JPH0592886U - Automatic transaction equipment - Google Patents

Abstract

(57) [Abstract] [Purpose] To provide an automatic transaction device with high reliability against crime prevention by ensuring detection of vandalism such as melting and opening the outer door part by heat. A temperature rise detection unit 6 attached to the outer door 101 portion for detecting a temperature rise of the outer door 101 portion, and an inference portion 7 for judging whether or not there is an abnormality based on the output of the detection portion 6. An automatic transaction device comprising:

Description

[Detailed description of the device]

[0001]

[Industrial applications]

 The present invention relates to a crime prevention mechanism in an automatic transaction device such as a vending machine or a cash dispenser.

[0002]

[Prior Art]

 As a conventional crime prevention mechanism for vending machines, etc., a vibration detection unit is provided to detect vibration due to impact when the vending machine is destroyed or collapsed, and an alarm is issued accordingly. There is something.

[0003]

[Problems to be solved by the device]

 However, recently, vandals such as burners and lasers that do not cause impact have been used to melt and open the outer door portion with heat, and no crime prevention mechanism is provided for such vandalism.

The present invention has been made in view of the above-mentioned points, and enables reliable detection of vandalism such as heat that melts and opens the outer door portion, thereby improving the reliability of crime prevention. It is intended to provide an automatic transaction device with high cost.

[0005]

[Means for Solving the Problems]

 According to the present invention, in order to achieve the above-mentioned object, an automatic transaction device has a temperature rise detection unit attached to an outer door portion for detecting a temperature rise of the outer door portion and an abnormality based on an output of the detection portion. And an inference unit for determining whether or not

[0006]

[Action]

 According to the present invention, when the outer door is melted and opened by heat using a burner, a laser, etc., the temperature rise detection unit detects the temperature rise of the outer door, and the detection output is used by the reasoning unit. It is determined to be abnormal, and an alarm or the like is issued accordingly.

[0007]

【Example】

 Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

FIG. 1 is a block diagram of a main part of an embodiment of a crime prevention mechanism to which the present invention is applied, for example, to a telephone card vending machine.

The crime prevention mechanism of this embodiment includes a temperature rise detection unit 6 that detects a temperature rise and outputs temperature rise data, a vibration detection unit 5 that detects vibration and outputs vibration data, and an automatic vending machine. The operating state detection unit 30 detects an operating state such as whether a coin has been inserted and is on sale, or a coin has not been inserted and is not on sale, and the output of each detection unit 5, 6, 30. An inference device 7 as an inference unit that determines whether or not there is an abnormality by performing fuzzy inference based on the above, and an alarm device 8 that issues an alarm when there is an abnormality based on the determination of this inference device 7. It is basically composed of

FIG. 2 shows the schematic arrangement of the temperature rise detection unit 6, the vibration detection unit 5, and the control unit 110 in the vending machine. The vending machine has an outer door 101 and an inner door at the front opening of the main body 100. The door 102 is provided in double, and the vibration detection unit 5 is provided in the locking portion 104 of the outer door 101. The vibration detectors 5 are provided at four locations in the vicinity of the hook portion, and the controller 110 is provided on the inner surface side of the inner door 102.

The temperature rise detection unit 6 is provided as described above to detect a temperature change near the vending machine, particularly a temperature rise near the mounting shaft support of the outer door 101 and near the hook when locking up and down. However, temperature rise data per unit time, that is, temperature rise rate data is output to the inference device 7 as the temperature rise data. As described above, the temperature rise detecting portion 6 is provided near the mounting shaft support portion of the outer door 101 and near the hook portion at the time of locking the upper and lower sides by melting the outer door 101 with heat using a burner, a laser, or the like. When opening, it is possible that the mounting shaft support part and the hook part are blown out, and the reason is to be able to detect the temperature rise of that part more effectively at that time.

The vibration detection unit 5 detects the vibration applied to the vending machine, particularly the vibration applied to the locking unit 104, and as described later, determines the excitation factor of the vibration based on the vibration state, and detects the normal condition. It determines whether the vibration is normal or abnormal and outputs the corresponding vibration data. As described above, the vibration detection unit 5 is provided in the locking unit 104. In order to open the outer door 101 by breaking the vending machine with a hammer or the like, the locking unit 104 is often broken with a hammer. The reason is to be able to detect vibration efficiently.

The operation state detection unit 30 gives an output corresponding to whether a coin is put into the vending machine and is on sale, or whether the coin is not put into the vending machine and is not on sale. Also, it is shared in the control unit 110.

The inference device 7 is also used by the control unit 110, and the inference device 7 is automatically operated based on the temperature rise rate data from the temperature rise detection unit 6, the vibration data from the vibration detection unit 5, and the output from the operation state detection unit 30. Whether the vibration or temperature rise applied to the vending machine is due to normal factors such as the vibration of a passing vehicle or abnormal factors such as drills, hammers, burners, etc. is explained below. Fuzzy inference to judge.

The alarm device 8 issues an alarm sound or lights an alarm light to issue an alarm when the determination result of the inference device 7 is abnormal.

Next, the configuration of the vibration detector 5 will be described in more detail.

