Intelligent self-checking method and system of red-violet composite flame detector

Abstract

The application discloses an intelligent self-checking method and system of a red-ultraviolet composite flame detector, wherein the detector comprises an infrared sensor, an ultraviolet sensor, an infrared light source, an ultraviolet light source, a window heating module and a control unit; the method comprises a factory individual characteristic calibration stage, an environment compensation coefficient calibration stage and an online self-checking stage; wherein the factory individual characteristic calibration stage is carried out in a controlled environment, and comprises the following steps: S101, infrared reference acquisition, collecting an infrared signal under a clean light path, calculating and storing an infrared reference mean value I100; S102, ultraviolet background noise statistics, collecting background noise pulses of the ultraviolet sensor under a darkroom environment, calculating and storing a background noise mean value μ_bg and a standard deviation σ_bg.

Description

Technical Field

[0001] This invention belongs to the fields of fire protection electronic technology and fire detection technology, and more specifically, it relates to an intelligent self-testing method and system for an infrared-ultraviolet composite flame detector. Background Technology

[0002] Infrared-ultraviolet composite flame detectors are widely used for fire detection in high-risk locations such as petroleum, chemical, military, and gas industries. They typically include infrared and ultraviolet sensors, determining the fire situation by detecting specific wavelengths of radiation emitted by the flame. Existing technologies include flame detectors employing optical self-testing schemes, such as the "Ultraviolet-Infrared Composite Flame Detector and Alarm Device and Method with Optical Self-Testing Function" disclosed in Chinese Invention Patent Publication No. CN101383075A. This device uses infrared and ultraviolet light-emitting diodes inside the detector to receive reflected signals and thus detect the optical path and circuitry.

[0003] However, existing technologies still have the following drawbacks in practical applications: 1) They cannot effectively distinguish whether the failure is due to the UV sensor itself (such as decreased sensitivity or breakdown self-excitation) or optical path attenuation caused by detector window contamination, which may lead to misjudgment; 2) They do not consider the discreteness of individual UV sensor response and background noise, which can easily lead to inconsistent self-testing standards; 3) They cannot fully cover the two completely different failure modes of UV sensors: aging (too few pulses) and breakdown (too many pulses); 4) They lack effective compensation for changes in ambient temperature, and measurement results are prone to drift; 5) In low temperature and high humidity environments, condensation or icing on the window can seriously affect detection, and traditional solutions lack intelligent proactive countermeasures. Summary of the Invention

