Surveillance system and method

Abstract

A storage unit (214) stores: a prediction model (191, 197) for predicting a delay state of a work period that is required for a plurality of work items and that is from the start of a maintenance service to the completion of report of an execution result of the maintenance service to a client; and an actually-taken period relative to a reference period for each of a plurality of work items. A prediction unit (130) predicts a delay state on the basis of the prediction model (191, 197) and an actually-taken period for each of the plurality of work items. A display unit (131) displays the delay state predicted by the prediction unit (130). The prediction model (191, 197) is a model for predicting a delay state by using a difference between a reference period for each of the plurality of work items and an actually-taken period corresponding to the reference period.

Description

[Technical field]

[0001] The present disclosure relates to a monitoring system and a monitoring method for monitoring the provision of elevator maintenance services. [Background technology]

[0002] In recent years, service level agreements (SLAs) have been defined for the provision of elevator maintenance services. For example, in the facility remote monitoring system described in JP 2019-191662 A (Patent Document 1), evaluation items are defined for each maintenance service provided, and the target value of the quality level to be provided for each evaluation item is previously determined as an SLA contract. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-191662 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the remote facility monitoring system described in Patent Document 1, the SLA covers the period from the start to the completion of the maintenance service. However, no consideration was given to improving the quality level of the entire maintenance service (time limit management from the start of the maintenance service to the delivery of the maintenance results to the customer).

[0005] The present disclosure has been made to solve such problems, and its purpose is to provide technology that can improve the compliance rate with the SLA covering the entire maintenance service in a maintenance service that specifies an SLA, thereby increasing customer satisfaction. [Means for solving the problem]

[0006] A monitoring system according to the present disclosure monitors the provision of elevator maintenance services. The monitoring system includes a memory unit, a prediction unit, and a display unit. The memory unit stores a prediction model that predicts a delay state of a work period required for multiple tasks from the start of the maintenance service to the completion of reporting the results of the maintenance service to a customer, and an actual period for each of the multiple tasks relative to a reference period. The prediction unit predicts the delay state based on the prediction model and the actual period for each of the multiple tasks. The display unit displays the delay state predicted by the prediction unit. The prediction model is a model that predicts the delay state using the difference between the reference period for each of the multiple tasks and the actual period for the reference period.

[0007] A monitoring method according to the present disclosure is a method for monitoring the provision of an elevator maintenance service. The monitoring method includes a step of storing a prediction model for predicting a delay state of a work period required for a plurality of tasks from the start of the maintenance service to the completion of a report of the implementation result of the maintenance service to a customer, a step of storing an actual period for each of the plurality of tasks with respect to a reference period, a step of predicting the delay state based on the prediction model and the actual period for each of the plurality of tasks, and a step of displaying the delay state predicted by the predicting step. The prediction model is a model for predicting the delay state using a difference between the reference period for each of the plurality of tasks and the actual period with respect to the reference period. Effect of the Invention

[0008] According to the present disclosure, in a maintenance service in which an SLA is defined, it is possible to improve the compliance rate with the SLA covering the entire maintenance service, thereby increasing customer satisfaction. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an overview of a monitoring system according to a first embodiment. [Diagram 2] 1 is a diagram illustrating an example of a hardware configuration of a monitoring system according to a first embodiment. [Diagram 3] FIG. 1 is a diagram showing an example of a functional block diagram of a monitoring system according to a first embodiment. [Figure 4] 4 is a flowchart of a process executed by the monitoring system according to the first embodiment. [Diagram 5] FIG. 4 is a diagram for explaining a delay state of a work period according to the first embodiment. [Figure 6] FIG. 4 is a diagram for explaining a delay state of a work period according to the first embodiment. [Figure 7] FIG. 4 is a diagram for explaining a delay state of a work period according to the first embodiment. [Figure 8] FIG. 4 is a diagram for explaining a delay state of a work period according to the first embodiment. [Figure 9] FIG. 4 is a diagram for explaining a delay state of a work period according to the first embodiment. [Figure 10] FIG. 11 is a diagram showing an example of a functional block diagram of a monitoring system according to a second embodiment. [Figure 11] 10 is a flowchart of a process executed by a monitoring system according to a second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment will be described with reference to the drawings. In the following description, the same components are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated.

[0011] [First embodiment] First, a monitoring system 1 according to the first embodiment will be described. Fig. 1 is a diagram illustrating an overview of the monitoring system 1 according to the first embodiment. Fig. 2 is a diagram illustrating an example of a hardware configuration of the monitoring system according to the first embodiment.

[0012] The monitoring system 1 is a system that monitors the provision of elevator maintenance services. In the following, an elevator is taken as an example of an elevator, and elevator maintenance services will be described. The monitoring system 1 includes a monitoring device 200, a maintenance company terminal 400, and an elevator control device 100.

[0013] In this embodiment, it is assumed that the contract between the customer and the maintenance company specifies the entire elevator maintenance service as an evaluation item of the SLA. The monitoring device 200 manages the delay state of the entire elevator maintenance service. Here, the "entire maintenance service" refers to the period from the start of the maintenance service (also referred to as "maintenance work") to the completion of reporting the results of the maintenance service to the customer (also referred to as "maintenance result report").

