Abnormality identification program and fire monitoring system therewith

Abstract

To provide an abnormality identification program for early identification of the occurrence of an abnormality that has a low probability of occurring under normal conditions, and a fire monitoring system equipped with the program.SOLUTION: An abnormality identification program for a fire monitoring system includes processes for: collecting a plurality of physical quantities x measured by a plurality of sensors over a predetermined time period and converting them into a normalized physical quantity y; calculating the sum Sprev of the physical quantity y multiplied by a weight w, where the weight w corresponding to the plurality of sensors are set in advance; collecting a plurality of physical quantity x measured by a plurality of sensors after a predetermined time elapses, converting them into a normalized physical quantity y, and calculating the sum Snew of the physical quantity y multiplied by the weight w; calculating the mean value and standard deviation σnew of the sum Snew; and determining whether the probability calculated by applying the mean value of the sum Snew, the standard deviation σnew, and the mean value of the sum Sprev to the cumulative distribution function is less than or equal to a threshold value, and determining that an abnormality has occurred if the probability is less than or equal to the threshold value.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an abnormality determination program that determines whether or not an abnormality has occurred based on physical quantities measured by a plurality of sensors, and a fire monitoring system equipped with the program. [Background technology]

[0002] For example, fire detectors, which are widely used in homes and other places, measure physical quantities such as room temperature and smoke density, and alert the user to a fire if the measured physical quantity exceeds a threshold. Meanwhile, large-scale facilities such as office buildings, tenant buildings, and logistics warehouses use fire monitoring systems that combine multiple analog sensors and receivers. The multiple analog sensors are installed in various monitored areas within the large-scale facility. Each of the multiple analog sensors outputs an analog signal to the receiver in accordance with its measured value. The receiver determines whether a fire or other abnormality has occurred based on the analog signals received from each of the multiple analog sensors. For example, the receiver can issue a warning to alert users if a physical quantity measured is below a threshold for determining whether a fire has occurred. The threshold for determining whether to issue a warning can be set to a different value for each analog sensor or each monitored area.

[0003] Another known conventional fire detector is a combined spot detector, which is capable of measuring two or more physical quantities, such as temperature and smoke density, and alerts the occurrence of a fire when any one of the physical quantities exceeds a threshold value.

[0004] Furthermore, Patent Publication No. 2018-88630 (Patent Document 1) and Patent Publication No. 2018-101416 (Patent Document 2) propose a fire monitoring system that generates a large number of images of a monitored area taken by a surveillance camera, and uses these images to perform deep learning on a multi-layer neural network, thereby improving the accuracy of fire detection. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2018-88630 [Patent Document 2] Japanese Patent Application Publication No. 2018-101416 Summary of the Invention [Problem to be solved by the invention]

[0006] A receiver in a fire monitoring system can issue a warning when a physical quantity measured is below a threshold for determining whether a fire has occurred. However, the receiver can only compare the physical quantity measured by each of multiple analog detectors with a threshold. The receiver cannot issue a warning that takes into account the interrelationships between the multiple analog detectors, such as the relationship between different physical quantities such as temperature and smoke density, the relative positions of the multiple analog detectors, the correlation between the measurement results of the multiple analog detectors, or the effects of external disturbances on each of the multiple analog detectors.

[0007] Conventional combined spot detectors alert the occurrence of a fire when one of two or more physical quantities, such as temperature and smoke density, exceeds a threshold. In other words, combined spot detectors compare each of the two or more physical quantities with a threshold to determine whether a fire has occurred. For this reason, combined spot detectors cannot determine whether a fire has occurred by combining two or more physical quantities.

[0008] The fire monitoring systems of JP 2018-88630 A and JP 2018-101416 A both require deep learning of a multilayer neural network based on a large number of images. Furthermore, image-based deep learning does not allow weighting that takes into account the interrelationships between multiple analog detectors, as described above. Furthermore, the computational load required for fire detection using a multilayer neural network is extremely large.

[0009] The present invention has been made in consideration of the above-mentioned problems, and aims to provide an abnormality detection program and a fire monitoring system equipped with the same that can quickly detect the occurrence of an abnormality that is unlikely to occur under normal circumstances by combining the measurement results of multiple sensors and taking into account the interrelationships between the multiple sensors. [Means for solving the problem]

[0010] (A) In order to achieve the above object, the anomaly discrimination program of the present invention is an anomaly discrimination program that discriminates whether an anomaly has occurred based on signals output from a plurality of sensors for measuring physical quantities x that change due to the occurrence of a fire, and includes a first process of collecting a plurality of physical quantities x measured by the plurality of sensors over a predetermined time or a predetermined number of times under normal conditions in which no fire has occurred, and converting each of the collected physical quantities x into a normalized physical quantity y; and a second process of converting a weight w corresponding to each of the plurality of sensors into a sum S of the physical quantities y multiplied by the weight w. prev and a second process of collecting a plurality of physical quantities x measured by a plurality of sensors after the predetermined time has elapsed or the predetermined number of times has been exceeded, converting each of the collected physical quantities x into a normalized physical quantity y, and calculating a sum S of the physical quantities y multiplied by a weight w. new The third process calculates the sum S new Average value of [Table 1] and standard deviation σ new The fourth process calculates the average value [Table 2] , standard deviation σ new and the sum S prev Average value of [Table 3] and fifth processing of determining whether or not the probability calculated by applying the above formula to the cumulative distribution function is equal to or less than a preset probability threshold, and determining that an abnormality has occurred if the probability is equal to or less than the threshold.

[0011] (B) Preferably, in the anomaly discrimination program of (A) above, each of the physical quantities x collected in the first process and the third process is converted into a normalized physical quantity y by the following formula (1):

number

[0012] (C) Preferably, in the abnormality discrimination program of (A) or (B), the sum S in the second process and the third process prev and the sum S new is calculated by one of the following formulas (2) to (4).

number

number

number

[0013] (D) Preferably, in the anomaly discrimination program of any one of (A) to (C) above, the probability in the fifth process is calculated based on the value of t calculated by the following formula (5):

number

[0014] (E) Preferably, in any of the abnormality discrimination programs (A) to (D) above, the value of the weight w used in the second process and the third process is set or changed based on the influence of a disturbance on at least one of the multiple sensors.

[0015] (F) Preferably, in the abnormality detection program of (E) above, when at least one of the plurality of sensors is affected by the temperature of the air conditioner, the value of the weight w applied to at least one of the plurality of sensors is changed based on information regarding the operation of the air conditioner.

[0016] (G) Preferably, in the abnormality detection program of (E) above, when at least one of the plurality of sensors is affected by temperature due to sunlight, the value of the weight w applied to at least one of the plurality of sensors is changed based on at least one of the time of sunlight, the amount of sunlight, and the temperature.

