Information processing apparatus, information processing method, and information processing program

Abstract

To provide an information processing device capable of accurately specifying a relationship between a position of a car and measurement data SOLUTION: An information processor (10A) includes an acquirer (12) that acquires a first time-series datum as a result of measuring an acceleration of an elevator car during movement and a second time-series datum as a result of measuring a measuring object related to the elevator at the same time as measuring the acceleration, a similarity calculator (111A) that calculates a similarity by comparing a first waveform representing the first time-series datum acquired by the acquirer (12) with a model waveform while shifting a time, and a synchronizer (112) that synchronizes the second time-series datum with the model waveform by using a time difference representing a time lag at which the similarity calculated by the similarity calculator (111A) is the highest.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program for processing the results of various measurements made regarding the operation of an elevator car. [Background technology]

[0002] As disclosed in Patent Document 1, a technique is known in which data such as vibrations and noises inside an elevator car is recorded by a terminal device and transmitted to a management terminal. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-168560 Summary of the Invention [Problem to be solved by the invention]

[0004] In the above-described technology, the relationship between the recorded data and the car's position may be identified by measuring acceleration using a terminal device in addition to the recorded data and comparing the car's travel distance data calculated from the acceleration with the recorded data. In such cases, when measuring acceleration, depending on how the terminal device is held, the axis of the terminal device's acceleration sensor may not be aligned with the direction of travel, making it impossible to accurately measure acceleration. This makes the relationship between the car's position and the recorded data unclear.

[0005] An object of one aspect of the present disclosure is to provide an information processing device that can accurately identify the relationship between the position of a car and measurement data. [Means for solving the problem]

[0006] In order to solve the above problems, the information processing device of the present invention is configured to include an acquisition unit that acquires first time series data resulting from measuring the acceleration of an elevator car when it is moving and second time series data resulting from measuring an object to be measured related to the elevator simultaneously with the measurement of the acceleration; a similarity calculation unit that calculates a similarity by comparing a first waveform indicating the first time series data acquired by the acquisition unit with a model waveform indicating a time series of standard acceleration whose relationship with the position of the car is clear, while shifting the time; and a synchronization unit that synchronizes the second time series data with the model waveform using a time difference indicating the time shift at which the similarity calculated by the similarity calculation unit is highest.

[0007] Furthermore, in order to solve the above-mentioned problems, the information processing method of the present invention includes an acquisition step of acquiring first time series data resulting from measurement of acceleration when an elevator car is moving and second time series data resulting from measurement of an object to be measured related to the elevator simultaneously with the measurement of the acceleration; a similarity calculation step of calculating a similarity between a first waveform representing the first time series data acquired in the acquisition step and a model waveform representing a time series of standard acceleration whose relationship with the position of the car is clear, while shifting the time; and a synchronization step of synchronizing the second time series data with the model waveform using the time difference at which the similarity calculated in the similarity calculation step is highest.

[0008] According to the above configuration and method, it is possible to recognize the relationship between the second time series data and the elevator car position by synchronizing the second time series data with the model waveform, thereby enabling the relationship between the car position and the second time series data as measurement data to be identified with high accuracy.

[0009] An information processing device according to aspect 2 of the present invention may be configured in the above-mentioned aspect 1 such that the similarity calculation unit calculates the similarity by comparing each of the plurality of types of model waveforms with the first waveform while shifting the time, and the synchronization unit synchronizes the model waveform having the highest similarity with the second time series data.

[0010] According to the above configuration, a model waveform with a high degree of similarity is selected from among a plurality of model waveforms, and this model waveform can be synchronized with the second time-series data. Therefore, it is possible to accurately identify the relationship between the second time-series data measured under various conditions and the car position.

[0011] An information processing device according to aspect 3 of the present invention may be configured in the above aspect 1 such that the acquisition unit acquires three pieces of the first time series data as a result of measuring acceleration in each of three mutually perpendicular directions in a time series, and the similarity calculation unit calculates the similarity by comparing the first waveform having the largest absolute value among signal waveforms obtained by applying a low-pass filter to each of the first waveforms representing the three pieces of the first time series data with the model waveform.