[0014] As an SLA contract, a target value for the period from the start of the maintenance service to the completion of the maintenance result report ("target number of days X" described below) is set. In this embodiment, the monitoring device 200 manages the delay state of the entire maintenance service, thereby alerting the user to work delays and encouraging the user to take action to meet the deadline for the maintenance result report. This makes it possible to improve the compliance rate with the SLA.

[0015] Here, it is assumed that the maintenance company has a contract (maintenance service contract) with a client who is the owner of building 10 to periodically inspect the elevators of building 10. For example, if an SLA contract is made with a target number of days X=28 days, the monitoring device 200 manages the delay status of work so that the inspection results can be reported within 28 days. A specific method for this will be described below.

[0016] In this example, building 10 is a four-story building owned by a customer. An elevator is installed in building 10. Elevator car 341 moves in hoistway 351. Cage 341 can stop at each floor, and passengers can board car 341 from hall 331 on the first floor to hall 334 on the fourth floor. An elevator control device 100 is installed in a machine room of building 10. Elevator control device 100 controls car 341.

[0017] In this embodiment, the elevator control device 100 can be connected to a monitoring device 200 via a network. The monitoring device 200 is a device that monitors the provision of elevator maintenance services. The monitoring device 200 is installed, for example, in an information center 20 of a maintenance company.

[0018] The maintenance company terminal 400 can be connected to the monitoring device 200 via a network. The maintenance company terminal 400 is installed, for example, in a branch office 40 of the maintenance company. The maintenance company terminal 400 is a terminal for inputting information related to the maintenance service of the elevator.

[0019] In this example, it is assumed that a customer is in a building 10 and can access a customer terminal 300 owned by the customer. The customer terminal 300 can be connected to the monitoring device 200 via a network.

[0020] 2, the monitoring device 200 has a CPU (Central Processing Unit) 211, a ROM (Read Only Memory) 212, a RAM (Random Access Memory) 213, a storage unit 214, a communication interface 215, and an I / O interface 216. These are connected to each other via a bus so as to be able to communicate with each other.

[0021] The CPU 211 performs overall control of the monitoring device 200. The CPU 211 loads a program stored in the ROM 212 into the RAM 213 and executes it. The ROM 212 stores a program in which the processing procedures of the processes performed by the monitoring device 200 are written.

[0022] The RAM 213 serves as a working area when the CPU 211 executes a program, and temporarily stores the program, data when the program is executed, etc. The storage unit 214 is a non-volatile storage device, such as a hard disk drive (HDD) or a solid state drive (SSD).

[0023] The communication interface 215 controls communication with the elevator control device 100, the maintenance company terminal 400, and the customer terminal 300. The monitoring device 200 can communicate with these devices or systems via the communication interface 115.

[0024] The I / O interface 216 is an interface for connecting the CPU 211 to an external device. For example, the CPU 211 can be connected via the I / O interface 216 to a display device such as a display, or an input device such as a keyboard or a mouse.

[0025] Although not shown, the customer terminal 300 and the maintenance company terminal 400 are also assumed to have a CPU, a ROM, a RAM, a storage unit, a communication interface, and an I / O interface, similar to the monitoring device.

[0026] The elevator control device 100 includes a CPU 111, a ROM 112, a RAM 113, and a communication interface 115. These are connected to each other via a bus so as to be able to communicate with each other.

[0027] The CPU 111 performs overall control of the entire elevator control device 100. The CPU 111 loads a program stored in the ROM 112 into the RAM 113 and executes it. The ROM 112 stores a program in which the processing procedures of the processes performed by the elevator control device 100 are written.

[0028] The RAM 113 serves as a working area when the CPU 111 executes a program, and temporarily stores the program, data required for executing the program, and the like.

[0029] The communication interface 115 controls communication with the car 341, devices installed at the landings of each floor (landing devices including call buttons), and the monitoring device 200. The elevator control device 100 can communicate with these devices and the like via the communication interface 115.

[0030] The elevator control device 100 can be connected to a maintenance computer 30 carried by a maintenance worker 21. The maintenance computer 30 is a terminal (for example, a tablet terminal) used during maintenance work, and can be connected to the elevator control device 100.

[0031] By connecting to the elevator control device 100, the maintenance computer 30 can acquire various information (signals) held by the elevator control device 100, change the settings of the elevator control device 100, and the like.

[0032] Returning to FIG. 1, when starting maintenance work, the maintenance worker 21 connects the maintenance computer 30 to the elevator control device 100.

[0033] The elevator control device 100 issues report data indicating that maintenance work has started by connecting to the maintenance computer 30. The monitoring device 200 receives and records this report data via the network (details will be described later). After that, in this example, the maintenance worker 21 performs inspection work at the landing 333 on the third floor.

[0034] A number of tasks need to be performed from the start of the maintenance service to the completion of the maintenance result report. In this embodiment, N tasks (e.g., 10 tasks) need to be performed as the number of tasks. The names of the N tasks are flow 1, flow 2, flow 3, . . . Flow N.

[0035] The details will be described later with reference to Figs. 4 to 6, but flow 1 is the maintenance work performed by a maintenance worker. Flow 2 is the clerical work performed by an office worker. Flow 3 is the confirmation work performed by a checker. And the last flow N is the reporting work performed by a reporter. Once the reporter has performed the reporting work, the customer can view the maintenance work report on the customer terminal 300 in building 10.

