WO2022149495A1

WO2022149495A1 - Coin identification device and coin identification method

Info

Publication number: WO2022149495A1
Application number: PCT/JP2021/048229
Authority: WO
Inventors: 嵐稀結崎; 和矢多田
Original assignee: グローリー株式会社
Priority date: 2021-01-06
Filing date: 2021-12-24
Publication date: 2022-07-14
Also published as: JP2022106203A

Abstract

A coin identification device of the present disclosure comprises: a magnetic sensor that includes a resonance-type coil and that detects a magnetic characteristic of a coin which is conveyed; a first waveform generation unit that generates, from an output of the magnetic sensor, first waveform data indicating a material feature quantity which is a feature quantity that changes according to the material of a coin; a first material detection unit that detects, from the first waveform data, a first material feature quantity corresponding to an outer edge portion of the coin; a second material detection unit that detects, from the first waveform data, a second material feature quantity corresponding to a center portion of the coin; and an identification processing unit that performs an identification process of the coin on the basis of the first material feature quantity and the second material feature quantity.

Description

Coin identification device and coin identification method

This disclosure relates to a coin identification device and a coin identification method.

Conventionally, a coin processing machine that performs processing such as counting coins is equipped with a coin identification device including a plurality of sensors for performing coin identification processing. Further, there are known coins in which the material is different between the central portion of a bimetal coin composed of two or more kinds of materials, for example, a bicolor coin, and the outer edge portion around the coin.

For example, in Patent Document 1, a ring detection sensor, which is a magnetic sensor in which a resonance circuit is connected to a coil and detects the material of the outer edge of a coin, and a resonance circuit are connected to the coil to detect the material of the central portion of the coin. A coin identification device that includes a core detection sensor, which is a magnetic sensor, and determines whether or not a coin is a bimetal coin based on the output of the ring detection sensor and the core detection sensor is disclosed.

Further, Patent Document 2 includes a reflection type magnetic sensor including a primary coil and a secondary coil, and bicolor coins are obtained from the output of the secondary coil when a high frequency (250 kHz) signal is applied to the primary coil. A coin identification device for detection is disclosed.

Japanese Unexamined Patent Publication No. 2017-22019 Japanese Patent No. 4157335

In the market, monometal coins that are made of the same material as the center of the bimetal coin and are very similar in size to the bimetal coin are in circulation. Especially overseas, the material of the center and outer edge of the bimetal coin is accurate. Is required to be detected.

However, in the coin identification device disclosed in Patent Document 1, as shown in FIG. 2 of Patent Document 1, in order to detect the material of the outer edge portion and the central portion of the coin, the ring detection sensor 32 and the core detection having different frequencies are detected. Since it is necessary to arrange the sensor 33 at a position where the outer edge portion and the central portion of the bimetal coin are conveyed, a space and a control circuit for arranging these sensors are required, and there is room for improvement in terms of space and cost. there were.

In the coin identification device disclosed in Patent Document 2, a signal of 250 kHz is applied as a high frequency to the primary coil of the reflective magnetic sensor, but in the reflective magnetic sensor, the frequency of the signal applied to the primary coil is increased. Is difficult, and it is not easy to actually measure bicolor coins with a reflective magnetic sensor at this level of frequency, and it is difficult to actually commercialize it. This is because the higher the frequency of the signal applied to the primary coil, the more difficult it is to detect the phase shift between the phase of the signal applied to the primary coil and the phase of the signal detected by the secondary coil.

The present disclosure has been made in view of the above situation, and an object of the present disclosure is to provide a coin identification device and a coin identification method capable of accurately identifying bimetal coins with a simpler configuration.

In order to solve the above-mentioned problems and achieve the object, the coin identification device according to the present disclosure includes a resonance type coil, a magnetic sensor for detecting the magnetic characteristics of the conveyed coin, and the output of the magnetic sensor. A first waveform generator that generates first waveform data representing a material feature amount that is a feature amount that changes according to the material of the coin, and a first material feature amount corresponding to the outer edge of the coin from the first waveform data. The first material detection unit for detecting the above, the second material detection unit for detecting the second material feature amount corresponding to the center of the coin from the first waveform data, the first material feature amount, and the second material. It is provided with an identification processing unit that performs identification processing of coins based on a feature amount.

The first material detection unit has a first-order differential processing unit that differentially processes the first waveform data to generate first-order differential data, and a second-order differential data that differentially processes the first-order differential data to generate second-order differential data. The first-order differential processing unit may be included, and the first material feature amount may be detected based on the first-order differential data and the second-order differential data.

The first material detection unit detects a first point in which the differential value is less than a predetermined first threshold value in the first-order differential data and the differential value exceeds a predetermined second threshold value in the second-order differential data. May be good.

When a predetermined number of points whose differential value is less than the first threshold value are continuous from the first point to the second point in the first-order differential data, the first material detection unit is the first in the first waveform data. The first material feature amount may be detected based on a plurality of material feature amounts from the point to the second point.

The first material detection unit determines the material feature amount of the first waveform data at the point where the differential value is the smallest among the predetermined number of points from the first point to the second point in the first-order differential data. It may be detected as the first material feature amount.

The coin identification device may further include a second waveform generation unit that generates second waveform data representing a distance feature amount, which is a feature amount that changes according to the unevenness of the coin, from the output of the magnetic sensor. ..

The second material detection unit may detect the third point and the fourth point corresponding to both ends of the coin in the transport direction in the second waveform data, and the third point and the third point and the fourth point in the first waveform data. The material feature amount at the central point between the fifth and sixth points corresponding to the fourth point may be detected as the second material feature amount.

The identification processing unit may determine whether or not the coin is a bimetal coin based on the first material feature amount and the second material feature amount.

The coin identification device may further include a storage unit for storing reference data relating to the respective material feature amounts of the outer edge portion and the central portion of the genuine bimetal coin, and the identification processing unit may further include the first material feature. The amount and the second material feature amount may be compared with the reference data.

When the first material feature amount and the second material feature amount match the reference data, the identification processing unit may determine that the coin is a genuine bimetal coin.

The identification processing unit may determine the denomination of the coin by comparing the first material feature amount and the second material feature amount with the reference data.

The bimetal coin may be at least one of a bicolor coin and a bicolor clad coin.

The bimetal coin may be a bicolor coin.

The coin identification method according to the present disclosure is a coin identification method using a magnetic sensor, wherein the magnetic sensor includes a resonance type coil, and a step of detecting the magnetic characteristics of the coin conveyed by the magnetic sensor, and From the output of the magnetic sensor, a step of generating a first waveform data representing a material feature amount, which is a feature amount that changes according to the material of the coin, and a first step corresponding to the outer edge of the coin from the first waveform data. Based on the step of detecting the material feature amount, the step of detecting the second material feature amount corresponding to the center of the coin from the first waveform data, and the first material feature amount and the second material feature amount. It comprises a step of performing a coin identification process.

The steps for detecting the first material feature amount are a step of differentiating the first waveform data to generate first-order differential data and a step of differentiating the first-order differential data to generate second-order differential data. And may be included, and the first material feature amount may be detected based on the first-order differential data and the second-order differential data.

In the step of detecting the first material feature amount, the first point where the differential value is less than the predetermined first threshold value in the first-order differential data and the differential value exceeds the predetermined second threshold value in the second-order differential data. May be detected.

The step of detecting the first material feature amount is the first waveform data when a predetermined number of points whose differential value is less than the first threshold value are continuous from the first point to the second point in the first-order differential data. The first material feature amount may be detected based on a plurality of material feature amounts from the first point to the second point in.

