommendation result with respect to the user by Embodiment 1 of this invention. It is a block diagram of the recommendation apparatus by Embodiment 2 of this invention. It is a figure which shows an example of the candidate selection log | history data accumulate | stored in the candidate selection log | history data storage part by Embodiment 2 of this invention. It is a flowchart which shows operation | movement of the primary recommendation candidate extraction part by Embodiment 2 of this invention. It is a figure which shows an example of the building equipment matrix produced | generated in the primary recommendation candidate extraction part by Embodiment 2 of this invention. It is a flowchart which shows operation | movement of the data correction part by Embodiment 2 of this invention. It is a block diagram of the recommendation apparatus by Embodiment 3 of this invention. It is a figure which shows an example of the purchase history data accumulate | stored in the purchase history data storage part by Embodiment 3 of this invention. It is a figure which shows an example of the work period history data accumulate | stored in the purchase history data storage part by Embodiment 3 of this invention. It is a flowchart which shows operation | movement of the primary recommendation candidate extraction part by Embodiment 3 of this invention. It is a figure which shows an example of the building equipment matrix produced | generated in the primary recommendation candidate extraction part by Embodiment 3 of this invention.

  Embodiments of the present invention will be described below with reference to the drawings.

<Embodiment 1>
FIG. 1 is a configuration diagram of a recommendation device 101 according to the first embodiment. As shown in FIG. 1, elevator signal data as operation history is totaled from elevators 102 a to 102 c (hereinafter also simply referred to as elevator 102) installed in each building, and operation history data described later (see FIG. 4). ) Is stored in the operation history data storage unit 107. The elevator signal data includes date and time, car position, running state (running / stopped), car load, door open button pressed state, door close button pressed state, landing button pressed state, door sensor sensing state, etc. It is. In addition, the elevator signal is received and stored in the operation history data storage unit 107 by online communication via a telephone line or an Internet line using communication software installed in a control device (not shown) of the elevator 102. There are a collection method, an offline collection method in which a maintenance worker visiting a building connects to the elevator 102, extracts data from a signal storage device that stores the history of elevator signals, and takes it home.

  FIG. 2 is a diagram illustrating an example of building facility information data stored in the building facility information data storage unit 106 according to the first embodiment. As shown in FIG. 2, the building facility information data storage unit 106 stores building facility information data that is information about parameters already set in the elevator 102 and optional devices that have been adopted. The building facility information data stored in the building facility information data storage unit 106 includes elevator specification information and elevator setting information.

  The elevator specification information is information relating to specifications and usage conditions in buildings and elevators, and includes an elevator number field 201, a building usage field 202, a resident number field 203, and the like. These pieces of information are related to the user group of the elevator, the utilization rate, the degree of congestion, and the waiting time. In addition to the above, the elevator specification information includes information such as model, address, elevator usage, number of tenants, number of startups, capacity, date of completion, number of floors, elevator speed, travel time, whether pets are allowed in apartments, etc. May be included.

  Further, the elevator setting information is settable parameters related to the control of the elevator 102 and information of devices added as options, and includes a door sensor field 204, a door opening / closing time field 205, a non-interference time field 206, and the like. As the optional device information, the type of the device may be input as in the door sensor field 204, or information on the presence / absence of the optional device may be simply input as “1”, “0”, or the like.

  The building equipment parameter option accumulating unit 105 is a building equipment parameter option information related to elevator control parameters and optional equipment (hereinafter also referred to as “service”) (that is, control parameters that can be set in the elevator 102 and information on optional equipment that can be adopted). ). FIG. 3 is a diagram illustrating an example of building facility parameter option information stored in the building facility parameter option storage unit 105 according to the first embodiment.

  As shown in FIG. 3, a service name field 303 indicates the name of a service, and a category field 302 indicates a category in which the service indicated in the service name field 303 is classified by function or the like. The category indicates items that the building owner or user is interested in for the elevator 102, and includes user safety, crime prevention, user service, disaster prevention, energy saving, and the like. The service number field 301 is an identification number uniquely assigned to each service. However, if the category is different even for the same service as in the “Place Monitor” shown in FIG. 3, it is treated as a different service. Different service numbers may be attached, and a plurality of categories may be written in the category field. The maximum value definition field 304 and the minimum value definition field 305 define a maximum value and a minimum value that can be set for the control parameter. The mandatory condition field 306 indicates other services that are essential when applying the service. The elevator 102 that does not apply the service defined in the mandatory condition field 306 is excluded from the recommendation target or recommended at the same time. It indicates to the user that it needs to be done or simply other services need to be applied. The combination impossible field 307 represents service numbers of services that cannot be applied at the same time. It is difficult to recommend a service that cannot be combined with a service that has already been installed, or recommends a service that cannot be combined with a service that has already been installed. Used to call attention. In addition, an indispensable condition field may be provided to indicate that it is a necessary condition that another service has already been introduced in order to introduce a service. A price field 308 and a construction period field 309 respectively indicate a price and a construction period necessary for applying the service. In addition to the above, the building facility parameter option information may include a default value field indicating a general value of the control parameter or a value at the time of shipment.

  The operation history data storage unit 107 stores operation history data obtained by tabulating elevator signals. FIG. 4 is a diagram illustrating an example of operation history data stored in the operation history data storage unit 107 according to the first embodiment.

