Extraction system and extraction method

Abstract

To provide a system and method for extracting training items to enable a manager managing a building to efficiently acquire jobs.SOLUTION: A control unit 21 acquires a routine job procedure and an atypical job procedure from a routine job procedure document DB 62 and an atypical job procedure document DB 63. The control unit 21 extracts high-frequency jobs having an occurrence frequency of equal to or higher than a reference value from the atypical jobs. The control unit 21 extracts a procedure other than the procedure included in the routine job from the procedures included in the high-frequency jobs as a training item.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an extraction system and an extraction method. [Background technology]

[0002] A manager who manages a building (such as a resident manager who resides in the building) needs to master the work of managing the building. For example, Japanese Patent Application Laid-Open No. 2018-132556 (Patent Document 1) discloses a worker training device that trains workers. The worker training device improves knowledge by presenting trainees with questions that simulate work content corresponding to the work site and requesting the trainees to answer them.

[0003] The tasks performed by the building manager include, for example, tasks that are repeated periodically (routine tasks, etc.) and other tasks that are performed in response to incidents that occur in relation to building management, such as complaints or inquiries (non-routine tasks, etc.). Because routine tasks are repeated periodically, the manager will first become proficient in the routine tasks. After becoming proficient in the routine tasks, the manager will need to become proficient in the non-routine tasks as the next step. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2018-132556 Summary of the Invention [Problem to be solved by the invention]

[0005] Education for managerial tasks is often carried out through on-the-job training (OJT). However, when learning non-routine tasks, it is often not clearly defined which items should be given priority in training. As a result, the items to be taught depend on the experience of the OJT trainer, and are highly personal.

[0006] If trainers do not prioritize training items, employees will become proficient in their work through on-site experience whenever a complaint, inquiry, or other incident occurs. In other words, the order of training will be determined by the order in which the incidents occur.

[0007] During the OJT training period, if a series of low-frequency events occur, the trainees' proficiency in dealing with those events will increase, but the training of high-frequency events, which should be prioritized, will be put off, resulting in poor training efficiency.

[0008] On the other hand, among the procedures included in non-routine work, there are procedures that do not require training if the worker has mastered routine work. Therefore, if training is conducted without taking into account procedures that overlap with routine work, the same procedures will be taught redundantly in training for non-routine work, which reduces training efficiency.

[0009] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to provide a system and control method for extracting training items that will enable building managers to efficiently master their duties. [Means for solving the problem]

[0010] According to an aspect of the present disclosure, the extraction system includes a storage device and a control device. The storage device stores a task database in which the procedures of tasks performed by a manager who manages a building are recorded. The control device extracts, as training items, the procedures of tasks that the manager should prioritize mastering. The tasks performed by the manager include a first task that the manager repeats periodically and a second task that is different from the first task. The control device acquires the procedures of the first task and the procedures of the second task from the task database. The control device extracts, from the second tasks, high-frequency tasks whose occurrence frequency is equal to or greater than a reference value. The control device extracts, from the procedures included in the high-frequency tasks, procedures excluding the procedures included in the first task, as training items.

[0011] According to another aspect of the present disclosure, an extraction method includes the steps of: storing, in a storage device, a task database in which task procedures performed by a building manager who manages a building are recorded; and extracting, as training items, task procedures that the manager should prioritize mastering. The tasks performed by the manager include a first task that the manager regularly repeats and a second task that is different from the first task.

[0012] The extraction step includes a step of acquiring the procedures of the first task and the procedures of the second task from the task database, a step of extracting high-frequency tasks from the second task whose occurrence frequency is equal to or greater than a reference value, and a step of extracting, as training items, the procedures included in the high-frequency tasks, excluding the procedures included in the first task. [Effects of the Invention]

[0013] According to the present disclosure, building managers can efficiently learn their jobs. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a schematic diagram showing an example of the overall configuration of an education item extraction system according to an embodiment of the present invention. [Figure 2] 1 is a schematic diagram illustrating an example of a hardware configuration of an education item extraction system according to an embodiment of the present invention. [Figure 3] 10 is a flowchart of a main process. [Figure 4] 10 is a flowchart of a main process. [Figure 5] FIG. 10 is a diagram illustrating an example of a daily equipment report DB. [Figure 6] FIG. 10 is a diagram illustrating an example of a high-priority education item DB. [Figure 7] FIG. 2 is a diagram illustrating an example of a routine procedure DB. [Figure 8] FIG. 10 is a diagram illustrating an example of an unstructured procedure manual DB. [Figure 9] FIG. 10 is a diagram illustrating an example of a priority training procedure table. [Figure 10]FIG. 10 is a diagram illustrating the learning of an estimation model. [Figure 11] 10 is a flowchart of a learning process. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, embodiments will be described with reference to the drawings. In the following description, the same components are denoted by the same reference numerals. The names and functions of these components are also the same. Therefore, detailed descriptions thereof will not be repeated.