[0004] The purpose of this invention is to provide a self-testing method and system for the ultraviolet sensor of an infrared-ultraviolet composite flame detector, aiming to solve the technical problems in the prior art, such as the inability to distinguish between optical path contamination and sensor failure, the inability to cope with sensor response dispersion, the inability to detect pulse surge faults caused by sensor breakdown, the large influence of ambient temperature, and the impact of condensation and icing on the viewing window on detection reliability.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent self-testing method for an infrared-ultraviolet composite flame detector, wherein the detector includes an infrared sensor, an ultraviolet sensor, an infrared light source, an ultraviolet light source, a window heating module, and a control unit; characterized in that the method includes a factory individual characteristic calibration stage, an environmental compensation coefficient calibration stage, and an online self-testing stage; The factory-specific characteristic calibration stage is conducted in a controlled environment and includes: Step S101: Infrared reference acquisition. Infrared signals are acquired under a clean optical path, and the infrared reference mean value I100 is calculated and stored. Step S102: Ultraviolet background noise statistics. Collect the background noise pulses of the ultraviolet sensor in a darkroom environment, calculate the background noise mean μ_bg and standard deviation σ_bg, and store them. Step S103: Data collection under different pollution degrees. Insert standard attenuation sheets with different transmittances into the optical path in sequence, and collect and calculate the infrared mean I_T, ultraviolet effective pulse mean P_T and their upper and lower limits L_T, U_T under each transmittance T. Step S104: Establish a continuous mapping model. Taking the normalized infrared value x = I_T / I100 as the independent variable, perform curve fitting on the ultraviolet pulse expected value P_T, lower limit L_T, and upper limit U_T to obtain continuous functions f(x), f_lower(x), f_upper(x), and store their coefficients. Among them, the environmental compensation coefficient calibration stage includes: Step S201: Place the detector at multiple temperature points and repeat the calibration process of steps S103 - S104. Step S202: Establish a temperature compensation coefficient look-up table, and record the infrared measurement compensation coefficient K_IR and ultraviolet threshold compensation coefficient K_UV at different temperatures. Among them, the on-line self-check stage is periodically executed during the on-site operation of the detector, and includes: Step S301: Infrared pollution detection. Turn on the infrared light source, measure the current infrared value I_cur, calculate the current relative transmittance τ, and if τ is lower than the minimum effective transmittance, issue an optical path pollution warning. Step S302: Dynamic threshold generation. According to τ and the stored fitting function, calculate the dynamic lower limit D_min and dynamic upper limit D_max of the ultraviolet pulse under the current pollution and environment, which includes background noise compensation and temperature compensation. Step S303: Ultraviolet measurement and processing. Turn on the ultraviolet light source, collect ultraviolet pulses and perform filtering processing to obtain the current ultraviolet pulse average value P_avg. Step S304: Fault judgment. Compare P_avg with the dynamic threshold [D_min, D_max]. If P_avg < D_min, it is determined that the sensitivity of the ultraviolet sensor has decreased. If P_avg > D_max, it is determined that the ultraviolet sensor is broken down or self-excited, otherwise it is determined to be normal. Step S305: Historical trend analysis and self-check cycle adaptive adjustment. Record the self-check history and adaptively adjust the self-check cycle according to the pollution change trend or environmental mutation. Step S306: Condensation / icing risk prediction. Calculate the dew point temperature T_dew according to the current environmental temperature T_cur and relative humidity RH. Step S307: Pollution type identification, combining the τ value and the relationship between T_cur and T_dew to distinguish between dust and oil pollution and condensation and icing pollution; Step S308: Graded heating control, based on the degree of contamination and condensation risk, implement standby heating, adaptive heating or enhanced heating strategies; Step S309: Heating effect verification. During the heating process, the change of τ value is continuously monitored to verify the heating effectiveness. If it is ineffective, it is determined to be irreversible contamination and an alarm is triggered. Step S310: Overheat protection, monitor the temperature in the viewing window, and cut off heating when the temperature exceeds the safety threshold; Step S311: Heating history record and life prediction, record the cumulative heating time, and issue an early warning when the heater is close to its lifespan.

[0006] An intelligent self-testing system for an infrared-ultraviolet composite flame detector that implements the above method includes: Optical module, including infrared emitter, ultraviolet emitter, infrared sensor and ultraviolet sensor; The signal conditioning module includes an infrared signal processing circuit and an ultraviolet signal processing circuit that are respectively connected to the infrared sensor and the ultraviolet sensor; The control module, connected to the signal conditioning module, is used to store and execute the algorithms for the factory individual characteristic calibration, environmental compensation coefficient calibration, and online self-test stages. An environmental monitoring module, including a temperature sensor and a humidity sensor, is connected to the control module; A window heating module, connected to the control module, is used to heat the detector window; A communication module, connected to the control module, is used to upload self-test results and alarm information; The power supply module provides power to each module.

[0007] Preferably, the wavelengths of the infrared and ultraviolet emitting diodes are selected to avoid interference peaks in the characteristic spectrum of the flame. The control module is a microcontroller or digital signal processor with built-in non-volatile memory.

[0008] This invention provides an intelligent self-testing method and system for an infrared-ultraviolet composite flame detector, which has the following beneficial effects: 1. Comprehensive coverage of failure modes: By setting dynamic upper and lower thresholds, it can not only detect the decrease in the sensitivity of the ultraviolet sensor (fewer pulses), but also effectively detect sensor breakdown or self-excitation (more pulses), thus eliminating serious safety accidents caused by false fire alarms due to sensor abnormalities. 2. Eliminate the impact of individual discreteness: Through individualized calibration at the factory stage, the unique background noise and response characteristics of each sensor are recorded, avoiding self-test misjudgment caused by batch differences of components and improving the accuracy of judgment; 3. Accurately distinguish between contamination and malfunction: The infrared light path is used to measure the degree of contamination in real time and the ultraviolet judgment threshold is dynamically adjusted, which fundamentally solves the interference of window contamination on the self-test results; 4. Ambient temperature compensation: A temperature compensation coefficient is introduced to eliminate the influence of ambient temperature changes on infrared measurement and ultraviolet threshold, enabling the detector to adapt to various harsh outdoor environments. 5. Intelligent Adaptive: Through historical trend analysis and self-check cycle adaptive adjustment (default every 2 minutes), it can quickly respond to increased pollution or sudden environmental changes while ensuring safety, and can provide early warning of potential faults; 6. Intelligent window cleaning: The infrared light path monitors the degree of pollution in real time. Combined with temperature and humidity sensors, it automatically triggers window heating to defog, preventing condensation and ice from obstructing the window and ensuring the detector works reliably in various harsh weather conditions. 7. On-demand heating and energy saving: Unlike traditional timed heating or continuous heating solutions, this invention only starts heating when light path attenuation and temperature and humidity conditions meet specific thresholds, which significantly reduces power consumption and extends heater life. 8. Multi-level alarm strategy: Provides detailed fault classification and corresponding suggested measures, which facilitates rapid response by on-site maintenance personnel and reduces the difficulty and cost of operation and maintenance. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the system architecture of the infrared-ultraviolet composite flame detector in an embodiment of the present invention.

