Evaluation method and evaluation system for scissors

Abstract

To provide an evaluation method and an evaluation system for scissors that can quantitatively evaluate scissors.SOLUTION: An evaluation method for scissors of the present invention includes: a step S200 of acquiring, by a computer, time-series data of the load applied to scissors within a period from an open state to a closed state of the scissors; and a step S202 of determining, by the computer, the similarity between the acquired time-series data and reference data that is time-series data of load to be a reference of evaluation determination of scissors.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] The present invention relates to a method and system for evaluating scissors. [Background technology]

[0002] Conventionally, there is known a technique for evaluating scissors by inspecting their sharpness (for example, Patent Document 1). In the technique described in Patent Document 1, when one blade of the scissors is moved toward the other blade, the sharpness of the scissors is inspected (evaluated) by determining whether a test sheet suspended between the blades is cut. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Utility Model Application Publication No. 6-49985 Summary of the Invention [Problem to be solved by the invention]

[0004] However, with conventional techniques, only a qualitative (sensory) evaluation of whether or not the test sheet was cut was possible, making it difficult to quantitatively evaluate the scissors.

[0005] The present invention has been made in consideration of the above-mentioned problems, and has an object to provide a scissors evaluation method and evaluation system that are capable of quantitatively evaluating scissors. [Means for solving the problem]

[0006] In order to solve the above problems, firstly, the present invention provides a method for evaluating scissors, comprising the steps of: acquiring, by a computer, time series data of the load applied to the scissors during the transition from an open state to a closed state; and determining the similarity between the acquired time series data and reference data, which is time series data of the load that serves as the basis for judging the evaluation of the scissors.

[0007] According to this invention, a similarity is determined between time series data of the load applied to the scissors to be evaluated during the transition from the open state to the closed state and reference data, which is time series data of the load that serves as the basis for the evaluation judgment of the scissors, so that the fit of the scissors can be quantitatively evaluated based on the determined similarity.

[0008] Secondly, the present invention provides a scissors evaluation system comprising: an acquisition means for acquiring time series data of the load applied to the scissors during the transition from an open state to a closed state; and a determination means for determining the similarity between the acquired time series data and reference data, which is time series data of the load that serves as the basis for judging the evaluation of the scissors. Effect of the Invention

[0009] According to the scissors evaluation method and evaluation system of the present invention, scissors can be quantitatively evaluated. [Brief description of the drawings]

[0010] [Figure 1] 1 is a diagram illustrating a schematic basic configuration of a scissors evaluation system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram showing a configuration of an evaluation device. [Diagram 3] FIG. 2 is a functional block diagram for explaining functions that play a major role in the evaluation system. [Figure 4] FIG. 4A is a diagram showing an example of the configuration of acquired data, and FIG. 4B is a diagram showing an example of changes over time in the load cell output and the rotary encoder output. [Diagram 5] 11 is a flowchart illustrating an example of pre-processing of time series data. [Figure 6] FIG. 2A is a diagram showing an example of time series data, and FIG. 2B is a diagram showing an example of time series data after difference or differentiation processing. [Figure 7]FIG. 1A is a diagram showing an example of time series data after low-pass filter processing, and FIG. 1B is a diagram showing an example of time series data to be subjected to moving averaging and downsampling. [Figure 8] 4 is a flowchart showing an example of main processing of the evaluation system according to one embodiment of the present invention. [Figure 9] 13A and 13B are diagrams illustrating an example of how functions of the evaluation system are shared between an evaluation device and a server. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. However, this embodiment is merely an example, and the present invention is not limited thereto.

[0012] (1) Basic configuration of scissors evaluation system Fig. 1 is a diagram showing a schematic basic configuration of a scissors evaluation system according to an embodiment of the present invention. As shown in Fig. 1, in this evaluation system, a measurement device 10 measures the load applied to the scissors S during the transition from the open state to the closed state of the scissors S, and an evaluation device 20 determines the similarity between the time series data of the measured load and reference data, which is the time series data of the load that serves as the basis for the evaluation judgment of the scissors S. Here, the measurement device 10 and the evaluation device 20 are connected to each other so as to be able to communicate with each other via, for example, a wired LAN (Local Area Network).