[0016] First, an overview of a training item extraction system (also simply referred to as an "extraction system") 1 according to this embodiment will be described. Fig. 1 is a schematic diagram showing an example of the overall configuration of the training item extraction system 1 according to this embodiment. The training item extraction system 1 is a system that extracts, as training items (hereinafter also referred to as "priority training procedures"), work procedures that a manager 5 who manages a building should master on a priority basis.

[0017] The tasks performed by the manager 5 include a first task that the manager 5 performs periodically, and a second task that is different from the first task. The second task refers to a task performed in response to events that occur in relation to building management, such as complaints or inquiries. In this embodiment, the first task is exemplified as a "routine task," and the second task is exemplified as a "non-routine task." Note that the "first task" may refer to a simple task that anyone can perform if it is performed according to a manual (procedure manual). On the other hand, the "second task" may refer to a task that includes complex procedures that are difficult to document in a manual.

[0018] The procedures for the work performed by the administrator 5 are recorded in a work procedure manual. The work procedure manual includes a routine work procedure manual 72 and an unroutine work procedure manual 73. The procedures for the routine work are stored as document data in the routine work procedure manual 72. The procedures for the unroutine work are stored as document data in the unroutine work procedure manual 73.

[0019] Meanwhile, requests, inquiries, complaints (claims) made to the manager 5, and incidents that occur in relation to the management of the building, such as breakdowns of building equipment, and responses to those incidents, are stored as document data in the form of an equipment daily report 71. When the manager 5 responds to the above-mentioned incidents, he records the details in the form of a daily report. The work of responding to the events recorded in the equipment daily report 71 corresponds to non-routine work.

[0020] The data of the routine work procedure manual 72, the non-routine work procedure manual 73, and the equipment daily report 71 are recorded using, for example, spreadsheet software, etc. These data are stored, for example, in a server installed in the building or in a server in the management company that manages the building, and can be viewed or updated by each manager 5.

[0021] The training item extraction system 1 stores an equipment daily report DB (database) 61, a fixed procedure manual DB 62, an unfixed procedure manual DB 63, a high priority training item DB 64, and a priority training procedure table 65. Details will be described later, but first, the flow of processing executed by the training item extraction system 1 will be described below.

[0022] The training item extraction system 1 generates an equipment daily report DB 61 (FIG. 5) based on the equipment daily report 71, and further generates a high-priority training item DB 64 (FIG. 6) based on the equipment daily report DB 61 (Step 1).

[0023] The training item extraction system 1 generates a routine procedure manual DB 62 (FIG. 7) based on the routine work procedure manual 72 (Step 2). The training item extraction system 1 generates an unroutine procedure manual DB 63 (FIG. 8) based on the unroutine work procedure manual 73 (Step 3).

[0024] The training item extraction system 1 generates a priority training procedure table 65 (FIG. 9) in which priority training procedures are recorded, based on the high priority training item DB 64, the fixed procedure DB 62, and the unfixed procedure DB 63 (step 4).

[0025] The terminal 100 displays the priority training procedures recorded in the priority training procedure table 65 (step 5). This allows the building manager 5 and his / her trainer to confirm the priority training procedures. The trainer then uses the priority training procedures to train the manager 5 on the work procedures.

[0026] 2 is a schematic diagram showing an example of a hardware configuration of the education item extraction system 1 according to this embodiment. The education item extraction system 1 includes a server device 10. The server device 10 is configured to be able to communicate with a terminal 100.

[0027] The server device 10 includes a control unit 21, a read only memory (ROM) 22, a random access memory (RAM) 23, a communication interface 24, and a storage unit 25. The control unit 21 is, for example, a central processing unit (CPU).

[0028] The control unit 21 performs overall control of the entire server device 10. The control unit 21 loads a program stored in the ROM 22 or the storage unit 25 into the RAM 23 and executes it. The RAM 23 serves as a working area when the control unit 21 executes a program, and temporarily stores the program, data used when the program is executed, and the like.

[0029] The communication interface 24 is an interface for communicating with the terminal 100. The storage unit 25 is a non-volatile storage device. The storage unit 25 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD).

[0030] The memory unit 25 stores an estimated model (also referred to as a "trained model") 31, a learning dataset 32, a word dictionary 34, an equipment daily report DB 61, a standard procedure manual DB 62, an unstandard procedure manual DB 63, a high priority training item DB 64, and a priority training procedure table 65.

[0031] The trained model 31 and the training dataset 32 ​​are used for training to determine event classification names, which will be described later. The training will be described in detail with reference to FIGS. 10 and 11.

