JPH05314357A - Security device - Google Patents

Abstract

PURPOSE:To prevent malfunction and to improve reliability by issuing an alarm to any action, which can not be detected by the conventional device, due to heat utilizing burners not accompanying impulse. CONSTITUTION:Abnormality is judged based on an output from any one of an operating state detection part 30, time detection part, key detection part, brightness detection part, installing state detection part and sound detection part at least in addition to the outputs of a vibration detection part 5 and a temperature increase detection part 6.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a crime prevention device for preventing theft of a vending machine, a cash dispenser or the like.

[0002]

2. Description of the Related Art Conventionally, in this type of security device, an impact detector that is turned on and off according to the magnitude of the impact applied to the vending machine from the outside, a tilt detector that is turned on and off according to the magnitude of the tilt of the vending machine, and the like. It is composed of an alarm device.

In such a crime prevention device, when the outer door of a locked vending machine or the like is forcibly opened, or when the vending machine or the like is about to be knocked down, the outer door is opened without a key. Alternatively, before the vending machine etc. is completely knocked down, the detector detects the theft situation and issues an alarm, or displays and informs the surrounding people and manager to prevent theft. We prevent the destruction of vending machines.

[0004]

However, in such a conventional security device, for example, a person or an object is inadvertently hit and a shock is applied to the vending machine, or an earthquake occurs. Even in such a case, the impact or tilt may exceed the set value of the above-mentioned detector and malfunction, and conversely, even if the act is to destroy the vending machine etc., the impact or tilt does not If the set value is not exceeded,
Alternatively, there is a problem that an alarm cannot be issued for an action such as a burner or a laser which does not cause impact and is melted by heat, and the notification is delayed.

The present invention has been made in view of the above-mentioned points, and prevents malfunction due to inadvertent shock or earthquake, and acts of destruction by heat using a burner without impact. It is an object of the present invention to enhance the reliability of the crime prevention device by making it possible to detect reliably.

[0006]

In order to achieve the above object, the present invention is configured as follows.

That is, the present invention according to claim 1 is
A security device for preventing theft of a vending machine or the like, which is a vibration detecting section for detecting vibration, a temperature rise detecting section for detecting a temperature rise, and an operating state for detecting an operating state of the vending machine, etc. Detection unit, time detection unit for detecting time, key detection unit for detecting whether or not a regular key is inserted in the vending machine, brightness detection unit for detecting ambient brightness, the vending machine, etc. Of at least one of the installation state detection unit and the sound detection unit for detecting the installation state of, and an inference unit that determines whether or not there is an abnormality based on the output of each of the detection units. There is.

Further, the present invention according to claim 2 is configured such that the vibration detecting section gives an output corresponding to the presence or absence of vibration and an excitation factor of the vibration to the inference section.

[0009]

According to the first aspect of the present invention, in addition to the outputs of the vibration detecting section and the temperature rise detecting section, the operating state detecting section, the time detecting section, the key detecting section, the brightness detecting section, and the installation state detecting section are detected. Since the abnormality is determined based on the output of at least one of the detector and the sound detector, it is possible to reliably detect a vandalism such as melting using a burner, a laser, or the like without impact.

According to the second aspect of the present invention, the vibration detecting section detects the vibration and gives the output corresponding to the excitation factor of the vibration to the inference section. It is possible to prevent malfunctions due to earthquakes and earthquakes.

[0011]

Embodiments of the present invention will now be described in detail with reference to the drawings.

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

The security device of this embodiment includes a temperature rise detecting section 6 for detecting temperature rise and outputting temperature rise data, a vibration detecting section 5 for detecting vibration and outputting vibration data, and a vending machine. Based on the output of the operating state detection unit 30 that detects an operating state such as whether a coin has been inserted and is on sale, or whether a coin has not been inserted and is not on sale, and each detection unit 5, 6, 30 And an inference device 7 for determining whether or not there is an abnormality by performing fuzzy inference,
It basically comprises an alarm device 8 which issues an alarm based on the judgment of the inference device 7 when there is an abnormality.

The temperature rise detection unit 6 detects a temperature change near the vending machine, and outputs temperature rise data per unit time, that is, temperature rise rate data to the inference device 7 as temperature rise data.

The vibration detector 5 detects the vibration applied to the vending machine, determines the excitation factor of the vibration from the vibration state, and determines whether the vibration is normal or abnormal, as described later. Then, the corresponding vibration data is output.