[0036] The input operations of flows 1 to N are performed using a maintenance company terminal 400. The maintenance company terminal 400 may be a plurality of terminals respectively owned by a plurality of workers. The data inputted in the maintenance company terminal 400 is stored in the storage unit 214 of the monitoring device 200 via a network.

[0037] The monitoring device 200 predicts delay conditions for flows 1 to N. The predicted delay conditions can be confirmed on a display device connected to the maintenance company terminal 400 or a display device connected to the monitoring device 200. The monitoring device 200 and the maintenance company terminal 400 may be configured as a single device.

[0038] Next, there will be described various processes executed by the monitoring system 1. Fig. 3 is a diagram showing an example of a functional block diagram of the monitoring system 1 according to the first embodiment.

[0039] 3, the monitoring system 1 includes a prediction unit 130, a display unit 131, and a generation unit 132. The storage unit 214 stores information such as a first prediction model 191, implementation history information 192, report data 193, and past history information 194. Note that some or all of the information stored in the storage unit 214 may be stored in a storage device included in the maintenance company terminal 400, or may be stored in a storage device included in another external device (such as a database server).

[0040] The various processes executed by the monitoring system 1 may be executed, for example, by a user operating an input device connected to the maintenance company terminal 400 or the monitoring device 200. The various processes executed by the monitoring system 1 may be executed by either the monitoring device 200 or the maintenance company terminal 400, or part of the processes may be executed by the monitoring device 200 and the rest by the maintenance company terminal 400.

[0041] The monitoring device 200 may be a device equipped with a WEB server that can be connected from the maintenance company terminal 400 by accessing a specific URL. By accessing the specific URL, the monitoring device 200 may be configured to display the input screen for the flows 1 to N and the delay status predicted by the monitoring device 200 on a browser started by the maintenance company terminal 400. Alternatively, dedicated software may be installed in the maintenance company terminal 400, and the input screen for the flows 1 to N and the delay status may be displayed on a display device of the maintenance company terminal 400.

[0042] In the first embodiment, the prediction unit 130 predicts a delay in the work period using the first prediction model 191 as a prediction model. Here, the "work period" refers to the period required for multiple tasks (flows 1 to N) from the start of the maintenance service to the completion of the maintenance result report, that is, the period until the entire maintenance service is completed.

[0043] The prediction model (first prediction model 191) is a model that predicts a delay state using the difference between the reference period of each of a plurality of tasks (flows 1 to N) and the actual period for that reference period. The "reference period" (also called "reference number of days") specifies the number of days that are expected to be required for each task (flow). The "actual period" (also called "actual number of days") is the number of days that are actually required for each task (flow).

[0044] The performance period refers to the period from when a task is assigned to a worker (the start of the flow) to when the worker completes the task (the end of the flow). For example, in flow 1, the start of the flow does not necessarily occur when a maintenance worker begins inspection work at the site. The performance period also includes the period from when a task is assigned to a maintenance worker to when the maintenance worker actually goes to the site. The base period may be calculated from the average value of past performance periods, but the base period may also be corrected by taking into account the worker's work efficiency or monthly working hours.

[0045] Hereinafter, the reference period of flow 1 will be referred to as "reference number of days 1", and the performance period of flow 1 will be referred to as "performance number of days 1". The reference period of flow 2 will be referred to as "reference number of days 2", and the performance period of flow 2 will be referred to as "performance number of days 2". Similarly, below, the reference period of flow N will be referred to as "reference number of days N", and the performance period of flow N will be referred to as "performance number of days N". For example, the prediction model can be expressed by the following formula.

[0046] Forecast model = -1 x [X - (base number of days 1 + base number of days 2... + base number of days N) + {(base number of days 1 - actual number of days 1) x m1 + (base number of days 2 - actual number of days 2) x m2... + (base number of days N - actual number of days N) x mN}] + Y When using this prediction model, it is shown that the larger the prediction result output, the greater the delay that will occur until the maintenance work results are reported.

[0047] Here, X is the target number of days from the start of maintenance work to the completion of the maintenance result report. m1, m2...mN is set to "1" if each flow has been performed, and set to "0" if each flow has not been performed. For flows that have not been performed, "0" is set in the number of days achieved. For flows that have been performed, data is set in the number of days achieved.

[0048] Y is set to 100 when the report data has not been reported (this state is also called the "unreported state"), and is set to "0" when the report data has been reported (this state is also called the "reported state"). Note that the report data does not have to be used in the prediction model. In this case, Y is always 0.

[0049] The generating unit 132 generates a first prediction model 191 by using a reference period for each of a plurality of tasks (flows 1 to N). Specifically, the generating unit 132 determines the reference number of days 1 to N, respectively, based on past history information 194. Then, the generating unit 132 generates the first prediction model 191 by setting the determined reference number of days 1 to N.

[0050] The past history information 194 is history information of the entire maintenance service for all buildings managed by the maintenance company. The generating unit 132 can acquire a set of performance days 1 to N from the past history information 194 for each building, each elevator in the building, or each elevator model.

[0051] For example, the average value of the acquired multiple numbers of performance days 1 is determined as the reference number of days 1. Similarly, the average value of the acquired multiple numbers of performance days 2 is determined as the reference number of days 2, and the average value of the acquired multiple numbers of performance days N is determined as the reference number of days N.