[0013] In this embodiment, the scissors S are, for example, Western-style scissors for hairdressing and beauty, and include a pair of blades S1, S2 arranged to overlap each other. One blade S1 of the pair of blades S1, S2 is rotatably supported on a support shaft held by the other blade S2. In this embodiment, the transition of the scissors S from the open state to the closed state means that the tip end (e.g., the grip) of one blade S1 approaches the tip end (e.g., the grip) of the other blade S2, that is, the angle of the pair of blades S1, S2 in the rotation direction of the blade S1 approaches 0°. The scissors S are not limited to hairdressing and beauty scissors, and may be scissors used for other purposes such as medical use.

[0014] In this embodiment, the measuring device 10 includes an arm-shaped moving member 11, a load cell 12 provided at the tip of the moving member 11, and a rotary encoder 13. The moving member 11 is provided so as to contact the tip (e.g., grip) of one blade S1 of the scissors S in the open state via the load cell 12. The moving member 11 is rotatably supported on the shaft of a predetermined motor (not shown) and moves along the rotation direction of the motor shaft (here, the direction in which the tip (e.g., grip) of one blade S1 of the scissors S approaches the tip (e.g., grip) of the other blade S2) in response to the driving of the motor.

[0015] The load cell 12 detects the load applied from the moving member 11 to one blade S1 of the scissors S while the scissors S transition from the open state to the closed state, and outputs information about the detected load to the evaluation device 20. The rotary encoder 13 has a shaft that rotates in conjunction with the shaft of the motor described above, and detects the amount of rotational displacement of the motor shaft (i.e., the amount of rotational displacement of the moving member 11 and one blade S1 of the scissors S). The rotary encoder 13 also outputs to the evaluation device 20 a number of pulse signals corresponding to the amount of detected rotational displacement.

[0016] It should be noted that the configuration of the measuring device 10 in this embodiment is merely an example, and other configurations capable of measuring the load applied to the scissors S during the transition of the scissors S from the open state to the closed state may be used.

[0017] The evaluation device 20 is configured to acquire information related to the load transmitted from the measurement device 10 over time, and determine the similarity between the acquired time-series data of the load and reference data. The evaluation device 20 may be a terminal device operated by an individual user, such as a mobile terminal, a smartphone, a PDA (Personal Digital Assistant), a personal computer, or a television receiver with a two-way communication function (including so-called multi-function smart televisions).

[0018] (2) Evaluation device configuration The configuration of the evaluation device 20 will be described with reference to Fig. 2. Fig. 2 is a block diagram showing the internal configuration of the evaluation device 20. As shown in Fig. 2, the evaluation device 20 includes a CPU 21, a ROM 22, a RAM 23, a storage device 24, a display processing unit 25, a display unit 26, an input unit 27, and a communication interface unit 28, and is provided with a bus 20a for transmitting control signals or data signals between the units.

[0019] When the power is turned on to the evaluation device 20, the CPU 21 loads various programs stored in the ROM 22 or the storage device 24 into the RAM 23 and executes them. In this embodiment, the CPU 21 reads out and executes the programs stored in the ROM 22 or the storage device 24, thereby realizing the functions of an acquisition means 31, a determination means 32, and a presentation means 33 (shown in FIG. 3) described below.

[0020] The storage device 24 may be, for example, a non-volatile storage device such as a flash memory, an SSD, a magnetic storage device (e.g., an HDD, a floppy disk (registered trademark), a magnetic tape, etc.), an optical disk, or a volatile storage device such as a RAM, and stores the programs executed by the CPU 21 and the data referenced by the CPU 21. The storage device 24 also stores acquired data (shown in FIG. 4(a)) which will be described later.

