Anti-false alarm method of photoelectric smoke fire detection alarm based on particle size inversion

Abstract

The invention belongs to the technical field of photoelectric smoke fire detection, and discloses a photoelectric smoke fire detection alarm false alarm resisting method based on particle size inversion, which comprises the following steps: S1, regularly detecting light intensity information scattered by particles; s2, judging whether a suspected fire signal is detected or not; s3, repeatedly and continuously sampling, and calculating the particle size r through a particle size inversion method; s4, judging the size relationship between the particle size r and a preset fire smoke particle r *; if r is far greater than r * or far less than r *, determining that the signal is a non-fire signal, and the fire detector does not give an alarm; if r is close to r *, fire particles are judged, and the alarm gives an alarm. According to dynamic change characteristics of scattered light intensity brought by thermal motion of particulate matters with different particle sizes, particle sizes of the particulate matters are inverted by adopting a particle size inversion method, fire smoke particles and large particles such as dust, fog drops and eggs are distinguished according to the particle sizes, and false alarm resistance of the fire detector is formed.

Description

Technical Field

[0001] This invention relates to the field of photoelectric smoke detection technology, specifically a method for preventing false alarms in photoelectric smoke detection alarms based on particle size inversion. Background Technology

[0002] Fire detectors play a vital role in reducing fire risks and protecting people's lives and property. Among them, point-type photoelectric smoke detectors (hereinafter referred to as "fire detectors"), based on the principle of light scattering, are widely used due to their low cost and reliable performance. However, in practical applications, factors such as water mist in high-humidity environments and dust in industrial plants or roadsides can also cause light scattering, leading to misjudgments of non-fire scenarios by fire detectors and resulting in a high false alarm rate.

[0003] To address the aforementioned issues, Chinese Patent (Patent Publication No.: CN116863636A) discloses a method for improving the alarm reliability of smoke detectors. This method separates the background value for calculating the aerosol scattered light power ratio from the background value for alarm decision-making, employing different background value update mechanisms to adapt to different factors, thus enabling more accurate calculation of the aerosol scattered light power ratio. Chinese Patent (Patent Publication No.: CN112907884A) discloses a smoke detection method with a low false alarm rate, which calculates the aerosol scattered light power ratio by calculating the unit average increment. Chinese Patent (Patent Publication No.: CN113538837A) discloses a photoelectric smoke detection method, detection device, and computer-readable storage medium. This method uses a set first threshold to determine whether smoke has entered the maze, and a set second threshold as an alarm drift compensation value to correct the alarm threshold, reduce the impact of dust on the alarm, and improve alarm sensitivity. The above solutions all reduce the impact of interference factors such as dust on fire detectors by setting multiple detection thresholds, thereby reducing the false alarm rate. However, this method of setting or correcting thresholds does not take into account the differences in the properties of interference factors such as smoke particles and dust, and its false alarm rate is still relatively high.

[0004] Studies have shown that dust and mist particles generally have a diameter greater than 1 micrometer, while the diameter of smoke particles from fires is generally between tens and hundreds of nanometers. For example, the peak particle size of smoke particles from cotton rope during the stable smoldering stage is 180-200 nanometers. Based on this research, it can be concluded that if the differences in particle size can be reflected in the detection method, the performance of fire detectors against false alarms caused by dust and water mist can be further improved. Summary of the Invention

[0005] This invention aims to provide a method for preventing false alarms in photoelectric smoke detectors based on particle size inversion. By utilizing the dynamic changes in scattered light intensity caused by the thermal motion of particles of different sizes, the particle size inversion method is used to determine the particle size. Based on particle size, fire smoke particles are distinguished from larger particles such as dust, droplets, and insect eggs, thus enhancing the fire detector's ability to prevent false alarms. This addresses the aforementioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for preventing false alarms in photoelectric smoke detectors based on particle size inversion includes the following steps:

[0008] S1. Periodically detect the light intensity information scattered by particles;

[0009] S2. Determine whether a suspected fire signal has been detected; if yes, proceed to step S3; if no, return to step S1.

[0010] S3. Repeated sampling is performed, and the particle size r is calculated using the particle size inversion method;

[0011] S4, compare the particle size r obtained in step S3 with the preset fire smoke particle size r. * The relationship between their magnitudes; if r is much larger than r * Or r is much smaller than r * If the signal is not a fire signal, the fire detector will not trigger an alarm and the process will return to step S1; if r is close to r * If the particles are identified as fire particles, the alarm will sound.