[0052] When obtaining all the actual days 1 to N for building 10 and calculating the reference days 1 to N, it is possible to construct a prediction model according to the unique circumstances of building 10. It is possible to take into account circumstances such as the fact that work times will be longer if the building is large.

[0053] When all the actual days 1 to N for elevator unit A installed in building 10 are obtained and the reference days 1 to N are calculated, a prediction model can be constructed according to the circumstances specific to unit A. When all the actual days 1 to N for elevator model X are obtained and the reference days 1 to N are calculated, a prediction model can be constructed according to the circumstances specific to model X. In this way, a prediction model can be constructed according to the situation, such as for each elevator or each site.

[0054] The storage unit 214 of the monitoring device 200 stores the alert data 193 that is issued by the elevator when maintenance service is performed. As described above, when the elevator control device 100 detects a connection with the maintenance computer 30, it issues alert data indicating that maintenance service has started.

[0055] The monitoring device 200 receives the issued notification data via the network. The received notification data is stored in the storage unit 214. The prediction unit 130 acquires the notification data from the storage unit 214 when predicting a delay state.

[0056] The prediction unit 130 predicts the delay state so that the unalarmed state is evaluated as being delayed more than the alerted state. Specifically, Y=100 is set in the unalarmed state, and Y=0 is set in the alerted state. In this way, it is possible to reliably grasp that maintenance work has been performed from the alert data, and to call attention to the performance of maintenance work (promotion of the performance of maintenance work).

[0057] The prediction model may also have weights W1 and W2 added as follows: W1 is set based on the number of past trouble occurrences in the target case, or the malfunction history of the elevator itself (for example, the value of W1 is set large for cases where trouble is likely to occur). W2 is set based on the speed of customer response (for example, the value of W2 is set small (negative) for cases where customer response is fast).

[0058] Forecast model = -1 x [X - (base number of days 1 + base number of days 2... + base number of days N) + {(base number of days 1 - actual number of days 1) x m1 + (base number of days 2 - actual number of days 2) x m2... + (base number of days N - actual number of days N) x mN}] + Y + W1 + W2 Fig. 4 is a flowchart of the process executed by the monitoring system 1 according to the first embodiment. The flowchart in Fig. 4 shows the process executed by the prediction unit 130 and the display unit 131. Hereinafter, a "step" may also be simply referred to as "S".

[0059] 4, when the above process starts, in S1, the prediction unit 130 of the monitoring system 1 acquires a prediction model stored in the storage unit 214 and an actual performance period for a reference period of each of a plurality of tasks (flows 1 to N), and proceeds to the process in S2. Specifically, the prediction unit 130 acquires a first prediction model 191 and the actual performance days 1 to N from the implementation history information 192.

[0060] In S2, the prediction unit 130 of the monitoring system 1 predicts a delay state based on the first prediction model 191 and the performance periods of each of the multiple tasks (flows 1 to N), and proceeds to S2. Specifically, the prediction unit 130 predicts a delay state based on the first prediction model 191 and the performance days 1 to N.

[0061] In S3, the display unit 131 of the monitoring system 1 displays the delay state predicted by the prediction unit 130, and the process ends. Specific examples will be described below with reference to Fig. 5 to Fig. 9. Fig. 5 to Fig. 9 are diagrams for explaining the delay state of the work period according to the first embodiment.

[0062] First, the calculation image of the delay state will be explained using Figures 5 and 6. For simplicity, the flow is assumed to consist of flows 1 to 3. In this example, the report data is not used to predict the delay state (Y=0 at all times).

[0063] First, using Figure 5, we will show an example where a maintenance result report was completed without delay. Here, it is assumed that the target number of days X is set to 28 days. "Flow 1 standard" indicates the base number of days 1 for flow 1, where base number of days 1 = 7 days. "Flow 2 standard" indicates the base number of days 2 for flow 2, where base number of days 2 = 7 days. "Flow 3 standard" indicates the base number of days 3 for flow 3, where base number of days 3 = 7 days.

[0064] If all goes according to plan, the maintenance result report is scheduled to be completed in 21 days (=7+7+7). This number of days is denoted as "estimated days to completion." In the figure, "reserve" (also called "reserve days") = target days X - estimated days to completion. In this case, reserve = 7 days (=28-21). In other words, in this case, the number of days delayed from target days X = -7 days. The number of days delayed is the result of the delay state predicted by prediction unit 130.

[0065] Now, let's say that Flow 1 is implemented and the actual number of days for Flow 1 (Flow 1 actual) = 4, meaning that the work for Flow 1 is completed 3 days (= 7 - 4) earlier than planned. This increases the number of spare days by 3 days to 10 days (= 7 + 3) (number of days delayed = -10 days).

[0066] Furthermore, suppose that Flow 2 is implemented and the actual number of days for Flow 2 (Flow 2 actual) = 4, meaning that the work for Flow 2 is completed 3 days (= 7 - 4) earlier than planned. This increases the number of spare days by 3, making it 13 days (= 10 + 3) (number of delay days = -13 days).

[0067] Finally, suppose that Flow 3 is implemented and the actual number of days for Flow 3 (Flow 3 actual) = 4, in other words, the work for Flow 3 is completed 3 days (= 7 - 4) earlier than planned. This increases the number of spare days by 3, making it 16 days (= 16 + 3) (number of days delayed = -16 days).