The step of detecting the first material feature amount is the step of detecting the first waveform data at the point where the differential value is the smallest among the predetermined number of points from the first point to the second point in the first-order differential data. The material feature amount may be detected as the first material feature amount.

The coin identification method may further include a step of generating second waveform data representing a distance feature amount, which is a feature amount that changes according to the unevenness of the coin, from the output of the magnetic sensor.

In the step of detecting the second material feature amount, the third point and the fourth point corresponding to both ends of the coin in the transport direction may be detected by the second waveform data, and the first waveform data may be used to detect the third point and the fourth point. The material feature amount of the central point between the fifth point and the sixth point corresponding to the third point and the fourth point, respectively, may be detected as the second material feature amount.

In the step of performing the identification process, it may be determined whether or not the coin is a bimetal coin based on the first material feature amount and the second material feature amount.

In the step of performing the identification process, the first material feature amount and the second material feature amount may be compared with the reference data relating to the respective material feature amounts of the outer edge portion and the central portion of the genuine bimetal coin.

In the step of performing the identification process, if the first material feature amount and the second material feature amount match the reference data, the coin may be determined to be the genuine bimetal coin.

In the step of performing the identification process, the denomination of the coin may be determined by comparing the first material feature amount and the second material feature amount with the reference data.

The bimetal coin may be at least one of a bicolor coin and a bicolor clad coin.

The bimetal coin may be a bicolor coin.

According to the present disclosure, it is possible to provide a coin identification device and a coin identification method capable of accurately identifying a bimetal coin with a simpler configuration.

(A) is a schematic plan view showing a coin surface of a bicolor coin, and (b) is a schematic cross-sectional view of a bicolor coin. (A) is a schematic plan view showing a coin surface of a clad coin (plating), (b) is a schematic cross-sectional view of a clad coin (plating), and (c) is a clad coin (three-layer structure). It is a plan schematic diagram which shows the coin surface of, and (d) is a cross-sectional schematic diagram of a clad coin (three-layer structure). (A) is a plan view showing a coin surface of a bicolor clad coin, and (b) is a sectional schematic view of a bicolor clad coin. It is a schematic diagram explaining the structure of the coin identification apparatus which concerns on 1st Embodiment, and is the figure which looked at the coin transport path from above. It is a schematic diagram explaining the structure of the coin identification apparatus which concerns on 1st Embodiment, and is sectional drawing in the line AB of FIG. It is a block diagram explaining an example of the structure of the coin identification apparatus which concerns on 1st Embodiment. It is a graph which shows an example of the 1st waveform data generated by the 1st waveform generation part schematically. It is a graph which shows the relationship between the material (conductivity) of a coin, and the material feature amount. It is a block diagram explaining the structure of the 1st material detection part which concerns on 1st Embodiment. It is a graph which shows another example of the 1st waveform data generated by the 1st waveform generation part schematically. It is a graph which shows typically an example of the 1st waveform data, the 1st derivative data, and the 2nd derivative data. It is a flowchart explaining an example of the operation of the coin identification apparatus which concerns on 1st Embodiment. It is a flowchart explaining an example of the operation of the 1st material detection part which concerns on 1st Embodiment. It is a block diagram explaining an example of the structure of the magnetic sensor which concerns on 1st Embodiment. It is a block diagram explaining another example of the structure of the coin identification apparatus which concerns on 1st Embodiment. It is a flowchart explaining another example of operation of the coin identification apparatus which concerns on 1st Embodiment. It is a block diagram explaining an example of the structure of the coin identification apparatus which concerns on 2nd Embodiment. It is a graph which shows an example of the 2nd waveform data generated by the 2nd waveform generation part schematically. It is a graph which shows the relationship between the distance between a coin and a resonance type coil, and a distance feature quantity. It is a graph which shows another example of the 1st waveform data generated by the 1st waveform generation part schematically. It is a flowchart explaining an example of the operation of the coin identification apparatus which concerns on 2nd Embodiment. It is a flowchart explaining an example of the operation of the 2nd material detection part which concerns on 2nd Embodiment. It is a block diagram explaining another example of the structure of the coin identification apparatus which concerns on 2nd Embodiment. It is a flowchart explaining another example of operation of the coin identification apparatus which concerns on 2nd Embodiment. It is a graph which shows typically an example of the 1st waveform data, the 1st derivative data, and the 2nd derivative data.

Hereinafter, embodiments of the coin identification device and the coin identification method according to the present disclosure will be described with reference to the drawings. In the following, the present disclosure will be described by taking as an example a coin identification device and a coin identification method for coins as money. However, the coins subject to the present disclosure include coins as money and game machines. Also includes coins used in. The following description is an example of a coin identification device and a coin identification method.

The coin identification device and the coin identification method according to the present disclosure can identify bimetal coins, which are coins composed of two or more kinds of materials, but also identify monometal coins, which are coins composed of one kind of material. It is possible to do. In the following, the case of identifying bimetal coins will be mainly described.

First, a bimetal coin will be described. FIG. 1A is a schematic plan view showing a coin surface of a bicolor coin, and FIG. 1B is a schematic cross-sectional view of a bicolor coin. FIG. 2A is a schematic plan view showing a coin surface of a clad coin (plating), FIG. 2B is a schematic cross-sectional view of a clad coin (plating), and FIG. 2C is a clad. It is a plane schematic diagram which shows the coin surface of a coin (three-layer structure), and FIG. 2 (d) is a cross-sectional schematic diagram of a clad coin (three-layer structure). FIG. 3A is a schematic plan view showing the coin surface of the bicolor clad coin, and FIG. 3B is a schematic cross-sectional view of the bicolor clad coin. Bimetallic coins include bicolor coins, clad coins, and bicolor clad coins, and the coin identification device and coin identification method according to the present disclosure can identify bicolor coins and bicolor clad coins. Yes, especially bicolor coins can be identified. As shown in FIGS. 1A and 1B, the bicolor coin 51 has a different material (metal or metal) between the circular core portion 51a at the center and the ring portion 51b at the outer edge portion surrounding the core portion 51a. It is formed by using an alloy), and is formed by fitting the core portion 51a into the ring portion 51b. As shown in FIGS. 2A and 2B, the clad coin 52 is formed by using different materials (metals or alloys) for the core material 52a and the surface layer 52b covering the core material 52a. For example, a circular core material (base material) 52a is plated and engraved. As shown in FIGS. 2 (c) and 2 (d), the clad coin 52 is formed by punching a plate having a three-layer structure into a circular shape to form a surface layer 52b, a core material 52a, and a surface layer 52b, and engraving the surface layer 52b. You may. As shown in FIGS. 3A and 3B, the bicolor clad coin 53 has a central circular core portion 53a having a clad coin structure and a ring portion of an outer edge portion surrounding the core portion 53a. It is formed by using a material (metal or alloy) different from that of 53b, and is formed by fitting the core portion 53a into the ring portion 53b. The core portion 53a in the bicolor clad coin 53 is formed by using a different material (metal or alloy) between the core material 53a1 and the surface layer 53a2 covering the core material 53a1. The core portion 53a may be a core material 53a1 plated with a surface layer 53a2, or may be a plate having a three-layer structure punched out in a circular shape.

Hereinafter, the central portion (core portion) and the outer edge portion (ring portion) of the bicolor coin or the bicolor clad coin may be referred to as an inner portion and an outer portion of the bimetal coin, respectively.

In the following description, the same reference numerals are appropriately used for configurations having the same or similar functions in different embodiments and drawings, and repeated description of the configurations will be omitted as appropriate.