The operation state detecting section 30 provides an output corresponding to whether a coin is put into the vending machine and is on sale, or whether the coin is not put and is not on sale. The output is taken out from the control unit of the vending machine.

The reasoning device 7 is 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, and the vibration applied to the vending machine. It is determined by fuzzy inference as described later whether the temperature rise or the temperature rise is due to a normal factor such as vibration of a passing vehicle or an abnormal factor such as a drill, a hammer, or a burner.

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 structure of the vibration detector 5 will be described in more detail.

As shown in FIG. 2, the vibration detector 5 includes a seismic switch 1 for generating a pulse train by vibration,
A feature amount extraction unit 2 that extracts a plurality of types of feature amounts from the pulse wave from the seismic switch 1 as described later, and a fuzzy inference is performed by inputting the extracted feature amounts as input factors of vibration. The inference unit 3 for making a determination and the determination output unit 4 for basically outputting output values corresponding to four types of determination results to the inference apparatus 7 as vibration data based on these outputs.

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

As shown in the flow chart of FIG. 3, the determination output unit 4 determines whether or not there is vibration based on the output of the seismic switch 1 (step 600). If there is no vibration, the determination is made. The result is set to "none" (step 605), and when there is vibration, it is determined whether or not the total number of pulses extracted by the feature amount extraction unit 2 is 3 or more as described later (step 601), 3 When it is less than, the judgment result is "normal" (step 604), and when it is 3 or more, it is "abnormal 1" and "abnormal 2" based on the result by fuzzy inference by the inference unit 3 (step 602). The output value corresponding to is determined (step 603).

The structure of the vibration detector 5 will be described in more detail below.

FIG. 4 is a sectional view showing the details of the seismic switch 1. As shown in FIG. 4, a so-called automatic horizontal seismic sensor is well known in the art and is not directly related to the gist of the present invention. In the following, the names of each member in the drawings will be pointed out.

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,
Reference numeral 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. 5 is a functional block diagram of the feature quantity extraction unit 2 for extracting the feature quantity based on the pulse wave P from the seismic switch 1 as shown in FIG.

Therefore, as shown in the figure, the feature quantity extraction unit 2 includes a counting function 2a, a time counting function 2b, and a program function 2.
c and a calculation function 2d are included, and the extracted feature amount D is configured to be sent to the inference unit 3.

By the way, in the present embodiment, when the pulse wave P as shown in FIG. 6 is obtained, the feature amount extracting section 2 utilizes the above-mentioned respective functions so that the following five types within the predetermined time period are obtained. 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 time t ₁ and the first pulse P ₁ rises, the rising state of each pulse within the time period of 3 seconds is checked.

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
ON time T ₁ , T ₂ , of four pulses P ₁ , P ₂ , P ₃ , P ₄
The maximum value is selected from T ₃ and T ₄ , and since T ₂ is the maximum in this example, 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 respective pulses. In this example, since T ₇ is the maximum, the OFF value is set. 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 measured for 3 seconds is OF.
Not used for F time.

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

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

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

Therefore, in this example, T ₆ / T ₅ , T ₇
The maximum of / T ₆ will be selected.

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. 7 to 11, the calculation processing procedure of each feature amount in the feature amount extraction unit 2 will be described below. explain.

FIG. 7 is a flow chart showing the procedure for calculating the number of pulses in (1).

In this process, when the program is started, it is checked whether the input signal is ON (step 100), and if it is ON, the timer is started (step 1).
02), the counter value is set to 1, and the flag is set to 1 (step 104).

Next, it is checked whether or not the timer is operating (step 106). When the timer is operating, the input signal ON,
If the flag is 0 (YES in steps 106, 108 and 110), the counter is incremented (step 112).

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

Next, FIG. 8 is a flow chart showing the procedure for calculating the maximum ON time value and the maximum OFF time value in (2) and (3).

Although the maximum ON time value and the maximum OFF time value are obtained in one step for convenience of description in the figure, 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 by exactly the same processing procedure.

The processing shown in FIG. 8 is performed after the processing of steps 100 and 102 in FIG. 7. First, in step 200, the ON time of the first detected pulse is the temporary maximum ON time on-value. It is set to max.