[0012] According to the above configuration, it is possible to select appropriate first time series data from the three pieces of first time series data and calculate the similarity without being affected by noise, etc. Therefore, it is possible to more accurately identify the relationship between the car position and the second time series data as measurement data.

[0013] An information processing device according to a fourth aspect of the present invention may be configured such that, in the first aspect described above, the acquisition unit acquires three pieces of the first time series data as a result of measuring acceleration in each of three mutually perpendicular directions in a time series manner, the similarity calculation unit calculates the similarity between the three pieces of first time series data and the model waveform, calculates the time difference at which each of the similarity values ​​is maximized, and selects the first time series data at which the similarity value at the time difference is maximized, and the synchronization unit synchronizes the second time series data with the model waveform using the time difference corresponding to the first time series data selected by the similarity calculation unit.

[0014] According to the above configuration, it is possible to select the first time series data with the highest similarity from the three first time series data and calculate the similarity, thereby making it possible to more accurately identify the relationship between the car position and the second time series data as measurement data.

[0015] The information processing device according to aspect 5 of the present invention may be configured in the above aspect 1, further comprising a model generation unit that accepts input of operational specification information regarding the running of the car and generates the model waveform based on the operational specification information.

[0016] According to the above configuration, a model waveform is generated according to the operational specification information of the elevator to be measured, which allows for a more appropriate comparison with the model waveform, thereby enabling more accurate identification of the relationship between the car position and the second time-series data as measurement data.

[0017] The information processing program according to aspect 7 of the present invention is an information processing program for causing a computer to function as the information processing device in aspect 1 above, and may be configured to cause a computer to function as the acquisition unit, the similarity calculation unit, and the synchronization unit. [Effects of the Invention]

[0018] According to one aspect of the present disclosure, the relationship between the car position and the measurement data can be identified with high accuracy. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a block diagram showing the configuration of a main part of an information processing device according to a first embodiment of the present invention. [Figure 2] 5 is a flowchart showing an example of processing of an information processing method performed by the information processing device according to the first embodiment of the present invention. [Figure 3] FIG. 10 is a diagram showing an overview of the state of measurement by an information processing device inside an elevator car. [Figure 4]FIG. 10 is a diagram showing an example of a state in which a time lag and a value lag occur between the model waveform W(t) and the first waveform X(t). [Figure 5] This figure shows a graph showing the change in acceleration over time, a graph showing the change in volume measurements over time, and a graph showing the change in elevator car position over time, all displayed side by side in a synchronized manner. [Figure 6] FIG. 10 is a block diagram showing the configuration of a main part of an information processing device according to a second embodiment of the present invention. [Figure 7] 10 is a flowchart showing an example of processing of an information processing method performed by an information processing device according to a second embodiment of the present invention. [Figure 8] FIG. 10 is a block diagram showing the configuration of a main part of an information processing device according to a third embodiment of the present invention. [Figure 9] 10 is a flowchart showing an example of processing of a first method according to a third embodiment of the present invention. [Figure 10] 10 is a flowchart showing an example of processing of a second method according to the third embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] [Embodiment 1] Hereinafter, one embodiment of the present invention will be described in detail.

[0021] First, an outline of an example of a method of using the information processing device 10A according to the first embodiment of the present invention will be described. Fig. 3 shows a state in which a measurer is in the car of an elevator to be measured, holding the information processing device 10A, which is a portable terminal such as a smartphone, in his / her hand. In this state, the measurer operates the elevator to move the car.

[0022] While the person travels in the car, an acquisition unit 12 (described later) of the information processing device 10A records the change over time in the acceleration of the elevator car as measured acceleration time series data D1 (first time series data), specifically as a function of acceleration (first waveform) X(t) with time as a variable. At the same time, an acquisition unit 12 (described later) of the information processing device 10A records the change over time in the volume of the running sound of the elevator car as measured volume time series data D2 (second time series data), specifically as a function of volume (second waveform) V(t) with time as a variable.