[0017] (H) Preferably, in the anomaly determination program of any one of (A) to (G) above, in the first process, a random number is generated to increase the significant figures of the physical quantity x, and the random number is added to each of the plurality of physical quantities x measured by the plurality of sensors, and then a normalized physical quantity y is calculated.

[0018] (I) In order to achieve the above object, the fire monitoring system of the present invention is a fire monitoring system that operates according to any of the abnormality discrimination programs (A) to (H) above, and includes a plurality of sensors and a receiver, the receiver includes a receiving unit, a control unit, and an alarm unit, and the abnormality discrimination program is installed, the plurality of sensors are installed in a monitored area, measure a physical quantity x that changes due to the occurrence of a fire, and output a signal, the receiving unit receives the signals output from the plurality of sensors, a processor of the control unit executes the first process to the fifth process in accordance with the abnormality discrimination program based on the plurality of physical quantities x measured by the plurality of sensors, and the alarm unit notifies that an abnormality has occurred if it is determined that an abnormality has occurred in the fifth process.

[0019] (J) Preferably, in the fire monitoring system of (I) above, the anomaly discrimination program includes a sixth process of determining whether a physical quantity x measured by at least one of the plurality of sensors is equal to or greater than a predetermined threshold value of the physical quantity x, and discriminating that a fire has occurred if the physical quantity x measured by at least one of the plurality of sensors is equal to or greater than the threshold value, and the processor of the control unit executes the sixth process in accordance with the anomaly discrimination program based on each of the plurality of physical quantities x measured by the plurality of sensors, and the alarm unit issues an alarm if it is determined in the sixth process that a fire has occurred.

[0020] (K) In order to achieve the above object, the fire monitoring system of the present invention is a fire monitoring system that operates according to any of the abnormality discrimination programs (A) to (H) above, and includes a plurality of sensors, at least one repeater, and a receiver, the repeater includes a receiving unit and a control unit, and the abnormality discrimination program is installed, the plurality of sensors are installed in a monitored area, measure a physical quantity x that changes due to the occurrence of a fire, and output a signal, the receiving unit receives the signals output from the plurality of sensors, and a processor of the control unit executes the first process to the fifth process in accordance with the abnormality discrimination program based on the plurality of physical quantities x measured by the plurality of sensors, and if it is determined in the fifth process that an abnormality has occurred, transmits a signal to another repeater or the receiver.

[0021] (L) Preferably, the fire monitoring system of (K) above includes a sixth process of determining whether a physical quantity x measured by at least one of the plurality of sensors is equal to or greater than a preset threshold value of the physical quantity x, and determining that a fire has occurred if the physical quantity x is equal to or greater than the threshold value, and the processor of the control unit executes the sixth process in accordance with the abnormality determination program based on each of the plurality of physical quantities x measured by the plurality of sensors, and transmits a signal to another repeater or the receiver if it is determined in the sixth process that a fire has occurred.

[0022] (M) In order to achieve the above object, the abnormality discrimination program of the present invention is an abnormality discrimination program that discriminates whether an abnormality has occurred based on signals output from a plurality of sensors for measuring physical quantities x, and includes a first process of collecting a plurality of physical quantities x measured by the plurality of sensors over a predetermined time or a predetermined number of times, and converting each of the collected physical quantities x into a normalized physical quantity y; and a second process of converting the sum S of the physical quantities y into a normalized physical quantity y. prev a second process of collecting a plurality of physical quantities x measured by a plurality of sensors after the predetermined time has elapsed or the predetermined number of times has been exceeded, converting each of the collected physical quantities x into a normalized physical quantity y, and calculating a sum S of the physical quantities y; newThe third process calculates the sum S new Average value of [Table 6] and standard deviation σ new The fourth process calculates the average value [Table 7] , standard deviation σ new and the sum S prev Average value of [Table 8] and fifth processing of determining whether or not the probability calculated by applying the above formula to the cumulative distribution function is equal to or less than a preset probability threshold, and determining that an abnormality has occurred if the probability is equal to or less than the threshold.

[0023] (N) Preferably, in the abnormality discrimination program of (M) above, a weight w corresponding to each of the plurality of sensors is set in advance, and in the second process, the sum S of the physical quantities y multiplied by the weight w is calculated. prev is calculated, and in the third process, the sum S of the physical quantities y multiplied by the weight w is calculated. new is calculated. [Effects of the Invention]

[0024] According to the abnormality detection program of the present invention and the fire monitoring system equipped with the same, by combining the measurement results of multiple sensors and taking into account the interrelationships between the multiple sensors, it becomes possible to quickly determine that an abnormality has occurred that is unlikely to occur under normal circumstances. [Brief explanation of the drawings]

[0025] [Figure 1] FIG. 1 is a schematic diagram showing a first embodiment of a fire monitoring system equipped with an abnormality determination program of the present invention. [Figure 2]FIG. 2 is a block diagram showing a receiver that constitutes the above-mentioned fire monitoring system. [Figure 3] FIG. 3 is an explanatory diagram for explaining a physical quantity x measured by a plurality of sensors and a normalized physical quantity y. [Figure 4] FIG. 4 is an explanatory diagram for explaining the sum S of the physical quantities y. [Figure 5] FIG. 5 is an explanatory diagram for explaining the principle of abnormality discrimination in the abnormality discrimination program. [Figure 6] Figure 6 shows the conditions of the monitoring area set to simulate the abnormality detection program, where Figure 6(a) is a side view of the ceiling, sensors 1 to 4, and the fire source, and Figure 6(b) is a front view of the ceiling and sensors 1 to 4. [Figure 7] FIG. 7(a) is a graph showing the temperature at the position of sensor 1 calculated in the above simulation, and FIG. 7(b) is a graph showing the temperature at the position of sensor 2 calculated in the above simulation. [Figure 8] FIG. 7(a) is a graph showing the temperature at the position of sensor 3 calculated in the above simulation, and FIG. 7(b) is a graph showing the temperature at the position of sensor 4 calculated in the above simulation. [Figure 9] FIG. 9 is a graph showing the values ​​of t calculated in the above simulation and the probability values ​​of the t distribution calculated from the values ​​of t. [Figure 10] FIG. 10 is a schematic diagram showing a second embodiment of a fire monitoring system equipped with an abnormality determination program of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0026] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of an abnormality discrimination program and a fire monitoring system including the program according to the present invention will be described below with reference to the accompanying drawings.

[0027] The anomaly discrimination program of the present invention discriminates whether an anomaly has occurred based on the probability that a measured physical quantity x occurs. Therefore, the discrimination target of the anomaly discrimination program of the present invention is not limited to anomalies caused by fires. However, for convenience of explanation, an embodiment of the anomaly discrimination program and the fire monitoring system will be described using an anomaly caused by a fire as an example of the discrimination target.

