Coin identification device and coin identification method

Abstract

To provide a coin identification device and a coin identification method that can accurately identify bimetallic coins with a simpler configuration.SOLUTION: A coin identification device includes: a magnetic sensor that includes a resonant coil and detects magnetic characteristics of a coin being conveyed; a first waveform generator that generates, from the output of the magnetic sensor, first waveform data representing a material feature, which is a feature that varies depending on the material of the coin; a first material detection unit that detects, from the first waveform data, a first material feature corresponding to the outer edge of the coin; a second material detection unit that detects, from the first waveform data, a second material feature corresponding to the center of the coin; and an identification processing unit that identifies coins based on the first material feature and the second material feature.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present disclosure relates to a coin validator and a coin validator method. [Background technology]

[0002] Conventionally, coin processing machines that perform processes such as coin counting are equipped with a coin identification device equipped with multiple sensors for identifying coins. Also known are bimetallic coins made of two or more materials, such as bicolor coins, in which the material of the center and the material of the surrounding outer edge are different.

[0003] For example, Patent Document 1 discloses a coin identification device that includes a ring detection sensor, which is a magnetic sensor with a resonant circuit connected to a coil that detects the material of the outer edge of a coin, and a core detection sensor, which is a magnetic sensor with a resonant circuit connected to a coil that detects the material of the center of the coin, and that determines whether a coin is a bimetal coin based on the outputs of the ring detection sensor and the core detection sensor.

[0004] Patent document 2 also discloses a coin identification device that includes a reflective magnetic sensor with a primary coil and a secondary coil, and detects bicolor coins from the output of the secondary coil when a high-frequency (250 kHz) signal is applied to the primary coil. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-220119 [Patent Document 2] Patent No. 4157335 Summary of the Invention [Problem to be solved by the invention]

[0006] Monometallic coins, which are made of the same material as the core of bimetallic coins and are very similar in size to bimetallic coins, are in circulation on the market. Particularly overseas, there is a need to accurately detect the material of the core and outer edge of bimetallic coins.

[0007] However, in the coin identification device disclosed in Patent Document 1, as shown in Figure 2 of Patent Document 1, in order to detect the material of the outer edge and center of the coin, it is necessary to place a ring detection sensor 32 and a core detection sensor 33 with different frequencies at positions where the outer edge and center of the bimetal coin are transported, which requires space and control circuits to place these sensors, leaving room for improvement in terms of space and cost.

[0008] In the coin identification device disclosed in Patent Document 2, a high-frequency signal of 250 kHz is applied to the primary coil of the reflective magnetic sensor, but it is difficult to increase the frequency of the signal applied to the primary coil with a reflective magnetic sensor, and it is not easy to actually measure bicolor coins with a reflective magnetic sensor at this frequency level, making it difficult to commercialize. This is because the higher the frequency of the signal applied to the primary coil, the more difficult it becomes to detect a phase shift between the signal applied to the primary coil and the signal detected by the secondary coil.

[0009] The present disclosure has been made in consideration of the above-mentioned current situation, and aims to provide a coin identification device and coin identification method that can accurately identify bimetal coins with a simpler configuration. [Means for solving the problem]

[0010] In order to solve the above-mentioned problems and achieve the objectives, the coin identification device of the present disclosure comprises a magnetic sensor including a resonant coil that detects the magnetic characteristics of a coin being transported, a first waveform generation unit that generates first waveform data representing a material feature, which is a feature that changes depending on the material of the coin, from the output of the magnetic sensor, a first material detection unit that detects a first material feature corresponding to the outer edge of the coin from the first waveform data, a second material detection unit that detects a second material feature corresponding to the center of the coin from the first waveform data, and an identification processing unit that performs an identification process for the coin based on the first material feature and the second material feature.

[0011] The first material detection unit may include a first-order differential processing unit that performs differential processing on the first waveform data to generate first-order differential data, and a second-order differential processing unit that performs differential processing on the first-order differential data to generate second-order differential data, and may detect the first material feature amount based on the first-order differential data and the second-order differential data.

[0012] The first material detection unit may detect a first point where a derivative value in the first-order derivative data is less than a predetermined first threshold value and a derivative value in the second-order derivative data exceeds a predetermined second threshold value.

[0013] When a predetermined number of points in the first-order derivative data whose derivative value is less than the first threshold value are consecutive from the first point to the second point, the first material detection unit may detect the first material feature based on a plurality of material feature values ​​in the first waveform data from the first point to the second point.

[0014] The first material detection unit may detect, as the first material feature, a material feature of the first waveform data at a point in the first-order differential data where a differential value is smallest among the predetermined number of points from the first point to the second point.

[0015] The coin identification device may further include a second waveform generation unit that generates, from the output of the magnetic sensor, second waveform data that represents a distance feature amount, which is a feature amount that changes depending on the unevenness of the coin.

[0016] The second material detection unit may detect a third point and a fourth point in the second waveform data corresponding to both ends of the coin in the conveying direction, and may detect a material feature value of a center point between a fifth point and a sixth point corresponding to the third point and the fourth point, respectively, in the first waveform data as the second material feature value.

[0017] The identification processing unit may determine whether or not the coin is a bimetal coin based on the first material characteristic amount and the second material characteristic amount.

[0018] The coin identification device may further include a memory unit that stores reference data relating to the material characteristics of the outer edge and center of a genuine bimetal coin, and the identification processing unit may compare the first material characteristic and the second material characteristic with the reference data.

[0019] The identification processing unit may determine that the coin is the genuine bimetal coin when the first material characteristic amount and the second material characteristic amount match the reference data.

[0020] The recognition 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.

[0021] The bimetallic coin may be at least one of a bicolor coin and a bicolor clad coin.

[0022] The bimetallic coin may be a bicolor coin.

[0023] The coin identification method of the present disclosure is a coin identification method using a magnetic sensor, wherein the magnetic sensor includes a resonant coil, and includes the steps of: detecting the magnetic characteristics of a coin being transported using the magnetic sensor; generating first waveform data representing a material feature, which is a feature that changes depending on the material of the coin, from the output of the magnetic sensor; detecting a first material feature corresponding to the outer edge of the coin from the first waveform data; detecting a second material feature corresponding to the center of the coin from the first waveform data; and performing a coin identification process based on the first material feature and the second material feature.

[0024] The step of detecting the first material feature may include 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 the first material feature may be detected based on the first-order differential data and the second-order differential data.

[0025] The step of detecting the first material feature may include detecting a first point where a derivative value in the first-order derivative data is less than a predetermined first threshold value and where a derivative value in the second-order derivative data exceeds a predetermined second threshold value.

[0026] The step of detecting the first material feature may include detecting the first material feature based on a plurality of material features from the first point to the second point in the first waveform data when a predetermined number of consecutive points from the first point to the second point have a derivative value less than the first threshold value in the first-order derivative data.

[0027] The step of detecting the first material feature may detect, as the first material feature, a material feature of the first waveform data at a point in the first-order differential data where a differential value is smallest among the predetermined number of points from the first point to the second point.

[0028] The coin identifying method may further comprise the step of generating, from the output of the magnetic sensor, second waveform data representing a distance feature amount, which is a feature amount that changes depending on the unevenness of the coin.