(First Embodiment)
In the first embodiment, a high-frequency magnetic sensor is placed at a position where the inner portion and the outer portion of the bimetallic coin pass, and the positions of the inner portion and the outer portion are estimated from the sensor output from the arrival of the coin to the passage. By acquiring those material feature quantities, it is possible to detect the material of bimetallic coins with a single sensor.

4 and 5 are schematic views illustrating the configuration of the coin identification device according to the first embodiment, FIG. 4 is a view of a coin transport path from above, and FIG. 5 is FIG. It is sectional drawing in AB line. 4 and 5 show XYZ coordinate systems that are orthogonal to each other. As shown in FIGS. 4 and 5, the coin identification device 1 according to the present embodiment includes a single magnetic sensor 10 arranged in the transport path 110 of the coin processing device.

The coins C are conveyed in the transport direction (+ X direction in FIG. 4) one by one on the transport path 110 of the coin processor by means of transport (not shown, for example, fins) of the coin processor. Will be done. The transport path 110 has a smooth transport surface 111 that supports the lower surface of the coin C, and a guide surface 112 that is in contact with the peripheral surface of the coin C and guides the coin C in a one-sided manner. The transport surface 111 is parallel to the XY plane in FIG. 4, and the coin C is offset to the end of the transport path 110 on the guide surface 112 side (in the −Y direction in FIG. 4), that is, , It is conveyed on the conveying surface 111 in a state of being in contact with the guide surface 112.

The magnetic sensor 10 includes a resonance type coil 11 (hereinafter, may be simply referred to as a coil 11) arranged below the transport path 110 (in the −Z direction in FIG. 5), and the coin C is conveyed through the transport path 110. Detects the magnetic properties of. The magnetic sensor 10 including the resonance type coil 11 can apply a signal having a higher frequency to the coil 11 than the reflection type magnetic sensor, and even in that case, a coin as will be described later from the output of the magnetic sensor 10. It is possible to detect the material (material feature amount) of each part of. Further, the control circuit for the magnetic sensor 10 does not require a complicated circuit as compared with the conventional one, and for example, a circuit for a ring detection sensor or a core detection sensor disclosed in Patent Document 1 can be used. Therefore, in the present embodiment, the bimetal coin can be detected with a simpler sensor configuration and circuit configuration than before, so that space saving and cost reduction can be achieved.

Further, as shown in FIG. 5, the magnetic sensor 10 includes a cylindrical pot core 12. The pot core 12 is an E-shaped core made of a magnetic material in a cross-sectional view, and a winding is wound around the central axis of the pot core 12 to form a coil 11. In the transport direction of the coin C, the width of the coil 11 (width in the X direction) is designed to be smaller than the width of each of the inner portion Ba and the outer portion Bb of the bimetal coin B.

The magnetic sensor 10 generates a magnetic field (magnetic flux) in the transport path 110 according to the oscillation frequency given by the resonance circuit (see FIG. 13 described later), and changes the magnetic field when the coin C passes through the transport path 110. Detect. The resonance circuit is connected to the coil 11 and resonates with the coil 11 at a high frequency suitable for detecting the material of the coin C. Specifically, it resonates at a frequency of 1 to 2 MHz, more specifically 1.4 to 1.5 MHz. As a result, the material near the surface of the coin C can be effectively detected, so that bimetal coins, particularly bicolor clad coins, can be detected more effectively. Further, since the material in a narrow region can be detected, the resolution of the magnetic sensor 10 can be improved.

The coil 11 of the magnetic sensor 10 is arranged with a gap between it and the guide surface 112 in the width direction of the transport path 110. Therefore, when the coin C is conveyed in a state of being offset to the guide surface 112 side, one outer edge portion, the central portion, and the other outer edge portion of the coin C sequentially pass over the coil 11 (in the magnetic field by the coil 11). However, the output signal of the magnetic sensor 10 also changes. That is, the magnetic sensor 10 outputs a signal according to the material of the outer edge portion and the central portion of the coin C. In this way, the magnetic sensor 10 detects the materials of the inner portion Ba and the outer portion Bb of the bimetal coin B. When the coin C is composed of a non-magnetic material, when the coin C arrives on the coil 11, the output of the magnetic sensor 10 is attenuated (amplitude decreases), and the higher the conductivity of the coin C, the more the attenuation factor of the output. Will grow.

FIG. 6 is a block diagram illustrating an example of the configuration of the coin identification device according to the first embodiment. As shown in FIG. 6, in the coin identification device 1 according to the present embodiment, in addition to the magnetic sensor 10, the first waveform generation unit 21, the first material detection unit 22, the second material detection unit 23, and the identification processing unit 24 It is equipped with. The first waveform generation unit 21, the first material detection unit 22, the second material detection unit 23, and the identification processing unit 24 function by executing the corresponding programs by the control unit 20 described later.

The first waveform generation unit 21 generates first waveform data representing a material feature amount from the output of the magnetic sensor 10. More specifically, the first waveform generation unit 21 generates the first waveform data based on the outputs of the magnetic sensors 10 at consecutive different timings. Therefore, the first waveform data shows the time change of the material feature amount. For example, the first waveform data may be generated based on a time series (digital signal) obtained by sampling the output (analog signal) of the magnetic sensor 10 at a predetermined time interval. The material feature amount is a feature amount that changes depending on the material of the coin, particularly the surface material.

FIG. 7 is a graph schematically showing an example of the first waveform data generated by the first waveform generation unit. FIG. 7 shows an example in the case where a bimetal coin is detected, and the side cross section of the bimetal coin B (inner portion Ba and outer portion Bb) is shown at the corresponding position in the graph at the upper part. As shown in FIG. 7, the first waveform data is data representing a material feature amount with respect to time, and the horizontal axis of FIG. 7 represents the time direction. However, since the magnetic sensor 10 usually detects coins transported at a predetermined speed, the first waveform data is also data representing the amount of material features with respect to the position of the coins in the transport direction. In this case, FIG. 7 shows. The horizontal axis represents the position of the coin in the transport direction.

Further, as shown in FIG. 7, the material feature amount is the intrusion portion of the coin (bimetal coin B), the center of the outer portion Bb of the bimetal coin B, and the joint between the inner portion Ba and the outer portion Bb of the bimetal coin B. At the center of the portion and the central portion (inner portion Ba) of the coin, there is a tendency to show an extreme value or become a stable point where the value hardly fluctuates. Utilizing this characteristic, the positions of the inner portion Ba and the outer portion Bb of the bimetal coin B are detected from the material feature amount.

FIG. 8 is a graph showing the relationship between the material (conductivity) of the coin and the material feature amount. The first waveform data is generated by, for example, the following method. The magnetic sensor 10 outputs a change in the voltage of the resonance circuit and a change in the oscillation frequency of the resonance circuit by transporting coins in the vicinity of the resonance type coil 11. These vary depending on the material of the coin, especially the conductivity. In addition, the amount of material features changes according to the conductivity of the coin. From these things, the material feature amount can be obtained from the output of the magnetic sensor 10. That is, there is a predetermined relationship between the voltage of the resonant circuit, the oscillation frequency of the resonant circuit, and the material (conductivity) of the coin. In addition, there is a predetermined relationship between the material (conductivity) of the coin and the amount of material features. Specifically, as shown in FIG. 8, the larger the conductivity of the coin, the smaller the material feature amount. Therefore, a table based on the three-way relationship between the voltage and oscillation frequency of the resonance circuit and the material (conductivity) of the coin, and a table showing the relationship between the material (conductivity) of the coin and the material feature amount. The material (conductivity) of the coin can be obtained by collating the output of the magnetic sensor 10, that is, the voltage and oscillation frequency of the resonance circuit with the former table, and the material (conductivity) can be referred to as the latter table. By collating, the material characteristic amount of the coin can be obtained. Then, the first waveform data can be generated by obtaining the material feature amount at each timing by using the voltage and the oscillation frequency of the resonance circuit related to the outputs of the magnetic sensors 10 having different consecutive timings and these tables. can. The relationship shown in FIG. 8 was obtained by collecting data in advance using various coins having different materials (conductivity).