[0021] The display processing unit 25 displays the display data provided from the CPU 21 on the display unit 26. The display unit 26 is, for example, an LCD (Liquid Crystal Display) monitor including thin film transistors arranged in a matrix on a pixel basis, and displays the data to be displayed on the display screen by driving the thin film transistors based on the display data.

[0022] When the evaluation device 20 is a terminal device of a button input type, the input unit 27 includes a button group including a plurality of instruction input buttons such as a direction indication button and a decision button for accepting user operation input, and a button group including a plurality of instruction input buttons such as a numeric keypad, and includes an interface circuit for recognizing the depression (operation) input of each button and outputting it to the CPU 21.

[0023] When the evaluation device 20 is a terminal device of a touch panel input type, the input unit 27 mainly accepts input of the touch panel type by touching the display screen with a fingertip or a pen. The touch panel input type may be a known type such as a capacitance type.

[0024] Furthermore, in the case where the evaluation device 20 is a device capable of receiving voice input, the input unit 27 may be configured to include a microphone for voice input, or may be provided with an interface circuit for outputting voice data input via an external microphone to the CPU 21. Furthermore, in the case where the evaluation device 20 is a device capable of receiving moving images and / or still images, the input unit 27 may be configured to include a digital camera or digital video camera for image input, or may be provided with an interface circuit for receiving image data captured by an external digital camera or digital video camera and outputting it to the CPU 21.

[0025] The communication interface unit 28 includes an interface circuit for performing communication via a communication network such as the Internet or a LAN.

[0026] (3) Overview of each function in the evaluation system The functions realized by the evaluation system of this embodiment will be described with reference to Fig. 3. Fig. 3 is a functional block diagram for explaining the functions that play a major role in the evaluation system of this embodiment. In the functional block diagram of Fig. 3, an acquisition means 31 and a determination means 32 correspond to the main components of the present invention. The other means (presentation means 33) are not necessarily essential components, but are components that make the present invention more preferable.

[0027] The acquiring means 31 has a function of acquiring time-series data of the load applied to the scissors S during the transition of the scissors S from the open state to the closed state.

[0028] Here, the load applied to the scissors S may be measured by a predetermined measuring device 10. This makes it possible to evaluate the scissors S using time-series data of the load measured by the measuring device 10.

[0029] Furthermore, the scissors S may be transitioned from the open state to the closed state by a predetermined moving member 11. This makes it possible to automatically apply a load to the scissors S by moving the moving member 11, so that the scissors S can be efficiently evaluated.

[0030] The function of the acquisition means 31 is realized, for example, as follows. The CPU 21 of the evaluation device 20 receives (acquires) information on the load output from the load cell 12 of the measurement device 10 and information output from the rotary encoder 13 of the measurement device 10 via the communication interface unit 28, for example, every time a predetermined time (for example, 0.1 milliseconds) has elapsed. Then, the CPU 21 stores the received (acquired) information in the acquired data shown in FIG. 4(a). Here, the acquired data shown in FIG. 4(a) is data in which the load cell output (voltage corresponding to the load applied to the scissors S) and the rotary encoder output (voltage of the pulse signal) are described in a state in which they are associated with each other for each acquisition date and time when the CPU 21 receives (acquires) information from the measurement device 10. Note that the relationship between the output of the load cell 12 and the output of the rotary encoder 13 when the load is applied to the scissors S during the transition from the open state to the closed state is shown, for example, as shown in FIG. 4(b).

[0031] When starting evaluation of the scissors S, the CPU 21 may transmit a predetermined start instruction signal to the measuring device 10 via the communication interface unit 28. In this case, when the measuring device 10 receives the start instruction signal transmitted from the CPU 21, the measuring device 10 may perform measurement processing using the load cell 12 and the rotary encoder 13 by driving a predetermined motor (not shown) to move the moving member 11. This makes it possible for the measuring device 10 to be controlled by the evaluation device 20.