[0029] The step of detecting the second material feature may include detecting a third point and a fourth point in the second waveform data corresponding to both ends of the coin in the conveying direction, and may also include detecting a material feature at a center point between a fifth point and a sixth point in the first waveform data corresponding to the third point and the fourth point, respectively, as the second material feature.

[0030] The step of performing the identification process may determine whether or not the coin is a bimetal coin based on the first material characteristic amount and the second material characteristic amount.

[0031] The step of performing the identification process may include comparing the first material characteristic amount and the second material characteristic amount with reference data relating to the material characteristic amounts of the outer edge and center of a genuine bimetal coin, respectively.

[0032] The step of performing the identification process may determine that the coin is the genuine bimetal coin if the first material characteristic amount and the second material characteristic amount match the reference data.

[0033] The step of performing the identification process may determine the denomination of the coin by comparing the first material feature amount and the second material feature amount with the reference data.

[0034] The bimetallic coin may be at least one of a bicolor coin and a bicolor clad coin.

[0035] The bimetallic coin may be a bicolor coin. [Effects of the Invention]

[0036] According to the present disclosure, it is possible to provide a coin identification device and a coin identification method that can accurately identify bimetal coins with a simpler configuration. [Brief explanation of the drawings]

[0037] [Figure 1] (a) is a schematic plan view showing the coin face of a bicolor coin, and (b) is a schematic cross-sectional view of the bicolor coin. [Figure 2] (a) is a schematic plan view showing the coin surface of a clad coin (plated), (b) is a schematic cross-sectional view of a clad coin (plated), (c) is a schematic plan view showing the coin surface of a clad coin (three-layer structure), and (d) is a schematic cross-sectional view of a clad coin (three-layer structure). [Figure 3] (a) is a schematic plan view showing the coin face of a bicolor clad coin, and (b) is a schematic cross-sectional view of the bicolor clad coin. [Figure 4] 1 is a schematic diagram illustrating the configuration of a coin identification device according to a first embodiment, showing a coin transport path as viewed from above. FIG. [Figure 5] 5 is a schematic diagram illustrating the configuration of the coin discriminating device according to the first embodiment, and is a cross-sectional view taken along line AB in FIG. 4. FIG. [Figure 6] 1 is a block diagram illustrating an example of the configuration of a coin identification device according to a first embodiment. [Figure 7] 4 is a graph schematically showing an example of first waveform data generated by a first waveform generating section. [Figure 8] 10 is a graph showing the relationship between the material (conductivity) of a coin and the material feature amount. [Figure 9] FIG. 2 is a block diagram illustrating the configuration of a first material detection unit according to the first embodiment. [Figure 10] 10 is a graph schematically showing another example of first waveform data generated by the first waveform generating section. [Figure 11] 4 is a graph schematically showing an example of first waveform data, its first-order differential data, and its second-order differential data. [Figure 12] 4 is a flowchart illustrating an example of the operation of the coin identifying device according to the first embodiment. [Figure 13]6 is a flowchart illustrating an example of an operation of a first material detection unit according to the first embodiment. [Figure 14] FIG. 1 is a block diagram illustrating an example of the configuration of a magnetic sensor according to a first embodiment. [Figure 15] FIG. 3 is a block diagram illustrating another example of the configuration of the coin identifying device according to the first embodiment. [Figure 16] 10 is a flowchart illustrating another example of the operation of the coin identifying device according to the first embodiment. [Figure 17] FIG. 10 is a block diagram illustrating an example of the configuration of a coin identifying device according to a second embodiment. [Figure 18] 4 is a graph schematically showing an example of second waveform data generated by a second waveform generating section. [Figure 19] 10 is a graph showing the relationship between the distance between the coin and the resonance coil and the distance feature amount. [Figure 20] 10 is a graph schematically showing another example of first waveform data generated by the first waveform generating section. [Figure 21] 10 is a flowchart illustrating an example of the operation of the coin identifying device according to the second embodiment. [Figure 22] 10 is a flowchart illustrating an example of an operation of a second material detection unit according to the second embodiment. [Figure 23] FIG. 10 is a block diagram illustrating another example of the configuration of the coin identifying device according to the second embodiment. [Figure 24] 10 is a flowchart illustrating another example of the operation of the coin identifying device according to the second embodiment. [Figure 25] 4 is a graph schematically showing an example of first waveform data, its first-order differential data, and its second-order differential data. DETAILED DESCRIPTION OF THE INVENTION

[0038] Hereinafter, embodiments of a coin identification device and a coin identification method according to the present disclosure will be described with reference to the drawings. Hereinafter, the present disclosure will be described using as an example a coin identification device and a coin identification method that are targeted at currency coins, but the coins targeted by the present disclosure include not only currency coins but also coins used in gaming machines. The following description is an example of a coin identification device and a coin identification method.

[0039] The coin identification device and coin identification method according to the present disclosure are suitable for bimetallic coins, which are coins made of two or more types of material, but can also identify monometallic coins, which are coins made of one type of material. The following mainly describes the case of identifying bimetallic coins.

[0040] First, we will explain bimetallic coins. Figure 1(a) is a schematic plan view showing the face of a bicolor coin, and Figure 1(b) is a schematic cross-sectional view of the bicolor coin. Figure 2(a) is a schematic plan view showing the face of a clad coin (plated), Figure 2(b) is a schematic cross-sectional view of the clad coin (plated), Figure 2(c) is a schematic plan view showing the face of a clad coin (three-layer structure), and Figure 2(d) is a schematic cross-sectional view of the clad coin (three-layer structure). Figure 3(a) is a schematic plan view showing the face of a bicolor-clad coin, and Figure 3(b) is a schematic cross-sectional view of the bicolor-clad coin. Bimetallic coins include bicolor coins, clad coins, and bicolor-clad coins, with bicolor coins and bicolor-clad coins being preferred, and bicolor coins being particularly preferred. As shown in Figures 1(a) and 1(b), the bicolor coin 51 is formed by inserting the core portion 51a into the ring portion 51b, using different materials (metals or alloys) for the central circular core portion 51a and the outer ring portion 51b surrounding the core portion 51a. As shown in Figures 2(a) and 2(b), the clad coin 52 is formed by inserting the core portion 51a into the ring portion 51b, using different materials (metals or alloys) for the core portion 52a and the surface layer 52b covering the core portion 52a. For example, the clad coin 52 may be formed by plating a circular core portion (base material) 52a and then stamping it. As shown in Figures 2(c) and 2(d), the clad coin 52 may be formed by punching a three-layer plate into a circle, forming the surface layer 52b, the core portion 52a, and the surface layer 52b, and then stamping it. As shown in Figures 3(a) and 3(b), the bicolor clad coin 53 is formed by fitting the core part 53a into the ring part 53b, which are made of different materials (metals or alloys) for the central circular core part 53a having the clad coin structure and the outer ring part 53b surrounding the core part 53a. The core part 53a in the bicolor clad coin 53 is formed by using different materials (metals or alloys) for the core material 53a1 and the surface layer 53a2 covering the core material 53a1. The surface layer 53a2 of the core part 53a may be plated, or may be punched into a circular shape from a three-layer plate.