  As shown in FIG. 4, the elevator number field 401 is an identification number uniquely assigned to each elevator 102, and each elevator specification information can be obtained by referring to the elevator number field 201 of FIG. 2. The elevator identification number shown in the elevator number field 401 is not limited to the format shown in FIG. 4, but is a combination of a building identification number and an elevator number, or a combination of a building's latitude / longitude and an elevator number. There may be. Further, the building number such as the identification number representing the building and the latitude and longitude and the elevator number may be defined in separate fields. Alternatively, the operation history data table may be generated for each elevator number without the elevator number field 401.

  The total period field 402 indicates a target period of the operation history data, and the operation history data is updated when data newer than the total period is collected.

  The activation number field 403, the door opening button operation number field 404, the door closing button operation number field 405, the run-in number field 406, and the average boarding rate field 407 are each one day to eliminate the influence of the number of days included in the counting period. Shows the averaged results. Note that these fields may be added in order to take into account differences due to the day of the week, weekdays / holidays, and floors used.

  Next, the operation of the primary recommendation candidate extraction unit 108 will be described. FIG. 5 is a flowchart showing the operation of the primary recommendation candidate extraction unit 108 according to the first embodiment. As shown in FIG. 5, the primary recommendation candidate extraction unit 108 acquires building facility parameter option information from the building facility parameter option storage unit 105 (step S501), and the building facility information data from the building facility information data storage unit 106. Is acquired (step S502), and operation history data is acquired from the operation history data storage unit 107 (step S503). Next, a building equipment matrix is generated based on the acquired building equipment parameter option information, building equipment information data, and operation history data (step S504). In order to apply collaborative filtering that is often used at a merchandise purchase site or the like to the building equipment matrix generated in step S504, the building equipment matrix is normalized (step S505). After normalizing the building equipment matrix in step S505, collaborative filtering is applied to extract primary recommendation candidates (parameters and optional equipment appropriate for the recommendation target elevator 102, or a category including the parameters and optional equipment). (Step S506). As for the extraction of the primary recommendation candidates, those having a high predicted value by collaborative filtering are extracted as higher candidates.

  Next, generation of a building equipment matrix in step S504 in FIG. 5 will be described. FIG. 6 is a diagram illustrating an example of a building equipment matrix generated by the primary recommendation candidate extraction unit 108 according to the first embodiment. As shown in FIG. 6, the office data field 602, the condominium data field 603, and the hospital data field 604 are data stored in the building usage field 202 shown in FIG. 2, and “1” is entered in the corresponding building usage data field. , “0” is entered in the non-applicable data field.

  Also, the photoelectric door sensor data field 606, the line signal door sensor data field 607, and the multi-beam door sensor data field 608 are data stored in the door sensor field 204 shown in FIG. 2, and among the door sensors adopted by each elevator, “1” is entered in the corresponding data field. In addition, it is unclear whether the door sensor currently installed is the one that the building owner has positively selected, or whether the door sensor was selected because no other door sensor was sold at the time of installation. The column of the door sensor that is not installed is left blank. For example, when the photoelectric door sensor was sold at the time of installation of the elevator number “268004-001” but the line signal door sensor was not sold, the intention that the user did not select the photoelectric door sensor is clarified. Therefore, the line signal door sensor data field 607 is entered to indicate that the user has not intentionally introduced the line signal door sensor by inputting “0” into the photoelectric door sensor data field 606 of the elevator. It may be left blank.

  Further, the door opening / closing time setting value field 609 is data stored in the door opening / closing time field 205 shown in FIG. 2, and a value of the set door opening / closing time is input. In addition, when not changing from the value at the time of shipment, it is good also as a blank as shown in FIG. In the security camera data field 610 and the hall monitor data field 611, “1” is input when each service is adopted. The door open button operation frequency history data field 612 and the run-in frequency history data field 613 are values stored in the door open button operation frequency field 405 and the drive-in frequency data field 406 shown in FIG. To do.

  Note that the building equipment matrix shown in FIG. 6 is an example, and is a matrix created by combining the data stored in each of the building equipment parameter option storage unit 105, the building equipment information data storage unit 106, and the operation history data storage unit 107. If it is.

  Next, normalization of the building equipment matrix in step S505 of FIG. 5 will be described. In the building equipment matrix as shown in FIG. 6, if there is a column having a large value that protrudes beyond the other columns, the column column is given a large weight and the recommendation greatly affected by the column. Candidates are extracted. Therefore, normalization is generally performed when applying collaborative filtering.

  For example, in the building equipment matrix shown in FIG. 6, the occupancy data field 605 has a very large value compared to other columns. In this case, even if the other columns are hardly similar, an elevator having a close occupancy value is extracted as a similar elevator. Therefore, the maximum value max (x) and the minimum value min (x) of the values input to the column x are obtained, and the value of the column x is set so as to be a value of 0 to 1 by the following equation (1). Normalized x ′ is calculated.

x ′ = x / (max (x) −min (x)) (1)
Expression (1) is an example of an expression for performing normalization, and may not be normalized by such a linear function. Similarly, the door opening / closing time data field 609, the door opening button operation frequency field 612, and the run-in frequency field 613 are normalized so as to have a value of 0-1. The door opening / closing time set value field 609 defines a range of values that can be set in the building equipment parameter option information. Therefore, instead of max (x) and min (x), the maximum value definition field 304 and the minimum value are set. The value of the value definition field 305 (see FIG. 3) may be used.