[0068] The prediction unit 130 calculates the number of delay days as the delay state. The delay state may include the performance period (performance days 1 to 3). The display unit 131 displays the number of delay days. In addition, the display unit 131 may display the performance period, or may display a diagram such as that shown in FIG. 5 that makes it easy to grasp the overall delay situation. In this way, the delay state (expected delay) before the start of the flow, after the execution of flow 1, and after the execution of flow 2 can be grasped.

[0069] Next, an example where a delay finally occurred is shown using Figure 6. As in Figure 5, assume that the target number of days X = 28 days, the base number of days 1 = 7 days, the base number of days 2 = 7 days, and the base number of days 3 = 7 days. The planned number of days to complete = 21 days, and the reserve (reserve days) = 7 days (number of days delayed = -7 days).

[0070] Now, let's say that flow 1 is implemented and the actual number of days for flow 1 (flow 1 actual) = 10, meaning that the work for flow 1 is delayed by 3 days (= 10 - 7) from the plan. As a result, the number of spare days is reduced by 3 days to 4 days (= 7 - 3) (number of days delayed = -4 days).

[0071] Furthermore, suppose that flow 2 is implemented and the actual number of days for flow 2 (flow 1 actual) = 12, meaning that the work for flow 1 is delayed by 5 days (= 12 - 7) from the plan. This reduces the number of reserve days by 5 days, to -1 day (= 4 - 5) (number of days delayed = 1 day). In this way, if the number of days delayed is > 0, it is expected that a delay will occur from the target number of days X. In other words, there is a possibility that the SLA contract will not be met.

[0072] Finally, flow 3 is implemented, and the actual number of days for flow 3 (flow 3 actual) = 9, meaning that the work for flow 3 is delayed by 2 days (= 9 - 7) compared to the plan. This reduces the number of spare days by 2 days, becoming -3 days (= -1 - 2). In other words, the number of days delayed from the target number of days X = 3 days.

[0073] In this example, when flow 2 is performed before the final work process, flow 3, is performed, it is expected that there will be a delay of one day from the target number of days X. Therefore, in order to complete the work within the target number of days X, it is necessary to create a work plan that shortens the work of flow 3 by one day, so that the actual number of days 3 = 6 days (= 7 - 1).

[0074] In this way, in this embodiment, it is possible to constantly grasp the delay status of work related to the entire maintenance service. This makes it possible to call attention to delays at an intermediate stage and suppress delays in work.

[0075] Next, a specific example will be described with reference to Figures 7 to 9. Here, it is assumed that the flow is made up of flows 1 to N. In addition, in this example, it is assumed that report data is used to predict the delay state.

[0076] Figure 7 shows a state where none of the flows have been implemented. Information such as "Work content," "Person in charge," "Base number of days," and "Actual number of days" is set for each flow. In this state, "Work content," "Person in charge," and "Base number of days" are set in advance, but "Actual number of days" is not set.

[0077] Flow 1 is elevator maintenance work performed by a maintenance worker. For Flow 1, "Elevator maintenance" is set as the "Work content," "Maintenance worker" is set as the person in charge, and "Base number of days (Base number of days 1)" is set as "3 days." However, since none of the flows have been carried out yet, the "Actual number of days (Actual number of days 1)" has yet to be set.

[0078] Flow 2 is an administrative task performed by an office worker. Specifically, the results of the maintenance work performed by the maintenance worker are entered. "Maintenance result input" is set as the "Work content" of Flow 2, "Office worker" is set as the person in charge, and "1 day" is set as the base number of days (base number of days 2)." No "Actual number of days (actual number of days 2)" is set.

[0079] Flow 3 is the confirmation work performed by the confirmer. Specifically, the maintenance staff and the office staff's superior confirm and verify the contents of the maintenance results that were entered. "Confirmation work" is set as the "Work content" of Flow 3, "Confirmer (superior)" is set as the person in charge, and "3 days" is set as the base number of days (base number of days 3)." No "Actual number of days (actual number of days 3)" is set.

[0080] The last flow N is a reporting task performed by the reporter. Specifically, it is a task of making the report viewable from the customer terminal 300. For the "task content" of flow N, "reporting task," for the person in charge, "reporter," and for the "reference number of days" are set to "3 days." The "number of days of results" is not set.

[0081] The above example is an example of a flow, but is not limited to this. As a confirmer, multiple people may verify using multiple flows, or the work process may be further divided. The report work may be divided into a flow for uploading the report to the server, an approval work for the upload, and a publication work for the uploaded report (at this point, the report becomes viewable from the customer terminal 300).

[0082] In the state of Figure 5, base number of days 1 = 3, base number of days 2 = 1...base number of days N = 3, m1 = 0, m2 = 0..., mN = 0, Y = 100 (no report issued state). Therefore, the prediction model = -1 x [28 - (3 + 1... + 3) + {(3 - number of days in actual performance 1) x 0 + (1 - number of days in actual performance 2) x 0... + (3 - number of days in actual performance N) x 0}] + 100.

[0083] Here, it is assumed that the sum of the reference days 1 to N is 28 (that is, coincides with X). In this case, the prediction model (first prediction model 191)=100. That is, as the estimation result by the first prediction model 191, a delay=100 is obtained.