[0032] In this embodiment, the output of the rotary encoder 13 is required when obtaining the output of the load cell 12 as time-series data, in order to determine whether the load cell 12 is rotating correctly at a predetermined rotational speed and whether the time-series data is being obtained.

[0033] The determining means 32 has a function of determining the degree of similarity between the acquired time-series data and reference data, which is time-series data of the load that serves as a reference for the evaluation and judgment of the scissors S.

[0034] The determining means 32 may also determine the similarity between the acquired time series data and the reference data by calculating a statistical distance in the data space between the acquired time series data and the reference data. This makes it possible to evaluate the degree of engagement of the scissors S using the statistical distance in the data space between the acquired time series data and the reference data.

[0035] Here, the statistical distance in the data space may be, for example, Euclidean distance, Manhattan distance, Mahalanobis distance, etc., with Mahalanobis distance being particularly preferable.

[0036] In addition, in this embodiment, when calculating the Mahalanobis distance, the determination means 32 calculates the Mahalanobis distance based on the acquired time series data and a variance-covariance matrix calculated based on a plurality of data in the reference data. This makes it possible to determine the similarity between the time series data and the reference data using the Mahalanobis distance.

[0037] Furthermore, the determination means 32 may perform pre-processing of the acquired time series data in order to calculate the statistical distance in the data space, which makes it possible to easily and accurately calculate the statistical distance in the data space.

[0038] Here, when preprocessing the time series data, at least one of the following may be performed: calculating a difference value or a derivative value of the weight for each predetermined time; applying a low-pass filter to the time series data; calculating a moving average of the time series data; and downsampling the time series data. For example, when a difference value or a derivative value of the weight for each predetermined time is calculated, it is possible to calculate the slope of the change in the weight for each predetermined time. For example, when a low-pass filter is applied to the time series data, it is possible to remove high-frequency noise contained in the time series data. Furthermore, for example, when a moving average of the time series data is calculated, it is possible to smooth the time series data. Furthermore, when the time series data is downsampled, it is possible to reduce the number of weight data used to calculate a statistical distance (for example, Mahalanobis distance) in the data space, so that the statistical distance in the data space can be calculated efficiently.

[0039] The function of the determining means 32 is realized, for example, as follows. Here, a case will be described as an example in which preprocessing is performed on the time series data acquired based on the function of the acquiring means 31, and the Mahalanobis distance between the preprocessed time series data and the reference data is calculated, thereby determining the similarity between the time series data and the reference data.

[0040] First, the contents of the pre-processing executed on the acquired time-series data will be described with reference to FIGS.

[0041] The CPU 21 of the evaluation device 20 accesses the acquired data and extracts time series data (here, load cell output) of the load applied to the scissors S during the transition of the scissors S from the open state to the closed state. An example of the waveform of the extracted time series data of the load is shown in FIG. 6(a). Then, the CPU 21 calculates the difference value or derivative value of the load for each predetermined time (e.g., 0.1 milliseconds) of the extracted time series data (step S100 in FIG. 5). Here, an example of the waveform of the time series data of the load from which the derivative value has been calculated is shown in FIG. 6(b).

[0042] Next, CPU 21 applies a low-pass filter of a predetermined frequency (e.g., 40 Hz) to the time-series data of the load for which the differential value has been calculated (step S102 in FIG. 5). FIG. 7(a) shows an example of a waveform of the time-series data of the load after the low-pass filter has been applied. Next, CPU 21 calculates a moving average of the time-series data of the load after the low-pass filter has been applied (step S104 in FIG. 5). Here, CPU 21 may calculate a moving average of the time-series data of the load within a predetermined period (period T in the example shown in FIG. 7(b)) during which the load changes significantly, from among the entire time-series data of the load applied to scissors S while the scissors S transition from the open state to the closed state.