As shown in FIG. 3, the vibration detection unit 5 uses a seismic switch 1 that generates a pulse train by vibration, and a plurality of types of characteristic amounts from pulse waves from the seismic switch 1 as described below. Based on each of these outputs, a feature amount extraction unit 2 for extracting, an inference unit 3 for determining an excitation factor of vibration by executing fuzzy inference with the extracted feature amount as an input, The determination output unit 4 is configured to output the output value corresponding to the type determination result as vibration data to the inference apparatus 7.

The determination result from the determination output unit 4 of the vibration detection device 5, that is, the vibration data corresponds to four types of “none”, “normal”, “abnormal 1” and “abnormal 2”, “None” indicates that there is no vibration, “Normal” indicates that the product was inadvertently bumped by an object or passed by a vehicle, “Abnormal 1” indicates that it was a vibration caused by an earthquake, and “Abnormal”. "2" indicates that the vibration is due to a drill, a hammer, or shaking.

As shown in the flowchart of FIG. 4, the determination output unit 4 determines whether or not there is vibration based on the output of the seismic switch 1 (step 600). The determination result is set to “none” (step 605), and when there is vibration, it is determined whether the total number of pulses extracted by the feature amount extraction unit 2 is 3 or more as described later (step 601). ) When it is less than 3, the judgment result is "normal" (step 604), and when it is 3 or more, it is fuzzy inference by the inference unit 3 (step 602), and "abnormal 1", " The output value corresponding to "abnormality 2" is set as the determination result (step 603).

Hereinafter, the configuration of the vibration detection unit 5 will be described in more detail.

FIG. 5 is a cross-sectional view showing the details of the seismic switch 1. As shown in the figure, the so-called automatic horizontal seismic sensor is well known in the art and is directly related to the gist of the present invention. Since these are not available, the names of each member in the drawings will be pointed out below.

That is, 11 is a case, 12 is a guide, 13 is a plunger, 14 is a base (insert), 15 is a metal fitting, 16 is a cap, 17 is a jig, 18 is a ring, 20 is a case, 21 is a terminal, 22 Is a movable piece, 23 is a contact point, and 24 is a hard sphere.

Next, FIG. 6 is a functional block diagram of the feature quantity extraction unit 2, and the feature quantity is extracted based on the pulse wave P from the seismic switch 1 as shown in FIG.

Therefore, the feature quantity extraction unit 2 includes a counting function 2a, a time counting function 2b, a program function 2c, and a calculation function 2d, respectively, as shown in FIG. It is configured to be delivered.

By the way, in the present embodiment, when the pulse wave P as shown in FIG. 7 is obtained, the feature quantity extraction unit 2 uses the above-mentioned functions to perform the following five types within a predetermined time period. Each feature quantity is extracted.

(1) Number of pulses (2) Maximum value of pulse ON time (3) Maximum value of pulse OFF time (4) Maximum value of ON time ratio (5) Maximum value of OFF time ratio Now, with reference to FIG. When the pulse wave P is detected at the time t ₁ and the first pulse P ₁ rises, the rising state of each pulse within the time period of 3 seconds is examined.

Here, the number of pulses in (1) is 4 because four pulses P ₁ , P ₂ , P ₃ , and P ₄ are detected in this example.

Next, the maximum value of the pulse ON time in (2) is the maximum value among the ON times T ₁ , T ₂ , T ₃ , T _{4 of the four} pulses P ₁ , P ₂ , P ₃ , P _4. Is selected, and T ₂ is the maximum in this example, so the maximum ON time is T ₂ .

Similarly, the maximum value of the pulse OFF time of (3) is selected from among the times T ₅ , T ₆ , and T ₇ between the pulses, and since T ₇ is the maximum in this example, the OFF The maximum time value is T ₇ .

Since the maximum OFF time is measured only for the time between each pulse, the time T ₈ from the fall of the last pulse T ₄ to the end of the time measurement of 3 seconds is not adopted as the OFF time. ..

Next, the maximum value of the ON time ratio of (4) is selected from the maximum ON time ratio calculated by the following equation (1) ON time ratio = current ON time / previous ON time (1). Be done.

Therefore, in this example, the maximum one is selected from T ₂ / T ₁ , T ₃ / T ₂ , and T ₄ / T ₃ .

Further, the maximum value of the OFF time ratio of (5) is selected from the OFF time ratio calculated by the following equation (2) OFF time ratio = current OFF time / previous OFF time (2). ..

Therefore, in this example, the maximum one is selected from T ₆ / T ₅ and T ₇ / T ₆ .

When the number of detected pulses is 1, the maximum value of the ON time ratio in (4) and the maximum value of the OFF time ratio in (5) are set to 0.

The above is the configuration of the seismic switch 1 and the feature amount extraction unit 2. First, referring to the flowcharts of FIGS. 8 to 12, the calculation process procedure of each feature amount in the feature amount extraction unit 2 will be described below. explain.

FIG. 8 is a flowchart showing the procedure of (1) pulse number calculation processing.

In this process, when the program is started, it is checked whether or not the input signal is ON (step 100). If it is ON, the timer is started (step 102), the counter value is 1, and the flag is 1 (Step 104).