[0028] 1. First embodiment of fire monitoring system FIG. 1 shows a fire monitoring system according to a first embodiment of the present invention. The fire monitoring system according to this embodiment is installed in a large-scale facility, such as an office building, a tenant building, or a logistics warehouse. The fire monitoring system is primarily composed of multiple sensors 1 to 4 and a receiver 10. The sensors 1 to 4 are installed, for example, on the ceiling of a predetermined monitoring area in the large-scale facility. Although not shown, numerous sensors, each with the same or different configuration as the sensors 1 to 4, are installed in monitoring areas throughout the large-scale facility. Signals output from the sensors 1 to 4 and other sensors are input to the receiver 10 via a signal line 5 indicated by a dashed line in FIG. 1. The receiver 10 monitors the entire large-scale facility based on the signals output from the sensors 1 to 4 and other sensors. The receiver 10 can monitor for fires and other problems. Problems other than fires include, for example, gas-related problems, electrical problems, problems with fire prevention equipment installed in the large-scale facility, and abnormalities in communication between the receiver 10 and peripheral devices linked to the receiver 10.

[0029] 1.2 Multiple Sensors The multiple sensors 1 to 4 are, for example, analog fire detectors. The sensors 1 to 4 have the same configuration and include at least one detection element for measuring temperature and / or smoke density (physical quantity x). In a predetermined monitoring area, each of the multiple sensors 1 to 4 measures the physical quantity x that changes due to the occurrence of a fire and generates an analog signal corresponding to the physical quantity x. Furthermore, each of the multiple sensors 1 to 4 converts the analog signal corresponding to the physical quantity x into a digital signal and outputs it. This digital signal includes an address assigned to each of the multiple sensors 1 to 4. The digital signal output from each of the multiple sensors 1 to 4 is input to the receiver 10 via a signal line 5.

[0030] The multiple sensors 1 to 4 are not limited to multiple fire detectors. For example, the multiple sensors 1 to 4 may be differential distributed detectors. A differential distributed detector is typically configured with multiple thermocouples electrically connected at predetermined intervals along a single electric wire. Each of the multiple thermocouples generates an electromotive force corresponding to the amount of heat detected.

[0031] The sensors 1 to 4 may also be temperature measuring devices using optical fibers. This temperature measuring device is primarily configured with a single optical fiber, a laser light source, and a light detection unit. The optical fiber is installed, for example, on the ceiling of a predetermined monitoring area in a large-scale facility. The laser light source emits pulsed laser light into the input end of the optical fiber. A portion of the incident light is reflected by glass particles in the optical fiber, generating reflected light of Rayleigh scattered light, Brillouin scattered light, and Raman scattered light. These reflected light beams return to the input end. Of these reflected light components, the intensity of the Raman scattered light and the frequency of the Brillouin scattered light change depending on the temperature of the optical fiber. Therefore, it is possible to measure the temperature based on the intensity of the Raman scattered light and the frequency of the Brillouin scattered light. Furthermore, it is possible to identify the position on the optical fiber where the reflected light originates based on the time it takes for the reflected light to return after the incident light enters.

[0032] 1.3 Receiver A receiver 10 shown in FIG. 1 is installed, for example, in a disaster prevention center provided in a large-scale facility. The receiver 10 is mainly composed of a receiving unit 11, a control unit 12, and a notification unit 13 shown in FIG. 2. In FIG. 2, the receiving unit 11 is electrically connected to a plurality of sensors 1 to 4 via a signal line 5 in FIG. 1. The receiving unit 11 receives digital signals output from the plurality of sensors 1 to 4. These digital signals include physical quantities x measured by each of the plurality of sensors 1 to 4. The receiving unit 11 transmits the received digital signals to a control unit 12. For convenience of explanation, the digital signals processed by the control unit 12 will be expressed as the physical quantities x measured by the plurality of sensors 1 to 4.

[0033] The control unit 12 includes a processor for executing calculation processing. The processor of the control unit 12 executes the processes of steps S1 to S10, S11 to S14, and S21 to S26 shown in Fig. 2 in accordance with the abnormality discrimination program according to this embodiment. The abnormality discrimination program is capable of discriminating the occurrence of a fire in the same manner as conventional fire monitoring systems, and also of discriminating slight abnormalities in the early stages of a fire. The abnormality discrimination program according to this embodiment will be described in detail later.

[0034] 1, and an LED and a speaker (not shown). The notification unit 13 displays various information on the touch panel based on a signal output from the control unit 11. For example, when the control unit 12 detects the occurrence of a fire or an abnormality, the notification unit 13 notifies the occurrence of the fire or abnormality by an image displayed on the touch panel, the color of the LED light, and a sound output from the speaker.

[0035] 1.4 Abnormality detection program The processing of the control unit 12 according to the abnormality determination program of this embodiment will be described. In Fig. 2, steps S1 to S10 are the processing of the main routine of the abnormality determination program. Furthermore, steps S11 to S14 and S21 to S26 are the processing of subroutines accompanying the main routine.

[0036] 1.4.1 Fire detection processing The control unit 12 executes a fire detection process in step S1 based on the physical quantity x measured by the multiple sensors 1 to 4. In the fire detection process S1, the control unit 12 compares the physical quantity x with a first threshold value set in advance in step S11. For example, if the physical quantity x is a temperature, a temperature of "70°C" is set as the first threshold value. In this case, the control unit 12 determines whether the physical quantity x is 70°C or higher. If the result of this determination is that the physical quantity x is 70°C or higher (YES), the control unit 12 transmits a signal to the alarm unit 13 to notify the occurrence of a fire. On the other hand, if the temperature is not 70°C or higher (NO), the control unit 12 executes a physical quantity x collection process in step S2.

[0037] In the abnormality determination program of this embodiment, the fire determination process S1 is executed at the earliest stage, so that even if a fire does break out, the occurrence of the fire is quickly notified by the alarm unit 13. As a result, the disaster prevention center where the receiver 10 is installed can quickly extinguish the fire in its initial stage.

[0038] 1.4.2 Physical quantity collection processing In the physical quantity x collection process of step S2, the control unit 12 continuously collects the physical quantity x measured by the multiple sensors 1 to 4. The control unit 12 collects the physical quantity x at predetermined time intervals for each of the multiple sensors 1 to 4. The time interval for collecting the physical quantity x is determined depending on the number of the multiple sensors 1 to 4, the length of the signal line 5, the processing capacity of the processor of the control unit 12, etc.

[0039] In step S21, the collected physical quantities x are stored in chronological order in databases corresponding to the plurality of sensors 1 to 4. Every time a new physical quantity x is measured, the physical quantity x is accumulated in the database. When the storage area of ​​the database is filled with the physical quantities x, the oldest physical quantity x is deleted and the latest physical quantity x is added. In this way, the physical quantities x stored in the database are constantly updated during operation of the abnormality determination program of this embodiment.