The first material detection unit 22 detects the first material feature amount corresponding to the outer edge portion of the coin, here, the outer portion of the bimetal coin, from the first waveform data. The second material detection unit 23 detects the second material feature amount corresponding to the central portion of the coin, here, the inner portion of the bimetal coin, from the first waveform data. As described above, the first waveform data has a characteristic showing a characteristic change according to each part of the bimetal coin. Therefore, the first material detection unit 22 has a material characteristic of a point corresponding to the outer part of the bimetal coin. The amount can be accurately detected as the first material feature amount, and the second material detection unit 23 can accurately detect the material feature amount at the point corresponding to the inner part of the bimetal coin as the second material feature amount. can.

The identification processing unit 24 performs coin identification processing based on the first material feature amount and the second material feature amount detected by the first material detection unit 22 and the second material detection unit 23, respectively. Since the first material feature amount and the second material feature amount accurately indicate the material feature amount at the points corresponding to the outer part and the inner part of the bimetal coin, respectively, the identification processing unit 24 accurately identifies the bimetal coin. It is possible to do.

For example, the identification processing unit 24 may determine whether or not the coin is a bimetal coin based on the first material feature amount and the second material feature amount. Further, the material of the outer portion and the inner portion of the bimetal coin may be determined.

From the above, in the present embodiment, it is possible to accurately identify the bimetal coin with a simpler configuration than in the conventional case.

FIG. 9 is a block diagram illustrating the configuration of the first material detection unit according to the first embodiment. As shown in FIG. 9, the first material detection unit 22 has a first-order differential processing unit 22a that differentially processes the first waveform data to generate first-order differential data, and a second-order differential processing unit 22a that differentially processes the first-order differential data to generate second-order differential data. The second-order differential processing unit 22b that generates the differential data may be included, and the first-order material feature amount may be detected based on the first-order differential data and the second-order differential data.

FIG. 10 is a graph schematically showing another example of the first waveform data generated by the first waveform generator. FIG. 10 shows an example when a bimetal coin is detected. FIG. 11 is a graph schematically showing an example of the first waveform data, the first-order differential data, and the second-order differential data. FIG. 11 shows an example in the case where a bimetal coin is detected, and the side cross section of the bimetal coin B (inner portion Ba and outer portion Bb) is shown at the corresponding position in the graph at the upper part. The first waveform data in FIG. 11 corresponds to the first waveform data sandwiched between the broken lines in FIG. In the example shown in FIG. 10, the first waveform data shows a stable region in which the fluctuation of the value is small in the region corresponding to the center of the outer portion and the center of the inner portion of the bimetal coin (the region surrounded by a circle in the figure).

Further, as shown in FIG. 11, the first-order differential data represents the gradient information of the first waveform data (slope of the waveform), and the second-order differential data is the gradient intensity of the first waveform data (slope of the waveform). It represents the rate of change) and the direction of the slope (whether the waveform is rotating clockwise or counterclockwise). When the sign of the second-order differential data changes, the rotation direction of the waveform of the first waveform data changes. Therefore, the slope of the waveform of the first waveform data can be quantified and its shape can be evaluated. Further, in the bimetal coin B whose material is different between the inner portion Ba and the outer portion Bb, there is a stable region in the region corresponding to the outer portion Bb where the slope of the waveform is small and the first waveform data stably changes. Therefore, by including the first-order differential processing unit 22a and the second-order differential processing unit 22b, the first material feature amount can be obtained more accurately from the material feature amount in the stable region, and the material feature of the outer portion Bb is enhanced. It can be detected with high accuracy.

The first-order differential processing unit 22a may simultaneously perform differential processing on the first waveform data and noise removal (for example, sine wave filter correction processing) on the first waveform data. Similarly, the second-order differential processing unit 22b may simultaneously perform differential processing on the first-order differential data and noise removal (for example, sinusoidal filter correction processing) on the first-order differential data.

As shown in FIG. 11, in the first-order differential data, the differential value is less than the predetermined first threshold Th1 in the first-order differential data, and the differential value exceeds the predetermined second threshold Th2 in the second-order differential data. The first point P1 may be detected. This prevents the region containing the extreme value of the first waveform data (see FIG. 7) caused by the combined portion of the bimetal coin B from being erroneously detected as the stable region corresponding to the outer portion Bb of the bimetal coin B. It is possible to do. Therefore, since the stable region corresponding to the outer portion Bb of the bimetal coin B can be detected more reliably, the material characteristics of the outer portion Bb can be detected with higher accuracy.

The first threshold value Th1 can be appropriately set, but can be, for example, 30 to 50% (for example, 40%) of the minimum differential value (for example, Min in FIG. 11) of the first-order differential data. By setting it to 30 to 50%, it is possible to make it less susceptible to fluctuations in the coin transport speed, so even if the coin transport speed fluctuates, the material characteristics of the outer portion Bb can be made more accurate. Can be detected. The second threshold value Th2 can be appropriately set, but can be, for example, 5 to 25% (for example, 10%) of the maximum differential value (for example, Max in FIG. 11) of the second-order differential data.

More specifically, the first material detection unit 22 may search for points having a first threshold value of less than Th1 in order from the start of the first-order differential data, and if a point having a first threshold value of less than Th1 is found, the point is found. It may be determined whether or not the differential value of the second-order differential data at the point exceeds the second threshold value Th2. As a result, if it exceeds, the point may be determined as the first point P1. If it does not exceed, the search process and the determination process may be repeated in the same manner for the first-order differential data after that point until the first point P1 is detected.

As shown in FIG. 11, the first material detection unit 22 has a predetermined number n (n is 2 or more) from the first point P1 to the second point P2 where the differential value is less than the first threshold Th1 in the first-order differential data. When only the natural number) is continuous, the first material feature amount may be detected based on a plurality of material feature amounts from the first point P1 to the second point P2 in the first waveform data. As a result, it is possible to effectively prevent the false detection of momentary noise as a stable region and more effectively detect the stable region of the first waveform data. Therefore, the material characteristics of the outer portion Bb of the bimetal coin B are further improved. It can be detected with high accuracy. The predetermined number n from the first point P1 to the second point P2 can be appropriately set, but may be, for example, 3 to 10 (for example, 5).

More specifically, the first material detection unit 22 may perform the following processing. That is, first, the number of points count is set to 1 (first step). Next, it is determined whether or not the number of points count is smaller than the predetermined number n (second step). If it is small (second step: Yes), it is determined whether or not the differential value of the first-order differential data of the point next to the first point P1 is less than the first threshold Th1 (third step). If it is less than the first threshold Th1 (third step: Yes), the number of points count is incremented by +1 (fourth step), and the process returns to the second step. When it is determined in the second step that the number of points count is not smaller than the predetermined number n (second step: No), the point finally determined to be less than the first threshold Th1 in the third step is the second point. As P2, the first material feature amount is detected based on a plurality of material feature amounts from the first point P1 to the second point P2. If it is determined in the third step that the threshold value is not less than Th1 (third step: No), the first material detection unit 22 may newly detect the first point P1 as described above. good.