[0084] As described above, in the unaltered state, Y is set to 100, and in the alredied state, Y is set to 0. In this way, by setting a large value in the unaltered state, it is possible to promote the implementation of the maintenance work (flow 1).

[0085] Here, assume that flow 1 (maintenance work) is performed and the number of days achieved is two days. As a result, "two days" is set as the "number of days achieved (number of days achieved 1)" for flow 1, as shown in Figure 8. In addition, as a result of the maintenance work being performed, notification data is issued. As a result, the delay evaluation due to the notification state changes from Y=-100 to Y=0.

[0086] Forecast model = -1 x [0 + {(3 - number of days for performance 1) x 1 + (1 - number of days for performance 2) x 0 + (3 - number of days for performance N) x 0}] + 0 = -(3 - number of days for performance 1). Number of days for performance 1 = 2. Therefore, the estimation result obtained by the first forecast model 191 is delay = -(3 - 2) = -1.

[0087] Next, assume that flow 2 (maintenance result input) is performed and the number of days of performance is 3. As a result, "3 days" is set as the "number of days of performance (number of days of performance 2)" of flow 2, as shown in FIG.

[0088] The prediction model is -(3 - number of days for performance 1) -(1 - number of days for performance 2). Number of days for performance 1 = 2, and number of days for performance 2 = 3. Therefore, the estimation result obtained by the first prediction model 191 is delay = -(3 - 2) - (1 - 3) = 1. Although not shown, when flows 3 to N are performed, the estimation result is updated in the same manner as above.

[0089] The display unit 131 displays the delay (number of days of delay). Additionally, the display unit 131 may display the actual period, or may display a diagram that makes it easy to grasp the overall delay situation, as shown in Figures 7 to 9. In this way, the delay state (expected delay) before the start of the flow and after each flow is performed can be grasped.

[0090] As described above, by displaying the delay status of work for the entire maintenance service, it is possible to call attention to delays in work for the entire maintenance service and suppress delays in work. In addition, by displaying the base number of days, the number of actual days, and the number of delayed days for each flow as the delay status, it is possible to visualize and monitor the flow from when maintenance is performed to when the maintenance results are reported to the customer. With the above configuration, in a maintenance service that specifies an SLA, it is possible to improve the compliance rate with the SLA for the entire maintenance service and increase customer satisfaction.

[0091] [Second embodiment] The second embodiment will be described below. In the description of the second embodiment, differences from the first embodiment will be described, and descriptions of common parts will be omitted.

[0092] 10 is a diagram showing an example of a functional block diagram of the monitoring system 1 according to the second embodiment. In the first embodiment, the monitoring system 1 includes a prediction unit 130, a display unit 131, and a generation unit 132, and the storage unit 214 is configured to store a first prediction model 191, implementation history information 192, report data 193, and past history information 194.

[0093] 10, the monitoring system 1 further includes a first update unit 134 and a second update unit 135. The storage unit 214 further stores a second prediction model 197, inspection information 195, and company-wide inspection information 196.

[0094] In the second embodiment, the prediction model further includes a second prediction model 197. Here, the first prediction model 191 is a model that predicts a delay state when no trouble occurs during maintenance service. The second prediction model 197 is a model that predicts a delay state when a trouble occurs during maintenance service. When a trouble occurs, trouble information is recorded in the inspection information 195.

[0095] In addition, the first prediction model 191 in the first embodiment may be a model that predicts a delay state when no trouble occurs during maintenance service, or it may be a model that predicts a delay state both when no trouble occurs during the work period and when a trouble occurs.

[0096] The flow of the process will be described below with reference to the flowchart. Fig. 11 is a flowchart of the process executed by the monitoring system 1 according to the second embodiment. As shown in Fig. 11, when the process executed by the elevator control device 100 starts, the process of S21 is executed.

[0097] In S21, the prediction unit 130 of the monitoring system 1 determines whether or not a problem has occurred during the maintenance service. Whether or not a problem has occurred is determined based on the problem information recorded in the inspection information 195. The inspection information 195 accumulates the results of regular inspections including the problem information.

[0098] If the prediction unit 130 determines that a trouble has occurred during the maintenance service (YES in S21), the process proceeds to S23. If the prediction unit 130 does not determine that a trouble has occurred during the maintenance service (NO in S21), the process proceeds to S22.

[0099] In S22, the prediction unit 130 selects the first prediction model 191 as the prediction model, and proceeds to the process at S23. The first update unit 134 of the monitoring system 1 determines whether or not an update of the first prediction model 191 has been requested in S23.

[0100] The update of the first prediction model 191 may be requested by a user through an input device, or may be requested every predetermined period (for example, every four months). Alternatively, an update request may be made at all times. In this case, the first prediction model 191 is constantly updated.

[0101] If the first update unit 134 determines that an update of the first prediction model 191 has been requested (YES in S23), the process proceeds to S24. If the first update unit 134 determines that an update of the first prediction model 191 has not been requested (NO in S23), the process proceeds to S28.

[0102] In S24, the first update unit 134 updates the first prediction model 191 based on the results of past maintenance services, and proceeds to S28. The processing content of the first update unit 134 is the same as the processing content of the generation unit 132, and determines each of the reference days 1 to N based on the past history information 194. Then, the first update unit 134 updates the first prediction model 191 by setting the determined reference days 1 to N. In this way, the prediction model can be updated according to the implementation status of past maintenance services.

