CN113223265A - Multi-scene smoke detector based on bidirectional blue light detection and self-adaptive identification method - Google Patents
Multi-scene smoke detector based on bidirectional blue light detection and self-adaptive identification method Download PDFInfo
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Abstract
The invention relates to a multi-scene smoke detector based on bidirectional blue light detection and a self-adaptive identification method, and belongs to the technical field of fire-fighting fire alarm systems. The invention comprises the following steps: s1: the fire detector converts the detected aerosol concentration into an electric signal; s2: the voltage signal is amplified, filtered and A/D converted by a signal processing circuit; s3: the digital signal obtained by A/D conversion is analyzed in a microprocessor to realize the extraction, analysis and identification of signal characteristics, and finally, a scene is judged to judge whether an alarm is needed or not; the multi-scene smoke detector comprises two blue light emitting diodes and a blue light receiving tube, wherein the two blue light emitting diodes respectively form 70 degrees and 135 degrees with the blue light receiving tube in space to form forward scattering and backward scattering; the fire disaster data real-time identification, scene judgment and threshold value adjustment can be realized through the bidirectional blue light technology, and a foundation is laid for the practical application of fire disaster detection.
Description
Technical Field
The invention relates to a multi-scene smoke detector based on bidirectional blue light detection and a self-adaptive identification method, and belongs to the technical field of fire-fighting fire alarm systems.
Background
When a fire disaster occurs, smoke is generated in most places, and the smoke is measured according to the fire disaster reference quantity, so that whether the fire disaster occurs in a detected area can be judged through comprehensive analysis. And interference factors such as dust, water vapor, electromagnetism and the like in the detected area may exist, and if the non-fire factors cannot be identified, the detector is easy to generate false alarm and feed back incorrect information. Scene adaptation mainly involves signal detection and recognition. The signal detection is to generate light signals through two blue light tubes distributed at different positions, the light signals received by the receiving tube are converted into current signals, and then a signal reference quantity is obtained, and the scene recognition is to perform feature extraction and analysis on the signal reference quantity to achieve the purpose of scene judgment.
Most of the current schemes in the industry are unidirectional infrared, and the schemes can only judge the magnitude of the signal quantity to determine whether a fire disaster occurs, have no other contrast objects, cannot identify a scene, and further have the condition of missed report or false report.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a multi-scene smoke detector based on bidirectional blue light detection and a self-adaptive identification method.
The invention discloses a multi-scene smoke detector self-adaptive identification method based on bidirectional blue light detection, which comprises the following steps of:
s1: the fire detector converts the detected aerosol concentration into an electric signal;
s2: the voltage signal is amplified, filtered and A/D converted by a signal processing circuit;
s3: the digital signal obtained by A/D conversion is analyzed in a microprocessor to realize the extraction, analysis and identification of signal characteristics, and finally, a scene is judged to judge whether an alarm is needed or not;
s4: and processing the data acquired by the microprocessing to output characteristic graphs of different scenes.
Preferably, in S1, two blue light emitting tubes disposed at different positions in the detector are used to emit light, and when the light encounters the aerosol, the light is scattered onto a receiving tube, and the receiving tube converts the light signal into a current signal.
Preferably, in S2, RC filtering is used in hardware to filter out high-frequency and low-frequency interference, two stages of operational amplifiers are used for amplification, and a dc blocking filter is added between the first stage and the second stage.