[0041] Hereinafter, the central part (core part) and outer edge part (ring part) of a bicolor coin or a bicolor clad coin may be referred to as the inner part and outer part of the bimetal coin, respectively.

[0042] In the following description, the same reference numerals will be used appropriately for components having the same or similar functions in common across different embodiments and drawings, and repeated description of those components will be omitted as appropriate.

[0043] (First embodiment) In the first embodiment, a high-frequency magnetic sensor is placed at a position where the inner and outer parts of the bimetal coin pass, and the positions of the inner and outer parts are estimated from the sensor output from the time the coin arrives until it passes, and their material features are obtained, thereby enabling material detection of the bimetal coin using a single sensor.

[0044] 4 and 5 are schematic diagrams illustrating the configuration of the coin identification device according to the first embodiment, where FIG. 4 is a diagram of the coin transport path seen from above and FIG. 5 is a cross-sectional view taken along line AB in FIG. 4. XYZ coordinate systems which are orthogonal to each other are shown in FIGS. 4 and 5. As shown in FIGS. 4 and 5, the coin identification device 1 according to this embodiment includes a single magnetic sensor 10 arranged on the transport path 110 of the coin processing device.

[0045] The coins C are transported one by one at intervals in the transport direction (+X direction in FIG. 4) on a transport path 110 of the coin processing device by a transport means (not shown, for example, fins) of the coin processing device. The transport path 110 has a smooth transport surface 111 that supports the underside of the coin C, and a guide surface 112 that contacts the circumferential surface of the coin C and guides the coin C to one side. The transport surface 111 is parallel to the XY plane in FIG. 4, and the coin C is transported on the transport surface 111 in a state where it is biased to the end of the transport path 110 on the guide surface 112 side (in the -Y direction in FIG. 4), i.e., in a state where it is in contact with the guide surface 112.

[0046] The magnetic sensor 10 includes a resonance coil 11 (hereinafter, sometimes simply referred to as coil 11) disposed below the conveyance path 110 (in the -Z direction in FIG. 5 ) and detects the magnetic characteristics of the coin C conveyed along the conveyance path 110. The magnetic sensor 10 including the resonance coil 11 can apply a higher frequency signal to the coil 11 than a reflective magnetic sensor, and even in this case, the material (material feature quantity) of each part of the coin can be detected from the output of the magnetic sensor 10, as will be described later. Furthermore, the control circuit for the magnetic sensor 10 does not require a more complex circuit than conventional circuits; for example, a circuit for a ring detection sensor or core detection sensor disclosed in Patent Document 1 can be used. Therefore, in this embodiment, bimetal coins can be detected with a simpler sensor and circuit configuration than conventional ones, thereby saving space and reducing costs.

[0047] 5, the magnetic sensor 10 is equipped with a cylindrical pot core 12. The pot core 12 is a core made of a magnetic material that is E-shaped in cross section, and a winding is wound around the central axis of the pot core 12 to form the coil 11. In the coin C conveyance direction, the width of the coil 11 (width in the X direction) is designed to be smaller than the widths of the inner part Ba and outer part Bb of the bimetal coin B.

[0048] The magnetic sensor 10 generates a magnetic field (magnetic flux) in the conveyance path 110 in accordance with an oscillation frequency provided by a resonant circuit (see FIG. 13 described later), and detects changes in the magnetic field when a coin C passes through the conveyance path 110. The resonant circuit is connected to the coil 11 and resonates together 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. This allows the material near the surface of the coin C to be effectively detected, making it possible to more effectively detect bimetal coins, especially bicolor clad coins. Furthermore, the ability to detect materials in a narrow area allows the resolution of the magnetic sensor 10 to be improved.

[0049] The coil 11 of the magnetic sensor 10 is disposed with a gap between it and the guide surface 112 in the width direction of the conveying path 110. Therefore, when a coin C is conveyed while being biased toward the guide surface 112, one outer edge, the center, and the other outer edge of the coin C pass over the coil 11 (within the magnetic field generated by the coil 11) in sequence, and the output signal of the magnetic sensor 10 also changes. In other words, the magnetic sensor 10 outputs a signal according to the material of the outer edge and center of the coin C. In this way, the magnetic sensor 10 detects the material of the inner part Ba and the outer part Bb of the bimetal coin B. If the coin C is made of a non-magnetic material, when the coin C arrives on the coil 11, the output of the magnetic sensor 10 attenuates (the amplitude decreases), and the higher the conductivity of the coin C, the greater the attenuation rate of the output.

[0050] Fig. 6 is a block diagram illustrating an example of the configuration of the coin recognition device according to the first embodiment. As shown in Fig. 6, the coin recognition device 1 according to this embodiment includes, in addition to the magnetic sensor 10, a first waveform generation unit 21, a first material detection unit 22, a second material detection unit 23, and a recognition processing unit 24. The first waveform generation unit 21, the first material detection unit 22, the second material detection unit 23, and the recognition processing unit 24 function by the control unit 20, which will be described later, executing corresponding programs.

[0051] The first waveform generating unit 21 generates first waveform data representing the material feature quantity from the output of the magnetic sensor 10. More specifically, the first waveform generating unit 21 generates the first waveform data based on the output of the magnetic sensor 10 at different successive timings. Therefore, the first waveform data indicates a temporal change in the material feature quantity. 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 quantity is a feature quantity that changes depending on the material of the coin, particularly the surface material.

[0052] FIG. 7 is a graph showing a schematic example of first waveform data generated by the first waveform generating unit. FIG. 7 shows an example of when a bimetal coin is detected, and the top part shows a side cross section of bimetal coin B (inner part Ba and outer part Bb) at the corresponding position on the graph. As shown in FIG. 7, the first waveform data is data that represents material feature amounts over time, and the horizontal axis of FIG. 7 represents the time direction. However, since the magnetic sensor 10 normally detects coins that are transported at a predetermined speed, the first waveform data is also data that represents material feature amounts over position in the coin transport direction, and in this case, the horizontal axis of FIG. 7 represents the position in the coin transport direction.

[0053] 7, the material feature tends to show extreme values ​​or become stable points where the value hardly fluctuates at the entry point of the coin (bimetal coin B), the center of the outer part Bb of the bimetal coin B, the joint where the inner part Ba of the bimetal coin B joins the outer part Bb, and the center of the coin (inner part Ba). Utilizing this characteristic, the positions of the inner part Ba and outer part Bb of the bimetal coin B can be detected from the material feature.

[0054] FIG. 8 is a graph showing the relationship between the material (conductivity) of a coin and the material feature amount. The first waveform data is generated, for example, by the following method. When a coin is transported near the resonant coil 11, the magnetic sensor 10 outputs a change in the voltage of the resonant circuit and a change in the oscillation frequency of the resonant circuit. These change depending on the material of the coin, particularly its conductivity. Furthermore, the material feature amount changes depending on the conductivity of the coin. From these facts, the material feature amount can be obtained from the output of the magnetic sensor 10. In other words, 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. There is also a predetermined relationship between the material (conductivity) of the coin and the material feature amount. Specifically, as shown in FIG. 8, the higher 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 resonant circuit and the material (conductivity) of the coin, and a table showing the relationship between the material (conductivity) of the coin and the material characteristic amount are prepared in advance, and the material (conductivity) of the coin is determined by comparing the output of the magnetic sensor 10, i.e., the voltage and oscillation frequency of the resonant circuit, with the former table, and the material characteristic amount of the coin is determined by comparing the material (conductivity) with the latter table.The first waveform data can then be generated by using the voltage and oscillation frequency of the resonant circuit related to the output of the magnetic sensor 10 at successive different timings and these tables to determine the material characteristic amount at each timing.The relationship shown in FIG. 8 was obtained in advance by collecting data using various coins with different materials (conductivities).