  Next, extraction of primary recommendation candidates in step S506 in FIG. 5 will be described. FIG. 7 is a flowchart showing an operation of extracting a primary recommendation candidate in the primary recommendation candidate extraction unit 108 according to the first embodiment. As shown in FIG. 7, among the services in the recommended elevator 102 of the building equipment matrix (described here as the elevator number “268001-001” shown in FIG. 6), the first category, for example, user safety In the service related to the category, at least one service for which a value has been input, that is, the column of the photoelectric door sensor field 606 is blanked (step S701). Next, collaborative filtering is performed on services related to the user safety category, ie, photoelectric door sensor field 606, line signal door sensor data field 607, multi-beam door sensor data field 608, and door opening / closing time field 609. (Step S702). The value predicted here is considered to represent the probability that the user introduces the optional device or the appropriate value of the setting parameter. When setting a value such as a service having the maximum value or a door opening / closing time, the service having the maximum difference from the default value or the current value is extracted and output (step S703). Subsequently, the above operation is similarly executed for other categories, and is executed for all categories. That is, for each category, the primary recommendation candidate extraction unit 108 extracts primary recommendation candidates by leaving at least one control parameter or optional device information belonging to each category of the building equipment matrix blank.

  For example, a photoelectric door sensor and a multi-beam door sensor cannot be installed at the same time. As in the case of conventional collaborative filtering, if the service adopted is “1”, the photoelectric door sensor and the multi-beam door sensor cannot be installed at the same time. Therefore, in the case of an elevator that uses a photoelectric door sensor, The value is “1”, and the value of the multi-beam door sensor is “0”. Therefore, since a photoelectric door sensor is installed and a building without a multi-beam door sensor is extracted as a similar building, the predicted value for the multi-beam door sensor is very low and the possibility of being recommended is very high. Lower. By executing the above operation, the employed service is temporarily blanked. Therefore, according to the first embodiment, the service that is estimated to be most appropriate for each category regardless of whether or not it is currently employed. Can be output.

  The recommendation result output unit 109 (primary recommendation result output unit) outputs the primary recommendation candidate extracted by the primary recommendation candidate extraction unit 108 to the user terminal 103 as a recommendation result (primary recommendation result). And present it to the user. FIG. 8 is a diagram illustrating an example of a screen that presents the primary recommendation result to the user according to the first embodiment. As shown in FIG. 8, the recommended service display 801 displays a message indicating a recommendation result (recommended service). The recommended services are displayed in descending order of predicted values by collaborative filtering, that is, in the order appropriate for the user.

  The recommended service display 801 may be a parameter or an option device category as shown in FIG. 8, that is, a message indicating an effect such as “I want to make the user safe to use” or a recommendation such as “Multi-beam door sensor”. The service name (name of parameter or optional device) may be used, or a message indicating a problem such as “the customer's building has more rushes than a similar building” may be used. When the user presses the detail request buttons 802a to 802e (hereinafter also simply referred to as the detail request button 802), the candidate selection receiving unit 110 receives the item selected by the user terminal 103 and corrects the selected service item by data correction. It transmits to the part 111. That is, the candidate selection receiving unit 110 receives the primary recommendation candidate selected by the user from the recommendation results presented by the recommendation result output unit 109. When the end button 803 is pressed, the display of the recommended service is ended.

  FIG. 9 is a diagram illustrating an example of a building equipment matrix generated by the primary recommendation candidate extraction unit 108 according to the first embodiment.

  Instead of the building equipment matrix shown in FIG. 6, service items may be classified by category as shown in FIG. In this case, if any one of the services in each category is adopted, “1” is input, and if none is adopted, it is left blank. Alternatively, the number of employed services may be totaled. For example, since the elevator number “268003-001” employs the line signal door sensor and the door opening / closing time adjustment among the services related to the user safety category, “2” may be input. Further, when a building equipment matrix is generated by counting the number of services, normalization is performed in the same manner as described above (see step S505 in FIG. 5) because the maximum value may vary greatly depending on the category or elevator. By doing in this way, since the number of columns of a building equipment matrix can be reduced, the calculation amount of the matrix in collaborative filtering can be reduced and calculation time can be shortened. The result recommended by the primary recommendation candidate extraction unit 108 using the building facility matrix shown in FIG. 9 is presented to the user as shown in FIG. 8, and the user presses the detail request button 802. The candidate selection receiving unit 110 receives the item selected by the user terminal 103 and transmits the selected category to the data correction unit 111.

  The data correction unit 111 corrects the accumulated data based on the item selected by the user, and transmits the corrected data to the secondary recommendation candidate extraction unit 112. That is, the data correction unit 111 temporarily corrects the building facility information data or the operation history data based on the primary recommendation candidate received by the candidate selection reception unit 110. FIG. 10 is a flowchart showing the operation of the data correction unit 111 according to the first embodiment. As shown in FIG. 10, first, building facility parameter option information is acquired from the building facility parameter option storage unit 105 (step S1001), and building facility information data is acquired from the building facility information data storage unit 106 (step S1002). Operation history data is acquired from the operation history data storage unit 107 (step S1003). Note that steps S1001 to S1003 in FIG. 10 are the same as the operations in steps S501 to S503 in FIG.