[0040] Here, the significance of the physical quantity x stored in the database in step S21 will be explained. First, the physical quantity x stored in the database is determined to be less than the first threshold in the fire discrimination process S1 described above. That is, all of the physical quantities x stored in the database are measured under conditions in which no fire has been reported. Next, the physical quantities x stored in the database may have been measured under either normal or abnormal conditions. A normal condition is a condition in which no event that could cause a fire has occurred. An abnormal condition is a condition in which no fire has been reported yet, but an event that could cause a fire has occurred. Then, the collection of time-series physical quantities x measured under normal conditions serves as a basis for determining whether or not an abnormality has occurred thereafter.

[0041] 1.4.3 Random number addition process In the random number addition process of step S3, the control unit 12 generates a random number for increasing the significant digits of the physical quantity x and adds the random number to each of the physical quantities x collected in step S2. The random number addition process S3 is selectively executed depending on the performance of the multiple sensors 1 to 4. That is, the random number addition process S3 is a process for improving the accuracy of the probability calculation process of step S9 (described later) and is executed when the least significant digit of the physical quantity x is insufficient. In the random number addition process S3, the control unit 12 generates a random number with n decimal places that does not substantially affect the physical quantity x measured by the multiple sensors 1 to 4 and adds this random number to the physical quantity x. In step S21 of FIG. 2, the physical quantity x to which the random number has been added is stored in chronological order in a database corresponding to each of the multiple sensors 1 to 4. On the other hand, if the physical quantity x measured by the multiple sensors 1 to 4 has n significant digits, the control unit 12 proceeds to the physical quantity y conversion process of step S4 without executing the random number addition process S3.

[0042] 1.4.4 Physical quantity y conversion process In the physical quantity y conversion process in step S4, the control unit 12 converts each of the physical quantities x collected in step S2 or each of the physical quantities x to which a random number has been added in step S3 into a normalized physical quantity y using the following equation (1).

number

[0043] Here, normalization in the conversion process to the physical quantity y will be described with reference to FIG. 3. As shown in FIG. 3, the physical quantity x measured by the multiple sensors 1 to 4 is a continuous variable and follows a normal distribution N(μ,σ) with a mean value μ and a standard deviation σ. However, the multiple sensors 1 to 4 are installed under different conditions in the predetermined monitoring area shown in FIG. 1. For example, the multiple sensors 1 to 4 are installed at different positions on the ceiling and at different distances from the air outlet of the air conditioner 100. Therefore, the mean value μ and standard deviation σ of the physical quantity x measured by the multiple sensors 1 to 4 will be different from one another. Therefore, the physical quantity x measured by the multiple sensors 1 to 4 is converted into a normalized physical quantity y using the above formula (1). As shown in FIG. 3, the normalized physical quantity y follows a standard normal distribution N(0,1) with a mean value 0 and a standard deviation 1. That is, the normal distribution N(μ,σ) of the physical quantity x measured by the multiple sensors 1 to 4 is aligned with the standard normal distribution N(0,1) of the normalized physical quantity y. A collection of the normalized physical quantities y forms a population that follows the normal distribution N(0,1). In step S22 of FIG. 2, the normalized physical quantities y are stored in chronological order in databases corresponding to the multiple sensors 1 to 4, respectively.

[0044] The average value in the above formula (1) [Table 10] The physical quantity x stored in the database in step S21 is used to calculate the average value. [Table 11] The number of data points of the physical quantity x used to calculate is not particularly limited. For example, the average value [Table 12] At the timing of calculating the physical quantity x, all the physical quantities x stored in the database in step S21 may be used. Also, for example, among the physical quantities x stored in the database in step S21, the physical quantities x for the most recent tens of minutes or tens of seconds may be calculated as an average value. [Table 13] It may also be used to calculate

[0045] 1.4.5 Sum S prev Calculation process The sum S of step S5 prev In the calculation process, the control unit 12 acquires the physical quantity y from the database corresponding to each of the plurality of sensors 1 to 4 in step S22, and calculates the sum S of the physical quantities y multiplied by the weight w. prev is calculated using the following formula (2).

number

[0046] In step S12, the weight w is set in advance for each of the multiple sensors 1 to 4 based on the interrelationships between the sensors 1 to 4 (including the interrelationships between the same sensors). For example, Table 1 below shows weights w set based on the mutual distances r between the multiple sensors 1 to 4. [Table 14]

[0047] The weight w shown in Table 1 is set to "1" when the distance r from sensor P to sensor Q is in the relationship of r≦0.18H with the height H (m) from the floor to the ceiling, and set to "r -3 / 2 "

[0048] The sum S of the above formula (2) prev is the physical quantity y of sensor Q for each sensor P. (Q) Weight w (Q,P) 4(a) to (d) in FIG. 4 show the physical quantity y in the above formula (2) when the sensors P and Q are sensors 1 to 4, respectively. (Q) and weight w (Q,P) The physical quantities y (Q) Each of these has a weight w (Q,P) The sum of all the multiplied values ​​is the sum S of the above formula (2). prev is.

[0049] In addition, the total S prev The formula for calculating may be either formula (3) or (4) below.

number

number

[0050] Sumwa S prev The physical quantity y used to calculate (p) and physical quantity y (Q)The number of data is not particularly limited. For example, the sum S prev At the timing of calculating all the physical quantities y (p) and physical quantity y (Q) Alternatively, for example, the physical quantity y stored in the database in step S22 may be used. (p) and physical quantity y (Q) Among these, the physical quantity y for the most recent few tens of minutes or tens of seconds (p) and physical quantity y (Q) The sum S prev It may also be used to calculate

[0051] Sumwa S prev In the calculation process S5, the sum S is repeatedly calculated until it reaches a preset number of times. prev is calculated. The repeated sum S prev The time interval at which the latest physical quantity y is calculated is not particularly limited. For example, as the shortest time interval, (p) and physical quantity y (Q) Each time is stored in the database in step S22, the sum S prev If the time interval is longer than the shortest time interval, the sum S prev The time interval at which is calculated can be set arbitrarily.

[0052] Sumwa S prev The sum S repeatedly calculated by the calculation process S5 prev is stored in the database in step S23 of FIG. 2. The sum S prev Based on the sum S prev Average value of [Table 15] is calculated. [Table 16] is used to determine whether or not there is an abnormality in the physical quantity x newly measured by the plurality of sensors 1 to 4 in step S9, which will be described later.

[0053] 1.4.6 Sum S new Calculation process The control unit 12 calculates the sum S prev After the calculation process S5 is completed, the sum S new Proceed to calculation process. Sum S new In the calculation process S6, similarly to steps S2 to S5 described above, the control unit 12 collects a plurality of physical quantities x measured by the plurality of sensors 1 to 4, converts each of the collected physical quantities x into a normalized physical quantity y, and calculates the sum S of the physical quantities y multiplied by the weight w. new The above formula (1) is used to convert the measured physical quantity x into the normalized physical quantity y. new To calculate the sum, any one of the above formulas (2) to (4) is used. new In the calculation process S6, the sum S is repeatedly calculated until it reaches a preset number of times. new is calculated. The sum of the repeated calculations S new is stored in the database in step S24.