As shown in FIG. 11, the first material detection unit 22 has the smallest differential value among the predetermined number n points from the first point P1 to the second point P2 in the first-order differential data (in FIG. 11). The material feature amount (SMx in FIG. 11) of the first waveform data in Px) may be detected as the first material feature amount. As a result, it is possible to detect the material feature amount in which the fluctuation of the first waveform data is particularly small in the stable region of the first waveform data, so that the material feature of the outer portion Bb of the bimetal coin B can be detected with particular accuracy.

The second material detection unit 23 may detect, for example, the second material feature amount corresponding to the central portion of the coin, here, the inner portion of the bimetal coin, from the first waveform data by the following method. That is, first, the first waveform data is monitored, and the point where the material feature amount changes by a predetermined amount or more from the material feature amount in the standby state before the coin arrives at the magnetic sensor 10 is set as the "medium-filled" point. If a predetermined number of material features similar to those in standby are consecutive at the points after the above point, the first point of the continuous predetermined number of points is regarded as the "medium missing" point. Then, the material feature amount at the central point between the "medium-filled" point and the "medium-excluded" point is detected as the second material feature amount.

Next, the operation of the coin identification device according to the present embodiment will be described with reference to FIG. FIG. 12 is a flowchart illustrating an example of the operation of the coin identification device according to the first embodiment.

As shown in FIG. 12, first, the magnetic sensor 10 detects the magnetic characteristics of the coins to be conveyed (step S11).

Next, the first waveform generation unit 21 generates first waveform data representing the material feature amount from the output of the magnetic sensor 10 (step S12).

Next, the first material detection unit 22 detects the first material feature amount corresponding to the outer edge portion of the coin (the outer portion of the bimetal coin) from the first waveform data generated in step S12 (step S13).

Next, the second material detection unit 23 detects the second material feature amount corresponding to the central portion of the coin (inner portion of the bimetal coin) from the first waveform data generated in step S12 (step S14).

The timings of steps S13 and S14 may be opposite or simultaneous.

After that, the identification processing unit 24 performs coin identification processing based on the first material feature amount detected in step S13 and the second material feature amount detected in step S14 (step S15), and the coin identification device. The operation of 1 ends.

FIG. 13 is a flowchart illustrating an example of the operation of the first material detection unit according to the first embodiment. The first material detection unit 22 (step S13) may detect the first material feature amount according to the flow shown in FIG.

In this case, first, the first-order differential processing unit 22a differentially processes the first waveform data to generate the first-order differential data (step S21).

Next, the second-order differential processing unit 22b differentially processes the first-order differential data to generate the second-order differential data (step S22).

Next, the first material detection unit 22 detects the first point P1 whose differential value is less than a predetermined first threshold value in the first-order differential data and whose differential value exceeds a predetermined second threshold value in the second-order differential data. (Step S23).

Next, the first material detection unit 22 determines whether or not the points whose differential value is less than the first threshold value are continuous from the first point P1 to the second point P2 by a predetermined number n (step S24). In the case of a predetermined number of consecutive cases (step S24: Yes), the first material feature amount is detected based on a plurality of material feature amounts from the first point P1 to the second point P2 in the first waveform data (step S25), and the first step. 1 The process of detecting the material feature amount is completed.

In step S25, for example, the first material detection unit 22 has the material feature amount of the first waveform data at the point Px having the smallest differential value among the points from the first point P1 to the second point P2 in the first-order differential data. SMx is detected as the first material feature amount.

In step S24, when the points where the differential value is less than the first threshold value are not continuous by a predetermined number n from the first point P1 (step S24: No), the first material detection unit 22 has the differential value as the first threshold value. The processing of steps S23 and S24 is repeated until the points less than or equal to are continuous from the first point P1 to the second point P2 by a predetermined number of n. That is, from the data after the points detected up to step S24, the first material detection unit 22 detects a new first point P1 satisfying the above conditions (step S23), and the point where the differential value is less than the first threshold value is. , It is determined whether or not the new first point P1 to the second point P2 are continuous by a predetermined number n (step S24).

Next, a more specific embodiment of the coin identification device 1 according to the present embodiment will be described.

FIG. 14 is a block diagram illustrating an example of the configuration of the magnetic sensor according to the first embodiment. In addition to the resonance type coil 11, the magnetic sensor 10 may further include a resonance circuit 13 and a detection circuit 14, as shown in FIG.

The resonance circuit (LC resonance circuit) 13 is connected to the coil 11 and includes an AC power supply that excites the coil 11 connected to the coil 11 and a capacitor connected in parallel to the coil 11. The frequency of the AC power supply is set to the oscillation frequency (resonance frequency) peculiar to the coil 11 and the resonance circuit 13, and the resonance circuit 13 has a high frequency (resonance frequency) suitable for detecting the material of the coin together with the coil 11 as described above. Specifically, it resonates at 1 to 2 MHz, more specifically 1.4 to 1.5 MHz). In this way, an AC voltage (sine wave) is applied to the coil 11 by the resonance circuit 13, and an AC magnetic flux is generated in the transport path 110. When a coin enters this magnetic flux, an induced current (eddy current) is generated in the coin, and the magnetic flux changes. As a result, the output (voltage and oscillation frequency) of the resonance circuit 13 changes according to this change in magnetic flux.

The detection circuit 14 is connected to the resonance circuit 13, and includes an amplifier circuit that amplifies the output of the resonance circuit 13 and a direct current conversion circuit that converts the output (alternating current) of the amplifier circuit into direct current.

FIG. 15 is a block diagram illustrating another example of the configuration of the coin identification device according to the first embodiment. The coin identification device 1 includes the above-mentioned magnetic sensor 10, a first waveform generation unit 21, a first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), a second material detection unit 23, and identification processing. In addition to the unit 24, as shown in FIG. 15, an AD converter 30, a storage unit 40, and a control unit (arithmetic processing unit) 20 may be further provided.

The AD converter 30 is connected to the magnetic sensor 10 (detection circuit 14), samples analog signals input from the detection circuit 14 of the magnetic sensor 10 at predetermined time intervals, and makes a time series (digital signal). Convert. Sampling by the AD converter 30 is performed for a period before the coin arrives at the magnetic sensor 10 and after passing through the magnetic sensor 10.

The storage unit 40 is composed of, for example, a storage device such as a RAM (RandomAccessMemory), a ROM (ReadOnlyMemory), a flash memory, an HDD (HardDiskDrive), an SSD (SolidStateDrive), and various data (for example,). Thresholds, tables, reference data, etc.) and programs are configured to be writable and readable.

The control unit 20 is a controller that controls each unit of the coin identification device 1, and is controlled by, for example, a program for realizing various processes, a CPU (Central Processing Unit) that executes the program, and the CPU. It is composed of various hardware (for example, FPGA (Field Programmable Gate Array)) and the like.

The control unit 20 acquires sampling data (time series) sampled by the AD converter 30 from the AD converter 30 and stores it in the storage unit 40.

By executing the corresponding program, the control unit 20 has the above-mentioned first waveform generation unit 21, first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), and second material detection unit 22. It can function as 23 and the identification processing unit 24.

The first waveform generation unit 21 generates the first waveform data based on the sampling data stored in the storage unit 40, that is, the time series in which the output of the magnetic sensor 10 is sampled at a predetermined time interval.