Preferably, in S3, the digital signal obtained by a/D conversion is analyzed by the microprocessor, which includes the following specific steps:
s31: initializing an I/O port, configuring an ADC (analog to digital converter), selecting an A/D conversion channel, enabling an ADC module and closing interruption;
s32: starting a blue light generating tube for emission, starting ADC (analog to digital converter) conversion, closing the blue light emitting tube, inquiring whether the ADC is finished or not, starting interruption, and obtaining an ADC conversion result;
s33: repeating the steps, and collecting forward and backward data;
s34: and (3) software filtering treatment: the data collected in the front-back direction are put into a cache, the data are subjected to average processing for 4 times, the collected data are analyzed, the fluctuation interference value is removed, amplitude limiting processing is carried out, and the reasonable value is obtained and subjected to average processing for 8 times;
s35: and (3) judging the fault of the sensor: when the sampling value of the sensor is lower than the fault threshold value, continuously timing for more than 60 seconds, and judging the fault of the sensor;
s36: when the sampling value of the sensor is higher than the early warning threshold value, starting fluctuation calculation, and recording by comparing whether the difference between two adjacent sampling values is larger than 1/2 of the previous sampling value; when the sampling value of the sensor is larger than the ratio calculation threshold, calculating the variable quantity of the forward sampling value and the backward sampling value, calculating the relation of the variable quantity of the two sampling values, and storing the calculation result;
s37: when the sampling value is greater than the model identification threshold value, analyzing and judging the model according to the recorded fluctuation condition and the specific value condition, and respectively:
the relationship between the fluctuation and the ratio of the black smoke model is divided into: a1, B1-B2;
the relationship between the white smoke model fluctuation and the ratio is divided into: a2, B3-B4;
the relationship between the fluctuation and the ratio of the raise dust model is divided into: a3, B5-B6;
the relation between the fluctuation and the ratio of the water vapor model is divided into: a4, B7-B8;
the ratio and the fluctuation are analyzed to which model:
if the black smoke model is met, adjusting the alarm threshold value to C1;
if the white smoke model is met, adjusting the alarm threshold value to C2;
if the dust model is met, adjusting the alarm threshold value to C3;
if the water vapor model is met, adjusting the alarm threshold value to C4;
when the sampling value meets the alarm threshold value, continuous judgment is needed for 3 times, and the sampling values are all in line, an alarm signal is triggered, false alarm is reduced to a certain extent, and the detection precision is improved.
Preferably, in S4, the detector is connected to a big basket of a debugging tool of a company, the big basket is connected to a computer through a USB, and the control of the detector is implemented by TC-BUS software.
Preferably, in S4, the detector may upload the collected data, store the data through the TC-BUS, and draw data of the sensor in different scenes, thereby establishing models of black smoke, white smoke, dust, and water vapor for parameter correction and model establishment of the detector.
The multi-scene smoke detector based on bidirectional blue light detection comprises two blue light emitting diodes and a blue light receiving tube, wherein the two blue light emitting diodes and the blue light receiving tube form 70 degrees and 135 degrees in space respectively to form forward scattering and backward scattering; smoke particles detected by the forward scattering blue light emitting diode show that the scattering intensity is larger than that detected by the backward scattering blue light emitting diode, the forward blue light emitting tube is used as a main reference, and the backward blue light emitting tube is used as an auxiliary reference.
According to the multi-scene smoke detector based on bidirectional blue light detection and the self-adaptive identification method, an ideal model is established by comprehensively analyzing and processing the characteristics expressed by different particles in a scene, an ideal judgment threshold value is determined, the judgment threshold value is adjusted according to different scenes, the false alarm of raised dust, water vapor and electromagnetic interference is reduced to a certain extent, and the detection sensitivity of black smoke is improved; the fire disaster data real-time identification, scene judgment and threshold value adjustment can be realized through the bidirectional blue light technology, and a foundation is laid for the practical application of fire disaster detection.
Drawings
Fig. 1 is a schematic structural view of the present invention.
FIG. 2 is a graph of the results of an experiment with the filtering algorithm of the present invention.
Fig. 3(a) is a diagram showing the results of an electromagnetic forward sensor fluctuation detection experiment.
Fig. 3(b) is a diagram showing the results of an experiment for detecting the fluctuation of the electromagnetic rear sensor.
Fig. 4(a) is a diagram showing the results of an experiment for determining the ratio of forward/backward direction of dust.
Fig. 4(b) is a diagram showing the results of an experiment for determining the ratio of the forward/backward water vapor ratio.
FIG. 4(c) is a graph showing the results of an experiment for determining the forward/backward ratio of n-heptane.
FIG. 4(d) is a graph showing the results of the determination experiment of the forward/backward ratio of the cotton rope.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
as shown in fig. 1, the adaptive identification method for a multi-scene smoke detector based on bidirectional blue light detection according to the present invention includes the following steps:
s1: the fire detector converts the detected aerosol concentration into an electric signal;
s2: the voltage signal is amplified, filtered and A/D converted by a signal processing circuit;
s3: the digital signal obtained by A/D conversion is analyzed in a microprocessor to realize the extraction, analysis and identification of signal characteristics, and finally, a scene is judged to judge whether an alarm is needed or not;
s4: and processing the data acquired by the microprocessing to output characteristic graphs of different scenes.
Preferably, in S1, two blue light emitting tubes disposed at different positions in the detector are used to emit light, and when the light encounters the aerosol, the light is scattered onto a receiving tube, and the receiving tube converts the light signal into a current signal.