[0032] The terminal 100 includes a control unit 121, a ROM 122, a RAM 123, a communication interface 124, a storage unit 125, an input unit 126, and a display unit 127. The control unit 121 is, for example, a CPU. The terminal 100 may be a desktop computer, a notebook computer, or a tablet terminal.

[0033] The server device 10 may also be configured to operate as a web server and display the priority training procedure by accessing a predetermined URL from the terminal 100. Alternatively, software for displaying the priority training procedure may be installed on the terminal 100, and the software may be run to display the priority training procedure.

[0034] The control unit 121 performs overall control of the entire terminal 100. The control unit 121 loads programs stored in the ROM 122 or the storage unit 125 into the RAM 123 and executes them. The RAM 123 serves as a work area when the control unit 121 executes a program, and temporarily stores programs, data used for executing the programs, and the like. The storage unit 125 is a non-volatile storage device. The storage unit 125 may be, for example, an HDD or SSD. The communication interface 124 is an interface for communicating with the server device 10.

[0035] The input unit 126 accepts input from the administrator 5. The input unit 126 is, for example, a keyboard, a mouse, or a touch panel. The display unit 127 displays various images such as the priority training procedure. The display unit 127 is, for example, a liquid crystal display or a display.

[0036] The main processing executed by the control unit 21 will be described below using a flowchart. Figures 3 and 4 are flowcharts of the main processing. The main processing is processing that executes steps 1 to 5 shown in Figure 1. In the main processing, the control unit 21 extracts, as training items (priority training procedures), the procedures of the work that the manager 5 should be prioritized to master.

[0037] The main processing starts based on an execution instruction from the administrator 5 via the input unit 126 of the terminal 100. Hereinafter, "step" may also be simply referred to as "S".

[0038] S101 to S106 shown in Fig. 3 correspond to step 1 shown in Fig. 1. The control unit 21 records the equipment daily report data of the equipment daily report 71 in the equipment daily report DB 61 in S101.

[0039] 5 is a diagram showing an example of the equipment daily report DB 61. The equipment daily report DB 61 records "occurrence event details" such as requests and complaints from building users or malfunctions that have occurred, and the corresponding "event classification names." These occurrence event details correspond to non-routine tasks.

[0040] For example, the content of the incident (non-routine work) is recorded as, "A store employee contacted us and asked us to take action because the temperature in the hallway in front of the store is high."

[0041] Returning to FIG. 3, in S102, the control unit 21 uses the word dictionary 34 to extract word representative words from the equipment daily report data (content of the event that occurred). Then, the control unit 21 inputs the extracted word representative words as features to the trained model 31, and outputs an event classification name as an estimation result from the trained model 31. The equipment daily report data (content of the event that occurred) is a written record of an event that occurred regarding the management of a building. Then, based on the text recorded in the equipment daily report data, the control unit 21 simplifies the text and outputs an event classification name that represents the content of the event.

[0042] The trained model 31 is a model that has undergone machine learning processing using training data so that when a word representative word is input as a feature, an event classification name is output as an estimation result. The trained model 31, the word dictionary 34, etc. will be described later with reference to Figures 10 and 11.

[0043] In the example shown in Figure 5, the event classification name "Indoor temperature response" is determined based on the content of the event that occurred, "A store employee contacted us to request that something be done about the high temperature in the hallway in front of the store," and is recorded in the equipment daily report DB61.

[0044] In S104, the control unit 21 acquires data recorded in the equipment daily report DB 61. In S105, the control unit 21 tallies the number of cases for each event classification name. In S106, the control unit 21 ranks the event classification names in descending order of the number of cases and records the top 10 event classification names in the high-priority training item DB 64.

[0045] 6 is a diagram showing an example of the high-priority training item DB 64. The high-priority training item DB 64 records the number of event classification names and their rankings.

[0046] For example, the event classification name "temperature change request" has 152 occurrences, ranking first. Following that, in second place is "bulb replacement" (53 occurrences). In third place is "door malfunction" (51 occurrences). In fourth place is "power supply work request" (23 occurrences). In fifth place is "toilet clog" (20 occurrences). In sixth place is "alarm" (20 occurrences). In seventh place is "multiple equipment setting request" (7 occurrences). In eighth place is "blind malfunction" (6 occurrences). In ninth place is "uncomfortable indoor draft" (6 occurrences). In tenth place is "water heater room malfunction" (6 occurrences). In this way, the control unit 21 calculates the occurrence frequency (number of occurrences, ranking) of event classification names corresponding to the equipment daily report data recorded in the equipment daily report DB61.

[0047] Returning to Fig. 3, S107 and S108 correspond to step 2 shown in Fig. 1. In S107, the control unit 21 records the routine work procedure manual data of the routine work procedure manual 72 in the routine work procedure manual DB 62.