[0023] In the present embodiment, it is assumed that the information processing device 10A includes a microphone 121 and an acceleration sensor 122, which will be described later, but the present invention is not limited to this. For example, at least one of an external microphone and an acceleration sensor may be connected to the information processing device 10A, and the acquisition unit 12 may acquire at least one of the measured acceleration time-series data D1 and the measured volume time-series data D2.

[0024] Alternatively, the system may be such that an information processing terminal equipped with a microphone and an acceleration sensor is placed in an elevator car, and measurement results by the information processing terminal are transmitted to information processing device 10A via a communication network such as wirelessly. That is, acquisition unit 12 may acquire measured acceleration time-series data D1 and measured volume time-series data D2 from an external information processing terminal via the communication network. In this case, information processing device 10A may be realized by a PC (Personal Computer), a server, or the like.

[0025] Furthermore, the orientation of acceleration sensor 122 included in information processing device 10A or an external acceleration sensor does not need to coincide with the direction of acceleration / deceleration (up and down) of the elevator car and is arbitrary. This eliminates the need to use a tripod to hold information processing device 10A or an external acceleration sensor horizontally or vertically. That is, the acceleration of the elevator car and the volume of its running sound can be measured by a simple method, such as by having the person measuring hold information processing device 10A or an external acceleration sensor in their hands. Furthermore, since there is no need to take measures such as placing information processing device 10A on the floor to maintain horizontality, floor vibration sounds can be prevented from being recorded by microphone 121.

[0026] <Configuration of information processing device 10A> 1 is a block diagram showing the main configuration of an information processing device 10A according to embodiment 1 of the present invention. As shown in FIG. 1, the information processing device 10A includes a control unit 11A, an acquisition unit 12, and a storage unit 19.

[0027] The acquiring unit 12 acquires measured acceleration time-series data D1 as a result of measuring the acceleration of the elevator car while it is moving, and measured volume time-series data D2 as a result of measuring the elevator's running sound as a measurement target simultaneously with the acceleration measurement. In this embodiment, the acquiring unit 12 includes a microphone 121 and an acceleration sensor 122.

[0028] The microphone 121 can record sounds outside the information processing device 10A. The microphone 121 records the change in volume of the elevator car running sound over time as measured volume time-series data D2, specifically as a volume function V(t) with time as a variable. The microphone 121 then stores the measured volume time-series data D2 in the storage unit 19, which will be described later.

[0029] The acceleration sensor 122 can record the acceleration of the information processing device 10A. The acceleration sensor 122 records the change in the acceleration of the elevator car over time as measured acceleration time-series data D1, specifically, as an acceleration function X(t) with time as a variable. The acceleration sensor 122 then stores the measured acceleration time-series data D1 in the storage unit 19, which will be described later.

[0030] The storage unit 19 stores the measured acceleration time series data D1 and the measured volume time series data D2 acquired by the acquisition unit 12, as well as the standard acceleration time series data D9.

[0031] The standard acceleration time series data D9 is data showing a standard time change in the acceleration of a preset elevator car, in which the relationship between the elevator car position and acceleration is clear. Specifically, the standard acceleration time series data D9 is a function (model waveform) W(t) of acceleration with time as a variable. In this embodiment, the standard acceleration time series data D9 is assumed to be predetermined data generated in advance, and may be, for example, data downloaded by the information processing device 10A from a predetermined server via a communication network, or data recorded in the memory unit 19 when an app that realizes processing by the control unit 11A (described later) is installed.

[0032] The control unit 11A controls each unit of the information processing device 10 A. The control unit 11A includes a similarity calculation unit 111A and a synchronization unit 112.

[0033] The similarity calculation unit 111A calculates the similarity by comparing the first waveform X(t) representing the measured acceleration time series data D1 with the model waveform W(t) representing the standard acceleration time series data D9 while shifting the time between them. Specifically, the similarity calculation unit 111A calculates a cross-correlation function C XW (τ) is calculated. XW (τ) is C XW (τ)=ΣX(t)W(t-τ).