The storage unit 40 may store reference data relating to the respective material feature amounts of the outer portion and the inner portion of the genuine bimetallic coin, and the identification processing unit 24 may store the first material detection unit 22 and the second material detection. The first material feature amount and the second material feature amount detected by the unit 23 may be compared with this reference data, respectively. This makes it possible to determine with high accuracy the denomination (and authenticity) of the coin and whether or not the coin is a bimetal coin (particularly a bicolor coin or a bicolor clad coin).

In this case, the reference data is the permissible range of the material feature amount set based on the material feature amount detected from the outer part and the inner part of the genuine bimetallic coin (that is, the permissible amount of the material feature amount of the outer part of the genuine bimetal coin). The range and the permissible range of the material feature amount of the inner part of the bimetallic coin) may be included, and the identification processing unit 24 sets the first material feature amount and the second material feature amount as the corresponding permissible range, respectively. They may be compared, i.e. determined whether they are within or not within the corresponding tolerances. In the present specification, the "allowable range of material feature amount" indicates a numerical range of material feature amount that can be accepted as a genuine coin (for example, a bimetal coin) in which the range is set, and is usually used. , A numerical range including the amount of material features detected from a genuine coin (for example, a bimetal coin) and before and after it.

Further, when the first material feature amount and the second material feature amount match the reference data, the identification processing unit 24 determines that the coin is a genuine bimetal coin (for example, a bicolor coin or a bicolor clad coin). You may judge.

More specifically, the identification processing unit 24 compares the first material feature amount and the second material feature amount with the corresponding allowable ranges, and if they are included in the corresponding allowable range, the coin is regarded as a genuine bimetal coin. (For example, a bicolor coin or a bicolor clad coin) may be determined.

Further, the identification processing unit 24 may determine the denomination of the coin by comparing the first material feature amount and the second material feature amount with the reference data.

In this case, the reference data is the permissible range of the material features set based on the material features detected from the outer edge and the center of the genuine coin (that is, the permissible range of the material features of the outer edge of the genuine coin). And the permissible range of the material feature amount of the central part of the coin) may be included for each denomination, and the identification processing unit 24 sets the first material feature amount and the second material feature amount of each denomination, respectively. It may be compared with the corresponding tolerance, i.e., whether it is included in the corresponding tolerance or not. Then, if there is a denomination included in the corresponding allowable range for both the first material feature amount and the second material feature amount, it may be determined that the coin is the denomination.

Further, the identification processing unit 24 may compare the first material feature amount and the second material feature amount with each other and determine whether or not the material of the outer edge portion of the coin is the same as the material of the central portion of the coin. .. As a result, the identification processing unit 24 is a coin made of a single material, that is, a monometal coin (if they are the same), or a coin made of two or more materials, that is, a bimetal coin. (If they are not the same) can be determined.

Next, the operation of the coin identification device according to the present embodiment shown in FIG. 15 will be described with reference to FIG. FIG. 16 is a flowchart illustrating another example of the operation of the coin identification device according to the first embodiment.

As shown in FIG. 16, first, the magnetic sensor 10 detects the magnetic characteristics of the coins to be conveyed, as in the case shown in FIG. 12 (step S11).

Next, the AD converter 30 samples the output of the magnetic sensor 10 (analog signal input from the detection circuit 14) and converts it into a time series (step S31).

Next, the first waveform generation unit 21 generates the first waveform data representing the material feature amount from the sampling data (time series) sampled by the AD converter 30 (step S32).

After that, the processes of steps S13 to S15 are executed in the same manner as in the case shown in FIG. 12, and the operation of the coin identification device 1 is completed.

The first material detection unit 22 (step S13) may detect the first material feature amount according to the flow shown in FIG.

Further, in step S15, as described above, the identification processing unit 24 may compare the first material feature amount and the second material feature amount with the reference data, and the first material feature amount and the second material feature amount may be compared with the reference data. If is consistent with the reference data, the coin may be determined to be a genuine bimetal coin. Further, the identification processing unit 24 may determine the denomination of the coin by comparing the first material feature amount and the second material feature amount with the reference data. Further, the identification processing unit 24 may compare the first material feature amount and the second material feature amount with each other and determine whether or not the material of the outer edge portion of the coin is the same as the material of the center portion of the coin. good.

(Second Embodiment)
The present embodiment further includes a second waveform generation unit that generates the second waveform data, except that the second material detection unit detects the second material feature amount by using the second waveform data. It is the same as the first embodiment. FIG. 17 is a block diagram illustrating an example of the configuration of the coin identification device according to the second embodiment.

As shown in FIG. 17, the coin identification device 2 according to the present embodiment has a magnetic sensor 10, a first waveform generation unit 21, a first material detection unit 22, and a second material detection unit 23, as in the first embodiment. And the identification processing unit 24 is provided, and the second waveform generation unit 25 is further provided.

The first material detection unit 22 may include a first-order differential processing unit 22a and a second-order differential processing unit 22b, as in the first embodiment.

The second waveform generation unit 25 generates second waveform data representing the distance feature amount from the output of the magnetic sensor 10. More specifically, the second waveform generation unit 25 generates the second waveform data based on the outputs of the magnetic sensors 10 at consecutive different timings. Therefore, the second waveform data shows the temporal change of the distance feature amount. For example, the second waveform data may be generated based on a time series (digital signal) obtained by sampling the output (analog signal) of the magnetic sensor 10 at a predetermined time interval. The first waveform generation unit 21 and the second waveform generation unit 25 may generate the first waveform data and the second waveform data, respectively, based on the same output of the magnetic sensor 10, for example, based on the same time series. can. The distance feature amount is a feature amount that changes according to the unevenness of the coin, particularly the unevenness of the surface on the magnetic sensor 10 side, and changes according to the distance (interval) between the coin and the resonance type coil 11 of the magnetic sensor 10. do. The unevenness may be caused by, for example, engraving or edging of a coin.

FIG. 18 is a graph schematically showing an example of the second waveform data generated by the second waveform generator. FIG. 18 shows an example when a bimetal coin is detected, and a side cross section of the bimetal coin B (inner portion Ba and outer portion Bb) is shown at a corresponding position in the graph at the upper part. As shown in FIG. 18, the second waveform data is data representing a distance feature amount with respect to time, and the horizontal axis of FIG. 18 represents the time direction. However, since the magnetic sensor 10 usually detects coins transported at a predetermined speed, the second waveform data is also data representing a distance feature amount with respect to a position in the transport direction of the coins. In this case, FIG. 18 shows. The horizontal axis represents the position of the coin in the transport direction.

Further, as shown in FIG. 18, the value of the distance feature tends to change sharply immediately after entering the medium and immediately before leaving the medium. That is, the distance feature amount increases sharply when the coin (bimetal coin B) overlaps the resonance type coil 11, and decreases sharply immediately before the coin (bimetal coin B) passes through the resonance type coil 11. Using this characteristic, the positions of both ends of the coin are detected from the distance features. Further, the distance feature amount tends to decrease at the abutment portion of the bimetal coin B.