[0012] Furthermore, in S3, the specific method for calculating the particle size r using the particle size inversion method is as follows:

[0013] S3.1 The autocorrelation function is used to analyze the dynamic characteristics of the scattered light intensity of particulate matter. The autocorrelation function is a statistical measure that describes the correlation between a signal and itself at different time points. It represents the similarity between the signal and itself at different time points. The expression for the autocorrelation function of the scattered light intensity is as follows:

[0014]

[0015] In the formula, I(t) is the scattered light intensity signal that varies with time, τ is the delay time, and t is the measurement time;

[0016] S3.2 Determine the Sieger relation to represent the relationship between the autocorrelation function of scattered light intensity and the autocorrelation function of electric field. Its functional expression is as follows:

[0017] G (2) (τ)=B{1+β|g (1) (τ)|2} (2)

[0018] In the formula, B is the baseline of the autocorrelation function, representing the value of the autocorrelation function at zero delay, β is the coherence factor related to the experimental angle, and its value is related to the optical path settings in the experimental setup, g (1) (τ) is the electric field autocorrelation function, representing the correlation between electric field amplitudes under different delay times;

[0019] Among them, g in monodisperse particle system (1) The functional expression for (τ) is:

[0020] g (1) (τ)=exp(-Γτ) (3)

[0021] In the formula, Γ represents the attenuation linewidth, which is the attenuation rate of the optical signal by the photosensitive element, and represents the attenuation rate of the energy in the optical signal, which is related to the diffusion coefficient D of the particles and the scattering vector q.

[0022] The functional expression for Γ is:

[0023] Γ=D*q 2 (4)

[0024]

[0025] In the formula, D is the diffusion coefficient of the particle, which represents the distance the particle diffuses in the fluid per unit time; q is the scattering vector, n is the refractive index, λ is the incident light wavelength, and θ is the scattering angle.

[0026] S3.3 According to the Stokes-Einstein equations:

[0027]

[0028] In the formula, k b Where η is Boltzmann constant, T is absolute temperature, η is fluid viscosity, and r is the hydrodynamic radius of the particulate matter.

[0029] S3.4 By calculating equations (1) to (6), the particle size r can be inverted from the time-varying scattered light intensity signal sequence I(t).

[0030] The principles and beneficial effects of the technical solution are as follows:

[0031] 1. This invention provides a method for preventing false alarms in photoelectric smoke detectors based on particle size inversion, which offers high alarm accuracy and reliability. Compared to traditional fire detectors that rely solely on light intensity signals, this invention, based on the analysis of scattered light intensity signals, further inverts the particle size using the temporal information of the light intensity signals. By differentiating particle sizes, it can effectively distinguish between fire smoke particles and interfering factors such as dust and water mist, thereby reducing the false alarm rate of the fire detector and ensuring high accuracy and reliability of the fire alarm.

[0032] 2. The photoelectric smoke detector anti-false alarm method based on particle size inversion provided by this invention is easy to integrate and promote. This invention mainly improves the identification strategy; there are no major changes to the fire detector hardware. It is equivalent to adding a secondary discrimination based on the existing hardware and software. Applying this method to existing fire detectors does not require additional hardware development costs, facilitating large-scale deployment.

[0033] 3. The photoelectric smoke detector and alarm method based on particle size inversion provided by this invention can also distinguish different types of fires based on the inverted particle size, such as clarifying whether the fire is a smoldering fire, an oil fire, or a plastic fire, providing a reference for further guiding fire rescue work.

[0034] The principle of particulate matter particle size inversion is as follows: When a beam of light shines on continuously moving aerosol particles, the Brownian and translational motion of the particles causes dynamic changes in light scattering intensity. This results in a Doppler shift in the frequency of the scattered light relative to the incident light, and the intensity signal of the scattered light fluctuates continuously over time. By performing autocorrelation calculations on the time-varying scattered light intensity signal, the autocorrelation function of the scattered light intensity is obtained, thus revealing information such as the diffusion coefficient of the flowing aerosol, which includes information on the distribution of particle size. Therefore, by analyzing the dynamic changes in the scattered light intensity of particles, the particle size can be inferred. Attached Figure Description

[0035] Figure 1 This is a flowchart of the anti-false alarm method for photoelectric smoke detectors and alarms based on particle size inversion according to the present invention;

[0036] Figure 2 This is the particle size inversion process for a smoke particle sample with an average particle size of 500 nm in an embodiment of the present invention.