[0043] Here, the CPU 21 may calculate the moving average for each of a predetermined number (e.g., 10) of consecutive time-series data within a predetermined period (period T). As a result, if the number of time-series data within the predetermined period (period T) is 2,009, for example, 2,000 (=2,009-10+1) pieces of moving average data are calculated.

[0044] Then, the CPU 21 downsamples the time series data (step S106 in FIG. 5). For example, the CPU 21 may downsample the time series data by extracting the moving average data calculated in step S104. Here, the reduction ratio when downsampling may be any number of values, but in this embodiment, a reduction ratio of 1 / 10 is preferable.

[0045] By executing each of the above pre-processing steps S100 to S106, if the total number of load data during the transition of the scissors S from the open state to the closed state is, for example, 10,000, and the moving average data within a specified period of time is 2,000, it is possible to reduce the number of time series data to 200, and the computational load for calculating the spatial statistical distance (here, the Mahalanobis distance) can be significantly reduced.

[0046] In the present embodiment, the case where all of steps S100 to S106 in FIG. 5 are executed as pre-processing of the time-series data has been described as an example, but at least one of steps S100 to S106 may not be executed.

[0047] Next, the CPU 21 calculates the Mahalanobis distance based on the time series data on which preprocessing has been performed and the variance-covariance matrix calculated based on a plurality of data in the reference data. The reference data may be data on which the preprocessing shown in FIG. 5 has been performed. The reference data may be measured in advance using the measuring device 10 and stored in, for example, the RAM 23 or the storage device 24. Here, the CPU 21 calculates the variance-covariance matrix A (here, a matrix of 200×200 dimensions) using a plurality of data in the reference data (for example, 200 pieces of time series data corresponding to the period T in FIG. 7(b)). The CPU 21 also calculates the inverse matrix A of the variance-covariance matrix A. -1 Then, the CPU 21 calculates the calculated inverse matrix A -1 and the following equation (1), the Mahalanobis distance MD between the acquired time series data and the reference data is calculated.

number

[0048] In this manner, the degree of similarity between the acquired time-series data and the reference data, which is time-series data of the load that serves as the basis for the evaluation and judgment of the scissors S, is determined.

[0049] The CPU 21 may evaluate the scissors S based on the determined similarity. For example, when the calculated Mahalanobis distance MD is equal to or smaller than a predetermined threshold, the CPU 21 may evaluate (determine) that the engagement condition of the scissors S to be evaluated satisfies a predetermined standard (i.e., the scissors S are non-defective).

[0050] The presentation means 33 has a function of presenting information on the determined similarity. As a result, since the information on the determined similarity is presented, for example, an evaluator or the like can easily evaluate (determine) whether or not the bite condition of the scissors S to be evaluated satisfies a predetermined standard (i.e., whether or not the scissors S are a good product) based on the presented information.

[0051] The function of the presentation means 33 is realized, for example, as follows. When the CPU 21 of the evaluation device 20 determines the similarity based on the function of the determination means 32, the CPU 21 may display information about the determined similarity on, for example, the display unit 26. Here, the information about the similarity may be composed of text data or image data, etc. Furthermore, when the information about the similarity is composed of audio data, the CPU 21 may output the information about the similarity from an audio output device such as a speaker. Furthermore, the CPU 21 may transmit the information about the similarity to another computer (for example, a server, etc.) connected to the evaluation device 20 via a communication network such as the Internet, for example.

[0052] In addition, when the CPU 21 evaluates the scissors S based on the determined similarity, it may present information regarding the evaluation results (for example, information indicating that the bite of the scissors S being evaluated satisfies a predetermined standard or that the scissors S being evaluated is a good product).

[0053] (4) Flow of main processing of the evaluation system according to the present embodiment Next, an example of the flow of main processes performed by the evaluation system of this embodiment will be described with reference to the flowchart of FIG.