FIG. 19 is a graph showing the relationship between the distance between the coin and the resonant coil and the distance feature amount. The second waveform data is generated by, for example, the following method. The magnetic sensor 10 outputs a change in the voltage of the resonance circuit and a change in the oscillation frequency of the resonance circuit by transporting coins in the vicinity of the resonance type coil 11. These vary depending on the distance (interval) between the coin and the resonant coil 11. Further, the distance feature amount changes according to the distance between the coin and the resonance type coil 11. From these things, the distance feature amount can be obtained from the output of the magnetic sensor 10. That is, there is a predetermined relationship between the voltage of the resonant circuit, the oscillation frequency of the resonant circuit, and the distance between the coin and the resonant coil 11. Further, there is a predetermined relationship between the distance between the coin and the resonance type coil 11 and the distance feature amount. Specifically, as shown in FIG. 19, the larger the distance between the coin and the resonant coil 11, the smaller the distance feature amount. This is because when the coin is far from the resonant coil 11, it is far from the magnetic field generated by the coil 11. Therefore, between the table based on the tripartite relationship between the voltage and oscillation frequency of the resonant circuit and the distance between the coin and the resonant coil 11 and the distance and distance feature between the coin and the resonant coil 11. By preparing in advance a table showing the relationship between the coins and the output of the magnetic sensor 10, that is, collating the voltage and oscillation frequency of the resonance circuit with the former table, the distance between the coin and the resonance type coil 11 can be obtained, and the coins can be obtained. And by collating the distance between the resonance type coils 11 with the latter table, the distance feature amount of the coin can be obtained. Then, the second waveform data can be generated by obtaining the distance feature amount at each timing by using the voltage and the oscillation frequency of the resonance circuit related to the outputs of the magnetic sensors 10 having different consecutive timings and these tables. can. The relationship shown in FIG. 19 was obtained by previously changing the distance between the resonance type coil 11 and the coin surface to, for example, 0 mm, 0.2 mm, 0.3 mm, and collecting data. Further, a table showing such a relationship is prepared for each denomination, and a table of the corresponding denomination is used when obtaining the distance feature amount.

In the present embodiment, the second material detection unit 23 is based on the second waveform data generated by the second waveform generation unit 25, and from the first waveform data generated by the first waveform generation unit 21, the central portion of the coin. Here, the second material feature amount corresponding to the inner portion of the bimetal coin is detected.

FIG. 20 is a graph schematically showing another example of the first waveform data generated by the first waveform generator. FIG. 20 is the same as that of FIG. 10, and shows an example when a bimetal coin is detected. For example, as shown in FIG. 18, the second material detection unit 23 may detect the third point P3 and the fourth point P4 corresponding to both ends of the coin in the transport direction from the second waveform data. More specifically, the points where the distance feature amount exceeds the predetermined third threshold value Th3 may be searched in order from the start and end of the second waveform data, and the points where the distance feature amount exceeds the third threshold value Th3 may be searched for at the third point P3 and, respectively. It may be determined as the fourth point P4. Then, as shown in FIG. 20, the second material detection unit 23 is a central point between the fifth point P5 and the sixth point P6 corresponding to the third point P3 and the fourth point P4 in the first waveform data, respectively. The material feature amount of Pc may be detected as the second material feature amount. This makes it possible to detect the second material feature amount from the overall image of the first and second waveform data, and it is possible to detect the material feature of the inner portion of the bimetal coin with high accuracy.

Next, the operation of the coin identification device according to the present embodiment will be described with reference to FIG. 21. FIG. 21 is a flowchart illustrating an example of the operation of the coin identification device according to the second embodiment.

As shown in FIG. 21, first, the processes of steps S11 to S12 are executed as in the case of the first embodiment.

Next, the second waveform generation unit 25 generates the second waveform data representing the distance feature amount from the output of the magnetic sensor 10 (step S41).

The timings of step S12 and step S41 may be opposite or simultaneous.

Next, as in the case of the first embodiment, the first material detection unit 22 executes the process of step S13. The first material detection unit 22 (step S13) may detect the first material feature amount according to the flow shown in FIG.

Next, the second material detection unit 23 detects the second material feature amount corresponding to the center of the coin from the first waveform data generated in step S12 based on the second waveform data generated in step S41. (Step S42).

The timings of step S13 and step S42 may be opposite or simultaneous.

After that, the identification processing unit 24 performs coin identification processing based on the first material feature amount detected in step S13 and the second material feature amount detected in step S42 (step S15), and the coin identification device. The operation of 2 ends.

FIG. 22 is a flowchart illustrating an example of the operation of the second material detection unit according to the second embodiment. The second material detection unit 23 (step S42) may detect the second material feature amount according to the flow shown in FIG.

In this case, first, the second material detection unit 23 detects the third point P3 and the fourth point P4 corresponding to both ends of the coin in the transport direction in the second waveform data (step S51).

Subsequently, the second material detection unit 23 uses the first waveform data to correspond to the third point P3 and the fourth point P4, respectively, at the fifth point P5 and the sixth point P6, and the fifth point P5 and the sixth point P6. The process of detecting the center point Pc between the two, detecting the material feature amount of the center point Pc as the second material feature amount (step S52), and detecting the second material feature amount is completed.

Next, a more specific embodiment of the coin identification device 2 according to the present embodiment will be described.

In the present embodiment as well, as in the first embodiment, as shown in FIG. 14, the magnetic sensor 10 may further include a resonance circuit 13 and a detection circuit 14 in addition to the resonance type coil 11.

FIG. 23 is a block diagram illustrating another example of the configuration of the coin identification device according to the second embodiment. The coin identification device 2 includes the above-mentioned magnetic sensor 10, a first waveform generation unit 21, a second waveform generation unit 25, a first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), and a second. In addition to the material detection unit 23 and the identification processing unit 24, as shown in FIG. 23, even if the AD converter 30, the storage unit 40, and the control unit (arithmetic processing unit) 20 are further provided as in the first embodiment. good.

By executing the corresponding program, the control unit 20 executes the above-mentioned first waveform generation unit 21, second waveform generation unit 25, and first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b). ), The second material detection unit 23 and the identification processing unit 24 can function.

The second waveform generation unit 25 generates the second waveform data based on the sampling data stored in the storage unit 40, that is, the time series in which the output of the magnetic sensor 10 is sampled at a predetermined time interval.

Next, the operation of the coin identification device according to the present embodiment shown in FIG. 23 will be described with reference to FIG. 24. FIG. 24 is a flowchart illustrating another example of the operation of the coin identification device according to the second embodiment.

First, the processes of steps S11, S31 and S32 are executed as in the case of the first embodiment.

Next, the second waveform generation unit 25 generates the second waveform data representing the distance feature amount from the sampling data (time series) sampled by the AD converter 30 (step S61).

The timings of step S32 and step S61 may be opposite or simultaneous.

After that, the processes of steps S13, S42 and S15 are executed in the same manner as in the case shown in FIG. 21, and the operation of the coin identification device 2 is completed.

Further, the second material detection unit 23 (step S42) may detect the second material feature amount according to the flow shown in FIG.

Further, in step S15, as in the case of the first embodiment, the identification processing unit 24 may compare the first material feature amount and the second material feature amount with the reference data, and the first material feature amount and the first material feature amount and the second material feature amount may be compared with the reference data. If the second material feature quantity matches the reference data, the coin may be determined to be a genuine bimetal coin. Further, the identification processing unit 24 may determine the denomination of the coin by comparing the first material feature amount and the second material feature amount with the reference data. Further, the identification processing unit 24 may compare the first material feature amount and the second material feature amount with each other and determine whether or not the material of the outer edge portion of the coin is the same as the material of the center portion of the coin. good.

As described above, in the above embodiment, the magnetic sensor 10 including the resonance type coil 11 and detecting the magnetic characteristics of the conveyed coin, and the first waveform data representing the material feature amount from the output of the magnetic sensor 10 are obtained. From the first waveform generation unit 21 to be generated, the first material detection unit 22 that detects the first material feature amount corresponding to the outer edge portion of the coin from the first waveform data, and from the first waveform data to the center of the coin. Since it is provided with a second material detection unit 23 for detecting the corresponding second material feature amount and an identification processing unit 24 for performing coin identification processing based on the first material feature amount and the second material feature amount, it is more likely to be provided. It is possible to accurately identify bimetal coins with a simple configuration.