Next, it is checked whether or not the timer is in operation (step 106). If the input signal is ON and the flag is 0 during operation of the timer (Y ES in steps 106, 108 and 110), the counter is turned on. Increment (step 112).

Then, when the determination in step 106 becomes NO and the fixed time period ends, the counter value at that time is set as the pulse number (step 116).

Next, FIG. 9 is a flowchart showing a calculation processing procedure of the maximum ON time value and the maximum OFF time value in (2) and (3).

In the figure, the ON time maximum value and the OF F time maximum value are obtained in one step for convenience of description, but they are actually obtained separately. Therefore, in the following description, only the processing procedure for obtaining the ON time maximum value will be described in order to omit redundant description, but the OFF time maximum value can also be obtained in exactly the same processing procedure.

The processing shown in FIG. 9 is performed after the processing of steps 100 and 102 in FIG. 8. First, in step 200, the ON time of the first detected pulse is a temporary ON time maximum value on−. It is set to max.

Subsequently, in step 202, it is checked whether or not the timer is operating, and the ON time of the pulse detected next while the timer is operating is obtained (step 204).

Next, in step 206, the ON time detected in step 204 and the temporary ON time maximum value on-max detected up to that time are compared, and if the newly detected ON time is larger , On-max are updated (step 208).

In this way, the value of the temporary maximum ON time value on-max during the timer operation is sequentially updated, and the maximum on-max during the predetermined time period is obtained as the maximum ON time value (step). 210).

Next, FIG. 10 is a flowchart showing details of step 204 in FIG.

In this process, first, the value of the timer when the pulse rises when the input signal is ON is stored as time (step 300), and when the input signal is turned off (YES in step 302), the ON time is determined from step 304. And the value of the timer at that time is stored as time.

Next, when the input signal is turned on (YES in step 306), the off time is obtained in step 308.

In the process of step 310, when the timer stop is detected, the process of step 312 is performed, and while the timer is operating, the processes of steps 300 to 308 are repeated to sequentially detect the ON time and the OFF time. ing.

Next, FIG. 11 is a flowchart showing the calculation processing procedure of the maximum ON time ratio and the maximum OFF time ratio in (4) and (5).

In FIG. 11, the ON time ratio maximum value and the OFF time ratio maximum value are calculated in one step for convenience of explanation, but they are actually calculated separately.

Therefore, in the following description, only the processing procedure for obtaining the maximum ON time ratio will be described, but the maximum OFF time ratio can also be obtained by exactly the same processing procedure.

Similar to the process shown in FIG. 9, the process in FIG. 11 is also performed subsequent to the process of steps 100 and 102 shown in FIG.

Then, in step 400, the ON time ratio calculated for the first time is set to the provisional ON time ratio maximum value on ratio −max, and while the timer is operating (YES in step 402), the ON time ratio is sequentially increased. Is calculated (step 404), and if the temporary ON time maximum value on ratio −max calculated up to that time is exceeded (YES in step 406), the temporary ON time maximum value on ratio −max is updated. (Step 408).

In this manner, the temporary ON time ratio maximum value is sequentially updated during the timer operation, and the maximum on ratio −max during a fixed time period is obtained as the ON time ratio maximum value (step 410).

Next, FIG. 12 is a flowchart showing details of step 404 in FIG.

In this process, it is first checked whether the counter value is 2 or more (step 500). If the counter value is 2 or more, the ON time at the previous detection is on time “a” and the OFF time is off time. It is set to "a" (step 502).

Then, the ON time and the OFF time of this time are calculated to be on time and off time (steps 506 and 508), and the ON time ratio and the OFF time ratio are calculated by calculating the equation of step 510. ..

The above is the processing procedure when the feature amount extraction unit 2 extracts five types of feature amounts.

By the way, as a result of diligent research conducted by the inventors of the present application, the five types of feature amounts extracted by the feature amount extracting unit 2 as described above and the earthquake or the non-earthquake shock that is the excitation factor of the vibration Have the following relationships.

That is, FIG. 13 shows the relationship between the pulse number and the excitation factor. The figure (a) shows the case where the vibration is due to an earthquake, and the figure (b) shows the case where the impact is not an earthquake, It can be seen that the total number of pulses is large in the case of an earthquake, while the total number of pulses is small in the case of an impact. Especially, in the case of normal vibration such as inadvertent collision with an object or vibration due to passing a car, the number of pulses is small. Therefore, as described above, the determination output unit 4 determines that the number of pulses is “normal” when the total number of pulses is less than three.

Next, FIG. 14 shows the relationship between the ON time or OFF time of the pulse and the excitation factor. (A) shows the case where the vibration is caused by an earthquake, and (b) shows the case caused by the shock which is not the earthquake. Yes, as shown in (a) of the figure, the maximum value of ON time or OFF time increases in the case of seismic waves, while the maximum value of ON time or OFF time decreases in the case of impact as shown in (b). I understand.

Further, FIG. 15 shows the relationship between the ON time ratio (solid line) or the OFF time ratio (dotted line) and the excitation factor. In the same figure (a), when the vibration is caused by an earthquake, b) shows the case of non-earthquake impact, and it can be seen that the maximum values of the ON time ratio and OFF time ratio become large in the case of an earthquake, as shown in FIG.