[0055] The first material detection unit 22 detects a first material feature value corresponding to the outer edge of the coin, in this case the outer part of the bimetal coin, from the first waveform data. The second material detection unit 23 detects a second material feature value corresponding to the center of the coin, in this case the inner part of the bimetal coin, from the first waveform data. Because the first waveform data has the property of exhibiting characteristic changes depending on each part of the bimetal coin, as described above, the first material detection unit 22 can accurately detect the material feature value of the point corresponding to the outer part of the bimetal coin as the first material feature value, and the second material detection unit 23 can accurately detect the material feature value of the point corresponding to the inner part of the bimetal coin as the second material feature value.

[0056] The recognition processing unit 24 performs a coin recognition process based on the first material characteristic amount and the second material characteristic amount detected by the first material detection unit 22 and the second material detection unit 23, respectively. The first material characteristic amount and the second material characteristic amount accurately indicate the material characteristic amount at the points corresponding to the outer part and the inner part of the bimetal coin, respectively, and therefore the recognition processing unit 24 can accurately recognize the bimetal coin.

[0057] For example, the recognition processing unit 24 may determine whether a coin is a bimetal coin based on the first material characteristic amount and the second material characteristic amount, or may determine the materials of the outer and inner parts of the bimetal coin.

[0058] As described above, in this embodiment, it is possible to accurately identify bimetal coins with a simpler configuration than conventional methods.

[0059] Fig. 9 is a block diagram illustrating the configuration of a first material detection unit according to the first embodiment. As shown in Fig. 9, the first material detection unit 22 may include a first-order differential processing unit 22a that performs differential processing on the first waveform data to generate first-order differential data, and a second-order differential processing unit 22b that performs differential processing on the first-order differential data to generate second-order differential data, and may detect a first material feature amount based on the first-order differential data and the second-order differential data.

[0060] FIG. 10 is a graph showing a schematic representation of another example of first waveform data generated by the first waveform generating unit. FIG. 10 shows an example of a case where a bimetal coin is detected. FIG. 11 is a graph showing a schematic representation of an example of the first waveform data, its first-order differential data, and its second-order differential data. FIG. 11 shows an example of a case where a bimetal coin is detected, and the top of the graph shows a side cross section of bimetal coin B (inner part Ba and outer part Bb) at the corresponding position on the graph. The first waveform data in FIG. 11 corresponds to the first waveform data sandwiched between the dashed lines in FIG. 10. In the example shown in FIG. 10, the first waveform data shows a stable region with small fluctuations in values ​​in the region corresponding to the center of the outer part and the center of the inner part of the bimetal coin (the region surrounded by circles in the figure).

[0061] As shown in FIG. 11 , the first-order differential data represents the gradient information (the gradient of the waveform) of the first waveform data, and the second-order differential data represents the gradient strength (the rate of change in the gradient of the waveform) and gradient direction (whether the waveform rotates clockwise or counterclockwise) of the first waveform data. When the sign of the second-order differential data changes, the rotation direction of the waveform of the first waveform data changes. Therefore, the gradient of the waveform of the first waveform data can be quantified and its shape can be evaluated. In addition, in a bimetal coin B whose inner portion Ba and outer portion Bb are made of different materials, a stable region exists in the region corresponding to the outer portion Bb, where the gradient of the waveform is small and the first waveform data transitions stably. Therefore, by including the first-order differential processing unit 22a and the second-order differential processing unit 22b, the first material characteristic amount can be more accurately obtained from the material characteristic amount of the stable region, enabling highly accurate detection of the material characteristic of the outer portion Bb.

[0062] The first-order differential processing unit 22a may simultaneously perform differential processing on the first waveform data and noise removal (e.g., 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 (e.g., sine wave filter correction processing) on ​​the first-order differential data.

[0063] 11, the first material detection unit 22 may detect a first point P1 where the differential value in the first-order differential data is less than a predetermined first threshold value Th1 and the differential value in the second-order differential data exceeds a predetermined second threshold value Th2. This makes it possible to prevent an area including an extreme value in the first waveform data due to the joint of the bimetal coin B (see FIG. 7) from being mistakenly detected as a stable area corresponding to the outer part Bb of the bimetal coin B. This makes it possible to more reliably detect the stable area corresponding to the outer part Bb of the bimetal coin B, thereby enabling more accurate detection of the material characteristics of the outer part Bb.

[0064] The first threshold value Th1 can be set as appropriate, for example, to 30 to 50% (e.g., 40%) of the minimum differential value of the first-order differential data (e.g., Min in FIG. 11). By setting it to 30 to 50%, it is possible to reduce the influence of fluctuations in the coin conveyance speed, so that the material characteristics of the outer part Bb can be detected with higher accuracy even when the coin conveyance speed fluctuates. The second threshold value Th2 can be set as appropriate, for example, to 5 to 25% (e.g., 10%) of the maximum differential value of the second-order differential data (e.g., Max in FIG. 11).

[0065] More specifically, the first material detection unit 22 may search for points where the differential value is less than the first threshold value Th1, starting from the beginning of the first-order differential data, and when a point where the differential value is less than the first threshold value Th1 is found, it may determine whether the differential value of the second-order differential data at that point exceeds the second threshold value Th2. If the differential value exceeds the second threshold value Th2, the first material detection unit 22 may determine that point as the first point P1. If the differential value does not exceed the second threshold value Th2, the first material detection unit 22 may repeat the same search process and determination process for the first-order differential data from that point onwards until the first point P1 is detected.

[0066] 11, the first material detection unit 22 may detect a first material characteristic quantity based on a plurality of material characteristic quantities from the first point P1 to the second point P2 in the first waveform data when there are a predetermined number n (n is a natural number equal to or greater than 2) of consecutive points in the first-order differential data where the differential value is less than the first threshold value Th1 from the first point P1 to the second point P2. This effectively prevents momentary noise from being mistakenly detected as a stable region and more effectively detects the stable region of the first waveform data, thereby enabling more accurate detection of the material characteristic of the outer part Bb of the bimetal coin B. The predetermined number n from the first point P1 to the second point P2 can be set as appropriate and may be, for example, 3 to 10 (e.g., 5).

[0067] More specifically, the first material detection unit 22 may perform the following process. That is, first, the point number count is set to 1 (first step). Next, it is determined whether the point number count is smaller than a predetermined number n (second step). If it is smaller (second step: Yes), it is determined whether the differential value of the first-order differential data of the point next to the first point P1 is smaller than a first threshold value Th1 (third step). If it is smaller than the first threshold value Th1 (third step: Yes), the point number count is incremented by 1 (fourth step), and the process returns to the second step. If it is determined in the second step that the point number count is not smaller than the predetermined number n (second step: No), the point last determined in the third step to be smaller than the first threshold value Th1 is set as the second point P2, and the first material feature amount is detected based on the multiple material feature amounts from the first point P1 to the second point P2. If it is determined in the third step that the difference is not less than the first threshold value Th1 (third step: No), the first material detection unit 22 may newly detect the first point P1 as described above.