[0054] First, the CPU 21 of the evaluation device 20 acquires time-series data of the load applied to the scissors S while the scissors S transitions from the open state to the closed state based on the function of the acquisition means 31 (step S200). Specifically, the CPU 21 receives (acquires) information on the load output from the load cell 12 of the measurement device 10 and information output from the rotary encoder 13 of the measurement device 10 via the communication interface unit 28, for example, every time a predetermined time (e.g., 0.1 milliseconds) elapses.

[0055] Next, the CPU 21 of the evaluation device 20 determines the similarity between the acquired time series data and reference data, which is time series data of load serving as a reference for evaluation and judgment of the scissors S, based on the function of the determination means 32 (step S202). Here, the CPU 21 may determine the similarity between the acquired time series data and the reference data by calculating a spatial statistical distance (e.g., Mahalanobis distance) between the acquired time series data and the reference data. Furthermore, the CPU 21 may perform pre-processing of the acquired time series data in order to calculate the spatial statistical distance.

[0056] In this way, it becomes possible to quantitatively evaluate the degree of engagement of the scissors S based on the determined similarity.

[0057] Although not shown in the flowchart of FIG. 8, the CPU 21 of the evaluation device 20 may present information on the determined similarity based on the function of the presentation means 33.

[0058] As described above, according to the evaluation system and evaluation method for scissors S of this embodiment, the similarity between the time series data of the load applied to the scissors S to be evaluated during the transition from the open state to the closed state and the reference data, which is the time series data of the load that serves as the basis for the evaluation judgment of the scissors S, is determined, and the condition of the engagement of the scissors S can be quantitatively evaluated based on the determined similarity.

[0059] The program for making a computer realize the functions of the above-mentioned means 31 to 33 may be stored in a computer-readable storage medium. The storage medium on which the program is recorded may be the ROM 22, RAM 23, or storage device 24 of the evaluation device 20 shown in Fig. 2. The storage medium may be a CD-ROM or the like that can be read by being inserted into a program reading device such as a CD-ROM drive. Furthermore, the storage medium may be a magnetic tape, a cassette tape, a flexible disk, an MO / MD / DVD, or a semiconductor memory.

[0060] The above-described embodiments are described for the purpose of facilitating understanding of the present invention, and are not described for the purpose of limiting the present invention. Therefore, each element disclosed in the above embodiment is intended to include all design modifications and equivalents that fall within the technical scope of the present invention.

[0061] For example, in the above embodiment, the similarity between the acquired time series data and the reference data is determined by calculating the Mahalanobis distance between them, but the present invention is not limited to this case. For example, the similarity between the acquired time series data and the reference data may be calculated (determined) using other similarity calculation techniques (e.g., Euclidean distance, Manhattan distance, cosine similarity, DTW (Dynamic Time Warping), etc.) or clustering techniques (e.g., k-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), etc.).

[0062] In the above embodiment, the similarity between the output of the load cell 12 and the reference data is calculated as time-series data to calculate the Mahalanobis distance between the output and the reference data, and this has been described as an example. However, by using the output of the rotary encoder 13, the time-series data may be converted into data on the degree of opening, and evaluation may be performed using data corresponding to the degree of opening of the load as a characteristic quantity of the physical phenomenon.

[0063] In the above embodiment, the case where one measurement device 10 communicates with one evaluation device 20 has been described as an example, but the present invention is not limited to this case. For example, each of the multiple measurement devices 10 may communicate with one evaluation device 20, one measurement device 10 may communicate with each of the multiple evaluation devices 20, or each of the multiple measurement devices 10 may communicate with each of the multiple evaluation devices 20.

[0064] Furthermore, in the above embodiment, the case where the measurement device 10 and the evaluation device 20 are provided separately has been described as an example, but the present invention is not limited to this case. For example, the measurement device 10 may be provided within the evaluation device 20. In this case, the evaluation device 20 can perform the above-mentioned measurement process.