Therefore, membership functions relating to the number of pulses, the maximum value of ON time, the maximum value of OFF time, the maximum value of ON time ratio, and the maximum value of OFF time ratio are provided as shown in FIGS. Based on the five types of feature amount data extracted by the feature amount extraction unit, a fuzzy rule is created, and the vibration excitation factor is an earthquake, that is, "abnormal 1", or a shock that is not an earthquake, that is, "abnormal 2". This example is intended to determine whether or not

Rule 1 if ON time ratio maximum value = greater then Judgment = abnormal 1 (earthquake) Rule 2 if if OFF time ratio maximum value = greater then Judgment = abnormality 1 (earthquake) Rule 3 if pulse count = greater and ON time maximum Value = Large then Judgment = Abnormal 1 (Earthquake) Rule 4 if Maximum ON time = Small and maximum OFF time = Small Ten judgment = Abnormal 2 (Impact other than earthquake) Rule 5 if Maximum OFF time = Large then Judgment = Abnormality 1 (Earthquake) Rule 6 if Number of pulses = Small then Judgment = Abnormality 2 (Impact other than earthquake) In this case, in Rule 1, the membership function of MF _{1 in} FIG. The conformance is calculated by applying the maximum value of the ON time ratio output from 2.

In Rule 2, the membership function of MF _{2 in} FIG. 16 (e) is used, and the fitness is calculated similarly.

In rule 3, the membership function of MF _{3 in} FIG. 16A and the membership function of MF ₄ in FIG. 16B are used, and the goodness of fit is calculated by AND (logical product) of those goodnesses. It

In Rule 4, the membership functions of MF ₅ and MF ₆ of FIGS. 16B and 16C are used, and the fitness degree by AND (logical product) of both membership functions is calculated.

In Rule 5, the membership function of MF _{7 in} FIG. 16C is used to calculate the suitability.

In rule 6, the membership function of MF _{8 in} FIG. 16 (d) is used and the goodness of fit is calculated.

When the goodness of fit in each rule is calculated in this way, an inference result as shown in, for example, FIG. 17 is obtained.

In the figure, it is determined that the numerical value of the judgment value J closer to −1 is more likely to be abnormal 2 (impact other than earthquake), and the numerical value closer to 1 is likely to be abnormal 1 (earthquake). It means a high judgment.

Then, in this example, the suitability of the determination that the abnormality 1 is made by using the rules 1, 2, 3, and 5 is S ₁ (0.54), and the abnormality 2 is made by using the rules 4 and 6. When the suitability for the determination is S ₂ (1), the determination value J is J ₀ (−0.3).

By the way, the closer the judgment value J is to -1, the more abnormal 2 is shown, and the closer it is to 1, the more abnormal 1 is shown.

Therefore, for example, if the judgment value J is a positive number, it may be judged to be abnormal 1, and if the judgment value J is a negative number, it may be judged to be abnormal 2, or a predetermined threshold value may be set, and for example, the judgment value J may be −1. ~ -0. If it is determined that the abnormality is 2 in the range of 2, and the abnormality 1 is in the range of the determination value J of 0.2 to 1, it is possible to perform the determination processing with higher accuracy.

In this embodiment, the judgment output unit 4 receives the output of the inference unit 3 and gives the output value shown in FIG. 18 to the inference device 7 according to the flow chart of FIG. That is, an output value of 0 is given in the case of “none”, an output value of 0.2 is given in the case of “normal”, and an output value from the inference unit 3 is given in the case of “abnormal 1” and “abnormal 2”. The judgment values −1 to 1 in FIG. 17 are given as output values in correspondence with 0.5 to 1. At this time, the output value 0.5 side corresponds to the abnormality 2 and the output value 1 side corresponds to the abnormality 1.

Next, based on the outputs of the vibration detection unit 5, the temperature rise detection unit 6, and the operation state detection unit 30 having the above configurations, the vibration and temperature rise applied to the vending machine are normal factors. The reasoning device 7 for determining whether the cause is due to an abnormal factor such as a drill, a hammer, a burner or the like by fuzzy reasoning will be described in detail.

In this embodiment, the judgment by the inference unit 7 is performed by the membership function shown in FIGS. 19 and 20 and the fuzzy rules in Tables 1 and 2 below.

That is, FIG. 19A shows a membership function of vibration data, which has four labels of “none”, “normal”, “abnormal 1”, and “abnormal 2”. (B) shows a membership function of temperature rise data, which has two labels of "absence" and "absence" of rapid temperature rise, and FIG. 19 (C) shows the membership function of the operating state. And has two labels, "at the time of non-release" and "at the time of sale".

FIG. 20 shows the membership function of the judgment output, which is the consequent part, and has three labels of “abnormal”, “slightly abnormal”, and “normal”.

Fuzzy rules are as shown in Tables 1 and 2 below. Table 1 shows rules when the operating state of the vending machine is on sale, and Table 2 shows the rules of the vending machine. It shows the rules when the operating status is not on sale.

[0083]

[Table 1]

[0084]

[Table 2]

FIG. 21 is a flowchart of the determination process of this embodiment.