[0037] In the figure, (a) is the scattered light intensity signal, (b) is the light intensity autocorrelation function curve, and (c) is the calculated particle size;

[0038] Figure 3This is the particle size inversion process for a smoke particle sample with an average particle size of 2 μm in an embodiment of the present invention;

[0039] In the figure, (a) is the scattered light intensity signal, (b) is the light intensity autocorrelation function curve, and (c) is the calculated particle size; Detailed Implementation

[0040] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments:

[0041] As shown in the figure, the method for preventing false alarms in photoelectric smoke detectors based on particle size inversion includes the following steps:

[0042] S1. Periodically detect the light intensity information scattered by particles;

[0043] S2. Determine whether a suspected fire signal has been detected; if yes, proceed to step S3; if no, return to step S1.

[0044] S3. Repeated sampling is performed, and the particle size r is calculated using the particle size inversion method;

[0045] The specific method for calculating the particle size r using the particle size inversion method is as follows:

[0046] S3.1 The autocorrelation function is used to analyze the dynamic characteristics of the scattered light intensity of particulate matter. The autocorrelation function is a statistical measure that describes the correlation between a signal and itself at different time points. It represents the similarity between the signal and itself at different time points. The expression for the autocorrelation function of the scattered light intensity is as follows:

[0047]

[0048] In the formula, I(t) is the scattered light intensity signal that varies with time, τ is the delay time, and t is the measurement time;

[0049] S3.2 Determine the Sieger relation to represent the relationship between the autocorrelation function of scattered light intensity and the autocorrelation function of electric field. Its functional expression is as follows:

[0050] G (2) (τ)=B{1+β|g (1) (τ)| 2} (2)

[0051] In the formula, B is the baseline of the autocorrelation function, representing the value of the autocorrelation function at zero delay, β is the coherence factor related to the experimental angle, and its value is related to the optical path settings in the experimental setup, g (1) (τ) is the electric field autocorrelation function, representing the correlation between electric field amplitudes under different delay times;

[0052] Among them, g in monodisperse particle system( 1 ) The functional expression for (τ) is:

[0053] g (1) (τ)=exp(-Γτ) (3)

[0054] In the formula, Γ represents the attenuation linewidth, which is the attenuation rate of the optical signal by the photosensitive element, and represents the attenuation rate of the energy in the optical signal, which is related to the diffusion coefficient D of the particles and the scattering vector q.

[0055] The functional expression for Γ is:

[0056] Γ=D*q 2 (4)

[0057]

[0058] In the formula, D is the diffusion coefficient of the particle, which represents the distance the particle diffuses in the fluid per unit time; q is the scattering vector, n is the refractive index, λ is the incident light wavelength, and θ is the scattering angle.

[0059] S3.3 According to the Stokes-Einstein equations:

[0060]

[0061] In the formula, k b Where η is Boltzmann constant, T is absolute temperature, η is fluid viscosity, and r is the hydrodynamic radius of the particulate matter.

[0062] S3.4 By calculating equations (1) to (6), the particle size r can be inverted from the time-varying scattered light intensity signal sequence I(t);

[0063] S4, compare the particle size r obtained in step S3 with the preset fire smoke particle size r. * The relationship between their magnitudes; if r is much larger than r * Or r is much smaller than r * If the signal is not a fire signal, the fire detector will not trigger an alarm and the process will return to step S1; if r is close to r * If the particles are identified as fire particles, the alarm will sound.

[0064] The specific implementation process is as follows:

[0065] like Figure 2 and Figure 3 As shown, optical measurements were performed on smoke particle samples with average particle sizes of 500 nm and 2 μm, and the results were as follows. Figure 2 (a) Figure 3(a) shows the scattered light intensity signal I; then, using the above equation (1), the obtained scattered light intensity signal I is plotted as a light intensity autocorrelation function curve ( Figure 2 (b) Figure 3 (b)); Then, by introducing equations (2) to (6) above and setting the corresponding parameters, the particle size r can be calculated, such as Figure 2 (c) Figure 3 As shown in (c).

[0066] The parameters used in the above inversion process are shown in Table 1:

[0067] Table 1. Key parameter values ​​in the particulate matter size inversion process.