[0103] In S25, the prediction unit 130 selects the second prediction model 197 as the prediction model, and proceeds to S26. In S26, the second update unit 135 of the monitoring system 1 determines whether or not an update of the second prediction model 197 has been requested.

[0104] The second prediction model 197 may be configured to be updated by a request from a user via an input device, or may be requested every predetermined period (for example, every four months). Alternatively, an update request may be made at all times. In this case, the second prediction model 197 is always updated when a problem occurs.

[0105] If the second update unit 135 determines that an update of the second prediction model 197 has been requested (YES in S26), the process proceeds to S27. If the second update unit 135 determines that an update of the second prediction model 197 has not been requested (NO in S26), the process proceeds to S28.

[0106] In S27, the second update unit updates the second prediction model 197 based on the results of past maintenance service in the case where a problem occurs during maintenance service, and the process proceeds to S28.

[0107] Specifically, the second update unit updates the second prediction model 197 based on the company-wide inspection information 196. The company-wide inspection information 196 is information that accumulates the results of past maintenance services when trouble occurs for elevators in each building. In this way, the prediction model can be updated according to the implementation status of past maintenance services when a trouble occurs.

[0108] If a problem occurs, not only will the maintenance work for flow 1 take time, but there is a high possibility that the work in the subsequent processes will also take time. As mentioned above, by switching the prediction model, it is possible to grasp the overall extent of delays that will occur, making it easier to deal with delays when a problem occurs.

[0109] In S28, the prediction unit 130 predicts the delay state using the selected prediction model, and proceeds to the process at S29. Specifically, when the first prediction model 191 is selected as the prediction model, the delay state is predicted based on the first prediction model 191 and the actual days 1 to N. On the other hand, when the second prediction model 197 is selected as the prediction model, the delay state is predicted based on the second prediction model 197 and the actual days 1 to N. In S29, the display unit 131 displays the delay state predicted by the prediction unit 130, and the process ends.

[0110] As described above, when no trouble occurs during the maintenance service, the prediction unit 130 predicts the delay state using the first prediction model 191. When a trouble occurs during the maintenance service, the prediction unit 130 predicts the delay state using the second prediction model 197. In this way, it is possible to grasp the delay state suited to the trouble occurrence situation, and it is possible to suppress delays in work under the trouble occurrence situation. With the configuration as described above, in a maintenance service in which an SLA is specified, it is possible to improve the compliance rate with the SLA for the entire maintenance service, and to further increase customer satisfaction.

[0111] [Main components and effects] The main configurations and effects of the above-described embodiment will be described below.

[0112] (1) The monitoring system 1 monitors the provision of a maintenance service for an elevator. The monitoring system 1 includes a storage unit 214, a prediction unit 130, and a display unit 131. The storage unit 214 stores a first prediction model 191 (a second prediction model 197) for predicting a delay state of a work period required for a plurality of tasks from the start of the maintenance service to the completion of a report of the implementation result of the maintenance service to the customer, and a performance period for each of the plurality of tasks with respect to a reference period. The prediction unit 130 predicts a delay state based on the first prediction model 191 and the performance period for each of the plurality of tasks. The display unit 131 displays the delay state predicted by the prediction unit 130. The first prediction model 191 is a model for predicting a delay state using a difference between a reference period for each of the plurality of tasks and a performance period with respect to the reference period. In this way, by displaying the delay state of the tasks for the entire maintenance service, attention can be drawn to delays in the tasks for the entire maintenance service, and delays in the tasks can be suppressed. This makes it possible to improve compliance with SLAs across all maintenance services, thereby increasing customer satisfaction in maintenance services that specify SLAs.

[0113] (2) The monitoring system 1 further includes a generating unit 132. The generating unit 132 generates a first prediction model 191 by using a reference period for each of a plurality of tasks. This makes it possible to build a prediction model according to the situation, such as for each elevator or each work site.

[0114] (3) The storage unit 214 further stores the notification data issued by the elevator when the maintenance service is performed. The prediction unit 130 predicts the delay state so that when the notification data is not issued, the delay is evaluated as being greater than when the notification data is issued. In this manner, the notification data can reliably grasp that the maintenance work has been performed, and can call attention to the execution of the maintenance work. In this manner, in a maintenance service that specifies an SLA, the compliance rate with the SLA for the entire maintenance service can be improved, thereby increasing customer satisfaction.

[0115] (4) The storage unit 214 further stores the results of past maintenance services. The monitoring system 1 further includes a first update unit that updates the first prediction model 191 based on the results of past maintenance services. In this way, the prediction model can be updated according to the implementation status of past maintenance services. This makes it possible to improve the compliance rate with the SLA for the entire maintenance service in a maintenance service that specifies an SLA, thereby increasing customer satisfaction.

[0116] (5) The prediction model includes a first prediction model 191 that predicts a delay state when no trouble occurs during the maintenance service, and a second prediction model 197 that predicts a delay state when a trouble occurs during the maintenance service. When no trouble occurs during the maintenance service, the prediction unit 130 predicts a delay state using the first prediction model 191. When a trouble occurs during the maintenance service, the prediction unit 130 predicts a delay state using the second prediction model 197. In this way, it is possible to grasp a delay state suited to the trouble occurrence situation, and to suppress delays in work under the trouble occurrence situation. As a result, in a maintenance service in which an SLA is specified, it is possible to improve the compliance rate with the SLA for the entire maintenance service, thereby increasing customer satisfaction.