First, after starting, the outputs of the detection units 5, 6 and 30 are fetched (steps 700 to 702), it is determined whether or not there is vibration (step 703), and when there is vibration, Fuzzy inference is performed (step 704), the result of the inference is judged to be abnormal (step 706), and if abnormal, the alarm device 8 is activated to notify (step 707). Returning to the start, when it is determined that there is no vibration in step 703, it is determined whether or not there is a temperature increase (step 705), and when there is a temperature increase, fuzzy reasoning is performed (step 704), and the temperature is increased. If there is no climb, return to the start.

Next, the fuzzy inference operation by the inference device 7 will be described.

First, given temperature rise rate data, vibration data, and operating state data, the goodness of fit is determined in the membership function corresponding to each fuzzy rule. Then, for each fuzzy rule, the smallest value of the conformance of each antecedent part is selected as the antecedent conformity (MIN calculation). Thus, each membership function related to the consequent part of each fuzzy rule is cut by the goodness of fit of the antecedent part obtained in each fuzzy rule, and each membership function related to all the cut fuzzy rules is superimposed. (MAX operation), the output value of the position corresponding to the position of the center of gravity of the superposed figure is obtained, and it is judged from this output value whether or not the position is normal.

The alarm device 8 issues an alarm sound or lights an alarm light to issue an alarm when the inference device 7 determines that there is an abnormality.

When it is determined that there is some abnormality, the standby state is set immediately before the alarm is issued.

In this way, it is determined whether normal or abnormal based on the temperature rise, vibration, and the operating state of the vending machine. For example, in the case of carelessly hitting a person or object or an earthquake, No alarm is issued when it is judged to be normal.On the other hand, if there is no impact and damage caused by heat using a burner or the like, it is possible to reliably judge as an abnormality and issue an alarm. The reliability of the crime prevention mechanism will be improved.

FIG. 22 is a block diagram of another embodiment of the present invention, in which parts corresponding to those in the above embodiment are designated by the same reference numerals.

The crime prevention mechanism of this embodiment is based on the temperature rise detecting unit 6, the vibration detecting unit 5, the time measuring unit 31 as a time detecting unit for detecting the time, and the outputs of the respective detecting units 6, 5, 31. Te, essentially of by performing fuzzy inference, and determines inference apparatus ₇₁ whether or not abnormal, on the basis of the determination of the inference device _71, alarm device 8 which emits an alarm when an abnormality Is configured.

The temperature rise detection unit 6, the vibration detection unit 5, and the alarm device 8 are the same as those in the above-described embodiment.

The clock unit 31 outputs the time data to the inference device, and in this embodiment, it is possible to consider that the theft is more likely to occur at night than at daytime.

[0096] inference apparatus 7 _1, vibration data of the temperature increase rate from the temperature rise detector 6, on the basis of time data from the vibration data and timing unit 31 from the vibration detecting unit 5, was added to the vending machine Whether the temperature rise or the temperature rise is due to normal factors or abnormal factors is determined by fuzzy inference as described later.

In this embodiment, the inference unit 7 ₁ makes the determination based on the membership functions shown in FIGS. 23 and 24 and the fuzzy rules shown in Tables 3 and 4 below.

That is, FIG. 23A shows a membership function of vibration data, which has four labels of “none”, “normal”, “abnormal 1”, and “abnormal 2”. (B) shows the membership function of the temperature rise data, which has two labels of “absence” and “presence” of the rapid temperature rise, and FIG. 23 (C) shows the membership of the time data. It shows a function and has two labels, "day" and "night".

FIG. 24 shows the membership function of the judgment output, which is the consequent part, and has three labels, “abnormal”, “slightly abnormal”, and “normal”.

The fuzzy rules are as shown in Tables 3 and 4 below. Table 3 shows the rules when the time data is daytime, and Table 2 shows the rules when the time data is night. Is shown.

[0101]

[Table 3]

[0102]

[Table 4]

FIG. 25 is a flowchart of the determination process of this embodiment.

First, at start, each output of each detection unit 5, 6, 31 is fetched (step 800-802), and it is judged whether or not there is vibration (step 803). Makes a fuzzy inference (step 804), judges whether or not there is an abnormality as a result of the inference, and if it is abnormal, activates the alarm device 8 to notify (step 807), and if not abnormal, returns to the start. If it is determined in step 803 that there is no vibration, it is determined whether or not there is a temperature rise (step 805). If there is a temperature rise, fuzzy inference is performed (step 804). , Go back to the start.

The fuzzy inference operation by the inference device 7 ₁ is performed in the same manner as in the above-described embodiment, and it is determined whether or not it is normal.

FIG. 26 is a block diagram of still another embodiment of the present invention, in which parts corresponding to the above-mentioned embodiments are designated by the same reference numerals.

The crime prevention mechanism of this embodiment is based on the outputs of the temperature rise detection unit 6, the vibration detection unit 5, the brightness detection unit 32 for detecting the ambient brightness, and the detection units 5, 6, 32. Then, the inference device 7 ₂ that determines whether or not there is an abnormality by performing fuzzy inference, and the alarm device 8 that issues an alarm when there is an abnormality based on the determination of this inference device 7 _2. It is basically composed.