[0034] Next, the similarity calculation unit 111A calculates the time difference at which the similarity is highest. Specifically, the similarity calculation unit 111A calculates the cross-correlation function C XW The time lag τ when (τ) reaches its maximum value XW 4 shows an example of a state in which there is a time lag and a value lag between the model waveform W(t) and the first waveform X(t).

[0035] The synchronization unit 112 synchronizes the measured volume time series data D2 with the standard acceleration time series data D9 using the time difference that maximizes the similarity calculated by the similarity calculation unit 111A. As described above, the standard acceleration time series data D9 is data that clearly indicates the relationship between the elevator car position and acceleration. That is, by synchronizing the measured volume time series data D2 with the standard acceleration time series data D9, the relationship between the measured volume time series data D2 and the elevator car position becomes clear.

[0036] The synchronization unit 112 may control the display of the relationship between the time change in acceleration, the time change in the volume measurement value obtained from the measured volume time-series data D2, and the elevator car position on a display unit included in the information processing device 10A. For example, as shown in FIG. 5, the synchronization unit 112 may display a graph showing the time change in acceleration, a graph showing the time change in the volume measurement value, and a graph showing the time change in the elevator car position side by side in a synchronized state. The synchronization unit 112 may also display the acceleration, the volume measurement value, and the car position values ​​for each time in a table format. Furthermore, the synchronization unit 112 may transmit data showing the relationship between the time change in acceleration, the time change in the volume measurement value, and the elevator car position to an external server or information processing terminal via a communication network.

[0037] <Example of Information Processing Method by Information Processing Device 10A> Next, an example of the information processing method performed by the information processing device 10A will be described with reference to Fig. 2. Fig. 2 is a flowchart showing an example of the information processing method performed by the information processing device 10A according to the first embodiment of the present invention.

[0038] First, the acquisition unit 12 acquires the measured acceleration time series data D1 and the measured sound volume time series data D2 of the elevator car (step S11).

[0039] Next, the similarity calculation unit 111A compares the first waveform X(t) representing the measured acceleration time series data D1 with the model waveform W(t) representing the standard acceleration time series data D9, with a time shift, to calculate a cross-correlation function as the similarity (step S12).

[0040] Next, the similarity calculation unit 111A calculates the time difference at which the cross-correlation function is maximized (step S13).

[0041] Finally, the synchronization unit 112 synchronizes the measured volume time series data D2 with the standard acceleration time series data D9 using the time difference at which the cross-correlation function is at its highest (step S14).

[0042] As described above, the information processing device 10A according to this embodiment synchronizes the measured volume time series data D2 with the standard acceleration time series data D9. This allows the position of the elevator car at time t in the measured volume time series data D2 to be identified, making it possible to accurately determine, for example, the position at which the elevator car passes when the measured volume becomes loud. This makes it possible to efficiently identify the cause of problems associated with the car's movement.

[0043] Furthermore, by synchronizing the time change in acceleration with the time change in sound volume during measurement, it becomes possible to confirm whether or not there is a causal relationship between the change in acceleration and the generation of noise.

[0044] [Embodiment 2] Other embodiments of the present invention will be described below. For ease of explanation, the same reference numerals will be used to designate components having the same functions as those described in the above embodiment, and the description thereof will not be repeated.

[0045] <Configuration of information processing device 10B> 6 is a block diagram showing the main configuration of an information processing device 10B according to embodiment 2 of the present invention. As shown in FIG. 6, the information processing device 10B includes a control unit 11B instead of the control unit 11A in embodiment 1. The control unit 11B includes a similarity calculation unit 111B instead of the similarity calculation unit 111A in embodiment 1. The control unit 11B also includes a model generation unit 113.

[0046] In the information processing device 10A according to the first embodiment, the similarity calculation unit 111A calculates the similarity between the measured acceleration time series data D1 and one standard acceleration time series data D9, and calculates the time difference at which the similarity is highest. In contrast, in the information processing device 10B according to the present embodiment, the similarity calculation unit 111B calculates the similarity between the measured acceleration time series data D1 and each of the multiple standard acceleration time series data D9 created by the model generation unit 113 using the time difference at which the similarity is highest.