[0054] Sumwa S new The physical quantity y used to calculate (p) and physical quantity y (Q) The number of data is not particularly limited, but the above-mentioned sum S prev In the calculation process S5, the sum S prev The physical quantity y used to calculate (p) and physical quantity y (Q) It is preferable that the number of data is the same as or close to the number of data.

[0055] Also, the sum S new The number of times and the time interval at which the calculation of the sum S prev In the calculation process S5, the sum S prev It is preferable that the number of times and the time intervals at which the calculation of is repeated are the same as or approximate to the number of times and the time intervals at which the calculation of is repeated.

[0056] 1.4.7 Average [Table 17] Calculation process Average value of step S7 [Table 18] In the calculation process, the control unit 12 calculates the sum S new Average value of [Table 19] Calculate the average value [Table 20] The sum S calculated in the calculation process S7 new Average value of [Table 21] is stored in the database in step S25.

[0057] 1.4.8 Standard deviation σ new Calculation process Standard deviation σ of step S8 new In the calculation process, the control unit 12 calculates the sum S new Standard deviation σ new Calculate the standard deviation σ new The standard deviation σ calculated in the calculation process S8 new is stored in the database in step S26.

[0058] 1.4.9 Probability calculation process In the probability calculation process of step S9, the control unit 12 calculates the average value [Table 22] , standard deviation σ new and the sum S prev Average value of [Table 23] is applied to the cumulative distribution function. For example, the following formula (5) is used as a variable for the cumulative distribution function. The control unit 12 calculates the probability value of the t-distribution based on the value of t calculated by the following formula (5).

number

[0059] 1.4.10 Abnormality detection process In the abnormality determination process in step S10, the control unit 12 compares the probability value calculated in the probability calculation process in step S9 with a second threshold value set in advance in step S13. The second threshold value is, for example, 1.0×10 -4 ~1.0×10 -8 A low probability value within the range is set.

[0060] The control unit 12 determines whether the calculated probability value is equal to or less than the second threshold value. If it is determined that the calculated probability value is equal to or less than the second threshold value (YES), the control unit 12 transmits a signal to the alarm unit 13 to notify the occurrence of an abnormality. Thereafter, the control unit 12 acquires the updated physical quantity y from the database in step S22 and repeats the processing of steps S5 to S10. On the other hand, if it is determined that the calculated probability value is not equal to or less than the second threshold value (NO), the control unit 12 acquires the updated physical quantity y from the database in step S22 without transmitting a signal to the alarm unit 13 and repeats the processing of steps S5 to S10.

[0061] 1.4.11 Principle of abnormality detection The principle of anomaly discrimination in the above-mentioned anomaly discrimination program will be described with reference to FIG. 5. The mean value of a sample population sampled from a population of normal distribution N(μ,σ) with mean value μ and standard deviation σ is [Table 25] , standard deviation is σ samp When t is calculated by the following formula (6), it follows the t distribution shown in FIG.

number

[0062] Therefore, in the abnormality discrimination program of this embodiment, in steps S1 to S5 of FIG. 2, the sum S of the physical quantities y that form the population is calculated based on the physical quantity x in the normal state. prev Then, in step S6 of FIG. 2, the newly measured physical quantity x is converted into a normalized physical quantity y, and the sum S of the physical quantities y is calculated. new Calculate the sum S new is the sum S prev Although it is not a sample population sampled from prev 2, t is calculated, and the probability value of the t distribution is derived from the value of t. In step S10, if the probability value is equal to or less than the second threshold, it is determined that an event with a low probability of occurring under normal circumstances, i.e., an abnormality, has occurred.

[0063] 1.4.12 Changing the weight w setting The value of the weight w preset in step S12 of FIG. 2 may be changed based on the influence of a disturbance on at least one of the plurality of sensors 1 to 4.

[0064] For example, if at least one of the multiple sensors 1 to 4 is affected by the temperature caused by the air conditioner 100 shown in FIG. 1, the value of the weight w applied to at least one of the multiple sensors 1 to 4 is changed based on information related to the operation of the air conditioner 100. Information related to the operation of the air conditioner 100 includes, for example, information on whether the air conditioner 100 is ON / OFF, information on the set temperature of the air conditioner 100, etc. This information is provided to the control unit 12, which then executes the setting change process of step S14. For example, if the sensor 3 shown in FIG. 1 is affected by the temperature caused by the air conditioner 100, the control unit 12 changes the setting of the weight w value set corresponding to the sensor 3 to "0" as shown in Table 2 below. As a result, the sum S prev and the sum S new The measurement result of sensor 3 is excluded from the calculation. [Table 27]

[0065] For example, if at least one of the multiple sensors 1 to 4 is affected by the temperature due to sunlight, the value of the weight w applied to at least one of the multiple sensors may be changed based on at least one of the time of sunlight, the amount of sunlight, and the temperature of the ceiling.

[0066] 1.5 Effects According to the anomaly detection program of this embodiment and the fire monitoring system equipped with the same, by combining the physical quantities x measured by the multiple sensors 1 to 4 and applying a weight w that takes into account the interrelationships between the multiple sensors 1 to 4, it becomes possible to quickly detect the occurrence of an anomaly that is unlikely to occur under normal circumstances.

[0067] In addition, the sum S is used to determine whether an abnormality has occurred. previs calculated based on the physical quantity x measured by the multiple sensors 1 to 4, there is no need to perform deep learning on the neural network. Furthermore, the calculation processing executed in steps S3 to S10 in Fig. 2 makes it possible to determine whether an abnormality has occurred, and the calculation load for the determination is extremely small.

[0068] 2. Simulation of anomaly detection program A simulation of the anomaly discrimination program of the present invention was carried out using spreadsheet software "Microsoft Excel (registered trademark)" from Microsoft Corporation (registered trademark).

[0069] 2.1 Setting monitoring area conditions First, the conditions of the monitoring area to which the abnormality discrimination program of the present invention is applied were set as shown in Figures 6(a) and (b). In Figures 6(a) and (b), the monitoring area was set to have a ceiling and a floor, with no walls between the ceiling and the floor. Four sensors 1 to 4 were installed on the ceiling, and one fire source was installed on the floor.

[0070] The ceiling is 10m x 10m = 100m 2 The ceiling is a square and is set to be located at a height H = 8m from the floor. The four sensors 1 to 4 are set to be located at a distance r of 5m from each other and 2.5m from each of the two sides of the ceiling. In other words, the ceiling is divided into four parts, 5m x 5m = 25m. 2 One sensor will be located at the center of the

[0071] As shown in Figure 6(b), the fire source was set to be located 1 m vertically and 1 m horizontally from the center of the ceiling toward sensor 1. A six-tiered crib made of evenly spaced wooden pieces was assumed as the fire source. The assumption of a six-tiered crib is reflected in the value of the fire growth coefficient α used to calculate the heat release rate Q of the fire source, as described below.