[0068] 11, the first material detection unit 22 may detect, as the first material feature, the material feature (SMx in FIG. 11) of the first waveform data at the point (Px in FIG. 11) where the differential value is smallest among a predetermined number n of points from the first point P1 to the second point P2 in the first-order differential data. This makes it possible to detect a material feature where the fluctuation of the first waveform data is particularly small even in the stable region of the first waveform data, thereby making it possible to detect the material feature of the outer part Bb of the bimetal coin B with particularly high accuracy.

[0069] The second material detection unit 23 may detect the second material feature corresponding to the center of the coin, in this case the inner part of a bimetal coin, from the first waveform data, for example, by the following method. That is, first, the first waveform data is monitored, and the point at which the material feature has changed by more than a predetermined amount from the material feature at the time of standby before the coin arrives at the magnetic sensor 10 is determined to be the "medium in" point. If a predetermined number of points after the medium in- stalled point show a material feature of the same level as at the time of standby, the first of the predetermined number of consecutive points is determined to be the "medium out" point. Then, the material feature at the center point between the "medium in" point and the "medium out" point is detected as the second material feature.

[0070] Next, the operation of the coin recognition device according to this embodiment will be described with reference to Fig. 12. Fig. 12 is a flowchart illustrating an example of the operation of the coin recognition device according to the first embodiment.

[0071] As shown in FIG. 12, first, the magnetic sensor 10 detects the magnetic characteristics of the coin being transported (step S11).

[0072] Next, the first waveform generating unit 21 generates first waveform data representing the material feature quantity from the output of the magnetic sensor 10 (step S12).

[0073] Next, the first material detection unit 22 detects a first material feature corresponding to the outer edge of the coin (the outer part of the bimetal coin) from the first waveform data generated in step S12 (step S13).

[0074] Next, the second material detection unit 23 detects a second material feature corresponding to the center of the coin (the inner part of the bimetal coin) from the first waveform data generated in step S12 (step S14).

[0075] The timing of steps S13 and S14 may be reversed or may be simultaneous.

[0076] Thereafter, the recognition processing unit 24 performs a recognition process for the coin based on the first material characteristic detected in step S13 and the second material characteristic detected in step S14 (step S15), and the operation of the coin recognition device 1 is completed.

[0077] 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.

[0078] In this case, first, the first-order differential processing unit 22a performs differential processing on the first waveform data to generate first-order differential data (step S21).

[0079] Next, the second-order differential processing unit 22b performs differential processing on the first-order differential data to generate second-order differential data (step S22).

[0080] Next, the first material detection unit 22 detects a first point P1 where the differential value in the first-order differential data is less than a predetermined first threshold value and the differential value in the second-order differential data exceeds a predetermined second threshold value (step S23).

[0081] Next, the first material detection unit 22 determines whether or not there are a predetermined number n of consecutive points from the first point P1 to the second point P2 where the differential value is less than the first threshold (step S24). If there are the predetermined number of consecutive points (step S24: Yes), the first material feature is detected based on the multiple material feature values ​​from the first point P1 to the second point P2 in the first waveform data (step S25), and the process of detecting the first material feature value ends.

[0082] In step S25, for example, the first material detection unit 22 detects the material feature SMx of the first waveform data at point Px, which has the smallest differential value among the points from the first point P1 to the second point P2 in the first-order differential data, as the first material feature.

[0083] In step S24, if there are no points where the differential value is less than the first threshold value in succession for the predetermined number n from the first point P1 (step S24: No), the first material detection unit 22 repeats the processes of steps S23 and S24 until there are n consecutive points where the differential value is less than the first threshold value from the first point P1 to the second point P2. That is, the first material detection unit 22 detects a new first point P1 that satisfies the above condition from the data after the points detected up to step S24 (step S23), and determines whether there are n consecutive points where the differential value is less than the first threshold value in succession from the new first point P1 to the second point P2 (step S24).

[0084] Next, a more specific embodiment of the coin discriminating device 1 according to this embodiment will be described.

[0085] 14 is a block diagram illustrating an example of the configuration of the magnetic sensor according to the first embodiment. In addition to the resonant coil 11, the magnetic sensor 10 may further include a resonant circuit 13 and a detection circuit 14, as shown in FIG.

[0086] The resonant circuit (LC resonant circuit) 13 is connected to the coil 11 and includes an AC power supply connected to the coil 11 for exciting the coil 11, and a capacitor connected in parallel to the coil 11. The frequency of the AC power supply is set to an oscillation frequency (resonant frequency) specific to the coil 11 and the resonant circuit 13, and the resonant circuit 13, as described above, resonates with the coil 11 at a high frequency (specifically, 1 to 2 MHz, more specifically, 1.4 to 1.5 MHz) suitable for detecting the material of the coin. In this way, the resonant circuit 13 applies an AC voltage (sine wave) to the coil 11, generating an AC magnetic flux in the conveyance path 110. When a coin enters this magnetic flux, an induced current (eddy current) is generated in the coin, causing the magnetic flux to change. As a result, the output (voltage and oscillation frequency) of the resonant circuit 13 changes in response to this change in magnetic flux.

[0087] The detection circuit 14 is connected to the resonant circuit 13 and includes an amplifier circuit that amplifies the output of the resonant circuit 13, and a DC conversion circuit that converts the output (AC) of the amplifier circuit into DC.

[0088] Fig. 15 is a block diagram illustrating another example of the configuration of the coin recognition device according to the first embodiment. In addition to the above-mentioned magnetic sensor 10, first waveform generating unit 21, first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), second material detection unit 23, and recognition processing unit 24, the coin recognition device 1 may further include an AD converter 30, a storage unit 40, and a control unit (arithmetic processing unit) 20, as shown in Fig. 15.

[0089] The AD converter 30 is connected to the magnetic sensor 10 (detection circuit 14), samples the analog signal input from the detection circuit 14 of the magnetic sensor 10 at predetermined time intervals, and converts it into a time series (digital signal). Sampling by the AD converter 30 is performed from before the coin arrives at the magnetic sensor 10 until after it has passed through the magnetic sensor 10.

[0090] The memory unit 40 is composed of storage devices such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, HDD (Hard Disk Drive), SSD (Solid State Drive), etc., and is configured to be able to write and read various data (e.g., thresholds, tables, reference data, etc.) and programs.

[0091] The control unit 20 controls each part of the coin identification device 1, and is composed of, for example, a program for realizing various processes, a CPU (Central Processing Unit) that executes the program, and various hardware (for example, an FPGA (Field Programmable Gate Array)) controlled by the CPU.

[0092] The control unit 20 acquires the sampling data (time series) sampled by the AD converter 30 from the AD converter 30 and stores it in the storage unit 40.

[0093] The control unit 20 can function as the above-mentioned first waveform generating unit 21, first material detecting unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), second material detecting unit 23 and identification processing unit 24 by executing corresponding programs.