  Next, the operation history data acquired in step S1003 is corrected so as to improve the value (index value) of the selected category (step S1004). That is, when the candidate selection receiving unit 110 receives a user's selection in a category, the data correction unit 111 corrects the operation history data related to the category. For example, when there is a request from the user to “reduce the waiting time”, the index value of “waiting time” that is the related operation history data is “20 seconds” when the actual value is “30 seconds”, for example. And temporarily correct it to a value that will improve it. It should be noted that how much the index value is improved may be corrected so as to improve X% uniformly, or may be corrected to be an average value if it is larger than the average value of other similar buildings. Good.

  Next, a building equipment matrix is generated based on the building equipment parameter option information, the building equipment data, and the corrected operation history data (step S1005). The building equipment matrix generation method is the same as the method in step S504 in FIG.

  Next, the building equipment matrix is corrected based on the category selected by the user. Specifically, if a value is entered for a service belonging to the category selected by the user, reflecting that the user may be interested in the category, the service of the category is selected. In order to make a recommendation target, at least one cell of the category in the matrix is left blank (step S1006). And, in order to reflect that the user is not interested in other than the category and exclude it from the recommendation target, if a value is not input for a service belonging to a category other than the category selected by the user, That is, if the matrix cell is blank, “0” is input (step S1007). That is, when the candidate selection receiving unit 110 receives a user's selection in a category, the data correction unit 111 blanks information on at least one control parameter or optional equipment belonging to the category of the building equipment matrix, and the building equipment Information on at least one control parameter or optional device that does not belong to the matrix category is set to zero. By doing in this way, it is possible to prevent a service of a category that the user is not interested in from being recommended, and make a recommendation that reflects that the user is not interested in the category. In addition, for a category of interest, a more appropriate service can be recommended by setting it as a recommendation target regardless of whether it is currently employed.

  Next, the operation of the secondary recommendation candidate extraction unit 112 will be described. FIG. 11 is a flowchart showing the operation of the secondary recommendation candidate extraction unit 112 according to the first embodiment. As shown in FIG. 11, first, the corrected building equipment matrix is normalized (step S1101). Note that the normalization method in step S1101 is the same as that in step S505 in FIG. Next, collaborative filtering is applied to predict the value of a blank cell, and at least one service is extracted as a secondary recommendation candidate in descending order of the predicted value (step S1102). That is, the secondary recommendation candidate extraction unit 112 controls the control parameters appropriate for the recommendation target elevator 102 based on the building facility parameter option information, the building facility information data and the operation history data corrected by the data correction unit 111. And optional devices are extracted as secondary recommendation candidates.

  The recommendation result output unit 109 (secondary recommendation result output unit) outputs the second recommendation candidate extracted by the second recommendation candidate extraction unit 112 to the user terminal 103 as a recommendation result (secondary recommendation result), Present to the user. FIG. 12 is a diagram illustrating an example of a screen that presents the second recommendation result to the user according to the first embodiment. As shown in FIG. 12, the recommended service display 1201 displays the service name of the recommendation result. The recommended services are displayed in the descending order of predicted values by collaborative filtering in the case of an optional device, that is, in the order appropriate for the user, and in the case of setting parameters in the order of large difference from the current state or default. When the user presses the detail request buttons 1202a and 1202b (hereinafter also simply referred to as the detail request button 1202), the detail display unit (not shown) displays details such as service information or catalog stored in the building facility parameter option storage unit 105. Present to the user. Alternatively, a message to the effect that the user has selected a service is sent to the maintenance worker or maintenance company server, or a mail creation screen is opened on the user terminal 103 so that the maintenance worker or maintenance company server can be contacted. You may make it do. Note that FIG. 12 is an example of the display of the recommendation result for the user. In addition, it is described as a part of the periodic inspection result report or as an appendix, or distributed to a monitor mounted in the car or the hall. May be.

  From the above, according to the first embodiment, it is possible to make an accurate recommendation that reflects not only the track record of adoption in other similar buildings but also the demand (intention) of the building owner.

<Embodiment 2>
FIG. 13 is a configuration diagram of a recommendation device 1301 according to the second embodiment. As shown in FIG. 13, the second embodiment is characterized in that a candidate selection history data storage unit 1302 is provided. In FIG. 13, the components given the same reference numerals as those in FIG. 1 are the same as those in the first embodiment, and thus the description thereof is omitted here.

  The candidate selection history data storage unit 1302 stores candidate selection history data in which the history of the services selected by the user and the services not selected for the recommendation results presented to the user in the past are recorded. That is, the candidate selection history data storage unit 1302 selects a primary recommendation candidate selected by the user or a primary recommendation candidate not selected by the user from the recommendation results presented by the recommendation result output unit 109. It is stored as selection history data.

  FIG. 14 is a diagram illustrating an example of candidate selection history data stored in the candidate selection history data storage unit 1302 according to the second embodiment. As shown in FIG. 14, the elevator number field 1401 indicates the partner who presented the recommendation result. For example, the elevator number unique to each elevator, the unique building number for each building, and the unique user for each building owner May be a user number or the like. The service number field 1402 indicates a service presented to the user, and may be a service number or category unique to each service, for example. The selection / non-selection field 1403 indicates whether or not the user has selected the presented recommendation result, and “1” is recorded when selected, and “0” is recorded when not selected.