[0047] The model generation unit 113 generates multiple standard acceleration time series data D9 based on operation specification information (travel specification information) about the elevator car. Specifically, the model generation unit 113 generates multiple standard acceleration time series data D9 based on operation specification information input by a user and stores the multiple standard acceleration time series data D9 in the storage unit 19. Examples of operation specification information include the car's travel path, maximum acceleration, jerk, and constant speed. Here, if the type of information input as operation specification information is sufficient, the standard acceleration time series data D9 can be uniquely identified. However, requiring the user to input enough operation specification information to uniquely identify the standard acceleration time series data D9 places a heavy burden on the user. Therefore, the model generation unit 113 limits the type of information input as operation specification information and generates multiple types of standard acceleration time series data D9. This reduces the user's effort in inputting operation specification information to generate the standard acceleration time series data D9.

[0048] Note that the user may input elevator model information as the operational specification information. The model generation unit 113 can generate the standard acceleration time series data D9 by referencing a database in which model information and physical operational specification information are associated with each other. For example, in this case, input of the car's travel path may be omitted, and the model generation unit 113 may generate multiple standard acceleration time series data D9 taking into account variations in the car's travel path.

[0049] In addition, a large number of variations of the standard acceleration time series data D9 may be stored in the storage unit 19, and the model generation unit 113 may generate multiple standard acceleration time series data D9 by narrowing down the standard acceleration time series data D9 that may be relevant according to the input operation specification information.

[0050] The similarity calculation unit 111B compares the first waveform X(t) representing the measured acceleration time series data D1 with the model waveform w(t) representing the plurality of standard acceleration time series data D9, shifting the time between the first waveform X(t) and the model waveform w(t), and calculates the similarity between the first waveform X(t) and the model waveform w(t), with the time difference τ as a variable. Xw Next, the similarity calculation unit 111B calculates the time difference with the highest similarity for each similarity and the similarity at that time difference (Xw maximum similarity). Specifically, the similarity calculation unit 111B calculates the cross-correlation function C Xw The time lag τ when (τ) reaches its maximum value Xw , and the maximum value C Xw (τ Xw ) is calculated.

[0051] The synchronization unit 112 synchronizes the model waveform w(t) with the highest similarity with the measured volume time series data D2. Specifically, the synchronization unit 112 identifies the maximum of the Xw maximum similarities and selects the standard acceleration time series data D9 (D9A) from which that similarity was calculated. Then, the synchronization unit 112 synchronizes the selected standard acceleration time series data D9 (D9A) with the measured volume time series data D2.

[0052] <Example of Information Processing Method by Information Processing Device 10B> Next, an example of the information processing method performed by the information processing device 10B will be described with reference to Fig. 7. Fig. 7 is a flowchart showing an example of the information processing method performed by the information processing device 10B according to the second embodiment of the present invention.

[0053] First, the acquisition unit 12 acquires the measured acceleration time series data D1 of the elevator car and the measured sound volume time series data D2 (step S21).

[0054] Next, the similarity calculation unit 111B compares the first waveform X(t) representing the measured acceleration time series data D1 with the model waveform w(t) representing the multiple standard acceleration time series data D9, each with a time shift, to calculate the similarity between them (step S22).

[0055] Next, similarity calculation section 111B calculates, for each similarity, the time difference at which the similarity is highest, and the similarity at that time difference (Xw maximum similarity) (step S23).

[0056] Next, the synchronization unit 112 identifies the maximum of the Xw maximum similarities and selects the standard acceleration time-series data D9 (D9A) for which that similarity has been calculated (step S24).

[0057] Finally, the synchronization unit 112 synchronizes the measured volume time series data D2 with the standard acceleration time series data D9 (D9A) using the time difference at which the cross-correlation function is at its highest (step S25).

[0058] In this embodiment, the model generation unit 113 generates a plurality of standard acceleration time-series data D9, but the present invention is not limited to this. For example, the plurality of standard acceleration time-series data D9 may be data downloaded by the information processing device 10A from a predetermined server via a communication network, or data recorded in the storage unit 19 when an application that realizes processing by the control unit 11A (described later) is installed.