[0010] Figure 2 This is a flowchart of the factory-outgoing individual characteristic calibration stage in an embodiment of the present invention.

[0011] Figure 3 This is a flowchart of the online self-test stage in an embodiment of the present invention (including heating and defogging logic). Detailed Implementation

[0012] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0013] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0014] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0015] Example 1: System Architecture like Figure 1 As shown, the system of the present invention includes an optical module, a signal conditioning module, a control module, an environmental monitoring module, a communication module, a power supply module, and a window heating module.

[0016] The optical module includes an infrared emitting diode (such as an IR LED with a center wavelength of 940nm), an ultraviolet emitting diode (such as a UV LED with a center wavelength of 185nm), an infrared photoelectric sensor, and an ultraviolet photoelectric sensor (such as an R2868 ultraviolet tube).

[0017] The signal conditioning module includes an infrared signal processing circuit (including IV conversion, PGA, and ADC) and an ultraviolet signal processing circuit (including pulse shaping, comparator, and counter).

[0018] The control module uses an STM32 series MCU, which is responsible for algorithm execution and logic control.

[0019] The environmental monitoring module includes a temperature sensor (such as the DS18B20) and a humidity sensor.

[0020] The window heating module includes a transparent heating film (such as an ITO film) attached to the window and its driving circuit.

[0021] The communication module uses RS-485 or CAN bus.

[0022] The power module supplies power to the entire system.

[0023] Example 2: Factory Calibration like Figure 2 The calibration process is illustrated using a specific detector as an example. In a clean environment at 25℃, infrared AD values ​​were collected, and the infrared reference value I100 was calculated to be 2048.

[0024] Ultraviolet background noise was collected in a dark room, and the calculated values ​​were μ_bg = 3.2 particles / second and σ_bg = 0.8.

[0025] Pollution was simulated using standard attenuation filters with transmittance ranging from 10% to 90%, and data were collected. For example, at a transmittance of T=10%, the measured infrared mean I10=205, the ultraviolet effective pulse mean P̄10=15.2, and the standard deviation σ_P10=1.1 were obtained. Taking k=2, the lower limit L10=13.0 and the upper limit U10=17.4 were obtained.

[0026] Using the normalized infrared value x = I_T / I100 as the abscissa and P_T as the ordinate, a quadratic polynomial fit was performed to obtain the function f(x) = -5.2x² + 35.8x + 1.2 (goodness of fit R² = 0.998). Similarly, upper and lower bound functions f_lower(x) and f_upper(x) were fitted. All coefficients were stored in the Flash memory of the control module.

[0027] Example 3: Online self-testing and heating defogging like Figure 3 As shown, assume the detector is operating in a chemical plant, the lens is contaminated and the ultraviolet sensor is broken down, the ambient temperature is -15℃ and the humidity is 90%.

[0028] (S301) Infrared contamination detection: I_cur = 818.5 was measured. The temperature compensation coefficient K_IR(-15℃) = 1.05 was found. The relative transmittance τ = (818.5 / 2048)*1.05 ≈ 0.42 (i.e. 42%) was calculated, indicating that contamination exists.

[0029] (S302) Dynamic threshold generation: Using the fitting function, calculate the expected value P=15.1 when x=0.42, f_lower(0.42)=13.0, f_upper(0.42)=17.5. Find K_UV(-15℃)=0.97. Calculate the dynamic lower limit D_min=13.0*0.97+3.2=15.8, and the dynamic upper limit D_max=17.5 * 0.97+3.2+2.0=22.2 (assume a margin δ=2.0).