  It should be noted that the selection / non-selection field 1403 only needs to indicate whether or not the user is interested or indicate the degree of interest. For example, the selection / non-selection field 1403 records the number of times the user has selected or the time when the detailed information is browsed. Also good. In this way, by providing the candidate selection history data storage unit 1302, it is possible to reflect in the recommendation result services that have been interested in the past and services that have not been interested in, so that it is possible to appropriately reflect the interest of the user. Can recommend services. In addition, it is possible to prevent a service that has already been obtained detailed information such as an inquiry or a service that is not of interest from being repeatedly recommended.

  Further, the data stored in the candidate selection history data storage unit 1302 may be periodically discarded. For example, candidate selection history data older than one year is discarded. In this way, services that have been inquired in the past but have not been adopted or services that are not interested in can be targeted for recommendation, so they are forgotten over time while considering adoption. If a request changes as time passes or over time, a timely service can be recommended.

  Next, the operation of the primary recommendation candidate extraction unit 1308 will be described. FIG. 15 is a flowchart showing the operation of the primary recommendation candidate extraction unit 1308 according to the second embodiment. As shown in FIG. 15, the primary recommendation candidate extraction unit 1308 acquires building facility parameter option information from the building facility parameter option storage unit 105 (step S1501), and the building facility information data from the building facility information data storage unit 106. Is acquired (step S1502), and operation history data is acquired from the operation history data storage unit 107 (step S1503). Also, candidate selection history data is acquired from the candidate selection history data storage unit 1302 (step S1504). Next, a building equipment matrix is generated based on each data acquired in steps S1501 to S1504 (step S1505). After step S1505, in order to apply collaborative filtering to the building equipment matrix, the building equipment matrix is normalized (step S1506). After step S1506, collaborative filtering is applied to extract primary recommendation candidates (step S1507). Note that steps S1501 to S1503 in FIG. 15 are the same as the operations in steps S501 to S503 in FIG. Further, Step S1506 and Step S1507 in FIG. 15 are the same as the operations in Step S505 and Step S506 in FIG.

  Next, generation of a building equipment matrix in step S1505 in FIG. 15 will be described. FIG. 16 is a diagram illustrating an example of a building equipment matrix generated by the primary recommendation candidate extraction unit 1308 according to the second embodiment. As shown in FIG. 16, first, as in FIG. 6, the building facility parameter option information stored in the building facility parameter option storage unit 105, the building facility information data stored in the building facility information data storage unit 106, and the operation A building equipment matrix is generated by combining the operation history data stored in the history data storage unit 107.

  Next, the building equipment matrix is updated based on the candidate selection history data acquired in step S1504 of FIG. For example, when a multi-beam door sensor and a landing monitor have been recommended to a user who owns the elevator number “268001-001” in the past, the user browsed the detailed information of the multi-beam door sensor. Suppose you did not browse. In this case, in the building equipment matrix, the user inputs “0” in the hall monitor field 1611 reflecting that he / she is not interested in the hall monitor. The numerical value “0” may be a value that indicates that there is no possibility of installation or is very low, and may be “0.1” or the like. On the other hand, by inputting a value larger than “0” and close to “1” such as “0.5” in the multi-beam door sensor field 1608, the user's intention (intention) that he / she is interested in but does not adopt is reflected. Can do.

  In addition, as shown in FIG. 9, in the case of a building equipment matrix in which service items are generated for each category, for example, a service category that the user browsed in the past is given a weight of 1 or more to the original. Past recommendation by multiplying the value, adding a positive number, giving the weight of less than 1 to the service category that was not viewed, multiplying the original value, or adding a negative number Generate a building equipment matrix that reflects the user's response to the results.

  By doing so, it is possible to make a recommendation reflecting the user's interest. In addition, for services that are not expected to be adopted or those that are not yet adopted among those that have not been adopted, by temporarily entering a fixed value, the service can be excluded from the recommendation and again Can never be recommended.

  The recommendation result output unit 109 outputs the recommendation candidates extracted by the primary recommendation candidate extraction unit 1308 to the user terminal 103 and presents the recommendation candidates to the user using, for example, a screen as shown in FIG. At this time, the predicted value obtained by collaborative filtering in the primary recommendation candidate extraction unit 1308 is corrected by giving a weight of 1 or more to a category that has been interested in the past or adding a positive number. The corrected prediction values are displayed in descending order, that is, in the order appropriate for the user.

  The candidate selection receiving unit 1303 receives the candidate selected by the user from the recommendation results output to the user terminal 103 by the recommendation result output unit 109, stores the selection result in the candidate selection history data storage unit 1302, and The selection result is also transmitted to the data correction unit 1304.

  The data correction unit 1304 corrects the accumulated data based on the item selected by the user, and transmits the corrected data to the secondary recommendation candidate extraction unit 112. FIG. 17 is a flowchart showing the operation of the data correction unit 1304 according to the second embodiment. As shown in FIG. 17, first, building facility parameter option information is acquired from the building facility parameter option storage unit 105 (step S1701), and building facility information data is acquired from the building facility information data storage unit 106 (step S1702). Candidate selection history data is acquired from the candidate selection history data storage unit 1302 (step S1703), and operation history data is acquired from the operation history data storage unit 107 (step S1704). Note that steps S1701, S1702, and S1704 in FIG. 17 are the same as the operations in steps S501, S502, and S503 in FIG.