[0094] The first waveform generating section 21 generates first waveform data based on the sampling data stored in the storage section 40, that is, the time series obtained by sampling the output of the magnetic sensor 10 at predetermined time intervals.

[0095] The memory unit 40 may store reference data relating to the material feature quantities of the outer and inner parts of a genuine bimetal coin, and the identification processing unit 24 may compare the first and second material feature quantities detected by the first material detection unit 22 and the second material detection unit 23, respectively, with this reference data. This makes it possible to determine with high accuracy the denomination (and authenticity) of the coin and whether the coin is a bimetal coin (particularly a bicolor coin or a bicolor clad coin).

[0096] In this case, the reference data may include an acceptable range of material features set based on material features detected from the outer and inner parts of the genuine bimetal coin, and the identification processing unit 24 may compare the first material feature and the second material feature with the corresponding acceptable range, i.e., determine whether they are included in the corresponding acceptable range or not.

[0097] In addition, the identification processing unit 24 may determine that the coin is a genuine bimetal coin (e.g., a bicolor coin or a bicolor clad coin) if the first material characteristic amount and the second material characteristic amount match the reference data.

[0098] More specifically, the identification processing unit 24 may compare the first material feature and the second material feature with their corresponding tolerance ranges, and if they are within the corresponding tolerance ranges, determine that the coin is a genuine bimetal coin (e.g., a bicolor coin or a bicolor clad coin).

[0099] The recognition processing unit 24 may also determine the denomination of the coin by comparing the first material characteristic amount and the second material characteristic amount with reference data.

[0100] In this case, the reference data may include an acceptable range of material feature values ​​for each denomination, which is set based on the material feature values ​​detected from the outer edge and center of a genuine coin, and the recognition processing unit 24 may compare the first material feature value and the second material feature value with the acceptable range for each denomination, i.e., determine whether or not they are included in the acceptable range. If there is a denomination for which both the first material feature value and the second material feature value are included in the acceptable range, the recognition processing unit 24 may determine that the coin is of that denomination.

[0101] The recognition processor 24 may also compare the first material feature value and the second material feature value with each other to determine whether the material of the outer edge of the coin is the same as the material of the center of the coin. This allows the recognition processor 24 to determine whether the coin is made of a single material, i.e., a monometallic coin (if they are the same), or whether it is made of two or more materials, i.e., a bimetallic coin (if they are not the same).

[0102] Next, the operation of the coin identification device according to this embodiment shown in Fig. 15 will be described with reference to Fig. 16. Fig. 16 is a flowchart illustrating another example of the operation of the coin identification device according to the first embodiment.

[0103] As shown in FIG. 16, first, similarly to the case shown in FIG. 12, the magnetic sensor 10 detects the magnetic characteristics of the coin being conveyed (step S11).

[0104] Next, the AD converter 30 samples the output of the magnetic sensor 10 (the analog signal input from the detection circuit 14) and converts it into a time series signal (step S31).

[0105] Next, the first waveform generating section 21 generates first waveform data representing the material feature amount from the sampling data (time series) sampled by the AD converter 30 (step S32).

[0106] Thereafter, similarly to the case shown in FIG. 12, the processes of steps S13 to S15 are executed, and the operation of the coin discriminating device 1 is completed.

[0107] The first material detection unit 22 (step S13) may detect the first material feature amount according to the flow shown in FIG.

[0108] In step S15, as described above, the recognition processor 24 may compare the first and second material feature amounts with reference data, and may determine that the coin is a genuine bimetal coin if the first and second material feature amounts match the reference data. The recognition processor 24 may also determine the denomination of the coin by comparing the first and second material feature amounts with the reference data. Furthermore, the recognition processor 24 may compare the first and second material feature amounts with each other to determine whether the material of the outer edge of the coin is the same as the material of the center of the coin.

[0109] (Second embodiment) This embodiment is substantially the same as the first embodiment, except that it further includes a second waveform generating unit that generates second waveform data, and a second material detecting unit that detects a second material feature using the second waveform data. Fig. 17 is a block diagram illustrating an example of the configuration of a coin identifying device according to the second embodiment.

[0110] As shown in Figure 17, the coin identification device 2 of this embodiment is equipped with a magnetic sensor 10, a first waveform generating unit 21, a first material detection unit 22, a second material detection unit 23, and an identification processing unit 24, as in the first embodiment, and further equipped with a second waveform generating unit 25.

[0111] The first material detection unit 22 may include a first-order differential processing unit 22a and a second-order differential processing unit 22b, similar to the first embodiment.

[0112] The second waveform generating unit 25 generates second waveform data representing the distance feature quantity from the output of the magnetic sensor 10. More specifically, the second waveform generating unit 25 generates the second waveform data based on the output of the magnetic sensor 10 at different consecutive timings. Therefore, the second waveform data indicates a temporal change in the distance feature quantity. 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 generating unit 21 and the second waveform generating unit 25 can 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. The distance feature quantity changes depending on the unevenness of the coin, particularly the unevenness of the surface on the magnetic sensor 10 side, and changes depending on the distance (spacing) between the coin and the resonant coil 11 of the magnetic sensor 10. The unevenness may be due to, for example, the marking or edging of the coin.

[0113] FIG. 18 is a graph showing a schematic example of second waveform data generated by the second waveform generating unit. FIG. 18 shows an example of when a bimetal coin is detected, and the top part shows a side cross section of bimetal coin B (inner part Ba and outer part Bb) at the corresponding position on the graph. As shown in FIG. 18, the second waveform data is data representing distance feature amounts with respect to time, and the horizontal axis of FIG. 18 represents the time direction. However, since the magnetic sensor 10 normally detects coins transported at a predetermined speed, the second waveform data is also data representing distance feature amounts with respect to the position in the coin transport direction, and in this case, the horizontal axis of FIG. 18 represents the position in the coin transport direction.

[0114] 18, the distance feature value tends to change suddenly immediately after the medium is inserted and immediately before the medium is removed. That is, the distance feature value increases suddenly when the coin (bimetal coin B) begins to overlap the resonance coil 11, and decreases suddenly immediately before the coin (bimetal coin B) passes through the resonance coil 11. This characteristic is utilized to detect the positions of both ends of the coin from the distance feature value. The distance feature value also tends to decrease at the engagement portion of the bimetal coin B.