[0030] (S303) Ultraviolet measurement: Turn on the ultraviolet light source and measure the average value of the ultraviolet pulse P_avg=189.6.

[0031] (S304) Fault diagnosis: Since P_avg=189.6 >D_max=22.2, it is determined to be "UV sensor breakdown or self-excitation fault".

[0032] (S306-S307) Icing Risk and Pollution Type Identification: Calculate the dew point T_dew≈-16℃. If T_cur is close to T_dew and below 0℃, icing risk is identified. Since τ<80% and T_cur<0℃, it is classified as icing pollution.

[0033] (S308-S309) Graded heating control and verification: Start enhanced heating (100% PWM). Monitor the τ value every 30 seconds after heating: τ=55% after 30 seconds, τ=70% after 60 seconds, τ=88% after 90 seconds, and τ=96% after 120 seconds, returning to the normal level, and stop heating.

[0034] Finally, the system reported the fault code "UV_SHORT" and the message "Automatic de-icing completed" through the communication module.

[0035] Example 4: Self-test cycle adaptive Assume the initial self-test cycle is 2 minutes. If three consecutive self-tests yield τ values ​​of 95%, 88%, and 75% respectively, the algorithm detects a decreasing trend in contamination and automatically shortens the cycle to 30 seconds for close monitoring. If τ recovers to above 90% after subsequent cleaning, the cycle automatically returns to 2 minutes.

[0036] Example 5: Fault Classification and Alarms This invention sets up a multi-level alarm strategy, for example: Warning level: 50%≤τ<80%, output "Optical path contamination warning".

[0037] Warning level: τ<50%, output "Severe dirt warning".

[0038] Level 1 fault: P_avg is slightly lower than D_min, output "UV sensor performance has degraded".

[0039] Level 2 fault: P_avg is much lower than D_min, output "UV sensor failed".

[0040] Critical fault: P_avg≥D_max, output "UV sensor breakdown / self-excitation".

[0041] Heating alert: Condensation risk detected, output "Automatic heating started".

[0042] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent self-testing method for an infrared-ultraviolet composite flame detector, the detector comprising an infrared sensor, an ultraviolet sensor, an infrared light source, an ultraviolet light source, a viewing window heating module, and a control unit; characterized in that, The method includes a factory individual characteristic calibration stage, an environmental compensation coefficient calibration stage, and an online self-check stage; The factory individual characteristic calibration stage is carried out in a controlled environment and is used to establish a calibration data model unique to each detector that reflects the relationship between the degree of optical path contamination and the response of the ultraviolet sensor, and record the individual background noise of the ultraviolet sensor; The online self-check stage is periodically executed during the on-site operation of the detector and includes: Step S1: Infrared contamination detection, turn on the infrared light source and measure, and calculate the relative transmittance (τ) of the current optical path; Step S2: Dynamic threshold generation, calculate the dynamic lower limit (D_min) and dynamic upper limit (D_max) of the ultraviolet pulse in the current state according to the current relative transmittance (τ) and the calibration data model; Step S3: Ultraviolet measurement and processing, turn on the ultraviolet light source and measure to obtain the current average value (P_avg) of the ultraviolet pulse; Step S4: Fault judgment, compare the current average value (P_avg) of the ultraviolet pulse with the dynamic lower limit (D_min) and dynamic upper limit (D_max). If P_avg < D_min, it is determined that the sensitivity of the ultraviolet sensor has decreased. If P_avg > D_max, it is determined that the ultraviolet sensor has broken down or self-excited.

2. The intelligent self-testing method according to claim 1, characterized in that, The factory individual characteristic calibration stage includes: Step S101: Infrared reference acquisition, collect infrared signals under a clean optical path, calculate and store the infrared reference mean value (I100); Step S102: Ultraviolet background noise statistics, collect the background noise pulses of the ultraviolet sensor in a darkroom environment, calculate the background noise mean value (μ_bg) and standard deviation (σ_bg) and store them; Step S103: Data acquisition at different contamination levels, sequentially insert standard attenuation sheets with different known transmittances into the optical path, respectively collect and calculate the infrared measurement values (I_T), the average value of ultraviolet effective pulses (P_T) and their standard deviations at each transmittance (T), and then determine the lower limit (L_T) and upper limit (U_T) of the reasonable interval of the ultraviolet pulse at this transmittance; Step S104: Establish a continuous mapping model, use the normalized infrared value (x = I_T / I100) as the independent variable, perform curve fitting on the average value of the ultraviolet effective pulse (P_T), the lower limit (L_T), and the upper limit (U_T) respectively, obtain continuous expected value functions (f(x)), lower limit functions (f_lower(x)), and upper limit functions (f_upper(x)), and store the fitting coefficients.