The temperature rise detection unit 6, the vibration detection unit 5, and the alarm device 8 are the same as those in the above-described embodiment.

[0109] brightness detection unit 32, for example, is composed of a photodiode, and outputs the brightness data corresponding to the brightness around the vending machine to the inference unit 7 _2, in this embodiment This is because the theft is likely to occur when the vending machines installed indoors are dark around.

[0110] Reasoning apparatus 7 _2, data rates of temperature increase from the temperature rise detector 6, on the basis of the luminance data from the vibration data and the brightness detection unit 32 from the vibration detecting unit 5, in addition to the automatic vending machine Fuzzy inference is performed as described later to determine whether the generated vibration or temperature rise is due to a normal factor or an abnormal factor.

In this embodiment, the inference unit 7 ₂ makes the determination based on the membership functions shown in FIGS. 27 and 28 and the fuzzy rules shown in Tables 5 and 6 below.

That is, FIG. 27A shows a membership function of vibration data, which has four labels of “none”, “normal”, “abnormal 1”, and “abnormal 2”. (B) shows the membership function of the temperature rise data, which has two labels of "absence" and "presence" of the rapid temperature rise, and FIG. 27 (C) shows the membership function of the brightness data. , And has two labels, “bright” and “dark”.

FIG. 28 shows the membership function of the judgment output which is the consequent part, and has three labels of “abnormal”, “slightly abnormal”, and “normal”.

Fuzzy rules are as shown in Tables 5 and 6 below. Table 5 shows rules when the brightness data is “bright”, and Table 6 shows that the brightness data is “dark”. Shows the rules for when.

[0115]

[Table 5]

[0116]

[Table 6]

FIG. 29 is a flowchart of the determination process of this embodiment.

First, after starting, the outputs of the detection units 5, 6 and 32 are fetched (steps 902 to 902) and it is judged whether or not there is vibration (step 903). Fuzzy inference is performed (step 904), the result of the inference is judged to be abnormal or not (step 906), and if abnormal, the alarm device 8 is activated to notify (step 907). If not abnormal, Returning to the start, when it is determined that there is no vibration in step 903, it is determined whether or not there is a temperature increase (step 905), and when there is a temperature increase, fuzzy reasoning is performed (step 904), If there is no climb, return to the start.

The fuzzy inference operation by the inference device 7 ₂ is performed in the same manner as in the above-described embodiment, and it is determined whether or not it is normal.

FIG. 30 is a block diagram of another embodiment of the present invention, in which parts corresponding to the above-mentioned embodiments are designated by the same reference numerals.

The crime prevention mechanism of this embodiment includes a temperature rise detection unit 6, a vibration detection unit 5, an inclination detection unit 33 as an installation state detection unit that detects the installation state of a vending machine, and the detection units 5 and 5. based on the output of the 6, 33, by performing the fuzzy inference, and determines inference apparatus 7 ₃ whether abnormalities, based on the determination of the inference apparatus 7 ₃ issues an alarm when an abnormality It is basically composed of an alarm device 8.

The temperature rise detection unit 6, the vibration detection unit 5, and the alarm device 8 are the same as those in the above-described embodiments.

[0123] inclination detecting unit 33 is, for example, is composed of a tilt sensor, and outputs a tilt data corresponding to the magnitude of the slope of the vending machine to the inference unit 7 _3, this embodiment, for example, , It effectively prevents the main body of the vending machine from being loaded into the car and carried away. It should be noted that the installation state detection unit is not limited to the detection of the inclination, and for example, a switch may be provided on the foot of the vending machine so that an output is given when the vending machine is lifted from the installation surface. Good.

[0124] reasoning apparatus 7 _3, data rates of temperature increase from the temperature rise detector 6, on the basis of the inclination data from the vibration data and the tilt detection unit 33 from the vibration detecting unit 5, was added to the vending machine As will be described later, fuzzy inference is used to determine whether the vibration or temperature rise is due to normal factors or abnormal factors.

In this embodiment, the inference unit 7 ₃ makes the determination based on the membership functions shown in FIGS. 31 and 32 and the fuzzy rules in Tables 7 and 8 below.

That is, FIG. 31A shows a membership function of vibration data, which has four labels of “none”, “normal”, “abnormal 1”, and “abnormal 2”. (B) shows the membership function of the temperature rise data, which has two labels of “absence” and “presence” of the rapid temperature rise, and FIG. 31 (C) shows the membership data of the slope data. It shows a function and has two labels, "small" and "large".

FIG. 32 shows the membership function of the judgment output which is the consequent part, and has three labels of “abnormal”, “slightly abnormal”, and “normal”.

The fuzzy rules are as shown in Tables 7 and 8 below. Table 7 shows the rules when the tilt data is “large”, and Table 8 shows that the tilt data is “small”. It shows the rule when.

[0129]

[Table 7]

[0130]

[Table 8]

FIG. 33 is a flowchart of the determination process of this embodiment.

First, at start, the outputs of the detection units 5, 6, and 33 are fetched (steps 1000 to 1002), fuzzy inference is performed (step 1003), and it is determined whether or not there is an abnormality as a result of the inference. (Step 1004) If there is an abnormality, the alarm device 8 is activated to give a notification (step 1005). If there is no abnormality, the process returns to the start.