[0059] [Embodiment 3] Other embodiments of the present invention will be described below. For ease of explanation, the same reference numerals will be used to designate components having the same functions as those described in the above embodiment, and the description thereof will not be repeated.

[0060] <Configuration of information processing device 10C> Fig. 8 is a block diagram showing the main configuration of an information processing device 10C according to embodiment 3 of the present invention. As shown in Fig. 8, the information processing device 10C includes a control unit 11C instead of the control unit 11A in embodiment 1. The control unit 11C includes a similarity calculation unit 111C instead of the similarity calculation unit 111A in embodiment 1.

[0061] In the information processing device 10A according to the first embodiment, the similarity calculation unit 111A calculates the similarity between one piece of measured acceleration time series data D1 and one piece of standard acceleration time series data D9, and outputs the time difference with the highest similarity. In contrast, in the information processing device 10C according to the present embodiment, the similarity calculation unit 111C selects one piece of measured acceleration time series data D1 measured by the acceleration sensor 122, calculates the similarity between the selected piece of measured acceleration time series data D1 (D1A) and the standard acceleration time series data D9, and calculates the time difference with the highest similarity. Below, a first method and a second method for selecting one piece of measured acceleration time series data D1 will be described.

[0062] <Method 1> First, a first method for selecting one of the plurality of measured acceleration time series data D1 will be described. In this embodiment, as an example, the measured acceleration time series data D1 uses three pieces of measured acceleration time series data D1 as the result of measuring acceleration in three mutually orthogonal directions in time series. The similarity calculation unit 111C calculates a cross-correlation function by comparing the first waveform U(t) having the largest absolute value among signal waveforms obtained by applying a low-pass filter to each of the three first waveforms U(t) representing the measured acceleration time series data D1 with the model waveform W(t).

[0063] For example, if the information processing device 10A is a smartphone with a display screen, the three measured acceleration time series data D1 are assumed to be acceleration measurement data corresponding to three directions: the normal direction of the display screen, the up-down direction of the display screen, and the left-right direction of the display screen. In this case, the measurement data that most sensitively detects the acceleration of the elevator car is determined depending on the direction in which the information processing device 10A is held by the user during measurement. In other words, it is basically preferable to select the measured acceleration time series data D1 corresponding to the first waveform U(t) having the largest absolute value.

[0064] However, it is conceivable that the first waveform U(t) may temporarily contain data with a large absolute value due to sudden noise or the like. Therefore, in this embodiment, in order to remove momentary high-frequency fluctuations, the measured acceleration time-series data D1 corresponding to the first waveform U(t) with the largest absolute value is selected from the signal waveform obtained by applying a low-pass filter to the first waveform U(t). This makes it possible to select more appropriate measured acceleration time-series data D1 without being affected by noise and calculate a cross-correlation function as a similarity.

[0065] <An example of the first method> Next, an example of the processing of the first method will be described with reference to the flowchart shown in FIG.

[0066] First, the acquisition unit 12 acquires three pieces of measured acceleration time series data D1 and measured sound volume time series data D2 of the elevator car (step S31).

[0067] Next, the similarity calculation unit 111C calculates a cross-correlation function by comparing the first waveform U(t) having the largest absolute value among the signal waveforms obtained by applying a low-pass filter to each of the first waveforms U(t) representing the three pieces of measured acceleration time-series data D1 with the model waveform W(t) (step S32).

[0068] Next, similarity calculation section 111C calculates the time difference at which the cross-correlation function is maximized (step S33).

[0069] Finally, the synchronization unit 112 synchronizes the measured volume time series data D2 with the standard acceleration time series data D9 using the time difference at which the cross-correlation function is at its highest (step S34).