[0072] 2.2 Calculation of heat release rate Q of fire source A quadratic function model Q=αt is used as a fire growth model for the fire source. 2 was used. Q is the heat release rate of the fire source, and represents the amount of heat (kW) per second. α is the fire growth coefficient, and t is time (seconds). The fire growth coefficient α is 0.007kW / s assuming a six-stage crib. 2 The heat release rate Q of the fire source is calculated repeatedly over a predetermined period of time, for example, by adding time t in increments of 1 second. When repeatedly calculating the heat release rate Q, the time t = 0 is set as the ignition point of the fire source.

[0073] 2.3 Calculation of ceiling temperature T By substituting the heat release rate Q (kW) of the fire source, the ceiling height H (m), and the horizontal distance r (m) between the fire source and the sensor into Alpert's equation (7) below, the ceiling flow temperature rise ΔT (°C) at each position of sensors 1 to 4 is calculated. For example, the ceiling flow temperature rise ΔT over a specified time period is calculated based on the heat release rate Q calculated every second.

number

[0074] ΔT indicates the "temperature rise" of the ceiling airflow, and ΔT plus the ambient temperature T0 (= ΔT + T0) is the "temperature" of the ceiling airflow at each of the positions of sensors 1 to 4. Furthermore, in this simulation, a random number is added to the ceiling airflow temperature T calculated by ΔT + T0, so that it follows a standard normal distribution N(0,1) with a mean value of 0 and a standard deviation of 1.

[0075] 2.4 Example of calculation of ceiling temperature T Below, we will explain a specific example of the calculation of the ceiling air temperature T at each position of sensors 1 to 4. The following specific example is a single calculation when time t=250 seconds and ambient temperature T0=20°C.

[0076] First, calculate the heat release rate Q of the fire source. When time t = 250 seconds, the heat release rate Q is 0.007 x 2502 It becomes 437.5 kW. This means that the amount of heat of 437.5 kJ is generated by combustion per second.

[0077] Next, the horizontal distance r (m) between the ignition source and the sensor is calculated. When the horizontal distance between the ignition source and sensor 1 shown in Fig. 6(b) is r1, the horizontal distance between the ignition source and sensor 2 is r2, the horizontal distance between the ignition source and sensor 3 is r3, and the horizontal distance between the ignition source and sensor 4 is r4, the respective horizontal distances r1, r2, r3, and r4 are as shown in the following formulas (8) to (11).

Number

Number

Number

Number

[0078] Next, the ceiling flow temperature rise ΔT (°C) at each position of sensors 1 to 4 is calculated using the above formula (7).

[0079] Since the horizontal distance r1 of sensor 1 is 2.12 m and the ceiling height H is 8 m, r1 / H = 0.265. Therefore, the ceiling flow temperature rise ΔT1 at the position of sensor 1 is calculated using the formula corresponding to (0.18 < r / H) among the two formulas in the above formula (7). The ceiling flow temperature rise ΔT1 is as shown in the following formula (12), and ΔT1 = 23.5 °C.

Number

[0080] Since the horizontal distance r2 of sensor 2 is 3.81 m and the ceiling height H is 8 m, r2 / H = 0.476. Therefore, the ceiling flow temperature rise ΔT2 at the position of sensor 2 is calculated using the equation corresponding to (0.18 < r / H) among the two equations in the above equation (7). The ceiling flow temperature rise ΔT2 is as shown in the following equation (13), and ΔT2 = 15.9 °C.

Number

[0081] Since the horizontal distance r3 of sensor 3 is 4.95 m and the ceiling height H is 8 m, r3 / H = 0.619. Therefore, the ceiling flow temperature rise ΔT3 at the position of sensor 3 is calculated using the equation corresponding to (0.18 < r / H) among the two equations in the above equation (7). The ceiling flow temperature rise ΔT3 is as shown in the following equation (14), and ΔT3 = 13.3 °C.

Number

[0082] Since the horizontal distance r4 of sensor 4 is 3.81 m and the ceiling height H is 8 m, r4 / H = 0.476. Therefore, the ceiling flow temperature rise ΔT4 at the position of sensor 4 is calculated using the equation corresponding to (0.18 < r / H) among the two equations in the above equation (7). The ceiling flow temperature rise ΔT4 is as shown in the following equation (15), and ΔT4 = 15.9 °C.

Number

[0083] Next, the ambient temperature T0 is added to each of the ceiling flow temperature rises ΔT1 to ΔT4 to calculate the ceiling flow temperatures T1 to T4 at the respective positions of sensors 1 to 4. As described above, the ambient temperature T0 was set to 20 °C. The ceiling flow temperatures T1 to T4 at the respective positions of sensors 1 to 4 are as shown in the following equations (16) to (19).

Number

number

number

number

[0084] Finally, by adding random numbers to each of the ceiling flow temperatures T1 to T4 so that they follow a standard normal distribution N(0,1) with a mean value of 0 and a standard deviation of 1, the temperatures at each of the positions of sensors 1 to 4 at time t = 250 seconds are obtained.

[0085] 2.5 Calculated temperatures of sensors 1 to 4 Based on the conditions of the monitoring area shown in Figure 6(a) and (b), and the heat generation rate of the fire source Q = αt 2 Using the above formulas (7) to (19), the temperatures were calculated every second from -60 seconds to 360 seconds at the positions of sensors 1 to 4. The ambient temperature T0 in this case was set to 20°C.

[0086] Figure 7(a) shows the temperature of sensor 1, Figure 7(b) shows the temperature of sensor 2, Figure 8(a) shows the temperature of sensor 3, and Figure 8(b) shows the temperature of sensor 4. In these figures, the time 0 seconds is set as the ignition point of the fire source, and the temperatures from -60 seconds to 0 seconds are calculated with the heat release rate of the fire source Q = 0 (kW). In other words, before the ignition point of the fire source, the ceiling flow temperature rises ΔT1 to ΔT4 are all 0, and the temperatures of sensors 1 to 4 are the ambient temperature T0 of 20°C plus a random number. After the ignition point, the heat release rate of the fire source Q = αt 2 The time t was added in increments of 1 second, and the temperature was calculated from 1 second to 360 seconds.

[0087] 2.6 Conversion of physical quantity x to physical quantity y Each of the temperatures measured every second at sensors 1 to 4 shown in Figures 7(a), (b) and 8(a), (b) corresponds to a physical quantity x to which a random number is added. Using the above formula (1), each of the temperatures measured every second at sensors 1 to 4 (physical quantity x) is converted to a physical quantity y. The average value in the above formula (1) is [Table 28] In calculating the standard deviation σ, a plurality of physical quantities x calculated before the physical quantity x to be converted into the physical quantity y are used. For example, when converting the physical quantity x at the time point of 270 seconds into the physical quantity y, the average value in the above formula (1) is calculated using 20 physical quantities x from 250 seconds to 269 seconds. [Table 29] and the standard deviation σ is calculated.