The fuzzy inference operation by the inference device 7 ₃ is performed in the same manner as in the above-described embodiment, and it is determined whether or not it is normal.

FIG. 34 is a block diagram of still another embodiment of the present invention, in which parts corresponding to the above-mentioned embodiments are designated by the same reference numerals.

The crime prevention mechanism of this embodiment includes a temperature rise detection unit 6, a vibration detection unit 5, and a key detection unit 34 that detects whether a regular key has been inserted to open the outer door of the vending machine. And an inference device 7 ₄ that determines whether or not there is an abnormality by performing fuzzy inference based on the output of each detection unit 5, 6, 34, and an abnormality based on the determination of this inference device 7 _4. It basically comprises an alarm device 8 for issuing an alarm when

The temperature rise detection unit 6, the vibration detection unit 5, and the alarm device 8 are the same as those in the above-described embodiment.

The key detection unit 34 turns on the contact only when the legitimate key is inserted, and turns off the contact when an object other than the legitimate key is inserted or nothing is inserted. It is configured.

[0138] inference device 7 _4, data rates of temperature increase from the temperature rise detector 6, on the basis of the output of the vibration data and the key detection unit 34 from the vibration detecting unit 5, the vibrating Ya applied to the vending machine Fuzzy inference is performed as described later to determine whether the temperature rise is due to a normal factor or an abnormal factor.

In this embodiment, the inference device makes the determination by the membership function shown in FIGS. 35 and 36 and the fuzzy rules in Tables 9 and 10 below.

That is, FIG. 35A shows a membership function of vibration data, which has four labels of “none”, “normal”, “abnormal 1”, and “abnormal 2”. (B) shows a membership function of the temperature rise data, which has two labels of “absence” and “presence” of the rapid temperature rise, and FIG. 35 (C) shows the output of the key detection unit 34. It shows the membership function and has two labels, "regular" and "false".

FIG. 36 shows the membership function of the judgment output which is the consequent part, and has three labels of “abnormal”, “slightly abnormal”, and “normal”.

Fuzzy rules are as shown in Tables 9 and 10 below. Table 9 shows rules when the output of the key detection unit 34 is “regular”, that is, when a regular key is inserted. Table 10 shows the rule when the output of the key detection unit is “false”, that is, when a person other than the regular key is inserted or nothing is inserted.

[0143]

[Table 9]

[0144]

[Table 10]

FIG. 37 is a flowchart of the determination process of this embodiment.

First, at start, the outputs of the detection units 5, 6 and 34 are fetched (step 1100 to 1102) and fuzzy inference is performed (step 1103). As a result of the inference, it is determined whether or not there is an abnormality. If it is abnormal (step 1104), the alarm device 8 is operated to notify (step 1105). If it is not abnormal, the process returns to the start.

The fuzzy inference operation by the inference device 7 ₄ is performed in the same manner as in the above-described embodiment, and it is determined whether or not it is normal.

FIG. 38 is a block diagram of another embodiment of the present invention, in which parts corresponding to those in the above embodiment are designated by the same reference numerals.

The crime prevention mechanism of this embodiment performs fuzzy inference based on the outputs of the temperature rise detection unit 6, the vibration detection unit 5, the sound detection unit 35, and the detection units 5, 6, and 35. Accordingly, the determined inference apparatus 7 ₅ whether abnormalities, based on the determination of the inference apparatus 7 ₅ are basically formed from the alarm device 8 which emits an alarm when an abnormality. The temperature rise detection unit 6, the vibration detection unit 5, and the alarm device 8 are the same as those in the above-described embodiment.

The sound detection unit 35 is composed of a microphone installed inside the vending machine and provides an output according to the detected sound volume. In this embodiment, the sound generated by a hammer or a drill is used. To prevent theft.

[0151] reasoning apparatus 7 _5, data rates of temperature increase from the temperature rise detector 6, on the basis of the output of the vibration data, the sound detector 35 from the vibration detecting unit 5, the vibrating Ya applied to the vending machine Fuzzy inference is performed as described later to determine whether the temperature rise is due to a normal factor or an abnormal factor.

[0152] In this embodiment, the determination by the inference unit 7 ₅ performs the membership functions and fuzzy rules table 11 and 12 below are shown in FIGS. 39 and 40.

That is, FIG. 39A shows a membership function of vibration data, which has four labels of “none”, “normal”, “abnormal 1”, and “abnormal 2”. (B) shows the membership function of the temperature rise data, which has two labels of “absence” and “presence” of the rapid temperature rise, and FIG. 39 (C) shows the output of the sound detecting unit 35. It shows the membership function and has two labels, "large" and "small".

FIG. 40 shows the membership function of the judgment output, which is the consequent part, and has three labels of “abnormal”, “slightly abnormal”, and “normal”.

The fuzzy rules are as shown in Tables 11 and 12 below. Table 11 shows the rules when the output of the sound detecting unit 35 is “large”, and Table 12 shows the fuzzy rules of the sound detecting unit 35. It shows the rule when the output is "small".