  Next, as in step S1004 of FIG. 10, the index value of the selected category is corrected so as to be improved (step S1705).

  Next, a building equipment matrix is generated based on building equipment parameter option information, building equipment information, candidate selection history data, and corrected operation history data (step S1706).

  Next, the building equipment matrix is corrected based on the category selected by the user. Specifically, if a value is entered for a service belonging to the category selected by the user, reflecting that the user may be interested in the category, the service of the category is selected. In order to make a recommendation target, at least one cell of the category in the matrix is left blank (step S1707). Then, in order to reflect that the user is not interested in other than the category and exclude it from the recommendation target, if a value for a service belonging to a category other than the category selected by the user is not input, that is, a building If the cell in the equipment matrix is blank, “0” is input (step S1708).

  Note that steps S1706 to S1708 in FIG. 17 are the same as the operations in steps S1005 to S1007 in FIG.

  In this way, while recommending candidates that were recommended in the past but not selected, that is, services in categories that the user is not interested in, recommendations that reflect that they are not interested in the categories are made. It can be carried out. In addition, regarding a category of interest, regardless of whether or not it is currently employed, it is possible to recommend a more appropriate service within the category by making it a recommendation target.

  The operation of the secondary recommendation candidate extraction unit 112 is the same as the flowchart shown in FIG. That is, the secondary recommendation candidate extraction unit 112 according to the second embodiment is based on the building equipment parameter option information, the candidate selection history data, and the building equipment information data and the operation history data corrected by the data correction unit 1304. To extract secondary recommendation candidates.

  The recommendation result output unit 109 outputs the recommendation result extracted by the secondary recommendation candidate extraction unit 112 to the user terminal 103, and presents, for example, a screen as shown in FIG.

  As described above, according to the second embodiment, by providing the candidate selection history data storage unit 1302, it is possible to reflect in the recommendation result services that the user has been interested in or has not been interested in in the past. Therefore, it is possible to recommend an appropriate service that reflects the user's interest. Further, it is possible to eliminate the recommendation of a service for which detailed information such as an inquiry has already been obtained or a service that is not of interest is repeated many times.

<Embodiment 3>
FIG. 18 is a configuration diagram of a recommendation device 1801 according to the third embodiment. As shown in FIG. 18, the third embodiment is characterized in that a purchase history data storage unit 1802 is provided. In FIG. 18, the components given the same reference numerals as those in FIG. 1 are the same as those in the first embodiment, and thus the description thereof is omitted here.

  The purchase history data storage unit 1802 is a database that stores the price of a service purchased by a building owner or the like and applied to an elevator, that is, purchase history data. That is, the purchase history data accumulation unit 1802 accumulates, as purchase history data, a history in which the user has set, purchased, or contracted control parameters and optional devices in the past.

  FIG. 19 is a diagram illustrating an example of purchase history data stored in the purchase history data storage unit 1802 according to the third embodiment. As shown in FIG. 19, the elevator number field 1901 indicates the other party who presented the recommendation result. The elevator number unique to each elevator 102, the unique building number for each building, and the unique number for each user such as the building owner It may be a user number or the like. The service number field 1902 indicates a service presented to the user, and may be a service number unique to each service, a category, or the like. The purchase price field 1903 indicates information regarding the price at which the building owner of the building where the elevator 102 is installed or the monthly amount of the contract service, etc., and not only the price but also the discount rate from the list price. Good. In addition, information such as a purchase date, a contract date, and a contract period may be included.

  Further, the purchase history data storage unit 1802 may store work period history data as shown in FIG. FIG. 20 is a diagram illustrating an example of work period history data stored in the purchase history data storage unit 1802 according to the third embodiment. As shown in FIG. 20, the elevator number field 2001 shows the other party who presented the recommendation result. The elevator number unique to each elevator 102, the unique building number for each building, and the unique number for each user such as the building owner It may be a user number or the like. The service number field 2002 indicates the service presented to the user, and may be a service number unique to each service, a category, or the like. The work period field 2003 is information related to the time or period required for the work to apply the service to the elevator 102, that is, the work period, and may be the time ratio with respect to the standard work period instead of the work period itself.

  The purchase history data storage unit 1802 may include both a purchase price history as shown in FIG. 19 and a construction period history as shown in FIG.

  Next, the operation of the primary recommendation candidate extraction unit 1804 will be described. FIG. 21 is a flowchart showing the operation of the primary recommendation candidate extraction unit 1804 according to the third embodiment. As shown in FIG. 21, the primary recommendation candidate extraction unit 1802 acquires building facility parameter option information from the building facility parameter option storage unit 105 (step S2101), and the building facility information data from the building facility information data storage unit 106. Is acquired (step S2102), and operation history data is acquired from the operation history data storage unit 107 (step S2103). Further, purchase history data is acquired from the purchase history data storage unit 1802 (step S2104). Next, a building equipment matrix is generated based on each data acquired in steps S2101 to S2104 (step S2105). After step S2105, in order to apply collaborative filtering to the building equipment matrix, the building equipment matrix is normalized (step S2106). After step S2106, collaborative filtering is applied to extract primary recommendation candidates (step S2107). The predicted purchase price here is considered to be a reasonable price that can be spent for the user, and the predicted work period is an acceptable proper work period, that is, a standard representing how much importance is attached to the category. Therefore, with regard to the extraction of the primary recommendation candidates, those with a predicted value of the purchase price that exceeds the total of the purchased prices and a large difference are extracted as higher candidates. When the purchase history data is the work period, the predicted value of the work period exceeds the total of the installed work periods, and those having a large difference are extracted as higher candidates. That is, a category including a device or service that is expected to be more interested as it is adopted even if a larger price or work period is spent is extracted. Note that steps S2101 to S2103 in FIG. 21 are the same as the operations in steps S501 to S503 in FIG. Further, Step S2106 and Step S2107 in FIG. 21 are the same as the operations in Step S505 and Step S506 in FIG.