Preferably, in S2, RC filtering is used in hardware to filter out high-frequency and low-frequency interference, two stages of operational amplifiers are used for amplification, and a dc blocking filter is added between the first stage and the second stage.
Preferably, in S3, the digital signal obtained by a/D conversion is analyzed by the microprocessor, which includes the following specific steps:
s31: initializing an I/O port, configuring an ADC (analog to digital converter), selecting an A/D conversion channel, enabling an ADC module and closing interruption;
s32: starting a blue light generating tube for emission, starting ADC (analog to digital converter) conversion, closing the blue light emitting tube, inquiring whether the ADC is finished or not, starting interruption, and obtaining an ADC conversion result;
s33: repeating the steps, and collecting forward and backward data;
s34: and (3) software filtering treatment: the data collected in the front-back direction are put into a cache, the data are subjected to average processing for 4 times, the collected data are analyzed, the fluctuation interference value is removed, amplitude limiting processing is carried out, and the reasonable value is obtained and subjected to average processing for 8 times;
s35: and (3) judging the fault of the sensor: when the sampling value of the sensor is lower than the fault threshold value, continuously timing for more than 60 seconds, and judging the fault of the sensor;
s36: when the sampling value of the sensor is higher than the early warning threshold value, starting fluctuation calculation, and recording by comparing whether the difference between two adjacent sampling values is larger than 1/2 of the previous sampling value; when the sampling value of the sensor is larger than the ratio calculation threshold, calculating the variable quantity of the forward sampling value and the backward sampling value, calculating the relation of the variable quantity of the two sampling values, and storing the calculation result;
s37: when the sampling value is greater than the model identification threshold value, analyzing and judging the model according to the recorded fluctuation condition and the specific value condition, and respectively:
the relationship between the fluctuation and the ratio of the black smoke model is divided into: a1, B1-B2;
the relationship between the white smoke model fluctuation and the ratio is divided into: a2, B3-B4;
the relationship between the fluctuation and the ratio of the raise dust model is divided into: a3, B5-B6;
the relation between the fluctuation and the ratio of the water vapor model is divided into: a4, B7-B8;
the ratio and the fluctuation are analyzed to which model:
if the black smoke model is met, adjusting the alarm threshold value to C1;
if the white smoke model is met, adjusting the alarm threshold value to C2;
if the dust model is met, adjusting the alarm threshold value to C3;
if the water vapor model is met, adjusting the alarm threshold value to C4;
when the sampling value meets the alarm threshold value, continuous judgment is needed for 3 times, and the sampling values are all in line, an alarm signal is triggered, false alarm is reduced to a certain extent, and the detection precision is improved.
Preferably, in S4, the detector is connected to a big basket of a debugging tool of a company, the big basket is connected to a computer through a USB, and the control of the detector is implemented by TC-BUS software.
Preferably, in S4, the detector may upload the collected data, store the data through the TC-BUS, and draw data of the sensor in different scenes, thereby establishing models of black smoke, white smoke, dust, and water vapor for parameter correction and model establishment of the detector.
According to the multi-scene smoke detector based on bidirectional blue light detection and the self-adaptive identification method, an ideal model is established by comprehensively analyzing and processing the characteristics expressed by different particles in a scene, an ideal judgment threshold value is determined, the judgment threshold value is adjusted according to different scenes, the false alarm of raised dust, water vapor and electromagnetic interference is reduced to a certain extent, and the detection sensitivity of black smoke is improved; the fire disaster data real-time identification, scene judgment and threshold value adjustment can be realized through the bidirectional blue light technology, and a foundation is laid for the practical application of fire disaster detection.
Example 2:
at present, smoke detectors in the fire alarm system industry are generally of a one-way infrared type, the smoke detectors cannot classify smoke, namely black smoke and white smoke are distinguished, so that the smoke detectors are extremely sensitive to white smoke, insensitive to black smoke and slow in response to black smoke, threshold values are set to be low in false alarm of white smoke, sensitivity is adjusted to be high, response to black smoke is quite slow, the early-stage alarm function is lost, and false alarm can be easily generated if the threshold values are set to be low in order to improve sensitivity of black smoke, so that social loss is caused. Dust, water vapor and electromagnetism can not be distinguished, and further false alarm occurs, so that social loss is caused.
The invention solves the technical problem that the general smoke detector cannot identify black and white smoke, dust and water vapor by adopting unidirectional infrared in an automatic fire alarm system, cannot improve the sensitivity of the black smoke and avoids the false alarm condition caused by the dust and the water vapor.