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

Next, in step 206, step 20
ON time detected in 4 and temporary ON detected up to that time
The time maximum value on-max is compared, and if the newly detected ON time is larger, the value of on-max is updated (step 208).

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

Next, FIG. 9 shows step 204 in FIG.
3 is a flowchart showing the details of FIG.

In this process, first, the value of the timer at the time of pulse rise when the input signal is ON is stored as time (step 300), and when the input signal is turned off (YES in step 302), it is turned on from step 304.
The time is obtained, 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 stop of the timer 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. There is.

Next, FIG. 10 is a flow chart showing the procedure for calculating the maximum value of the ON time ratio and the maximum value of the OFF time ratio in (4) and (5).

It should be noted that in FIG.
Although the maximum value of the ON time ratio and the maximum value of the OFF time ratio are obtained by one step, they are actually obtained separately.

Therefore, in the following description, ON
Only the processing procedure for obtaining the maximum time ratio will be explained,
The maximum value of the OFF time ratio can be obtained by the same processing procedure.

Similar to the process shown in FIG. 8, the process in FIG. 10 is also performed after the processes of steps 100 and 102 shown in FIG.

Then, in step 400, the ON time ratio calculated in the first time is set to the provisional ON time ratio maximum value on ratio-max, and while the timer is operating (YE in step 402).
S), the sequential ON time ratio is calculated (step 404),
Temporary ON time ratio maximum value calculated by that time on ratio-m
If it exceeds ax (YES in step 406), provisional ON
The maximum time ratio value on ratio-max is updated (step 4).
08).

Thus, during the operation of the timer, the provisional ON time ratio maximum value is successively updated, and the maximum on ratio -max during a fixed time period is obtained as the ON time ratio maximum value (step 410).

Next, FIG. 11 shows step 4 in FIG.
It is a flowchart which shows the detail of 04.

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

Then, the ON time and OFF time of this time are obtained and set as on time and off time (steps 506 and 508), and the ON time ratio and the OFF time ratio are obtained 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, between the five types of feature quantities extracted by the feature quantity extracting unit 2 as described above and the earthquake or the shock other than the earthquake which is the excitation factor of the vibration. Have the following relationships.

That is, FIG. 12 shows the relationship between the number of pulses and the excitation factors. The figure (a) shows the case where the vibration is due to an earthquake, and the figure (b) shows the case where there is a shock other than the earthquake,
It can be seen that the total pulse number is large in the case of an earthquake, while the total pulse number is small in the case of an impact. In particular, the number of pulses is small in the case of normal vibration such as inadvertent collision with an object or vibration due to passage of a car. 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. 13 shows the relationship between the ON time or OFF time of the pulse and the excitation factor.
Shows the case where the vibration is due to an earthquake, and (b) shows the case due to a shock that is not an earthquake. As shown in (a) of the figure, in the case of an earthquake wave, the maximum value of ON time or OFF time becomes large, while as shown in (b). In case of impact, the maximum value of ON time or OFF time becomes smaller.

Further, FIG. 14 shows the relationship between the ON time ratio (solid line) or the OFF time ratio (dotted line) and the excitation factor. In the case of FIG. 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 increase in the case of an earthquake, as shown in FIG.

Therefore, the number of pulses, maximum ON time, OF
Membership functions relating to the maximum F time value, the maximum ON time ratio value, and the maximum OFF time ratio value are provided as shown in FIGS. 15 (a) to 15 (e), and the following fuzzy rules are created and extracted by the feature amount extraction unit. Whether the excitation factor of vibration is an earthquake, that is, "abnormality 1" based on the five types of feature data
In this embodiment, an attempt is made to determine whether the shock is not an earthquake, that is, "abnormal 2".

Rule 1 if ON time ratio maximum value = greater then judgment = abnormal 1 (earthquake) Rule 2 if if OFF time ratio maximum = 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 adaptability is calculated by applying the maximum value of the ON time ratio output from No. 2.

In Rule 2, the membership function of MF ₂ shown in FIG. 15 (e) is used, and the fitness is calculated in the same manner.

In rule 3, the membership function of MF _{3 in} FIG. 15A and the membership function of MF ₄ in FIG. 15B are used, and the fitness level is calculated by AND (logical product) of those fitness levels. It

In rule 4, MF ₅ , M in FIGS. 15 (b) and 15 (c) are used.
The membership function of F ₆ is used, and similarly the fitness by AND (logical product) of both membership functions is calculated.