  Next, generation of a building equipment matrix in step S2105 in FIG. 21 will be described. FIG. 22 is a diagram illustrating an example of a building equipment matrix generated by the primary recommendation candidate extraction unit 1804 according to the third embodiment. As shown in FIG. 22, 601 to 605 and 612 and 613 are the same as those in FIG. Photoelectric door sensor purchase history field 2201, line signal door sensor purchase history field 2202, multi-beam door sensor purchase history field 2203, door opening / closing time contract history field 2204, security camera purchase history field 2205, landing monitor purchase history field 2206, purchase history data This data is generated based on the purchase history data stored in the storage unit 1802, and the price of the service purchased by the owner of the building where each elevator 102 is installed, the monthly price of the contract service, etc. The work schedule is shown. In place of the building equipment matrix shown in FIG. 22, the service items as shown in FIG. 9 are classified for each category, the value of each cell is the sum of the prices of services adopted in each category, or the value of each cell is It may be the total construction period of services adopted in the category.

  The recommendation result output unit 109 outputs the recommendation candidates extracted by the primary recommendation candidate extraction unit 1804 to the user terminal 103 and presents them to the user using a screen as shown in FIG. 8, for example.

  The candidate selection receiving unit 110 receives the candidate selected by the user from the recommendation results output to the user terminal 103 by the recommendation result output unit 109 and transmits the selection result to the data correction unit 1803.

  The data correction unit 1803 corrects the accumulated data based on the item selected by the user, and transmits the corrected data to the secondary recommendation candidate extraction unit 112. In step S1703 of the flowchart shown in FIG. 17, the data correction unit 1803 acquires purchase history data from the purchase history data storage unit 1802 instead of the candidate selection history data, and generates a building equipment matrix.

  The other operations in the data correction unit 1803 are the same as those in FIG. The acquisition of purchase history data in step S1703 is the same as that in step S2104 in FIG.

  Since the operation of the secondary recommendation candidate extraction unit 112 performs the same operation as Step S1101 and Step S1102 of FIG. 11, description thereof is omitted here. That is, the second recommendation candidate extraction unit 112 according to the third embodiment performs the first operation based on the building facility parameter option information, the purchase history data, the building facility information data and the operation history data corrected by the data correction unit 1803. Extract secondary recommendation candidates.

  The recommendation result output unit 109 outputs the recommendation result extracted by the secondary recommendation candidate extraction unit 112 to the user terminal 103, and presents, for example, a screen as shown in FIG.

  From the above, according to the third embodiment, by providing the purchase history data storage unit 1802, the price that the user can spend on the service and the category and the allowable work period, that is, how much the user attaches importance Can be recommended.

  It should be noted that the present invention can be freely combined with each other within the scope of the invention, and each embodiment can be appropriately modified or omitted.

  101 recommendation device, 102 elevator, 103 user terminal, 105 building facility parameter option storage unit, 106 building facility information data storage unit, 107 operation history data storage unit, 108 primary recommendation candidate extraction unit, 109 recommendation result output unit, 110 Candidate selection acceptance unit, 111 data correction unit, 112 secondary recommendation candidate extraction unit, 201 elevator number field, 202 building use field, 203 occupant number field, 204 door sensor field, 205 door opening / closing time field, 206 non-interference time field, 301 service number field, 302 category field, 303 service name field, 304 maximum value definition field, 305 minimum value definition field, 306 essential condition field, 307 308 Price field, 309 Work period field, 401 Elevator number field, 402 Total period field, 403 Start count field, 404 Door open button operation count field, 405 Door close button operation count field, 406 Ride count field, 407 Average boarding rate Field, 601 Elevator number field, 602 Office data field, 603 Condominium data field, 604 Hospital data field, 605 Residents field, 606 Photoelectric door sensor data field, 607 Line signal door sensor data field, 608 Multi-beam door sensor data field, 609 Open / close time setting value field, 610 security camera data field, 611 hall monitor Data field, 612 Door open button operation frequency history data field, 613 Run-in frequency history data field, 901 User safety field, 902 Security field, 903 User service field, 904 Energy saving field, 1301 Recommended device, 1302 Candidate selection history data storage unit 1303 Candidate selection acceptance unit, 1304 Data correction unit, 1606 Photoelectric door sensor data field, 1607 Line signal door sensor data field, 1608 Multi-beam door sensor data field, 1609 Door opening / closing time set value field, 1610 Security camera data field, 1611 Hall monitor Data field, 1801 recommendation device, 1802 purchase history data storage unit, 1803 data correction unit, 1804 primary Recommendation candidate extraction unit, 1901 elevator number field, 1902 service number field, 1903 purchase price field, 2001 elevator number field, 2002 service number field, 2003 construction period field, 2201 photoelectric door sensor purchase history field, 2202 line signal door sensor purchase history field, 2203 Multi-beam door sensor purchase history field, 2204 Door opening / closing time contract history field, 2205 security camera purchase history field, 2206 landing monitor purchase history field.