Seven factors that typically contribute to false positives:
1: non-fire smoke factors (cooking fumes, smoking, moisture, dust, insecticides, etc.);
2: environmental factors (electromagnetic interference, airflow, drastic changes in air temperature);
3: product quality issues;
4: human factors;
5: engineering design reasons (bad configuration position, improper type selection);
6: construction problems (decoration pollution, poor connection quality, substandard grounding, etc.);
7: component aging, dust accumulation, and insect intrusion (60% false alarm source dust accumulation).
The invention adopts a blue light emitting diode and a blue light receiving tube, wherein the two blue light emitting diodes respectively form an angle of 70 degrees and 135 degrees with the blue light receiving tube in space, the working central wavelength of the blue light emitting diode is 470nm, the wavelength range is 420 nm-500 nm, and the receiving wavelength of the blue light receiving tube is 400 nm-1100 nm. In the industry, forward scattering is generally called when the included angle between the transmitting tube and the receiving tube is larger than 90 degrees, and backward scattering is called when the included angle between the transmitting tube and the receiving tube is smaller than 90 degrees. According to the scattering model of light in physics, 1: rayleigh scattering region (i.e. particle diameter d <0.1), light will radiate in all directions and with substantially uniform intensity. 2: the Mie scattering region (i.e., 0.1 λ < d <4 λ), light is much stronger in forward radiation than in backward radiation. 3: in a Brickel scattering region (namely d >4 lambda), the light is extremely strong in the forward radiation intensity, extremely small in the backward radiation intensity and extremely asymmetric.
Based on the above-described principle, two blue light emitting tubes are disposed at 70 ° and 135 ° from the receiving tube, respectively, to form a forward direction and a backward direction.
While the smoke particles are generally 0.5um to 1.0um, and the particles of dust and water vapor are larger. And therefore are generally in the mie and the brilliant scattering regions. According to the theory, the forward blue light emission tube shows stronger scattering when meetting the granule, consequently regard forward blue light emission tube as main reference, then to blue light emission tube as supplementary, through the signal intensity according to forward blue light emission tube and backward blue light emission tube intensity contrast, judge the granule, and raise dust scene general condition all is brought with the wind, its certain volatility and periodicity, and steam also exists this kind of condition. Therefore, scenes are identified by taking the signal intensity of the front and rear blue light emitting tubes and the fluctuation of the signal intensity as characteristic values, the sensitivity of the detector is adjusted, the intensity dual threshold of the front and rear blue light emitting tubes is judged, and false alarm is reduced.
Through the technical scheme disclosed by the invention, the performance of the smoke detector is improved, and the false alarm rate is reduced. The following are partial experimental results, as shown in fig. 2 to 4 (d):
the invention solves the problem of insensitive black smoke of the traditional smoke detector by designing a black smoke and white smoke model identification scene, identifies non-fire factors such as fire factors dust and water vapor by designing a dust and water vapor model, further adjusts the threshold value, reduces the false alarm, and tests prove that the false alarm rate can be reduced by more than 99 percent, and can effectively filter electromagnetic interference and the like by a self-designed filtering algorithm. The method can basically achieve zero missing report and zero false report in normal environment places, and can reduce the false report rate by more than 99% for places frequently generating non-fire factors.
The invention can be widely applied to the occasions of fire-fighting fire alarm systems.
It is well within the skill of those in the art to implement and protect the present invention without undue experimentation and without undue experimentation that the present invention is directed to software and process improvements.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A multi-scene smoke detector self-adaptive identification method based on bidirectional blue light detection is characterized by comprising the following steps:
s1: the fire detector converts the detected aerosol concentration into an electric signal;
s2: the voltage signal is amplified, filtered and A/D converted by a signal processing circuit;
s3: the digital signal obtained by A/D conversion is analyzed in a microprocessor to realize the extraction, analysis and identification of signal characteristics, and finally, a scene is judged to judge whether an alarm is needed or not;
s4: and processing the data acquired by the microprocessing to output characteristic graphs of different scenes.
2. The adaptive identification method for the multi-scene smoke detector based on the bidirectional blue light detection as claimed in claim 1, wherein in S1, two blue light emitting tubes disposed at different positions in the detector are used to emit light, and when encountering aerosol, the light is scattered onto a receiving tube, and the receiving tube converts the light signal into a current signal.