3. The intelligent self-testing method according to claim 2, characterized in that, The environmental compensation coefficient calibration stage includes: Step S201: Place the detector at multiple different temperature points and repeat the calibration process of steps S103 - S104; Step S202: Establish a temperature compensation coefficient lookup table or fitting function, and record the infrared measurement compensation coefficient (K_IR(T_emp)) and ultraviolet threshold compensation coefficient (K_UV(T_emp)) at different temperatures.

4. The intelligent self-testing method according to claim 3, characterized in that, The formula for calculating the current relative transmittance (τ) in step S1 is: τ = (I_cur / I100) * K_IR(T_cur), where I_cur is the current infrared measurement value, and K_IR(T_cur) is the infrared compensation coefficient obtained by looking up a table or calculating (T_cur) based on the current ambient temperature. The formulas for calculating the dynamic lower limit (D_min) and dynamic upper limit (D_max) in step S2 are as follows: D_min=f_lower(τ)*K_UV(T_cur)+μ_bg; D_max=f_upper(τ)*K_UV(T_cur)+μ_bg+δ; Wherein, K_UV(T_cur) is the ultraviolet compensation coefficient obtained by looking up a table or calculating based on the current ambient temperature (T_cur), and δ is the preset margin.

5. The intelligent self-testing method according to claim 1, characterized in that, The online self-test phase also includes step S5: intelligent heating and defogging control, which includes: Step S51: Condensation risk prediction, calculate dew point temperature (T_dew) based on current ambient temperature (T_cur) and relative humidity (RH); Step S52: Pollution type identification, combining the current relative transmittance (τ) and the closeness of T_cur and T_dew, to distinguish between dust and oil pollution and condensation and icing pollution; Step S53: Graded heating control, according to the type and degree of pollution, control the window heating module to perform heating at different power levels.

6. The intelligent self-testing method according to claim 5, characterized in that, The graded heating control in step S53 includes: When T_cur < 5℃ and RH > 80%, start low-power standby heating to prevent condensation; When the contamination is determined to be condensation / icing and 80%≤τ<95%, start the medium-power adaptive heating. When condensation / icing contamination is detected and τ < 80%, full-power enhanced heating is activated.

7. The intelligent self-testing method according to claim 5 or 6, characterized in that, The intelligent heating and defogging control also includes step S54: heating effect verification. After heating is started, the change of the relative transmittance (τ) is periodically monitored. If the τ value continues to rise after heating, the heating is determined to be effective. If the τ value does not improve significantly after a predetermined heating time, the heating is determined to be ineffective and an alarm requiring manual cleaning is issued.

8. The intelligent self-testing method according to claim 1, characterized in that, The online self-inspection phase also includes step S6: self-inspection cycle adaptive adjustment, recording the τ value and / or P_avg value of each self-inspection. If the pollution is detected to be continuously aggravated, the self-inspection cycle is automatically shortened; if the environment is stable or the pollution is cleared, the self-inspection cycle is restored to the default self-inspection cycle.

9. An intelligent self-testing system for an infrared-ultraviolet composite flame detector that implements the method described in any one of claims 1-8, characterized in that, include: Optical module, including infrared emitter, ultraviolet emitter, infrared sensor and ultraviolet sensor; The signal conditioning module includes an infrared signal processing circuit and an ultraviolet signal processing circuit that are respectively connected to the infrared sensor and the ultraviolet sensor; The control module, connected to the signal conditioning module, is used to store and execute the algorithms for the factory individual characteristic calibration, environmental compensation coefficient calibration, and online self-test stages. An environmental monitoring module, including a temperature sensor and a humidity sensor, is connected to the control module; A window heating module, connected to the control module, is used to heat the detector window; A communication module, connected to the control module, is used to upload self-test results and alarm information; The power supply module provides power to each module.

10. The intelligent self-testing system according to claim 9, characterized in that, The control module is a microcontroller or digital signal processor and has built-in non-volatile memory; the window heating module includes a transparent conductive heating film attached to the detector window and its driving circuit.

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