Claims (8)

An operation history data storage unit for storing operation history data including at least one of an operation history of an elevator user and an operation history of an optional device adopted in the elevator ;
And building equipment parameter option information storage section for storing said an elevator settable parameters and information adoptable optional equipment building equipment parameter option information,
And building equipment information data storage unit for storing building equipment information data is configured of parameters and information already adopted optional equipment in the elevator,
Based on the operation history data, the building facility parameter option information, and the building facility information data, parameters and option devices suitable for the recommended elevator or a category including the parameters and option devices are extracted as primary recommendation candidates. A primary recommendation candidate extraction unit,
A primary recommendation result output unit that outputs the primary recommendation candidate extracted by the primary recommendation candidate extraction unit as a primary recommendation result and presents it to a user;
Among the primary recommendation results presented by the primary recommendation result output unit, a candidate selection receiving unit that receives the primary recommendation candidate selected by the user;
A data correction unit that temporarily corrects the building facility information data or the operation history data based on the first recommendation candidate received by the candidate selection reception unit;
Based on the building equipment parameter option information, the building equipment information data corrected by the data correction unit, and the operation history data, parameters and option devices appropriate for the recommendation target elevator are set as secondary recommendation candidates. A secondary recommendation candidate extraction unit to extract;
A second recommendation result output unit that outputs the second recommendation candidate extracted by the second recommendation candidate extraction unit as a second recommendation result and presents it to the user;
Equipped with a,
The building equipment information data, and elevator specification information which is information relating to specifications and usage in the elevator, you comprising a lift setting information which is the parameter and the information of the optional device, parameters in elevators and Equipment recommendation device. A candidate selection history data accumulating unit that accumulates the first recommended candidates selected by the user as candidate selection history data among the first recommendation results presented by the first recommendation result output unit; ,
The primary recommendation candidate extraction unit extracts the primary recommendation candidates based on the operation history data, the building equipment parameter option information, the building equipment information data, and the candidate selection history data,
The second recommended candidate extraction unit is based on the building facility parameter option information, the candidate selection history data, the building facility information data corrected by the data correction unit, and the operation history data. The apparatus for recommending parameters and equipment in an elevator according to claim 1, wherein secondary recommendation candidates are extracted. A purchase history data storage unit that stores, as purchase history data, a history of setting, purchasing, or contracting the parameter and the optional device in the past by the user;
The primary recommendation candidate extraction unit extracts the primary recommendation candidate based on the operation history data, the building facility parameter option information, the building facility information data, and the purchase history data,
The second recommended candidate extraction unit is configured to perform the first based on the building facility parameter option information, the purchase history data, the building facility information data corrected by the data correction unit, and the operation history data. The apparatus for recommending parameters and equipment in an elevator according to claim 1, wherein secondary recommendation candidates are extracted. The primary recommendation candidate extraction unit, said each category of said parameter and the optional equipment, at least one information of the building equipment before belonging to each of said categories of matrix Symbol parameter and the optional equipment as blank There line the extraction of primary recommendation candidates,
4. The building equipment matrix according to claim 1, wherein the building equipment matrix is a matrix created by combining the operation history data, the building equipment parameter option information, and the building equipment information data . Parameter and equipment recommendation equipment for elevators. When the candidate selection accepting unit accepts the user's selection in the category of the parameter and the optional device,
Wherein the data correcting section, at least one information of the previous SL parameter and the optional device belonging to the category of building facilities matrix and blank,
The building facilities matrix, the operation history data, the building equipment parameter option information, and wherein the matrix der Rukoto said created by combining the building equipment information data, according to one of claims 1 to 4 Parameter and equipment recommendation equipment for elevators. When the candidate selection accepting unit accepts the user's selection in the category of the parameter and the optional device,
Wherein the data correcting section, at least one information of the building equipment before have such belong to the category of the matrix's rating parameter and the optional device to zero,
The building facilities matrix, the operation history data, and wherein said building equipment parameter option information, and a matrix der Rukoto created by combining the building equipment information data, according to one of claims 1 to 5 Parameter and equipment recommendation equipment for elevators. When the candidate selection accepting unit accepts the user's selection in the category of the parameter and the optional device,
The elevator according to any one of claims 1 to 6, wherein the data correction unit temporarily corrects the value so that the operation history data related to the category is improved more than actual. Parameter and equipment recommendation device. In the building equipment matrix, the primary recommendation candidate extraction unit is left blank for the information on the parameter and the optional device that has not been released or applied at the time of installation of the elevator, and has been sold or applied, Set the parameters not installed in the elevator and the optional equipment information to zero ,
The building facilities matrix, the operation history data, and wherein said building equipment parameter option information, and a matrix der Rukoto created by combining the building equipment information data, according to one of claims 1 to 3 Parameter and equipment recommendation equipment for elevators.

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