[0070] In the above example, the similarity calculation unit 111C selects the measured acceleration time series data D1 corresponding to the first waveform U(t) having the largest absolute value among the signal waveforms obtained by applying a low-pass filter to the first waveform U(t), but this is not limiting. For example, the similarity calculation unit 111C may select the measured acceleration time series data D1 corresponding to the first waveform U(t) having the largest average value of the absolute values ​​of the first waveform U(t) representing the three measured acceleration time series data D1.

[0071] <Second Method> Next, a second method for selecting one of the plurality of measured acceleration time series data D1 will be described. As with the first method, in this embodiment, as an example, three pieces of measured acceleration time series data D1 are used as the measured acceleration time series data D1, which are the results of measuring acceleration in three mutually orthogonal directions in time series. The similarity calculation unit 111C calculates the similarities between the three pieces of measured acceleration time series data D1 and the model waveform W(t) representing the standard acceleration time series data D9, calculates the time differences at which the respective similarity values ​​are maximized, and selects the measured acceleration time series data D1 at which the similarity value at the time differences is maximized.

[0072] Specifically, the similarity calculation unit 111C calculates a cross-correlation function C xW (τ), C yW (τ), C zW Calculate (τ). C xW (τ)=ΣW(t)αx(t-τ) C yW (τ)=ΣW(t)αy(t-τ) C zW (τ)=ΣW(t)αz(t-τ) In the above equation, αx, αy, and αz represent acceleration data on each axis.

[0073] Next, the similarity calculation unit 111C calculates the time difference τ at which the value of the cross-correlation function is maximized for each cross-correlation function. x , τ y , τ z Then, the similarity calculation unit 111C calculates the time difference τ x , τ y , τ z C when xW (τ), C yW (τ), C zW The values ​​of (τ) are calculated, and the measured acceleration time series data D1 with the largest value is selected.

[0074] The synchronization unit 112 synchronizes the measured volume time series data D2 with the standard acceleration time series data D9 using the time difference corresponding to the measured acceleration time series data D1 selected by the similarity calculation unit 111C.

[0075] <An example of processing using the second method> Next, an example of the processing of the second method will be described with reference to the flowchart shown in FIG.

[0076] First, the acquisition unit 12 acquires three pieces of measured acceleration time series data D1 and measured sound volume time series data D2 of the elevator car (step S41).

[0077] Next, the similarity calculation unit 111C calculates a cross-correlation function C with the model waveform W(t) for each of the three pieces of measured acceleration time series data D1. xW (τ), C yW (τ), C zW (τ) is calculated (step S42).

[0078] Next, the similarity calculation unit 111C calculates the time difference τ at which the value of the cross-correlation function is maximized for each cross-correlation function. x , τ y , τ z is calculated (step S43).

[0079] Next, the similarity calculation unit 111C calculates the time difference τ x , τ y , τ z C when xW (τ), C yW (τ), C zW The values ​​of (τ) are calculated, and the measured acceleration time series data D1 with the largest value is selected (step S44).

[0080] Finally, the synchronization unit 112 synchronizes the measured volume time series data D2 with the standard acceleration time series data D9 using the time difference corresponding to the measured acceleration time series data D1 selected by the similarity calculation unit 111C (step S45).

[0081] [Modification] In each embodiment, the acquisition unit 12 may further include a video camera. The video camera can capture images of the outside of the elevator car and record the external video, for example, by the following method. A user, such as a maintenance worker, stands on the elevator car to be measured and performs the measurement described in embodiment 1 while capturing images of the outside of the elevator car. The synchronization unit 112 shifts the start time of the external video by the time difference at which the similarity is highest. This synchronizes the time t of the external video with the time t of the standard acceleration time series data D9. Therefore, the user can identify the position of the elevator car at time t in the external video from the position of the elevator car at time t in the standard acceleration time series data D9. In other words, the relationship between the car position and the external situation can be accurately determined. The video camera may record the measured volume time series data D2 instead of the microphone 121.

[0082] In each embodiment, the information processing devices 10A, 10B, and 10C may include a mechanism that notifies the user to remeasure if the similarity between the measured acceleration time series data D1 (D1A) and the standard acceleration time series data D9 (D9A) at the time difference when the similarity output by the similarity calculation units 111A, 111B, and 111C is highest falls below a predetermined value. This promotes the acquisition of measured acceleration time series data D1 suitable for comparison with the standard acceleration time series data D9, and enables the relationship between the car position and the recorded data to be identified with high accuracy.