Further, in rule 5, the membership function of MF _{7 in} FIG. 15 (c) is used to calculate the goodness of fit.

In rule 6, the membership function of MF _{8 in} FIG. 15 (d) is used to calculate the goodness of fit.

When the degree of conformity in each rule is calculated in this way, an inference result as shown in FIG. 16, for example, is obtained.

In the figure, it is judged 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 more likely to be abnormal 1 (earthquake). It means the judgment.

Then, in this example, rules 1, 2, 3,
The suitability of the determination of abnormality 1 using S ₅ is S ₁ (0.
54), if the suitability for the determination of abnormality 2 using rules 4 and 6 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 it is, and the closer it is to 1, the more abnormal it is.

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, for example, the judgment value J may be -1. ~-
If it is determined that the abnormality is 2 in the range of 0.2 and the abnormality 1 is in the range of the determination value J of 0.2 to 1, it is possible to perform a more accurate determination process.

In this embodiment, the judgment output unit 4 receives the output of the inference unit 3 and gives the output value shown in FIG. 17 to the inference unit 7 according to the flow chart of FIG. That is, when "none", the output value of 0 is changed to "normal".
In the case of, an output value of 0.2 is given, and when "abnormal 1" or "abnormal 2", the judgment values -1 to 1 of FIG. 16 from the inference unit 3 are made to correspond to 0.5 to 1. Give as output value. At this time, the output value 0.5 side is abnormal 2 and
The output value 1 side corresponds to abnormality 1.

Next, the vibration detecting section 5 having the above configuration,
Based on the outputs of the temperature rise detection unit 6 and the operation state detection unit 30, the vibration and temperature rise applied to the vending machine are due to normal factors or abnormal factors such as drills, hammers, burners, etc. The inference apparatus 7 that determines whether or not the object is a fuzzy inference will be described in detail.

In this embodiment, the inference unit 7 makes the determination based on the membership functions shown in FIGS. 18 and 19 and the fuzzy rules shown in Tables 1 and 2 below.

That is, FIG. 18A shows the 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,
The rapid temperature rise has two labels, "without" and "with", and FIG. 18C shows the membership function in the operating state. It has individual labels.

FIG. 19 shows the membership function of the judgment output, which is the consequent part, and includes "abnormal", "slightly abnormal",
It has three labels of "normal".

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

[0086]

[Table 1]

[0087]

[Table 2]

FIG. 20 is a flow chart of the judgment processing of this embodiment.

First, after starting, each detecting section 5, 6, 3
The output of 0 is taken in (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). As a result of the inference, it is determined whether or not there is an abnormality. Judge (step 706)
If it is abnormal, the alarm device 8 is operated to notify it (step 707). If it is not abnormal, the process returns to the start. If it is determined in step 703 that there is no vibration, it is determined whether or not there is a temperature rise. If the temperature rises (step 705), fuzzy inference is performed (step 704). If the temperature does not rise, the process returns 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 degree of conformity is found in the membership function corresponding to each fuzzy rule. Then, for each fuzzy rule, the smallest value of the matching degree of each antecedent part is selected as the antecedent part matching degree (MIN calculation). In this way, each membership function related to the consequent part of each fuzzy rule is cut by the conformance of the antecedent part obtained in each fuzzy rule.
Membership functions related to all the cut fuzzy rules are overlaid (MAX operation), an output value at a position corresponding to the position of the center of gravity of the overlaid figure is obtained, and it is determined whether the output value is normal or not. To be done.

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 judged to be slightly abnormal, 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, for damage caused by heat using a burner without impact, it is possible to reliably judge as abnormal and issue an alarm. Reliability will be improved.

FIG. 21 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 security device of this embodiment is based on the outputs of 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 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 detecting section 6, the vibration detecting section 5 and the alarm device 8 are the same as those in the above-mentioned embodiment.

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

[0099] 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 a normal factor or an abnormal factor is determined by fuzzy inference as described later.

In this embodiment, the judgment by the inference unit 7 _{1 is made} by the membership functions shown in FIGS. 22 and 23 and the fuzzy rules shown in Tables 3 and 4 below.

That is, FIG. 22A 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,
The rapid temperature rise has two labels of "absence" and "absence", and FIG. 22C shows the membership function of the time data, and there are two labels of "day" and "night". have.