[0048] In S108, the control unit 21 determines a procedure classification name from the procedure content of each routine in the routine procedure manual DB 62 and records it in the routine procedure manual DB 62. This procedure content is a sentence that explains the procedure of the routine task. Based on the sentence (procedure content) that explains the procedure of the routine task, the control unit 21 simplifies the sentence and outputs a first classification name (procedure classification name) that represents the content of the procedure of the routine task. The method for determining the procedure classification name will be described later using Figures 10 and 11.

[0049] 7 is a diagram showing an example of the routine procedure DB 62. The routine procedure DB 62 stores task names, procedure numbers, procedure contents, and procedure classification names. The routine procedure DB 62 stores routine procedures for tasks such as "building temperature inspection (daily)" and "toilet inspection (daily)."

[0050] The steps for "Temperature patrol in the building (daily)" are "Carry a thermometer" (step number 1), "Move to the temperature measurement location" (step number 2), "Measure the temperature" (step number 3), and "Record the measured temperature in a record sheet" (step number 4). In contrast, the procedure classification names for steps 1 to 4 are determined to be "Carry a thermometer," "Move to the temperature measurement location," "Measure the temperature," and "Record the temperature."

[0051] The steps for "Toilet patrol (daily)" are "Move to the toilet" (step number 1), "Check the opening and closing operation of the toilet entrance door" (step number 2), "Check for damage to the urinal" (step number 3), "Check for damage to the toilet bowl" (step number 4), "Check for water leakage" (step number 5), and "Check for clogged toilet" (step number 6). In contrast, the procedure classification names for steps 1 to 6 are determined as "Move to the toilet," "Open and close the toilet door," "Check for damage to the urinal," "Check for damage to the toilet bowl," "Check for water leakage in the toilet," and "Check for clogged toilet."

[0052] Returning to Fig. 3, S109 and S110 correspond to step 3 shown in Fig. 1. In S109, the control unit 21 records the non-routine work procedure manual data of the non-routine work procedure manual 73 in the non-routine work procedure manual DB 63.

[0053] In S110, the control unit 21 determines a procedure classification name from the procedure content of each procedure in the non-routine procedure manual DB 63 and records it in the non-routine procedure manual DB 63. This procedure content is a sentence that explains the procedure of the non-routine task. Based on the sentence (procedure content) that explains the procedure of the non-routine task, the control unit 21 simplifies the sentence and outputs a second classification name (procedure classification name) that represents the content of the procedure of the non-routine task. The method for determining the procedure classification name will be described later using Figures 10 and 11.

[0054] 8 is a diagram showing an example of the non-routine procedure manual DB 63. The non-routine procedure manual DB 63 records the business name, procedure number, procedure content, procedure classification name, whether it is a high priority item, and whether it is a routine procedure.

[0055] The non-routine procedure manual DB63 contains non-routine procedures for the tasks "Temperature change request," "Shutter malfunction," and "Toilet clog."

[0056] The steps for "Temperature change request" are "Accept request for temperature being too hot / cold" (step number 1), "Move to temperature measurement location" (step number 2), "Change temperature setting" (step number 3), and "Report temperature change to requester" (step number 4). In contrast, the procedure classification names for steps 1 to 4 have been determined as "Temperature abnormality request," "Move to temperature measurement," "Change temperature setting," and "Temperature change report."

[0057] The procedures for "shutter malfunction" are "Accept notification of shutter malfunction" (procedure number 1) and "Move to the location of the target shutter" (procedure number 2). In contrast, the procedure classification names for procedure numbers 1 and 2 are determined to be "Shutter malfunction request" and "Move to the shutter."

[0058] The steps for "toilet clog" are "Accept notification of toilet malfunction" (step number 1), "Move to the target toilet" (step number 2), "Check the extent of the blockage in the target toilet" (step number 3), "Check for leaks" (step number 4), "Flush the clogged toilet with a plunger" (step number 5), and "Wipe up the leaking water around the toilet" (step number 6). In contrast, the procedure classification names for steps 1 to 6 are determined as "Accept notification of toilet malfunction," "Move to toilet," "Check for clogged toilet," "Check for toilet leak," "Pluggage work," and "Wipe up the toilet leak."

[0059] In this way, the storage unit 25 stores the fixed procedure manual DB 62 and the unfixed procedure manual DB 63 as a business database in which the procedures of the business performed by the manager 5 are recorded.

[0060] S111 to S114 shown in Fig. 4 correspond to step 4 shown in Fig. 1. In S111, the control unit 21 acquires data recorded in the high-priority training item DB 64, the routine task DB 62, and the non-routine task DB 63. As a result, the occurrence frequency (number of occurrences, ranking) of the event classification name recorded in the high-priority training item DB 64, the routine task procedures recorded in the routine procedure manual DB 62, the non-routine task procedures recorded in the non-routine procedure manual DB 63, etc. are acquired.