[0115] FIG. 19 is a graph showing the relationship between the distance between the coin and the resonance coil and the distance feature amount. The second waveform data is generated, for example, by the following method. When a coin is transported near the resonance coil 11, 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. These change depending on the distance (spacing) between the coin and the resonance coil 11. The distance feature amount also changes depending on the distance between the coin and the resonance coil 11. From these facts, 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 resonance circuit, the oscillation frequency of the resonance circuit, and the distance between the coin and the resonance coil 11. There is also a predetermined relationship between the distance between the coin and the resonance coil 11 and the distance feature amount. Specifically, as shown in FIG. 19, the greater the distance between the coin and the resonance coil 11, the smaller the distance feature amount. This is because as the coin moves farther from the resonance coil 11, it moves farther from the magnetic field generated by the coil 11. Therefore, a table based on the three-way relationship between the voltage and oscillation frequency of the resonant circuit and the distance between the coin and the resonance coil 11, and a table showing the relationship between the distance between the coin and the resonance coil 11 and the distance feature value are prepared in advance. The distance between the coin and the resonance coil 11 is determined by comparing the output of the magnetic sensor 10, i.e., the voltage and oscillation frequency of the resonant circuit, with the former table, and the distance feature value of the coin is determined by comparing the distance between the coin and the resonance coil 11 with the latter table. The second waveform data can be generated by using the voltage and oscillation frequency of the resonant circuit associated with the output of the magnetic sensor 10 at successive different timings and these tables to determine the distance feature value at each timing. The relationship shown in FIG. 19 was obtained by collecting data while varying the distance between the resonance coil 11 and the coin surface, for example, 0 mm, 0.2 mm, and 0.3 mm. Furthermore, a table showing such relationships is prepared for each denomination, and the table for the corresponding denomination is used to determine the distance feature value.

[0116] In this embodiment, the second material detection unit 23 detects a second material feature corresponding to the center of the coin, in this case the inner part of a bimetal coin, from the first waveform data generated by the first waveform generation unit 21 based on the second waveform data generated by the second waveform generation unit 25.

[0117] FIG. 20 is a graph showing another example of the first waveform data generated by the first waveform generator. Similar to FIG. 10, FIG. 20 illustrates an example in which a bimetal coin is detected. For example, as shown in FIG. 18, the second material detection unit 23 may detect a third point P3 and a fourth point P4 in the second waveform data, which correspond to both ends of the coin in the conveyance direction. More specifically, the second material detection unit 23 may search for points where the distance feature exceeds a predetermined third threshold Th2, starting from the beginning and ending of the second waveform data, and determine the points where the distance feature exceeds the third threshold Th2 as the third point P3 and the fourth point P4, respectively. Then, as shown in FIG. 20, the second material detection unit 23 may detect, as the second material feature, the material feature of a center point Pc between a fifth point P5 and a sixth point P6, which correspond to the third point P3 and the fourth point P4, in the first waveform data. This enables the second material feature to be detected from the overall image of the first and second waveform data, enabling the material feature of the inner part of the bimetal coin to be detected with high accuracy.

[0118] Next, the operation of the coin recognition device according to this embodiment will be described with reference to Fig. 21. Fig. 21 is a flowchart illustrating an example of the operation of the coin recognition device according to the second embodiment.

[0119] As shown in FIG. 21, first, the processes of steps S11 to S12 are executed in the same manner as in the first embodiment.

[0120] Next, the second waveform generating unit 25 generates second waveform data representing the distance feature amount from the output of the magnetic sensor 10 (step S41).

[0121] The timing of step S12 and step S41 may be reversed or may be simultaneous.

[0122] Next, similarly to 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.

[0123] Next, the second material detection unit 23 detects a second material feature 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).

[0124] The timing of steps S13 and S42 may be reversed or may be simultaneous.

[0125] Thereafter, the recognition processing unit 24 performs a recognition process on the coin based on the first material characteristic detected in step S13 and the second material characteristic detected in step S42 (step S15), and the operation of the coin recognition device 2 is completed.

[0126] 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.

[0127] 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 conveying direction using the second waveform data (step S51).

[0128] Next, the second material detection unit 23 detects the fifth point P5 and the sixth point P6 corresponding to the third point P3 and the fourth point P4, respectively, in the first waveform data, and the center point Pc between the fifth point P5 and the sixth point P6, and detects the material feature of the center point Pc as the second material feature (step S52), and the process of detecting the second material feature is completed.

[0129] Next, a more specific embodiment of the coin discriminating device 2 according to this embodiment will be described.

[0130] In this embodiment, similarly to the first embodiment, the magnetic sensor 10 may further include a resonant circuit 13 and a detection circuit 14 in addition to the resonant coil 11, as shown in FIG.

[0131] Fig. 23 is a block diagram illustrating another example of the configuration of the coin recognition device according to the second embodiment. In addition to the above-mentioned magnetic sensor 10, first waveform generating unit 21, second waveform generating unit 25, first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), second material detection unit 23, and recognition processing unit 24, the coin recognition device 2 may further include an AD converter 30, a storage unit 40, and a control unit (arithmetic processing unit) 20, as in the first embodiment, as shown in Fig. 23.

[0132] By executing corresponding programs, the control unit 20 can function as the above-mentioned first waveform generating unit 21, second waveform generating unit 25, first material detecting unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b), second material detecting unit 23 and identification processing unit 24.

[0133] The second waveform generating section 25 generates second waveform data based on the sampling data stored in the storage section 40, that is, the time series obtained by sampling the output of the magnetic sensor 10 at predetermined time intervals.

[0134] Next, the operation of the coin recognition device according to this 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 recognition device according to the second embodiment.

[0135] First, similarly to the first embodiment, the processes of steps S11, S31 and S32 are executed.

[0136] Next, the second waveform generating section 25 generates second waveform data representing distance feature amounts from the sampling data (time series) sampled by the AD converter 30 (step S61).

[0137] The timing of steps S32 and S61 may be reversed or may be simultaneous.

[0138] Thereafter, similarly to the case shown in FIG. 21, the processes of steps S13, S42 and S15 are executed, and the operation of the coin discriminating device 2 is completed.

[0139] The first material detection unit 22 (step S13) may detect the first material feature amount according to the flow shown in FIG.

[0140] Alternatively, the second material detection unit 23 (step S42) may detect the second material feature amount according to the flow shown in FIG.

[0141] In step S15, as in the first embodiment, the recognition processor 24 may compare the first and second material feature amounts with reference data, and may determine that the coin is a genuine bimetal coin if the first and second material feature amounts match the reference data. The recognition processor 24 may also determine the denomination of the coin by comparing the first and second material feature amounts with the reference data. Furthermore, the recognition processor 24 may compare the first and second material feature amounts with each other to determine whether the material of the outer edge of the coin is the same as the material of the center of the coin.

[0142] As described above, the above embodiment includes a magnetic sensor 10 that includes a resonant coil 11 and detects the magnetic characteristics of the coin being transported, a first waveform generation unit 21 that generates first waveform data representing a material feature from the output of the magnetic sensor 10, a first material detection unit 22 that detects the first material feature corresponding to the outer edge of the coin from the first waveform data, a second material detection unit 23 that detects the second material feature corresponding to the center of the coin from the first waveform data, and an identification processing unit 24 that performs an identification process on the coin based on the first material feature and the second material feature, making it possible to accurately identify bimetal coins with a simpler configuration.

[0143] Modifications of each embodiment will be described below.

[0144] While the above embodiment has been described primarily for identifying bimetallic coins, the device and method of the present disclosure can also identify monometallic coins. The device and method use a stable region with a small slope in the waveform of the first waveform data, rather than differences in material, so it is possible to detect the position of the outer edge of a coin regardless of the coin's structure, and can also accurately detect the material characteristics of the outer edge of coins with internal structures different from those of bicolor coins, such as monometallic coins and clad coins.