FIG. 23 shows the membership function of the judgment output, which is the consequent part, and is "abnormal", "slightly abnormal",
It has three labels of "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. ing.

[0104]

[Table 3]

[0105]

[Table 4]

FIG. 24 is a flow chart of the judgment processing of this embodiment.

First, after starting, each of the detection units 5, 6, 3
Each output of No. 1 is fetched (steps 800 to 802), it is judged whether or not there is vibration (step 803), and when there is vibration, fuzzy inference is performed (step 804), and as a result of the inference, whether or not there is an abnormality If it is abnormal, the alarm device 8 is operated to notify (step 80).
7) If there is no abnormality, the process returns to the start, and if it is determined in step 803 that there is no vibration, it is determined whether or not there is a temperature increase (step 80).
5) If there is a temperature rise, fuzzy inference is performed (step 804), and if there is no temperature rise, the process returns to the start.

The fuzzy inference operation by the inference unit 7 ₁ is
It is performed in the same manner as in the above-described embodiment, and it is determined whether it is normal.

FIG. 25 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 security device of this embodiment is based on the outputs of the temperature rise detecting unit 6, the vibration detecting unit 5, the brightness detecting unit 32 for detecting the ambient brightness, and the detecting units 5, 6, 32. Then, a fuzzy inference is performed to determine whether or not there is an abnormality, and an inference apparatus 7 ₂ that basically issues an alarm when there is an abnormality based on the determination of this inference apparatus 7 ₂ is basically used. Is configured.

The temperature rise detecting section 6, the vibration detecting section 5 and the alarm device 8 are the same as those in the above-mentioned embodiment.

[0112] 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 machine installed in the room is dark.

[0113] 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,
Whether the vibration or temperature rise applied to the vending machine is due to normal factors or abnormal factors is determined by fuzzy inference as described later.

In this embodiment, the judgment by the inference unit 7 _{2 is made} by the membership functions shown in FIGS. 26 and 27 and the fuzzy rules shown in Tables 5 and 6 below.

That is, FIG. 26A 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,
The abrupt temperature rise has two labels of "absence" and "absence", and FIG. 26 (C) shows the membership function of the brightness data, that is, "bright" and "dark". Have a label.

FIG. 27 shows the membership function of the judgment output, which is the consequent part, and includes "abnormal", "slightly abnormal",
It has three labels of "normal".

The fuzzy rules are as shown in Tables 5 and 6 below. In Table 5, the brightness data is "bright".
The table below shows the rules for when the brightness data is "dark".
It shows the rules for.

[0118]

[Table 5]

[0119]

[Table 6]

FIG. 28 is a flow chart of the judgment processing of this embodiment.

First, after starting, each detecting section 5, 6, 3
The output of No. 2 is taken in (steps 900 to 902), it is determined whether or not there is vibration (step 903), and when there is vibration, fuzzy inference is performed (step 904). Judge (step 906)
When it is abnormal, the alarm device 8 is operated to notify it (step 907), when it is not abnormal, the process returns to the start, and when it is determined that there is no vibration in step 903, it is determined whether or not there is a temperature rise. If the temperature rises (step 905), the fuzzy reasoning is performed (step 904). If the temperature does not rise, the process returns to the start.

The fuzzy inference operation by the inference unit 7 ₂ is
It is performed in the same manner as in the above-described embodiment, and it is determined whether it is normal.

FIG. 29 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 security device of this embodiment includes a temperature rise detecting section 6, a vibration detecting section 5, an inclination detecting section 33 as an installation state detecting section for detecting the installation state of a vending machine, and each detecting section 5, 5. An inference device 7 that determines whether or not there is an abnormality by performing fuzzy inference based on the outputs of 6 and 33.
_3, based on the determination of the inference apparatus 7 _3, it is basically formed from the alarm device 8 which emits an alarm when an abnormality.

The temperature rise detecting section 6, the vibration detecting section 5 and the alarm device 8 are the same as those in the above-mentioned embodiment.

[0126] 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 tilt, 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.

[0127] 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 Whether the vibration or the temperature rise is due to a normal factor or an abnormal factor is determined by fuzzy inference as described later.

In this embodiment, the inference unit 7 ₃ makes the determination based on the membership functions shown in FIGS. 30 and 31 and the fuzzy rules shown in Tables 7 and 8 below.