[0156]

[Table 11]

[0157]

[Table 12]

FIG. 41 is a flowchart of the determination processing of this embodiment.

First, at start, the outputs of the detection units 5, 6, and 35 are fetched (step 12000 to 1202), fuzzy inference is performed (step 1203), and it is determined whether or not there is an abnormality as a result of the inference. If it is abnormal (step 1204), the alarm device 8 is operated to notify it (step 1205). If it is not abnormal, the process returns to the start.

[0160] Fuzzy inference operation by the inference apparatus 7 ₅ is carried out similar to the above-described embodiment, whether normal or not.

[0161]

As described above, according to the present invention, when a burner, a laser or the like is used to melt and open the outer door portion by heat, the temperature rise detection unit detects the temperature rise of the outer door. The detection output determines that there is something wrong with the inference unit and issues an alarm, etc., so that it is possible to reliably detect vandalism such as the outer door being melted and opened by heat. The security of the transaction device is improved.

[Brief description of drawings]

FIG. 1 is a block diagram showing an overall configuration of a crime prevention mechanism in an embodiment to which the present invention is applied.

FIG. 2 is a schematic external perspective view of a vending machine according to an embodiment to which the present invention is applied.

FIG. 3 is a block diagram of a vibration detector of FIG.

FIG. 4 is a flowchart of a determination process of a vibration detection unit.

5 is a detailed cross-sectional view of the seismic switch in FIG.

FIG. 6 is a functional block diagram of a feature amount extraction unit in FIG.

FIG. 7 is an explanatory diagram of a characteristic amount of a pulse wave generated by a characteristic amount extraction unit.

FIG. 8 is a flowchart showing a procedure for calculating the number of pulses.

FIG. 9 is a flowchart showing a procedure for calculating the maximum ON time value and the maximum OFF time value.

10 is a flowchart showing details of a calculation processing procedure of ON time and OFF time in FIG.

FIG. 11 is a flowchart showing a calculation processing procedure of an ON time ratio maximum value and an OFF time ratio maximum value.

12 is a flowchart showing details of a calculation processing procedure of an ON time ratio and an OFF time ratio in FIG.

FIG. 13 is an explanatory diagram showing a relationship between the number of pulses and an excitation factor of vibration.

FIG. 14 is an explanatory diagram showing a relationship between ON time and OFF time and a vibration excitation factor.

FIG. 15 is an explanatory diagram showing the relationship between the maximum value of the ON time ratio and the maximum value of the OFF time ratio and the excitation factor of vibration.

FIG. 16 is an explanatory diagram of a membership function used in the vibration detection device.

FIG. 17 is an explanatory diagram of an inference result of the vibration detection unit.

FIG. 18 is a diagram showing an output value of a vibration detection unit.

19 is a diagram showing a membership function of the antecedent part used in the inference device of FIG. 1. FIG.

20 is a diagram showing a membership function of a consequent part used in the inference apparatus of FIG. 1. FIG.

21 is a flowchart of the determination process of FIG.

FIG. 22 is a block diagram of another embodiment of the present invention.

23 is a diagram showing a membership function of the antecedent part used in the inference apparatus of FIG. 22.

24 is a diagram showing a membership function of a consequent part used in the inference apparatus of FIG.

FIG. 25 is a flowchart of the determination process of FIG.

FIG. 26 is a block diagram of another embodiment of the present invention.

27 is a diagram showing a membership function of the antecedent part used in the inference apparatus of FIG. 26.

28 is a diagram showing a membership function of the consequent part used in the inference apparatus of FIG. 26.

29 is a flowchart of the determination process of FIG.

FIG. 30 is a block diagram of another embodiment of the present invention.

31 is a diagram showing the membership function of the antecedent part used in the inference apparatus of FIG. 30.

32 is a diagram showing the membership function of the consequent part used in the inference apparatus of FIG. 30.

33 is a flowchart of the determination process of FIG.

FIG. 34 is a block diagram of another embodiment of the present invention.

35 is a diagram showing a membership function of the antecedent part used in the inference apparatus of FIG. 34.

36 is a diagram showing the membership function of the consequent part used in the inference apparatus of FIG. 34.

37 is a flowchart of the determination process of FIG. 34.

FIG. 38 is a block diagram of another embodiment of the present invention.

39 is a diagram showing a membership function of the antecedent part used in the inference apparatus of FIG. 38.

40 is a diagram showing a membership function of the consequent part used in the inference apparatus of FIG. 38. FIG.

41 is a flowchart of the determination process of FIG. 38.

[Explanation of symbols]

5 Vibration detection part 6 Temperature rise detection part 7, 7 ₁ , 7 ₂ , 7 ₃ , 7 ₄ , 7 ₅ Inference device (inference part) 8 Alarm device 30 Operating state detection part 31 Clock part 32 Brightness detection part 33 Tilt detection Part 34 Key detection part 35 Sound detection part 101 Outer door

Claims (1)

[Scope of utility model registration request] 1. A temperature rise detection unit attached to an outer door unit for detecting a temperature rise of the outer door unit, and an inference unit for determining whether or not there is an abnormality based on an output of the detection unit. An automatic transaction device characterized in that