[0145] 25 is a graph showing an example of the first waveform data, its first-order differential data, and its second-order differential data. FIG. 25 shows an example of detecting a monometal coin, with a side cross section of the monometal coin M shown at the top at the corresponding position on the graph. As shown in FIG. 25, even for a monometal coin M, the first material detection unit 22 (first-order differential processing unit 22a and second-order differential processing unit 22b) can detect a first point P1 and a second point P2 based on the first waveform data, the first-order differential data, and the second-order differential data, just as in the case shown in FIG. 11. The material feature SMx at point Px, where the differential value in the first-order differential data is smallest, can be detected as the first material feature corresponding to the outer edge of the monometal coin M.

[0146] Also, as in the cases shown in Figures 18 and 20, the second material detection unit 23 can detect the third point P3 and the fourth point P4 based on the second waveform data, and detect the material feature at the center point Pc between the fifth point P5 and the sixth point P6 based on the first waveform data as the second material feature corresponding to the center of the monometal coin M.

[0147] In the above embodiment, the magnetic sensor 10 equipped with the resonant coil 11 is used alone as a sensor, but the magnetic sensor 10 may be used in combination with a material sensor (magnetic sensor) of another frequency. This makes it possible to detect monometal coins, plated clad coins, three-layer clad coins, two-color clad coins, etc.

[0148] In the above embodiment, the first waveform data and the second waveform data are generated based on a time series obtained by sampling the output of the magnetic sensor 10 at predetermined time intervals, i.e., based on data obtained by sampling the output of the magnetic sensor 10 over time. However, the diameter of the coin may be detected using a diameter detection sensor (e.g., a magnetic sensor or an optical sensor) that detects the diameter of the coin, and the first waveform data and the second waveform data may be generated based on data obtained by sampling the output of the magnetic sensor 10 in response to changes in the coin diameter (output of the diameter detection sensor). This makes it possible to collect data similar to that obtained when the coin is being transported normally at a constant speed, even if the coin transport speed fluctuates or if the coin temporarily stops on the magnetic sensor 10 and then starts moving again. In this case, a time series obtained by sampling the output of the magnetic sensor 10 at predetermined time intervals may be used, and only necessary elements may be selected from the time series based on the output of the diameter detection sensor.

[0149] In the above embodiment, the first waveform data, first-order differential data, and second-order differential data are processed in order from the beginning, respectively. However, the first waveform data, first-order differential data, and second-order differential data may also be processed in order from the end, respectively. Even in this case, it is possible to detect the material features of the outer edge and center of the coin using a similar method.

[0150] In the above embodiment, a case where the coin is made of a non-magnetic material has been described. However, according to the device and method disclosed herein, even if the coin contains a magnetic material (e.g., a ferromagnetic material), it is possible to detect material characteristics of the outer edge and center of the coin using a similar technique, and identify the coin. For example, each part or at least a part of a bimetal coin may be made of a magnetic material (e.g., a ferromagnetic material). Even in this case, for example, the above-mentioned threshold value can be used as is.

[0151] Although the embodiments have been described above with reference to the drawings, the present disclosure is not limited to the above-described embodiments. Furthermore, the configurations of the respective embodiments may be appropriately combined or modified without departing from the spirit and scope of the present disclosure. [Industrial Applicability]

[0152] As described above, the present disclosure provides a useful technology for accurately identifying bimetal coins with a simpler configuration. [Explanation of symbols]

[0153] 1, 2: Coin identification device 10: Magnetic sensor 11: Resonant coil 12: Pot Core 13: Resonant circuit 14: Detection circuit 20: Control unit (arithmetic processing unit) 21: First waveform generation section 22: First material detection unit 22a: First-order differential processing section 22b: Second-order differential processing section 23: Second material detection unit 24: Identification processing unit 25:Second waveform generation section 30: AD converter 40: Storage part 51: Bicolor coins 51a, 53a: Core part 51b, 53b: Ring part 52: Clad coins 52a, 53a1: Core material 52b, 53a2: Surface layer 53: Bicolor clad coins 110: Transport path 111:Transport surface 112: Guide surface B: Bimetallic coin Ba: Inner part Bb: outer part C: Coin M: Monometal coin

Claims

1. a magnetic sensor including a resonant coil for detecting the magnetic characteristics of the coin being transported; a first waveform generating unit that generates first waveform data representing a material feature, which is a feature that changes depending on the material of the coin, from the output of the magnetic sensor; a second waveform generating unit that generates second waveform data representing a distance feature amount, which is a feature amount that changes depending on the unevenness of the coin, from the output of the magnetic sensor; a first material detection unit that detects a first material feature corresponding to an outer edge of the coin from the first waveform data; a second material detection unit that detects a second material feature corresponding to a center portion of the coin from the first waveform data; a recognition processing unit that performs a coin recognition process based on the first material feature amount and the second material feature amount; A coin identification device comprising:

2. The coin identification device according to claim 1, characterized in that the first material detection unit includes a first-order differential processing unit that differentiates the first waveform data to generate first-order differential data, and a second-order differential processing unit that differentiates the first-order differential data to generate second-order differential data, and detects the first material feature based on the first-order differential data and the second-order differential data.

3. The coin identification device according to claim 2, characterized in that the first material detection unit detects a first point where a differential value in the first-order differential data is less than a predetermined first threshold value and where a differential value in the second-order differential data exceeds a predetermined second threshold value.

4. The coin identification device described in claim 3, characterized in that the first material detection unit detects the first material feature based on multiple material feature values ​​from the first point to the second point in the first waveform data when a predetermined number of consecutive points in the first-order differential data where the differential value is less than the first threshold value are found from the first point to the second point.

5. The coin identification device according to claim 4, characterized in that the first material detection unit detects the material feature of the first waveform data at the point in the first derivative data where the differential value is smallest among the predetermined number of points from the first point to the second point as the first material feature.

6. A coin identification device as described in any one of claims 1 to 5, characterized in that the second material detection unit detects a third point and a fourth point corresponding to both ends of the coin in the conveying direction in the second waveform data, and detects a material feature value of a center point between a fifth point and a sixth point corresponding to the third point and the fourth point, respectively, in the first waveform data as the second material feature value.

7. The coin identification device according to any one of claims 1 to 6, 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.

8. Further, a memory unit is provided for storing reference data relating to material characteristics of the outer edge and the center of the genuine bimetal coin, 8. The coin identifying device according to claim 1, wherein the identifying processing unit compares the first material feature amount and the second material feature amount with the reference data.

9. The coin identification device according to claim 8, wherein the identification processing unit determines that the coin is a genuine bimetal coin when the first material feature and the second material feature match the reference data.

10. The coin identification device according to claim 8 or 9, 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.

11. A coin identification method using a magnetic sensor, comprising: the magnetic sensor includes a resonant coil; detecting magnetic characteristics of the coin being conveyed by the magnetic sensor; generating, from the output of the magnetic sensor, first waveform data representing a material feature, which is a feature that changes depending on the material of the coin; generating second waveform data representing a distance feature quantity, which is a feature quantity that changes depending on the unevenness of the coin, from the output of the magnetic sensor; detecting a first material feature corresponding to an outer edge of the coin from the first waveform data; detecting a second material feature corresponding to a center portion of the coin from the first waveform data; performing a coin identification process based on the first material feature amount and the second material feature amount; A coin identification method comprising:

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