Hereinafter, modified examples in each embodiment will be described.

In the above embodiment, the case of identifying a bimetal coin has been mainly described, but according to the apparatus and method according to the present disclosure, even a monometal coin can be identified. Since the device and method use a stable region where the slope of the waveform of the first waveform data is small, not the difference in material, it is possible to detect the position of the outer edge of the coin regardless of the structure of the coin, and it is possible to detect the position of the outer edge of the coin. It is possible to detect with high accuracy the material characteristics of the outer edge of coins whose internal structure is different from that of bicolor coins such as coins and clad coins.

FIG. 25 is a graph schematically showing an example of the first waveform data, the first-order differential data, and the second-order differential data. FIG. 25 shows an example when a monometal coin is detected, and a side cross section of the monometal coin M is shown at a corresponding position in the graph at the upper part. As shown in FIG. 25, even in the case of the monometal coin M, the first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b) is the same as in the case shown in FIG. The first point P1 and the second point P2 can be detected based on the first-order waveform data, the first-order differential data, and the second-order differential data. It can be detected as the first material feature amount corresponding to the outer edge portion of the metal coin M.

Further, as in the case shown in FIGS. 18 and 20, the second material detection unit 23 detects the third point P3 and the fourth point P4 based on the second waveform data, and is based on the first waveform data. Therefore, the material feature amount at the center point Pc between the fifth point P5 and the sixth point P6 can be detected as the second material feature amount corresponding to the central portion of the monometal coin M.

In the above embodiment, the case where the magnetic sensor 10 provided with the resonance type coil 11 is used alone as the sensor has been described, but the magnetic sensor 10 may be used in combination with a material sensor (magnetic sensor) having another frequency. .. This makes it possible to detect monometal coins, plated clad coins, three-layer clad coins, bicolor clad coins, and the like.

In the above embodiment, the first waveform data and the second waveform are based on the time series in which the output of the magnetic sensor 10 is sampled at predetermined time intervals, that is, based on the data obtained by sampling the output of the magnetic sensor 10 according to time. The case of generating data has been described, but the diameter of a coin is detected using a diameter detection sensor (for example, a magnetic sensor or an optical sensor) that detects the diameter of the coin, and the change in the diameter of the coin (output of the diameter detection sensor) is used. Accordingly, the first waveform data and the second waveform data may be generated based on the data obtained by sampling the output of the magnetic sensor 10. As a result, even if the transport speed of the coin fluctuates, or if the coin temporarily stops on the magnetic sensor 10 and then starts moving again, the same data as when the coin is normally transported at a constant speed. Can be collected. In this case, a time series in which the output of the magnetic sensor 10 is sampled at a predetermined time interval may be collected, and only necessary elements may be selected and used from the time series based on the output of the diameter detection sensor.

In the above embodiment, the case where the first waveform data, the first-order differential data, and the second-order differential data are processed in order from the start is described, but the first waveform data, the first-order differential data, and the second-order differential data are processed from the end, respectively. It may be processed in order, and even in this case, it is possible to detect the material feature amount of each of the outer edge portion and the center portion of the coin by the same method.

In the above embodiment, the case where the coin is composed of a non-magnetic material has been described, but according to the apparatus and method according to the present disclosure, the same method can be used even when the coin contains a magnetic material (for example, a ferromagnetic material). It is possible to detect the material feature amount of each of the outer edge portion and the central portion of the coin, and the coin can be identified. For example, each part or at least a part of the bimetal coin may be composed of a magnetic material (for example, a ferromagnetic material). Even in this case, for example, the above threshold value can be used as it is.

Although the embodiments have been described above with reference to the drawings, the present disclosure is not limited to the above embodiments. Further, the configurations of the respective embodiments may be appropriately combined or modified as long as they do not deviate from the gist of the present disclosure.

As described above, the present disclosure is a technique useful for accurately identifying bimetal coins with a simpler configuration.

Claims

A magnetic sensor that includes a resonant coil and detects the magnetic properties of the conveyed coin,
A first waveform generator that generates first waveform data representing a material feature amount that is a feature amount that changes according to the material of the coin from the output of the magnetic sensor.
From the first waveform data, a first material detection unit that detects the first material feature amount corresponding to the outer edge portion of the coin, and a first material detection unit.
From the first waveform data, a second material detection unit that detects the second material feature amount corresponding to the center of the coin, and a second material detection unit.
An identification processing unit that performs coin identification processing based on the first material feature amount and the second material feature amount, and
A coin identification device comprising.
The first material detection unit has a first-order differential processing unit that differentially processes the first waveform data to generate first-order differential data, and a second-order differential data that differentially processes the first-order differential data to generate second-order differential data. The coin identification device according to claim 1, further comprising a third-order differential processing unit and detecting the first material feature amount based on the first-order differential data and the second-order differential data.
The first material detection unit detects a first point in which the differential value is less than a predetermined first threshold value in the first-order differential data and the differential value exceeds a predetermined second threshold value in the second-order differential data. 2. The coin identification device according to claim 2.
When a predetermined number of points whose differential value is less than the first threshold value are continuous from the first point to the second point in the first-order differential data, the first material detection unit is the first in the first waveform data. The coin identification device according to claim 3, wherein the first material feature amount is detected based on a plurality of material feature amounts from the point to the second point.
The first material detection unit determines the material feature amount of the first waveform data at the point where the differential value is the smallest among the predetermined number of points from the first point to the second point in the first-order differential data. The coin identification device according to claim 4, wherein the coin identification device is detected as the first material feature amount.
Claims 1 to 5 further include a second waveform generator that generates second waveform data representing a distance feature amount that is a feature amount that changes according to the unevenness of a coin from the output of the magnetic sensor. The coin identification device described in any of the above.
The second material detection unit detects the third point and the fourth point corresponding to both ends of the coin in the transport direction in the second waveform data, and the third point and the fourth point in the first waveform data. The coin identification device according to claim 6, wherein the material feature amount of the central point between the fifth point and the sixth point corresponding to the points is detected as the second material feature amount.
The invention according to any one of claims 1 to 7, wherein the identification processing unit determines whether or not the coin is a bimetal coin based on the first material feature amount and the second material feature amount. Coin identification device.
Further equipped with a storage unit for storing reference data relating to the respective material feature amounts of the outer edge portion and the central portion of the genuine bimetal coin.
The coin identification device according to any one of claims 1 to 8, wherein the identification processing unit compares the first material feature amount and the second material feature amount with the reference data.
The ninth aspect of the present invention, wherein the identification processing unit determines that the coin is a genuine bimetal coin when the first material feature amount and the second material feature amount match the reference data. Coin identification device.
The coin according to claim 9 or 10, wherein the identification processing unit determines the denomination of the coin by comparing the first material feature amount and the second material feature amount with the reference data. Identification device.
It is a coin identification method using a magnetic sensor.
The magnetic sensor includes a resonant coil.
The step of detecting the magnetic characteristics of the coins to be conveyed by the magnetic sensor,
From the output of the magnetic sensor, a step of generating first waveform data representing a material feature amount, which is a feature amount that changes depending on the material of the coin, and
From the first waveform data, a step of detecting the first material feature amount corresponding to the outer edge of the coin, and
From the first waveform data, a step of detecting the second material feature amount corresponding to the center of the coin, and
A step of identifying coins based on the first material feature amount and the second material feature amount, and
A coin identification method comprising.