[0117] (6) The monitoring system 1 further includes a second update unit. The second update unit updates the second prediction model 197 based on the results of past maintenance services when a problem occurs during maintenance services. In this way, the prediction model can be updated according to the implementation status of past maintenance services when a problem occurs. This makes it possible to improve the compliance rate with the SLA for the entire maintenance service in a maintenance service that specifies an SLA, thereby increasing customer satisfaction.

[0118] (7) The monitoring method is a method for monitoring the provision of an elevator maintenance service. The monitoring method includes a step of storing a first prediction model 191 (second prediction model 197) for predicting a delay state of a work period required for a plurality of tasks from the start of the maintenance service to the completion of a report of the implementation result of the maintenance service to the customer, a step of storing an actual period for each of the plurality of tasks with respect to a reference period, a step of predicting a delay state based on the first prediction model 191 and each of the actual periods for the plurality of tasks, and a step of displaying the delay state predicted by the predicting step. The first prediction model 191 is a model for predicting a delay state using a difference between a reference period for each of the plurality of tasks and an actual period with respect to the reference period. In this way, by displaying the delay state of the tasks related to the entire maintenance service, it is possible to call attention to delays in the tasks related to the entire maintenance service, and to suppress delays in the tasks. As a result, in a maintenance service for which an SLA is specified, it is possible to improve the compliance rate with the SLA for the entire maintenance service and increase customer satisfaction.

[0119] The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present disclosure is defined by the claims, not the above description, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0120] 1,1a monitoring system, 10 building, 20 information center (maintenance company), 21 maintenance staff, 30 maintenance computer, 40 branch office (maintenance company), 100 elevator control device, 111,211 CPU, 112,212 ROM, 113,213 RAM, 214 storage unit, 115,215 communication interface, 116,216 I / O interface, 130 prediction unit, 131 display unit, 132 generation unit, 134 first update unit, 135 second update unit, 190 prediction model, 191 first prediction model, 192 implementation history information, 193 alarm data, 194 past history information, 195 inspection information, 196 company-wide inspection information, 197 second prediction model, 200 monitoring device, 300 customer terminal, 341 cage, 351 elevator shaft, 331-334 Platform, 400 maintenance company terminal.

Claims

1. A monitoring system for monitoring provision of elevator maintenance services, comprising: a storage unit that stores a prediction model that predicts a delay state of a work period required for a plurality of tasks from the start of the maintenance service to the completion of a report of the implementation result of the maintenance service to a customer, and stores an actual period for each of the plurality of tasks relative to a reference period; a prediction unit that predicts the delay state based on the prediction model and an actual performance period of each of the plurality of operations; a display unit that displays the delay state predicted by the prediction unit, The customer has a contract for the maintenance service with a maintenance company for the elevator, the plurality of tasks include the maintenance service, an administrative task of inputting the results of the maintenance service, and a reporting task of making a report of the maintenance service available for viewing by the customer; A monitoring system, wherein the prediction model is a model that predicts the delay state using a difference between a reference period for each of the plurality of tasks and an actual period for that reference period.

2. The monitoring system according to claim 1 , further comprising a generating unit configured to generate the prediction model using a reference period of each of the plurality of operations.

3. The storage unit further stores alarm data that is issued by the elevator when the maintenance service is performed, The monitoring system according to claim 1 or claim 2, wherein the prediction unit predicts the delay state so that when the alert data is not issued, the alert data is evaluated as being delayed more than when the alert data is issued.

4. The storage unit further stores results of the past maintenance services, The monitoring system according to any one of claims 1 to 3, further comprising a first update unit that updates the prediction model based on results of the past maintenance services.

5. the prediction model includes a first prediction model that predicts the delay state when no trouble occurs during the maintenance service, and a second prediction model that predicts the delay state when a trouble occurs during the maintenance service, The prediction unit is If no trouble occurs during the maintenance service, predicting the delay state using the first prediction model; 5. The monitoring system according to claim 1, further comprising: a second prediction model that predicts the delay state when a trouble occurs during the maintenance service.

6. The monitoring system according to claim 5 , further comprising a second update unit configured to update the second prediction model based on a result of the past maintenance service when a trouble occurs during the maintenance service.

7. A computer-implemented monitoring method for monitoring provision of elevator maintenance services, comprising: storing a prediction model for predicting a delay state of a work period required for a plurality of works from the start of the maintenance service to the completion of a report of the implementation result of the maintenance service to a customer; storing an actual performance period with respect to a reference period for each of the plurality of tasks; predicting the delay state based on the prediction model and a performance period of each of the plurality of operations; and displaying the delay state predicted in the predicting step, The customer has a contract for the maintenance service with a maintenance company for the elevator, the plurality of tasks include the maintenance service, an administrative task of inputting the results of the maintenance service, and a reporting task of making a report of the maintenance service available for viewing by the customer; A monitoring method, wherein the prediction model is a model that predicts the delay state using a difference between a reference period for each of the plurality of tasks and an actual period for that reference period.

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