[0088] 2.7 Sum S prev Calculation of In this simulation, the sum S of the physical quantities y of sensors 1 to 4 was calculated using the physical quantities y calculated before the ignition point of 0 seconds shown in FIGS. 7(a), (b) and 8(a), (b) using the above formula (2). prev For example, the sum S prev In the calculation, 20 physical quantities y from -40 seconds to -21 seconds are used. (Q,P) The values ​​in Table 1 above are used for .

[0089] 2.8 Sum S new Calculation of Sum of physical quantities y of sensors 1 to 4 prev After calculating the physical quantity y, the sum S of the physical quantities y of sensors 1 to 4 is calculated using the above formula (2) every second when a new physical quantity y is calculated. new For example, the sum S at 270 seconds is calculated. new The calculation uses 20 physical quantities y from 251 seconds to 270 seconds. (Q,P) The values ​​in Table 1 above are used for .

[0090] 2.9 Average [Table 30] Calculation of Sum of physical quantities y of sensors 1 to 4 new Every second that is calculated, the sum S new Average value of [Table 31] For example, the sum S at 270 seconds is calculated. new At the time when is calculated, the sum of 20 times from 250 seconds to 269 seconds S new Using the average value [Table 32] Calculate.

[0091] 2.10 Standard deviation σ new Calculation of Sum of physical quantities y of sensors 1 to 4 new Every second that is calculated, the sum S new Standard deviation σ new For example, the sum S at 270 seconds is calculated. new At the time when is calculated, the sum of 20 times from 250 seconds to 269 seconds S new Using the standard deviation σ new Calculate.

[0092] 2.11 Calculating the t-value and probability value of the t-distribution Average [Table 33] , standard deviation σ new and the sum S prev Average value of [Table 34] is applied to the above formula (5) to calculate the value of t, and the probability value of the t distribution is calculated based on this value of t. Fig. 9 is a graph showing the value of t calculated every second from -22 seconds to 360 seconds, and the probability value of the t distribution calculated from this value of t. Note that the sum S in the above formula (5) prev Average value of [Table 35] For example, the sum S of 20 values ​​calculated every second from -40 seconds to -21 seconds is prev Calculate using:

[0093] As shown in Figure 9, at approximately 90 seconds after the fire source was ignited, the value of t was approximately 6, and the probability value of the t distribution was 1.0 × 10 -4 ~1.0×10 -5 If the probability threshold is set to 1.0×10 in the anomaly detection program of the present invention, -4 ~1.0×10 -5 If the setting is made between 100 and 120 seconds, it will be possible to determine temperature abnormalities caused by the outbreak of a fire approximately 90 seconds after the fire source has ignited.

[0094] On the other hand, as shown in Figures 7(a), (b) and 8(a), (b), the temperatures measured by each of the four sensors 1 to 4 at approximately 90 seconds after the fire source was ignited were approximately 25°C. -4 ~1.0×10 -5 If the setting is made between , it is possible to detect a temperature abnormality caused by the outbreak of a fire when the temperature rises by approximately 5°C from the ambient temperature T0 of 20°C.

[0095] In contrast, general fixed temperature heat detectors are classified into the following types in order of response time: "Special" > "Type 1" > "Type 2" > "Type 3." The "Special" fixed temperature heat detector, which has the fastest response time, is designed to alert the occurrence of a fire when it measures a temperature of 60°C or higher, so it will not activate at all if the temperature rises by about 5°C from the initial temperature of 20°C.

[0096] 3. Second embodiment of fire monitoring system FIG. 10 shows a fire monitoring system according to a second embodiment of the present invention. The fire monitoring system according to this embodiment is mainly composed of a plurality of sensors 1 to 4 installed in a plurality of monitoring areas, at least one repeater 20, and a receiver 10. The repeater 20 is electrically connected between the sensors 1 to 4 installed in any one of the monitoring areas and the receiver 10 via a signal line 5. The repeater 20 includes a receiving unit 11 and a control unit 12 similar to those in the first embodiment, and has installed therein an abnormality detection program similar to that in the first embodiment. On the other hand, the receiver 10 according to this embodiment is an existing product that is applied as is, and no abnormality detection program is installed in the receiver 10.

[0097] The receiver 11 of the repeater 20 is electrically connected to the plurality of sensors 1 to 4 via a signal line 5. The receiver 11 receives digital signals output from the plurality of sensors 1 to 4. These digital signals include physical quantities x measured by each of the plurality of sensors 1 to 4. The receiver 11 transmits the received digital signals to the controller 12. The controller 12 includes a processor for executing arithmetic processing.

[0098] First, the processor of the control unit 12 transmits digital signals including the physical quantity x received from the multiple sensors 1 to 4 to the receiver 10 and / or other repeaters, other receivers, fire extinguishing equipment, etc. For example, the receiver 10 executes a fire determination process similar to step S1 shown in Fig. 2 based on the digital signals including the physical quantity x to determine whether or not a fire has occurred. If the receiver 10 determines that a fire has occurred, it causes the alarm unit 13 to notify the occurrence of a fire.

[0099] Second, the processor of the control unit 12 executes the processes of steps S2 to S10, S11 to S14, and S21 to S26 shown in Fig. 2 according to the same abnormality determination program as in the first embodiment. If the control unit 12 determines in step S10 that an abnormality has occurred, it transmits an abnormality detection signal 5a to the receiver 10 and / or another repeater, another receiver, or fire extinguishing equipment. For example, the receiver 10 causes the notification unit 13 to notify the occurrence of an abnormality based on the abnormality detection signal 5a.

[0100] In the fire monitoring system of the present embodiment described above, an abnormality detection program is installed in the repeater 20. Therefore, by adding the repeater 20 to an existing fire monitoring system installed in, for example, an office building, a tenant building, a logistics warehouse, etc., it becomes possible to detect the occurrence of an abnormality in a specified monitoring area. In other words, the fire monitoring system of the present invention can be constructed using the existing sensors 1 to 4, signal line 5, and receiver 10.