[0061] In S112, when an event classification name recorded in the high priority training item DB 64 matches a task name in the non-routine task DB 63, the control unit 21 records a circle in the high priority item column of the non-routine task DB 63. In S113, when a procedure classification name with a circle in the high priority item column of the non-routine task DB 63 matches a procedure classification name in the routine task DB 62, the control unit 21 records a circle in the routine procedure column of the non-routine task DB 63. A specific example will be described below.

[0062] Returning to Figure 8, in the non-routine procedure manual DB 63, "temperature change request" is "number one" (within the top 10) in the high-priority training item DB 64. Also, in the non-routine procedure manual DB 63, "toilet clog" is "number five" (within the top 10) in the high-priority training item DB 64. On the other hand, "shutter malfunction" is not in the top 10 in the high-priority training item DB 64. For this reason, "temperature change request" and "toilet clog" are set as "high-priority items" in the non-routine procedure manual DB 63.

[0063] The "event classification name" in the high priority training item DB 64 (or the equipment daily report DB 61) corresponds to the "task name" in the non-routine task DB 63. Therefore, when the "task name" in the non-routine task DB 63 is entered in the "event classification name" in the high priority training item DB 64, it indicates that the non-routine task occurs with a high frequency (within the top 10 frequency of occurrence). In this way, the control unit 21 sets the occurrence frequency of the non-routine task (sets the high priority item) based on the frequency of occurrence (number of occurrences, ranking) of the event classification name.

[0064] Furthermore, the system extracts items that overlap between the procedure classification names in the non-routine procedure manual DB 63 that have a circle in the high priority item column and the procedure classification names in the routine procedure manual DB 62. Since there is overlap between the non-routine procedure manual DB 63 and the routine procedure manual DB 62 for "changing the temperature setting," "moving the toilet," and "checking for clogged toilets," which have a circle in the high priority item column, these are set as "routine procedures."

[0065] Returning to FIG. 4, in S114, the control unit 21 extracts procedures that have a circle in the high priority item column and no circle in the routine procedure column of the unroutine procedure DB 63, and records them in the priority training procedure table 65.

[0066] 9 is a diagram showing an example of the priority training procedure table 65. The priority training procedure table 65 records the task names of tasks for which training should be given priority, the procedure contents, and the procedure classification names.

[0067] In the non-routine procedure manual DB 63, items that are set as "high priority items" but are not set as "routine procedures" are extracted and recorded in the priority training procedure table 65. As a result, procedure numbers 1 (request for abnormal temperature), 3 (change in temperature setting), and 4 (report of temperature change) for "temperature change request" and procedure numbers 1 (receive toilet malfunction notification), 4 (check for toilet leak), 5 (apply plunger), and 6 (wipe toilet leak) for "toilet clog" are extracted and recorded in the priority training procedure table 65.

[0068] In this way, the control unit 21 extracts, from among the non-routine tasks, high-frequency tasks (tasks with a circle in the high priority item column) whose occurrence frequency is equal to or greater than a reference value (ranking within 10). The control unit 21 determines that the first procedure and the second procedure match when the first classification name (temperature measurement movement) corresponding to the first procedure (procedure number: 2) included in the routine task (for example, indoor temperature inspection (daily)) matches the second classification name (temperature measurement movement) corresponding to the second procedure (procedure number: 2) included in the non-routine task (temperature change request). The control unit 21 extracts, from among the procedures included in the high-frequency tasks (tasks with a circle in the high priority item column), procedures included in the routine tasks (tasks with a circle in the routine procedure column) excluding those included in the routine tasks, as training items (priority training procedures).

[0069] 4, S115 corresponds to step 5 shown in Fig. 1. In S115, the control unit 21 reads out the priority training procedure table 65 and generates display information as the priority training procedure. The generated display information is displayed on the terminal 100.

[0070] As explained above, the control unit 21 extracts non-routine tasks that occur frequently, compares the procedures of the extracted non-routine tasks that occur frequently with the procedures of routine tasks, and extracts procedures that cannot be mastered through routine tasks as items that should be trained with priority (priority training procedures).In this case, the comparison is made after classifying the procedures into comparable formats (classification names) based on the procedure manual text.

[0071] By learning the priority training procedures, Manager 5 can learn procedures for events that occur frequently (non-routine tasks) other than the routine tasks that he or she has already mastered. Therefore, whoever becomes the OJT trainer can efficiently enable Manager 5, who manages the building, to learn the tasks (non-routine tasks).

[0072] The learning process of the estimation model in this embodiment will be described below with reference to Fig. 10 and Fig. 11. Fig. 10 is a diagram for explaining the learning of the estimation model. Hereinafter, the "trained model 31" before learning is completed will also be referred to as the "estimation model 31."