That is, FIG. 30A 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,
The rapid temperature rise has two labels of "absence" and "absence", and FIG. 30C shows the membership function of the slope data, and the two labels "small" and "large". have.

FIG. 31 shows the membership function of the judgment output which is the consequent part, and includes "abnormal", "slightly abnormal",
It has three labels of "normal".

Fuzzy rules are as shown in Tables 7 and 8 below. Table 7 shows rules when the tilt data is "large", and Table 8 shows when the tilt data is "small". Shows the rules of.

[0132]

[Table 7]

[0133]

[Table 8]

FIG. 32 is a flow chart of the judgment processing of this embodiment.

First, after starting, each detecting section 5, 6, 3
3 output is captured (steps 1000-1002),
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 notify (step 1005). Return to.

The fuzzy inference operation by the inference unit 7 ₃ is
It is performed in the same manner as in the above-described embodiment, and it is determined whether it is normal.

FIG. 33 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 security device of this embodiment has a temperature rise detecting section 6, a vibration detecting section 5, and a key detecting section 34 for detecting 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 detecting section 6, the vibration detecting section 5 and the alarm device 8 are the same as those in the above-mentioned embodiment.

The key detection unit 34 is constructed so that the contact is turned on only when the regular key is inserted, and the contact is turned off when an object other than the regular key is inserted or nothing is inserted. Has been done.

[0141] 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 Whether the temperature rise is due to a normal factor or an abnormal factor is determined by fuzzy inference as described later.

In this embodiment, the judgment by the inference device is
This is performed by the membership functions shown in FIGS. 34 and 35 and the fuzzy rules in Tables 9 and 10 below.

That is, FIG. 34A 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,
The rapid temperature rise has two labels, "absent" and "absent", and FIG. 34C shows the membership function of the output of the key detection unit 34, which includes "regular" and "false". It has two labels.

FIG. 35 shows the membership function of the judgment output which is the consequent part, and includes "abnormal", "slightly abnormal",
It has three labels of "normal".

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

[0146]

[Table 9]

[0147]

[Table 10]

FIG. 36 is a flow chart of the judgment processing of this embodiment.

First, after starting, each detecting section 5, 6, 3
4 output is taken (steps 1100 to 1102),
Fuzzy inference is performed (step 1103), and as a result of the inference, it is determined whether or not there is an abnormality (step 1104). If there is an abnormality, the alarm device 8 is activated to notify (step 1105). Return to.

The fuzzy inference operation by the inference unit 7 ₄ is
It is performed in the same manner as in the above-described embodiment, and it is determined whether it is normal.

FIG. 37 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 security device of this embodiment includes the temperature rise detecting section 6, the vibration detecting section 5, the sound detecting section 35, the detecting sections 5 and 5.
Based on the output of the 6, 35, by performing the fuzzy inference, and determines inference apparatus 7 ₅ whether abnormalities, based on the determination of the inference apparatus 7 ₅ issues an alarm when an abnormality It is basically composed of an alarm device 8.

The temperature rise detecting section 6, the vibration detecting section 5 and the alarm device 8 are the same as those in the above-mentioned embodiment.

The sound detecting section 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 generated. It detects and prevents theft.

[0155] 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 Whether the temperature rise is due to a normal factor or an abnormal factor is determined by fuzzy inference as described later.

[0156] 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. 38 and 39.

That is, FIG. 38A 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,
38C shows the membership function of the output of the sound detection unit 35, which has two labels of "absence" and "presence". It has two labels.

FIG. 39 shows the membership function of the judgment output which is the consequent part, and includes "abnormal", "slightly abnormal",
It has three labels of "normal".

The fuzzy rules are as shown in Tables 11 and 12 below. Table 11 shows the rules when the output of the sound detector 35 is "high", and Table 12 shows the sound detector 3
5 shows the rule when the output of 5 is "small".

[0160]

[Table 11]

[0161]

[Table 12]

FIG. 40 is a flow chart of the judgment processing of this embodiment.

First, after starting, each of the detection units 5, 6, 3
Capture the output of step 5 (steps 1200 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 (step 1204). If there is an abnormality, the alarm device 8 is activated to notify (step 1205). Return to.

[0164] The fuzzy inference operation by the inference apparatus 7 _5,
It is performed in the same manner as in the above-described embodiment, and it is determined whether it is normal.