[0083] [Software implementation example] The functions of the information processing devices 10A, 10B, and 10C (hereinafter referred to as "devices") can be realized by a program that causes a computer to function as the device, and a program that causes a computer (including a smartphone) to function as each control block of the device (particularly each part included in the control units 11A, 11B, and 11C).

[0084] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program, thereby realizing the functions described in each of the above embodiments.

[0085] The program may be non-transitory and may be recorded on one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0086] Furthermore, some or all of the functions of the control blocks can be realized by logic circuits. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of the control blocks can also be realized by, for example, a quantum computer.

[0087] Furthermore, each process described in each of the above embodiments may be executed by AI (Artificial Intelligence). In this case, the AI ​​may run on the control device or on another device (for example, an edge computer or a cloud server).

[0088] [Additional Notes] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0089] 10A, 10B, 10C Information processing device 11A, 11B, 11C control section 12 Acquisition Department 19 Memory section 111A, 111B, 111C Similarity calculation unit 112 Synchronization section 113 Model Generation Unit 121 Microphone 122 Acceleration Sensor D1 Measured acceleration time series data D2 Measured volume time series data D9 Standard acceleration time series data

Claims

1. an acquisition unit that acquires first time series data obtained by measuring acceleration during movement of an elevator car, and second time series data obtained by measuring an object to be measured related to the elevator simultaneously with the measurement of the acceleration; a similarity calculation unit that calculates a similarity between a first waveform indicating the first time series data acquired by the acquisition unit and a model waveform indicating a time series of standard acceleration having a clear relationship with the position of the car, while shifting the time between the first waveform and the model waveform; a synchronization unit that synchronizes the second time series data with the model waveform using a time difference that indicates the time difference when the similarity calculated by the similarity calculation unit becomes highest.

2. the similarity calculation unit calculates a similarity by comparing each of the plurality of types of model waveforms with the first waveform while shifting the time; The information processing apparatus according to claim 1 , wherein the synchronization unit synchronizes the model waveform having the highest similarity with the second time series data.

3. the acquisition unit acquires three pieces of the first time-series data as a result of measuring accelerations in three mutually orthogonal directions in a time series manner; 2. The information processing device according to claim 1, wherein the similarity calculation unit calculates the similarity by comparing a first waveform having a largest absolute value among signal waveforms obtained by applying a low-pass filter to each of the three first waveforms representing the first time series data with the model waveform.

4. the acquisition unit acquires three pieces of the first time-series data as a result of measuring accelerations in three mutually orthogonal directions in a time series manner; the similarity calculation unit calculates the similarities between the three first time series data and the model waveform, calculates the time difference at which each of the similarities is maximized, and selects the first time series data at which the similarity value is maximized at the time difference; The information processing apparatus according to claim 1 , wherein the synchronization unit synchronizes the second time series data with the model waveform using the time difference corresponding to the first time series data selected by the similarity calculation unit.

5. 2. The information processing apparatus according to claim 1, further comprising a model generating unit that receives input of operational specification information related to the running of said car, and generates said model waveform based on said operational specification information.

6. an acquiring step of acquiring first time series data obtained by measuring acceleration during movement of an elevator car, and second time series data obtained by measuring a measurement object related to the elevator simultaneously with the measurement of the acceleration; a similarity calculation step of calculating a similarity between a first waveform indicating the first time series data acquired in the acquisition step and a model waveform indicating a time series of standard acceleration having a clear relationship with the position of the car, while shifting the time between the first waveform and the model waveform; a synchronization step of synchronizing the second time series data with the model waveform using the time difference at which the similarity calculated in the similarity calculation step is highest.

7. 2. An information processing program for causing a computer to function as the information processing device according to claim 1, the information processing program causing a computer to function as the acquisition unit, the similarity calculation unit, and the synchronization unit.

Images