[0065] Furthermore, in the above-described embodiment, the case where communication is performed between the measurement device 10 and the evaluation device 20 via a wired LAN has been described as an example, but the present invention is not limited to this case. For example, communication may be performed between the measurement device 10 and the evaluation device 20 via the Internet. Also, a wired communication method other than a wired LAN may be used, and a wireless communication method such as a wireless LAN (e.g., Wi-Fi (registered trademark)), Bluetooth (registered trademark), ZigBee (registered trademark), UWB, or optical wireless communication (e.g., infrared) may be used.

[0066] In the above embodiment, the case where one evaluation device 20 is provided has been described as an example, but the present invention is not limited to this case. For example, a plurality of evaluation devices 20 may be provided, and in this case, the operation contents and processing results of any one of the evaluation devices 20 may be presented in real time on the other evaluation devices 20, or the processing results of any one of the evaluation devices 20 may be shared among the plurality of evaluation devices 20.

[0067] Furthermore, in the above-mentioned embodiment, the evaluation device 20 is configured to realize the functions of the acquisition means 31, the determination means 32, and the presentation means 33, but the present invention is not limited to this configuration. For example, a computer or the like (a server 40 in the example shown in FIG. 9) communicably connected to the evaluation device 20 via the Internet, a LAN, or the like may be provided. In this case, the server 40 can have substantially the same hardware configuration as the evaluation device 20, so that the server 40 can realize the function of at least one of the means 31 to 33 described in the above-mentioned embodiment. For example, the functions of the functional block diagram shown in FIG. 3 may be arbitrarily shared between the evaluation device 20 and the server 40 as shown in FIGS. 9(a) and 9(b).

[0068] Furthermore, the function of at least one of the above-mentioned means 31 to 33 may be realized by the measuring device 10. [Industrial Applicability]

[0069] The above-described method and system for evaluating scissors S of the present invention makes it possible to quantitatively evaluate scissors S, and therefore can be suitably used, for example, in services and evaluation organizations that evaluate the quality of scissors S. Therefore, the industrial applicability thereof is extremely high. [Explanation of symbols]

[0070] 10. Measurement equipment 11...Moving member 20...Evaluation device 31…Acquisition means 32...Decision-making means 33…Presentation means S...Scissors

Claims

1. The computer acquiring time series data of a load applied to the scissors during a transition from an open state to a closed state; determining a degree of similarity between the acquired time series data and reference data, which is time series data of loads serving as a reference for evaluation and judgment of the scissors; How to evaluate scissors.

2. The determining step includes a step of calculating a statistical distance in a data space between the acquired time series data and the reference data. A method for evaluating scissors according to claim 1.

3. The calculating step includes a step of performing pre-processing of the acquired time series data to calculate a statistical distance in the data space. A method for evaluating scissors according to claim 2.

4. The step of performing the pre-processing includes at least one of a step of calculating a difference value or a derivative value of the load for each predetermined time, a step of applying a low-pass filter to the time series data, a step of calculating a moving average of the time series data, and a step of down-sampling the time series data. A method for evaluating scissors according to claim 3.

5. The statistical distance in the data space is a Mahalanobis distance calculated based on the acquired time series data and a variance-covariance matrix calculated based on a plurality of data in the reference data. A method for evaluating scissors according to claim 2.

6. and a step of the computer presenting information regarding the determined similarity. A method for evaluating scissors according to claim 1.

7. The load applied to the scissors is measured by a predetermined measuring device. A method for evaluating scissors according to claim 1.

8. The scissors are transitioned from an open state to a closed state by a predetermined moving member. A method for evaluating scissors according to claim 1.

9. an acquisition means for acquiring time series data of a load applied to the scissors during a transition from an open state to a closed state; A determination means for determining a similarity between the acquired time series data and reference data which is time series data of load serving as a reference for evaluation and judgment of the scissors. Scissors rating system.

Images