3. The adaptive identification method for the multi-scene smoke detector based on the bidirectional blue light detection as claimed in claim 1, wherein in S2, RC filtering is used in hardware to filter out high-frequency and low-frequency interference, two stages of operational amplification are used, and a dc blocking filter is added between the first stage and the second stage.
4. The adaptive identification method for the multi-scene smoke detector based on the bidirectional blue light detection as claimed in claim 1, wherein in S3, the digital signal obtained by the a/D conversion is analyzed by the microprocessor, which includes the following specific steps:
s31: initializing an I/O port, configuring an ADC (analog to digital converter), selecting an A/D conversion channel, enabling an ADC module and closing interruption;
s32: starting a blue light generating tube for emission, starting ADC (analog to digital converter) conversion, closing the blue light emitting tube, inquiring whether the ADC is finished or not, starting interruption, and obtaining an ADC conversion result;
s33: repeating the steps, and collecting forward and backward data;
s34: and (3) software filtering treatment: the data collected in the front-back direction are put into a cache, the data are subjected to average processing for 4 times, the collected data are analyzed, the fluctuation interference value is removed, amplitude limiting processing is carried out, and the reasonable value is obtained and subjected to average processing for 8 times;
s35: and (3) judging the fault of the sensor: when the sampling value of the sensor is lower than the fault threshold value, continuously timing for more than 60 seconds, and judging the fault of the sensor;
s36: when the sampling value of the sensor is higher than the early warning threshold value, starting fluctuation calculation, and recording by comparing whether the difference between two adjacent sampling values is larger than 1/2 of the previous sampling value; when the sampling value of the sensor is larger than the ratio calculation threshold, calculating the variable quantity of the forward sampling value and the backward sampling value, calculating the relation of the variable quantity of the two sampling values, and storing the calculation result;
s37: when the sampling value is greater than the model identification threshold value, analyzing and judging the model according to the recorded fluctuation condition and the specific value condition, and respectively:
the relationship between the fluctuation and the ratio of the black smoke model is divided into: a1, B1-B2;
the relationship between the white smoke model fluctuation and the ratio is divided into: a2, B3-B4;
the relationship between the fluctuation and the ratio of the raise dust model is divided into: a3, B5-B6;
the relation between the fluctuation and the ratio of the water vapor model is divided into: a4, B7-B8;
the ratio and the fluctuation are analyzed to which model:
if the black smoke model is met, adjusting the alarm threshold value to C1;
if the white smoke model is met, adjusting the alarm threshold value to C2;
if the dust model is met, adjusting the alarm threshold value to C3;
if the water vapor model is met, adjusting the alarm threshold value to C4;
when the sampling value meets the alarm threshold value, continuous judgment is needed for 3 times, and the sampling values are all met, an alarm signal is triggered, false alarm is reduced to a certain extent, and the detection precision is improved.
5. The adaptive identification method for the multi-scene smoke detector based on the bidirectional blue light detection of claim 4, wherein in S4, the detector is connected with a big blue shell of a debugging tool of a company through a USB, the big blue shell is connected with a computer through a USB, and the control of the detector is realized by using TC-BUS software.
6. The self-adaptive identification method for the multi-scene smoke detector based on the bidirectional blue light detection of claim 5, wherein in S4, the detector uploads the collected data, the data is stored through TC-BUS, the obtained data is drawn on the data of the sensor in different scenes, and then models of black smoke, white smoke, dust and water vapor are built for parameter correction and model building of the detector.
7. A multi-scene smoke detector based on bidirectional blue light detection is characterized by comprising two blue light emitting diodes and a blue light receiving tube, wherein the two blue light emitting diodes respectively form a forward scattering angle and a backward scattering angle with the blue light receiving tube at 70 degrees and 135 degrees in space; smoke particles detected by the forward scattering blue light emitting diode show that the scattering intensity is larger than that detected by the backward scattering blue light emitting diode, the forward blue light emitting tube is used as a main reference, and the backward blue light emitting tube is used as an auxiliary reference.
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| CN113670786A (en) * | 2021-10-21 | 2021-11-19 | 中国民航大学 | Dual-wavelength fire smoke detection system and method based on phase-locked amplification |
| CN114913667A (en) * | 2022-05-06 | 2022-08-16 | 合肥科大立安安全技术有限责任公司 | Anti-interference sensing device for smoke in early stage of fire and anti-interference method thereof |
| CN116665397A (en) * | 2023-08-01 | 2023-08-29 | 中国科学技术大学 | Fire smoke alarm method and alarm device, alarm and readable storage medium |
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