[0073] As explained using Fig. 5, in this embodiment, the "event classification name" is determined based on the "occurrence event content" (text from the equipment daily report) recorded in the equipment daily report DB 61. In the example of Fig. 5, the event classification name "indoor temperature response" is determined based on the occurrence event content "a store employee contacted us to ask us to take action because the temperature in the hallway in front of the store is high."

[0074] In this embodiment, the equipment daily report DB61 is configured to record only incidents (complaints, requests, inquiries, etc.), but in addition to the incidents, the cause of the incident and the results of the manager 5's response to the incident may also be recorded.

[0075] In building management, there are industry-specific terminologies and building-specific expressions, and the terms entered into the daily equipment report are often not standardized. For this reason, in this embodiment, the terminology is standardized using the word dictionary 34, and then the event classification name is determined using the trained model.

[0076] The sentence described as "content of the occurrence event" has a sentence structure specific to building management and can be expressed concisely using characteristic words used in building management. In the embodiment, such concise words are replaced with a combination of two words. These two words should be a combination of a word corresponding to the subject and a word corresponding to the predicate, or a combination of a word corresponding to the predicate and a word corresponding to the object.

[0077] For example, in the example in Figure 5, it can be expressed with two words: "temperature inside the building" + "response." Other examples include "bulb replacement" = "bulb" + "replace," and "toilet clog" = "toilet" + "clog."

[0078] The word dictionary 34 is a dictionary in which word representative words representing words used in the equipment daily report data (contents of occurrence events) are associated with word related words having meanings related to the word representative words. The trained model 31 is a model used to output estimation results based on the equipment daily report data (contents of occurrence events). The word representative words include words that correspond to superordinate concepts of the word related words, but do not necessarily have to be words that correspond to superordinate concepts as long as they have a related meaning.

[0079] For example, the attribute related word corresponding to the word representative word "water supply" is "faucet." The attribute related words corresponding to the word representative word "lighting" are "light," "fluorescent light," "lamp," and "light." The attribute related words corresponding to the word representative word "desk" are "table" and "desk." The attribute related word corresponding to the word representative word "correspondence" is "deal." There is no attribute related word corresponding to the word representative word "window."

[0080] The above combinations of word-related words corresponding to word representative words are merely examples, and other words may be used for association. The word-related words for a word representative word may be selected manually or by machine learning.

[0081] A method for outputting an event classification name (hereinafter also simply referred to as "classification name") from equipment daily report data (content of an event that has occurred) will be described below.

[0082] 10, the control unit 21 extracts a word representative word from the equipment daily report data (occurrence event content) using the word dictionary 34. Specifically, if the occurrence event content includes a word representative word or a word related word, the control unit 21 extracts the corresponding word representative word. For example, if the occurrence event content includes any of illumination, light, fluorescent light, lamp, and light, the control unit 21 extracts illumination (word representative word).

[0083] In this example, three words, "corridor," "temperature," and "response," are extracted from the equipment daily report data 45a, "A store employee has requested that you take action because the temperature in the hallway in front of the store is high." By extracting words using the word dictionary 34, only specialized terms used in building management and words suitable for extracting classification names are extracted. Note that the above is merely an example, and other words (such as "store") may also be extracted.

[0084] The control unit 21 inputs words having meanings related to words included in the equipment daily report data (event content) into the trained model 31, and outputs a classification name from the trained model 31.

[0085] In this example, as shown in the estimation result 45b, when the words "corridor," "temperature," and "response" are input to the trained model 31, the trained model 31 outputs "indoor temperature response" as the classification name. The control unit 21 registers the output event classification name (classification name) in the equipment daily report DB 61.

[0086] The classification name is not limited to a combination of two representative words, but may be any other combination of words, a combination of three or more words, or a sentence.

[0087] The learning process will be specifically described below. In the learning process, the learning dataset is a combination of data in which "words" are input and "category names" are output.

[0088] The trained model 31 (estimation model 31) is a model that has undergone machine learning processing using training data so as to output an event classification name (classification name) when a word having a meaning related to a word contained in the equipment daily report data (event content) is input. Here, the word having a meaning related to a word contained in the event content is a word extracted from the words contained in the event content using the word dictionary 34, and is a word that is characteristically used when managing a building (in this embodiment, a "representative word").

[0089] The training data set 32 ​​includes a training data set consisting of words 01 and classification names 01, a training data set consisting of words 02 and classification names 02, and a training data set consisting of words 03 and classification names 03.

[0090] A "word" is one or more words extracted using the word dictionary 34. For example, word 01 is "corridor, temperature, correspondence." The classification name 01 corresponding to "corridor, temperature, correspondence" is "indoor temperature correspondence." In other words, if the words extracted using the word dictionary 34 include "corridor, temperature, correspondence," learning is performed so that the classification name becomes "indoor temperature correspondence."