[0101] The repeater 20 can be selectively applied to multiple monitoring areas, and can detect the occurrence of an abnormality in any monitoring area selected by the user. Furthermore, the repeater 20 can be easily installed by connecting signal lines 5 to the input and output terminals. In addition, by installing an abnormality detection program in the repeater 20, the calculation load on the receiver 10 can be reduced, and the amount of information stored in memory can be reduced. [Explanation of symbols]

[0102] 1~4 sensors 5 Signal line 10 Receivers 11 Receiving unit 12 Control unit (processor) 13. Information Department 20 Repeater 22 Control unit (processor) 5a Abnormality detection signal

Claims

1. An abnormality determination program that determines whether or not an abnormality has occurred based on signals output from a plurality of sensors that measure a physical quantity x that changes due to the occurrence of a fire, a first process of collecting a plurality of physical quantities x measured by a plurality of sensors for a predetermined time or a predetermined number of times under normal conditions where no fire has occurred, and converting each of the collected physical quantities x into a normalized physical quantity y; A weight w corresponding to each of the plurality of sensors is set in advance, and the sum S of the physical quantities y multiplied by the weight w is prev A second process of calculating After the predetermined time has elapsed or the predetermined number of times has been exceeded, a plurality of physical quantities x measured by a plurality of sensors are collected, each of the collected physical quantities x is converted into a normalized physical quantity y, and the sum S of the physical quantities y multiplied by a weight w is calculated. new A third process of calculating Sumwa S new Average value of Table 36 and standard deviation σ new A fourth process of calculating Average Table 37 , standard deviation σ new and the sum S prev Average value of Table 38 a fifth process of determining whether the probability calculated by applying the above formula to the cumulative distribution function is equal to or less than a preset probability threshold, and determining that an abnormality has occurred if the probability is equal to or less than the threshold; An abnormality determination program that causes a processor to execute a process including the steps of:

2. 2. The abnormality determination program according to claim 1, wherein each of the physical quantities x collected in the first process and the third process is converted into a normalized physical quantity y by the following formula (1): [Equation 25] however, 【number】 is the mean value of the physical quantity x, and σ is the standard deviation of the physical quantity x.

3. The sum S in the second process and the third process prev and the sum S new is calculated by any one of the following formulas (2) to (4): [Equation 26] [0000] [0000] where P and Q each represent a plurality of sensors used to measure a physical quantity x. (p) is the value of the physical quantity y of the sensor P, and y (Q) is the value of the physical quantity y of the sensor Q. (Q,P) is a weight w that is set based on the correlation between the sensor Q and the sensor P.

4. 2. The abnormality determination program according to claim 1, wherein the probability in the fifth process is calculated based on a value of t calculated by the following equation (5): [0000] where n is the average value 【number】 The sum S used to calculate new is the number of data.

5. 2. The abnormality determination program according to claim 1, wherein the value of the weight w used in the second process and the third process is set and changed based on the influence of a disturbance on at least one of the plurality of sensors.

6. An abnormality detection program as described in claim 5, wherein when at least one of the plurality of sensors is affected by the temperature of the air conditioner, the value of the weight w applied to at least one of the plurality of sensors is set or changed based on information regarding the operation of the air conditioner.

7. 6. The abnormality detection program according to claim 5, wherein when at least one of the plurality of sensors is affected by temperature due to sunlight, the value of the weight w applied to at least one of the plurality of sensors is changed based on at least one of the time of sunlight, the amount of sunlight, and the temperature.

8. 2. The abnormality determination program according to claim 1, wherein in the first processing, a random number is generated to increase the significant digits of the physical quantity x, and the random number is added to each of a plurality of physical quantities x measured by a plurality of sensors, and then a normalized physical quantity y is calculated.

9. A fire monitoring system that operates according to the abnormality determination program according to any one of claims 1 to 8, a plurality of sensors and receivers; the receiver includes a receiving unit, a control unit, and a notification unit, and the abnormality determination program is installed therein; The plurality of sensors are installed in the monitoring area, measure a physical quantity x that changes due to the occurrence of a fire, and output a signal; the receiving unit receives signals output from a plurality of sensors; the processor of the control unit executes the first process to the fifth process in accordance with the abnormality determination program based on a plurality of physical quantities x measured by a plurality of sensors; The notification unit notifies the occurrence of an abnormality when it is determined that an abnormality has occurred in the fifth process. A fire monitoring system characterized by:

10. the abnormality determination program includes a sixth process of determining whether a physical quantity x measured by at least one of the plurality of sensors is equal to or greater than a predetermined threshold value of the physical quantity x, and determining that a fire has occurred when the physical quantity x measured by at least one of the plurality of sensors is equal to or greater than the threshold value; the processor of the control unit executes the sixth process in accordance with the abnormality determination program based on each of a plurality of physical quantities x measured by a plurality of sensors; The fire monitoring system according to claim 9 , wherein the notification unit notifies the occurrence of a fire when it is determined in the sixth process that a fire has occurred.

11. A fire monitoring system that operates according to the abnormality determination program according to any one of claims 1 to 8, a plurality of sensors, at least one repeater, and a receiver; the repeater includes a receiving unit and a control unit, and the abnormality determination program is installed therein; The plurality of sensors are installed in the monitoring area, measure a physical quantity x that changes due to the occurrence of a fire, and output a signal; the receiving unit receives signals output from a plurality of sensors; The processor of the control unit executes the first process to the fifth process in accordance with the abnormality determination program based on a plurality of physical quantities x measured by a plurality of sensors, and when it determines that an abnormality has occurred in the fifth process, transmits a signal to another repeater or the receiver. A fire monitoring system characterized by:

12. the abnormality determination program includes a sixth process of determining whether a physical quantity x measured by at least one of the plurality of sensors is equal to or greater than a predetermined threshold value of the physical quantity x, and determining that a fire has occurred when the physical quantity x measured by at least one of the plurality of sensors is equal to or greater than the threshold value; The fire monitoring system of claim 10, wherein the processor of the control unit executes the sixth process in accordance with the abnormality determination program based on each of the plurality of physical quantities x measured by the plurality of sensors, and if it determines in the sixth process that a fire has occurred, transmits a signal to another repeater or the receiver.

13. An abnormality determination program that determines whether or not an abnormality has occurred based on signals output from a plurality of sensors that measure a physical quantity x, a first process of collecting a plurality of physical quantities x measured by a plurality of sensors over a predetermined time or a predetermined number of times, and converting each of the collected physical quantities x into a normalized physical quantity y; Sum of physical quantity y prev A second process of calculating After the predetermined time has elapsed or the predetermined number of times has been exceeded, a plurality of physical quantities x measured by a plurality of sensors are collected, each of the collected physical quantities x is converted into a normalized physical quantity y, and a sum S of the physical quantities y is calculated. new A third process of calculating Sumwa S new Average value of Table 41 and standard deviation σ new A fourth process of calculating Average Table 42 , standard deviation σ new and the sum S prev Average value of Table 43 a fifth process of determining whether the probability calculated by applying the above formula to the cumulative distribution function is equal to or less than a preset probability threshold, and determining that an abnormality has occurred if the probability is equal to or less than the threshold; An abnormality determination program that causes a processor to execute a process including the steps of:

14. A weight w corresponding to each of the plurality of sensors is set in advance, and in the second process, the sum S of the physical quantities y multiplied by the weight w is calculated. prev is calculated, and in the third process, the sum S of the physical quantities y multiplied by the weight w is calculated. new The abnormality determination program according to claim 13, wherein the following is calculated:

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