[0165]

As described above, according to the present invention, it is possible to reliably detect a sabotage of a vending machine or the like, such as melting using a burner or a laser without impact.

Further, it is possible to prevent malfunction due to inadvertent shock or earthquake.

[Brief description of drawings]

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

FIG. 2 is a block diagram of a vibration detector of FIG.

FIG. 3 is a flowchart of a determination process of a vibration detection unit.

FIG. 4 is a detailed cross-sectional view of the seismic switch in FIG.

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

FIG. 6 is an explanatory diagram of a characteristic amount of a pulse wave generated by a characteristic amount extraction unit.

FIG. 7 is a flowchart showing a procedure for calculating the number of pulses.

FIG. 8 is a flowchart showing a procedure for calculating a maximum ON time value and a maximum OFF time value.

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

FIG. 10 is a flowchart showing a procedure for calculating an ON time ratio maximum value and an OFF time ratio maximum value.

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

FIG. 12 is an explanatory diagram showing a relationship between a pulse number and a vibration excitation factor.

FIG. 13 is an explanatory diagram showing a relationship between ON time and OFF time and a vibration excitation factor.

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

FIG. 15 is an explanatory diagram of a membership function used in the vibration detection device.

FIG. 16 is an explanatory diagram of an inference result of the vibration detection unit.

FIG. 17 is a diagram showing an output value of a vibration detection unit.

18 is a diagram showing a membership function of an antecedent part used in the inference apparatus of FIG. 1. FIG.

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

20 is a flowchart of the determination process of FIG.

FIG. 21 is a block diagram of another embodiment of the present invention.

22 is a diagram showing a membership function of the antecedent part used in the inference apparatus of FIG. 21.

23 is a diagram showing the membership function of the consequent part used in the inference apparatus of FIG. 21.

24 is a flowchart of the determination process of FIG.

FIG. 25 is a block diagram of another embodiment of the present invention.

26 is a diagram showing a membership function of the antecedent part used in the inference apparatus of FIG. 25.

27 is a diagram showing the membership function of the consequent part used in the inference apparatus of FIG. 25.

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

FIG. 29 is a block diagram of another embodiment of the present invention.

30 is a diagram showing a membership function of the antecedent part used in the inference apparatus of FIG. 29.

31 is a diagram showing a membership function of a consequent part used in the inference apparatus of FIG. 29.

32 is a flowchart of the determination process of FIG.

FIG. 33 is a block diagram of another embodiment of the present invention.

34 is a diagram showing a membership function of the antecedent part used in the inference apparatus of FIG. 33.

35 is a diagram showing a membership function of a consequent part used in the inference apparatus of FIG. 33.

36 is a flowchart of the determination process of FIG. 33.

FIG. 37 is a block diagram of another embodiment of the present invention.

38 is a diagram showing the membership function of the antecedent part used in the inference apparatus of FIG. 37.

39 is a diagram showing the membership function of the consequent part used in the inference apparatus of FIG. 37.

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

[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 8 Alarm device 30 Operating state detection part 31 Timing part 32 Brightness detection part 33 Tilt detection part 34 Key detection Part 35 Sound detector

Claims (5)

[Claims] 1. A crime prevention device for preventing theft of a vending machine or the like, the vibration detecting section detecting vibration, a temperature rise detecting section detecting temperature rise, and an operating state of the vending machine etc. An operation state detecting unit for detecting the time, a time detecting unit for detecting the time, a key detecting unit for detecting whether a regular key is inserted in the vending machine or the like, a brightness detecting unit for detecting ambient brightness, At least one detection unit of an installation state detection unit and a sound detection unit that detects the installation state of the vending machine, and an inference unit that determines whether there is an abnormality based on the output of each detection unit. And a security device. 2. The crime prevention device according to claim 1, wherein the vibration detection unit gives an output corresponding to the presence or absence of vibration and an excitation factor of the vibration to the inference unit. 3. The crime prevention device according to claim 1, wherein the inference unit performs fuzzy inference to determine whether or not there is an abnormality. 4. The security device according to claim 3, wherein the fuzzy inference uses the MAX-MIN arithmetic method and the centroid method for defuzzification. 5. The crime prevention device according to any one of claims 1 to 4, further comprising an alarm device for issuing an alarm when an abnormality is determined based on the output of the inference unit.