[0091] An example of generating an event classification name (classification name) based on equipment daily report data (occurrence event content) has been described above. Using a similar method, a "procedure classification name (also referred to as a "classification name")" is generated from the "procedure content" of the fixed procedure manual DB 62 in Fig. 7, and a "procedure classification name (classification name)" is generated from the "procedure content" of the unfixed procedure manual DB 63 in Fig. 8.

[0092] For example, an estimation result 46b is obtained from a procedure content 46a in the routine procedure manual DB 62. As with the equipment daily report data 45a, words (representative words) are extracted from the procedure content 46a, "move to the toilet," using the word dictionary 34. Here, two words, "toilet" and "move," are extracted from the procedure content 46a, "move to the toilet."

[0093] The control unit 21 inputs words (word representative words) having meanings related to the words included in the procedure content into the trained model, and outputs a procedure classification name (classification name) from the trained model. The trained model used here is a model that has undergone a training process using a combination of data in which "words" are input and "procedure classification names (classification names)" are output, as in the trained model 31. For example, the training dataset includes the word 01 "toilet, move" and its corresponding classification name 01 "toilet move." The method for obtaining a procedure classification name (classification name) from the procedure content of the unstructured procedure manual DB 63 is similar.

[0094] 11 is a flowchart of the learning process. The control unit 21 executes the learning process. As shown in FIG. 11, when the learning process starts, the control unit 21 selects learning data from the learning dataset 32 ​​in S11 and proceeds to S12. In S12, the control unit 21 inputs the word data of the selected learning data into the estimation model 31 and proceeds to S13.

[0095] In S13, the control unit 21 outputs an estimation result by the estimation process using the estimation model 31, and proceeds to S14. In S14, the control unit 21 updates the parameters of the estimation model 31 based on the error between the estimation result and the ground truth data corresponding to the learning data, and proceeds to S15.

[0096] In S15, the control unit 21 determines whether learning has been performed based on all of the learning data. If it is determined that learning has been performed based on all of the learning data (YES in S15), the process proceeds to S16. If it is determined that learning has not been performed based on all of the learning data (NO in S15), the process returns to S11. In S16, the control unit 21 stores the trained estimation model 31 as the trained model 31, and ends the learning process.

[0097] As described above, in this embodiment, the training item extraction system 1 includes a storage unit 25 and a control unit 21. The storage unit 25 stores a routine procedure manual DB 62 and an unroutine procedure manual DB 63 as task databases in which the procedures of tasks performed by the manager 5 who manages the building are recorded. The control unit 21 extracts, as training items, procedures for tasks that the manager 5 should prioritize mastering. The tasks performed by the manager 5 include routine tasks as an example of a first task that the manager 5 periodically repeats, and unroutine tasks as an example of a second task different from the first task. The control unit 21 acquires procedures for routine tasks and procedures for unroutine tasks from the task databases (the routine procedure manual DB 62 and the unroutine procedure manual DB 63). The control unit 21 extracts, from the unroutine tasks, high-frequency tasks whose occurrence frequency is equal to or greater than a reference value. The control unit 21 extracts, as training items, procedures included in the high-frequency tasks, excluding procedures included in the routine tasks.

[0098] The manager 5 can learn procedures for events (non-routine tasks) that occur frequently and are not routine tasks that he or she has already mastered. This allows the manager 5 who manages the building to efficiently learn non-routine tasks, regardless of who becomes the OJT trainer.

[0099] The memory unit 25 further stores an equipment daily report DB (database) 61 in which equipment daily report data in which events occurring in relation to building management are recorded in text form is recorded. The control unit 21 outputs an event classification name that expresses the content of the event by simplifying the text recorded in the equipment daily report data based on the text. The control unit 21 calculates the occurrence frequency of the event classification name corresponding to the equipment daily report data recorded in the equipment daily report DB 61. The control unit 21 sets the occurrence frequency of non-routine tasks based on the occurrence frequency of the event classification name. This allows the manager 5 to prioritize learning non-routine tasks that actually occur frequently in the buildings he manages.

[0100] The storage unit 25 further stores a word dictionary 34 in which word representative words representing words used in the equipment daily report data are associated with word-related words having meanings related to the word representative words, and a trained model 31 used to output estimation results based on the equipment daily report data. The trained model 31 is a model that has undergone machine learning processing using training data so that, when a word representative word is input as a feature, an event classification name is output as an estimation result. The control unit 21 uses the word dictionary 34 to extract word representative words from the equipment daily report data. The control unit 21 inputs the extracted word representative words as features to the trained model, and outputs an event classification name as an estimation result from the trained model. This makes it possible to classify the content of equipment daily report data input as text without manual intervention, and to grasp the frequency of non-routine tasks based on equipment daily report data actually input by the manager 5 in the buildings he manages.