[0068]

[0069] from Figure 2 , Figure 3 It can be seen that the inverted particle sizes of the two types of smoke particles are in good agreement with the original particle sizes, with peak values ​​of 516.58 nm and 2029.42 nm, respectively, which are close to 500 nm and 2000 nm, with errors of 3.3% and 1.5%, respectively. This shows that the method of inverting particle size based on the dynamic characteristics of scattered light intensity can accurately invert the true particle size.

[0070] Furthermore, regarding the false alarm prevention method for photoelectric smoke detectors based on particle size inversion provided by this invention, in practical applications, fire detectors are often battery-powered for ease of installation and maintenance, resulting in very low energy consumption. The particle size inversion method is only activated when a suspected fire signal is detected, continuously sampling the scattered light intensity signal over a short period. Then, this scattered light intensity signal, containing time-series information, is analyzed and calculated using the particle size inversion method to determine the hydrodynamic radius of the particles. This radius is then compared with the preset particle size of the main smoke particles. If the particle size is close to the preset value, it indicates that the detected particle size is comparable to the fire smoke particle size, the fire signal is clear, and the fire detector issues an alarm signal. Otherwise, if the particle size is much larger or smaller than the preset value, it indicates a significant difference between the detected particle size and the fire smoke particle size, and the signal is determined to be non-fire, so the fire detector does not issue an alarm. The workflow is as follows: Figure 1 As shown.

[0071] The above descriptions are merely embodiments of the present invention, and common knowledge regarding specific technical solutions or characteristics is not elaborated upon here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for resisting false alarms in photoelectric smoke detectors and alarms based on particle size inversion, characterized in that, Includes the following steps: S1. Periodically detect the light intensity information scattered by particles; S2. Determine whether a suspected fire signal has been detected; if yes, proceed to step S3; if no, return to step S1. S3. Repeated sampling is performed, and the particle size r is calculated using the particle size inversion method; S4, judging the size relation between the particle size r obtained in step S3 and the preset fire smoke particle r * ; if r is much larger than r * or r is much smaller than r * , it is determined as non-fire signal, the fire detector does not alarm, and returns to step S1; if r is close to r * , it is determined as fire particle, and the alarm is given.

2. The method for resisting false alarms in a photoelectric smoke detector alarm based on particle size inversion according to claim 1, characterized in that, In S3, the specific method for calculating the particle size r using the particle size inversion method is as follows: S3.1 The autocorrelation function is used to analyze the dynamic characteristics of the scattered light intensity of particulate matter. The autocorrelation function is a statistical measure that describes the correlation between a signal and itself at different time points. It represents the similarity between the signal and itself at different time points. The expression for the autocorrelation function of the scattered light intensity is as follows: In the formula, I(t) is the scattered light intensity signal that varies with time, τ is the delay time, and t is the measurement time; S3.2 Determine the Sieger relation to represent the relationship between the autocorrelation function of scattered light intensity and the autocorrelation function of electric field. Its functional expression is as follows: G (2) (τ) = B {1 + β | g (1) (τ)| 2} (2) where B is the baseline of the autocorrelation function, representing the value of the autocorrelation function at zero delay, β is a coherence factor related to the experimental angle, whose value is related to the optical path setup in the experimental apparatus, g (1) (τ) is the electric field autocorrelation function, representing the correlation between the amplitudes of the electric field at different delay times; wherein, in the monodisperse particle system, g (1) The functional expression of (τ) is: g (1) (τ) = exp(-Γτ) (3) In the formula, Γ represents the attenuation linewidth, which is the attenuation rate of the optical signal by the photosensitive element, and represents the attenuation rate of the energy in the optical signal, which is related to the diffusion coefficient D of the particles and the scattering vector q. The functional expression for Γ is: Γ = D * q 2 (4) In the formula, D is the diffusion coefficient of the particle, which represents the distance the particle diffuses in the fluid per unit time; q is the scattering vector, n is the refractive index, λ is the incident light wavelength, and θ is the scattering angle. S3.3 According to the Stokes-Einstein equations: In the formula, k b Where η is Boltzmann constant, T is absolute temperature, η is fluid viscosity, and r is the hydrodynamic radius of the particulate matter. S3.4 By calculating equations (1) to (6), the particle size r can be inverted from the time-varying scattered light intensity signal sequence I(t).

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