[0101] Based on a sentence describing a procedure for a routine task, the control unit 21 simplifies the sentence and outputs a first classification name that represents the content of the procedure for the routine task. Based on a sentence describing a procedure for an atypical task, the control unit 21 simplifies the sentence and outputs a second classification name that represents the content of the procedure for the atypical task. When the first classification name corresponding to a first procedure included in the routine task matches the second classification name corresponding to a second procedure included in the atypical task, the control unit 21 determines that the first procedure and the second procedure match. This makes it possible to easily determine, without human intervention, whether the content of the procedure for a routine task input as text matches the content of the procedure for an atypical task input as text.

[0102] The embodiments disclosed herein are merely examples and are not limited to the above. The scope of the present invention is defined by the claims, and it is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0103] 1 Training item extraction system, 5 Administrator, 10 Server device, 21,121 Control unit, 22,122 ROM, 23,123 RAM, 24,124 Communication interface, 25,125 Memory unit, 31 Trained model, 32 Training dataset, 34 Word dictionary, 45a Equipment daily report data, 45b Estimation result, 46a Procedure content, 46b Estimation result, 61 Equipment daily report DB, 62 Routine procedure manual DB, 63 Non-routine procedure manual DB, 64 High priority training item DB, 65 Priority training procedure table, 71 Equipment daily report, 72 Routine work procedure manual, 73 Non-routine work procedure manual, 100 Terminal, 126 Input unit, 127 Display unit.

Claims

1. a storage device that stores a business database in which business procedures performed by a manager who manages a building are recorded, and an equipment daily report database in which equipment daily report data in which events occurring in relation to the management of the building are recorded in text form; a control device that extracts, as training items, work procedures that the manager should be prioritized to master; The tasks performed by the administrator include a first task that the administrator periodically repeats and a second task that is different from the first task, The control device acquiring a first procedure included in the first task and a second procedure included in the second task from the task database; extracting, from the second tasks, high-frequency tasks whose occurrence frequency calculated based on the event classification name corresponding to the equipment daily report data is equal to or greater than a reference value; extracting, from among the procedures included in the high-frequency work, procedures excluding the first procedure by determining whether a first classification name corresponding to the first procedure matches a second classification name corresponding to the second procedure, as the training items; The event classification name is a name that represents the content of the event, the first classification name is a name that represents the content of the procedure of the first business; the second classification name is a name that represents the content of the second business procedure, an extraction system, wherein, in the match determination, when the first classification name and the second classification name match, it is determined that the first procedure and the second procedure match.

2. The control device Based on the sentence recorded in the equipment daily report data, the sentence is simplified and the event classification name is output; Calculating the frequency of occurrence of the event classification name corresponding to the equipment daily report data recorded in the equipment daily report database; The extraction system according to claim 1 , wherein the occurrence frequency of the second task is set based on the appearance frequency of the event classification name.

3. The storage device further stores a word dictionary in which word representative words representing words used in the equipment daily report data are associated with word-related words having meanings related to the word representative words, and a trained model used to output an estimation result based on the equipment daily report data, the trained model is a model that has been subjected to machine learning processing using training data so as to output the event classification name as the estimation result when the word representative word is input as a feature; The control device extracting the representative words from the daily equipment report data using the word dictionary; The extraction system according to claim 2, wherein the extracted word representative words are input to the trained model as the features, and the event classification name is output from the trained model as the estimation result.

4. The control device based on a sentence explaining the procedure of the first business, simplifying the sentence and outputting the first classification name; based on a sentence explaining the procedure of the second business, simplifying the sentence and outputting the second classification name; An extraction system according to any one of claims 1 to 3, wherein the matching determination is performed to determine that the first procedure and the second procedure match when the first classification name and the second classification name match.

5. A computer-implemented extraction method, comprising: a step of storing in a storage device a business database in which business procedures performed by a manager who manages a building are recorded, and an equipment daily report database in which equipment daily report data in which events occurring in relation to the management of the building are recorded in writing; extracting, as training items, work procedures that the manager should be prioritized to master; The tasks performed by the administrator include a first task that the administrator periodically repeats and a second task that is different from the first task, The extracting step includes: acquiring a first procedure included in the first task and a second procedure included in the second task from the task database; extracting, from the second tasks, high-frequency tasks whose occurrence frequency calculated based on the event classification name corresponding to the equipment daily report data is equal to or greater than a reference value; extracting, from among the procedures included in the high-frequency work, procedures excluding the first procedure by determining whether a first classification name corresponding to the first procedure matches a second classification name corresponding to the second procedure, as the training items; The event classification name is a name that represents the content of the event, the first classification name is a name that represents the content of the procedure of the first business; the second classification name is a name that represents the content of the second business procedure, an extraction method, wherein the first procedure and the second procedure are determined to match when the first classification name and the second classification name match in the match determination;

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